diff --git a/.gitignore b/.gitignore
index 599859f..ae6297e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -172,10 +172,12 @@ log/
 .ruff_cache/
 
 # model files
+**/model_base/*
 *.pkl
 
 # reports
-reports/report_*
+reports/scores/scores_*.csv
+reports/portfolios/portfolio_*.xlsx
 
 # references
 references
diff --git a/README.md b/README.md
index ace243c..7f44b55 100644
--- a/README.md
+++ b/README.md
@@ -5,7 +5,7 @@
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 
 
-This project implements an intelligent dynamic stock selection system using an **Adaptive Genetic Algorithm-optimized XGBoost** (GA-XGBoost) classifier to identify stocks with potential market outperformance. The model analyzes quarterly financial statements, market data, insider trading patterns and other external data to rank and select stocks that will outperform the S&P 500 index over a one-year horizon. The project includes a **Streamlit-based analytics dashboard** that provides comprehensive stock analysis tools, including technical indicators, financial metrics visualization, and model-driven insights.
+This project implements an intelligent dynamic stock selection system using **Adaptive Genetic Algorithm-optimized XGBoost** (GA-XGBoost) ensemble models to identify stocks with potential market outperformance in the medium to long term. The model analyzes quarterly financial statements, market data, insider trading patterns and other external data to rank and select stocks that will outperform the S&P 500 index over a one-year horizon. The project includes a **Streamlit-based analytics dashboard** that provides comprehensive stock analysis tools, including technical indicators, financial metrics visualization, and model-driven insights.
 
 
 ## Table of Contents
@@ -28,7 +28,7 @@ This project implements an intelligent stock selection system that identifies po
 The core engine combines three key components:
 
 1. **Data Pipeline**
-   - Automated collection of S&P 500 constituent data
+   - Automated collection of S&P500 constituent data
    - Integration of multiple data sources:
      - Quarterly financial statements and earnings reports
      - Daily market data and technical indicators
diff --git a/notebooks/classification.ipynb b/notebooks/classification.ipynb
new file mode 100644
index 0000000..1565ad5
--- /dev/null
+++ b/notebooks/classification.ipynb
@@ -0,0 +1,1770 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datetime as dt\n",
+    "\n",
+    "import plotly.express as px\n",
+    "import polars as pl\n",
+    "\n",
+    "from stocksense.config import config\n",
+    "from stocksense.database import DatabaseHandler\n",
+    "from stocksense.model import XGBoostClassifier\n",
+    "from stocksense.pipeline import clean, engineer_features\n",
+    "\n",
+    "features = config.model.features\n",
+    "date_col = config.model.date_col\n",
+    "aux_cols = [\"tic\", \"datadate\", \"rdq\"]\n",
+    "target = \"excess_return_4Q_pos\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2024-12-12 20:35:46.853\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mstocksense.database.schema\u001b[0m:\u001b[36mcreate_tables\u001b[0m:\u001b[36m121\u001b[0m - \u001b[32m\u001b[1mTables created successfully\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "constituents = DatabaseHandler().fetch_constituents(dt.datetime(2022, 6, 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2024-12-12 20:35:46.862\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mstocksense.pipeline.preprocess\u001b[0m:\u001b[36mengineer_features\u001b[0m:\u001b[36m20\u001b[0m - \u001b[1mSTART processing stock data\u001b[0m\n",
+      "\u001b[32m2024-12-12 20:35:46.864\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mstocksense.database.schema\u001b[0m:\u001b[36mcreate_tables\u001b[0m:\u001b[36m121\u001b[0m - \u001b[32m\u001b[1mTables created successfully\u001b[0m\n",
+      "\u001b[32m2024-12-12 20:35:50.783\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mstocksense.pipeline.preprocess\u001b[0m:\u001b[36mengineer_features\u001b[0m:\u001b[36m33\u001b[0m - \u001b[1mSTART feature engineering\u001b[0m\n",
+      "\u001b[32m2024-12-12 20:36:05.135\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mstocksense.pipeline.preprocess\u001b[0m:\u001b[36mengineer_features\u001b[0m:\u001b[36m46\u001b[0m - \u001b[32m\u001b[1mEND 58960 rows PROCESSED\u001b[0m\n",
+      "\u001b[32m2024-12-12 20:36:05.138\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mstocksense.pipeline.preprocess\u001b[0m:\u001b[36mclean\u001b[0m:\u001b[36m68\u001b[0m - \u001b[1mSTART cleaning data\u001b[0m\n",
+      "\u001b[32m2024-12-12 20:36:05.280\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mstocksense.pipeline.preprocess\u001b[0m:\u001b[36mclean\u001b[0m:\u001b[36m106\u001b[0m - \u001b[32m\u001b[1m37372 rows retained after CLEANING\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (5, 202)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>tdq</th><th>tic</th><th>datadate</th><th>rdq</th><th>saleq</th><th>cogsq</th><th>xsgaq</th><th>niq</th><th>ebitdaq</th><th>cshoq</th><th>actq</th><th>atq</th><th>cheq</th><th>rectq</th><th>invtq</th><th>ppentq</th><th>lctq</th><th>dlttq</th><th>ltq</th><th>req</th><th>seqq</th><th>oancfq</th><th>ivncfq</th><th>fincfq</th><th>dvq</th><th>capxq</th><th>icaptq</th><th>surprise_pct</th><th>stock_split</th><th>n_purch</th><th>val_purch</th><th>n_sales</th><th>val_sales</th><th>insider_balance</th><th>roa</th><th>roi</th><th>roe</th><th>&hellip;</th><th>f_score</th><th>f_score_gr1</th><th>f_score_gr4</th><th>forward_vol_yoy</th><th>forward_vol_sos</th><th>forward_vol_qoq</th><th>excess_return_1Q</th><th>sharpe_ratio_1Q</th><th>risk_return_1Q</th><th>fwd_return_1Q_pos</th><th>excess_return_1Q_pos</th><th>excess_return_2Q</th><th>sharpe_ratio_2Q</th><th>risk_return_2Q</th><th>fwd_return_2Q_pos</th><th>excess_return_2Q_pos</th><th>excess_return_3Q</th><th>sharpe_ratio_3Q</th><th>risk_return_3Q</th><th>fwd_return_3Q_pos</th><th>excess_return_3Q_pos</th><th>excess_return_4Q</th><th>sharpe_ratio_4Q</th><th>risk_return_4Q</th><th>fwd_return_4Q_pos</th><th>excess_return_4Q_pos</th><th>sector_communication_services</th><th>sector_consumer_discretionary</th><th>sector_consumer_staples</th><th>sector_energy</th><th>sector_financials</th><th>sector_health_care</th><th>sector_industrials</th><th>sector_information_technology</th><th>sector_materials</th><th>sector_real_estate</th><th>sector_utilities</th></tr><tr><td>date</td><td>str</td><td>date</td><td>date</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>u32</td><td>f64</td><td>u32</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>&hellip;</td><td>i8</td><td>i8</td><td>i8</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>i8</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>i8</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>i8</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td></tr></thead><tbody><tr><td>2007-03-01</td><td>&quot;A&quot;</td><td>2007-01-31</td><td>2007-02-15</td><td>1280.0</td><td>540.0</td><td>564.0</td><td>150.0</td><td>176.0</td><td>405.0</td><td>3749.0</td><td>7193.0</td><td>2090.0</td><td>671.0</td><td>648.0</td><td>775.0</td><td>1372.0</td><td>1500.0</td><td>3536.0</td><td>1705.0</td><td>3657.0</td><td>93.0</td><td>-93.0</td><td>-169.0</td><td>0.0</td><td>37.0</td><td>5157.0</td><td>0.011</td><td>0</td><td>0</td><td>0.0</td><td>1</td><td>8.598</td><td>8.598</td><td>0.089114</td><td>0.029087</td><td>0.17528</td><td>&hellip;</td><td>7</td><td>1</td><td>null</td><td>1.780824</td><td>1.749898</td><td>1.355307</td><td>8.952132</td><td>6.605244</td><td>10.89898</td><td>1</td><td>1</td><td>13.694795</td><td>10.104573</td><td>15.070857</td><td>1</td><td>1</td><td>8.583285</td><td>4.905021</td><td>9.151445</td><td>1</td><td>1</td><td>10.709357</td><td>6.013709</td><td>6.242292</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>2007-06-01</td><td>&quot;A&quot;</td><td>2007-04-30</td><td>2007-05-14</td><td>1320.0</td><td>540.0</td><td>586.0</td><td>123.0</td><td>194.0</td><td>395.958</td><td>3791.0</td><td>7283.0</td><td>2050.0</td><td>718.0</td><td>650.0</td><td>777.0</td><td>1554.0</td><td>1500.0</td><td>3728.0</td><td>1881.0</td><td>3555.0</td><td>302.0</td><td>-36.0</td><td>-320.0</td><td>0.0</td><td>42.0</td><td>5055.0</td><td>-0.023</td><td>0</td><td>0</td><td>0.0</td><td>3</td><td>2.695</td><td>2.695</td><td>0.089112</td><td>0.024332</td><td>0.18256</td><td>&hellip;</td><td>7</td><td>0</td><td>null</td><td>1.927423</td><td>1.918095</td><td>2.106162</td><td>1.404396</td><td>0.666804</td><td>-0.836463</td><td>0</td><td>1</td><td>-2.215787</td><td>-1.05205</td><td>-2.205524</td><td>0</td><td>0</td><td>-0.292617</td><td>-0.152556</td><td>-4.454244</td><td>0</td><td>0</td><td>-5.91296</td><td>-3.067807</td><td>-8.865139</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>2007-09-01</td><td>&quot;A&quot;</td><td>2007-07-31</td><td>2007-08-14</td><td>1374.0</td><td>563.0</td><td>580.0</td><td>185.0</td><td>231.0</td><td>386.548</td><td>3281.0</td><td>7024.0</td><td>1486.0</td><td>738.0</td><td>674.0</td><td>787.0</td><td>1510.0</td><td>1500.0</td><td>3736.0</td><td>2069.0</td><td>3288.0</td><td>176.0</td><td>-258.0</td><td>-484.0</td><td>0.0</td><td>36.0</td><td>4788.0</td><td>-0.01</td><td>0</td><td>0</td><td>0.0</td><td>2</td><td>5.747</td><td>5.747</td><td>0.086418</td><td>0.038638</td><td>0.184611</td><td>&hellip;</td><td>6</td><td>-1</td><td>null</td><td>1.772393</td><td>1.803532</td><td>1.789715</td><td>-1.703536</td><td>-0.951848</td><td>0.168495</td><td>1</td><td>0</td><td>-0.089423</td><td>-0.049965</td><td>-2.674412</td><td>0</td><td>0</td><td>-6.394643</td><td>-3.545622</td><td>-7.722925</td><td>0</td><td>0</td><td>10.925009</td><td>6.163987</td><td>-0.514147</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>2007-12-01</td><td>&quot;A&quot;</td><td>2007-10-31</td><td>2007-11-15</td><td>1446.0</td><td>611.0</td><td>598.0</td><td>180.0</td><td>237.0</td><td>370.0</td><td>3671.0</td><td>7554.0</td><td>1826.0</td><td>735.0</td><td>643.0</td><td>801.0</td><td>1663.0</td><td>2087.0</td><td>4320.0</td><td>2580.0</td><td>3234.0</td><td>398.0</td><td>-69.0</td><td>-7.0</td><td>0.0</td><td>39.0</td><td>5321.0</td><td>0.022</td><td>0</td><td>0</td><td>0.0</td><td>2</td><td>2.75</td><td>2.75</td><td>0.084459</td><td>0.033828</td><td>0.197279</td><td>&hellip;</td><td>5</td><td>-1</td><td>-1</td><td>3.004222</td><td>1.948736</td><td>1.848409</td><td>-3.735506</td><td>-2.02093</td><td>-6.186043</td><td>0</td><td>0</td><td>-8.003799</td><td>-4.330101</td><td>-8.594016</td><td>0</td><td>0</td><td>8.239344</td><td>4.228045</td><td>-2.316993</td><td>0</td><td>1</td><td>-2.055557</td><td>-0.684223</td><td>-10.49239</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>2008-03-01</td><td>&quot;A&quot;</td><td>2008-01-31</td><td>2008-02-13</td><td>1393.0</td><td>580.0</td><td>617.0</td><td>120.0</td><td>196.0</td><td>368.0</td><td>5070.0</td><td>7459.0</td><td>3148.0</td><td>726.0</td><td>674.0</td><td>801.0</td><td>2674.0</td><td>626.0</td><td>4286.0</td><td>2657.0</td><td>3173.0</td><td>4.0</td><td>-295.0</td><td>-168.0</td><td>0.0</td><td>34.0</td><td>3799.0</td><td>0.021</td><td>0</td><td>0</td><td>0.0</td><td>2</td><td>3.418</td><td>3.418</td><td>0.081512</td><td>0.031587</td><td>0.191617</td><td>&hellip;</td><td>6</td><td>1</td><td>-1</td><td>3.238751</td><td>1.741436</td><td>2.050642</td><td>1.053516</td><td>0.513749</td><td>2.536928</td><td>1</td><td>1</td><td>20.442391</td><td>9.968778</td><td>8.760186</td><td>1</td><td>1</td><td>6.726355</td><td>3.862535</td><td>-7.914079</td><td>0</td><td>1</td><td>-6.951969</td><td>-2.146497</td><td>-13.35568</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (5, 202)\n",
+       "┌────────────┬─────┬────────────┬────────────┬───┬────────────┬────────────┬───────────┬───────────┐\n",
+       "│ tdq        ┆ tic ┆ datadate   ┆ rdq        ┆ … ┆ sector_inf ┆ sector_mat ┆ sector_re ┆ sector_ut │\n",
+       "│ ---        ┆ --- ┆ ---        ┆ ---        ┆   ┆ ormation_t ┆ erials     ┆ al_estate ┆ ilities   │\n",
+       "│ date       ┆ str ┆ date       ┆ date       ┆   ┆ echnology  ┆ ---        ┆ ---       ┆ ---       │\n",
+       "│            ┆     ┆            ┆            ┆   ┆ ---        ┆ i8         ┆ i8        ┆ i8        │\n",
+       "│            ┆     ┆            ┆            ┆   ┆ i8         ┆            ┆           ┆           │\n",
+       "╞════════════╪═════╪════════════╪════════════╪═══╪════════════╪════════════╪═══════════╪═══════════╡\n",
+       "│ 2007-03-01 ┆ A   ┆ 2007-01-31 ┆ 2007-02-15 ┆ … ┆ 0          ┆ 0          ┆ 0         ┆ 0         │\n",
+       "│ 2007-06-01 ┆ A   ┆ 2007-04-30 ┆ 2007-05-14 ┆ … ┆ 0          ┆ 0          ┆ 0         ┆ 0         │\n",
+       "│ 2007-09-01 ┆ A   ┆ 2007-07-31 ┆ 2007-08-14 ┆ … ┆ 0          ┆ 0          ┆ 0         ┆ 0         │\n",
+       "│ 2007-12-01 ┆ A   ┆ 2007-10-31 ┆ 2007-11-15 ┆ … ┆ 0          ┆ 0          ┆ 0         ┆ 0         │\n",
+       "│ 2008-03-01 ┆ A   ┆ 2008-01-31 ┆ 2008-02-13 ┆ … ┆ 0          ┆ 0          ┆ 0         ┆ 0         │\n",
+       "└────────────┴─────┴────────────┴────────────┴───┴────────────┴────────────┴───────────┴───────────┘"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = engineer_features()\n",
+    "data = clean(data)\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
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+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "Feature Correlations with Target (excess_return_4Q_pos)"
+        },
+        "width": 1000,
+        "xaxis": {
+         "anchor": "y",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "Correlation"
+         }
+        },
+        "yaxis": {
+         "anchor": "x",
+         "domain": [
+          0,
+          1
+         ],
+         "tickfont": {
+          "size": 10
+         },
+         "title": {
+          "text": "Feature"
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = data.to_pandas()\n",
+    "df_standardized = (df[features] - df[features].mean()) / df[features].std()\n",
+    "corr = df_standardized.corrwith(df[target])\n",
+    "corr_df = pl.DataFrame({\"Feature\": corr.index, \"Correlation\": corr.values}).sort(\n",
+    "    \"Correlation\", descending=False\n",
+    ")\n",
+    "\n",
+    "fig = px.bar(\n",
+    "    corr_df,\n",
+    "    x=\"Correlation\",\n",
+    "    y=\"Feature\",\n",
+    "    orientation=\"h\",\n",
+    "    title=f\"Feature Correlations with Target ({target})\",\n",
+    "    width=1000,\n",
+    "    height=1200,\n",
+    ")\n",
+    "fig.update_layout(yaxis={\"tickfont\": {\"size\": 10}}, showlegend=False, margin={\"l\": 200})\n",
+    "fig.add_vline(x=0, line_dash=\"dash\", line_color=\"gray\")\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PR AUC: 0.40451412813410054\n",
+      "ROC AUC: 0.5182640890573731\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = data.select([\"tic\", date_col] + features + [\"fwd_return_4Q\", target])\n",
+    "data = data.filter((~pl.all_horizontal(pl.col(target).is_null())))\n",
+    "train = data.filter((pl.col(\"tdq\").dt.year() >= 2007) & (pl.col(\"tdq\").dt.year() < 2021))\n",
+    "val = data.filter(\n",
+    "    (pl.col(\"tdq\").dt.year() >= 2022)\n",
+    "    & (pl.col(\"tdq\").dt.year() <= 2023)\n",
+    "    & pl.col(\"tic\").is_in(constituents)\n",
+    ")\n",
+    "\n",
+    "X_train = train.select(features).to_pandas()\n",
+    "y_train = train.select(target).to_pandas().values.ravel()\n",
+    "X_val = val.select(features).to_pandas()\n",
+    "y_val = val.select(target).to_pandas().values.ravel()\n",
+    "\n",
+    "params = {\n",
+    "    \"learning_rate\": 0.01,\n",
+    "    \"n_estimators\": 100,\n",
+    "    \"max_depth\": 7,\n",
+    "    \"min_child_weight\": 3.21,\n",
+    "    \"gamma\": 0.45,\n",
+    "    \"subsample\": 0.50,\n",
+    "    \"colsample_bytree\": 0.53,\n",
+    "    \"reg_alpha\": 1.83,\n",
+    "    \"reg_lambda\": 1.2,\n",
+    "    \"nthread\": -1,\n",
+    "    \"seed\": 100,\n",
+    "}\n",
+    "\n",
+    "model = XGBoostClassifier(params)\n",
+    "model.train(X_train, y_train)\n",
+    "\n",
+    "print(f\"PR AUC: {model.get_pr_auc(X_val, y_val)}\")\n",
+    "print(f\"ROC AUC: {model.get_roc_auc(X_val, y_val)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (3_655, 91)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>tic</th><th>tdq</th><th>n_purch</th><th>n_sales</th><th>insider_balance</th><th>volume_ma20</th><th>volume_ma50</th><th>price_mom</th><th>price_qoq</th><th>price_yoy</th><th>price_2y</th><th>price_risk_qoq</th><th>price_risk_sos</th><th>price_risk_yoy</th><th>price_risk_2y</th><th>rsi_14d</th><th>rsi_30d</th><th>rsi_60d</th><th>rsi_90d</th><th>rsi_1y</th><th>vol_mom</th><th>vol_qoq</th><th>vol_sos</th><th>vol_yoy</th><th>vol_2y</th><th>rel_vol_mom</th><th>rel_vol_qoq</th><th>rel_vol_yoy</th><th>momentum_mom</th><th>momentum_qoq</th><th>momentum_yoy</th><th>momentum_2y</th><th>index_mom</th><th>index_qoq</th><th>index_sos</th><th>index_yoy</th><th>index_2y</th><th>&hellip;</th><th>niq_2y</th><th>ltq_yoy</th><th>ltq_2y</th><th>dlttq_yoy</th><th>gpm_yoy</th><th>gpm_2y</th><th>roa_yoy</th><th>roa_2y</th><th>roi_yoy</th><th>roi_2y</th><th>roe_yoy</th><th>fcf_yoy</th><th>der_yoy</th><th>dr_yoy</th><th>dr_2y</th><th>ltda_yoy</th><th>ev_ebitda_yoy</th><th>ltcr_yoy</th><th>pe_yoy</th><th>pe_2y</th><th>pb_yoy</th><th>ps_yoy</th><th>atr_yoy</th><th>size_yoy</th><th>sector_utilities</th><th>sector_health_care</th><th>sector_financials</th><th>sector_consumer_discretionary</th><th>sector_consumer_staples</th><th>sector_energy</th><th>sector_industrials</th><th>sector_information_technology</th><th>sector_communication_services</th><th>sector_materials</th><th>sector_real_estate</th><th>fwd_return_4Q</th><th>excess_return_4Q_pos</th></tr><tr><td>str</td><td>date</td><td>u32</td><td>u32</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>&hellip;</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>f64</td><td>i8</td></tr></thead><tbody><tr><td>&quot;A&quot;</td><td>2022-03-01</td><td>0</td><td>5</td><td>19.293</td><td>133.226941</td><td>117.561988</td><td>-3.74289</td><td>-10.984423</td><td>6.704944</td><td>60.949116</td><td>-150.0</td><td>-150.0</td><td>200.0</td><td>300.0</td><td>43.158399</td><td>42.053527</td><td>43.715374</td><td>45.665021</td><td>51.522485</td><td>2.161613</td><td>1.857107</td><td>1.641768</td><td>1.424811</td><td>1.894748</td><td>50.0</td><td>150.0</td><td>160.441865</td><td>50.0</td><td>50.0</td><td>50.0</td><td>50.0</td><td>-2.833813</td><td>-4.58184</td><td>-4.913681</td><td>11.264523</td><td>39.350786</td><td>&hellip;</td><td>43.654822</td><td>6.221766</td><td>11.175586</td><td>24.942792</td><td>0.418303</td><td>2.029907</td><td>39.358651</td><td>45.107156</td><td>-12.893125</td><td>20.895223</td><td>38.663007</td><td>7.142857</td><td>-0.991586</td><td>-0.494881</td><td>2.283261</td><td>17.042371</td><td>-23.129185</td><td>-14.246468</td><td>-30.140404</td><td>5.066142</td><td>-3.130584</td><td>-10.827631</td><td>-0.76069</td><td>0.711764</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>15.553968</td><td>1</td></tr><tr><td>&quot;A&quot;</td><td>2022-06-01</td><td>0</td><td>1</td><td>0.165</td><td>116.531326</td><td>115.558614</td><td>2.943879</td><td>-8.066325</td><td>-9.870404</td><td>36.327384</td><td>-150.0</td><td>-150.0</td><td>-200.0</td><td>300.0</td><td>49.272664</td><td>47.577979</td><td>46.541324</td><td>46.77372</td><td>50.376317</td><td>3.014695</td><td>2.469218</td><td>2.175493</td><td>1.758636</td><td>1.591078</td><td>50.0</td><td>147.055598</td><td>153.737685</td><td>-50.0</td><td>50.0</td><td>50.0</td><td>50.0</td><td>-1.303128</td><td>-5.25865</td><td>-10.198599</td><td>-2.540092</td><td>33.121373</td><td>&hellip;</td><td>171.287129</td><td>-4.56335</td><td>13.782803</td><td>0.110011</td><td>0.008074</td><td>0.958421</td><td>35.79613</td><td>67.232292</td><td>21.762787</td><td>126.509135</td><td>28.22335</td><td>-40.042373</td><td>-10.37675</td><td>-5.083665</td><td>2.899704</td><td>-0.435782</td><td>-28.314049</td><td>-40.10826</td><td>-35.577858</td><td>-28.576028</td><td>-17.395771</td><td>-21.606108</td><td>1.776929</td><td>0.059105</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>9.318676</td><td>1</td></tr><tr><td>&quot;A&quot;</td><td>2022-09-01</td><td>0</td><td>2</td><td>0.868</td><td>79.928753</td><td>82.561189</td><td>-4.184011</td><td>1.018566</td><td>-26.514679</td><td>27.501967</td><td>46.302237</td><td>-150.0</td><td>-200.0</td><td>300.0</td><td>46.529941</td><td>50.295003</td><td>49.851588</td><td>49.37193</td><td>50.749185</td><td>2.316539</td><td>2.19982</td><td>2.343169</td><td>2.031332</td><td>1.71518</td><td>50.0</td><td>148.682818</td><td>153.04007</td><td>50.0</td><td>-27.157106</td><td>50.0</td><td>50.0</td><td>-4.532181</td><td>-3.750642</td><td>-9.089974</td><td>-12.317168</td><td>12.482106</td><td>&hellip;</td><td>65.326633</td><td>-2.741208</td><td>18.138007</td><td>0.146628</td><td>1.743347</td><td>3.022338</td><td>34.230978</td><td>74.990489</td><td>22.24579</td><td>53.513405</td><td>30.320847</td><td>-2.39521</td><td>-5.511298</td><td>-2.67627</td><td>7.568238</td><td>0.213494</td><td>-37.978017</td><td>-2.538116</td><td>-46.471204</td><td>-36.421035</td><td>-30.24082</td><td>-33.770549</td><td>8.064162</td><td>-0.007209</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>-5.57725</td><td>0</td></tr><tr><td>&quot;A&quot;</td><td>2022-12-01</td><td>0</td><td>14</td><td>29.504</td><td>84.294291</td><td>79.954035</td><td>10.809849</td><td>20.742453</td><td>5.337014</td><td>35.332866</td><td>150.0</td><td>150.0</td><td>200.0</td><td>300.0</td><td>64.82618</td><td>61.518024</td><td>57.445711</td><td>55.2601</td><td>52.630406</td><td>2.743982</td><td>2.300815</td><td>2.214761</td><td>2.21394</td><td>1.804737</td><td>50.0</td><td>134.867777</td><td>145.846538</td><td>50.0</td><td>50.0</td><td>-50.0</td><td>50.0</td><td>5.717434</td><td>4.308392</td><td>-2.400146</td><td>-9.671307</td><td>11.307188</td><td>&hellip;</td><td>65.765766</td><td>-1.674191</td><td>9.949516</td><td>0.146574</td><td>0.950664</td><td>3.005054</td><td>5.338708</td><td>59.422189</td><td>-15.912726</td><td>47.596638</td><td>5.277354</td><td>1.587302</td><td>-0.117289</td><td>-0.059079</td><td>0.501708</td><td>1.791594</td><td>-10.702488</td><td>1.438619</td><td>-0.676569</td><td>-25.389269</td><td>4.565081</td><td>-5.016431</td><td>12.339549</td><td>-0.175597</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>-26.851359</td><td>0</td></tr><tr><td>&quot;A&quot;</td><td>2023-03-01</td><td>0</td><td>5</td><td>9.322</td><td>85.991285</td><td>81.411412</td><td>-9.37789</td><td>-11.272423</td><td>5.484807</td><td>10.467538</td><td>-150.0</td><td>150.0</td><td>200.0</td><td>300.0</td><td>27.642313</td><td>39.893702</td><td>46.853905</td><td>48.655084</td><td>50.816258</td><td>1.430282</td><td>1.57085</td><td>1.948803</td><td>2.161252</td><td>1.828145</td><td>50.0</td><td>148.188834</td><td>145.011024</td><td>50.0</td><td>50.0</td><td>-50.0</td><td>50.0</td><td>-1.652163</td><td>-3.154822</td><td>-1.965465</td><td>-9.660628</td><td>1.270428</td><td>&hellip;</td><td>22.222222</td><td>2.648367</td><td>9.034908</td><td>0.10989</td><td>1.406293</td><td>1.830478</td><td>3.839863</td><td>44.709833</td><td>17.55397</td><td>2.397589</td><td>0.886202</td><td>16.078431</td><td>-5.678431</td><td>-2.916963</td><td>-3.397409</td><td>-5.31781</td><td>-10.779239</td><td>15.951013</td><td>-6.946706</td><td>-34.993345</td><td>-6.122066</td><td>-4.984549</td><td>2.849477</td><td>0.60311</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>-3.250208</td><td>0</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;ZTS&quot;</td><td>2022-12-01</td><td>0</td><td>0</td><td>0.0</td><td>139.909142</td><td>110.406077</td><td>2.566788</td><td>-0.127517</td><td>-28.689795</td><td>-2.925137</td><td>-4.954486</td><td>-150.0</td><td>-200.0</td><td>-174.274658</td><td>61.163976</td><td>53.440047</td><td>49.431148</td><td>48.308086</td><td>49.582144</td><td>3.726094</td><td>2.573759</td><td>2.202554</td><td>2.014371</td><td>1.678464</td><td>50.0</td><td>150.0</td><td>132.699596</td><td>44.894051</td><td>-2.959725</td><td>50.0</td><td>-25.869712</td><td>5.717434</td><td>4.308392</td><td>-2.400146</td><td>-9.671307</td><td>11.307188</td><td>&hellip;</td><td>10.438413</td><td>-0.132979</td><td>-10.931014</td><td>-20.964806</td><td>-1.236584</td><td>0.362138</td><td>4.525028</td><td>24.757024</td><td>9.226616</td><td>13.914201</td><td>4.713626</td><td>-19.448698</td><td>0.27403</td><td>0.093427</td><td>-10.598813</td><td>-20.785627</td><td>-31.462157</td><td>1.918269</td><td>-32.655876</td><td>-23.432926</td><td>-29.481525</td><td>-33.197357</td><td>1.812715</td><td>-0.023773</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>12.704655</td><td>1</td></tr><tr><td>&quot;ZTS&quot;</td><td>2023-03-01</td><td>0</td><td>1</td><td>2.277</td><td>101.933659</td><td>103.999792</td><td>1.742569</td><td>8.712864</td><td>-13.46759</td><td>6.265461</td><td>150.0</td><td>150.0</td><td>-200.0</td><td>300.0</td><td>53.3973</td><td>54.744564</td><td>52.724753</td><td>51.216697</td><td>50.329901</td><td>1.935118</td><td>1.719409</td><td>2.152536</td><td>2.038631</td><td>1.717367</td><td>50.0</td><td>150.0</td><td>136.783662</td><td>-50.0</td><td>-50.0</td><td>50.0</td><td>50.0</td><td>-1.652163</td><td>-3.154822</td><td>-1.965465</td><td>-9.660628</td><td>1.270428</td><td>&hellip;</td><td>28.412256</td><td>12.462591</td><td>6.97438</td><td>-0.606796</td><td>-2.167728</td><td>1.872541</td><td>-3.347206</td><td>17.680081</td><td>13.220269</td><td>21.607934</td><td>7.103482</td><td>9.131075</td><td>16.06405</td><td>4.739029</td><td>-2.458001</td><td>-7.432795</td><td>-16.962455</td><td>9.797321</td><td>-17.252629</td><td>-19.069925</td><td>-11.374684</td><td>-17.355672</td><td>0.1066</td><td>0.745823</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>14.673911</td><td>0</td></tr><tr><td>&quot;ZTS&quot;</td><td>2023-06-01</td><td>0</td><td>2</td><td>3.059</td><td>113.412167</td><td>93.601906</td><td>-6.16167</td><td>-2.163021</td><td>-2.884217</td><td>-4.701764</td><td>-150.0</td><td>150.0</td><td>-150.545009</td><td>-274.970853</td><td>36.872081</td><td>44.714764</td><td>49.034177</td><td>49.615524</td><td>50.134387</td><td>1.691554</td><td>1.411919</td><td>1.591245</td><td>1.915851</td><td>1.709914</td><td>50.0</td><td>150.0</td><td>145.437831</td><td>-50.0</td><td>-50.0</td><td>-50.0</td><td>-50.0</td><td>2.462385</td><td>4.263394</td><td>6.655249</td><td>2.150699</td><td>0.451685</td><td>&hellip;</td><td>-1.252236</td><td>0.662899</td><td>-4.544518</td><td>25.459067</td><td>-1.050388</td><td>-0.081241</td><td>0.673463</td><td>17.098316</td><td>-17.010903</td><td>-4.570163</td><td>3.618482</td><td>77.669903</td><td>4.406098</td><td>1.438693</td><td>-4.253029</td><td>26.425961</td><td>-4.914543</td><td>41.615834</td><td>-2.508209</td><td>-20.640202</td><td>1.019514</td><td>-5.045032</td><td>-2.52209</td><td>-0.080502</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>3.072419</td><td>0</td></tr><tr><td>&quot;ZTS&quot;</td><td>2023-09-01</td><td>0</td><td>2</td><td>2.564</td><td>82.354987</td><td>88.746835</td><td>6.435205</td><td>13.153876</td><td>22.992401</td><td>-5.8858</td><td>150.0</td><td>150.0</td><td>200.0</td><td>-300.0</td><td>62.382436</td><td>59.24916</td><td>56.672467</td><td>55.061073</td><td>51.742329</td><td>1.613205</td><td>1.651363</td><td>1.542894</td><td>1.870124</td><td>1.778463</td><td>50.0</td><td>150.0</td><td>164.216626</td><td>50.0</td><td>50.0</td><td>50.0</td><td>50.0</td><td>0.308312</td><td>5.41382</td><td>11.620662</td><td>14.178762</td><td>-0.152789</td><td>&hellip;</td><td>31.054688</td><td>-0.674646</td><td>-6.061542</td><td>25.550661</td><td>3.758961</td><td>1.85494</td><td>6.046895</td><td>18.622645</td><td>11.177887</td><td>28.516994</td><td>4.854933</td><td>-94.940476</td><td>-1.641055</td><td>-0.522938</td><td>-3.875178</td><td>25.742425</td><td>9.935686</td><td>-95.970134</td><td>11.457617</td><td>-22.294022</td><td>16.969973</td><td>14.758768</td><td>6.728389</td><td>-0.016014</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>-6.69165</td><td>0</td></tr><tr><td>&quot;ZTS&quot;</td><td>2023-12-01</td><td>0</td><td>6</td><td>4.751</td><td>95.221124</td><td>96.035172</td><td>18.28447</td><td>-4.136785</td><td>16.212538</td><td>-19.325342</td><td>-150.0</td><td>150.0</td><td>200.0</td><td>-300.0</td><td>61.591662</td><td>54.922165</td><td>52.210853</td><td>51.633403</td><td>50.788053</td><td>1.606235</td><td>1.536062</td><td>1.58278</td><td>1.577631</td><td>1.809127</td><td>50.0</td><td>150.0</td><td>181.30939</td><td>50.0</td><td>-50.0</td><td>50.0</td><td>-50.0</td><td>8.418637</td><td>2.892184</td><td>7.291751</td><td>12.610438</td><td>0.60499</td><td>&hellip;</td><td>7.971014</td><td>0.221926</td><td>0.088652</td><td>25.758157</td><td>0.94583</td><td>-0.30245</td><td>6.926676</td><td>11.765138</td><td>-4.20124</td><td>4.637744</td><td>1.268399</td><td>10.076046</td><td>-7.988456</td><td>-2.847397</td><td>-2.75663</td><td>21.90678</td><td>1.849746</td><td>-12.470055</td><td>1.558202</td><td>-31.606518</td><td>2.927443</td><td>7.15239</td><td>5.857245</td><td>0.326611</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>4.299999</td><td>0</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (3_655, 91)\n",
+       "┌─────┬────────────┬─────────┬─────────┬───┬─────────────┬─────────────┬─────────────┬─────────────┐\n",
+       "│ tic ┆ tdq        ┆ n_purch ┆ n_sales ┆ … ┆ sector_mate ┆ sector_real ┆ fwd_return_ ┆ excess_retu │\n",
+       "│ --- ┆ ---        ┆ ---     ┆ ---     ┆   ┆ rials       ┆ _estate     ┆ 4Q          ┆ rn_4Q_pos   │\n",
+       "│ str ┆ date       ┆ u32     ┆ u32     ┆   ┆ ---         ┆ ---         ┆ ---         ┆ ---         │\n",
+       "│     ┆            ┆         ┆         ┆   ┆ i8          ┆ i8          ┆ f64         ┆ i8          │\n",
+       "╞═════╪════════════╪═════════╪═════════╪═══╪═════════════╪═════════════╪═════════════╪═════════════╡\n",
+       "│ A   ┆ 2022-03-01 ┆ 0       ┆ 5       ┆ … ┆ 0           ┆ 0           ┆ 15.553968   ┆ 1           │\n",
+       "│ A   ┆ 2022-06-01 ┆ 0       ┆ 1       ┆ … ┆ 0           ┆ 0           ┆ 9.318676    ┆ 1           │\n",
+       "│ A   ┆ 2022-09-01 ┆ 0       ┆ 2       ┆ … ┆ 0           ┆ 0           ┆ -5.57725    ┆ 0           │\n",
+       "│ A   ┆ 2022-12-01 ┆ 0       ┆ 14      ┆ … ┆ 0           ┆ 0           ┆ -26.851359  ┆ 0           │\n",
+       "│ A   ┆ 2023-03-01 ┆ 0       ┆ 5       ┆ … ┆ 0           ┆ 0           ┆ -3.250208   ┆ 0           │\n",
+       "│ …   ┆ …          ┆ …       ┆ …       ┆ … ┆ …           ┆ …           ┆ …           ┆ …           │\n",
+       "│ ZTS ┆ 2022-12-01 ┆ 0       ┆ 0       ┆ … ┆ 0           ┆ 0           ┆ 12.704655   ┆ 1           │\n",
+       "│ ZTS ┆ 2023-03-01 ┆ 0       ┆ 1       ┆ … ┆ 0           ┆ 0           ┆ 14.673911   ┆ 0           │\n",
+       "│ ZTS ┆ 2023-06-01 ┆ 0       ┆ 2       ┆ … ┆ 0           ┆ 0           ┆ 3.072419    ┆ 0           │\n",
+       "│ ZTS ┆ 2023-09-01 ┆ 0       ┆ 2       ┆ … ┆ 0           ┆ 0           ┆ -6.69165    ┆ 0           │\n",
+       "│ ZTS ┆ 2023-12-01 ┆ 0       ┆ 6       ┆ … ┆ 0           ┆ 0           ┆ 4.299999    ┆ 0           │\n",
+       "└─────┴────────────┴─────────┴─────────┴───┴─────────────┴─────────────┴─────────────┴─────────────┘"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "val"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average freturn: 0.40%\n",
+      "\n",
+      "Top 100 stocks: 34.00%\n",
+      "\n",
+      "Bottom 100 stocks: 42.00%\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(X_val)\n",
+    "val_subset = val.with_columns(pl.Series(\"pred\", y_pred)).sort(\"pred\", descending=True)\n",
+    "n = 100\n",
+    "top = val_subset.head(n)\n",
+    "bottom = val_subset.tail(n)\n",
+    "\n",
+    "top_freturn = top.select(pl.col(target)).mean().item()\n",
+    "bottom_freturn = bottom.select(pl.col(target)).mean().item()\n",
+    "freturn = val_subset.select(pl.col(target)).mean().item()\n",
+    "\n",
+    "print(f\"Average freturn: {freturn:.2f}%\")\n",
+    "\n",
+    "print(f\"\\nTop {n} stocks: {top_freturn * 100:.2f}%\")\n",
+    "print(f\"\\nBottom {n} stocks: {bottom_freturn * 100:.2f}%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.36363636363636365\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (77, 92)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>tic</th><th>tdq</th><th>n_purch</th><th>n_sales</th><th>insider_balance</th><th>volume_ma20</th><th>volume_ma50</th><th>price_mom</th><th>price_qoq</th><th>price_yoy</th><th>price_2y</th><th>price_risk_qoq</th><th>price_risk_sos</th><th>price_risk_yoy</th><th>price_risk_2y</th><th>rsi_14d</th><th>rsi_30d</th><th>rsi_60d</th><th>rsi_90d</th><th>rsi_1y</th><th>vol_mom</th><th>vol_qoq</th><th>vol_sos</th><th>vol_yoy</th><th>vol_2y</th><th>rel_vol_mom</th><th>rel_vol_qoq</th><th>rel_vol_yoy</th><th>momentum_mom</th><th>momentum_qoq</th><th>momentum_yoy</th><th>momentum_2y</th><th>index_mom</th><th>index_qoq</th><th>index_sos</th><th>index_yoy</th><th>index_2y</th><th>&hellip;</th><th>ltq_yoy</th><th>ltq_2y</th><th>dlttq_yoy</th><th>gpm_yoy</th><th>gpm_2y</th><th>roa_yoy</th><th>roa_2y</th><th>roi_yoy</th><th>roi_2y</th><th>roe_yoy</th><th>fcf_yoy</th><th>der_yoy</th><th>dr_yoy</th><th>dr_2y</th><th>ltda_yoy</th><th>ev_ebitda_yoy</th><th>ltcr_yoy</th><th>pe_yoy</th><th>pe_2y</th><th>pb_yoy</th><th>ps_yoy</th><th>atr_yoy</th><th>size_yoy</th><th>sector_utilities</th><th>sector_health_care</th><th>sector_financials</th><th>sector_consumer_discretionary</th><th>sector_consumer_staples</th><th>sector_energy</th><th>sector_industrials</th><th>sector_information_technology</th><th>sector_communication_services</th><th>sector_materials</th><th>sector_real_estate</th><th>fwd_return_4Q</th><th>excess_return_4Q_pos</th><th>pred</th></tr><tr><td>str</td><td>date</td><td>u32</td><td>u32</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>&hellip;</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>f64</td><td>i8</td><td>i64</td></tr></thead><tbody><tr><td>&quot;A&quot;</td><td>2022-03-01</td><td>0</td><td>5</td><td>19.293</td><td>133.226941</td><td>117.561988</td><td>-3.74289</td><td>-10.984423</td><td>6.704944</td><td>60.949116</td><td>-150.0</td><td>-150.0</td><td>200.0</td><td>300.0</td><td>43.158399</td><td>42.053527</td><td>43.715374</td><td>45.665021</td><td>51.522485</td><td>2.161613</td><td>1.857107</td><td>1.641768</td><td>1.424811</td><td>1.894748</td><td>50.0</td><td>150.0</td><td>160.441865</td><td>50.0</td><td>50.0</td><td>50.0</td><td>50.0</td><td>-2.833813</td><td>-4.58184</td><td>-4.913681</td><td>11.264523</td><td>39.350786</td><td>&hellip;</td><td>6.221766</td><td>11.175586</td><td>24.942792</td><td>0.418303</td><td>2.029907</td><td>39.358651</td><td>45.107156</td><td>-12.893125</td><td>20.895223</td><td>38.663007</td><td>7.142857</td><td>-0.991586</td><td>-0.494881</td><td>2.283261</td><td>17.042371</td><td>-23.129185</td><td>-14.246468</td><td>-30.140404</td><td>5.066142</td><td>-3.130584</td><td>-10.827631</td><td>-0.76069</td><td>0.711764</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>15.553968</td><td>1</td><td>1</td></tr><tr><td>&quot;A&quot;</td><td>2022-06-01</td><td>0</td><td>1</td><td>0.165</td><td>116.531326</td><td>115.558614</td><td>2.943879</td><td>-8.066325</td><td>-9.870404</td><td>36.327384</td><td>-150.0</td><td>-150.0</td><td>-200.0</td><td>300.0</td><td>49.272664</td><td>47.577979</td><td>46.541324</td><td>46.77372</td><td>50.376317</td><td>3.014695</td><td>2.469218</td><td>2.175493</td><td>1.758636</td><td>1.591078</td><td>50.0</td><td>147.055598</td><td>153.737685</td><td>-50.0</td><td>50.0</td><td>50.0</td><td>50.0</td><td>-1.303128</td><td>-5.25865</td><td>-10.198599</td><td>-2.540092</td><td>33.121373</td><td>&hellip;</td><td>-4.56335</td><td>13.782803</td><td>0.110011</td><td>0.008074</td><td>0.958421</td><td>35.79613</td><td>67.232292</td><td>21.762787</td><td>126.509135</td><td>28.22335</td><td>-40.042373</td><td>-10.37675</td><td>-5.083665</td><td>2.899704</td><td>-0.435782</td><td>-28.314049</td><td>-40.10826</td><td>-35.577858</td><td>-28.576028</td><td>-17.395771</td><td>-21.606108</td><td>1.776929</td><td>0.059105</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>9.318676</td><td>1</td><td>1</td></tr><tr><td>&quot;A&quot;</td><td>2022-09-01</td><td>0</td><td>2</td><td>0.868</td><td>79.928753</td><td>82.561189</td><td>-4.184011</td><td>1.018566</td><td>-26.514679</td><td>27.501967</td><td>46.302237</td><td>-150.0</td><td>-200.0</td><td>300.0</td><td>46.529941</td><td>50.295003</td><td>49.851588</td><td>49.37193</td><td>50.749185</td><td>2.316539</td><td>2.19982</td><td>2.343169</td><td>2.031332</td><td>1.71518</td><td>50.0</td><td>148.682818</td><td>153.04007</td><td>50.0</td><td>-27.157106</td><td>50.0</td><td>50.0</td><td>-4.532181</td><td>-3.750642</td><td>-9.089974</td><td>-12.317168</td><td>12.482106</td><td>&hellip;</td><td>-2.741208</td><td>18.138007</td><td>0.146628</td><td>1.743347</td><td>3.022338</td><td>34.230978</td><td>74.990489</td><td>22.24579</td><td>53.513405</td><td>30.320847</td><td>-2.39521</td><td>-5.511298</td><td>-2.67627</td><td>7.568238</td><td>0.213494</td><td>-37.978017</td><td>-2.538116</td><td>-46.471204</td><td>-36.421035</td><td>-30.24082</td><td>-33.770549</td><td>8.064162</td><td>-0.007209</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>-5.57725</td><td>0</td><td>1</td></tr><tr><td>&quot;A&quot;</td><td>2022-12-01</td><td>0</td><td>14</td><td>29.504</td><td>84.294291</td><td>79.954035</td><td>10.809849</td><td>20.742453</td><td>5.337014</td><td>35.332866</td><td>150.0</td><td>150.0</td><td>200.0</td><td>300.0</td><td>64.82618</td><td>61.518024</td><td>57.445711</td><td>55.2601</td><td>52.630406</td><td>2.743982</td><td>2.300815</td><td>2.214761</td><td>2.21394</td><td>1.804737</td><td>50.0</td><td>134.867777</td><td>145.846538</td><td>50.0</td><td>50.0</td><td>-50.0</td><td>50.0</td><td>5.717434</td><td>4.308392</td><td>-2.400146</td><td>-9.671307</td><td>11.307188</td><td>&hellip;</td><td>-1.674191</td><td>9.949516</td><td>0.146574</td><td>0.950664</td><td>3.005054</td><td>5.338708</td><td>59.422189</td><td>-15.912726</td><td>47.596638</td><td>5.277354</td><td>1.587302</td><td>-0.117289</td><td>-0.059079</td><td>0.501708</td><td>1.791594</td><td>-10.702488</td><td>1.438619</td><td>-0.676569</td><td>-25.389269</td><td>4.565081</td><td>-5.016431</td><td>12.339549</td><td>-0.175597</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>-26.851359</td><td>0</td><td>1</td></tr><tr><td>&quot;A&quot;</td><td>2023-03-01</td><td>0</td><td>5</td><td>9.322</td><td>85.991285</td><td>81.411412</td><td>-9.37789</td><td>-11.272423</td><td>5.484807</td><td>10.467538</td><td>-150.0</td><td>150.0</td><td>200.0</td><td>300.0</td><td>27.642313</td><td>39.893702</td><td>46.853905</td><td>48.655084</td><td>50.816258</td><td>1.430282</td><td>1.57085</td><td>1.948803</td><td>2.161252</td><td>1.828145</td><td>50.0</td><td>148.188834</td><td>145.011024</td><td>50.0</td><td>50.0</td><td>-50.0</td><td>50.0</td><td>-1.652163</td><td>-3.154822</td><td>-1.965465</td><td>-9.660628</td><td>1.270428</td><td>&hellip;</td><td>2.648367</td><td>9.034908</td><td>0.10989</td><td>1.406293</td><td>1.830478</td><td>3.839863</td><td>44.709833</td><td>17.55397</td><td>2.397589</td><td>0.886202</td><td>16.078431</td><td>-5.678431</td><td>-2.916963</td><td>-3.397409</td><td>-5.31781</td><td>-10.779239</td><td>15.951013</td><td>-6.946706</td><td>-34.993345</td><td>-6.122066</td><td>-4.984549</td><td>2.849477</td><td>0.60311</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>-3.250208</td><td>0</td><td>1</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;AFL&quot;</td><td>2022-06-01</td><td>1</td><td>4</td><td>0.706</td><td>99.709075</td><td>88.81247</td><td>4.380458</td><td>-0.482524</td><td>4.654418</td><td>60.606871</td><td>-32.525135</td><td>150.0</td><td>200.0</td><td>300.0</td><td>54.610327</td><td>49.381937</td><td>49.720993</td><td>50.589655</td><td>51.892807</td><td>1.588698</td><td>1.483543</td><td>1.512047</td><td>1.393566</td><td>1.645896</td><td>50.0</td><td>88.35318</td><td>121.823718</td><td>-50.0</td><td>9.175821</td><td>-50.0</td><td>50.0</td><td>-1.303128</td><td>-5.25865</td><td>-10.198599</td><td>-2.540092</td><td>33.121373</td><td>&hellip;</td><td>-6.770908</td><td>-6.145479</td><td>-3.956479</td><td>-3.434403</td><td>26.386743</td><td>-20.304906</td><td>42.956154</td><td>-12.73818</td><td>64.467749</td><td>-19.439403</td><td>-7.759883</td><td>1.362602</td><td>0.273612</td><td>-3.228601</td><td>3.300703</td><td>-2.620483</td><td>-3.960084</td><td>33.117502</td><td>4.705925</td><td>7.240255</td><td>5.287569</td><td>-6.460572</td><td>-0.608476</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>12.116626</td><td>1</td><td>1</td></tr><tr><td>&quot;AFL&quot;</td><td>2022-09-01</td><td>0</td><td>4</td><td>2.339</td><td>72.651825</td><td>78.859641</td><td>0.864264</td><td>0.60852</td><td>5.644303</td><td>62.179834</td><td>41.50154</td><td>-150.0</td><td>200.0</td><td>300.0</td><td>44.451863</td><td>50.626418</td><td>50.664731</td><td>50.748047</td><td>51.576331</td><td>1.266545</td><td>1.46626</td><td>1.460371</td><td>1.439141</td><td>1.525142</td><td>50.0</td><td>99.102487</td><td>108.424517</td><td>-19.069493</td><td>-16.224432</td><td>-45.824681</td><td>50.0</td><td>-4.532181</td><td>-3.750642</td><td>-9.089974</td><td>-12.317168</td><td>12.482106</td><td>&hellip;</td><td>-14.492357</td><td>-14.432077</td><td>-8.681197</td><td>12.046824</td><td>29.782344</td><td>-10.529083</td><td>72.427181</td><td>55.534091</td><td>89.702007</td><td>-3.933021</td><td>-46.985447</td><td>9.318996</td><td>1.813037</td><td>-0.89429</td><td>8.732322</td><td>-10.822963</td><td>-41.945633</td><td>32.813317</td><td>-2.643767</td><td>27.589742</td><td>8.089467</td><td>9.746982</td><td>-1.455381</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>22.114378</td><td>1</td><td>1</td></tr><tr><td>&quot;AFL&quot;</td><td>2022-12-01</td><td>0</td><td>5</td><td>4.705</td><td>102.120517</td><td>94.914768</td><td>7.633597</td><td>20.796246</td><td>33.985478</td><td>60.872489</td><td>150.0</td><td>150.0</td><td>200.0</td><td>300.0</td><td>68.400056</td><td>65.801368</td><td>60.926112</td><td>58.377648</td><td>53.893066</td><td>1.332801</td><td>1.56518</td><td>1.501103</td><td>1.502817</td><td>1.391457</td><td>50.0</td><td>91.746767</td><td>99.000261</td><td>50.0</td><td>50.0</td><td>-50.0</td><td>50.0</td><td>5.717434</td><td>4.308392</td><td>-2.400146</td><td>-9.671307</td><td>11.307188</td><td>&hellip;</td><td>-18.180172</td><td>-19.091503</td><td>-6.79395</td><td>3.774564</td><td>10.444318</td><td>50.016073</td><td>38.215451</td><td>136.185965</td><td>-17.299836</td><td>66.232068</td><td>-41.932002</td><td>13.664245</td><td>2.57626</td><td>1.659852</td><td>16.850992</td><td>-7.800877</td><td>-37.699326</td><td>5.309464</td><td>29.475684</td><td>75.058101</td><td>36.624981</td><td>12.404182</td><td>-1.886155</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>11.275425</td><td>1</td><td>1</td></tr><tr><td>&quot;AFL&quot;</td><td>2023-03-01</td><td>0</td><td>4</td><td>3.293</td><td>89.37487</td><td>101.888445</td><td>-6.736032</td><td>-5.296813</td><td>11.507616</td><td>39.819378</td><td>-150.0</td><td>150.0</td><td>200.0</td><td>300.0</td><td>39.16616</td><td>45.705162</td><td>50.513318</td><td>51.8188</td><td>52.48003</td><td>1.628044</td><td>1.347271</td><td>1.453388</td><td>1.491305</td><td>1.384386</td><td>50.0</td><td>127.097152</td><td>100.060404</td><td>50.0</td><td>50.0</td><td>-50.0</td><td>50.0</td><td>-1.652163</td><td>-3.154822</td><td>-1.965465</td><td>-9.660628</td><td>1.270428</td><td>&hellip;</td><td>-10.210879</td><td>-15.152022</td><td>-6.460533</td><td>-6.414139</td><td>-0.615698</td><td>16.158731</td><td>10.180748</td><td>-75.556261</td><td>-73.133092</td><td>60.375468</td><td>18.735632</td><td>48.25013</td><td>7.376441</td><td>6.326294</td><td>11.861382</td><td>7.034539</td><td>26.9364</td><td>12.998248</td><td>41.584964</td><td>81.221468</td><td>24.414011</td><td>-7.451168</td><td>-1.494696</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>21.965642</td><td>1</td><td>1</td></tr><tr><td>&quot;AFL&quot;</td><td>2023-06-01</td><td>0</td><td>6</td><td>7.647</td><td>88.305899</td><td>89.490315</td><td>-5.754658</td><td>-5.174434</td><td>6.802052</td><td>13.074643</td><td>-150.0</td><td>-150.0</td><td>200.0</td><td>300.0</td><td>42.372501</td><td>45.613209</td><td>47.780038</td><td>49.050053</td><td>51.380387</td><td>1.045422</td><td>1.50681</td><td>1.41434</td><td>1.459171</td><td>1.42419</td><td>50.0</td><td>150.0</td><td>110.769921</td><td>-50.0</td><td>-50.0</td><td>50.0</td><td>50.0</td><td>2.462385</td><td>4.263394</td><td>6.655249</td><td>2.150699</td><td>0.451685</td><td>&hellip;</td><td>-1.988615</td><td>-8.624875</td><td>-4.479918</td><td>6.868985</td><td>3.198673</td><td>15.975152</td><td>-7.573494</td><td>55.558723</td><td>35.743373</td><td>58.869795</td><td>-43.809524</td><td>46.278921</td><td>6.783799</td><td>7.075972</td><td>4.069514</td><td>4.007717</td><td>-41.174175</td><td>-4.443081</td><td>27.202983</td><td>51.811081</td><td>13.847278</td><td>5.969777</td><td>-0.720448</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>32.445487</td><td>1</td><td>1</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (77, 92)\n",
+       "┌─────┬────────────┬─────────┬─────────┬───┬────────────────┬───────────────┬───────────────┬──────┐\n",
+       "│ tic ┆ tdq        ┆ n_purch ┆ n_sales ┆ … ┆ sector_real_es ┆ fwd_return_4Q ┆ excess_return ┆ pred │\n",
+       "│ --- ┆ ---        ┆ ---     ┆ ---     ┆   ┆ tate           ┆ ---           ┆ _4Q_pos       ┆ ---  │\n",
+       "│ str ┆ date       ┆ u32     ┆ u32     ┆   ┆ ---            ┆ f64           ┆ ---           ┆ i64  │\n",
+       "│     ┆            ┆         ┆         ┆   ┆ i8             ┆               ┆ i8            ┆      │\n",
+       "╞═════╪════════════╪═════════╪═════════╪═══╪════════════════╪═══════════════╪═══════════════╪══════╡\n",
+       "│ A   ┆ 2022-03-01 ┆ 0       ┆ 5       ┆ … ┆ 0              ┆ 15.553968     ┆ 1             ┆ 1    │\n",
+       "│ A   ┆ 2022-06-01 ┆ 0       ┆ 1       ┆ … ┆ 0              ┆ 9.318676      ┆ 1             ┆ 1    │\n",
+       "│ A   ┆ 2022-09-01 ┆ 0       ┆ 2       ┆ … ┆ 0              ┆ -5.57725      ┆ 0             ┆ 1    │\n",
+       "│ A   ┆ 2022-12-01 ┆ 0       ┆ 14      ┆ … ┆ 0              ┆ -26.851359    ┆ 0             ┆ 1    │\n",
+       "│ A   ┆ 2023-03-01 ┆ 0       ┆ 5       ┆ … ┆ 0              ┆ -3.250208     ┆ 0             ┆ 1    │\n",
+       "│ …   ┆ …          ┆ …       ┆ …       ┆ … ┆ …              ┆ …             ┆ …             ┆ …    │\n",
+       "│ AFL ┆ 2022-06-01 ┆ 1       ┆ 4       ┆ … ┆ 0              ┆ 12.116626     ┆ 1             ┆ 1    │\n",
+       "│ AFL ┆ 2022-09-01 ┆ 0       ┆ 4       ┆ … ┆ 0              ┆ 22.114378     ┆ 1             ┆ 1    │\n",
+       "│ AFL ┆ 2022-12-01 ┆ 0       ┆ 5       ┆ … ┆ 0              ┆ 11.275425     ┆ 1             ┆ 1    │\n",
+       "│ AFL ┆ 2023-03-01 ┆ 0       ┆ 4       ┆ … ┆ 0              ┆ 21.965642     ┆ 1             ┆ 1    │\n",
+       "│ AFL ┆ 2023-06-01 ┆ 0       ┆ 6       ┆ … ┆ 0              ┆ 32.445487     ┆ 1             ┆ 1    │\n",
+       "└─────┴────────────┴─────────┴─────────┴───┴────────────────┴───────────────┴───────────────┴──────┘"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "top = val_subset.head(100).filter((pl.col(\"pe\") < 50) & (pl.col(\"f_score\") >= 5))\n",
+    "print(top.select(pl.col(target)).mean().item())\n",
+    "top"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "DATE 2022-03-01\n",
+      "\n",
+      "Top 50 stocks:\n",
+      "Average freturn: 34.90%\n",
+      "\n",
+      "Bottom 50 stocks:\n",
+      "Average freturn: -403.35%\n",
+      "\n",
+      "DATE 2022-06-01\n",
+      "\n",
+      "Top 50 stocks:\n",
+      "Average freturn: -103.18%\n",
+      "\n",
+      "Bottom 50 stocks:\n",
+      "Average freturn: -202.87%\n",
+      "\n",
+      "DATE 2022-09-01\n",
+      "\n",
+      "Top 50 stocks:\n",
+      "Average freturn: 984.15%\n",
+      "\n",
+      "Bottom 50 stocks:\n",
+      "Average freturn: 1141.68%\n",
+      "\n",
+      "DATE 2022-12-01\n",
+      "\n",
+      "Top 50 stocks:\n",
+      "Average freturn: 17.13%\n",
+      "\n",
+      "Bottom 50 stocks:\n",
+      "Average freturn: -434.97%\n",
+      "\n",
+      "DATE 2023-03-01\n",
+      "\n",
+      "Top 50 stocks:\n",
+      "Average freturn: 1340.95%\n",
+      "\n",
+      "Bottom 50 stocks:\n",
+      "Average freturn: -139.93%\n",
+      "\n",
+      "DATE 2023-06-01\n",
+      "\n",
+      "Top 50 stocks:\n",
+      "Average freturn: 2060.39%\n",
+      "\n",
+      "Bottom 50 stocks:\n",
+      "Average freturn: 2086.70%\n",
+      "\n",
+      "DATE 2023-09-01\n",
+      "\n",
+      "Top 50 stocks:\n",
+      "Average freturn: 877.32%\n",
+      "\n",
+      "Bottom 50 stocks:\n",
+      "Average freturn: 1577.30%\n",
+      "\n",
+      "DATE 2023-12-01\n",
+      "\n",
+      "Top 50 stocks:\n",
+      "Average freturn: 2006.79%\n",
+      "\n",
+      "Bottom 50 stocks:\n",
+      "Average freturn: 2286.45%\n"
+     ]
+    }
+   ],
+   "source": [
+    "eval_dates = [\n",
+    "    \"2022-03-01\",\n",
+    "    \"2022-06-01\",\n",
+    "    \"2022-09-01\",\n",
+    "    \"2022-12-01\",\n",
+    "    \"2023-03-01\",\n",
+    "    \"2023-06-01\",\n",
+    "    \"2023-09-01\",\n",
+    "    \"2023-12-01\",\n",
+    "]\n",
+    "\n",
+    "for date in eval_dates:\n",
+    "    y_pred = model.predict(X_val)\n",
+    "    val_subset = (\n",
+    "        val.with_columns(pl.Series(\"pred\", y_pred))\n",
+    "        .filter(pl.col(\"tdq\") == pl.lit(date).str.to_date())\n",
+    "        .sort(\"pred\", descending=True)\n",
+    "    )\n",
+    "    n = 50\n",
+    "    top = val_subset.head(n)\n",
+    "    bottom = val_subset.tail(n)\n",
+    "\n",
+    "    # Calculate average returns for top stocks\n",
+    "    top_freturn = top.select(pl.col(\"fwd_return_4Q\")).mean().item()\n",
+    "\n",
+    "    # Calculate average returns for bottom stocks\n",
+    "    bottom_freturn = bottom.select(pl.col(\"fwd_return_4Q\")).mean().item()\n",
+    "\n",
+    "    print(f\"\\nDATE {date}\")\n",
+    "    print(f\"\\nTop {n} stocks:\")\n",
+    "    print(f\"Average freturn: {top_freturn * 100:.2f}%\")\n",
+    "    print(f\"\\nBottom {n} stocks:\")\n",
+    "    print(f\"Average freturn: {bottom_freturn * 100:.2f}%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('sector_energy', 40.191165924072266),\n",
+       " ('momentum_yoy', 27.2480411529541),\n",
+       " ('size', 25.87224769592285),\n",
+       " ('vol_sos', 23.586788177490234),\n",
+       " ('fear_ma30', 20.31974220275879),\n",
+       " ('index_2y', 16.7189998626709),\n",
+       " ('high_fear', 16.481006622314453),\n",
+       " ('sector_real_estate', 15.660086631774902),\n",
+       " ('sector_health_care', 15.417682647705078),\n",
+       " ('vol_2y', 14.626969337463379),\n",
+       " ('gpm_2y', 14.605377197265625),\n",
+       " ('vol_yoy', 14.286331176757812),\n",
+       " ('rsi_1y', 14.145264625549316),\n",
+       " ('sector_information_technology', 13.999712944030762),\n",
+       " ('sector_utilities', 13.80540943145752),\n",
+       " ('index_sos', 13.57997989654541),\n",
+       " ('index_yoy', 13.11374282836914),\n",
+       " ('rel_vol_yoy', 12.731752395629883),\n",
+       " ('index_mom', 12.614106178283691),\n",
+       " ('rel_vol_qoq', 12.564565658569336),\n",
+       " ('low_fear', 12.17973804473877),\n",
+       " ('sector_communication_services', 11.731181144714355),\n",
+       " ('vol_qoq', 11.615520477294922),\n",
+       " ('price_qoq', 11.137831687927246),\n",
+       " ('gpm_yoy', 11.062193870544434),\n",
+       " ('sector_industrials', 10.805398941040039),\n",
+       " ('ps_yoy', 10.727892875671387),\n",
+       " ('sector_consumer_discretionary', 10.655860900878906),\n",
+       " ('index_qoq', 10.591582298278809),\n",
+       " ('sector_consumer_staples', 10.435930252075195),\n",
+       " ('price_yoy', 10.267444610595703),\n",
+       " ('ev_ebitda', 10.220476150512695),\n",
+       " ('roa_yoy', 9.932145118713379),\n",
+       " ('rsi_30d', 9.705886840820312),\n",
+       " ('roa', 9.501445770263672),\n",
+       " ('price_risk_2y', 9.3060884475708),\n",
+       " ('rsi_14d', 9.27425479888916),\n",
+       " ('roa_2y', 9.200528144836426),\n",
+       " ('ebitdam', 9.015608787536621),\n",
+       " ('rsi_90d', 8.802074432373047),\n",
+       " ('saleq_2y', 8.772933959960938),\n",
+       " ('pe_2y', 8.764995574951172),\n",
+       " ('momentum_2y', 8.54587173461914),\n",
+       " ('pb', 8.486556053161621),\n",
+       " ('ev_ebitda_yoy', 8.481590270996094),\n",
+       " ('dr_2y', 8.154480934143066),\n",
+       " ('rsi_60d', 8.108072280883789),\n",
+       " ('price_risk_sos', 8.001082420349121),\n",
+       " ('ltda_yoy', 7.980452060699463),\n",
+       " ('vol_mom', 7.976050853729248),\n",
+       " ('dr_yoy', 7.819016456604004),\n",
+       " ('price_mom', 7.79142951965332),\n",
+       " ('ps', 7.751694202423096),\n",
+       " ('gpm', 7.725594997406006),\n",
+       " ('f_score', 7.710827827453613),\n",
+       " ('price_2y', 7.5825581550598145),\n",
+       " ('ltq_2y', 7.467060565948486),\n",
+       " ('dlttq_yoy', 7.456555366516113),\n",
+       " ('saleq_yoy', 7.415136814117432),\n",
+       " ('sector_materials', 7.256232261657715),\n",
+       " ('ltcr_yoy', 7.2060346603393555),\n",
+       " ('price_risk_yoy', 7.108036041259766),\n",
+       " ('roe', 7.033833980560303),\n",
+       " ('roi_2y', 6.946475028991699),\n",
+       " ('earn_drift', 6.893874645233154),\n",
+       " ('size_yoy', 6.844686985015869),\n",
+       " ('pe', 6.815245151519775),\n",
+       " ('volume_ma20', 6.670749664306641),\n",
+       " ('niq_2y', 6.4354023933410645),\n",
+       " ('niq_yoy', 6.4303998947143555),\n",
+       " ('roe_yoy', 6.3927741050720215),\n",
+       " ('der_yoy', 6.38761043548584),\n",
+       " ('insider_balance', 6.332291126251221),\n",
+       " ('sector_financials', 6.288899898529053),\n",
+       " ('price_risk_qoq', 6.2268829345703125),\n",
+       " ('ltq_yoy', 6.033779621124268),\n",
+       " ('pb_yoy', 5.937747955322266),\n",
+       " ('fcf_yoy', 5.932516574859619),\n",
+       " ('atr_yoy', 5.818764686584473),\n",
+       " ('volume_ma50', 5.534381866455078),\n",
+       " ('n_purch', 5.444931983947754),\n",
+       " ('roi_yoy', 5.397580146789551),\n",
+       " ('n_sales', 5.303983211517334),\n",
+       " ('pe_yoy', 5.299224376678467),\n",
+       " ('momentum_mom', 4.928510665893555),\n",
+       " ('momentum_qoq', 4.616486072540283)]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.get_importance(\"gain\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('size', 249.0),\n",
+       " ('vol_2y', 247.0),\n",
+       " ('index_2y', 240.0),\n",
+       " ('fear_ma30', 212.0),\n",
+       " ('gpm_2y', 197.0),\n",
+       " ('ev_ebitda', 189.0),\n",
+       " ('ps', 188.0),\n",
+       " ('ebitdam', 176.0),\n",
+       " ('roa_2y', 174.0),\n",
+       " ('roa', 164.0),\n",
+       " ('dr_2y', 154.0),\n",
+       " ('vol_qoq', 153.0),\n",
+       " ('vol_sos', 153.0),\n",
+       " ('saleq_2y', 150.0),\n",
+       " ('pe_2y', 150.0),\n",
+       " ('ltda_yoy', 148.0),\n",
+       " ('index_mom', 147.0),\n",
+       " ('index_sos', 141.0),\n",
+       " ('vol_yoy', 140.0),\n",
+       " ('ltcr_yoy', 138.0),\n",
+       " ('pb', 136.0),\n",
+       " ('dlttq_yoy', 132.0),\n",
+       " ('roa_yoy', 124.0),\n",
+       " ('rsi_1y', 123.0),\n",
+       " ('ev_ebitda_yoy', 122.0),\n",
+       " ('gpm_yoy', 121.0),\n",
+       " ('price_2y', 120.0),\n",
+       " ('atr_yoy', 118.0),\n",
+       " ('ps_yoy', 114.0),\n",
+       " ('volume_ma20', 113.0),\n",
+       " ('rel_vol_yoy', 113.0),\n",
+       " ('pe', 113.0),\n",
+       " ('index_qoq', 112.0),\n",
+       " ('gpm', 112.0),\n",
+       " ('ltq_2y', 110.0),\n",
+       " ('roe', 106.0),\n",
+       " ('index_yoy', 101.0),\n",
+       " ('price_qoq', 100.0),\n",
+       " ('saleq_yoy', 98.0),\n",
+       " ('vol_mom', 97.0),\n",
+       " ('der_yoy', 97.0),\n",
+       " ('size_yoy', 97.0),\n",
+       " ('niq_2y', 96.0),\n",
+       " ('price_mom', 88.0),\n",
+       " ('insider_balance', 81.0),\n",
+       " ('volume_ma50', 81.0),\n",
+       " ('price_yoy', 81.0),\n",
+       " ('dr_yoy', 81.0),\n",
+       " ('roe_yoy', 80.0),\n",
+       " ('roi_yoy', 79.0),\n",
+       " ('roi_2y', 79.0),\n",
+       " ('rsi_90d', 77.0),\n",
+       " ('pe_yoy', 75.0),\n",
+       " ('earn_drift', 72.0),\n",
+       " ('rsi_60d', 71.0),\n",
+       " ('rsi_14d', 70.0),\n",
+       " ('pb_yoy', 67.0),\n",
+       " ('momentum_yoy', 65.0),\n",
+       " ('fcf_yoy', 63.0),\n",
+       " ('rel_vol_qoq', 62.0),\n",
+       " ('ltq_yoy', 59.0),\n",
+       " ('rsi_30d', 46.0),\n",
+       " ('niq_yoy', 46.0),\n",
+       " ('sector_energy', 46.0),\n",
+       " ('n_sales', 38.0),\n",
+       " ('sector_information_technology', 29.0),\n",
+       " ('momentum_qoq', 27.0),\n",
+       " ('price_risk_sos', 26.0),\n",
+       " ('low_fear', 26.0),\n",
+       " ('momentum_2y', 25.0),\n",
+       " ('f_score', 25.0),\n",
+       " ('momentum_mom', 23.0),\n",
+       " ('sector_health_care', 19.0),\n",
+       " ('price_risk_2y', 17.0),\n",
+       " ('sector_consumer_discretionary', 17.0),\n",
+       " ('sector_financials', 16.0),\n",
+       " ('sector_industrials', 14.0),\n",
+       " ('sector_utilities', 12.0),\n",
+       " ('price_risk_yoy', 10.0),\n",
+       " ('sector_communication_services', 9.0),\n",
+       " ('sector_consumer_staples', 8.0),\n",
+       " ('sector_real_estate', 8.0),\n",
+       " ('sector_materials', 5.0),\n",
+       " ('n_purch', 4.0),\n",
+       " ('price_risk_qoq', 3.0),\n",
+       " ('high_fear', 3.0)]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.get_importance(\"weight\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import shap\n",
+    "\n",
+    "\n",
+    "def plot_shap(model, X_train):\n",
+    "    explainer = shap.TreeExplainer(model.model)\n",
+    "    shap_values = explainer(X_train)\n",
+    "\n",
+    "    feature_names = [\n",
+    "        a + \": \" + str(b)\n",
+    "        for a, b in zip(X_train.columns, np.abs(shap_values.values).mean(0).round(2), strict=False)\n",
+    "    ]\n",
+    "\n",
+    "    shap.summary_plot(\n",
+    "        shap_values,\n",
+    "        X_train,\n",
+    "        max_display=X_train.shape[1],\n",
+    "        feature_names=feature_names,\n",
+    "        plot_size=(8, 13.5),\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "#  plot_shap(model, X_train)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "stocksense",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/db_analysis.ipynb b/notebooks/db_analysis.ipynb
deleted file mode 100644
index a897e2d..0000000
--- a/notebooks/db_analysis.ipynb
+++ /dev/null
@@ -1,857 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Stock Database Analysis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m2024-11-28 15:50:25.454\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mstocksense.database_handler.schema\u001b[0m:\u001b[36mcreate_tables\u001b[0m:\u001b[36m121\u001b[0m - \u001b[32m\u001b[1mTables created successfully\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "from stocksense.database_handler import DatabaseHandler\n",
-    "\n",
-    "db = DatabaseHandler()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div><style>\n",
-       ".dataframe > thead > tr,\n",
-       ".dataframe > tbody > tr {\n",
-       "  text-align: right;\n",
-       "  white-space: pre-wrap;\n",
-       "}\n",
-       "</style>\n",
-       "<small>shape: (936, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>tic</th><th>sector</th><th>name</th></tr><tr><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>&quot;AABA&quot;</td><td>&quot;Financials&quot;</td><td>null</td></tr><tr><td>&quot;AAP&quot;</td><td>&quot;Consumer Discretionary&quot;</td><td>null</td></tr><tr><td>&quot;ABC&quot;</td><td>&quot;Health Care&quot;</td><td>null</td></tr><tr><td>&quot;ABI&quot;</td><td>&quot;Industrials&quot;</td><td>null</td></tr><tr><td>&quot;ABKFQ&quot;</td><td>&quot;Financials&quot;</td><td>null</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;XYL&quot;</td><td>&quot;Industrials&quot;</td><td>&quot;Xylem Inc.&quot;</td></tr><tr><td>&quot;YUM&quot;</td><td>&quot;Consumer Discretionary&quot;</td><td>&quot;Yum! Brands&quot;</td></tr><tr><td>&quot;ZBRA&quot;</td><td>&quot;Information Technology&quot;</td><td>&quot;Zebra Technologies&quot;</td></tr><tr><td>&quot;ZBH&quot;</td><td>&quot;Health Care&quot;</td><td>&quot;Zimmer Biomet&quot;</td></tr><tr><td>&quot;ZTS&quot;</td><td>&quot;Health Care&quot;</td><td>&quot;Zoetis&quot;</td></tr></tbody></table></div>"
-      ],
-      "text/plain": [
-       "shape: (936, 3)\n",
-       "┌───────┬────────────────────────┬────────────────────┐\n",
-       "│ tic   ┆ sector                 ┆ name               │\n",
-       "│ ---   ┆ ---                    ┆ ---                │\n",
-       "│ str   ┆ str                    ┆ str                │\n",
-       "╞═══════╪════════════════════════╪════════════════════╡\n",
-       "│ AABA  ┆ Financials             ┆ null               │\n",
-       "│ AAP   ┆ Consumer Discretionary ┆ null               │\n",
-       "│ ABC   ┆ Health Care            ┆ null               │\n",
-       "│ ABI   ┆ Industrials            ┆ null               │\n",
-       "│ ABKFQ ┆ Financials             ┆ null               │\n",
-       "│ …     ┆ …                      ┆ …                  │\n",
-       "│ XYL   ┆ Industrials            ┆ Xylem Inc.         │\n",
-       "│ YUM   ┆ Consumer Discretionary ┆ Yum! Brands        │\n",
-       "│ ZBRA  ┆ Information Technology ┆ Zebra Technologies │\n",
-       "│ ZBH   ┆ Health Care            ┆ Zimmer Biomet      │\n",
-       "│ ZTS   ┆ Health Care            ┆ Zoetis             │\n",
-       "└───────┴────────────────────────┴────────────────────┘"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import polars as pl\n",
-    "\n",
-    "from stocksense.pipeline.scraper import Scraper\n",
-    "\n",
-    "base_df = pl.read_csv(\n",
-    "    \"../stocksense/data/interim/SP500.csv\", separator=\";\", columns=[\"tic\", \"name\", \"sector\"]\n",
-    ")\n",
-    "curr_df = Scraper.scrape_sp500_constituents()\n",
-    "base_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div><style>\n",
-       ".dataframe > thead > tr,\n",
-       ".dataframe > tbody > tr {\n",
-       "  text-align: right;\n",
-       "  white-space: pre-wrap;\n",
-       "}\n",
-       "</style>\n",
-       "<small>shape: (340, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>added</th><th>tic</th><th>name_added</th></tr><tr><td>date</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>2024-11-26</td><td>&quot;TPL&quot;</td><td>&quot;Texas Pacific Land Corporation&quot;</td></tr><tr><td>2024-09-30</td><td>&quot;AMTM&quot;</td><td>&quot;Amentum&quot;</td></tr><tr><td>2024-09-23</td><td>&quot;PLTR&quot;</td><td>&quot;Palantir Technologies&quot;</td></tr><tr><td>2024-09-23</td><td>&quot;DELL&quot;</td><td>&quot;Dell Technologies&quot;</td></tr><tr><td>2024-09-23</td><td>&quot;ERIE&quot;</td><td>&quot;Erie Indemnity&quot;</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>1999-06-09</td><td>&quot;WLP&quot;</td><td>&quot;Wellpoint&quot;</td></tr><tr><td>1998-12-11</td><td>&quot;FSR&quot;</td><td>&quot;Firstar&quot;</td></tr><tr><td>1998-12-11</td><td>&quot;CCL&quot;</td><td>&quot;Carnival Corp.&quot;</td></tr><tr><td>1998-12-11</td><td>&quot;CPWR&quot;</td><td>&quot;Compuware&quot;</td></tr><tr><td>1997-06-17</td><td>&quot;CCI&quot;</td><td>&quot;Countrywide Credit Industries&quot;</td></tr></tbody></table></div>"
-      ],
-      "text/plain": [
-       "shape: (340, 3)\n",
-       "┌────────────┬──────┬────────────────────────────────┐\n",
-       "│ added      ┆ tic  ┆ name_added                     │\n",
-       "│ ---        ┆ ---  ┆ ---                            │\n",
-       "│ date       ┆ str  ┆ str                            │\n",
-       "╞════════════╪══════╪════════════════════════════════╡\n",
-       "│ 2024-11-26 ┆ TPL  ┆ Texas Pacific Land Corporation │\n",
-       "│ 2024-09-30 ┆ AMTM ┆ Amentum                        │\n",
-       "│ 2024-09-23 ┆ PLTR ┆ Palantir Technologies          │\n",
-       "│ 2024-09-23 ┆ DELL ┆ Dell Technologies              │\n",
-       "│ 2024-09-23 ┆ ERIE ┆ Erie Indemnity                 │\n",
-       "│ …          ┆ …    ┆ …                              │\n",
-       "│ 1999-06-09 ┆ WLP  ┆ Wellpoint                      │\n",
-       "│ 1998-12-11 ┆ FSR  ┆ Firstar                        │\n",
-       "│ 1998-12-11 ┆ CCL  ┆ Carnival Corp.                 │\n",
-       "│ 1998-12-11 ┆ CPWR ┆ Compuware                      │\n",
-       "│ 1997-06-17 ┆ CCI  ┆ Countrywide Credit Industries  │\n",
-       "└────────────┴──────┴────────────────────────────────┘"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "additions, removals = Scraper.scrape_sp500_changes()\n",
-    "additions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = base_df.join(additions, on=\"tic\", how=\"left\")\n",
-    "df = df.join(removals, on=\"tic\", how=\"left\")\n",
-    "df = df.join(curr_df.select([\"tic\", \"date_added\"]), on=\"tic\", how=\"left\")\n",
-    "df = df.with_columns(pl.col(\"removed\").alias(\"date_removed\"))\n",
-    "df = df.with_columns(\n",
-    "    pl.when(pl.col(\"date_added\").is_null())\n",
-    "    .then(pl.col(\"added\"))\n",
-    "    .otherwise(pl.col(\"date_added\"))\n",
-    "    .alias(\"date_added\")\n",
-    ").with_columns(\n",
-    "    pl.when(pl.col(\"name\").is_null())\n",
-    "    .then(pl.col(\"name_removed\"))\n",
-    "    .otherwise(pl.col(\"name\"))\n",
-    "    .alias(\"name\")\n",
-    ")\n",
-    "df = df.filter(~(pl.col(\"date_added\").is_null() & pl.col(\"date_removed\").is_null()))\n",
-    "\n",
-    "df = df[[\"tic\", \"name\", \"sector\", \"date_added\", \"date_removed\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div><style>\n",
-       ".dataframe > thead > tr,\n",
-       ".dataframe > tbody > tr {\n",
-       "  text-align: right;\n",
-       "  white-space: pre-wrap;\n",
-       "}\n",
-       "</style>\n",
-       "<small>shape: (1, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>tic</th><th>name</th><th>sector</th><th>date_added</th><th>date_removed</th></tr><tr><td>str</td><td>str</td><td>str</td><td>date</td><td>date</td></tr></thead><tbody><tr><td>&quot;AMZN&quot;</td><td>&quot;Amazon&quot;</td><td>&quot;Consumer Discretionary&quot;</td><td>2005-11-18</td><td>null</td></tr></tbody></table></div>"
-      ],
-      "text/plain": [
-       "shape: (1, 5)\n",
-       "┌──────┬────────┬────────────────────────┬────────────┬──────────────┐\n",
-       "│ tic  ┆ name   ┆ sector                 ┆ date_added ┆ date_removed │\n",
-       "│ ---  ┆ ---    ┆ ---                    ┆ ---        ┆ ---          │\n",
-       "│ str  ┆ str    ┆ str                    ┆ date       ┆ date         │\n",
-       "╞══════╪════════╪════════════════════════╪════════════╪══════════════╡\n",
-       "│ AMZN ┆ Amazon ┆ Consumer Discretionary ┆ 2005-11-18 ┆ null         │\n",
-       "└──────┴────────┴────────────────────────┴────────────┴──────────────┘"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stocks = db.fetch_stock(\"AMZN\")\n",
-    "stocks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div><style>\n",
-       ".dataframe > thead > tr,\n",
-       ".dataframe > tbody > tr {\n",
-       "  text-align: right;\n",
-       "  white-space: pre-wrap;\n",
-       "}\n",
-       "</style>\n",
-       "<small>shape: (4_992, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>tic</th><th>date</th><th>close</th><th>adj_close</th><th>volume</th></tr><tr><td>str</td><td>date</td><td>f64</td><td>f64</td><td>i64</td></tr></thead><tbody><tr><td>&quot;AMZN&quot;</td><td>2005-01-03</td><td>2.226</td><td>2.226</td><td>208930000</td></tr><tr><td>&quot;AMZN&quot;</td><td>2005-01-04</td><td>2.107</td><td>2.107</td><td>388370000</td></tr><tr><td>&quot;AMZN&quot;</td><td>2005-01-05</td><td>2.0885</td><td>2.0885</td><td>167084000</td></tr><tr><td>&quot;AMZN&quot;</td><td>2005-01-06</td><td>2.0525</td><td>2.0525</td><td>174018000</td></tr><tr><td>&quot;AMZN&quot;</td><td>2005-01-07</td><td>2.116</td><td>2.116</td><td>196732000</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;AMZN&quot;</td><td>2024-10-25</td><td>187.830002</td><td>187.830002</td><td>29362100</td></tr><tr><td>&quot;AMZN&quot;</td><td>2024-10-28</td><td>188.389999</td><td>188.389999</td><td>27930800</td></tr><tr><td>&quot;AMZN&quot;</td><td>2024-10-29</td><td>190.830002</td><td>190.830002</td><td>35690200</td></tr><tr><td>&quot;AMZN&quot;</td><td>2024-10-30</td><td>192.729996</td><td>192.729996</td><td>37707600</td></tr><tr><td>&quot;AMZN&quot;</td><td>2024-10-31</td><td>186.399994</td><td>186.399994</td><td>74225400</td></tr></tbody></table></div>"
-      ],
-      "text/plain": [
-       "shape: (4_992, 5)\n",
-       "┌──────┬────────────┬────────────┬────────────┬───────────┐\n",
-       "│ tic  ┆ date       ┆ close      ┆ adj_close  ┆ volume    │\n",
-       "│ ---  ┆ ---        ┆ ---        ┆ ---        ┆ ---       │\n",
-       "│ str  ┆ date       ┆ f64        ┆ f64        ┆ i64       │\n",
-       "╞══════╪════════════╪════════════╪════════════╪═══════════╡\n",
-       "│ AMZN ┆ 2005-01-03 ┆ 2.226      ┆ 2.226      ┆ 208930000 │\n",
-       "│ AMZN ┆ 2005-01-04 ┆ 2.107      ┆ 2.107      ┆ 388370000 │\n",
-       "│ AMZN ┆ 2005-01-05 ┆ 2.0885     ┆ 2.0885     ┆ 167084000 │\n",
-       "│ AMZN ┆ 2005-01-06 ┆ 2.0525     ┆ 2.0525     ┆ 174018000 │\n",
-       "│ AMZN ┆ 2005-01-07 ┆ 2.116      ┆ 2.116      ┆ 196732000 │\n",
-       "│ …    ┆ …          ┆ …          ┆ …          ┆ …         │\n",
-       "│ AMZN ┆ 2024-10-25 ┆ 187.830002 ┆ 187.830002 ┆ 29362100  │\n",
-       "│ AMZN ┆ 2024-10-28 ┆ 188.389999 ┆ 188.389999 ┆ 27930800  │\n",
-       "│ AMZN ┆ 2024-10-29 ┆ 190.830002 ┆ 190.830002 ┆ 35690200  │\n",
-       "│ AMZN ┆ 2024-10-30 ┆ 192.729996 ┆ 192.729996 ┆ 37707600  │\n",
-       "│ AMZN ┆ 2024-10-31 ┆ 186.399994 ┆ 186.399994 ┆ 74225400  │\n",
-       "└──────┴────────────┴────────────┴────────────┴───────────┘"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mdf = db.fetch_market_data(\"AMZN\").sort(by=[\"date\"])\n",
-    "mdf"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>tic</th>\n",
-       "      <th>filling_date</th>\n",
-       "      <th>trade_date</th>\n",
-       "      <th>owner_name</th>\n",
-       "      <th>title</th>\n",
-       "      <th>transaction_type</th>\n",
-       "      <th>last_price</th>\n",
-       "      <th>qty</th>\n",
-       "      <th>shares_held</th>\n",
-       "      <th>owned</th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>PLTR</td>\n",
-       "      <td>2020-10-02</td>\n",
-       "      <td>2020-09-30</td>\n",
-       "      <td>Cohen Stephen Andrew</td>\n",
-       "      <td>Pres, Secretary</td>\n",
-       "      <td>S - Sale+OE</td>\n",
-       "      <td>$9.71</td>\n",
-       "      <td>-2,000,000</td>\n",
-       "      <td>2,402,647</td>\n",
-       "      <td>-45%</td>\n",
-       "      <td>-$19,422,100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>PLTR</td>\n",
-       "      <td>2020-10-02</td>\n",
-       "      <td>2020-09-30</td>\n",
-       "      <td>Glazer David A.</td>\n",
-       "      <td>CFO, Treasurer</td>\n",
-       "      <td>S - Sale</td>\n",
-       "      <td>$10.03</td>\n",
-       "      <td>-1,615,621</td>\n",
-       "      <td>3,940,152</td>\n",
-       "      <td>-29%</td>\n",
-       "      <td>-$16,206,016</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>PLTR</td>\n",
-       "      <td>2020-10-02</td>\n",
-       "      <td>2020-09-30</td>\n",
-       "      <td>Karp Alexander C.</td>\n",
-       "      <td>CEO</td>\n",
-       "      <td>S - Sale+OE</td>\n",
-       "      <td>$9.70</td>\n",
-       "      <td>-11,500,000</td>\n",
-       "      <td>9,026,496</td>\n",
-       "      <td>-56%</td>\n",
-       "      <td>-$111,606,220</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>PLTR</td>\n",
-       "      <td>2020-10-02</td>\n",
-       "      <td>2020-09-30</td>\n",
-       "      <td>Long Matthew A.</td>\n",
-       "      <td>GC</td>\n",
-       "      <td>S - Sale+OE</td>\n",
-       "      <td>$10.06</td>\n",
-       "      <td>-579,404</td>\n",
-       "      <td>874,006</td>\n",
-       "      <td>-40%</td>\n",
-       "      <td>-$5,830,660</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>PLTR</td>\n",
-       "      <td>2020-10-02</td>\n",
-       "      <td>2020-09-30</td>\n",
-       "      <td>Moore Alexander D.</td>\n",
-       "      <td>Dir</td>\n",
-       "      <td>S - Sale</td>\n",
-       "      <td>$9.54</td>\n",
-       "      <td>-285,000</td>\n",
-       "      <td>2,429,249</td>\n",
-       "      <td>-11%</td>\n",
-       "      <td>-$2,719,315</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>277</th>\n",
-       "      <td>PLTR</td>\n",
-       "      <td>2024-10-07</td>\n",
-       "      <td>2024-10-03</td>\n",
-       "      <td>Planishek Heather A.</td>\n",
-       "      <td>See Remarks</td>\n",
-       "      <td>S - Sale+OE</td>\n",
-       "      <td>$39.32</td>\n",
-       "      <td>-34,017</td>\n",
-       "      <td>570,136</td>\n",
-       "      <td>-6%</td>\n",
-       "      <td>-$1,337,500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>278</th>\n",
-       "      <td>PLTR</td>\n",
-       "      <td>2024-10-08</td>\n",
-       "      <td>2024-10-04</td>\n",
-       "      <td>Taylor Ryan D.</td>\n",
-       "      <td>See Remarks</td>\n",
-       "      <td>S - Sale+OE</td>\n",
-       "      <td>$40.00</td>\n",
-       "      <td>-195,500</td>\n",
-       "      <td>331,369</td>\n",
-       "      <td>-37%</td>\n",
-       "      <td>-$7,820,704</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>279</th>\n",
-       "      <td>PLTR</td>\n",
-       "      <td>2024-10-15</td>\n",
-       "      <td>2024-10-11</td>\n",
-       "      <td>Stat Lauren Elaina Friedman</td>\n",
-       "      <td>Dir</td>\n",
-       "      <td>S - Sale</td>\n",
-       "      <td>$43.90</td>\n",
-       "      <td>-7,321</td>\n",
-       "      <td>201,243</td>\n",
-       "      <td>-4%</td>\n",
-       "      <td>-$321,392</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>280</th>\n",
-       "      <td>PLTR</td>\n",
-       "      <td>2024-10-29</td>\n",
-       "      <td>2024-10-25</td>\n",
-       "      <td>Karp Alexander C.</td>\n",
-       "      <td>See Remarks</td>\n",
-       "      <td>S - Sale+OE</td>\n",
-       "      <td>$45.01</td>\n",
-       "      <td>-5,656,293</td>\n",
-       "      <td>6,432,258</td>\n",
-       "      <td>-47%</td>\n",
-       "      <td>-$254,610,669</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>281</th>\n",
-       "      <td>PLTR</td>\n",
-       "      <td>2024-10-29</td>\n",
-       "      <td>2024-10-25</td>\n",
-       "      <td>Taylor Ryan D.</td>\n",
-       "      <td>See Remarks</td>\n",
-       "      <td>S - Sale+OE</td>\n",
-       "      <td>$45.00</td>\n",
-       "      <td>-123,334</td>\n",
-       "      <td>331,369</td>\n",
-       "      <td>-27%</td>\n",
-       "      <td>-$5,550,030</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>282 rows × 11 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      tic filling_date trade_date                   owner_name  \\\n",
-       "0    PLTR   2020-10-02 2020-09-30         Cohen Stephen Andrew   \n",
-       "1    PLTR   2020-10-02 2020-09-30              Glazer David A.   \n",
-       "2    PLTR   2020-10-02 2020-09-30            Karp Alexander C.   \n",
-       "3    PLTR   2020-10-02 2020-09-30              Long Matthew A.   \n",
-       "4    PLTR   2020-10-02 2020-09-30           Moore Alexander D.   \n",
-       "..    ...          ...        ...                          ...   \n",
-       "277  PLTR   2024-10-07 2024-10-03         Planishek Heather A.   \n",
-       "278  PLTR   2024-10-08 2024-10-04               Taylor Ryan D.   \n",
-       "279  PLTR   2024-10-15 2024-10-11  Stat Lauren Elaina Friedman   \n",
-       "280  PLTR   2024-10-29 2024-10-25            Karp Alexander C.   \n",
-       "281  PLTR   2024-10-29 2024-10-25               Taylor Ryan D.   \n",
-       "\n",
-       "               title transaction_type last_price          qty shares_held  \\\n",
-       "0    Pres, Secretary      S - Sale+OE      $9.71   -2,000,000   2,402,647   \n",
-       "1     CFO, Treasurer         S - Sale     $10.03   -1,615,621   3,940,152   \n",
-       "2                CEO      S - Sale+OE      $9.70  -11,500,000   9,026,496   \n",
-       "3                 GC      S - Sale+OE     $10.06     -579,404     874,006   \n",
-       "4                Dir         S - Sale      $9.54     -285,000   2,429,249   \n",
-       "..               ...              ...        ...          ...         ...   \n",
-       "277      See Remarks      S - Sale+OE     $39.32      -34,017     570,136   \n",
-       "278      See Remarks      S - Sale+OE     $40.00     -195,500     331,369   \n",
-       "279              Dir         S - Sale     $43.90       -7,321     201,243   \n",
-       "280      See Remarks      S - Sale+OE     $45.01   -5,656,293   6,432,258   \n",
-       "281      See Remarks      S - Sale+OE     $45.00     -123,334     331,369   \n",
-       "\n",
-       "    owned          value  \n",
-       "0    -45%   -$19,422,100  \n",
-       "1    -29%   -$16,206,016  \n",
-       "2    -56%  -$111,606,220  \n",
-       "3    -40%    -$5,830,660  \n",
-       "4    -11%    -$2,719,315  \n",
-       "..    ...            ...  \n",
-       "277   -6%    -$1,337,500  \n",
-       "278  -37%    -$7,820,704  \n",
-       "279   -4%      -$321,392  \n",
-       "280  -47%  -$254,610,669  \n",
-       "281  -27%    -$5,550,030  \n",
-       "\n",
-       "[282 rows x 11 columns]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "db.fetch_insider_data(\"PLTR\").to_pandas()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div><style>\n",
-       ".dataframe > thead > tr,\n",
-       ".dataframe > tbody > tr {\n",
-       "  text-align: right;\n",
-       "  white-space: pre-wrap;\n",
-       "}\n",
-       "</style>\n",
-       "<small>shape: (78, 27)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>tic</th><th>datadate</th><th>rdq</th><th>saleq</th><th>cogsq</th><th>xsgaq</th><th>niq</th><th>ebitdaq</th><th>cshoq</th><th>actq</th><th>atq</th><th>cheq</th><th>rectq</th><th>invtq</th><th>ppentq</th><th>lctq</th><th>dlttq</th><th>ltq</th><th>req</th><th>seqq</th><th>oancfq</th><th>ivncfq</th><th>fincfq</th><th>dvq</th><th>capxq</th><th>icaptq</th><th>surprise_pct</th></tr><tr><td>str</td><td>date</td><td>date</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;AMZN&quot;</td><td>2005-03-31</td><td>2005-04-26</td><td>1902.0</td><td>1416.0</td><td>342.0</td><td>78.0</td><td>144.0</td><td>411.0</td><td>1793.0</td><td>2472.0</td><td>1151.0</td><td>null</td><td>403.0</td><td>245.0</td><td>1073.0</td><td>1561.0</td><td>2634.0</td><td>-2284.0</td><td>-162.0</td><td>-294.0</td><td>-197.0</td><td>-256.0</td><td>0.0</td><td>26.0</td><td>1399.0</td><td>-0.209</td></tr><tr><td>&quot;AMZN&quot;</td><td>2005-06-30</td><td>2005-07-26</td><td>1753.0</td><td>1275.0</td><td>350.0</td><td>52.0</td><td>128.0</td><td>412.0</td><td>1926.0</td><td>2601.0</td><td>1325.0</td><td>null</td><td>383.0</td><td>267.0</td><td>1144.0</td><td>1521.0</td><td>2665.0</td><td>-2229.0</td><td>-64.0</td><td>244.0</td><td>-144.0</td><td>9.0</td><td>0.0</td><td>47.0</td><td>1457.0</td><td>0.138</td></tr><tr><td>&quot;AMZN&quot;</td><td>2005-09-30</td><td>2005-11-29</td><td>1858.0</td><td>1365.0</td><td>380.0</td><td>30.0</td><td>113.0</td><td>414.0</td><td>2121.0</td><td>2832.0</td><td>1419.0</td><td>null</td><td>456.0</td><td>322.0</td><td>1313.0</td><td>1513.0</td><td>2826.0</td><td>-2213.0</td><td>6.0</td><td>153.0</td><td>-206.0</td><td>30.0</td><td>0.0</td><td>76.0</td><td>1519.0</td><td>null</td></tr><tr><td>&quot;AMZN&quot;</td><td>2005-12-31</td><td>2006-02-02</td><td>2977.0</td><td>2274.0</td><td>502.0</td><td>199.0</td><td>201.0</td><td>416.0</td><td>2929.0</td><td>3696.0</td><td>2000.0</td><td>null</td><td>566.0</td><td>348.0</td><td>1929.0</td><td>1521.0</td><td>3450.0</td><td>-2021.0</td><td>246.0</td><td>630.0</td><td>-231.0</td><td>24.0</td><td>0.0</td><td>55.0</td><td>1767.0</td><td>1.209</td></tr><tr><td>&quot;AMZN&quot;</td><td>2006-03-31</td><td>2006-04-25</td><td>2279.0</td><td>1692.0</td><td>441.0</td><td>51.0</td><td>146.0</td><td>417.0</td><td>2186.0</td><td>2990.0</td><td>1334.0</td><td>null</td><td>538.0</td><td>361.0</td><td>1407.0</td><td>1259.0</td><td>2666.0</td><td>-1967.0</td><td>324.0</td><td>-303.0</td><td>84.0</td><td>-296.0</td><td>0.0</td><td>46.0</td><td>1583.0</td><td>0.024</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;AMZN&quot;</td><td>2023-06-30</td><td>2023-08-03</td><td>134383.0</td><td>112609.0</td><td>13947.0</td><td>6750.0</td><td>19992.0</td><td>10313.0</td><td>140482.0</td><td>477607.0</td><td>63970.0</td><td>39925.0</td><td>36587.0</td><td>264116.0</td><td>148238.0</td><td>63092.0</td><td>309005.0</td><td>93115.0</td><td>168602.0</td><td>16476.0</td><td>-9673.0</td><td>-6539.0</td><td>null</td><td>11455.0</td><td>231694.0</td><td>0.857</td></tr><tr><td>&quot;AMZN&quot;</td><td>2023-09-30</td><td>2023-10-26</td><td>143083.0</td><td>118539.0</td><td>13112.0</td><td>9879.0</td><td>24044.0</td><td>10330.0</td><td>142995.0</td><td>486883.0</td><td>64169.0</td><td>38100.0</td><td>35406.0</td><td>267226.0</td><td>145214.0</td><td>61098.0</td><td>303910.0</td><td>102994.0</td><td>182973.0</td><td>21217.0</td><td>-11753.0</td><td>-8948.0</td><td>null</td><td>12479.0</td><td>244071.0</td><td>0.608</td></tr><tr><td>&quot;AMZN&quot;</td><td>2023-12-31</td><td>2024-02-01</td><td>169961.0</td><td>140686.0</td><td>15912.0</td><td>10624.0</td><td>27949.0</td><td>10383.0</td><td>172351.0</td><td>527854.0</td><td>86780.0</td><td>52253.0</td><td>33318.0</td><td>276690.0</td><td>164917.0</td><td>58314.0</td><td>325979.0</td><td>113618.0</td><td>201875.0</td><td>42465.0</td><td>-12601.0</td><td>-6746.0</td><td>null</td><td>14588.0</td><td>260189.0</td><td>0.246</td></tr><tr><td>&quot;AMZN&quot;</td><td>2024-03-31</td><td>2024-04-30</td><td>143313.0</td><td>115374.0</td><td>12404.0</td><td>10431.0</td><td>27736.0</td><td>10403.0</td><td>163989.0</td><td>530969.0</td><td>85074.0</td><td>42200.0</td><td>31147.0</td><td>283263.0</td><td>152965.0</td><td>57634.0</td><td>314308.0</td><td>124049.0</td><td>216661.0</td><td>18989.0</td><td>-17862.0</td><td>-1256.0</td><td>null</td><td>14925.0</td><td>274295.0</td><td>0.179</td></tr><tr><td>&quot;AMZN&quot;</td><td>2024-06-30</td><td>2024-08-01</td><td>147977.0</td><td>119655.0</td><td>13553.0</td><td>13485.0</td><td>27832.0</td><td>10490.0</td><td>173307.0</td><td>554818.0</td><td>89092.0</td><td>44200.0</td><td>34109.0</td><td>295292.0</td><td>158172.0</td><td>54889.0</td><td>318371.0</td><td>137534.0</td><td>236447.0</td><td>25281.0</td><td>-22138.0</td><td>-4490.0</td><td>null</td><td>17620.0</td><td>291336.0</td><td>0.2258</td></tr></tbody></table></div>"
-      ],
-      "text/plain": [
-       "shape: (78, 27)\n",
-       "┌──────┬────────────┬────────────┬──────────┬───┬──────┬─────────┬──────────┬──────────────┐\n",
-       "│ tic  ┆ datadate   ┆ rdq        ┆ saleq    ┆ … ┆ dvq  ┆ capxq   ┆ icaptq   ┆ surprise_pct │\n",
-       "│ ---  ┆ ---        ┆ ---        ┆ ---      ┆   ┆ ---  ┆ ---     ┆ ---      ┆ ---          │\n",
-       "│ str  ┆ date       ┆ date       ┆ f64      ┆   ┆ f64  ┆ f64     ┆ f64      ┆ f64          │\n",
-       "╞══════╪════════════╪════════════╪══════════╪═══╪══════╪═════════╪══════════╪══════════════╡\n",
-       "│ AMZN ┆ 2005-03-31 ┆ 2005-04-26 ┆ 1902.0   ┆ … ┆ 0.0  ┆ 26.0    ┆ 1399.0   ┆ -0.209       │\n",
-       "│ AMZN ┆ 2005-06-30 ┆ 2005-07-26 ┆ 1753.0   ┆ … ┆ 0.0  ┆ 47.0    ┆ 1457.0   ┆ 0.138        │\n",
-       "│ AMZN ┆ 2005-09-30 ┆ 2005-11-29 ┆ 1858.0   ┆ … ┆ 0.0  ┆ 76.0    ┆ 1519.0   ┆ null         │\n",
-       "│ AMZN ┆ 2005-12-31 ┆ 2006-02-02 ┆ 2977.0   ┆ … ┆ 0.0  ┆ 55.0    ┆ 1767.0   ┆ 1.209        │\n",
-       "│ AMZN ┆ 2006-03-31 ┆ 2006-04-25 ┆ 2279.0   ┆ … ┆ 0.0  ┆ 46.0    ┆ 1583.0   ┆ 0.024        │\n",
-       "│ …    ┆ …          ┆ …          ┆ …        ┆ … ┆ …    ┆ …       ┆ …        ┆ …            │\n",
-       "│ AMZN ┆ 2023-06-30 ┆ 2023-08-03 ┆ 134383.0 ┆ … ┆ null ┆ 11455.0 ┆ 231694.0 ┆ 0.857        │\n",
-       "│ AMZN ┆ 2023-09-30 ┆ 2023-10-26 ┆ 143083.0 ┆ … ┆ null ┆ 12479.0 ┆ 244071.0 ┆ 0.608        │\n",
-       "│ AMZN ┆ 2023-12-31 ┆ 2024-02-01 ┆ 169961.0 ┆ … ┆ null ┆ 14588.0 ┆ 260189.0 ┆ 0.246        │\n",
-       "│ AMZN ┆ 2024-03-31 ┆ 2024-04-30 ┆ 143313.0 ┆ … ┆ null ┆ 14925.0 ┆ 274295.0 ┆ 0.179        │\n",
-       "│ AMZN ┆ 2024-06-30 ┆ 2024-08-01 ┆ 147977.0 ┆ … ┆ null ┆ 17620.0 ┆ 291336.0 ┆ 0.2258       │\n",
-       "└──────┴────────────┴────────────┴──────────┴───┴──────┴─────────┴──────────┴──────────────┘"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "db.fetch_financial_data(\"AMZN\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "        vertical-align: middle;\n",
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-       "\n",
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-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>tic</th>\n",
-       "      <th>name</th>\n",
-       "      <th>sector</th>\n",
-       "      <th>date_added</th>\n",
-       "      <th>date_removed</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>AAP</td>\n",
-       "      <td>Advance Auto Parts</td>\n",
-       "      <td>Consumer Discretionary</td>\n",
-       "      <td>2015-07-08</td>\n",
-       "      <td>2023-08-25</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>ABMD</td>\n",
-       "      <td>Abiomed</td>\n",
-       "      <td>Health Care</td>\n",
-       "      <td>2018-05-31</td>\n",
-       "      <td>2022-12-22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>ABS</td>\n",
-       "      <td>Albertsons</td>\n",
-       "      <td>Consumer Staples</td>\n",
-       "      <td>NaT</td>\n",
-       "      <td>2006-06-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>ACAS</td>\n",
-       "      <td>American Capital</td>\n",
-       "      <td>Financials</td>\n",
-       "      <td>NaT</td>\n",
-       "      <td>2009-03-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>ADCT</td>\n",
-       "      <td>ADC Telecommunications</td>\n",
-       "      <td>Communication Services</td>\n",
-       "      <td>NaT</td>\n",
-       "      <td>2007-07-02</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>783</th>\n",
-       "      <td>XYL</td>\n",
-       "      <td>Xylem Inc.</td>\n",
-       "      <td>Industrials</td>\n",
-       "      <td>2011-11-01</td>\n",
-       "      <td>NaT</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>784</th>\n",
-       "      <td>YUM</td>\n",
-       "      <td>Yum! Brands</td>\n",
-       "      <td>Consumer Discretionary</td>\n",
-       "      <td>1997-10-06</td>\n",
-       "      <td>NaT</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>785</th>\n",
-       "      <td>ZBRA</td>\n",
-       "      <td>Zebra Technologies</td>\n",
-       "      <td>Information Technology</td>\n",
-       "      <td>2019-12-23</td>\n",
-       "      <td>NaT</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>786</th>\n",
-       "      <td>ZBH</td>\n",
-       "      <td>Zimmer Biomet</td>\n",
-       "      <td>Health Care</td>\n",
-       "      <td>2001-08-07</td>\n",
-       "      <td>NaT</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>787</th>\n",
-       "      <td>ZTS</td>\n",
-       "      <td>Zoetis</td>\n",
-       "      <td>Health Care</td>\n",
-       "      <td>2013-06-21</td>\n",
-       "      <td>NaT</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>788 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      tic                    name                  sector date_added  \\\n",
-       "0     AAP      Advance Auto Parts  Consumer Discretionary 2015-07-08   \n",
-       "1    ABMD                 Abiomed             Health Care 2018-05-31   \n",
-       "2     ABS              Albertsons        Consumer Staples        NaT   \n",
-       "3    ACAS        American Capital              Financials        NaT   \n",
-       "4    ADCT  ADC Telecommunications  Communication Services        NaT   \n",
-       "..    ...                     ...                     ...        ...   \n",
-       "783   XYL              Xylem Inc.             Industrials 2011-11-01   \n",
-       "784   YUM             Yum! Brands  Consumer Discretionary 1997-10-06   \n",
-       "785  ZBRA      Zebra Technologies  Information Technology 2019-12-23   \n",
-       "786   ZBH           Zimmer Biomet             Health Care 2001-08-07   \n",
-       "787   ZTS                  Zoetis             Health Care 2013-06-21   \n",
-       "\n",
-       "    date_removed  \n",
-       "0     2023-08-25  \n",
-       "1     2022-12-22  \n",
-       "2     2006-06-02  \n",
-       "3     2009-03-03  \n",
-       "4     2007-07-02  \n",
-       "..           ...  \n",
-       "783          NaT  \n",
-       "784          NaT  \n",
-       "785          NaT  \n",
-       "786          NaT  \n",
-       "787          NaT  \n",
-       "\n",
-       "[788 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "st = db.fetch_stock().to_pandas()\n",
-    "st"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>tic</th>\n",
-       "      <th>risk</th>\n",
-       "      <th>beta</th>\n",
-       "      <th>volume</th>\n",
-       "      <th>trailing_pe</th>\n",
-       "      <th>forward_pe</th>\n",
-       "      <th>trailing_peg</th>\n",
-       "      <th>peg</th>\n",
-       "      <th>trailing_eps</th>\n",
-       "      <th>forward_eps</th>\n",
-       "      <th>...</th>\n",
-       "      <th>currency</th>\n",
-       "      <th>shares_outstanding</th>\n",
-       "      <th>enterprise_value</th>\n",
-       "      <th>short_ratio</th>\n",
-       "      <th>curr_price</th>\n",
-       "      <th>fiftytwo_wc</th>\n",
-       "      <th>rec_key</th>\n",
-       "      <th>target_low</th>\n",
-       "      <th>target_high</th>\n",
-       "      <th>target_mean</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>AAPL</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.24</td>\n",
-       "      <td>47923696.0</td>\n",
-       "      <td>37.00658</td>\n",
-       "      <td>27.079428</td>\n",
-       "      <td>2.3294</td>\n",
-       "      <td>None</td>\n",
-       "      <td>6.08</td>\n",
-       "      <td>8.31</td>\n",
-       "      <td>...</td>\n",
-       "      <td>USD</td>\n",
-       "      <td>1.511580e+10</td>\n",
-       "      <td>3.454948e+12</td>\n",
-       "      <td>3.24</td>\n",
-       "      <td>225.0</td>\n",
-       "      <td>0.175242</td>\n",
-       "      <td>None</td>\n",
-       "      <td>184.0</td>\n",
-       "      <td>300.0</td>\n",
-       "      <td>244.47739</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1 rows × 22 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    tic  risk  beta      volume  trailing_pe  forward_pe  trailing_peg   peg  \\\n",
-       "0  AAPL     1  1.24  47923696.0     37.00658   27.079428        2.3294  None   \n",
-       "\n",
-       "   trailing_eps  forward_eps  ...  currency  shares_outstanding  \\\n",
-       "0          6.08         8.31  ...       USD        1.511580e+10   \n",
-       "\n",
-       "  enterprise_value  short_ratio  curr_price  fiftytwo_wc  rec_key  target_low  \\\n",
-       "0     3.454948e+12         3.24       225.0     0.175242     None       184.0   \n",
-       "\n",
-       "  target_high  target_mean  \n",
-       "0       300.0    244.47739  \n",
-       "\n",
-       "[1 rows x 22 columns]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inf = db.fetch_info(\"AAPL\")\n",
-    "inf"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "np.int64(1)"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "inf.loc[0, [\"risk\"]].values[0]"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "stocksense",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.0"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/eda.ipynb b/notebooks/eda.ipynb
index 5d9a419..80e66a3 100644
--- a/notebooks/eda.ipynb
+++ b/notebooks/eda.ipynb
@@ -14,72 +14,77 @@
    "outputs": [],
    "source": [
     "import plotly.express as px\n",
-    "from pipeline import Preprocess"
+    "import polars as pl\n",
+    "\n",
+    "from stocksense.config import config\n",
+    "from stocksense.pipeline import clean, engineer_features\n",
+    "\n",
+    "features = config.model.features\n",
+    "date_col = config.model.date_col\n",
+    "aux_cols = [\"tic\", \"datadate\", \"rdq\"]\n",
+    "targets = config.model.targets"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = Preprocess().fetch_data()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2024-12-25 12:33:56.878\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mstocksense.pipeline.preprocess\u001b[0m:\u001b[36mengineer_features\u001b[0m:\u001b[36m20\u001b[0m - \u001b[1mSTART processing stock data\u001b[0m\n",
+      "\u001b[32m2024-12-25 12:33:56.881\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mstocksense.database.schema\u001b[0m:\u001b[36mcreate_tables\u001b[0m:\u001b[36m121\u001b[0m - \u001b[32m\u001b[1mTables created successfully\u001b[0m\n",
+      "\u001b[32m2024-12-25 12:34:01.047\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mstocksense.pipeline.preprocess\u001b[0m:\u001b[36mengineer_features\u001b[0m:\u001b[36m33\u001b[0m - \u001b[1mSTART feature engineering\u001b[0m\n",
+      "\u001b[32m2024-12-25 12:34:14.881\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mstocksense.pipeline.preprocess\u001b[0m:\u001b[36mengineer_features\u001b[0m:\u001b[36m47\u001b[0m - \u001b[32m\u001b[1mEND 58960 rows PROCESSED\u001b[0m\n",
+      "\u001b[32m2024-12-25 12:34:14.883\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mstocksense.pipeline.preprocess\u001b[0m:\u001b[36mclean\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSTART cleaning data\u001b[0m\n",
+      "\u001b[32m2024-12-25 12:34:15.280\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mstocksense.pipeline.preprocess\u001b[0m:\u001b[36mclean\u001b[0m:\u001b[36m110\u001b[0m - \u001b[32m\u001b[1m37806 rows retained after CLEANING\u001b[0m\n"
+     ]
+    },
     {
      "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (5, 230)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>tdq</th><th>tic</th><th>datadate</th><th>rdq</th><th>saleq</th><th>cogsq</th><th>xsgaq</th><th>niq</th><th>ebitdaq</th><th>cshoq</th><th>actq</th><th>atq</th><th>cheq</th><th>rectq</th><th>invtq</th><th>ppentq</th><th>lctq</th><th>dlttq</th><th>ltq</th><th>req</th><th>seqq</th><th>oancfq</th><th>ivncfq</th><th>fincfq</th><th>dvq</th><th>capxq</th><th>icaptq</th><th>surprise_pct</th><th>stock_split</th><th>n_purch</th><th>val_purch</th><th>n_sales</th><th>val_sales</th><th>insider_balance</th><th>roa</th><th>roi</th><th>roe</th><th>&hellip;</th><th>eps_2y</th><th>ev_ebitda_yoy</th><th>ltcr_yoy</th><th>itr_yoy</th><th>rtr_yoy</th><th>atr_yoy</th><th>size_yoy</th><th>roa_sec_qoq</th><th>roa_sec_yoy</th><th>f_score</th><th>forward_vol_yoy</th><th>forward_vol_sos</th><th>forward_vol_qoq</th><th>excess_return_1Q</th><th>sharpe_ratio_1Q</th><th>risk_return_1Q</th><th>fwd_return_1Q_hit</th><th>excess_return_1Q_hit</th><th>risk_return_1Q_hit</th><th>excess_return_2Q</th><th>sharpe_ratio_2Q</th><th>risk_return_2Q</th><th>fwd_return_2Q_hit</th><th>excess_return_2Q_hit</th><th>risk_return_2Q_hit</th><th>excess_return_3Q</th><th>sharpe_ratio_3Q</th><th>risk_return_3Q</th><th>fwd_return_3Q_hit</th><th>excess_return_3Q_hit</th><th>risk_return_3Q_hit</th><th>excess_return_4Q</th><th>sharpe_ratio_4Q</th><th>risk_return_4Q</th><th>fwd_return_4Q_hit</th><th>excess_return_4Q_hit</th><th>risk_return_4Q_hit</th></tr><tr><td>date</td><td>str</td><td>date</td><td>date</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>u32</td><td>f64</td><td>u32</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>&hellip;</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>i8</td><td>i8</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>i8</td><td>i8</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>i8</td><td>i8</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>i8</td><td>i8</td></tr></thead><tbody><tr><td>2007-03-01</td><td>&quot;A&quot;</td><td>2007-01-31</td><td>2007-02-15</td><td>1280.0</td><td>540.0</td><td>564.0</td><td>150.0</td><td>176.0</td><td>405.0</td><td>3749.0</td><td>7193.0</td><td>2090.0</td><td>671.0</td><td>648.0</td><td>775.0</td><td>1372.0</td><td>1500.0</td><td>3536.0</td><td>1705.0</td><td>3657.0</td><td>93.0</td><td>-93.0</td><td>-169.0</td><td>0.0</td><td>37.0</td><td>5157.0</td><td>0.011</td><td>0</td><td>0</td><td>0.0</td><td>1</td><td>8.598</td><td>8.598</td><td>8.911442</td><td>2.908668</td><td>0.17528</td><td>&hellip;</td><td>null</td><td>-45.435715</td><td>131.460674</td><td>25.76978</td><td>22.719366</td><td>11.302357</td><td>-1.218827</td><td>-86.503836</td><td>-83.655657</td><td>6</td><td>1.780824</td><td>1.749898</td><td>1.355307</td><td>8.952132</td><td>6.605244</td><td>10.89898</td><td>1</td><td>1</td><td>1</td><td>13.694795</td><td>10.104573</td><td>15.070857</td><td>1</td><td>1</td><td>1</td><td>8.583285</td><td>4.905021</td><td>9.151445</td><td>0</td><td>0</td><td>0</td><td>10.709357</td><td>6.013709</td><td>6.242292</td><td>0</td><td>0</td><td>0</td></tr><tr><td>2007-06-01</td><td>&quot;A&quot;</td><td>2007-04-30</td><td>2007-05-14</td><td>1320.0</td><td>540.0</td><td>586.0</td><td>123.0</td><td>194.0</td><td>395.958</td><td>3791.0</td><td>7283.0</td><td>2050.0</td><td>718.0</td><td>650.0</td><td>777.0</td><td>1554.0</td><td>1500.0</td><td>3728.0</td><td>1881.0</td><td>3555.0</td><td>302.0</td><td>-36.0</td><td>-320.0</td><td>0.0</td><td>42.0</td><td>5055.0</td><td>-0.023</td><td>0</td><td>0</td><td>0.0</td><td>3</td><td>2.695</td><td>2.695</td><td>8.911163</td><td>2.433234</td><td>0.18256</td><td>&hellip;</td><td>null</td><td>-29.485541</td><td>7.883817</td><td>19.259654</td><td>24.025332</td><td>18.364403</td><td>-1.122916</td><td>13.440324</td><td>-81.598553</td><td>6</td><td>1.927423</td><td>1.918095</td><td>2.106162</td><td>1.404396</td><td>0.666804</td><td>-0.836463</td><td>0</td><td>0</td><td>0</td><td>-2.215787</td><td>-1.05205</td><td>-2.205524</td><td>0</td><td>0</td><td>0</td><td>-0.292617</td><td>-0.152556</td><td>-4.454244</td><td>0</td><td>0</td><td>0</td><td>-5.91296</td><td>-3.067807</td><td>-8.865139</td><td>0</td><td>0</td><td>0</td></tr><tr><td>2007-09-01</td><td>&quot;A&quot;</td><td>2007-07-31</td><td>2007-08-14</td><td>1374.0</td><td>563.0</td><td>580.0</td><td>185.0</td><td>231.0</td><td>386.548</td><td>3281.0</td><td>7024.0</td><td>1486.0</td><td>738.0</td><td>674.0</td><td>787.0</td><td>1510.0</td><td>1500.0</td><td>3736.0</td><td>2069.0</td><td>3288.0</td><td>176.0</td><td>-258.0</td><td>-484.0</td><td>0.0</td><td>36.0</td><td>4788.0</td><td>-0.01</td><td>0</td><td>0</td><td>0.0</td><td>2</td><td>5.747</td><td>5.747</td><td>8.6418</td><td>3.863826</td><td>0.184611</td><td>&hellip;</td><td>null</td><td>-17.061919</td><td>300.0</td><td>18.769156</td><td>29.327755</td><td>22.359853</td><td>-1.068351</td><td>-14.122377</td><td>-85.588435</td><td>5</td><td>1.772393</td><td>1.803532</td><td>1.789715</td><td>-1.703536</td><td>-0.951848</td><td>0.168495</td><td>0</td><td>0</td><td>0</td><td>-0.089423</td><td>-0.049965</td><td>-2.674412</td><td>0</td><td>0</td><td>0</td><td>-6.394643</td><td>-3.545622</td><td>-7.722925</td><td>0</td><td>0</td><td>0</td><td>10.925009</td><td>6.163987</td><td>-0.514147</td><td>0</td><td>0</td><td>0</td></tr><tr><td>2007-12-01</td><td>&quot;A&quot;</td><td>2007-10-31</td><td>2007-11-15</td><td>1446.0</td><td>611.0</td><td>598.0</td><td>180.0</td><td>237.0</td><td>370.0</td><td>3671.0</td><td>7554.0</td><td>1826.0</td><td>735.0</td><td>643.0</td><td>801.0</td><td>1663.0</td><td>2087.0</td><td>4320.0</td><td>2580.0</td><td>3234.0</td><td>398.0</td><td>-69.0</td><td>-7.0</td><td>0.0</td><td>39.0</td><td>5321.0</td><td>0.022</td><td>0</td><td>0</td><td>0.0</td><td>2</td><td>2.75</td><td>2.75</td><td>8.445856</td><td>3.382823</td><td>0.197279</td><td>&hellip;</td><td>165.240102</td><td>-12.283396</td><td>-32.276673</td><td>10.125696</td><td>14.20785</td><td>12.76951</td><td>0.27844</td><td>-3.477092</td><td>-87.309223</td><td>4</td><td>3.004222</td><td>1.948736</td><td>1.848409</td><td>-3.735506</td><td>-2.02093</td><td>-6.186043</td><td>0</td><td>0</td><td>0</td><td>-8.003799</td><td>-4.330101</td><td>-8.594016</td><td>0</td><td>0</td><td>0</td><td>8.239344</td><td>4.228045</td><td>-2.316993</td><td>0</td><td>0</td><td>0</td><td>-2.055557</td><td>-0.684223</td><td>-10.49239</td><td>0</td><td>0</td><td>0</td></tr><tr><td>2008-03-01</td><td>&quot;A&quot;</td><td>2008-01-31</td><td>2008-02-13</td><td>1393.0</td><td>580.0</td><td>617.0</td><td>120.0</td><td>196.0</td><td>368.0</td><td>5070.0</td><td>7459.0</td><td>3148.0</td><td>726.0</td><td>674.0</td><td>801.0</td><td>2674.0</td><td>626.0</td><td>4286.0</td><td>2657.0</td><td>3173.0</td><td>4.0</td><td>-295.0</td><td>-168.0</td><td>0.0</td><td>34.0</td><td>3799.0</td><td>0.021</td><td>0</td><td>0</td><td>0.0</td><td>2</td><td>3.418</td><td>3.418</td><td>8.151227</td><td>3.158726</td><td>0.191617</td><td>&hellip;</td><td>-76.630435</td><td>-22.566969</td><td>-228.366043</td><td>5.357524</td><td>1.528223</td><td>5.558859</td><td>0.408891</td><td>-20.5599</td><td>-25.300507</td><td>5</td><td>3.238751</td><td>1.741436</td><td>2.050642</td><td>1.053516</td><td>0.513749</td><td>2.536928</td><td>0</td><td>0</td><td>0</td><td>20.442391</td><td>9.968778</td><td>8.760186</td><td>1</td><td>1</td><td>1</td><td>6.726355</td><td>3.862535</td><td>-7.914079</td><td>0</td><td>0</td><td>0</td><td>-6.951969</td><td>-2.146497</td><td>-13.35568</td><td>0</td><td>0</td><td>0</td></tr></tbody></table></div>"
+      ],
       "text/plain": [
-       "(44884, 91)"
+       "shape: (5, 230)\n",
+       "┌────────────┬─────┬────────────┬────────────┬───┬────────────┬────────────┬───────────┬───────────┐\n",
+       "│ tdq        ┆ tic ┆ datadate   ┆ rdq        ┆ … ┆ risk_retur ┆ fwd_return ┆ excess_re ┆ risk_retu │\n",
+       "│ ---        ┆ --- ┆ ---        ┆ ---        ┆   ┆ n_4Q       ┆ _4Q_hit    ┆ turn_4Q_h ┆ rn_4Q_hit │\n",
+       "│ date       ┆ str ┆ date       ┆ date       ┆   ┆ ---        ┆ ---        ┆ it        ┆ ---       │\n",
+       "│            ┆     ┆            ┆            ┆   ┆ f64        ┆ i8         ┆ ---       ┆ i8        │\n",
+       "│            ┆     ┆            ┆            ┆   ┆            ┆            ┆ i8        ┆           │\n",
+       "╞════════════╪═════╪════════════╪════════════╪═══╪════════════╪════════════╪═══════════╪═══════════╡\n",
+       "│ 2007-03-01 ┆ A   ┆ 2007-01-31 ┆ 2007-02-15 ┆ … ┆ 6.242292   ┆ 0          ┆ 0         ┆ 0         │\n",
+       "│ 2007-06-01 ┆ A   ┆ 2007-04-30 ┆ 2007-05-14 ┆ … ┆ -8.865139  ┆ 0          ┆ 0         ┆ 0         │\n",
+       "│ 2007-09-01 ┆ A   ┆ 2007-07-31 ┆ 2007-08-14 ┆ … ┆ -0.514147  ┆ 0          ┆ 0         ┆ 0         │\n",
+       "│ 2007-12-01 ┆ A   ┆ 2007-10-31 ┆ 2007-11-15 ┆ … ┆ -10.49239  ┆ 0          ┆ 0         ┆ 0         │\n",
+       "│ 2008-03-01 ┆ A   ┆ 2008-01-31 ┆ 2008-02-13 ┆ … ┆ -13.35568  ┆ 0          ┆ 0         ┆ 0         │\n",
+       "└────────────┴─────┴────────────┴────────────┴───┴────────────┴────────────┴───────────┴───────────┘"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Missing Values Count:\n",
-      " itr                 11174\n",
-      "fperf                5979\n",
-      "adj_fperf            5973\n",
-      "adj_freturn          5973\n",
-      "freturn              5973\n",
-      "                    ...  \n",
-      "sector_Utilities        0\n",
-      "n_purch                 0\n",
-      "n_sales                 0\n",
-      "rdq                     0\n",
-      "datadate                0\n",
-      "Length: 91, dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "missing_values = df.isnull().sum().sort_values(ascending=False)\n",
-    "print(\"\\nMissing Values Count:\\n\", missing_values)"
+    "data = engineer_features()\n",
+    "data = clean(data)\n",
+    "data.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -91,7 +96,8 @@
        "data": [
         {
          "alignmentgroup": "True",
-         "hovertemplate": "x=%{x}<br>y=%{y}<extra></extra>",
+         "bingroup": "x",
+         "hovertemplate": "ev_ebitda=%{x}<br>count=%{y}<extra></extra>",
          "legendgroup": "",
          "marker": {
           "color": "#636efa",
@@ -100,2105 +106,37820 @@
           }
          },
          "name": "",
+         "nbinsx": 50,
          "offsetgroup": "",
          "orientation": "v",
          "showlegend": false,
-         "textposition": "auto",
-         "type": "bar",
+         "type": "histogram",
          "x": [
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-   "source": [
-    "sector_counts = df[\"fperf\"].value_counts()\n",
-    "fig_sector = px.bar(\n",
-    "    x=sector_counts.index,\n",
-    "    y=sector_counts.values,\n",
-    "    title=\"Number of Over/Under performers\",\n",
-    ")\n",
-    "fig_sector.show()"
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-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Single Stock Visualization"
-   ]
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+          20.727643714394524,
+          21.787013702999417,
+          20.07461631151699
          ],
          "xaxis": "x",
-         "y": [
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-         ],
          "yaxis": "y"
         }
        ],
@@ -2207,9 +37928,6 @@
         "legend": {
          "tracegroupgap": 0
         },
-        "margin": {
-         "t": 60
-        },
         "template": {
          "data": {
           "bar": [
@@ -3026,6 +38744,9 @@
           }
          }
         },
+        "title": {
+         "text": "Distribution"
+        },
         "xaxis": {
          "anchor": "y",
          "domain": [
@@ -3033,7 +38754,7 @@
           1
          ],
          "title": {
-          "text": "rdq"
+          "text": "ev_ebitda"
          }
         },
         "yaxis": {
@@ -3043,7 +38764,7 @@
           1
          ],
          "title": {
-          "text": "roa_qoq"
+          "text": "count"
          }
         }
        }
@@ -3054,7 +38775,8 @@
     }
    ],
    "source": [
-    "fig = px.bar(aapl_df, x=\"rdq\", y=\"roa_qoq\")\n",
+    "# Create histogram of vol_yoy distribution\n",
+    "fig = px.histogram(data, x=\"ev_ebitda\", title=\"Distribution\", nbins=50)\n",
     "fig.show()"
    ]
   },
@@ -3073,21 +38795,31 @@
        "  white-space: pre-wrap;\n",
        "}\n",
        "</style>\n",
-       "<small>shape: (5, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>date</th><th>close</th><th>adj_close</th><th>volume</th><th>tic</th></tr><tr><td>date</td><td>f64</td><td>f64</td><td>i64</td><td>str</td></tr></thead><tbody><tr><td>2005-01-03</td><td>1202.079956</td><td>1202.079956</td><td>1510800000</td><td>&quot;^GSPC&quot;</td></tr><tr><td>2005-01-04</td><td>1188.050049</td><td>1188.050049</td><td>1721000000</td><td>&quot;^GSPC&quot;</td></tr><tr><td>2005-01-05</td><td>1183.73999</td><td>1183.73999</td><td>1738900000</td><td>&quot;^GSPC&quot;</td></tr><tr><td>2005-01-06</td><td>1187.890015</td><td>1187.890015</td><td>1569100000</td><td>&quot;^GSPC&quot;</td></tr><tr><td>2005-01-07</td><td>1186.189941</td><td>1186.189941</td><td>1477900000</td><td>&quot;^GSPC&quot;</td></tr></tbody></table></div>"
+       "<small>shape: (72, 222)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>tdq</th><th>tic</th><th>datadate</th><th>rdq</th><th>saleq</th><th>cogsq</th><th>xsgaq</th><th>niq</th><th>ebitdaq</th><th>cshoq</th><th>actq</th><th>atq</th><th>cheq</th><th>rectq</th><th>invtq</th><th>ppentq</th><th>lctq</th><th>dlttq</th><th>ltq</th><th>req</th><th>seqq</th><th>oancfq</th><th>ivncfq</th><th>fincfq</th><th>dvq</th><th>capxq</th><th>icaptq</th><th>surprise_pct</th><th>stock_split</th><th>n_purch</th><th>val_purch</th><th>n_sales</th><th>val_sales</th><th>insider_balance</th><th>roa</th><th>roi</th><th>roe</th><th>&hellip;</th><th>ltda_2y</th><th>pe_yoy</th><th>pe_2y</th><th>pb_yoy</th><th>pb_2y</th><th>ps_yoy</th><th>ps_2y</th><th>eps_yoy</th><th>eps_2y</th><th>ev_ebitda_yoy</th><th>ltcr_yoy</th><th>itr_yoy</th><th>rtr_yoy</th><th>atr_yoy</th><th>size_yoy</th><th>roa_sec_qoq</th><th>roa_sec_yoy</th><th>f_score</th><th>forward_vol_yoy</th><th>forward_vol_sos</th><th>forward_vol_qoq</th><th>excess_return_1Q</th><th>sharpe_ratio_1Q</th><th>risk_return_1Q</th><th>excess_return_2Q</th><th>sharpe_ratio_2Q</th><th>risk_return_2Q</th><th>excess_return_3Q</th><th>sharpe_ratio_3Q</th><th>risk_return_3Q</th><th>excess_return_4Q</th><th>sharpe_ratio_4Q</th><th>risk_return_4Q</th><th>fwd_return</th><th>excess_return</th><th>sharpe_ratio</th><th>risk_return</th></tr><tr><td>date</td><td>str</td><td>date</td><td>date</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>u32</td><td>f64</td><td>u32</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>&hellip;</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2007-03-01</td><td>&quot;YUM&quot;</td><td>2006-12-31</td><td>2007-02-12</td><td>3016.0</td><td>2120.0</td><td>443.0</td><td>232.0</td><td>453.0</td><td>530.0</td><td>901.0</td><td>6353.0</td><td>325.0</td><td>220.0</td><td>93.0</td><td>3631.0</td><td>1724.0</td><td>2045.0</td><td>4916.0</td><td>1437.0</td><td>1437.0</td><td>295.0</td><td>-176.0</td><td>-76.0</td><td>40.0</td><td>291.0</td><td>3482.0</td><td>0.054</td><td>0</td><td>0</td><td>0.0</td><td>7</td><td>10.802</td><td>10.802</td><td>12.97025</td><td>6.662837</td><td>0.573417</td><td>&hellip;</td><td>NaN</td><td>3.071736</td><td>null</td><td>12.388907</td><td>NaN</td><td>8.986742</td><td>null</td><td>13.441292</td><td>null</td><td>1.669279</td><td>103.839795</td><td>-4.383214</td><td>8.732754</td><td>-1.754273</td><td>1.258251</td><td>-4.739447</td><td>8.712918</td><td>5</td><td>1.81229</td><td>1.489239</td><td>1.084487</td><td>5.600592</td><td>5.16428</td><td>10.530256</td><td>9.266337</td><td>8.544446</td><td>14.75092</td><td>19.383489</td><td>13.015698</td><td>18.005365</td><td>29.645544</td><td>16.358062</td><td>16.582675</td><td>26.814296</td><td>19.383489</td><td>13.015698</td><td>18.005365</td></tr><tr><td>2007-06-01</td><td>&quot;YUM&quot;</td><td>2007-03-31</td><td>2007-05-01</td><td>2223.0</td><td>1542.0</td><td>274.0</td><td>194.0</td><td>407.0</td><td>524.0</td><td>1041.0</td><td>6379.0</td><td>341.0</td><td>278.0</td><td>97.0</td><td>3570.0</td><td>1488.0</td><td>2208.0</td><td>4920.0</td><td>1459.0</td><td>1459.0</td><td>341.0</td><td>-42.0</td><td>-277.0</td><td>40.0</td><td>93.0</td><td>3667.0</td><td>0.099</td><td>0</td><td>0</td><td>0.0</td><td>14</td><td>5.478</td><td>5.478</td><td>13.29362</td><td>5.290428</td><td>0.58122</td><td>&hellip;</td><td>17.499715</td><td>18.21296</td><td>null</td><td>17.305894</td><td>39.707797</td><td>24.451279</td><td>null</td><td>13.427863</td><td>null</td><td>13.119532</td><td>-14.637681</td><td>-5.165468</td><td>5.76233</td><td>-4.40995</td><td>1.266497</td><td>6.106867</td><td>9.509898</td><td>4</td><td>1.914891</td><td>1.702851</td><td>1.799144</td><td>-0.985628</td><td>-0.547832</td><td>-2.307626</td><td>8.344666</td><td>4.638132</td><td>3.28782</td><td>16.45971</td><td>9.665973</td><td>4.820542</td><td>25.209564</td><td>13.165014</td><td>7.329741</td><td>8.208665</td><td>16.45971</td><td>9.665973</td><td>4.820542</td></tr><tr><td>2007-09-01</td><td>&quot;YUM&quot;</td><td>2007-06-30</td><td>2007-07-11</td><td>2367.0</td><td>1642.0</td><td>306.0</td><td>214.0</td><td>419.0</td><td>521.0</td><td>1104.0</td><td>6430.0</td><td>436.0</td><td>252.0</td><td>98.0</td><td>3552.0</td><td>1695.0</td><td>2088.0</td><td>4973.0</td><td>1457.0</td><td>1457.0</td><td>252.0</td><td>-74.0</td><td>-89.0</td><td>79.0</td><td>124.0</td><td>3545.0</td><td>0.076</td><td>1</td><td>0</td><td>0.0</td><td>17</td><td>9.413</td><td>9.413</td><td>13.530327</td><td>6.036671</td><td>0.597117</td><td>&hellip;</td><td>17.978904</td><td>15.687107</td><td>null</td><td>22.390525</td><td>34.768278</td><td>20.820814</td><td>null</td><td>14.553204</td><td>null</td><td>13.686995</td><td>-15.469023</td><td>-6.541634</td><td>-1.550681</td><td>-1.92138</td><td>1.061968</td><td>18.096718</td><td>36.440277</td><td>4</td><td>2.047201</td><td>2.082258</td><td>1.657973</td><td>13.330495</td><td>8.040238</td><td>9.249603</td><td>16.759604</td><td>10.108492</td><td>7.275507</td><td>26.707222</td><td>12.826085</td><td>9.207945</td><td>25.274567</td><td>12.345912</td><td>6.564224</td><td>19.17332</td><td>26.707222</td><td>12.826085</td><td>9.207945</td></tr><tr><td>2007-12-01</td><td>&quot;YUM&quot;</td><td>2007-09-30</td><td>2007-10-08</td><td>2564.0</td><td>1761.0</td><td>292.0</td><td>270.0</td><td>511.0</td><td>512.0</td><td>1165.0</td><td>6598.0</td><td>507.0</td><td>250.0</td><td>96.0</td><td>3600.0</td><td>1866.0</td><td>2124.0</td><td>5176.0</td><td>1422.0</td><td>1422.0</td><td>576.0</td><td>-140.0</td><td>-368.0</td><td>77.0</td><td>174.0</td><td>3546.0</td><td>0.119</td><td>0</td><td>0</td><td>0.0</td><td>20</td><td>13.894</td><td>13.894</td><td>13.792058</td><td>7.614213</td><td>0.639944</td><td>&hellip;</td><td>18.926604</td><td>5.857372</td><td>null</td><td>9.97847</td><td>53.229461</td><td>9.356404</td><td>-13.630451</td><td>15.157969</td><td>null</td><td>7.533914</td><td>17.199044</td><td>-4.85052</td><td>-0.001049</td><td>1.643236</td><td>1.285804</td><td>10.143046</td><td>31.47752</td><td>4</td><td>2.821542</td><td>2.110907</td><td>2.491781</td><td>3.722422</td><td>1.49388</td><td>-1.59581</td><td>12.303426</td><td>4.937603</td><td>1.77462</td><td>11.669343</td><td>5.528117</td><td>-0.514096</td><td>13.967799</td><td>4.950413</td><td>-5.492781</td><td>-1.085209</td><td>11.669343</td><td>5.528117</td><td>-0.514096</td></tr><tr><td>2008-03-01</td><td>&quot;YUM&quot;</td><td>2007-12-31</td><td>2008-02-04</td><td>3262.0</td><td>2286.0</td><td>496.0</td><td>231.0</td><td>480.0</td><td>499.0</td><td>1481.0</td><td>7242.0</td><td>789.0</td><td>225.0</td><td>128.0</td><td>3849.0</td><td>2062.0</td><td>2924.0</td><td>6103.0</td><td>1139.0</td><td>1139.0</td><td>398.0</td><td>-176.0</td><td>56.0</td><td>77.0</td><td>351.0</td><td>4063.0</td><td>0.05</td><td>0</td><td>1</td><td>0.397</td><td>10</td><td>52.2</td><td>51.803</td><td>12.551781</td><td>5.685454</td><td>0.798068</td><td>&hellip;</td><td>39.514914</td><td>2.930248</td><td>6.091993</td><td>43.255972</td><td>61.003821</td><td>4.227432</td><td>13.594083</td><td>17.168804</td><td>32.917804</td><td>4.647387</td><td>300.0</td><td>-16.468418</td><td>0.870166</td><td>-4.237736</td><td>1.495661</td><td>-1.909498</td><td>35.383382</td><td>5</td><td>2.89054</td><td>2.019357</td><td>1.730596</td><td>10.996881</td><td>6.354388</td><td>8.75172</td><td>9.166835</td><td>5.296922</td><td>3.864821</td><td>12.628194</td><td>6.253573</td><td>-3.902243</td><td>21.162932</td><td>7.321446</td><td>-5.238059</td><td>-7.880021</td><td>12.628194</td><td>6.253573</td><td>-3.902243</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>2023-12-01</td><td>&quot;YUM&quot;</td><td>2023-09-30</td><td>2023-11-01</td><td>1708.0</td><td>848.0</td><td>265.0</td><td>416.0</td><td>638.0</td><td>280.0</td><td>1705.0</td><td>6071.0</td><td>656.0</td><td>647.0</td><td>0.0</td><td>1920.0</td><td>1504.0</td><td>11152.0</td><td>14261.0</td><td>-7909.0</td><td>-8190.0</td><td>477.0</td><td>-30.0</td><td>-223.0</td><td>169.0</td><td>57.0</td><td>3335.0</td><td>0.129</td><td>0</td><td>0</td><td>0.0</td><td>4</td><td>2.604</td><td>2.604</td><td>24.789985</td><td>12.473763</td><td>-0.183761</td><td>&hellip;</td><td>5.382532</td><td>-18.531344</td><td>0.39124</td><td>0.404964</td><td>8.11113</td><td>-9.18962</td><td>-12.634302</td><td>20.979361</td><td>2.932784</td><td>-9.597317</td><td>-4.25024</td><td>NaN</td><td>-5.924602</td><td>1.088097</td><td>0.569069</td><td>-3.981822</td><td>11.475121</td><td>6</td><td>1.018172</td><td>1.005011</td><td>1.0</td><td>-3.457882</td><td>-3.457882</td><td>3.52162</td><td>-3.60439</td><td>-3.60439</td><td>9.055455</td><td>-13.235385</td><td>-13.169394</td><td>5.878279</td><td>-18.253729</td><td>-17.927948</td><td>7.184953</td><td>5.907735</td><td>-13.235385</td><td>-13.169394</td><td>5.878279</td></tr><tr><td>2024-03-01</td><td>&quot;YUM&quot;</td><td>2023-12-31</td><td>2024-02-07</td><td>2036.0</td><td>1063.0</td><td>352.0</td><td>463.0</td><td>677.0</td><td>281.0</td><td>1609.0</td><td>6231.0</td><td>512.0</td><td>757.0</td><td>0.0</td><td>1961.0</td><td>1277.0</td><td>11100.0</td><td>14089.0</td><td>-7616.0</td><td>-7858.0</td><td>448.0</td><td>-103.0</td><td>-508.0</td><td>170.0</td><td>106.0</td><td>3287.0</td><td>-0.102</td><td>0</td><td>0</td><td>0.0</td><td>3</td><td>2.265</td><td>2.265</td><td>25.629915</td><td>14.085793</td><td>-0.203232</td><td>&hellip;</td><td>-4.921051</td><td>-12.459328</td><td>5.068213</td><td>-19.180221</td><td>-13.518011</td><td>2.022077</td><td>-0.87169</td><td>24.388639</td><td>8.974524</td><td>-2.935362</td><td>-21.930041</td><td>NaN</td><td>-11.870986</td><td>-4.707506</td><td>0.735333</td><td>1.617368</td><td>6.543943</td><td>6</td><td>null</td><td>1.027403</td><td>1.060086</td><td>-0.018652</td><td>-0.017595</td><td>0.552444</td><td>-8.605506</td><td>-8.11774</td><td>-2.003117</td><td>-13.350619</td><td>-12.994526</td><td>-1.23868</td><td>-16.048234</td><td>null</td><td>null</td><td>-1.272624</td><td>-13.350619</td><td>-12.994526</td><td>-1.23868</td></tr><tr><td>2024-06-01</td><td>&quot;YUM&quot;</td><td>2024-03-31</td><td>2024-05-01</td><td>1598.0</td><td>798.0</td><td>284.0</td><td>314.0</td><td>557.0</td><td>281.0</td><td>1774.0</td><td>6224.0</td><td>652.0</td><td>686.0</td><td>0.0</td><td>1945.0</td><td>1188.0</td><td>11130.0</td><td>13980.0</td><td>-7492.0</td><td>-7756.0</td><td>363.0</td><td>45.0</td><td>-247.0</td><td>189.0</td><td>49.0</td><td>3432.0</td><td>-0.045</td><td>0</td><td>0</td><td>0.0</td><td>6</td><td>9.54</td><td>9.54</td><td>25.883676</td><td>9.149184</td><td>-0.20771</td><td>&hellip;</td><td>-8.220981</td><td>-22.027361</td><td>12.518807</td><td>-15.906324</td><td>-20.416082</td><td>1.161027</td><td>3.983611</td><td>33.273441</td><td>2.277321</td><td>-5.041189</td><td>-8.258328</td><td>NaN</td><td>-14.503515</td><td>-9.564719</td><td>0.917048</td><td>2.371978</td><td>19.124885</td><td>6</td><td>null</td><td>1.034203</td><td>1.0</td><td>-7.871843</td><td>-7.871843</td><td>-3.590689</td><td>-10.951294</td><td>-10.951294</td><td>-1.544948</td><td>-12.700341</td><td>-12.280318</td><td>1.550167</td><td>null</td><td>null</td><td>null</td><td>1.603188</td><td>-12.700341</td><td>-12.280318</td><td>1.550167</td></tr><tr><td>2024-09-01</td><td>&quot;YUM&quot;</td><td>2024-06-30</td><td>2024-08-06</td><td>1763.0</td><td>894.0</td><td>280.0</td><td>367.0</td><td>635.0</td><td>281.0</td><td>1635.0</td><td>6395.0</td><td>520.0</td><td>733.0</td><td>0.0</td><td>2137.0</td><td>1136.0</td><td>11140.0</td><td>14025.0</td><td>-7321.0</td><td>-7630.0</td><td>342.0</td><td>-298.0</td><td>-300.0</td><td>188.0</td><td>50.0</td><td>3534.0</td><td>0.0166</td><td>0</td><td>0</td><td>0.0</td><td>6</td><td>13.91</td><td>13.91</td><td>24.394058</td><td>10.384833</td><td>-0.204456</td><td>&hellip;</td><td>-12.5988</td><td>-4.958251</td><td>8.622772</td><td>-15.441698</td><td>-28.482903</td><td>2.736767</td><td>7.556778</td><td>9.50221</td><td>8.707928</td><td>-4.071181</td><td>11.142212</td><td>NaN</td><td>-5.879183</td><td>-3.958718</td><td>1.030876</td><td>-18.140158</td><td>-18.233879</td><td>5</td><td>null</td><td>null</td><td>1.061246</td><td>-3.667312</td><td>-3.455668</td><td>-0.116683</td><td>-3.536759</td><td>-3.332649</td><td>2.826218</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td></tr><tr><td>2024-12-01</td><td>&quot;YUM&quot;</td><td>2024-09-30</td><td>2024-11-07</td><td>1826.0</td><td>960.0</td><td>263.0</td><td>382.0</td><td>652.0</td><td>280.0</td><td>1625.0</td><td>6461.0</td><td>548.0</td><td>763.0</td><td>0.0</td><td>2190.0</td><td>1186.0</td><td>11169.0</td><td>14135.0</td><td>-7389.0</td><td>-7674.0</td><td>471.0</td><td>-39.0</td><td>-444.0</td><td>188.0</td><td>52.0</td><td>3519.0</td><td>0.0</td><td>0</td><td>0</td><td>0.0</td><td>3</td><td>2.843</td><td>2.843</td><td>23.618635</td><td>10.855357</td><td>-0.198853</td><td>&hellip;</td><td>-13.25831</td><td>7.616416</td><td>-12.326353</td><td>-16.455141</td><td>-15.983539</td><td>6.640488</td><td>-3.159368</td><td>1.395349</td><td>22.667445</td><td>1.659736</td><td>-0.38994</td><td>NaN</td><td>-6.883696</td><td>-0.883293</td><td>0.714714</td><td>-5.366536</td><td>-19.413059</td><td>5</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td></tr></tbody></table></div>"
       ],
       "text/plain": [
-       "shape: (5, 5)\n",
-       "┌────────────┬─────────────┬─────────────┬────────────┬───────┐\n",
-       "│ date       ┆ close       ┆ adj_close   ┆ volume     ┆ tic   │\n",
-       "│ ---        ┆ ---         ┆ ---         ┆ ---        ┆ ---   │\n",
-       "│ date       ┆ f64         ┆ f64         ┆ i64        ┆ str   │\n",
-       "╞════════════╪═════════════╪═════════════╪════════════╪═══════╡\n",
-       "│ 2005-01-03 ┆ 1202.079956 ┆ 1202.079956 ┆ 1510800000 ┆ ^GSPC │\n",
-       "│ 2005-01-04 ┆ 1188.050049 ┆ 1188.050049 ┆ 1721000000 ┆ ^GSPC │\n",
-       "│ 2005-01-05 ┆ 1183.73999  ┆ 1183.73999  ┆ 1738900000 ┆ ^GSPC │\n",
-       "│ 2005-01-06 ┆ 1187.890015 ┆ 1187.890015 ┆ 1569100000 ┆ ^GSPC │\n",
-       "│ 2005-01-07 ┆ 1186.189941 ┆ 1186.189941 ┆ 1477900000 ┆ ^GSPC │\n",
-       "└────────────┴─────────────┴─────────────┴────────────┴───────┘"
+       "shape: (72, 222)\n",
+       "┌────────────┬─────┬────────────┬────────────┬───┬────────────┬────────────┬───────────┬───────────┐\n",
+       "│ tdq        ┆ tic ┆ datadate   ┆ rdq        ┆ … ┆ fwd_return ┆ excess_ret ┆ sharpe_ra ┆ risk_retu │\n",
+       "│ ---        ┆ --- ┆ ---        ┆ ---        ┆   ┆ ---        ┆ urn        ┆ tio       ┆ rn        │\n",
+       "│ date       ┆ str ┆ date       ┆ date       ┆   ┆ f64        ┆ ---        ┆ ---       ┆ ---       │\n",
+       "│            ┆     ┆            ┆            ┆   ┆            ┆ f64        ┆ f64       ┆ f64       │\n",
+       "╞════════════╪═════╪════════════╪════════════╪═══╪════════════╪════════════╪═══════════╪═══════════╡\n",
+       "│ 2007-03-01 ┆ YUM ┆ 2006-12-31 ┆ 2007-02-12 ┆ … ┆ 26.814296  ┆ 19.383489  ┆ 13.015698 ┆ 18.005365 │\n",
+       "│ 2007-06-01 ┆ YUM ┆ 2007-03-31 ┆ 2007-05-01 ┆ … ┆ 8.208665   ┆ 16.45971   ┆ 9.665973  ┆ 4.820542  │\n",
+       "│ 2007-09-01 ┆ YUM ┆ 2007-06-30 ┆ 2007-07-11 ┆ … ┆ 19.17332   ┆ 26.707222  ┆ 12.826085 ┆ 9.207945  │\n",
+       "│ 2007-12-01 ┆ YUM ┆ 2007-09-30 ┆ 2007-10-08 ┆ … ┆ -1.085209  ┆ 11.669343  ┆ 5.528117  ┆ -0.514096 │\n",
+       "│ 2008-03-01 ┆ YUM ┆ 2007-12-31 ┆ 2008-02-04 ┆ … ┆ -7.880021  ┆ 12.628194  ┆ 6.253573  ┆ -3.902243 │\n",
+       "│ …          ┆ …   ┆ …          ┆ …          ┆ … ┆ …          ┆ …          ┆ …         ┆ …         │\n",
+       "│ 2023-12-01 ┆ YUM ┆ 2023-09-30 ┆ 2023-11-01 ┆ … ┆ 5.907735   ┆ -13.235385 ┆ -13.16939 ┆ 5.878279  │\n",
+       "│            ┆     ┆            ┆            ┆   ┆            ┆            ┆ 4         ┆           │\n",
+       "│ 2024-03-01 ┆ YUM ┆ 2023-12-31 ┆ 2024-02-07 ┆ … ┆ -1.272624  ┆ -13.350619 ┆ -12.99452 ┆ -1.23868  │\n",
+       "│            ┆     ┆            ┆            ┆   ┆            ┆            ┆ 6         ┆           │\n",
+       "│ 2024-06-01 ┆ YUM ┆ 2024-03-31 ┆ 2024-05-01 ┆ … ┆ 1.603188   ┆ -12.700341 ┆ -12.28031 ┆ 1.550167  │\n",
+       "│            ┆     ┆            ┆            ┆   ┆            ┆            ┆ 8         ┆           │\n",
+       "│ 2024-09-01 ┆ YUM ┆ 2024-06-30 ┆ 2024-08-06 ┆ … ┆ null       ┆ null       ┆ null      ┆ null      │\n",
+       "│ 2024-12-01 ┆ YUM ┆ 2024-09-30 ┆ 2024-11-07 ┆ … ┆ null       ┆ null       ┆ null      ┆ null      │\n",
+       "└────────────┴─────┴────────────┴────────────┴───┴────────────┴────────────┴───────────┴───────────┘"
       ]
      },
      "execution_count": 4,
@@ -3096,10 +38828,307 @@
     }
    ],
    "source": [
-    "from pipeline import Scraper\n",
+    "mcd = data.filter(pl.col(\"tic\") == \"YUM\")\n",
+    "mcd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (11, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>tdq</th><th>fwd_return_3Q</th><th>risk_return_3Q</th><th>fwd_return_4Q</th><th>risk_return_4Q</th></tr><tr><td>date</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2022-06-01</td><td>-0.950433</td><td>-0.252648</td><td>52.145192</td><td>13.9644</td></tr><tr><td>2022-09-01</td><td>96.810305</td><td>25.319246</td><td>200.0</td><td>61.334766</td></tr><tr><td>2022-12-01</td><td>153.846034</td><td>41.739487</td><td>164.975952</td><td>53.725829</td></tr><tr><td>2023-03-01</td><td>98.007867</td><td>31.764888</td><td>155.889126</td><td>55.370632</td></tr><tr><td>2023-06-01</td><td>44.232892</td><td>18.704916</td><td>126.35727</td><td>46.005311</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>2023-12-01</td><td>158.87167</td><td>52.031133</td><td>178.63735</td><td>54.725834</td></tr><tr><td>2024-03-01</td><td>57.718003</td><td>15.844878</td><td>70.369899</td><td>null</td></tr><tr><td>2024-06-01</td><td>26.123682</td><td>7.553415</td><td>null</td><td>null</td></tr><tr><td>2024-09-01</td><td>null</td><td>null</td><td>null</td><td>null</td></tr><tr><td>2024-12-01</td><td>null</td><td>null</td><td>null</td><td>null</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (11, 5)\n",
+       "┌────────────┬───────────────┬────────────────┬───────────────┬────────────────┐\n",
+       "│ tdq        ┆ fwd_return_3Q ┆ risk_return_3Q ┆ fwd_return_4Q ┆ risk_return_4Q │\n",
+       "│ ---        ┆ ---           ┆ ---            ┆ ---           ┆ ---            │\n",
+       "│ date       ┆ f64           ┆ f64            ┆ f64           ┆ f64            │\n",
+       "╞════════════╪═══════════════╪════════════════╪═══════════════╪════════════════╡\n",
+       "│ 2022-06-01 ┆ -0.950433     ┆ -0.252648      ┆ 52.145192     ┆ 13.9644        │\n",
+       "│ 2022-09-01 ┆ 96.810305     ┆ 25.319246      ┆ 200.0         ┆ 61.334766      │\n",
+       "│ 2022-12-01 ┆ 153.846034    ┆ 41.739487      ┆ 164.975952    ┆ 53.725829      │\n",
+       "│ 2023-03-01 ┆ 98.007867     ┆ 31.764888      ┆ 155.889126    ┆ 55.370632      │\n",
+       "│ 2023-06-01 ┆ 44.232892     ┆ 18.704916      ┆ 126.35727     ┆ 46.005311      │\n",
+       "│ …          ┆ …             ┆ …              ┆ …             ┆ …              │\n",
+       "│ 2023-12-01 ┆ 158.87167     ┆ 52.031133      ┆ 178.63735     ┆ 54.725834      │\n",
+       "│ 2024-03-01 ┆ 57.718003     ┆ 15.844878      ┆ 70.369899     ┆ null           │\n",
+       "│ 2024-06-01 ┆ 26.123682     ┆ 7.553415       ┆ null          ┆ null           │\n",
+       "│ 2024-09-01 ┆ null          ┆ null           ┆ null          ┆ null           │\n",
+       "│ 2024-12-01 ┆ null          ┆ null           ┆ null          ┆ null           │\n",
+       "└────────────┴───────────────┴────────────────┴───────────────┴────────────────┘"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.filter(pl.col(\"tic\") == \"NVDA\").select(\n",
+    "    [\"tdq\", \"fwd_return_3Q\", \"risk_return_3Q\", \"fwd_return_4Q\", \"risk_return_4Q\"]\n",
+    ").tail(11)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Null value counts and percentages:\n",
+      "                column  null_count  null_pct\n",
+      "58            ltcr_yoy        2152      5.86\n",
+      "56            ltda_yoy        1703      4.64\n",
+      "44           dlttq_yoy        1694      4.62\n",
+      "57       ev_ebitda_yoy        1462      3.98\n",
+      "36           ev_ebitda        1420      3.87\n",
+      "..                 ...         ...       ...\n",
+      "25           high_fear           0      0.00\n",
+      "26            low_fear           0      0.00\n",
+      "1              n_sales           0      0.00\n",
+      "41              niq_2y           0      0.00\n",
+      "75  sector_real_estate           0      0.00\n",
+      "\n",
+      "[76 rows x 3 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get null counts for each column and sort\n",
+    "null_counts = (\n",
+    "    data.select(features)\n",
+    "    .to_pandas()\n",
+    "    .isnull()\n",
+    "    .sum()\n",
+    "    .reset_index()\n",
+    "    .rename(columns={\"index\": \"column\", 0: \"null_count\"})\n",
+    "    .sort_values(\"null_count\", ascending=False)\n",
+    "    .assign(null_pct=lambda x: (x[\"null_count\"] / len(data) * 100).round(2))\n",
+    ")\n",
+    "print(\"\\nNull value counts and percentages:\")\n",
+    "print(null_counts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Total companies with missing data: 249\n",
+      "\n",
+      "Top 10 companies with most missing data:\n",
+      "    tic  total_nulls  null_pct\n",
+      "0  ALGN          205    106.77\n",
+      "1  ISRG          204    106.25\n",
+      "2  EXPD          204    106.25\n",
+      "3  TROW          204    106.25\n",
+      "4  GRMN          195    101.56\n",
+      "5  MNST          191     99.48\n",
+      "6  FFIV          172     89.58\n",
+      "7  LIFE          165     85.94\n",
+      "8  MPWR          156     81.25\n",
+      "9   CMG          156     81.25\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/b8/1sybx4y52l7fb3b8glvm3jk40000gn/T/ipykernel_35137/875865165.py:5: DeprecationWarning:\n",
+      "\n",
+      "DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get null counts per company\n",
+    "companies_with_nulls = (\n",
+    "    data.select([\"tic\"] + features)\n",
+    "    .to_pandas()\n",
+    "    .groupby(\"tic\")\n",
+    "    .apply(lambda x: x.isnull().sum().sum())\n",
+    "    .sort_values(ascending=False)\n",
+    "    .reset_index()\n",
+    "    .rename(columns={0: \"total_nulls\"})\n",
+    "    .assign(\n",
+    "        null_pct=lambda x: (\n",
+    "            x[\"total_nulls\"] / (len(data.columns) * x.groupby(\"tic\").size().values) * 100\n",
+    "        ).round(2)\n",
+    "    )\n",
+    ")\n",
     "\n",
-    "vix = Scraper(\"^GSPC\", \"yfinance\").get_market_data(\"2005-01-01\")\n",
-    "vix.head()"
+    "print(f\"\\nTotal companies with missing data: {(companies_with_nulls['total_nulls'] > 0).sum()}\")\n",
+    "print(\"\\nTop 10 companies with most missing data:\")\n",
+    "print(companies_with_nulls.head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (64, 192)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>tdq</th><th>tic</th><th>datadate</th><th>rdq</th><th>saleq</th><th>cogsq</th><th>xsgaq</th><th>niq</th><th>ebitdaq</th><th>cshoq</th><th>actq</th><th>atq</th><th>cheq</th><th>rectq</th><th>invtq</th><th>ppentq</th><th>lctq</th><th>dlttq</th><th>ltq</th><th>req</th><th>seqq</th><th>oancfq</th><th>ivncfq</th><th>fincfq</th><th>dvq</th><th>capxq</th><th>icaptq</th><th>surprise_pct</th><th>stock_split</th><th>n_purch</th><th>val_purch</th><th>n_sales</th><th>val_sales</th><th>insider_balance</th><th>roa</th><th>roi</th><th>roe</th><th>&hellip;</th><th>ps_yoy</th><th>ps_2y</th><th>eps_yoy</th><th>eps_2y</th><th>ev_ebitda_yoy</th><th>ev_ebitda_2y</th><th>ltcr_yoy</th><th>itr_yoy</th><th>rtr_yoy</th><th>atr_yoy</th><th>size_yoy</th><th>f_score</th><th>f_score_gr1</th><th>f_score_gr4</th><th>forward_vol_yoy</th><th>forward_vol_sos</th><th>forward_vol_qoq</th><th>fwd_return</th><th>risk_return</th><th>excess_return</th><th>performance_positive</th><th>risk_return_positive</th><th>fwd_sharpe_ratio</th><th>risk_return_2Q</th><th>risk_return_3Q</th><th>risk_return_4Q</th><th>sector_communication_services</th><th>sector_consumer_discretionary</th><th>sector_consumer_staples</th><th>sector_energy</th><th>sector_financials</th><th>sector_health_care</th><th>sector_industrials</th><th>sector_information_technology</th><th>sector_materials</th><th>sector_real_estate</th><th>sector_utilities</th></tr><tr><td>date</td><td>str</td><td>date</td><td>date</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>u32</td><td>f64</td><td>u32</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>&hellip;</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>i8</td><td>i8</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>i8</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td></tr></thead><tbody><tr><td>2008-03-01</td><td>&quot;L&quot;</td><td>2007-12-31</td><td>2008-02-11</td><td>3592.0</td><td>2857.0</td><td>0.0</td><td>384.0</td><td>0.0</td><td>529.684</td><td>0.0</td><td>76079.0</td><td>12872.0</td><td>11677.0</td><td>224.0</td><td>10425.0</td><td>0.0</td><td>6900.0</td><td>54590.0</td><td>13626.0</td><td>17591.0</td><td>1296.8</td><td>-1273.1</td><td>-2.8</td><td>82.5</td><td>901.7</td><td>28389.0</td><td>-0.239</td><td>0</td><td>1</td><td>0.191</td><td>4</td><td>1.724</td><td>1.533</td><td>0.02571</td><td>0.013526</td><td>0.111193</td><td>&hellip;</td><td>12.902874</td><td>34.885943</td><td>-19.334673</td><td>69.929798</td><td>NaN</td><td>NaN</td><td>200.0</td><td>-13.793438</td><td>-15.299251</td><td>-24.908028</td><td>-0.093202</td><td>5</td><td>0</td><td>-1</td><td>4.614202</td><td>1.761551</td><td>1.867523</td><td>-0.397332</td><td>-8.611077</td><td>-0.034295</td><td>0</td><td>0</td><td>-0.743249</td><td>4.959346</td><td>-9.255767</td><td>-8.611077</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>2008-06-01</td><td>&quot;L&quot;</td><td>2008-03-31</td><td>2008-04-28</td><td>3612.0</td><td>2661.0</td><td>0.0</td><td>555.0</td><td>0.0</td><td>529.702</td><td>0.0</td><td>76892.0</td><td>11667.0</td><td>11959.0</td><td>0.0</td><td>11086.0</td><td>0.0</td><td>7093.0</td><td>55847.0</td><td>13286.0</td><td>17257.0</td><td>1487.0</td><td>-1230.0</td><td>-200.0</td><td>82.0</td><td>846.0</td><td>28138.0</td><td>-0.158</td><td>0</td><td>0</td><td>0.0</td><td>1</td><td>0.082</td><td>0.082</td><td>0.024203</td><td>0.019724</td><td>0.10784</td><td>&hellip;</td><td>13.014081</td><td>51.193873</td><td>-27.442456</td><td>39.36741</td><td>NaN</td><td>NaN</td><td>200.0</td><td>11.309978</td><td>8.702655</td><td>-1.10861</td><td>0.017638</td><td>5</td><td>0</td><td>1</td><td>4.976012</td><td>5.668694</td><td>1.660324</td><td>-0.522072</td><td>-10.491769</td><td>-0.113731</td><td>0</td><td>0</td><td>-2.285586</td><td>-18.65718</td><td>-8.45061</td><td>-10.491769</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>2008-09-01</td><td>&quot;L&quot;</td><td>2008-06-30</td><td>2008-07-28</td><td>3922.0</td><td>2727.0</td><td>0.0</td><td>4859.0</td><td>0.0</td><td>436.268</td><td>0.0</td><td>74782.0</td><td>10375.0</td><td>11965.0</td><td>0.0</td><td>11782.0</td><td>0.0</td><td>7137.0</td><td>53747.0</td><td>13497.0</td><td>16781.0</td><td>1344.0</td><td>-1210.0</td><td>-173.0</td><td>83.0</td><td>933.0</td><td>28172.0</td><td>-0.16</td><td>0</td><td>1</td><td>0.492</td><td>4</td><td>2.734</td><td>2.242</td><td>0.083001</td><td>0.172476</td><td>0.369883</td><td>&hellip;</td><td>-16.715292</td><td>-3.152492</td><td>199.489123</td><td>300.0</td><td>NaN</td><td>NaN</td><td>-85.054034</td><td>NaN</td><td>28.610126</td><td>14.89984</td><td>-0.546362</td><td>7</td><td>2</td><td>3</td><td>5.018585</td><td>6.277926</td><td>7.985833</td><td>-0.327632</td><td>-6.528371</td><td>-0.070222</td><td>0</td><td>0</td><td>-1.399238</td><td>-5.264309</td><td>-7.203086</td><td>-6.528371</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>2008-12-01</td><td>&quot;L&quot;</td><td>2008-09-30</td><td>2008-10-27</td><td>2970.0</td><td>2929.0</td><td>0.0</td><td>-137.0</td><td>0.0</td><td>436.089</td><td>0.0</td><td>73417.0</td><td>9506.0</td><td>12201.0</td><td>0.0</td><td>12809.0</td><td>0.0</td><td>7401.0</td><td>53508.0</td><td>12461.0</td><td>15741.0</td><td>-1454.0</td><td>1494.0</td><td>24.0</td><td>28.0</td><td>1158.0</td><td>27310.0</td><td>-1.5</td><td>0</td><td>0</td><td>0.0</td><td>0</td><td>0.0</td><td>0.0</td><td>0.077107</td><td>-0.005016</td><td>0.359634</td><td>&hellip;</td><td>-55.743324</td><td>-46.738337</td><td>196.519991</td><td>285.704755</td><td>NaN</td><td>NaN</td><td>-200.0</td><td>NaN</td><td>-4.343053</td><td>-9.886907</td><td>-0.646001</td><td>4</td><td>-3</td><td>-1</td><td>3.214735</td><td>4.384858</td><td>4.661004</td><td>0.488965</td><td>15.21013</td><td>0.185788</td><td>1</td><td>1</td><td>5.779258</td><td>0.503758</td><td>5.307936</td><td>15.21013</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>2009-03-01</td><td>&quot;L&quot;</td><td>2008-12-31</td><td>2009-02-09</td><td>2743.0</td><td>3516.0</td><td>0.0</td><td>-958.0</td><td>0.0</td><td>435.092</td><td>0.0</td><td>69857.0</td><td>8867.0</td><td>11672.0</td><td>0.0</td><td>12876.0</td><td>0.0</td><td>8187.0</td><td>52735.0</td><td>9839.0</td><td>13126.0</td><td>1994.0</td><td>-2268.0</td><td>176.0</td><td>26.0</td><td>1060.0</td><td>25309.0</td><td>-5.0</td><td>0</td><td>0</td><td>0.0</td><td>0</td><td>0.0</td><td>0.0</td><td>0.061826</td><td>-0.037852</td><td>0.329042</td><td>&hellip;</td><td>-57.926143</td><td>-52.497407</td><td>168.812902</td><td>116.838807</td><td>NaN</td><td>NaN</td><td>99.234318</td><td>200.0</td><td>-21.355777</td><td>-17.362575</td><td>-0.759124</td><td>5</td><td>1</td><td>0</td><td>2.519552</td><td>3.215018</td><td>4.121507</td><td>0.837111</td><td>33.224616</td><td>0.330937</td><td>1</td><td>1</td><td>13.134767</td><td>11.145129</td><td>23.336302</td><td>33.224616</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>2022-12-01</td><td>&quot;L&quot;</td><td>2022-09-30</td><td>2022-11-29</td><td>3461.0</td><td>1665.0</td><td>383.0</td><td>130.0</td><td>282.0</td><td>240.76</td><td>10344.0</td><td>75111.0</td><td>886.0</td><td>9458.0</td><td>0.0</td><td>9949.0</td><td>42522.0</td><td>8475.0</td><td>60458.0</td><td>15377.0</td><td>14653.0</td><td>1246.0</td><td>-993.0</td><td>-303.0</td><td>23.0</td><td>0.0</td><td>21025.392</td><td>0.0</td><td>0</td><td>0</td><td>0.0</td><td>6</td><td>0.173</td><td>0.173</td><td>0.012981</td><td>0.006183</td><td>0.066539</td><td>&hellip;</td><td>7.43907</td><td>7.662498</td><td>-36.153086</td><td>201.876143</td><td>52.743225</td><td>170.610572</td><td>15.812824</td><td>NaN</td><td>-1.670822</td><td>10.193546</td><td>-0.746214</td><td>8</td><td>2</td><td>2</td><td>1.166898</td><td>1.369194</td><td>1.149776</td><td>0.117865</td><td>10.100698</td><td>0.041001</td><td>1</td><td>1</td><td>3.513711</td><td>-0.975316</td><td>3.507927</td><td>10.100698</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>2023-03-01</td><td>&quot;L&quot;</td><td>2022-12-31</td><td>2023-03-01</td><td>3793.0</td><td>1660.0</td><td>389.0</td><td>380.0</td><td>593.0</td><td>243.28</td><td>9935.0</td><td>75567.0</td><td>532.0</td><td>9403.0</td><td>0.0</td><td>10027.0</td><td>42961.0</td><td>8165.0</td><td>60366.0</td><td>14931.0</td><td>15201.0</td><td>622.0</td><td>-568.0</td><td>-416.0</td><td>26.0</td><td>0.0</td><td>21869.245</td><td>0.0</td><td>0</td><td>0</td><td>0.0</td><td>10</td><td>2.351</td><td>2.351</td><td>0.013392</td><td>0.017376</td><td>0.066575</td><td>&hellip;</td><td>0.095006</td><td>-3.823017</td><td>-31.407853</td><td>225.250223</td><td>43.112921</td><td>154.151421</td><td>-27.714593</td><td>NaN</td><td>1.402835</td><td>12.320162</td><td>-0.681952</td><td>8</td><td>0</td><td>2</td><td>1.093953</td><td>1.28216</td><td>1.592717</td><td>0.168217</td><td>15.376943</td><td>-0.047981</td><td>0</td><td>1</td><td>-4.385998</td><td>-0.713422</td><td>3.785912</td><td>15.376943</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>2023-06-01</td><td>&quot;L&quot;</td><td>2023-03-31</td><td>2023-05-30</td><td>3783.0</td><td>1653.0</td><td>379.0</td><td>375.0</td><td>614.0</td><td>233.62</td><td>10010.0</td><td>76560.0</td><td>594.0</td><td>9416.0</td><td>0.0</td><td>10098.0</td><td>44094.0</td><td>8150.0</td><td>61246.0</td><td>15293.0</td><td>15314.0</td><td>719.0</td><td>-134.0</td><td>-524.0</td><td>59.0</td><td>0.0</td><td>21779.533</td><td>0.0</td><td>0</td><td>0</td><td>0.0</td><td>6</td><td>0.21</td><td>0.21</td><td>0.013911</td><td>0.017218</td><td>0.069544</td><td>&hellip;</td><td>-18.051307</td><td>-23.587416</td><td>-30.888221</td><td>300.0</td><td>36.557222</td><td>-70.586369</td><td>85.263804</td><td>NaN</td><td>9.503445</td><td>17.517013</td><td>-0.294301</td><td>6</td><td>-2</td><td>-1</td><td>1.0</td><td>1.0</td><td>1.0</td><td>0.351408</td><td>35.140809</td><td>0.127103</td><td>1</td><td>1</td><td>12.710283</td><td>14.643191</td><td>26.813331</td><td>35.140809</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>2024-09-01</td><td>&quot;L&quot;</td><td>2024-06-30</td><td>2024-07-29</td><td>4193.0</td><td>0.0</td><td>0.0</td><td>369.0</td><td>0.0</td><td>219.707</td><td>0.0</td><td>81057.0</td><td>6879.0</td><td>10280.0</td><td>0.0</td><td>10710.0</td><td>0.0</td><td>8558.0</td><td>63856.0</td><td>16415.0</td><td>16384.0</td><td>952.0</td><td>-250.0</td><td>-787.0</td><td>14.0</td><td>159.0</td><td>25955.0</td><td>0.0</td><td>0</td><td>1</td><td>0.5</td><td>9</td><td>1.896</td><td>1.396</td><td>0.018814</td><td>0.014217</td><td>0.093079</td><td>&hellip;</td><td>-13.591337</td><td>10.740614</td><td>null</td><td>60.289661</td><td>200.0</td><td>300.0</td><td>NaN</td><td>NaN</td><td>200.0</td><td>200.0</td><td>NaN</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>1.339239</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>4.464145</td><td>null</td><td>null</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>2024-12-01</td><td>&quot;L&quot;</td><td>2024-09-30</td><td>2024-11-04</td><td>4399.0</td><td>0.0</td><td>0.0</td><td>401.0</td><td>0.0</td><td>218.931</td><td>0.0</td><td>83617.0</td><td>7355.0</td><td>10138.0</td><td>0.0</td><td>10689.0</td><td>0.0</td><td>8821.0</td><td>65369.0</td><td>16802.0</td><td>17357.0</td><td>934.0</td><td>-740.0</td><td>-122.0</td><td>0.0</td><td>140.0</td><td>26891.0</td><td>0.0</td><td>0</td><td>0</td><td>0.0</td><td>9</td><td>53.927</td><td>53.927</td><td>0.020008</td><td>0.014912</td><td>0.096388</td><td>&hellip;</td><td>-18.191216</td><td>11.455882</td><td>null</td><td>88.698479</td><td>200.0</td><td>300.0</td><td>-35.907563</td><td>NaN</td><td>-47.82718</td><td>-47.581579</td><td>0.794702</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (64, 192)\n",
+       "┌────────────┬─────┬────────────┬────────────┬───┬────────────┬────────────┬───────────┬───────────┐\n",
+       "│ tdq        ┆ tic ┆ datadate   ┆ rdq        ┆ … ┆ sector_inf ┆ sector_mat ┆ sector_re ┆ sector_ut │\n",
+       "│ ---        ┆ --- ┆ ---        ┆ ---        ┆   ┆ ormation_t ┆ erials     ┆ al_estate ┆ ilities   │\n",
+       "│ date       ┆ str ┆ date       ┆ date       ┆   ┆ echnology  ┆ ---        ┆ ---       ┆ ---       │\n",
+       "│            ┆     ┆            ┆            ┆   ┆ ---        ┆ i8         ┆ i8        ┆ i8        │\n",
+       "│            ┆     ┆            ┆            ┆   ┆ i8         ┆            ┆           ┆           │\n",
+       "╞════════════╪═════╪════════════╪════════════╪═══╪════════════╪════════════╪═══════════╪═══════════╡\n",
+       "│ 2008-03-01 ┆ L   ┆ 2007-12-31 ┆ 2008-02-11 ┆ … ┆ 0          ┆ 0          ┆ 0         ┆ 0         │\n",
+       "│ 2008-06-01 ┆ L   ┆ 2008-03-31 ┆ 2008-04-28 ┆ … ┆ 0          ┆ 0          ┆ 0         ┆ 0         │\n",
+       "│ 2008-09-01 ┆ L   ┆ 2008-06-30 ┆ 2008-07-28 ┆ … ┆ 0          ┆ 0          ┆ 0         ┆ 0         │\n",
+       "│ 2008-12-01 ┆ L   ┆ 2008-09-30 ┆ 2008-10-27 ┆ … ┆ 0          ┆ 0          ┆ 0         ┆ 0         │\n",
+       "│ 2009-03-01 ┆ L   ┆ 2008-12-31 ┆ 2009-02-09 ┆ … ┆ 0          ┆ 0          ┆ 0         ┆ 0         │\n",
+       "│ …          ┆ …   ┆ …          ┆ …          ┆ … ┆ …          ┆ …          ┆ …         ┆ …         │\n",
+       "│ 2022-12-01 ┆ L   ┆ 2022-09-30 ┆ 2022-11-29 ┆ … ┆ 0          ┆ 0          ┆ 0         ┆ 0         │\n",
+       "│ 2023-03-01 ┆ L   ┆ 2022-12-31 ┆ 2023-03-01 ┆ … ┆ 0          ┆ 0          ┆ 0         ┆ 0         │\n",
+       "│ 2023-06-01 ┆ L   ┆ 2023-03-31 ┆ 2023-05-30 ┆ … ┆ 0          ┆ 0          ┆ 0         ┆ 0         │\n",
+       "│ 2024-09-01 ┆ L   ┆ 2024-06-30 ┆ 2024-07-29 ┆ … ┆ 0          ┆ 0          ┆ 0         ┆ 0         │\n",
+       "│ 2024-12-01 ┆ L   ┆ 2024-09-30 ┆ 2024-11-04 ┆ … ┆ 0          ┆ 0          ┆ 0         ┆ 0         │\n",
+       "└────────────┴─────┴────────────┴────────────┴───┴────────────┴────────────┴───────────┴───────────┘"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.filter(pl.col(\"tic\") == \"L\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (51, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>tic</th><th>tdq</th><th>fwd_return_4Q</th><th>forward_vol_yoy</th><th>risk_return_4Q</th></tr><tr><td>str</td><td>date</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;AAL&quot;</td><td>2013-09-01</td><td>154.136085</td><td>2.126597</td><td>72.48016</td></tr><tr><td>&quot;ABMD&quot;</td><td>2017-09-01</td><td>165.335916</td><td>2.103878</td><td>78.586269</td></tr><tr><td>&quot;AMD&quot;</td><td>2016-03-01</td><td>200.0</td><td>4.775459</td><td>86.891669</td></tr><tr><td>&quot;BBWI&quot;</td><td>2020-06-01</td><td>200.0</td><td>3.755184</td><td>74.477339</td></tr><tr><td>&quot;BKNG&quot;</td><td>2010-06-01</td><td>168.915432</td><td>2.390081</td><td>70.673526</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;TSLA&quot;</td><td>2019-09-01</td><td>200.0</td><td>5.147724</td><td>97.705849</td></tr><tr><td>&quot;TSLA&quot;</td><td>2019-12-01</td><td>200.0</td><td>5.576061</td><td>100.0</td></tr><tr><td>&quot;TSLA&quot;</td><td>2020-03-01</td><td>200.0</td><td>5.316739</td><td>82.419614</td></tr><tr><td>&quot;WDC&quot;</td><td>2009-03-01</td><td>200.0</td><td>2.726421</td><td>74.235781</td></tr><tr><td>&quot;WHR&quot;</td><td>2009-03-01</td><td>200.0</td><td>3.696251</td><td>71.783289</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (51, 5)\n",
+       "┌──────┬────────────┬───────────────┬─────────────────┬────────────────┐\n",
+       "│ tic  ┆ tdq        ┆ fwd_return_4Q ┆ forward_vol_yoy ┆ risk_return_4Q │\n",
+       "│ ---  ┆ ---        ┆ ---           ┆ ---             ┆ ---            │\n",
+       "│ str  ┆ date       ┆ f64           ┆ f64             ┆ f64            │\n",
+       "╞══════╪════════════╪═══════════════╪═════════════════╪════════════════╡\n",
+       "│ AAL  ┆ 2013-09-01 ┆ 154.136085    ┆ 2.126597        ┆ 72.48016       │\n",
+       "│ ABMD ┆ 2017-09-01 ┆ 165.335916    ┆ 2.103878        ┆ 78.586269      │\n",
+       "│ AMD  ┆ 2016-03-01 ┆ 200.0         ┆ 4.775459        ┆ 86.891669      │\n",
+       "│ BBWI ┆ 2020-06-01 ┆ 200.0         ┆ 3.755184        ┆ 74.477339      │\n",
+       "│ BKNG ┆ 2010-06-01 ┆ 168.915432    ┆ 2.390081        ┆ 70.673526      │\n",
+       "│ …    ┆ …          ┆ …             ┆ …               ┆ …              │\n",
+       "│ TSLA ┆ 2019-09-01 ┆ 200.0         ┆ 5.147724        ┆ 97.705849      │\n",
+       "│ TSLA ┆ 2019-12-01 ┆ 200.0         ┆ 5.576061        ┆ 100.0          │\n",
+       "│ TSLA ┆ 2020-03-01 ┆ 200.0         ┆ 5.316739        ┆ 82.419614      │\n",
+       "│ WDC  ┆ 2009-03-01 ┆ 200.0         ┆ 2.726421        ┆ 74.235781      │\n",
+       "│ WHR  ┆ 2009-03-01 ┆ 200.0         ┆ 3.696251        ┆ 71.783289      │\n",
+       "└──────┴────────────┴───────────────┴─────────────────┴────────────────┘"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.filter(pl.col(\"risk_return_4Q\") > 70).select(\n",
+    "    [\"tic\", \"tdq\", \"fwd_return_4Q\", \"forward_vol_yoy\", \"risk_return_4Q\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (20, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>tic</th><th>tdq</th><th>avg_fwd_return</th><th>forward_vol</th><th>risk_return</th></tr><tr><td>str</td><td>date</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;NVDA&quot;</td><td>2023-09-01</td><td>1.473149</td><td>0.031268</td><td>47.113693</td></tr><tr><td>&quot;PGR&quot;</td><td>2023-09-01</td><td>0.625629</td><td>0.013195</td><td>47.413705</td></tr><tr><td>&quot;BK&quot;</td><td>2023-12-01</td><td>0.556011</td><td>0.010986</td><td>50.610972</td></tr><tr><td>&quot;BSX&quot;</td><td>2023-12-01</td><td>0.520267</td><td>0.010517</td><td>49.467115</td></tr><tr><td>&quot;COST&quot;</td><td>2023-12-01</td><td>0.557523</td><td>0.012445</td><td>44.798358</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;TMUS&quot;</td><td>2023-12-01</td><td>0.440126</td><td>0.01</td><td>44.012593</td></tr><tr><td>&quot;TRGP&quot;</td><td>2023-12-01</td><td>0.883907</td><td>0.014261</td><td>61.980651</td></tr><tr><td>&quot;TT&quot;</td><td>2023-12-01</td><td>0.712079</td><td>0.014324</td><td>49.71244</td></tr><tr><td>&quot;WAB&quot;</td><td>2023-12-01</td><td>0.538836</td><td>0.012491</td><td>43.138493</td></tr><tr><td>&quot;WELL&quot;</td><td>2023-12-01</td><td>0.482746</td><td>0.011473</td><td>42.075839</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (20, 5)\n",
+       "┌──────┬────────────┬────────────────┬─────────────┬─────────────┐\n",
+       "│ tic  ┆ tdq        ┆ avg_fwd_return ┆ forward_vol ┆ risk_return │\n",
+       "│ ---  ┆ ---        ┆ ---            ┆ ---         ┆ ---         │\n",
+       "│ str  ┆ date       ┆ f64            ┆ f64         ┆ f64         │\n",
+       "╞══════╪════════════╪════════════════╪═════════════╪═════════════╡\n",
+       "│ NVDA ┆ 2023-09-01 ┆ 1.473149       ┆ 0.031268    ┆ 47.113693   │\n",
+       "│ PGR  ┆ 2023-09-01 ┆ 0.625629       ┆ 0.013195    ┆ 47.413705   │\n",
+       "│ BK   ┆ 2023-12-01 ┆ 0.556011       ┆ 0.010986    ┆ 50.610972   │\n",
+       "│ BSX  ┆ 2023-12-01 ┆ 0.520267       ┆ 0.010517    ┆ 49.467115   │\n",
+       "│ COST ┆ 2023-12-01 ┆ 0.557523       ┆ 0.012445    ┆ 44.798358   │\n",
+       "│ …    ┆ …          ┆ …              ┆ …           ┆ …           │\n",
+       "│ TMUS ┆ 2023-12-01 ┆ 0.440126       ┆ 0.01        ┆ 44.012593   │\n",
+       "│ TRGP ┆ 2023-12-01 ┆ 0.883907       ┆ 0.014261    ┆ 61.980651   │\n",
+       "│ TT   ┆ 2023-12-01 ┆ 0.712079       ┆ 0.014324    ┆ 49.71244    │\n",
+       "│ WAB  ┆ 2023-12-01 ┆ 0.538836       ┆ 0.012491    ┆ 43.138493   │\n",
+       "│ WELL ┆ 2023-12-01 ┆ 0.482746       ┆ 0.011473    ┆ 42.075839   │\n",
+       "└──────┴────────────┴────────────────┴─────────────┴─────────────┘"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.filter(pl.col(\"risk_return\") > 40).select(\n",
+    "    [\"tic\", \"tdq\", \"avg_fwd_return\", \"forward_vol\", \"risk_return\"]\n",
+    ").sort(\"tdq\").tail(20)"
    ]
   }
  ],
diff --git a/notebooks/experiments_class.ipynb b/notebooks/experiments_class.ipynb
new file mode 100644
index 0000000..d33556a
--- /dev/null
+++ b/notebooks/experiments_class.ipynb
@@ -0,0 +1,365 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datetime as dt\n",
+    "\n",
+    "import polars as pl\n",
+    "\n",
+    "from stocksense.config import config\n",
+    "from stocksense.database import DatabaseHandler\n",
+    "from stocksense.model import XGBoostRegressor\n",
+    "from stocksense.pipeline import clean, engineer_features\n",
+    "\n",
+    "features = config.model.features\n",
+    "date_col = config.model.date_col\n",
+    "aux_cols = [\"tic\", \"datadate\", \"rdq\"]\n",
+    "\n",
+    "features = config.model.features\n",
+    "targets = [\"risk_return_3Q_hit\", \"fwd_return_4Q_hit\", \"risk_return_4Q_hit\"]\n",
+    "prediction_horizon = config.processing.prediction_horizon\n",
+    "min_train_years = config.model.min_train_years\n",
+    "\n",
+    "trade_date = dt.datetime(2023, 6, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2024-12-25 12:04:05.841\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mstocksense.pipeline.preprocess\u001b[0m:\u001b[36mengineer_features\u001b[0m:\u001b[36m20\u001b[0m - \u001b[1mSTART processing stock data\u001b[0m\n",
+      "\u001b[32m2024-12-25 12:04:05.842\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mstocksense.database.schema\u001b[0m:\u001b[36mcreate_tables\u001b[0m:\u001b[36m121\u001b[0m - \u001b[32m\u001b[1mTables created successfully\u001b[0m\n",
+      "\u001b[32m2024-12-25 12:04:09.281\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mstocksense.pipeline.preprocess\u001b[0m:\u001b[36mengineer_features\u001b[0m:\u001b[36m33\u001b[0m - \u001b[1mSTART feature engineering\u001b[0m\n",
+      "\u001b[32m2024-12-25 12:04:25.943\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mstocksense.pipeline.preprocess\u001b[0m:\u001b[36mengineer_features\u001b[0m:\u001b[36m47\u001b[0m - \u001b[32m\u001b[1mEND 58960 rows PROCESSED\u001b[0m\n",
+      "\u001b[32m2024-12-25 12:04:25.948\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mstocksense.pipeline.preprocess\u001b[0m:\u001b[36mclean\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSTART cleaning data\u001b[0m\n",
+      "\u001b[32m2024-12-25 12:04:26.355\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mstocksense.pipeline.preprocess\u001b[0m:\u001b[36mclean\u001b[0m:\u001b[36m110\u001b[0m - \u001b[32m\u001b[1m37806 rows retained after CLEANING\u001b[0m\n",
+      "\u001b[32m2024-12-25 12:04:26.357\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mstocksense.database.schema\u001b[0m:\u001b[36mcreate_tables\u001b[0m:\u001b[36m121\u001b[0m - \u001b[32m\u001b[1mTables created successfully\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "def prepare_data():\n",
+    "    \"\"\"Prepare data for model operations.\"\"\"\n",
+    "    data = engineer_features()\n",
+    "    data = clean(data)\n",
+    "    return data\n",
+    "\n",
+    "\n",
+    "data = prepare_data()\n",
+    "constituents = DatabaseHandler().fetch_constituents(trade_date)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (72, 230)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>tdq</th><th>tic</th><th>datadate</th><th>rdq</th><th>saleq</th><th>cogsq</th><th>xsgaq</th><th>niq</th><th>ebitdaq</th><th>cshoq</th><th>actq</th><th>atq</th><th>cheq</th><th>rectq</th><th>invtq</th><th>ppentq</th><th>lctq</th><th>dlttq</th><th>ltq</th><th>req</th><th>seqq</th><th>oancfq</th><th>ivncfq</th><th>fincfq</th><th>dvq</th><th>capxq</th><th>icaptq</th><th>surprise_pct</th><th>stock_split</th><th>n_purch</th><th>val_purch</th><th>n_sales</th><th>val_sales</th><th>insider_balance</th><th>roa</th><th>roi</th><th>roe</th><th>&hellip;</th><th>eps_2y</th><th>ev_ebitda_yoy</th><th>ltcr_yoy</th><th>itr_yoy</th><th>rtr_yoy</th><th>atr_yoy</th><th>size_yoy</th><th>roa_sec_qoq</th><th>roa_sec_yoy</th><th>f_score</th><th>forward_vol_yoy</th><th>forward_vol_sos</th><th>forward_vol_qoq</th><th>excess_return_1Q</th><th>sharpe_ratio_1Q</th><th>risk_return_1Q</th><th>fwd_return_1Q_hit</th><th>excess_return_1Q_hit</th><th>risk_return_1Q_hit</th><th>excess_return_2Q</th><th>sharpe_ratio_2Q</th><th>risk_return_2Q</th><th>fwd_return_2Q_hit</th><th>excess_return_2Q_hit</th><th>risk_return_2Q_hit</th><th>excess_return_3Q</th><th>sharpe_ratio_3Q</th><th>risk_return_3Q</th><th>fwd_return_3Q_hit</th><th>excess_return_3Q_hit</th><th>risk_return_3Q_hit</th><th>excess_return_4Q</th><th>sharpe_ratio_4Q</th><th>risk_return_4Q</th><th>fwd_return_4Q_hit</th><th>excess_return_4Q_hit</th><th>risk_return_4Q_hit</th></tr><tr><td>date</td><td>str</td><td>date</td><td>date</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>u32</td><td>f64</td><td>u32</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>&hellip;</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>i8</td><td>i8</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>i8</td><td>i8</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>i8</td><td>i8</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>i8</td><td>i8</td></tr></thead><tbody><tr><td>2007-03-01</td><td>&quot;AAPL&quot;</td><td>2006-12-31</td><td>2007-01-17</td><td>7115.0</td><td>4821.0</td><td>898.0</td><td>1004.0</td><td>1396.0</td><td>24086.16</td><td>16664.0</td><td>19461.0</td><td>11869.0</td><td>3113.0</td><td>303.0</td><td>1362.0</td><td>7337.0</td><td>0.0</td><td>8233.0</td><td>6634.0</td><td>11228.0</td><td>1813.0</td><td>-1234.0</td><td>188.0</td><td>0.0</td><td>142.0</td><td>11228.0</td><td>0.453</td><td>0</td><td>0</td><td>0.0</td><td>9</td><td>83.8</td><td>83.8</td><td>12.476235</td><td>8.941931</td><td>0.216245</td><td>&hellip;</td><td>null</td><td>-14.607498</td><td>NaN</td><td>-11.661243</td><td>-22.623626</td><td>-13.145444</td><td>3.310886</td><td>31.40398</td><td>171.993547</td><td>4</td><td>2.613724</td><td>2.198614</td><td>1.445804</td><td>9.625824</td><td>6.657767</td><td>10.682745</td><td>1</td><td>1</td><td>1</td><td>40.845842</td><td>28.251306</td><td>32.906736</td><td>1</td><td>1</td><td>1</td><td>77.23883</td><td>35.130699</td><td>38.510469</td><td>1</td><td>1</td><td>1</td><td>84.161474</td><td>32.199834</td><td>32.355575</td><td>1</td><td>1</td><td>1</td></tr><tr><td>2007-06-01</td><td>&quot;AAPL&quot;</td><td>2007-03-31</td><td>2007-04-25</td><td>5264.0</td><td>3346.0</td><td>863.0</td><td>770.0</td><td>1055.0</td><td>24211.404</td><td>16029.0</td><td>18711.0</td><td>12577.0</td><td>1667.0</td><td>208.0</td><td>1409.0</td><td>5485.0</td><td>0.0</td><td>6450.0</td><td>7413.0</td><td>12261.0</td><td>734.0</td><td>-978.0</td><td>180.0</td><td>0.0</td><td>105.0</td><td>12261.0</td><td>0.359</td><td>0</td><td>0</td><td>0.0</td><td>2</td><td>2.08</td><td>2.08</td><td>14.900326</td><td>6.280075</td><td>0.227388</td><td>&hellip;</td><td>null</td><td>20.089345</td><td>NaN</td><td>5.873221</td><td>11.085553</td><td>-11.127631</td><td>3.107109</td><td>59.90405</td><td>97.834928</td><td>5</td><td>2.792059</td><td>2.766874</td><td>2.792311</td><td>15.141031</td><td>5.4224</td><td>4.288529</td><td>1</td><td>1</td><td>1</td><td>39.002772</td><td>13.967916</td><td>13.097882</td><td>1</td><td>1</td><td>1</td><td>45.582556</td><td>16.474386</td><td>13.492304</td><td>1</td><td>1</td><td>1</td><td>42.675714</td><td>15.284672</td><td>11.282641</td><td>1</td><td>1</td><td>0</td></tr><tr><td>2007-09-01</td><td>&quot;AAPL&quot;</td><td>2007-06-30</td><td>2007-07-25</td><td>5410.0</td><td>3334.0</td><td>954.0</td><td>818.0</td><td>1122.0</td><td>24336.536</td><td>18745.0</td><td>21647.0</td><td>13767.0</td><td>2901.0</td><td>251.0</td><td>1626.0</td><td>6992.0</td><td>0.0</td><td>8243.0</td><td>8255.0</td><td>13404.0</td><td>1227.0</td><td>-1433.0</td><td>229.0</td><td>0.0</td><td>283.0</td><td>13404.0</td><td>0.271</td><td>0</td><td>0</td><td>0.0</td><td>8</td><td>38.399</td><td>38.399</td><td>14.477757</td><td>6.102656</td><td>0.233811</td><td>&hellip;</td><td>null</td><td>20.110764</td><td>NaN</td><td>12.470648</td><td>10.519002</td><td>-10.96548</td><td>3.733049</td><td>-17.000355</td><td>42.806873</td><td>5</td><td>2.702126</td><td>2.963765</td><td>2.710762</td><td>22.986262</td><td>8.47963</td><td>9.219309</td><td>1</td><td>1</td><td>1</td><td>19.298171</td><td>7.119094</td><td>5.386367</td><td>1</td><td>1</td><td>0</td><td>20.906667</td><td>7.054091</td><td>4.512087</td><td>0</td><td>1</td><td>0</td><td>36.943247</td><td>13.67192</td><td>9.291562</td><td>0</td><td>1</td><td>0</td></tr><tr><td>2007-12-01</td><td>&quot;AAPL&quot;</td><td>2007-09-30</td><td>2007-10-22</td><td>6217.0</td><td>4034.0</td><td>1030.0</td><td>904.0</td><td>1153.0</td><td>24425.212</td><td>21956.0</td><td>25347.0</td><td>15386.0</td><td>4029.0</td><td>346.0</td><td>1832.0</td><td>9299.0</td><td>0.0</td><td>10815.0</td><td>9164.0</td><td>14532.0</td><td>1696.0</td><td>396.0</td><td>142.0</td><td>0.0</td><td>205.0</td><td>14532.0</td><td>0.179</td><td>0</td><td>1</td><td>0.696</td><td>3</td><td>131.879</td><td>131.183</td><td>13.792559</td><td>6.220754</td><td>0.240573</td><td>&hellip;</td><td>null</td><td>15.326081</td><td>NaN</td><td>3.986648</td><td>-8.396816</td><td>-11.60651</td><td>3.972748</td><td>-12.499977</td><td>52.599244</td><td>5</td><td>3.635551</td><td>2.812971</td><td>3.086454</td><td>-13.717511</td><td>-4.444424</td><td>-6.938818</td><td>0</td><td>0</td><td>0</td><td>-5.03582</td><td>-1.631588</td><td>-4.185152</td><td>0</td><td>0</td><td>0</td><td>7.192317</td><td>2.55684</td><td>-1.977352</td><td>0</td><td>0</td><td>0</td><td>-6.577482</td><td>-1.809212</td><td>-9.914149</td><td>0</td><td>0</td><td>0</td></tr><tr><td>2008-03-01</td><td>&quot;AAPL&quot;</td><td>2007-12-31</td><td>2008-01-22</td><td>9608.0</td><td>6170.0</td><td>1206.0</td><td>1581.0</td><td>2232.0</td><td>24601.612</td><td>26189.0</td><td>30039.0</td><td>18448.0</td><td>4422.0</td><td>459.0</td><td>1870.0</td><td>10535.0</td><td>0.0</td><td>13235.0</td><td>10758.0</td><td>16804.0</td><td>2787.0</td><td>-3462.0</td><td>485.0</td><td>0.0</td><td>224.0</td><td>16804.0</td><td>0.089</td><td>0</td><td>0</td><td>0.0</td><td>2</td><td>6.884</td><td>6.884</td><td>13.55904</td><td>9.408474</td><td>0.242383</td><td>&hellip;</td><td>145.304665</td><td>-13.714245</td><td>NaN</td><td>-3.879321</td><td>-4.797032</td><td>-10.603272</td><td>4.395268</td><td>-4.206943</td><td>11.244333</td><td>5</td><td>3.64166</td><td>2.404911</td><td>2.40588</td><td>32.114818</td><td>13.348468</td><td>15.072915</td><td>1</td><td>1</td><td>1</td><td>40.92954</td><td>17.012291</td><td>15.982153</td><td>1</td><td>1</td><td>1</td><td>13.604471</td><td>5.656953</td><td>-2.870685</td><td>0</td><td>1</td><td>0</td><td>9.154933</td><td>2.513945</td><td>-7.455066</td><td>0</td><td>0</td><td>0</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>2023-12-01</td><td>&quot;AAPL&quot;</td><td>2023-09-30</td><td>2023-11-02</td><td>89498.0</td><td>49071.0</td><td>6151.0</td><td>22956.0</td><td>30653.0</td><td>15550.061</td><td>143566.0</td><td>352583.0</td><td>61555.0</td><td>60985.0</td><td>6331.0</td><td>43715.0</td><td>145308.0</td><td>95281.0</td><td>290437.0</td><td>-214.0</td><td>62146.0</td><td>21598.0</td><td>2394.0</td><td>-23153.0</td><td>3758.0</td><td>2163.0</td><td>173234.0</td><td>0.049</td><td>0</td><td>0</td><td>0.0</td><td>7</td><td>111.955</td><td>111.955</td><td>27.509835</td><td>13.25144</td><td>1.56076</td><td>&hellip;</td><td>11.107477</td><td>26.39167</td><td>-16.337621</td><td>-24.686506</td><td>2.25749</td><td>-0.510488</td><td>-0.003818</td><td>-5.737774</td><td>-0.656043</td><td>6</td><td>1.415201</td><td>1.343185</td><td>1.106571</td><td>-9.601592</td><td>-8.676889</td><td>-2.369563</td><td>0</td><td>0</td><td>0</td><td>-20.033258</td><td>-18.10391</td><td>-6.6633</td><td>0</td><td>0</td><td>0</td><td>-4.960896</td><td>-3.693383</td><td>10.558652</td><td>0</td><td>0</td><td>0</td><td>-6.181044</td><td>-4.367609</td><td>13.699963</td><td>0</td><td>0</td><td>0</td></tr><tr><td>2024-03-01</td><td>&quot;AAPL&quot;</td><td>2023-12-31</td><td>2024-02-01</td><td>119575.0</td><td>64720.0</td><td>6786.0</td><td>33916.0</td><td>43221.0</td><td>15460.223</td><td>143692.0</td><td>353514.0</td><td>73100.0</td><td>50102.0</td><td>6511.0</td><td>43666.0</td><td>133973.0</td><td>95088.0</td><td>279414.0</td><td>8242.0</td><td>74100.0</td><td>39895.0</td><td>1927.0</td><td>-30585.0</td><td>3825.0</td><td>2392.0</td><td>182140.0</td><td>0.039</td><td>0</td><td>0</td><td>0.0</td><td>1</td><td>1.058</td><td>1.058</td><td>28.545687</td><td>18.620841</td><td>1.361849</td><td>&hellip;</td><td>7.230683</td><td>14.453625</td><td>15.551229</td><td>-6.485394</td><td>5.764673</td><td>1.113202</td><td>0.151514</td><td>5.112562</td><td>6.896212</td><td>8</td><td>null</td><td>1.615673</td><td>1.491581</td><td>-1.781402</td><td>-1.194305</td><td>-0.78917</td><td>0</td><td>0</td><td>0</td><td>14.397554</td><td>9.652546</td><td>13.99829</td><td>1</td><td>1</td><td>1</td><td>14.486879</td><td>8.966466</td><td>16.441985</td><td>1</td><td>1</td><td>1</td><td>13.434717</td><td>null</td><td>null</td><td>0</td><td>0</td><td>null</td></tr><tr><td>2024-06-01</td><td>&quot;AAPL&quot;</td><td>2024-03-31</td><td>2024-05-02</td><td>90753.0</td><td>48482.0</td><td>6468.0</td><td>23636.0</td><td>30736.0</td><td>15337.686</td><td>128416.0</td><td>337411.0</td><td>67150.0</td><td>41150.0</td><td>6232.0</td><td>43546.0</td><td>123822.0</td><td>91831.0</td><td>263217.0</td><td>4339.0</td><td>74194.0</td><td>22690.0</td><td>-310.0</td><td>-30433.0</td><td>3710.0</td><td>1996.0</td><td>178784.0</td><td>0.02</td><td>0</td><td>0</td><td>0.0</td><td>7</td><td>90.244</td><td>90.244</td><td>29.752735</td><td>13.220422</td><td>1.353061</td><td>&hellip;</td><td>5.325788</td><td>-2.113215</td><td>-23.431138</td><td>7.40212</td><td>-5.535435</td><td>-5.969847</td><td>0.123374</td><td>6.398108</td><td>4.326248</td><td>7</td><td>null</td><td>1.485033</td><td>1.706893</td><td>11.739695</td><td>6.877816</td><td>9.385973</td><td>1</td><td>1</td><td>1</td><td>9.040261</td><td>5.296326</td><td>10.807127</td><td>1</td><td>1</td><td>1</td><td>9.423971</td><td>6.345968</td><td>15.97776</td><td>0</td><td>0</td><td>1</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td></tr><tr><td>2024-09-01</td><td>&quot;AAPL&quot;</td><td>2024-06-30</td><td>2024-08-01</td><td>85777.0</td><td>46099.0</td><td>6320.0</td><td>21448.0</td><td>28202.0</td><td>15222.259</td><td>125435.0</td><td>331612.0</td><td>61801.0</td><td>43172.0</td><td>6165.0</td><td>44502.0</td><td>131624.0</td><td>86196.0</td><td>264904.0</td><td>-4726.0</td><td>66708.0</td><td>28858.0</td><td>-127.0</td><td>-36017.0</td><td>3895.0</td><td>2151.0</td><td>168012.0</td><td>0.0399</td><td>0</td><td>0</td><td>0.0</td><td>4</td><td>38.092</td><td>38.092</td><td>30.74557</td><td>12.765755</td><td>1.528392</td><td>&hellip;</td><td>9.322555</td><td>9.742691</td><td>25.113659</td><td>25.471726</td><td>-6.621743</td><td>4.579645</td><td>-0.080792</td><td>0.61492</td><td>6.069032</td><td>7</td><td>null</td><td>null</td><td>1.223949</td><td>-3.655292</td><td>-2.986473</td><td>-0.091351</td><td>0</td><td>0</td><td>0</td><td>-3.465013</td><td>-2.83101</td><td>2.509138</td><td>0</td><td>0</td><td>0</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td></tr><tr><td>2024-12-01</td><td>&quot;AAPL&quot;</td><td>2024-09-30</td><td>2024-10-31</td><td>94930.0</td><td>51051.0</td><td>6523.0</td><td>14736.0</td><td>32502.0</td><td>15116.786</td><td>152987.0</td><td>364980.0</td><td>65171.0</td><td>66243.0</td><td>7286.0</td><td>45680.0</td><td>176392.0</td><td>85750.0</td><td>308030.0</td><td>-19154.0</td><td>56950.0</td><td>26811.0</td><td>1445.0</td><td>-24948.0</td><td>3804.0</td><td>2908.0</td><td>163579.0</td><td>0.0237</td><td>0</td><td>0</td><td>0.0</td><td>7</td><td>151.316</td><td>151.316</td><td>25.682503</td><td>9.008491</td><td>1.645935</td><td>&hellip;</td><td>1.432787</td><td>13.645782</td><td>36.659568</td><td>7.890986</td><td>-2.891936</td><td>4.703404</td><td>0.270542</td><td>-22.440193</td><td>-12.725447</td><td>5</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (72, 230)\n",
+       "┌────────────┬──────┬────────────┬────────────┬───┬────────────┬───────────┬───────────┬───────────┐\n",
+       "│ tdq        ┆ tic  ┆ datadate   ┆ rdq        ┆ … ┆ risk_retur ┆ fwd_retur ┆ excess_re ┆ risk_retu │\n",
+       "│ ---        ┆ ---  ┆ ---        ┆ ---        ┆   ┆ n_4Q       ┆ n_4Q_hit  ┆ turn_4Q_h ┆ rn_4Q_hit │\n",
+       "│ date       ┆ str  ┆ date       ┆ date       ┆   ┆ ---        ┆ ---       ┆ it        ┆ ---       │\n",
+       "│            ┆      ┆            ┆            ┆   ┆ f64        ┆ i8        ┆ ---       ┆ i8        │\n",
+       "│            ┆      ┆            ┆            ┆   ┆            ┆           ┆ i8        ┆           │\n",
+       "╞════════════╪══════╪════════════╪════════════╪═══╪════════════╪═══════════╪═══════════╪═══════════╡\n",
+       "│ 2007-03-01 ┆ AAPL ┆ 2006-12-31 ┆ 2007-01-17 ┆ … ┆ 32.355575  ┆ 1         ┆ 1         ┆ 1         │\n",
+       "│ 2007-06-01 ┆ AAPL ┆ 2007-03-31 ┆ 2007-04-25 ┆ … ┆ 11.282641  ┆ 1         ┆ 1         ┆ 0         │\n",
+       "│ 2007-09-01 ┆ AAPL ┆ 2007-06-30 ┆ 2007-07-25 ┆ … ┆ 9.291562   ┆ 0         ┆ 1         ┆ 0         │\n",
+       "│ 2007-12-01 ┆ AAPL ┆ 2007-09-30 ┆ 2007-10-22 ┆ … ┆ -9.914149  ┆ 0         ┆ 0         ┆ 0         │\n",
+       "│ 2008-03-01 ┆ AAPL ┆ 2007-12-31 ┆ 2008-01-22 ┆ … ┆ -7.455066  ┆ 0         ┆ 0         ┆ 0         │\n",
+       "│ …          ┆ …    ┆ …          ┆ …          ┆ … ┆ …          ┆ …         ┆ …         ┆ …         │\n",
+       "│ 2023-12-01 ┆ AAPL ┆ 2023-09-30 ┆ 2023-11-02 ┆ … ┆ 13.699963  ┆ 0         ┆ 0         ┆ 0         │\n",
+       "│ 2024-03-01 ┆ AAPL ┆ 2023-12-31 ┆ 2024-02-01 ┆ … ┆ null       ┆ 0         ┆ 0         ┆ null      │\n",
+       "│ 2024-06-01 ┆ AAPL ┆ 2024-03-31 ┆ 2024-05-02 ┆ … ┆ null       ┆ null      ┆ null      ┆ null      │\n",
+       "│ 2024-09-01 ┆ AAPL ┆ 2024-06-30 ┆ 2024-08-01 ┆ … ┆ null       ┆ null      ┆ null      ┆ null      │\n",
+       "│ 2024-12-01 ┆ AAPL ┆ 2024-09-30 ┆ 2024-10-31 ┆ … ┆ null       ┆ null      ┆ null      ┆ null      │\n",
+       "└────────────┴──────┴────────────┴────────────┴───┴────────────┴───────────┴───────────┴───────────┘"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.filter(pl.col(\"tic\") == \"AAPL\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def format_parameters(solution, scale: float) -> dict:\n",
+    "    return {\n",
+    "        \"objective\": \"binary:logistic\",\n",
+    "        \"learning_rate\": solution[0],\n",
+    "        \"n_estimators\": round(solution[1]),\n",
+    "        \"max_depth\": round(solution[2]),\n",
+    "        \"min_child_weight\": solution[3],\n",
+    "        \"gamma\": solution[4],\n",
+    "        \"subsample\": solution[5],\n",
+    "        \"colsample_bytree\": solution[6],\n",
+    "        \"reg_alpha\": solution[7],\n",
+    "        \"reg_lambda\": solution[8],\n",
+    "        \"scale_pos_weight\": scale,\n",
+    "        \"eval_metric\": \"logloss\",\n",
+    "        \"tree_method\": \"hist\",\n",
+    "        \"nthread\": -1,\n",
+    "        \"random_state\": 100,\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "START training model for risk_return_3Q_hit, 2023-06-01 00:00:00\n",
+      "2.2\n",
+      "START training model for fwd_return_4Q_hit, 2023-06-01 00:00:00\n",
+      "2.84\n",
+      "START training model for risk_return_4Q_hit, 2023-06-01 00:00:00\n",
+      "1.92\n"
+     ]
+    }
+   ],
+   "source": [
+    "def get_dataset_imbalance_scale(train: pl.DataFrame, target: str):\n",
+    "    \"\"\"\n",
+    "    Compute dataset class imbalance scale.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    train : pl.DataFrame\n",
+    "        Training dataset.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    float\n",
+    "        Class imbalance scale.\n",
+    "    \"\"\"\n",
+    "    min_year = pl.col(\"tdq\").dt.year().min()\n",
+    "    filtered_data = train.filter(pl.col(\"tdq\").dt.year() < min_year + min_train_years)\n",
+    "    neg_count = len(filtered_data.filter(pl.col(target) == 0))\n",
+    "    pos_count = len(filtered_data.filter(pl.col(target) == 1))\n",
+    "    return round(neg_count / pos_count, 2)\n",
+    "\n",
+    "\n",
+    "def train_model(data, trade_date, targets, features):\n",
+    "    models = {}\n",
+    "    for target in targets:\n",
+    "        print(f\"START training model for {target}, {trade_date}\")\n",
+    "\n",
+    "        train = data.filter(\n",
+    "            (pl.col(\"tdq\") < trade_date - dt.timedelta(days=360))\n",
+    "            & ~pl.all_horizontal(pl.col(target).is_null())\n",
+    "        ).select([\"tdq\", \"tic\"] + features + [target])\n",
+    "\n",
+    "        scale = get_dataset_imbalance_scale(train, target)\n",
+    "        print(scale)\n",
+    "\n",
+    "        params = [0.10, 192.50, 7.85, 6.65, 0.47, 0.52, 0.87, 6.89, 8.68]\n",
+    "        params = format_parameters(params, scale)\n",
+    "\n",
+    "        X_train = train.select(features).to_pandas()\n",
+    "        y_train = train.select(target).to_pandas().values.ravel()\n",
+    "\n",
+    "        model = XGBoostRegressor(params)\n",
+    "        model.train(X_train, y_train)\n",
+    "        models[target] = model\n",
+    "    return models\n",
+    "\n",
+    "\n",
+    "models = train_model(data, trade_date, targets, features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded model with params: {'objective': 'binary:logistic', 'learning_rate': 0.1, 'n_estimators': 192, 'max_depth': 8, 'min_child_weight': 6.65, 'gamma': 0.47, 'subsample': 0.52, 'colsample_bytree': 0.87, 'reg_alpha': 6.89, 'reg_lambda': 8.68, 'scale_pos_weight': 2.2, 'eval_metric': 'logloss', 'tree_method': 'hist', 'nthread': -1, 'random_state': 100}\n",
+      "loaded model with params: {'objective': 'binary:logistic', 'learning_rate': 0.1, 'n_estimators': 192, 'max_depth': 8, 'min_child_weight': 6.65, 'gamma': 0.47, 'subsample': 0.52, 'colsample_bytree': 0.87, 'reg_alpha': 6.89, 'reg_lambda': 8.68, 'scale_pos_weight': 2.84, 'eval_metric': 'logloss', 'tree_method': 'hist', 'nthread': -1, 'random_state': 100}\n",
+      "loaded model with params: {'objective': 'binary:logistic', 'learning_rate': 0.1, 'n_estimators': 192, 'max_depth': 8, 'min_child_weight': 6.65, 'gamma': 0.47, 'subsample': 0.52, 'colsample_bytree': 0.87, 'reg_alpha': 6.89, 'reg_lambda': 8.68, 'scale_pos_weight': 1.92, 'eval_metric': 'logloss', 'tree_method': 'hist', 'nthread': -1, 'random_state': 100}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (20, 13)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>tic</th><th>adj_close</th><th>f_score</th><th>pe</th><th>pb</th><th>saleq_yoy</th><th>price_mom</th><th>index_mom</th><th>risk_return_4Q</th><th>rank_risk_return_3Q_hit</th><th>rank_fwd_return_4Q_hit</th><th>rank_risk_return_4Q_hit</th><th>avg_score</th></tr><tr><td>str</td><td>f64</td><td>i8</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>u32</td><td>u32</td><td>u32</td><td>f64</td></tr></thead><tbody><tr><td>&quot;DPZ&quot;</td><td>290.792206</td><td>7</td><td>22.730088</td><td>-2.55163</td><td>1.310292</td><td>-4.788338</td><td>2.462385</td><td>40.475988</td><td>14</td><td>50</td><td>8</td><td>24.0</td></tr><tr><td>&quot;TECH&quot;</td><td>81.149132</td><td>5</td><td>48.662854</td><td>6.98652</td><td>1.298317</td><td>1.857862</td><td>2.462385</td><td>-4.873225</td><td>17</td><td>41</td><td>16</td><td>24.667</td></tr><tr><td>&quot;PH&quot;</td><td>320.088196</td><td>4</td><td>28.232592</td><td>4.332433</td><td>23.866511</td><td>0.298444</td><td>2.462385</td><td>46.037079</td><td>33</td><td>29</td><td>40</td><td>34.0</td></tr><tr><td>&quot;TSCO&quot;</td><td>200.908157</td><td>5</td><td>21.074042</td><td>11.938254</td><td>9.096594</td><td>-14.726179</td><td>2.462385</td><td>20.285739</td><td>24</td><td>71</td><td>20</td><td>38.333</td></tr><tr><td>&quot;IT&quot;</td><td>340.23999</td><td>6</td><td>29.337466</td><td>57.766067</td><td>11.572374</td><td>12.665983</td><td>2.462385</td><td>21.359485</td><td>15</td><td>52</td><td>66</td><td>44.333</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;GL&quot;</td><td>102.404884</td><td>5</td><td>13.999426</td><td>2.641819</td><td>1.434948</td><td>-3.433519</td><td>2.462385</td><td>-1.114624</td><td>12</td><td>188</td><td>3</td><td>67.667</td></tr><tr><td>&quot;KEYS&quot;</td><td>163.139999</td><td>6</td><td>24.747508</td><td>6.206601</td><td>2.886751</td><td>14.54852</td><td>2.462385</td><td>-4.196431</td><td>25</td><td>165</td><td>33</td><td>74.333</td></tr><tr><td>&quot;CTVA&quot;</td><td>53.034714</td><td>4</td><td>32.745783</td><td>1.49288</td><td>6.150837</td><td>-11.180737</td><td>2.462385</td><td>2.267512</td><td>97</td><td>61</td><td>80</td><td>79.333</td></tr><tr><td>&quot;ZBH&quot;</td><td>125.822914</td><td>7</td><td>59.561112</td><td>2.204098</td><td>10.088985</td><td>-10.943287</td><td>2.462385</td><td>-1.861312</td><td>61</td><td>117</td><td>62</td><td>80.0</td></tr><tr><td>&quot;MKTX&quot;</td><td>272.210175</td><td>7</td><td>40.296304</td><td>9.308773</td><td>9.197182</td><td>-13.081775</td><td>2.462385</td><td>-9.781731</td><td>144</td><td>53</td><td>47</td><td>81.333</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (20, 13)\n",
+       "┌──────┬────────────┬─────────┬───────────┬───┬─────────────┬─────────────┬────────────┬───────────┐\n",
+       "│ tic  ┆ adj_close  ┆ f_score ┆ pe        ┆ … ┆ rank_risk_r ┆ rank_fwd_re ┆ rank_risk_ ┆ avg_score │\n",
+       "│ ---  ┆ ---        ┆ ---     ┆ ---       ┆   ┆ eturn_3Q_hi ┆ turn_4Q_hit ┆ return_4Q_ ┆ ---       │\n",
+       "│ str  ┆ f64        ┆ i8      ┆ f64       ┆   ┆ t           ┆ ---         ┆ hit        ┆ f64       │\n",
+       "│      ┆            ┆         ┆           ┆   ┆ ---         ┆ u32         ┆ ---        ┆           │\n",
+       "│      ┆            ┆         ┆           ┆   ┆ u32         ┆             ┆ u32        ┆           │\n",
+       "╞══════╪════════════╪═════════╪═══════════╪═══╪═════════════╪═════════════╪════════════╪═══════════╡\n",
+       "│ DPZ  ┆ 290.792206 ┆ 7       ┆ 22.730088 ┆ … ┆ 14          ┆ 50          ┆ 8          ┆ 24.0      │\n",
+       "│ TECH ┆ 81.149132  ┆ 5       ┆ 48.662854 ┆ … ┆ 17          ┆ 41          ┆ 16         ┆ 24.667    │\n",
+       "│ PH   ┆ 320.088196 ┆ 4       ┆ 28.232592 ┆ … ┆ 33          ┆ 29          ┆ 40         ┆ 34.0      │\n",
+       "│ TSCO ┆ 200.908157 ┆ 5       ┆ 21.074042 ┆ … ┆ 24          ┆ 71          ┆ 20         ┆ 38.333    │\n",
+       "│ IT   ┆ 340.23999  ┆ 6       ┆ 29.337466 ┆ … ┆ 15          ┆ 52          ┆ 66         ┆ 44.333    │\n",
+       "│ …    ┆ …          ┆ …       ┆ …         ┆ … ┆ …           ┆ …           ┆ …          ┆ …         │\n",
+       "│ GL   ┆ 102.404884 ┆ 5       ┆ 13.999426 ┆ … ┆ 12          ┆ 188         ┆ 3          ┆ 67.667    │\n",
+       "│ KEYS ┆ 163.139999 ┆ 6       ┆ 24.747508 ┆ … ┆ 25          ┆ 165         ┆ 33         ┆ 74.333    │\n",
+       "│ CTVA ┆ 53.034714  ┆ 4       ┆ 32.745783 ┆ … ┆ 97          ┆ 61          ┆ 80         ┆ 79.333    │\n",
+       "│ ZBH  ┆ 125.822914 ┆ 7       ┆ 59.561112 ┆ … ┆ 61          ┆ 117         ┆ 62         ┆ 80.0      │\n",
+       "│ MKTX ┆ 272.210175 ┆ 7       ┆ 40.296304 ┆ … ┆ 144         ┆ 53          ┆ 47         ┆ 81.333    │\n",
+       "└──────┴────────────┴─────────┴───────────┴───┴─────────────┴─────────────┴────────────┴───────────┘"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def score_models(data, models, trade_date, targets, features, stocks):\n",
+    "    final_ranks = data.filter(\n",
+    "        (pl.col(\"tdq\") == trade_date) & pl.col(\"tic\").is_in(constituents)\n",
+    "    ).select(\n",
+    "        [\n",
+    "            \"tic\",\n",
+    "            \"adj_close\",\n",
+    "            \"f_score\",\n",
+    "            \"pe\",\n",
+    "            \"pb\",\n",
+    "            \"saleq_yoy\",\n",
+    "            \"price_mom\",\n",
+    "            \"index_mom\",\n",
+    "            \"risk_return_4Q\",\n",
+    "        ]\n",
+    "    )\n",
+    "\n",
+    "    rank_cols = []\n",
+    "    pred_cols = []\n",
+    "    for target in targets:\n",
+    "        test_df = (\n",
+    "            data.filter((pl.col(\"tdq\") == trade_date) & pl.col(\"tic\").is_in(stocks))\n",
+    "            .select(features)\n",
+    "            .to_pandas()\n",
+    "        )\n",
+    "\n",
+    "        model = models[target]\n",
+    "        print(f\"loaded model with params: {model.params}\")\n",
+    "\n",
+    "        prob_scores = model.predict(test_df)\n",
+    "        final_ranks = final_ranks.with_columns(\n",
+    "            [pl.Series(prob_scores).rank(\"dense\", descending=True).alias(f\"rank_{target}\")]\n",
+    "        )\n",
+    "        rank_cols.append(f\"rank_{target}\")\n",
+    "        pred_cols.append(f\"pred_{target}\")\n",
+    "\n",
+    "    # Calculate average rank\n",
+    "    return (\n",
+    "        final_ranks.with_columns(pl.mean_horizontal(rank_cols).alias(\"avg_score\"))\n",
+    "        .sort(\"avg_score\", descending=False)\n",
+    "        .with_columns(pl.col(\"avg_score\").round(3).alias(\"avg_score\"))\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "ranks = score_models(data, models, trade_date, targets, features, constituents)\n",
+    "ranks.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "DATE 2023-06-01 00:00:00\n",
+      "Average top return: 15.76% (79.0)\n",
+      "Average bottom return: 11.29% (80.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "top = ranks.head(100)\n",
+    "bottom = ranks.tail(100)\n",
+    "\n",
+    "top_freturn = top.select(pl.col(\"risk_return_4Q\")).mean().item()\n",
+    "bottom_freturn = bottom.select(pl.col(\"risk_return_4Q\")).mean().item()\n",
+    "\n",
+    "# Calculate hit rates (% of stocks with positive returns)\n",
+    "top_hits = top.select(pl.col(\"risk_return_4Q\") > 0).sum().item()\n",
+    "bottom_hits = bottom.select(pl.col(\"risk_return_4Q\") > 0).sum().item()\n",
+    "\n",
+    "top_hitrate = (top_hits / len(top)) * 100\n",
+    "bottom_hitrate = (bottom_hits / len(bottom)) * 100\n",
+    "\n",
+    "\n",
+    "print(f\"\\nDATE {trade_date}\")\n",
+    "print(f\"Average top return: {top_freturn:.2f}% ({top_hitrate:.1f})\")\n",
+    "print(f\"Average bottom return: {bottom_freturn:.2f}% ({bottom_hitrate:.1f})\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "stocksense",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/mock_data.ipynb b/notebooks/mock_data.ipynb
index 56c7058..7ec3378 100644
--- a/notebooks/mock_data.ipynb
+++ b/notebooks/mock_data.ipynb
@@ -2,14 +2,14 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2024-11-29 11:44:04.892\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mstocksense.database_handler.schema\u001b[0m:\u001b[36mcreate_tables\u001b[0m:\u001b[36m121\u001b[0m - \u001b[32m\u001b[1mTables created successfully\u001b[0m\n"
+      "\u001b[32m2024-12-23 10:14:10.240\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mstocksense.database.schema\u001b[0m:\u001b[36mcreate_tables\u001b[0m:\u001b[36m121\u001b[0m - \u001b[32m\u001b[1mTables created successfully\u001b[0m\n"
      ]
     }
    ],
@@ -18,7 +18,7 @@
     "\n",
     "import polars as pl\n",
     "\n",
-    "from stocksense.database_handler import DatabaseHandler\n",
+    "from stocksense.database import DatabaseHandler\n",
     "\n",
     "FIXTURE_PATH = Path(\"../tests/fixtures\")\n",
     "\n",
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -63,7 +63,7 @@
        "└──────┴────────────┴────────────┴──────────┴───┴────────┴────────┴──────────┴──────────────┘"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -75,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/notebooks/modeling.ipynb b/notebooks/modeling.ipynb
deleted file mode 100644
index 09c4db2..0000000
--- a/notebooks/modeling.ipynb
+++ /dev/null
@@ -1,1726 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import datetime as dt\n",
-    "\n",
-    "import plotly.express as px\n",
-    "import polars as pl\n",
-    "\n",
-    "from stocksense.config import config\n",
-    "from stocksense.database_handler import DatabaseHandler\n",
-    "from stocksense.model import XGBoostModel\n",
-    "from stocksense.pipeline import clean, engineer_features\n",
-    "\n",
-    "features = config.model.features\n",
-    "target = config.model.target\n",
-    "date_col = config.model.date_col\n",
-    "aux_cols = [\"tic\", \"datadate\", \"rdq\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m2024-11-28 22:28:31.426\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mstocksense.database_handler.schema\u001b[0m:\u001b[36mcreate_tables\u001b[0m:\u001b[36m121\u001b[0m - \u001b[32m\u001b[1mTables created successfully\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "constituents = DatabaseHandler().fetch_constituents(dt.datetime(2022, 6, 1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m2024-11-28 22:28:31.441\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mstocksense.pipeline.preprocess\u001b[0m:\u001b[36mengineer_features\u001b[0m:\u001b[36m20\u001b[0m - \u001b[1mSTART processing stock data\u001b[0m\n",
-      "\u001b[32m2024-11-28 22:28:31.443\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mstocksense.database_handler.schema\u001b[0m:\u001b[36mcreate_tables\u001b[0m:\u001b[36m121\u001b[0m - \u001b[32m\u001b[1mTables created successfully\u001b[0m\n",
-      "\u001b[32m2024-11-28 22:28:34.455\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mstocksense.pipeline.preprocess\u001b[0m:\u001b[36mengineer_features\u001b[0m:\u001b[36m33\u001b[0m - \u001b[1mSTART feature engineering\u001b[0m\n",
-      "\u001b[32m2024-11-28 22:28:35.830\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mstocksense.pipeline.preprocess\u001b[0m:\u001b[36mengineer_features\u001b[0m:\u001b[36m46\u001b[0m - \u001b[32m\u001b[1mEND 47906 rows PROCESSED\u001b[0m\n",
-      "\u001b[32m2024-11-28 22:28:35.832\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mstocksense.pipeline.preprocess\u001b[0m:\u001b[36mclean\u001b[0m:\u001b[36m68\u001b[0m - \u001b[1mSTART cleaning data\u001b[0m\n",
-      "\u001b[32m2024-11-28 22:28:35.951\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mstocksense.pipeline.preprocess\u001b[0m:\u001b[36mclean\u001b[0m:\u001b[36m92\u001b[0m - \u001b[32m\u001b[1m41158 rows retained after CLEANING\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div><style>\n",
-       ".dataframe > thead > tr,\n",
-       ".dataframe > tbody > tr {\n",
-       "  text-align: right;\n",
-       "  white-space: pre-wrap;\n",
-       "}\n",
-       "</style>\n",
-       "<small>shape: (5, 169)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>tic</th><th>datadate</th><th>rdq</th><th>saleq</th><th>cogsq</th><th>xsgaq</th><th>niq</th><th>ebitdaq</th><th>cshoq</th><th>actq</th><th>atq</th><th>cheq</th><th>rectq</th><th>invtq</th><th>ppentq</th><th>lctq</th><th>dlttq</th><th>ltq</th><th>req</th><th>seqq</th><th>oancfq</th><th>ivncfq</th><th>fincfq</th><th>dvq</th><th>capxq</th><th>icaptq</th><th>surprise_pct</th><th>tdq</th><th>stock_split</th><th>n_purch</th><th>val_purch</th><th>n_sales</th><th>val_sales</th><th>insider_balance</th><th>roa</th><th>roi</th><th>roe</th><th>&hellip;</th><th>dr_yoy</th><th>dr_2y</th><th>ltda_yoy</th><th>ltda_2y</th><th>pe_yoy</th><th>pe_2y</th><th>pb_yoy</th><th>pb_2y</th><th>ps_yoy</th><th>ps_2y</th><th>eps_yoy</th><th>eps_2y</th><th>ev_ebitda_yoy</th><th>ev_ebitda_2y</th><th>ltcr_yoy</th><th>itr_yoy</th><th>rtr_yoy</th><th>atr_yoy</th><th>f_score</th><th>f_score_gr1</th><th>f_score_gr4</th><th>index_freturn</th><th>freturn</th><th>adj_freturn</th><th>adj_fperf</th><th>fperf</th><th>sector_communication_services</th><th>sector_consumer_discretionary</th><th>sector_consumer_staples</th><th>sector_energy</th><th>sector_financials</th><th>sector_health_care</th><th>sector_industrials</th><th>sector_information_technology</th><th>sector_materials</th><th>sector_real_estate</th><th>sector_utilities</th></tr><tr><td>str</td><td>date</td><td>date</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>date</td><td>i8</td><td>u32</td><td>f64</td><td>u32</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>&hellip;</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>i8</td><td>i8</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td></tr></thead><tbody><tr><td>&quot;A&quot;</td><td>2007-01-31</td><td>2007-02-15</td><td>1280.0</td><td>540.0</td><td>564.0</td><td>150.0</td><td>176.0</td><td>405.0</td><td>3749.0</td><td>7193.0</td><td>2090.0</td><td>671.0</td><td>648.0</td><td>775.0</td><td>1372.0</td><td>1500.0</td><td>3536.0</td><td>1705.0</td><td>3657.0</td><td>93.0</td><td>-93.0</td><td>-169.0</td><td>0.0</td><td>37.0</td><td>5157.0</td><td>0.011</td><td>2007-03-01</td><td>0</td><td>0</td><td>0.0</td><td>1</td><td>8.598</td><td>8.598</td><td>0.089114</td><td>0.029087</td><td>0.17528</td><td>&hellip;</td><td>2.640314</td><td>3.928494</td><td>11.580703</td><td>29.655039</td><td>282.560607</td><td>null</td><td>-7.754803</td><td>9.151709</td><td>-19.208201</td><td>null</td><td>-77.612898</td><td>null</td><td>-42.418432</td><td>null</td><td>131.460674</td><td>25.76978</td><td>22.719366</td><td>11.302357</td><td>7</td><td>1</td><td>null</td><td>-5.169725</td><td>-2.639948</td><td>2.529778</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>&quot;A&quot;</td><td>2007-04-30</td><td>2007-05-14</td><td>1320.0</td><td>540.0</td><td>586.0</td><td>123.0</td><td>194.0</td><td>395.958</td><td>3791.0</td><td>7283.0</td><td>2050.0</td><td>718.0</td><td>650.0</td><td>777.0</td><td>1554.0</td><td>1500.0</td><td>3728.0</td><td>1881.0</td><td>3555.0</td><td>302.0</td><td>-36.0</td><td>-320.0</td><td>0.0</td><td>42.0</td><td>5055.0</td><td>-0.023</td><td>2007-06-01</td><td>0</td><td>0</td><td>0.0</td><td>3</td><td>2.695</td><td>2.695</td><td>0.089112</td><td>0.024332</td><td>0.18256</td><td>&hellip;</td><td>3.544885</td><td>9.453423</td><td>10.627489</td><td>30.16614</td><td>378.903526</td><td>null</td><td>16.399931</td><td>38.944827</td><td>-0.630294</td><td>null</td><td>-77.181676</td><td>null</td><td>-27.569721</td><td>null</td><td>7.883817</td><td>19.259654</td><td>24.025332</td><td>18.364403</td><td>7</td><td>0</td><td>null</td><td>-8.849601</td><td>-2.325005</td><td>6.524596</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>&quot;A&quot;</td><td>2007-07-31</td><td>2007-08-14</td><td>1374.0</td><td>563.0</td><td>580.0</td><td>185.0</td><td>231.0</td><td>386.548</td><td>3281.0</td><td>7024.0</td><td>1486.0</td><td>738.0</td><td>674.0</td><td>787.0</td><td>1510.0</td><td>1500.0</td><td>3736.0</td><td>2069.0</td><td>3288.0</td><td>176.0</td><td>-258.0</td><td>-484.0</td><td>0.0</td><td>36.0</td><td>4788.0</td><td>-0.01</td><td>2007-09-01</td><td>0</td><td>0</td><td>0.0</td><td>2</td><td>5.747</td><td>5.747</td><td>0.086418</td><td>0.038638</td><td>0.184611</td><td>&hellip;</td><td>4.419175</td><td>14.878382</td><td>10.037016</td><td>36.525701</td><td>472.325667</td><td>null</td><td>25.872772</td><td>5.91443</td><td>3.996541</td><td>null</td><td>-79.822287</td><td>null</td><td>-14.222296</td><td>null</td><td>2000.0</td><td>18.769156</td><td>29.327755</td><td>22.359853</td><td>6</td><td>-1</td><td>null</td><td>-12.968883</td><td>-4.505501</td><td>8.463382</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>&quot;A&quot;</td><td>2007-10-31</td><td>2007-11-15</td><td>1446.0</td><td>611.0</td><td>598.0</td><td>180.0</td><td>237.0</td><td>370.0</td><td>3671.0</td><td>7554.0</td><td>1826.0</td><td>735.0</td><td>643.0</td><td>801.0</td><td>1663.0</td><td>2087.0</td><td>4320.0</td><td>2580.0</td><td>3234.0</td><td>398.0</td><td>-69.0</td><td>-7.0</td><td>0.0</td><td>39.0</td><td>5321.0</td><td>0.022</td><td>2007-12-01</td><td>0</td><td>0</td><td>0.0</td><td>2</td><td>2.75</td><td>2.75</td><td>0.084459</td><td>0.033828</td><td>0.197279</td><td>&hellip;</td><td>13.590344</td><td>46.46352</td><td>35.725911</td><td>2000.0</td><td>456.222783</td><td>-60.359786</td><td>21.410892</td><td>-1.590112</td><td>-1.541225</td><td>-26.668893</td><td>-78.726207</td><td>165.240102</td><td>-5.153189</td><td>-45.143381</td><td>-32.276673</td><td>10.125696</td><td>14.20785</td><td>12.76951</td><td>5</td><td>-1</td><td>-1</td><td>-44.893122</td><td>-54.374817</td><td>-9.481695</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>&quot;A&quot;</td><td>2008-01-31</td><td>2008-02-13</td><td>1393.0</td><td>580.0</td><td>617.0</td><td>120.0</td><td>196.0</td><td>368.0</td><td>5070.0</td><td>7459.0</td><td>3148.0</td><td>726.0</td><td>674.0</td><td>801.0</td><td>2674.0</td><td>626.0</td><td>4286.0</td><td>2657.0</td><td>3173.0</td><td>4.0</td><td>-295.0</td><td>-168.0</td><td>0.0</td><td>34.0</td><td>3799.0</td><td>0.021</td><td>2008-03-01</td><td>0</td><td>0</td><td>0.0</td><td>2</td><td>3.418</td><td>3.418</td><td>0.081512</td><td>0.031587</td><td>0.191617</td><td>&hellip;</td><td>16.887848</td><td>19.974054</td><td>-59.754945</td><td>-55.094284</td><td>-6.732989</td><td>256.802843</td><td>1.959688</td><td>-5.947085</td><td>-18.681512</td><td>-34.301331</td><td>4.388523</td><td>-76.630435</td><td>-23.738073</td><td>-56.087186</td><td>-228.366043</td><td>5.357524</td><td>1.528223</td><td>5.558859</td><td>6</td><td>1</td><td>-1</td><td>-44.756241</td><td>-54.68801</td><td>-9.931769</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr></tbody></table></div>"
-      ],
-      "text/plain": [
-       "shape: (5, 169)\n",
-       "┌─────┬────────────┬────────────┬────────┬───┬─────────────┬─────────────┬────────────┬────────────┐\n",
-       "│ tic ┆ datadate   ┆ rdq        ┆ saleq  ┆ … ┆ sector_info ┆ sector_mate ┆ sector_rea ┆ sector_uti │\n",
-       "│ --- ┆ ---        ┆ ---        ┆ ---    ┆   ┆ rmation_tec ┆ rials       ┆ l_estate   ┆ lities     │\n",
-       "│ str ┆ date       ┆ date       ┆ f64    ┆   ┆ hnology     ┆ ---         ┆ ---        ┆ ---        │\n",
-       "│     ┆            ┆            ┆        ┆   ┆ ---         ┆ i8          ┆ i8         ┆ i8         │\n",
-       "│     ┆            ┆            ┆        ┆   ┆ i8          ┆             ┆            ┆            │\n",
-       "╞═════╪════════════╪════════════╪════════╪═══╪═════════════╪═════════════╪════════════╪════════════╡\n",
-       "│ A   ┆ 2007-01-31 ┆ 2007-02-15 ┆ 1280.0 ┆ … ┆ 0           ┆ 0           ┆ 0          ┆ 0          │\n",
-       "│ A   ┆ 2007-04-30 ┆ 2007-05-14 ┆ 1320.0 ┆ … ┆ 0           ┆ 0           ┆ 0          ┆ 0          │\n",
-       "│ A   ┆ 2007-07-31 ┆ 2007-08-14 ┆ 1374.0 ┆ … ┆ 0           ┆ 0           ┆ 0          ┆ 0          │\n",
-       "│ A   ┆ 2007-10-31 ┆ 2007-11-15 ┆ 1446.0 ┆ … ┆ 0           ┆ 0           ┆ 0          ┆ 0          │\n",
-       "│ A   ┆ 2008-01-31 ┆ 2008-02-13 ┆ 1393.0 ┆ … ┆ 0           ┆ 0           ┆ 0          ┆ 0          │\n",
-       "└─────┴────────────┴────────────┴────────┴───┴─────────────┴─────────────┴────────────┴────────────┘"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = engineer_features()\n",
-    "data = clean(data)\n",
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
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-    "df_standardized = (df[features] - df[features].mean()) / df[features].std()\n",
-    "corr = df_standardized.corrwith(df[target])\n",
-    "corr_df = pl.DataFrame({\"Feature\": corr.index, \"Correlation\": corr.values}).sort(\n",
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-    "    height=1200,\n",
-    ")\n",
-    "fig.update_layout(yaxis={\"tickfont\": {\"size\": 10}}, showlegend=False, margin={\"l\": 200})\n",
-    "fig.add_vline(x=0, line_dash=\"dash\", line_color=\"gray\")\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ROC-AUC: 0.5784589151826839\n",
-      "PR-AUC: 0.1965824005584638\n",
-      "Baseline 0.14311926605504588, PR-AUC = 0.1965824005584638\n"
-     ]
-    }
-   ],
-   "source": [
-    "def get_scale(train):\n",
-    "    scale = round(\n",
-    "        len(train.filter(pl.col(target) == 0)) / len(train.filter(pl.col(target) == 1)), 2\n",
-    "    )\n",
-    "    print(f\"Scale of training data (pos/neg): {scale}\")\n",
-    "    return scale\n",
-    "\n",
-    "\n",
-    "data = data.select([\"tic\", date_col] + features + [\"freturn\", \"adj_freturn\", target])\n",
-    "data = data.filter((~pl.all_horizontal(pl.col(target).is_null())))\n",
-    "train = data.filter((pl.col(\"tdq\").dt.year() >= 2007) & (pl.col(\"tdq\").dt.year() <= 2021))\n",
-    "val = data.filter(\n",
-    "    (pl.col(\"tdq\").dt.year() >= 2022)\n",
-    "    & (pl.col(\"tdq\").dt.year() <= 2023)\n",
-    "    & pl.col(\"tic\").is_in(constituents)\n",
-    ")\n",
-    "\n",
-    "\n",
-    "X_train = train.select(features).to_pandas()\n",
-    "y_train = train.select(target).to_pandas().values.ravel()\n",
-    "X_val = val.select(features).to_pandas()\n",
-    "y_val = val.select(target).to_pandas().values.ravel()\n",
-    "\n",
-    "params = {\n",
-    "    \"objective\": \"binary:logistic\",\n",
-    "    \"learning_rate\": 0.1,\n",
-    "    \"n_estimators\": 430,\n",
-    "    \"max_depth\": 3,\n",
-    "    \"min_child_weight\": 5,\n",
-    "    \"gamma\": 0,\n",
-    "    \"subsample\": 1,\n",
-    "    \"colsample_bytree\": 1,\n",
-    "    \"reg_alpha\": 0,\n",
-    "    \"reg_lambda\": 1,\n",
-    "    \"scale_pos_weight\": 1,\n",
-    "    \"eval_metric\": \"logloss\",\n",
-    "    \"nthread\": -1,\n",
-    "    \"seed\": 100,\n",
-    "}\n",
-    "\n",
-    "model = XGBoostModel(params)\n",
-    "model.train(X_train, y_train)\n",
-    "\n",
-    "print(f\"ROC-AUC: {model.get_roc_auc(X_val, y_val)}\")\n",
-    "print(f\"PR-AUC: {model.get_pr_auc(X_val, y_val)}\")\n",
-    "\n",
-    "baseline = float(len(val.filter(pl.col(target) == 1)) / len(val))\n",
-    "\n",
-    "pr_auc = model.get_pr_auc(X_val, y_val)\n",
-    "\n",
-    "print(f\"Baseline {baseline}, PR-AUC = {pr_auc}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Average freturn: 9.47%\n",
-      "Average adj_freturn: -4.87%\n",
-      "\n",
-      "Top 20 stocks:\n",
-      "Average freturn: 10.42%\n",
-      "Average adj_freturn: 1.22%\n",
-      "\n",
-      "Bottom 20 stocks:\n",
-      "Average freturn: 2.33%\n",
-      "Average adj_freturn: -18.89%\n"
-     ]
-    }
-   ],
-   "source": [
-    "y_proba = model.predict_proba(X_val)\n",
-    "val_subset = val.with_columns(pl.Series(\"proba\", y_proba)).sort(\"proba\", descending=True)\n",
-    "n = 20\n",
-    "top = val_subset.head(n)\n",
-    "bottom = val_subset.tail(n)\n",
-    "\n",
-    "# Calculate average returns for top stocks\n",
-    "top_freturn = top.select(pl.col(\"freturn\")).mean().item()\n",
-    "top_adj_freturn = top.select(pl.col(\"adj_freturn\")).mean().item()\n",
-    "\n",
-    "# Calculate average returns for bottom stocks\n",
-    "bottom_freturn = bottom.select(pl.col(\"freturn\")).mean().item()\n",
-    "bottom_adj_freturn = bottom.select(pl.col(\"adj_freturn\")).mean().item()\n",
-    "\n",
-    "freturn = val_subset.select(pl.col(\"freturn\")).mean().item()\n",
-    "adj_freturn = val_subset.select(pl.col(\"adj_freturn\")).mean().item()\n",
-    "\n",
-    "print(f\"Average freturn: {freturn:.2f}%\")\n",
-    "print(f\"Average adj_freturn: {adj_freturn:.2f}%\")\n",
-    "\n",
-    "print(f\"\\nTop {n} stocks:\")\n",
-    "print(f\"Average freturn: {top_freturn:.2f}%\")\n",
-    "print(f\"Average adj_freturn: {top_adj_freturn:.2f}%\")\n",
-    "print(f\"\\nBottom {n} stocks:\")\n",
-    "print(f\"Average freturn: {bottom_freturn:.2f}%\")\n",
-    "print(f\"Average adj_freturn: {bottom_adj_freturn:.2f}%\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div><style>\n",
-       ".dataframe > thead > tr,\n",
-       ".dataframe > tbody > tr {\n",
-       "  text-align: right;\n",
-       "  white-space: pre-wrap;\n",
-       "}\n",
-       "</style>\n",
-       "<small>shape: (25, 83)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>tic</th><th>tdq</th><th>n_purch</th><th>n_sales</th><th>insider_balance</th><th>volume_ma20</th><th>volume_ma50</th><th>price_mom</th><th>price_qoq</th><th>price_yoy</th><th>price_2y</th><th>rsi_14d</th><th>rsi_30d</th><th>rsi_60d</th><th>rsi_90d</th><th>rsi_1y</th><th>vol_mom</th><th>vol_qoq</th><th>vol_yoy</th><th>rel_vol_mom</th><th>rel_vol_qoq</th><th>rel_vol_yoy</th><th>momentum_mom</th><th>momentum_qoq</th><th>momentum_yoy</th><th>momentum_2y</th><th>fear_ma30</th><th>high_fear</th><th>low_fear</th><th>gpm</th><th>roa</th><th>roe</th><th>ebitdam</th><th>size</th><th>earn_drift</th><th>pe</th><th>pb</th><th>&hellip;</th><th>dlttq_yoy</th><th>gpm_yoy</th><th>gpm_2y</th><th>roa_yoy</th><th>roa_2y</th><th>roi_yoy</th><th>roi_2y</th><th>roe_yoy</th><th>fcf_yoy</th><th>cr_yoy</th><th>qr_yoy</th><th>der_yoy</th><th>dr_yoy</th><th>dr_2y</th><th>ltda_yoy</th><th>ev_ebitda_yoy</th><th>ltcr_yoy</th><th>pe_yoy</th><th>pe_2y</th><th>pb_yoy</th><th>ps_yoy</th><th>atr_yoy</th><th>sector_utilities</th><th>sector_health_care</th><th>sector_financials</th><th>sector_consumer_discretionary</th><th>sector_consumer_staples</th><th>sector_energy</th><th>sector_industrials</th><th>sector_information_technology</th><th>sector_communication_services</th><th>sector_materials</th><th>sector_real_estate</th><th>freturn</th><th>adj_freturn</th><th>fperf</th><th>proba</th></tr><tr><td>str</td><td>date</td><td>u32</td><td>u32</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>i8</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>&hellip;</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>f64</td><td>f64</td><td>i8</td><td>f32</td></tr></thead><tbody><tr><td>&quot;NCLH&quot;</td><td>2022-06-01</td><td>1</td><td>0</td><td>-1.513</td><td>124.622641</td><td>103.526423</td><td>-23.01108</td><td>-12.075903</td><td>-53.764741</td><td>-12.075903</td><td>41.95808</td><td>42.370377</td><td>44.103593</td><td>45.147276</td><td>47.488691</td><td>6.315755</td><td>4.839737</td><td>3.843011</td><td>318.711194</td><td>288.233099</td><td>335.951157</td><td>1765.834751</td><td>229.638831</td><td>2000.0</td><td>-36.459548</td><td>29.026334</td><td>0</td><td>0</td><td>-0.408999</td><td>-0.213919</td><td>-2.732226</td><td>-0.945112</td><td>9.865551</td><td>-5.675504</td><td>-1.550619</td><td>4.236646</td><td>&hellip;</td><td>3.134374</td><td>99.358855</td><td>-301.87222</td><td>-15.909664</td><td>-229.463478</td><td>15.598336</td><td>52.448423</td><td>-241.535514</td><td>56.45467</td><td>-63.13951</td><td>-67.044421</td><td>253.030999</td><td>19.81098</td><td>25.527472</td><td>1.62556</td><td>20.439897</td><td>57.778063</td><td>49.67879</td><td>55.127191</td><td>71.864992</td><td>-98.167327</td><td>2000.0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1.438851</td><td>-1.481981</td><td>0</td><td>0.749094</td></tr><tr><td>&quot;ETSY&quot;</td><td>2022-06-01</td><td>0</td><td>7</td><td>21.505</td><td>205.248617</td><td>152.552988</td><td>-16.616872</td><td>-46.598932</td><td>-53.785541</td><td>-1.123868</td><td>42.92273</td><td>40.965493</td><td>41.767758</td><td>43.09633</td><td>48.124634</td><td>7.123166</td><td>5.81571</td><td>4.309569</td><td>359.45551</td><td>346.357717</td><td>376.737023</td><td>1275.153069</td><td>886.138656</td><td>2000.0</td><td>-3.39318</td><td>29.026334</td><td>0</td><td>0</td><td>0.701355</td><td>0.116376</td><td>0.643923</td><td>0.188288</td><td>8.228224</td><td>-25.967254</td><td>27.238999</td><td>17.539809</td><td>&hellip;</td><td>77.297462</td><td>-5.280658</td><td>9.824616</td><td>-39.511808</td><td>129.860248</td><td>-60.310523</td><td>181.256116</td><td>-8.364883</td><td>-59.893578</td><td>-39.554311</td><td>-42.877877</td><td>70.906204</td><td>12.814907</td><td>11.93406</td><td>18.228022</td><td>-26.855093</td><td>-77.379021</td><td>-44.671304</td><td>-78.369369</td><td>-49.299485</td><td>-56.400242</td><td>-31.942871</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1.297252</td><td>-1.623581</td><td>1</td><td>0.727049</td></tr><tr><td>&quot;FTNT&quot;</td><td>2023-09-01</td><td>0</td><td>7</td><td>4.429</td><td>131.854071</td><td>119.712572</td><td>-19.654175</td><td>-12.467647</td><td>25.015405</td><td>-3.423877</td><td>43.434367</td><td>42.684041</td><td>46.42821</td><td>48.324029</td><td>51.04732</td><td>6.537614</td><td>4.102648</td><td>3.001493</td><td>868.397928</td><td>620.611558</td><td>263.562811</td><td>-2000.0</td><td>-230.292988</td><td>176.428702</td><td>2000.0</td><td>15.335333</td><td>0</td><td>1</td><td>0.771194</td><td>0.141516</td><td>3.298257</td><td>0.256652</td><td>8.920803</td><td>-19.654175</td><td>45.06931</td><td>148.650147</td><td>&hellip;</td><td>0.192036</td><td>1.938567</td><td>0.58674</td><td>11.16534</td><td>52.811018</td><td>-28.647416</td><td>200.683512</td><td>285.759374</td><td>35.528757</td><td>24.343649</td><td>23.619585</td><td>249.232638</td><td>-10.693622</td><td>18.002724</td><td>-29.139775</td><td>-17.165679</td><td>35.268992</td><td>-22.765214</td><td>-54.98243</td><td>243.470855</td><td>-7.125685</td><td>-4.060591</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>26.022672</td><td>0.941016</td><td>0</td><td>0.708306</td></tr><tr><td>&quot;UA&quot;</td><td>2022-06-01</td><td>0</td><td>1</td><td>0.045</td><td>223.140991</td><td>137.95481</td><td>-34.743413</td><td>-33.215048</td><td>-51.867007</td><td>11.890607</td><td>39.53302</td><td>37.795377</td><td>39.536906</td><td>41.367488</td><td>46.982674</td><td>8.541233</td><td>5.53475</td><td>3.522744</td><td>431.015238</td><td>329.624995</td><td>307.953823</td><td>2000.0</td><td>631.626876</td><td>2000.0</td><td>35.900102</td><td>29.026334</td><td>0</td><td>0</td><td>0.465173</td><td>null</td><td>null</td><td>-0.098157</td><td>8.401296</td><td>-4.46701</td><td>null</td><td>2.565776</td><td>&hellip;</td><td>-33.4338</td><td>-6.971913</td><td>0.403306</td><td>null</td><td>null</td><td>NaN</td><td>NaN</td><td>null</td><td>-113.458576</td><td>-14.729386</td><td>-20.459217</td><td>-11.298622</td><td>-4.38705</td><td>-9.982455</td><td>-26.535291</td><td>187.763754</td><td>-220.671116</td><td>null</td><td>null</td><td>-49.615828</td><td>-60.169398</td><td>14.203994</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>-28.692878</td><td>-31.61371</td><td>0</td><td>0.686251</td></tr><tr><td>&quot;UAA&quot;</td><td>2022-06-01</td><td>0</td><td>1</td><td>0.045</td><td>183.090596</td><td>119.10571</td><td>-33.654463</td><td>-32.306686</td><td>-54.493394</td><td>9.776834</td><td>39.056204</td><td>37.496494</td><td>39.349963</td><td>41.246014</td><td>47.151994</td><td>7.768437</td><td>5.229559</td><td>3.56519</td><td>392.01774</td><td>311.449181</td><td>311.664329</td><td>2000.0</td><td>614.353199</td><td>2000.0</td><td>29.518203</td><td>29.026334</td><td>0</td><td>0</td><td>0.465173</td><td>null</td><td>null</td><td>-0.098157</td><td>8.401296</td><td>-5.142336</td><td>null</td><td>2.816628</td><td>&hellip;</td><td>-33.4338</td><td>-6.971913</td><td>0.403306</td><td>null</td><td>null</td><td>NaN</td><td>NaN</td><td>null</td><td>-113.458576</td><td>-14.729386</td><td>-20.459217</td><td>-11.298622</td><td>-4.38705</td><td>-9.982455</td><td>-26.535291</td><td>168.975838</td><td>-220.671116</td><td>null</td><td>null</td><td>-52.89639</td><td>-62.762806</td><td>14.203994</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>-28.944819</td><td>-31.865651</td><td>0</td><td>0.677761</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;UHS&quot;</td><td>2022-09-01</td><td>0</td><td>0</td><td>0.0</td><td>86.96199</td><td>103.054174</td><td>-12.513497</td><td>-19.740859</td><td>-37.43969</td><td>-12.046667</td><td>29.4094</td><td>37.605303</td><td>41.292391</td><td>42.997616</td><td>47.757034</td><td>1.657969</td><td>2.164502</td><td>2.192553</td><td>135.478846</td><td>146.295739</td><td>165.186463</td><td>276.1032</td><td>526.332836</td><td>303.96346</td><td>-96.511491</td><td>22.448</td><td>0</td><td>0</td><td>0.893184</td><td>0.058227</td><td>0.131132</td><td>0.113523</td><td>9.495226</td><td>-11.671207</td><td>9.350152</td><td>1.226103</td><td>&hellip;</td><td>31.925161</td><td>-0.124192</td><td>-0.327423</td><td>-30.992824</td><td>-3.664582</td><td>-53.25149</td><td>-45.708539</td><td>-20.284474</td><td>-31.559398</td><td>1.814719</td><td>-2.097009</td><td>31.859796</td><td>14.1468</td><td>5.749757</td><td>27.128126</td><td>-8.130446</td><td>-48.121646</td><td>-24.0548</td><td>-27.000208</td><td>-39.4599</td><td>-49.104732</td><td>1.83589</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>39.195516</td><td>25.357839</td><td>0</td><td>0.584983</td></tr><tr><td>&quot;META&quot;</td><td>2023-06-01</td><td>0</td><td>18</td><td>12.571</td><td>64.818627</td><td>70.060886</td><td>13.948328</td><td>47.436449</td><td>40.781856</td><td>-17.172551</td><td>83.846139</td><td>75.504411</td><td>68.060151</td><td>62.740474</td><td>52.037767</td><td>1.314236</td><td>2.381925</td><td>3.68763</td><td>163.360161</td><td>260.872107</td><td>279.938834</td><td>566.455895</td><td>1112.645173</td><td>1896.213802</td><td>-2000.0</td><td>17.502</td><td>0</td><td>1</td><td>0.786769</td><td>0.116233</td><td>0.171834</td><td>0.340408</td><td>12.125356</td><td>30.186243</td><td>33.00203</td><td>5.670864</td><td>&hellip;</td><td>2000.0</td><td>0.247232</td><td>-2.136248</td><td>-48.878885</td><td>-43.668475</td><td>-30.046716</td><td>-40.360284</td><td>-43.289045</td><td>-0.554135</td><td>-26.429139</td><td>-27.137021</td><td>43.806835</td><td>29.632199</td><td>77.162513</td><td>2000.0</td><td>101.269498</td><td>NaN</td><td>138.226716</td><td>17.391634</td><td>35.100646</td><td>39.523607</td><td>-8.452555</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>71.426308</td><td>46.397056</td><td>1</td><td>0.575203</td></tr><tr><td>&quot;LYV&quot;</td><td>2022-06-01</td><td>1</td><td>11</td><td>216.181</td><td>138.292734</td><td>100.131035</td><td>-9.853104</td><td>-17.371914</td><td>3.959959</td><td>87.037412</td><td>49.517222</td><td>45.82605</td><td>46.818269</td><td>48.053892</td><td>51.06392</td><td>4.131438</td><td>3.501759</td><td>2.861427</td><td>208.484269</td><td>208.549091</td><td>250.1423</td><td>756.112009</td><td>330.349304</td><td>-155.898213</td><td>262.783224</td><td>29.026334</td><td>0</td><td>0</td><td>0.405915</td><td>-0.025786</td><td>2.834477</td><td>0.083298</td><td>9.680756</td><td>-0.651735</td><td>-50.808045</td><td>-144.011255</td><td>&hellip;</td><td>-2.707239</td><td>-24.693461</td><td>12.702804</td><td>84.631356</td><td>-119.333406</td><td>85.149732</td><td>77.342896</td><td>-39.027205</td><td>1506.8603</td><td>-8.735388</td><td>-6.300724</td><td>-286.398154</td><td>-2.605488</td><td>12.593251</td><td>-33.627259</td><td>417.245037</td><td>1551.572312</td><td>-371.794491</td><td>33.731891</td><td>-187.664741</td><td>-89.261639</td><td>338.791725</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>-14.157237</td><td>-17.078069</td><td>0</td><td>0.566635</td></tr><tr><td>&quot;ALK&quot;</td><td>2022-06-01</td><td>0</td><td>1</td><td>0.173</td><td>92.146291</td><td>101.058312</td><td>-12.231439</td><td>-5.229955</td><td>-31.353181</td><td>28.646405</td><td>42.64752</td><td>43.302575</td><td>45.293515</td><td>46.331371</td><td>48.795441</td><td>3.099475</td><td>3.172476</td><td>2.509058</td><td>156.40843</td><td>188.938519</td><td>219.338604</td><td>938.621737</td><td>99.454318</td><td>1234.33232</td><td>86.489184</td><td>29.026334</td><td>0</td><td>0</td><td>1.0</td><td>0.032794</td><td>0.128128</td><td>-0.059488</td><td>9.561701</td><td>-20.77237</td><td>12.590289</td><td>1.613163</td><td>&hellip;</td><td>-10.623656</td><td>0.0</td><td>0.0</td><td>138.242459</td><td>-17.78168</td><td>0.674811</td><td>43.721468</td><td>130.119933</td><td>71.856287</td><td>-3.338931</td><td>-9.073719</td><td>-26.60213</td><td>-6.808722</td><td>6.362716</td><td>-10.296593</td><td>195.060765</td><td>92.283863</td><td>278.139341</td><td>51.312011</td><td>-46.344549</td><td>-73.781989</td><td>112.016913</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>-3.714837</td><td>-6.635669</td><td>0</td><td>0.56633</td></tr><tr><td>&quot;NCLH&quot;</td><td>2022-09-01</td><td>0</td><td>2</td><td>1.918</td><td>93.353157</td><td>98.174405</td><td>0.153968</td><td>-17.396824</td><td>-48.290937</td><td>-23.199529</td><td>48.025777</td><td>48.174825</td><td>46.675417</td><td>46.397585</td><td>47.431641</td><td>4.901977</td><td>5.692104</td><td>4.459918</td><td>400.558813</td><td>384.721568</td><td>336.009173</td><td>-3.397219</td><td>463.835936</td><td>392.062013</td><td>-185.862289</td><td>22.448</td><td>0</td><td>0</td><td>0.095912</td><td>-0.204604</td><td>-4.336412</td><td>-0.169263</td><td>9.858134</td><td>7.52066</td><td>-1.394294</td><td>6.046198</td><td>&hellip;</td><td>2.639327</td><td>100.170747</td><td>100.570272</td><td>-8.171039</td><td>-77.160226</td><td>15.528627</td><td>22.246076</td><td>-360.243114</td><td>141.613919</td><td>-60.850189</td><td>-65.84393</td><td>407.229206</td><td>19.21419</td><td>26.609005</td><td>-0.510867</td><td>8.773698</td><td>140.543835</td><td>47.504828</td><td>30.385086</td><td>141.603944</td><td>-99.412464</td><td>2000.0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>26.056874</td><td>12.219197</td><td>0</td><td>0.564044</td></tr></tbody></table></div>"
-      ],
-      "text/plain": [
-       "shape: (25, 83)\n",
-       "┌──────┬────────────┬─────────┬─────────┬───┬────────────┬─────────────┬───────┬──────────┐\n",
-       "│ tic  ┆ tdq        ┆ n_purch ┆ n_sales ┆ … ┆ freturn    ┆ adj_freturn ┆ fperf ┆ proba    │\n",
-       "│ ---  ┆ ---        ┆ ---     ┆ ---     ┆   ┆ ---        ┆ ---         ┆ ---   ┆ ---      │\n",
-       "│ str  ┆ date       ┆ u32     ┆ u32     ┆   ┆ f64        ┆ f64         ┆ i8    ┆ f32      │\n",
-       "╞══════╪════════════╪═════════╪═════════╪═══╪════════════╪═════════════╪═══════╪══════════╡\n",
-       "│ NCLH ┆ 2022-06-01 ┆ 1       ┆ 0       ┆ … ┆ 1.438851   ┆ -1.481981   ┆ 0     ┆ 0.749094 │\n",
-       "│ ETSY ┆ 2022-06-01 ┆ 0       ┆ 7       ┆ … ┆ 1.297252   ┆ -1.623581   ┆ 1     ┆ 0.727049 │\n",
-       "│ FTNT ┆ 2023-09-01 ┆ 0       ┆ 7       ┆ … ┆ 26.022672  ┆ 0.941016    ┆ 0     ┆ 0.708306 │\n",
-       "│ UA   ┆ 2022-06-01 ┆ 0       ┆ 1       ┆ … ┆ -28.692878 ┆ -31.61371   ┆ 0     ┆ 0.686251 │\n",
-       "│ UAA  ┆ 2022-06-01 ┆ 0       ┆ 1       ┆ … ┆ -28.944819 ┆ -31.865651  ┆ 0     ┆ 0.677761 │\n",
-       "│ …    ┆ …          ┆ …       ┆ …       ┆ … ┆ …          ┆ …           ┆ …     ┆ …        │\n",
-       "│ UHS  ┆ 2022-09-01 ┆ 0       ┆ 0       ┆ … ┆ 39.195516  ┆ 25.357839   ┆ 0     ┆ 0.584983 │\n",
-       "│ META ┆ 2023-06-01 ┆ 0       ┆ 18      ┆ … ┆ 71.426308  ┆ 46.397056   ┆ 1     ┆ 0.575203 │\n",
-       "│ LYV  ┆ 2022-06-01 ┆ 1       ┆ 11      ┆ … ┆ -14.157237 ┆ -17.078069  ┆ 0     ┆ 0.566635 │\n",
-       "│ ALK  ┆ 2022-06-01 ┆ 0       ┆ 1       ┆ … ┆ -3.714837  ┆ -6.635669   ┆ 0     ┆ 0.56633  │\n",
-       "│ NCLH ┆ 2022-09-01 ┆ 0       ┆ 2       ┆ … ┆ 26.056874  ┆ 12.219197   ┆ 0     ┆ 0.564044 │\n",
-       "└──────┴────────────┴─────────┴─────────┴───┴────────────┴─────────────┴───────┴──────────┘"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "val_subset.head(25)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "DATE 2022-03-01\n",
-      "\n",
-      "Top 30 stocks:\n",
-      "Average freturn: -0.51%\n",
-      "Average adj_freturn: 7.73%\n",
-      "\n",
-      "Bottom 30 stocks:\n",
-      "Average freturn: -2.57%\n",
-      "Average adj_freturn: 5.68%\n",
-      "\n",
-      "DATE 2022-06-01\n",
-      "\n",
-      "Top 30 stocks:\n",
-      "Average freturn: 6.78%\n",
-      "Average adj_freturn: 3.86%\n",
-      "\n",
-      "Bottom 30 stocks:\n",
-      "Average freturn: -9.53%\n",
-      "Average adj_freturn: -12.45%\n",
-      "\n",
-      "DATE 2022-09-01\n",
-      "\n",
-      "Top 30 stocks:\n",
-      "Average freturn: 26.85%\n",
-      "Average adj_freturn: 13.01%\n",
-      "\n",
-      "Bottom 30 stocks:\n",
-      "Average freturn: -11.34%\n",
-      "Average adj_freturn: -25.18%\n",
-      "\n",
-      "DATE 2022-12-01\n",
-      "\n",
-      "Top 30 stocks:\n",
-      "Average freturn: 10.25%\n",
-      "Average adj_freturn: -2.46%\n",
-      "\n",
-      "Bottom 30 stocks:\n",
-      "Average freturn: -7.16%\n",
-      "Average adj_freturn: -19.87%\n",
-      "\n",
-      "DATE 2023-03-01\n",
-      "\n",
-      "Top 30 stocks:\n",
-      "Average freturn: 22.54%\n",
-      "Average adj_freturn: -7.47%\n",
-      "\n",
-      "Bottom 30 stocks:\n",
-      "Average freturn: 0.39%\n",
-      "Average adj_freturn: -29.61%\n",
-      "\n",
-      "DATE 2023-06-01\n",
-      "\n",
-      "Top 30 stocks:\n",
-      "Average freturn: 32.39%\n",
-      "Average adj_freturn: 7.36%\n",
-      "\n",
-      "Bottom 30 stocks:\n",
-      "Average freturn: 7.06%\n",
-      "Average adj_freturn: -17.97%\n"
-     ]
-    }
-   ],
-   "source": [
-    "for date in [\"2022-03-01\", \"2022-06-01\", \"2022-09-01\", \"2022-12-01\", \"2023-03-01\", \"2023-06-01\"]:\n",
-    "    y_proba = model.predict_proba(X_val)\n",
-    "    val_subset = (\n",
-    "        val.with_columns(pl.Series(\"proba\", y_proba))\n",
-    "        .filter(pl.col(\"tdq\") == pl.lit(date).str.to_date())\n",
-    "        .sort(\"proba\", descending=True)\n",
-    "    )\n",
-    "    n = 30\n",
-    "    top = val_subset.head(n)\n",
-    "    bottom = val_subset.tail(n)\n",
-    "\n",
-    "    # Calculate average returns for top stocks\n",
-    "    top_freturn = top.select(pl.col(\"freturn\")).mean().item()\n",
-    "    top_adj_freturn = top.select(pl.col(\"adj_freturn\")).mean().item()\n",
-    "\n",
-    "    # Calculate average returns for bottom stocks\n",
-    "    bottom_freturn = bottom.select(pl.col(\"freturn\")).mean().item()\n",
-    "    bottom_adj_freturn = bottom.select(pl.col(\"adj_freturn\")).mean().item()\n",
-    "\n",
-    "    print(f\"\\nDATE {date}\")\n",
-    "    print(f\"\\nTop {n} stocks:\")\n",
-    "    print(f\"Average freturn: {top_freturn:.2f}%\")\n",
-    "    print(f\"Average adj_freturn: {top_adj_freturn:.2f}%\")\n",
-    "    print(f\"\\nBottom {n} stocks:\")\n",
-    "    print(f\"Average freturn: {bottom_freturn:.2f}%\")\n",
-    "    print(f\"Average adj_freturn: {bottom_adj_freturn:.2f}%\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Baseline 0.14311926605504588, PR-AUC = 0.1965824005584638\n"
-     ]
-    }
-   ],
-   "source": [
-    "baseline = float(len(val.filter(pl.col(target) == 1)) / len(val))\n",
-    "\n",
-    "pr_auc = model.get_pr_auc(X_val, y_val)\n",
-    "\n",
-    "print(f\"Baseline {baseline}, PR-AUC = {pr_auc}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('vol_yoy', 149.04847717285156),\n",
-       " ('vol_qoq', 75.90708923339844),\n",
-       " ('sector_utilities', 33.48326110839844),\n",
-       " ('fear_ma30', 32.68614196777344),\n",
-       " ('high_fear', 28.813053131103516),\n",
-       " ('sector_energy', 27.4108943939209),\n",
-       " ('size', 25.666988372802734),\n",
-       " ('sector_information_technology', 25.195514678955078),\n",
-       " ('price_2y', 24.457368850708008),\n",
-       " ('price_mom', 24.078079223632812),\n",
-       " ('sector_financials', 22.985061645507812),\n",
-       " ('vol_mom', 19.32402992248535),\n",
-       " ('low_fear', 19.283903121948242),\n",
-       " ('momentum_2y', 18.569866180419922),\n",
-       " ('rel_vol_qoq', 17.964340209960938),\n",
-       " ('rsi_90d', 16.577228546142578),\n",
-       " ('rel_vol_mom', 16.358173370361328),\n",
-       " ('qr_yoy', 15.994983673095703),\n",
-       " ('ps', 14.994345664978027),\n",
-       " ('momentum_yoy', 14.966538429260254),\n",
-       " ('pb', 14.963623046875),\n",
-       " ('dr_2y', 14.914237022399902),\n",
-       " ('sector_real_estate', 14.572257995605469),\n",
-       " ('momentum_mom', 14.5684232711792),\n",
-       " ('price_qoq', 14.464409828186035),\n",
-       " ('sector_consumer_staples', 13.629505157470703),\n",
-       " ('rsi_14d', 13.33154010772705),\n",
-       " ('rel_vol_yoy', 13.179239273071289),\n",
-       " ('dr_yoy', 12.671536445617676),\n",
-       " ('price_yoy', 12.450226783752441),\n",
-       " ('cr_yoy', 12.400802612304688),\n",
-       " ('pb_yoy', 12.351264953613281),\n",
-       " ('roe_yoy', 12.236884117126465),\n",
-       " ('sector_communication_services', 12.203034400939941),\n",
-       " ('atr_yoy', 11.781717300415039),\n",
-       " ('gpm', 11.512931823730469),\n",
-       " ('ev_ebitda_yoy', 11.479098320007324),\n",
-       " ('roa_2y', 11.167205810546875),\n",
-       " ('ltda_yoy', 11.074289321899414),\n",
-       " ('rsi_30d', 11.054387092590332),\n",
-       " ('pe_2y', 10.926481246948242),\n",
-       " ('ltq_yoy', 10.92276382446289),\n",
-       " ('earn_drift', 10.818714141845703),\n",
-       " ('momentum_qoq', 10.755500793457031),\n",
-       " ('roe', 10.681802749633789),\n",
-       " ('saleq_2y', 10.606934547424316),\n",
-       " ('ev_ebitda', 10.497557640075684),\n",
-       " ('ps_yoy', 10.374456405639648),\n",
-       " ('sector_health_care', 10.353845596313477),\n",
-       " ('roa', 10.112092971801758),\n",
-       " ('rsi_60d', 10.040926933288574),\n",
-       " ('pe_yoy', 10.001860618591309),\n",
-       " ('ltq_2y', 9.910948753356934),\n",
-       " ('saleq_yoy', 9.76953125),\n",
-       " ('ebitdam', 9.640961647033691),\n",
-       " ('dlttq_yoy', 9.469494819641113),\n",
-       " ('sector_industrials', 9.43775463104248),\n",
-       " ('roa_yoy', 9.436161041259766),\n",
-       " ('roi_yoy', 9.279754638671875),\n",
-       " ('pe', 9.147547721862793),\n",
-       " ('gpm_2y', 8.906530380249023),\n",
-       " ('sector_consumer_discretionary', 8.177824020385742),\n",
-       " ('roi_2y', 8.136626243591309),\n",
-       " ('volume_ma50', 8.099648475646973),\n",
-       " ('ltcr_yoy', 8.079771995544434),\n",
-       " ('niq_yoy', 7.944287300109863),\n",
-       " ('rsi_1y', 7.701805591583252),\n",
-       " ('niq_2y', 7.684184551239014),\n",
-       " ('volume_ma20', 7.589004039764404),\n",
-       " ('der_yoy', 7.552547931671143),\n",
-       " ('fcf_yoy', 7.186034679412842),\n",
-       " ('gpm_yoy', 7.021056175231934),\n",
-       " ('f_score', 5.618443489074707),\n",
-       " ('n_sales', 5.314530372619629),\n",
-       " ('insider_balance', 4.604588508605957),\n",
-       " ('n_purch', 3.364511728286743),\n",
-       " ('sector_materials', 2.1850898265838623)]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.get_importance(\"gain\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('fear_ma30', 274.0),\n",
-       " ('size', 129.0),\n",
-       " ('vol_yoy', 104.0),\n",
-       " ('momentum_2y', 98.0),\n",
-       " ('ps', 90.0),\n",
-       " ('rel_vol_yoy', 85.0),\n",
-       " ('rsi_1y', 70.0),\n",
-       " ('ev_ebitda', 66.0),\n",
-       " ('roa', 64.0),\n",
-       " ('pb', 64.0),\n",
-       " ('price_2y', 59.0),\n",
-       " ('gpm', 56.0),\n",
-       " ('pe_2y', 56.0),\n",
-       " ('dlttq_yoy', 50.0),\n",
-       " ('qr_yoy', 49.0),\n",
-       " ('ev_ebitda_yoy', 47.0),\n",
-       " ('roe', 45.0),\n",
-       " ('rel_vol_mom', 44.0),\n",
-       " ('ebitdam', 44.0),\n",
-       " ('saleq_2y', 44.0),\n",
-       " ('momentum_yoy', 42.0),\n",
-       " ('saleq_yoy', 42.0),\n",
-       " ('momentum_mom', 41.0),\n",
-       " ('roa_2y', 41.0),\n",
-       " ('momentum_qoq', 40.0),\n",
-       " ('price_qoq', 39.0),\n",
-       " ('atr_yoy', 39.0),\n",
-       " ('dr_yoy', 38.0),\n",
-       " ('rsi_14d', 37.0),\n",
-       " ('ltq_2y', 37.0),\n",
-       " ('cr_yoy', 37.0),\n",
-       " ('dr_2y', 37.0),\n",
-       " ('roa_yoy', 36.0),\n",
-       " ('ltda_yoy', 35.0),\n",
-       " ('vol_qoq', 34.0),\n",
-       " ('pe', 34.0),\n",
-       " ('vol_mom', 33.0),\n",
-       " ('volume_ma20', 31.0),\n",
-       " ('rel_vol_qoq', 31.0),\n",
-       " ('ps_yoy', 30.0),\n",
-       " ('rsi_90d', 29.0),\n",
-       " ('ltq_yoy', 29.0),\n",
-       " ('roe_yoy', 29.0),\n",
-       " ('sector_utilities', 28.0),\n",
-       " ('price_mom', 27.0),\n",
-       " ('price_yoy', 26.0),\n",
-       " ('niq_yoy', 25.0),\n",
-       " ('rsi_60d', 24.0),\n",
-       " ('earn_drift', 24.0),\n",
-       " ('insider_balance', 23.0),\n",
-       " ('pe_yoy', 23.0),\n",
-       " ('gpm_2y', 22.0),\n",
-       " ('volume_ma50', 20.0),\n",
-       " ('gpm_yoy', 20.0),\n",
-       " ('f_score', 19.0),\n",
-       " ('pb_yoy', 19.0),\n",
-       " ('roi_2y', 18.0),\n",
-       " ('der_yoy', 18.0),\n",
-       " ('roi_yoy', 17.0),\n",
-       " ('high_fear', 15.0),\n",
-       " ('rsi_30d', 14.0),\n",
-       " ('ltcr_yoy', 14.0),\n",
-       " ('n_sales', 13.0),\n",
-       " ('low_fear', 13.0),\n",
-       " ('fcf_yoy', 13.0),\n",
-       " ('sector_information_technology', 13.0),\n",
-       " ('niq_2y', 12.0),\n",
-       " ('sector_consumer_staples', 12.0),\n",
-       " ('sector_energy', 10.0),\n",
-       " ('sector_communication_services', 10.0),\n",
-       " ('sector_real_estate', 10.0),\n",
-       " ('n_purch', 7.0),\n",
-       " ('sector_health_care', 7.0),\n",
-       " ('sector_financials', 6.0),\n",
-       " ('sector_consumer_discretionary', 5.0),\n",
-       " ('sector_industrials', 5.0),\n",
-       " ('sector_materials', 4.0)]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.get_importance(\"weight\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x1350 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import shap\n",
-    "\n",
-    "explainer = shap.TreeExplainer(model.model)\n",
-    "shap_values = explainer(X_train)\n",
-    "\n",
-    "feature_names = [\n",
-    "    a + \": \" + str(b)\n",
-    "    for a, b in zip(X_train.columns, np.abs(shap_values.values).mean(0).round(2), strict=False)\n",
-    "]\n",
-    "\n",
-    "shap.summary_plot(\n",
-    "    shap_values,\n",
-    "    X_train,\n",
-    "    max_display=X_train.shape[1],\n",
-    "    feature_names=feature_names,\n",
-    "    plot_size=(8, 13.5),\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "stocksense",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/regression.ipynb b/notebooks/regression.ipynb
new file mode 100644
index 0000000..56a2c1c
--- /dev/null
+++ b/notebooks/regression.ipynb
@@ -0,0 +1,1856 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datetime as dt\n",
+    "\n",
+    "import plotly.express as px\n",
+    "import polars as pl\n",
+    "\n",
+    "from stocksense.config import config\n",
+    "from stocksense.database import DatabaseHandler\n",
+    "from stocksense.model import XGBoostRegressor\n",
+    "from stocksense.pipeline import clean, engineer_features\n",
+    "\n",
+    "features = config.model.features\n",
+    "date_col = config.model.date_col\n",
+    "aux_cols = [\"tic\", \"datadate\", \"rdq\"]\n",
+    "target = \"fwd_return_4Q\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2024-12-16 21:23:16.444\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mstocksense.database.schema\u001b[0m:\u001b[36mcreate_tables\u001b[0m:\u001b[36m121\u001b[0m - \u001b[32m\u001b[1mTables created successfully\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "constituents = DatabaseHandler().fetch_constituents(dt.datetime(2022, 6, 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2024-12-16 21:23:16.452\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mstocksense.pipeline.preprocess\u001b[0m:\u001b[36mengineer_features\u001b[0m:\u001b[36m20\u001b[0m - \u001b[1mSTART processing stock data\u001b[0m\n",
+      "\u001b[32m2024-12-16 21:23:16.454\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mstocksense.database.schema\u001b[0m:\u001b[36mcreate_tables\u001b[0m:\u001b[36m121\u001b[0m - \u001b[32m\u001b[1mTables created successfully\u001b[0m\n",
+      "\u001b[32m2024-12-16 21:23:20.669\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mstocksense.pipeline.preprocess\u001b[0m:\u001b[36mengineer_features\u001b[0m:\u001b[36m33\u001b[0m - \u001b[1mSTART feature engineering\u001b[0m\n",
+      "\u001b[32m2024-12-16 21:23:36.935\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mstocksense.pipeline.preprocess\u001b[0m:\u001b[36mengineer_features\u001b[0m:\u001b[36m47\u001b[0m - \u001b[32m\u001b[1mEND 58960 rows PROCESSED\u001b[0m\n",
+      "\u001b[32m2024-12-16 21:23:36.938\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mstocksense.pipeline.preprocess\u001b[0m:\u001b[36mclean\u001b[0m:\u001b[36m69\u001b[0m - \u001b[1mSTART cleaning data\u001b[0m\n",
+      "\u001b[32m2024-12-16 21:23:37.087\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mstocksense.pipeline.preprocess\u001b[0m:\u001b[36mclean\u001b[0m:\u001b[36m107\u001b[0m - \u001b[32m\u001b[1m37806 rows retained after CLEANING\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (5, 223)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>tdq</th><th>tic</th><th>datadate</th><th>rdq</th><th>saleq</th><th>cogsq</th><th>xsgaq</th><th>niq</th><th>ebitdaq</th><th>cshoq</th><th>actq</th><th>atq</th><th>cheq</th><th>rectq</th><th>invtq</th><th>ppentq</th><th>lctq</th><th>dlttq</th><th>ltq</th><th>req</th><th>seqq</th><th>oancfq</th><th>ivncfq</th><th>fincfq</th><th>dvq</th><th>capxq</th><th>icaptq</th><th>surprise_pct</th><th>stock_split</th><th>n_purch</th><th>val_purch</th><th>n_sales</th><th>val_sales</th><th>insider_balance</th><th>roa</th><th>roi</th><th>roe</th><th>&hellip;</th><th>ltda_2y</th><th>pe_yoy</th><th>pe_2y</th><th>pb_yoy</th><th>pb_2y</th><th>ps_yoy</th><th>ps_2y</th><th>eps_yoy</th><th>eps_2y</th><th>ev_ebitda_yoy</th><th>ltcr_yoy</th><th>itr_yoy</th><th>rtr_yoy</th><th>atr_yoy</th><th>size_yoy</th><th>roa_sec_qoq</th><th>roa_sec_yoy</th><th>f_score</th><th>forward_vol_yoy</th><th>forward_vol_sos</th><th>forward_vol_qoq</th><th>excess_return_1Q</th><th>sharpe_ratio_1Q</th><th>risk_return_1Q</th><th>excess_return_2Q</th><th>sharpe_ratio_2Q</th><th>risk_return_2Q</th><th>excess_return_3Q</th><th>sharpe_ratio_3Q</th><th>risk_return_3Q</th><th>excess_return_4Q</th><th>sharpe_ratio_4Q</th><th>risk_return_4Q</th><th>fwd_return</th><th>excess_return</th><th>sharpe_ratio</th><th>risk_return</th></tr><tr><td>date</td><td>str</td><td>date</td><td>date</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>u32</td><td>f64</td><td>u32</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>&hellip;</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2007-03-01</td><td>&quot;A&quot;</td><td>2007-01-31</td><td>2007-02-15</td><td>1280.0</td><td>540.0</td><td>564.0</td><td>150.0</td><td>176.0</td><td>405.0</td><td>3749.0</td><td>7193.0</td><td>2090.0</td><td>671.0</td><td>648.0</td><td>775.0</td><td>1372.0</td><td>1500.0</td><td>3536.0</td><td>1705.0</td><td>3657.0</td><td>93.0</td><td>-93.0</td><td>-169.0</td><td>0.0</td><td>37.0</td><td>5157.0</td><td>0.011</td><td>0</td><td>0</td><td>0.0</td><td>1</td><td>8.598</td><td>8.598</td><td>8.911442</td><td>2.908668</td><td>0.17528</td><td>&hellip;</td><td>29.655039</td><td>282.560607</td><td>null</td><td>-7.754803</td><td>9.151709</td><td>-19.208201</td><td>null</td><td>-77.612898</td><td>null</td><td>-45.435715</td><td>131.460674</td><td>25.76978</td><td>22.719366</td><td>11.302357</td><td>-1.218827</td><td>-86.503836</td><td>-83.655657</td><td>6</td><td>1.780824</td><td>1.749898</td><td>1.355307</td><td>8.952132</td><td>6.605244</td><td>10.89898</td><td>13.694795</td><td>10.104573</td><td>15.070857</td><td>8.583285</td><td>4.905021</td><td>9.151445</td><td>10.709357</td><td>6.013709</td><td>6.242292</td><td>16.014092</td><td>8.583285</td><td>4.905021</td><td>9.151445</td></tr><tr><td>2007-06-01</td><td>&quot;A&quot;</td><td>2007-04-30</td><td>2007-05-14</td><td>1320.0</td><td>540.0</td><td>586.0</td><td>123.0</td><td>194.0</td><td>395.958</td><td>3791.0</td><td>7283.0</td><td>2050.0</td><td>718.0</td><td>650.0</td><td>777.0</td><td>1554.0</td><td>1500.0</td><td>3728.0</td><td>1881.0</td><td>3555.0</td><td>302.0</td><td>-36.0</td><td>-320.0</td><td>0.0</td><td>42.0</td><td>5055.0</td><td>-0.023</td><td>0</td><td>0</td><td>0.0</td><td>3</td><td>2.695</td><td>2.695</td><td>8.911163</td><td>2.433234</td><td>0.18256</td><td>&hellip;</td><td>30.16614</td><td>300.0</td><td>null</td><td>16.399931</td><td>38.944827</td><td>-0.630294</td><td>null</td><td>-77.181676</td><td>null</td><td>-29.485541</td><td>7.883817</td><td>19.259654</td><td>24.025332</td><td>18.364403</td><td>-1.122916</td><td>13.440324</td><td>-81.598553</td><td>6</td><td>1.927423</td><td>1.918095</td><td>2.106162</td><td>1.404396</td><td>0.666804</td><td>-0.836463</td><td>-2.215787</td><td>-1.05205</td><td>-2.205524</td><td>-0.292617</td><td>-0.152556</td><td>-4.454244</td><td>-5.91296</td><td>-3.067807</td><td>-8.865139</td><td>-8.543662</td><td>-0.292617</td><td>-0.152556</td><td>-4.454244</td></tr><tr><td>2007-09-01</td><td>&quot;A&quot;</td><td>2007-07-31</td><td>2007-08-14</td><td>1374.0</td><td>563.0</td><td>580.0</td><td>185.0</td><td>231.0</td><td>386.548</td><td>3281.0</td><td>7024.0</td><td>1486.0</td><td>738.0</td><td>674.0</td><td>787.0</td><td>1510.0</td><td>1500.0</td><td>3736.0</td><td>2069.0</td><td>3288.0</td><td>176.0</td><td>-258.0</td><td>-484.0</td><td>0.0</td><td>36.0</td><td>4788.0</td><td>-0.01</td><td>0</td><td>0</td><td>0.0</td><td>2</td><td>5.747</td><td>5.747</td><td>8.6418</td><td>3.863826</td><td>0.184611</td><td>&hellip;</td><td>36.525701</td><td>300.0</td><td>null</td><td>25.872772</td><td>5.91443</td><td>3.996541</td><td>null</td><td>-79.822287</td><td>null</td><td>-17.061919</td><td>300.0</td><td>18.769156</td><td>29.327755</td><td>22.359853</td><td>-1.068351</td><td>-14.122377</td><td>-85.588435</td><td>5</td><td>1.772393</td><td>1.803532</td><td>1.789715</td><td>-1.703536</td><td>-0.951848</td><td>0.168495</td><td>-0.089423</td><td>-0.049965</td><td>-2.674412</td><td>-6.394643</td><td>-3.545622</td><td>-7.722925</td><td>10.925009</td><td>6.163987</td><td>-0.514147</td><td>-13.928545</td><td>-6.394643</td><td>-3.545622</td><td>-7.722925</td></tr><tr><td>2007-12-01</td><td>&quot;A&quot;</td><td>2007-10-31</td><td>2007-11-15</td><td>1446.0</td><td>611.0</td><td>598.0</td><td>180.0</td><td>237.0</td><td>370.0</td><td>3671.0</td><td>7554.0</td><td>1826.0</td><td>735.0</td><td>643.0</td><td>801.0</td><td>1663.0</td><td>2087.0</td><td>4320.0</td><td>2580.0</td><td>3234.0</td><td>398.0</td><td>-69.0</td><td>-7.0</td><td>0.0</td><td>39.0</td><td>5321.0</td><td>0.022</td><td>0</td><td>0</td><td>0.0</td><td>2</td><td>2.75</td><td>2.75</td><td>8.445856</td><td>3.382823</td><td>0.197279</td><td>&hellip;</td><td>400.0</td><td>300.0</td><td>-60.359786</td><td>21.410892</td><td>-1.590112</td><td>-1.541225</td><td>-26.668893</td><td>-78.726207</td><td>165.240102</td><td>-12.283396</td><td>-32.276673</td><td>10.125696</td><td>14.20785</td><td>12.76951</td><td>0.27844</td><td>-3.477092</td><td>-87.309223</td><td>4</td><td>3.004222</td><td>1.948736</td><td>1.848409</td><td>-3.735506</td><td>-2.02093</td><td>-6.186043</td><td>-8.003799</td><td>-4.330101</td><td>-8.594016</td><td>8.239344</td><td>4.228045</td><td>-2.316993</td><td>-2.055557</td><td>-0.684223</td><td>-10.49239</td><td>-4.515208</td><td>8.239344</td><td>4.228045</td><td>-2.316993</td></tr><tr><td>2008-03-01</td><td>&quot;A&quot;</td><td>2008-01-31</td><td>2008-02-13</td><td>1393.0</td><td>580.0</td><td>617.0</td><td>120.0</td><td>196.0</td><td>368.0</td><td>5070.0</td><td>7459.0</td><td>3148.0</td><td>726.0</td><td>674.0</td><td>801.0</td><td>2674.0</td><td>626.0</td><td>4286.0</td><td>2657.0</td><td>3173.0</td><td>4.0</td><td>-295.0</td><td>-168.0</td><td>0.0</td><td>34.0</td><td>3799.0</td><td>0.021</td><td>0</td><td>0</td><td>0.0</td><td>2</td><td>3.418</td><td>3.418</td><td>8.151227</td><td>3.158726</td><td>0.191617</td><td>&hellip;</td><td>-55.094284</td><td>-6.732989</td><td>256.802843</td><td>1.959688</td><td>-5.947085</td><td>-18.681512</td><td>-34.301331</td><td>4.388523</td><td>-76.630435</td><td>-22.566969</td><td>-228.366043</td><td>5.357524</td><td>1.528223</td><td>5.558859</td><td>0.408891</td><td>-20.5599</td><td>-25.300507</td><td>5</td><td>3.238751</td><td>1.741436</td><td>2.050642</td><td>1.053516</td><td>0.513749</td><td>2.536928</td><td>20.442391</td><td>9.968778</td><td>8.760186</td><td>6.726355</td><td>3.862535</td><td>-7.914079</td><td>-6.951969</td><td>-2.146497</td><td>-13.35568</td><td>-13.781859</td><td>6.726355</td><td>3.862535</td><td>-7.914079</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (5, 223)\n",
+       "┌────────────┬─────┬────────────┬────────────┬───┬────────────┬────────────┬───────────┬───────────┐\n",
+       "│ tdq        ┆ tic ┆ datadate   ┆ rdq        ┆ … ┆ fwd_return ┆ excess_ret ┆ sharpe_ra ┆ risk_retu │\n",
+       "│ ---        ┆ --- ┆ ---        ┆ ---        ┆   ┆ ---        ┆ urn        ┆ tio       ┆ rn        │\n",
+       "│ date       ┆ str ┆ date       ┆ date       ┆   ┆ f64        ┆ ---        ┆ ---       ┆ ---       │\n",
+       "│            ┆     ┆            ┆            ┆   ┆            ┆ f64        ┆ f64       ┆ f64       │\n",
+       "╞════════════╪═════╪════════════╪════════════╪═══╪════════════╪════════════╪═══════════╪═══════════╡\n",
+       "│ 2007-03-01 ┆ A   ┆ 2007-01-31 ┆ 2007-02-15 ┆ … ┆ 16.014092  ┆ 8.583285   ┆ 4.905021  ┆ 9.151445  │\n",
+       "│ 2007-06-01 ┆ A   ┆ 2007-04-30 ┆ 2007-05-14 ┆ … ┆ -8.543662  ┆ -0.292617  ┆ -0.152556 ┆ -4.454244 │\n",
+       "│ 2007-09-01 ┆ A   ┆ 2007-07-31 ┆ 2007-08-14 ┆ … ┆ -13.928545 ┆ -6.394643  ┆ -3.545622 ┆ -7.722925 │\n",
+       "│ 2007-12-01 ┆ A   ┆ 2007-10-31 ┆ 2007-11-15 ┆ … ┆ -4.515208  ┆ 8.239344   ┆ 4.228045  ┆ -2.316993 │\n",
+       "│ 2008-03-01 ┆ A   ┆ 2008-01-31 ┆ 2008-02-13 ┆ … ┆ -13.781859 ┆ 6.726355   ┆ 3.862535  ┆ -7.914079 │\n",
+       "└────────────┴─────┴────────────┴────────────┴───┴────────────┴────────────┴───────────┴───────────┘"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = engineer_features()\n",
+    "data = clean(data)\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (72, 101)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>tdq</th><th>insider_balance</th><th>mkt_rel</th><th>volume_ma20</th><th>volume_ma50</th><th>volume_ratio</th><th>vol_mom</th><th>vol_qoq</th><th>vol_sos</th><th>vol_yoy</th><th>vol_2y</th><th>rel_vol_month</th><th>rel_vol_quarter</th><th>rel_vol_year</th><th>rel_vol_2year</th><th>price_mom</th><th>price_qoq</th><th>price_yoy</th><th>price_2y</th><th>price_risk_quarter</th><th>price_risk_semester</th><th>price_risk_year</th><th>price_risk_2year</th><th>rsi_14d</th><th>rsi_30d</th><th>rsi_60d</th><th>rsi_90d</th><th>momentum_month</th><th>momentum_quarter</th><th>momentum_year</th><th>momentum_2year</th><th>ma20_distance</th><th>ma50_distance</th><th>index_mom</th><th>index_qoq</th><th>index_sos</th><th>index_yoy</th><th>&hellip;</th><th>niq_yoy</th><th>niq_2y</th><th>ltq_yoy</th><th>ltq_2y</th><th>dlttq_yoy</th><th>gpm_yoy</th><th>gpm_2y</th><th>roa_yoy</th><th>roa_2y</th><th>roi_yoy</th><th>roi_2y</th><th>roe_yoy</th><th>fcf_yoy</th><th>der_yoy</th><th>dr_yoy</th><th>dr_2y</th><th>ltda_yoy</th><th>ev_ebitda_yoy</th><th>ltcr_yoy</th><th>pe_yoy</th><th>pe_2y</th><th>ps_yoy</th><th>atr_yoy</th><th>size_yoy</th><th>roa_sec_qoq</th><th>roa_sec_yoy</th><th>sector_utilities</th><th>sector_health_care</th><th>sector_financials</th><th>sector_consumer_discretionary</th><th>sector_consumer_staples</th><th>sector_energy</th><th>sector_industrials</th><th>sector_information_technology</th><th>sector_communication_services</th><th>sector_materials</th><th>sector_real_estate</th></tr><tr><td>date</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>&hellip;</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td></tr></thead><tbody><tr><td>2007-03-01</td><td>-0.122</td><td>0.020712</td><td>80.093026</td><td>91.637453</td><td>79.80161</td><td>1.427678</td><td>1.855661</td><td>1.967725</td><td>2.252324</td><td>2.375085</td><td>1.619844</td><td>2.975568</td><td>3.509585</td><td>3.691369</td><td>-1.483977</td><td>-6.952532</td><td>-0.519286</td><td>47.168571</td><td>-3.746661</td><td>-0.584397</td><td>-0.230555</td><td>19.859738</td><td>51.002121</td><td>51.250925</td><td>50.557955</td><td>50.308258</td><td>0.826645</td><td>-26.367919</td><td>-0.054284</td><td>2.84472</td><td>-0.000136</td><td>0.009391</td><td>-1.795181</td><td>0.263674</td><td>7.788567</td><td>9.566162</td><td>&hellip;</td><td>-47.671334</td><td>null</td><td>10.633053</td><td>400.0</td><td>-14.615968</td><td>-5.556238</td><td>NaN</td><td>-14.897831</td><td>null</td><td>-57.238137</td><td>null</td><td>-20.452074</td><td>-173.322606</td><td>-14.722747</td><td>-8.768468</td><td>NaN</td><td>-29.589614</td><td>-5.199647</td><td>-185.873909</td><td>-4.291725</td><td>null</td><td>-8.844133</td><td>-37.969979</td><td>2.152981</td><td>-38.187938</td><td>-49.284264</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>2007-06-01</td><td>2.107</td><td>0.02213</td><td>68.435574</td><td>73.865072</td><td>77.288318</td><td>1.402855</td><td>1.388149</td><td>1.647004</td><td>1.991601</td><td>2.275136</td><td>2.501956</td><td>2.261284</td><td>3.063647</td><td>3.601923</td><td>-0.348798</td><td>16.525684</td><td>17.484001</td><td>54.620165</td><td>11.904835</td><td>5.278561</td><td>8.778866</td><td>24.007433</td><td>53.937932</td><td>55.603691</td><td>54.991603</td><td>53.918255</td><td>-0.129088</td><td>1.636282</td><td>0.834038</td><td>1.88725</td><td>0.007381</td><td>0.031867</td><td>2.702011</td><td>10.099535</td><td>10.789489</td><td>20.963082</td><td>&hellip;</td><td>-48.989119</td><td>7.382229</td><td>13.747311</td><td>34.345981</td><td>1.406636</td><td>-5.75132</td><td>-2.860093</td><td>-38.320134</td><td>null</td><td>-57.511809</td><td>-31.156369</td><td>-40.337106</td><td>-185.705471</td><td>-7.701769</td><td>-4.581523</td><td>-16.372205</td><td>-14.933666</td><td>38.45163</td><td>-184.51663</td><td>60.314379</td><td>null</td><td>27.755588</td><td>-33.379754</td><td>1.948585</td><td>-67.487703</td><td>-84.956173</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>2007-09-01</td><td>-0.631</td><td>0.021312</td><td>98.885923</td><td>102.629492</td><td>58.79258</td><td>1.871273</td><td>1.891441</td><td>1.67721</td><td>1.818684</td><td>2.283939</td><td>1.173555</td><td>1.549188</td><td>2.294374</td><td>3.094745</td><td>-7.902651</td><td>-11.8786</td><td>3.328728</td><td>6.582278</td><td>-6.280184</td><td>2.355468</td><td>1.830295</td><td>2.881985</td><td>42.441533</td><td>43.679849</td><td>46.429625</td><td>47.889124</td><td>-64.994572</td><td>4.154032</td><td>0.257693</td><td>0.299499</td><td>-0.020061</td><td>-0.051457</td><td>0.121589</td><td>-2.859535</td><td>7.267924</td><td>12.91741</td><td>&hellip;</td><td>-7.351464</td><td>23.089042</td><td>21.120907</td><td>39.740641</td><td>15.017399</td><td>-4.569864</td><td>-6.291912</td><td>-44.060434</td><td>null</td><td>-23.612952</td><td>-22.53336</td><td>-44.264583</td><td>-300.0</td><td>-0.90273</td><td>-0.539754</td><td>-14.653898</td><td>-5.55174</td><td>22.129375</td><td>-300.0</td><td>51.320412</td><td>null</td><td>11.606056</td><td>-5.327607</td><td>2.17368</td><td>-118.33077</td><td>-102.846182</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>2007-12-01</td><td>8.751</td><td>0.030045</td><td>95.297027</td><td>97.789656</td><td>83.777889</td><td>1.588583</td><td>1.584528</td><td>1.755026</td><td>1.701752</td><td>2.078675</td><td>0.953015</td><td>1.297375</td><td>1.760496</td><td>2.545329</td><td>-6.568849</td><td>19.296728</td><td>14.876494</td><td>16.525898</td><td>12.178216</td><td>2.794439</td><td>8.741868</td><td>7.950207</td><td>47.973113</td><td>53.232412</td><td>54.00601</td><td>53.631722</td><td>1.491448</td><td>32.102213</td><td>2.549516</td><td>0.929133</td><td>-0.011529</td><td>0.017101</td><td>-4.404342</td><td>0.601103</td><td>-3.770841</td><td>5.835027</td><td>&hellip;</td><td>-31.802591</td><td>-40.655572</td><td>31.152631</td><td>52.453846</td><td>67.979791</td><td>-2.951328</td><td>-5.400916</td><td>-48.958167</td><td>null</td><td>-46.053528</td><td>-62.821352</td><td>-47.244684</td><td>-5.595445</td><td>8.336204</td><td>4.817463</td><td>-6.587688</td><td>34.249809</td><td>42.909197</td><td>37.138007</td><td>85.29262</td><td>null</td><td>27.663583</td><td>-15.212389</td><td>2.456842</td><td>-150.0</td><td>-121.803889</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>2008-03-01</td><td>2.361</td><td>0.053174</td><td>158.645323</td><td>119.30443</td><td>199.60587</td><td>4.440275</td><td>3.190493</td><td>2.480721</td><td>2.130664</td><td>2.193173</td><td>3.338771</td><td>2.431481</td><td>1.897736</td><td>2.399579</td><td>37.465343</td><td>43.742449</td><td>77.464567</td><td>76.54302</td><td>13.710248</td><td>31.580468</td><td>36.357008</td><td>34.900592</td><td>70.431836</td><td>66.804906</td><td>63.405984</td><td>61.437419</td><td>-20.173066</td><td>-4.30461</td><td>-14.984271</td><td>19.616899</td><td>0.168809</td><td>0.257977</td><td>-1.857196</td><td>-10.161768</td><td>-9.095071</td><td>-5.169725</td><td>&hellip;</td><td>48.869655</td><td>-22.098495</td><td>34.09146</td><td>48.349475</td><td>61.566968</td><td>1.040486</td><td>-4.573565</td><td>-34.787704</td><td>-44.502922</td><td>15.326912</td><td>-50.684064</td><td>-32.831076</td><td>-8.523085</td><td>7.418878</td><td>4.289772</td><td>-4.854843</td><td>25.658877</td><td>79.899363</td><td>32.830896</td><td>114.07494</td><td>104.887433</td><td>67.15411</td><td>6.558122</td><td>2.747373</td><td>56.772217</td><td>-115.248379</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>2023-12-01</td><td>0.0</td><td>0.166193</td><td>97.665305</td><td>89.390257</td><td>89.714905</td><td>1.516411</td><td>1.768725</td><td>1.645686</td><td>1.940887</td><td>2.41934</td><td>2.216459</td><td>2.168692</td><td>2.230567</td><td>1.954391</td><td>-0.499117</td><td>-7.15144</td><td>-12.914814</td><td>42.068964</td><td>-4.043274</td><td>6.341445</td><td>-6.654076</td><td>17.388615</td><td>46.867631</td><td>47.670449</td><td>49.346586</td><td>49.915493</td><td>-0.059287</td><td>-2.472678</td><td>-1.024137</td><td>69.536653</td><td>-0.004201</td><td>-0.015259</td><td>8.418637</td><td>2.892184</td><td>7.291751</td><td>12.610438</td><td>&hellip;</td><td>-28.871759</td><td>85.388128</td><td>-4.501743</td><td>3.128448</td><td>-0.789058</td><td>-23.441845</td><td>-23.441845</td><td>-2.07207</td><td>122.598236</td><td>-37.689507</td><td>57.666583</td><td>-9.295804</td><td>-75.151896</td><td>-17.950215</td><td>-11.415723</td><td>-12.17058</td><td>-7.971833</td><td>-13.941384</td><td>-74.954271</td><td>-17.255313</td><td>-44.684484</td><td>-10.1651</td><td>-25.646034</td><td>0.708413</td><td>-17.28508</td><td>-27.080045</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>2024-03-01</td><td>0.0</td><td>0.139799</td><td>110.386014</td><td>96.592588</td><td>91.552584</td><td>1.659868</td><td>1.515905</td><td>1.633424</td><td>1.756345</td><td>2.356433</td><td>2.194004</td><td>2.202264</td><td>2.266316</td><td>1.982106</td><td>2.030053</td><td>-6.067961</td><td>-1.61851</td><td>1.026802</td><td>-4.002863</td><td>-5.591688</td><td>-0.921522</td><td>0.435744</td><td>55.755637</td><td>50.025229</td><td>48.45228</td><td>48.651103</td><td>0.33754</td><td>-0.513965</td><td>-0.053938</td><td>0.058851</td><td>0.019861</td><td>0.005461</td><td>6.014264</td><td>11.806178</td><td>13.781348</td><td>30.006914</td><td>&hellip;</td><td>-12.692139</td><td>0.151134</td><td>-4.972276</td><td>-1.79995</td><td>-5.612648</td><td>-24.643449</td><td>-24.643449</td><td>-7.674443</td><td>41.953329</td><td>-21.316804</td><td>-13.918278</td><td>-13.663015</td><td>-54.065041</td><td>-16.173301</td><td>-10.358849</td><td>-14.385911</td><td>-10.962922</td><td>1.43756</td><td>-51.333565</td><td>-0.532335</td><td>-39.07599</td><td>5.81266</td><td>-16.04513</td><td>0.54894</td><td>18.70374</td><td>1.674354</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>2024-06-01</td><td>2.145</td><td>0.143276</td><td>103.185652</td><td>97.413139</td><td>127.59049</td><td>1.299601</td><td>1.19349</td><td>1.342016</td><td>1.514529</td><td>2.157803</td><td>2.211433</td><td>1.695456</td><td>2.105491</td><td>2.034331</td><td>-4.15544</td><td>7.185892</td><td>16.087242</td><td>-8.987945</td><td>6.020907</td><td>0.79207</td><td>10.621941</td><td>-4.165322</td><td>41.290501</td><td>46.281304</td><td>49.457109</td><td>50.073997</td><td>-0.804787</td><td>1.835193</td><td>0.612583</td><td>-0.333915</td><td>-0.020123</td><td>-0.02476</td><td>5.163401</td><td>3.915605</td><td>15.97444</td><td>26.261347</td><td>&hellip;</td><td>-11.56698</td><td>358.717949</td><td>3.736038</td><td>-2.397777</td><td>-0.792184</td><td>-32.276311</td><td>-32.276311</td><td>-28.232291</td><td>43.005523</td><td>-20.274459</td><td>258.68978</td><td>-30.362373</td><td>-67.18894</td><td>-7.816351</td><td>-4.99663</td><td>-16.993663</td><td>-9.143659</td><td>36.844403</td><td>-66.926941</td><td>43.861523</td><td>-48.09379</td><td>33.545346</td><td>-10.287148</td><td>0.827883</td><td>13.949088</td><td>1.261746</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>2024-09-01</td><td>2.752</td><td>0.142212</td><td>85.514673</td><td>89.689542</td><td>90.262057</td><td>1.294617</td><td>1.252109</td><td>1.254361</td><td>1.455543</td><td>1.882782</td><td>1.067134</td><td>1.340737</td><td>1.862192</td><td>1.925187</td><td>4.638134</td><td>7.529219</td><td>0.821798</td><td>1.777678</td><td>6.013228</td><td>8.734395</td><td>0.564599</td><td>0.944176</td><td>55.119995</td><td>53.099297</td><td>52.031383</td><td>51.599781</td><td>1.252353</td><td>1.115769</td><td>0.032732</td><td>0.04429</td><td>0.011037</td><td>0.01688</td><td>3.703535</td><td>6.748008</td><td>9.953511</td><td>25.106587</td><td>&hellip;</td><td>8.821636</td><td>-24.486148</td><td>5.4826</td><td>1.503759</td><td>-14.021164</td><td>-6.201767</td><td>-29.805938</td><td>-21.011835</td><td>10.765338</td><td>-0.665531</td><td>-40.123799</td><td>-22.466133</td><td>61.243386</td><td>-5.015377</td><td>-3.233756</td><td>-14.398287</td><td>-21.125863</td><td>5.063565</td><td>87.538462</td><td>11.199384</td><td>-19.999174</td><td>2.965207</td><td>1.211706</td><td>0.811123</td><td>-25.322084</td><td>-16.449068</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>2024-12-01</td><td>0.077</td><td>0.14241</td><td>92.098479</td><td>90.514742</td><td>57.951398</td><td>1.86895</td><td>1.601804</td><td>1.50518</td><td>1.423271</td><td>1.707263</td><td>2.132717</td><td>2.193246</td><td>1.84237</td><td>2.049972</td><td>10.570858</td><td>8.535589</td><td>8.130475</td><td>-2.48079</td><td>5.328733</td><td>4.646069</td><td>5.712529</td><td>-1.45308</td><td>55.804436</td><td>55.20115</td><td>53.540144</td><td>52.711877</td><td>2.809908</td><td>0.919698</td><td>0.249685</td><td>-0.047542</td><td>0.009158</td><td>0.03001</td><td>3.761995</td><td>9.280862</td><td>14.303529</td><td>32.562877</td><td>&hellip;</td><td>-17.586207</td><td>-41.380519</td><td>4.33004</td><td>-0.366631</td><td>-0.795334</td><td>-10.982647</td><td>-31.849957</td><td>-14.339626</td><td>-16.114569</td><td>-21.999731</td><td>-51.397648</td><td>-14.987108</td><td>75.885329</td><td>-2.076377</td><td>-1.330562</td><td>-12.594392</td><td>-6.17785</td><td>8.831324</td><td>77.29542</td><td>15.292542</td><td>-4.601547</td><td>6.966361</td><td>-10.609932</td><td>0.522133</td><td>-2.473932</td><td>-1.488222</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (72, 101)\n",
+       "┌───────────┬───────────┬──────────┬───────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
+       "│ tdq       ┆ insider_b ┆ mkt_rel  ┆ volume_ma ┆ … ┆ sector_in ┆ sector_co ┆ sector_ma ┆ sector_re │\n",
+       "│ ---       ┆ alance    ┆ ---      ┆ 20        ┆   ┆ formation ┆ mmunicati ┆ terials   ┆ al_estate │\n",
+       "│ date      ┆ ---       ┆ f64      ┆ ---       ┆   ┆ _technolo ┆ on_servic ┆ ---       ┆ ---       │\n",
+       "│           ┆ f64       ┆          ┆ f64       ┆   ┆ gy        ┆ es        ┆ i8        ┆ i8        │\n",
+       "│           ┆           ┆          ┆           ┆   ┆ ---       ┆ ---       ┆           ┆           │\n",
+       "│           ┆           ┆          ┆           ┆   ┆ i8        ┆ i8        ┆           ┆           │\n",
+       "╞═══════════╪═══════════╪══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
+       "│ 2007-03-0 ┆ -0.122    ┆ 0.020712 ┆ 80.093026 ┆ … ┆ 0         ┆ 0         ┆ 0         ┆ 0         │\n",
+       "│ 1         ┆           ┆          ┆           ┆   ┆           ┆           ┆           ┆           │\n",
+       "│ 2007-06-0 ┆ 2.107     ┆ 0.02213  ┆ 68.435574 ┆ … ┆ 0         ┆ 0         ┆ 0         ┆ 0         │\n",
+       "│ 1         ┆           ┆          ┆           ┆   ┆           ┆           ┆           ┆           │\n",
+       "│ 2007-09-0 ┆ -0.631    ┆ 0.021312 ┆ 98.885923 ┆ … ┆ 0         ┆ 0         ┆ 0         ┆ 0         │\n",
+       "│ 1         ┆           ┆          ┆           ┆   ┆           ┆           ┆           ┆           │\n",
+       "│ 2007-12-0 ┆ 8.751     ┆ 0.030045 ┆ 95.297027 ┆ … ┆ 0         ┆ 0         ┆ 0         ┆ 0         │\n",
+       "│ 1         ┆           ┆          ┆           ┆   ┆           ┆           ┆           ┆           │\n",
+       "│ 2008-03-0 ┆ 2.361     ┆ 0.053174 ┆ 158.64532 ┆ … ┆ 0         ┆ 0         ┆ 0         ┆ 0         │\n",
+       "│ 1         ┆           ┆          ┆ 3         ┆   ┆           ┆           ┆           ┆           │\n",
+       "│ …         ┆ …         ┆ …        ┆ …         ┆ … ┆ …         ┆ …         ┆ …         ┆ …         │\n",
+       "│ 2023-12-0 ┆ 0.0       ┆ 0.166193 ┆ 97.665305 ┆ … ┆ 0         ┆ 0         ┆ 0         ┆ 0         │\n",
+       "│ 1         ┆           ┆          ┆           ┆   ┆           ┆           ┆           ┆           │\n",
+       "│ 2024-03-0 ┆ 0.0       ┆ 0.139799 ┆ 110.38601 ┆ … ┆ 0         ┆ 0         ┆ 0         ┆ 0         │\n",
+       "│ 1         ┆           ┆          ┆ 4         ┆   ┆           ┆           ┆           ┆           │\n",
+       "│ 2024-06-0 ┆ 2.145     ┆ 0.143276 ┆ 103.18565 ┆ … ┆ 0         ┆ 0         ┆ 0         ┆ 0         │\n",
+       "│ 1         ┆           ┆          ┆ 2         ┆   ┆           ┆           ┆           ┆           │\n",
+       "│ 2024-09-0 ┆ 2.752     ┆ 0.142212 ┆ 85.514673 ┆ … ┆ 0         ┆ 0         ┆ 0         ┆ 0         │\n",
+       "│ 1         ┆           ┆          ┆           ┆   ┆           ┆           ┆           ┆           │\n",
+       "│ 2024-12-0 ┆ 0.077     ┆ 0.14241  ┆ 92.098479 ┆ … ┆ 0         ┆ 0         ┆ 0         ┆ 0         │\n",
+       "│ 1         ┆           ┆          ┆           ┆   ┆           ┆           ┆           ┆           │\n",
+       "└───────────┴───────────┴──────────┴───────────┴───┴───────────┴───────────┴───────────┴───────────┘"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hd = data.filter(pl.col(\"tic\") == \"EOG\").select([\"tdq\"] + features)\n",
+    "hd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "alignmentgroup": "True",
+         "hovertemplate": "Correlation=%{x}<br>Feature=%{y}<extra></extra>",
+         "legendgroup": "",
+         "marker": {
+          "color": "#636efa",
+          "pattern": {
+           "shape": ""
+          }
+         },
+         "name": "",
+         "offsetgroup": "",
+         "orientation": "h",
+         "showlegend": false,
+         "textposition": "auto",
+         "type": "bar",
+         "x": [
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+          -0.126438369659228,
+          -0.11612545677522385,
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+          "paper_bgcolor": "white",
+          "plot_bgcolor": "#E5ECF6",
+          "polar": {
+           "angularaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "radialaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "scene": {
+           "xaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "yaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "zaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "Feature Correlations with Target (fwd_return_4Q)"
+        },
+        "width": 1000,
+        "xaxis": {
+         "anchor": "y",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "Correlation"
+         }
+        },
+        "yaxis": {
+         "anchor": "x",
+         "domain": [
+          0,
+          1
+         ],
+         "tickfont": {
+          "size": 10
+         },
+         "title": {
+          "text": "Feature"
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = data.to_pandas()\n",
+    "df_standardized = (df[features] - df[features].mean()) / df[features].std()\n",
+    "corr = df_standardized.corrwith(df[target])\n",
+    "corr_df = pl.DataFrame({\"Feature\": corr.index, \"Correlation\": corr.values}).sort(\n",
+    "    \"Correlation\", descending=False\n",
+    ")\n",
+    "\n",
+    "fig = px.bar(\n",
+    "    corr_df,\n",
+    "    x=\"Correlation\",\n",
+    "    y=\"Feature\",\n",
+    "    orientation=\"h\",\n",
+    "    title=f\"Feature Correlations with Target ({target})\",\n",
+    "    width=1000,\n",
+    "    height=1200,\n",
+    ")\n",
+    "fig.update_layout(yaxis={\"tickfont\": {\"size\": 10}}, showlegend=False, margin={\"l\": 200})\n",
+    "fig.add_vline(x=0, line_dash=\"dash\", line_color=\"gray\")\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE: 27.280662521042228\n",
+      "MAE: 20.210763618884588\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = data.select([\"tic\", date_col] + features + [target])\n",
+    "data = data.filter((~pl.all_horizontal(pl.col(target).is_null())))\n",
+    "train = data.filter((pl.col(\"tdq\").dt.year() >= 2007) & (pl.col(\"tdq\").dt.year() < 2022))\n",
+    "val = data.filter(\n",
+    "    (pl.col(\"tdq\").dt.year() >= 2023)\n",
+    "    & (pl.col(\"tdq\").dt.year() <= 2023)\n",
+    "    & pl.col(\"tic\").is_in(constituents)\n",
+    ")\n",
+    "\n",
+    "X_train = train.select(features).to_pandas()\n",
+    "y_train = train.select(target).to_pandas().values.ravel()\n",
+    "X_val = val.select(features).to_pandas()\n",
+    "y_val = val.select(target).to_pandas().values.ravel()\n",
+    "\n",
+    "params = {\n",
+    "    \"objective\": \"reg:absoluteerror\",\n",
+    "    \"learning_rate\": 0.07,\n",
+    "    \"n_estimators\": 394,\n",
+    "    \"max_depth\": 4,\n",
+    "    \"min_child_weight\": 1.58,\n",
+    "    \"gamma\": 0.15,\n",
+    "    \"subsample\": 0.58,\n",
+    "    \"colsample_bytree\": 0.70,\n",
+    "    \"reg_alpha\": 5.47,\n",
+    "    \"reg_lambda\": 7.07,\n",
+    "    \"eval_metric\": \"mae\",\n",
+    "    \"nthread\": -1,\n",
+    "    \"seed\": 100,\n",
+    "}\n",
+    "\n",
+    "model = XGBoostRegressor(params)\n",
+    "model.train(X_train, y_train)\n",
+    "\n",
+    "print(f\"RMSE: {model.get_rmse(X_val, y_val)}\")\n",
+    "print(f\"MAE: {model.get_mae(X_val, y_val)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average freturn: 15.84%\n",
+      "\n",
+      "Top 100 stocks: 13.35%\n",
+      "\n",
+      "Bottom 100 stocks: 2.85%\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(X_val)\n",
+    "val_subset = val.with_columns(pl.Series(\"pred\", y_pred)).sort(\"pred\", descending=True)\n",
+    "n = 100\n",
+    "top = val_subset.head(n)\n",
+    "bottom = val_subset.tail(n)\n",
+    "\n",
+    "top_freturn = top.select(pl.col(target)).mean().item()\n",
+    "bottom_freturn = bottom.select(pl.col(target)).mean().item()\n",
+    "freturn = val_subset.select(pl.col(target)).mean().item()\n",
+    "\n",
+    "print(f\"Average freturn: {freturn:.2f}%\")\n",
+    "\n",
+    "print(f\"\\nTop {n} stocks: {top_freturn:.2f}%\")\n",
+    "print(f\"\\nBottom {n} stocks: {bottom_freturn:.2f}%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "16.401876551077624\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (83, 104)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>tic</th><th>tdq</th><th>insider_balance</th><th>mkt_rel</th><th>volume_ma20</th><th>volume_ma50</th><th>volume_ratio</th><th>vol_mom</th><th>vol_qoq</th><th>vol_sos</th><th>vol_yoy</th><th>vol_2y</th><th>rel_vol_month</th><th>rel_vol_quarter</th><th>rel_vol_year</th><th>rel_vol_2year</th><th>price_mom</th><th>price_qoq</th><th>price_yoy</th><th>price_2y</th><th>price_risk_quarter</th><th>price_risk_semester</th><th>price_risk_year</th><th>price_risk_2year</th><th>rsi_14d</th><th>rsi_30d</th><th>rsi_60d</th><th>rsi_90d</th><th>momentum_month</th><th>momentum_quarter</th><th>momentum_year</th><th>momentum_2year</th><th>ma20_distance</th><th>ma50_distance</th><th>index_mom</th><th>index_qoq</th><th>index_sos</th><th>&hellip;</th><th>ltq_yoy</th><th>ltq_2y</th><th>dlttq_yoy</th><th>gpm_yoy</th><th>gpm_2y</th><th>roa_yoy</th><th>roa_2y</th><th>roi_yoy</th><th>roi_2y</th><th>roe_yoy</th><th>fcf_yoy</th><th>der_yoy</th><th>dr_yoy</th><th>dr_2y</th><th>ltda_yoy</th><th>ev_ebitda_yoy</th><th>ltcr_yoy</th><th>pe_yoy</th><th>pe_2y</th><th>ps_yoy</th><th>atr_yoy</th><th>size_yoy</th><th>roa_sec_qoq</th><th>roa_sec_yoy</th><th>sector_utilities</th><th>sector_health_care</th><th>sector_financials</th><th>sector_consumer_discretionary</th><th>sector_consumer_staples</th><th>sector_energy</th><th>sector_industrials</th><th>sector_information_technology</th><th>sector_communication_services</th><th>sector_materials</th><th>sector_real_estate</th><th>fwd_return_4Q</th><th>pred</th></tr><tr><td>str</td><td>date</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>&hellip;</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>f64</td><td>f32</td></tr></thead><tbody><tr><td>&quot;KEY&quot;</td><td>2023-09-01</td><td>-0.115</td><td>0.024961</td><td>72.775971</td><td>100.218466</td><td>64.907363</td><td>2.251795</td><td>2.763218</td><td>4.56359</td><td>3.608747</td><td>2.912914</td><td>2.991082</td><td>4.179946</td><td>3.168861</td><td>2.35582</td><td>-3.986707</td><td>8.851229</td><td>-34.652346</td><td>-43.110233</td><td>3.203233</td><td>-8.085644</td><td>-9.602322</td><td>-14.799691</td><td>58.179644</td><td>53.431223</td><td>49.219843</td><td>47.646334</td><td>-12.930742</td><td>1.634932</td><td>-2.443961</td><td>282.155411</td><td>0.03819</td><td>0.046321</td><td>0.308312</td><td>5.41382</td><td>11.620662</td><td>&hellip;</td><td>4.990121</td><td>11.042813</td><td>74.640428</td><td>5.378151</td><td>4.131296</td><td>-36.01828</td><td>-40.947265</td><td>-58.353563</td><td>-69.406238</td><td>-30.461202</td><td>300.0</td><td>9.411476</td><td>0.66804</td><td>3.11643</td><td>67.451085</td><td>NaN</td><td>300.0</td><td>-1.601527</td><td>-12.105758</td><td>-41.610551</td><td>-22.903074</td><td>0.346307</td><td>-10.104881</td><td>10.131954</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>36.20497</td><td>28.27339</td></tr><tr><td>&quot;ZION&quot;</td><td>2023-09-01</td><td>0.0</td><td>0.012457</td><td>67.094137</td><td>97.966104</td><td>81.186726</td><td>2.125557</td><td>3.036259</td><td>5.065306</td><td>3.924108</td><td>3.187359</td><td>2.823399</td><td>4.592979</td><td>3.445782</td><td>2.577777</td><td>-2.592893</td><td>20.982728</td><td>-33.781575</td><td>-37.063907</td><td>6.910716</td><td>-5.129027</td><td>-8.608726</td><td>-11.628406</td><td>56.912647</td><td>55.227848</td><td>51.62677</td><td>49.658825</td><td>-8.409955</td><td>3.875771</td><td>-2.382548</td><td>242.582357</td><td>0.031525</td><td>0.065427</td><td>0.308312</td><td>5.41382</td><td>11.620662</td><td>&hellip;</td><td>-0.249537</td><td>3.501105</td><td>-20.417288</td><td>1.960784</td><td>1.973684</td><td>4.268139</td><td>-21.818088</td><td>1.052651</td><td>-14.119773</td><td>10.454691</td><td>11.627907</td><td>6.340073</td><td>0.38398</td><td>3.475001</td><td>-19.911856</td><td>35.058661</td><td>40.266527</td><td>-37.321426</td><td>-25.946268</td><td>-48.657936</td><td>1.767319</td><td>-0.055619</td><td>-6.271812</td><td>25.045509</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>29.613784</td><td>26.448109</td></tr><tr><td>&quot;JKHY&quot;</td><td>2023-09-01</td><td>0.0</td><td>0.02659</td><td>92.144434</td><td>88.356259</td><td>55.384387</td><td>1.876748</td><td>1.380782</td><td>1.93967</td><td>1.829635</td><td>1.681373</td><td>2.492904</td><td>2.088722</td><td>1.606613</td><td>1.35981</td><td>-7.641973</td><td>1.724587</td><td>-17.752339</td><td>-10.375327</td><td>1.248993</td><td>-2.477478</td><td>-9.702664</td><td>-6.170747</td><td>41.13505</td><td>45.223063</td><td>47.496861</td><td>47.810916</td><td>-24.786466</td><td>0.318553</td><td>-1.252037</td><td>67.906262</td><td>-0.017145</td><td>-0.028559</td><td>0.308312</td><td>5.41382</td><td>11.620662</td><td>&hellip;</td><td>8.508382</td><td>14.599002</td><td>139.130435</td><td>4.067127</td><td>8.427101</td><td>-10.563895</td><td>-0.858668</td><td>-3.401706</td><td>-4.140208</td><td>-13.222602</td><td>-40.776642</td><td>-6.797175</td><td>-3.941604</td><td>-3.483078</td><td>111.693195</td><td>-22.40834</td><td>-75.233868</td><td>-20.330936</td><td>-27.025187</td><td>-24.734773</td><td>-2.690912</td><td>1.561227</td><td>-2.883231</td><td>-7.993228</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>6.47807</td><td>25.784447</td></tr><tr><td>&quot;SEDG&quot;</td><td>2023-12-01</td><td>-1.453</td><td>0.010806</td><td>179.675028</td><td>200.457498</td><td>167.280681</td><td>3.490529</td><td>5.137851</td><td>4.354555</td><td>3.985649</td><td>4.249354</td><td>5.101922</td><td>6.29969</td><td>4.58051</td><td>3.432714</td><td>8.827025</td><td>-47.094287</td><td>-72.401791</td><td>-74.835245</td><td>-9.166144</td><td>-16.456838</td><td>-18.165623</td><td>-17.61097</td><td>48.30073</td><td>36.555129</td><td>36.010933</td><td>38.736523</td><td>1.04851</td><td>-16.283295</td><td>-5.741417</td><td>-123.696711</td><td>0.026696</td><td>-0.169845</td><td>8.418637</td><td>2.892184</td><td>7.291751</td><td>&hellip;</td><td>16.707671</td><td>52.730023</td><td>-6.362984</td><td>-25.785971</td><td>-39.933024</td><td>60.287896</td><td>-14.812534</td><td>-300.0</td><td>-169.859139</td><td>57.375175</td><td>-207.304786</td><td>-3.831358</td><td>-2.051456</td><td>-12.75753</td><td>-21.413826</td><td>-83.09423</td><td>-214.596546</td><td>-85.880095</td><td>-82.168313</td><td>-78.953779</td><td>-28.815308</td><td>2.118955</td><td>-65.627493</td><td>31.596035</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>-78.01517</td><td>25.131428</td></tr><tr><td>&quot;MKTX&quot;</td><td>2023-09-01</td><td>-0.193</td><td>0.020716</td><td>89.149361</td><td>99.575999</td><td>65.741831</td><td>1.757855</td><td>1.674163</td><td>2.120172</td><td>2.360708</td><td>2.278872</td><td>2.334976</td><td>2.532522</td><td>2.072951</td><td>1.843038</td><td>-3.960757</td><td>-10.321778</td><td>-3.922121</td><td>-49.815097</td><td>-6.165337</td><td>-16.680152</td><td>-1.661417</td><td>-21.859544</td><td>38.235778</td><td>39.718374</td><td>41.9051</td><td>43.905193</td><td>-12.846572</td><td>-1.906561</td><td>-0.276619</td><td>326.038579</td><td>-0.029092</td><td>-0.065222</td><td>0.308312</td><td>5.41382</td><td>11.620662</td><td>&hellip;</td><td>-19.898996</td><td>-12.007061</td><td>NaN</td><td>0.0</td><td>0.0</td><td>0.898636</td><td>-17.015312</td><td>-23.166482</td><td>-21.977535</td><td>-10.482985</td><td>12.555934</td><td>-31.194064</td><td>-22.445749</td><td>-16.42347</td><td>NaN</td><td>-7.445076</td><td>NaN</td><td>-8.85532</td><td>-43.765944</td><td>-9.887123</td><td>-0.470727</td><td>0.439161</td><td>-3.623944</td><td>7.144971</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>-8.389982</td><td>24.268373</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;GPN&quot;</td><td>2023-09-01</td><td>2.046</td><td>0.077576</td><td>85.441629</td><td>94.703277</td><td>91.06251</td><td>1.322276</td><td>1.747865</td><td>2.128585</td><td>2.339758</td><td>2.383192</td><td>1.756392</td><td>2.644013</td><td>2.054555</td><td>1.927407</td><td>6.304978</td><td>28.268208</td><td>4.097242</td><td>-20.48696</td><td>16.17299</td><td>6.880342</td><td>1.751139</td><td>-8.596437</td><td>70.080778</td><td>65.677938</td><td>59.536498</td><td>56.082813</td><td>20.44997</td><td>5.221491</td><td>0.28897</td><td>134.086648</td><td>0.047378</td><td>0.101361</td><td>0.308312</td><td>5.41382</td><td>11.620662</td><td>&hellip;</td><td>24.932156</td><td>37.902017</td><td>55.970201</td><td>6.540346</td><td>9.60402</td><td>300.0</td><td>-12.856001</td><td>136.149187</td><td>-4.188083</td><td>300.0</td><td>-30.271609</td><td>35.10324</td><td>14.618475</td><td>29.306683</td><td>43.094198</td><td>-26.888843</td><td>-55.293774</td><td>-90.72588</td><td>-24.8355</td><td>-7.893488</td><td>-0.452693</td><td>0.805557</td><td>55.132404</td><td>59.256669</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>-21.641651</td><td>18.963537</td></tr><tr><td>&quot;INCY&quot;</td><td>2023-12-01</td><td>0.55</td><td>0.027918</td><td>101.654669</td><td>99.953569</td><td>109.518473</td><td>1.465702</td><td>1.493562</td><td>1.328045</td><td>1.405066</td><td>1.575596</td><td>2.14234</td><td>1.831306</td><td>1.614773</td><td>1.272798</td><td>-0.643735</td><td>-14.511787</td><td>-32.195304</td><td>-20.230361</td><td>-9.716224</td><td>-10.258148</td><td>-22.913733</td><td>-12.839817</td><td>48.228694</td><td>43.781274</td><td>42.743043</td><td>43.266836</td><td>-0.076465</td><td>-5.017588</td><td>-2.553068</td><td>-33.439179</td><td>0.00103</td><td>-0.031128</td><td>8.418637</td><td>2.892184</td><td>7.291751</td><td>&hellip;</td><td>13.495948</td><td>38.719108</td><td>-100.0</td><td>0.096755</td><td>-1.719867</td><td>-58.156507</td><td>-47.747864</td><td>31.101005</td><td>-39.230151</td><td>-58.422091</td><td>-53.823288</td><td>-2.724745</td><td>-2.103387</td><td>-8.823372</td><td>-100.0</td><td>-32.561054</td><td>300.0</td><td>37.976977</td><td>5.477454</td><td>-38.225905</td><td>-4.034276</td><td>1.716381</td><td>150.0</td><td>-98.83571</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>27.714407</td><td>18.922745</td></tr><tr><td>&quot;TFX&quot;</td><td>2023-12-01</td><td>0.0</td><td>0.024673</td><td>96.106596</td><td>96.918702</td><td>70.814388</td><td>2.707236</td><td>2.073107</td><td>1.789919</td><td>1.824189</td><td>2.034271</td><td>3.957025</td><td>2.541906</td><td>2.096451</td><td>1.643326</td><td>21.417527</td><td>8.403958</td><td>-2.754996</td><td>-23.451689</td><td>4.053798</td><td>-2.079091</td><td>-1.510258</td><td>-11.528299</td><td>74.006478</td><td>61.525081</td><td>53.532745</td><td>51.00283</td><td>2.544061</td><td>2.905748</td><td>-0.218469</td><td>-38.763777</td><td>0.066087</td><td>0.092302</td><td>8.418637</td><td>2.892184</td><td>7.291751</td><td>&hellip;</td><td>11.060712</td><td>-5.98754</td><td>22.379548</td><td>2.436802</td><td>0.719404</td><td>-11.742672</td><td>-12.472989</td><td>16.319198</td><td>-39.325088</td><td>-11.639144</td><td>24.318864</td><td>0.276899</td><td>0.159409</td><td>-11.679147</td><td>10.367231</td><td>-13.122724</td><td>1.584673</td><td>-5.509794</td><td>-16.400898</td><td>-12.642744</td><td>1.885147</td><td>1.17156</td><td>-77.683541</td><td>-300.0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.195711</td><td>18.916733</td></tr><tr><td>&quot;MCO&quot;</td><td>2023-06-01</td><td>10.661</td><td>0.14529</td><td>104.252149</td><td>90.879996</td><td>94.203145</td><td>1.251627</td><td>1.330199</td><td>1.626425</td><td>2.027895</td><td>1.821209</td><td>1.555778</td><td>1.456854</td><td>1.539435</td><td>1.478219</td><td>5.854082</td><td>6.652828</td><td>7.328317</td><td>-2.190862</td><td>5.001379</td><td>7.249969</td><td>3.613755</td><td>-1.202971</td><td>65.477282</td><td>59.363546</td><td>55.241687</td><td>53.41355</td><td>2.377403</td><td>1.560453</td><td>3.407411</td><td>-4.850421</td><td>0.036974</td><td>0.053256</td><td>2.462385</td><td>4.263394</td><td>6.655249</td><td>&hellip;</td><td>-4.079954</td><td>13.489051</td><td>-4.420041</td><td>-2.365623</td><td>-6.035654</td><td>-30.219076</td><td>-42.280015</td><td>-0.943119</td><td>-41.819602</td><td>-41.160936</td><td>29.361702</td><td>-19.010609</td><td>-3.949619</td><td>-3.620277</td><td>-4.290168</td><td>56.192212</td><td>35.343961</td><td>62.715506</td><td>40.474351</td><td>28.548098</td><td>-2.250299</td><td>-0.014147</td><td>-1.771359</td><td>-32.214283</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>21.191345</td><td>18.791479</td></tr><tr><td>&quot;AXP&quot;</td><td>2023-09-01</td><td>0.0</td><td>0.272934</td><td>75.646224</td><td>86.040549</td><td>69.931907</td><td>0.925585</td><td>1.148399</td><td>1.614779</td><td>1.848025</td><td>2.04211</td><td>1.229464</td><td>1.737194</td><td>1.622761</td><td>1.651557</td><td>-4.080289</td><td>-7.100461</td><td>5.013155</td><td>-3.820204</td><td>-6.182921</td><td>-6.73258</td><td>2.71271</td><td>-1.870714</td><td>41.765679</td><td>44.713872</td><td>47.940857</td><td>49.006298</td><td>-13.23427</td><td>-1.311544</td><td>0.353568</td><td>25.003144</td><td>-0.011187</td><td>-0.027543</td><td>0.308312</td><td>5.41382</td><td>11.620662</td><td>&hellip;</td><td>19.849173</td><td>35.164216</td><td>15.384615</td><td>-7.035571</td><td>-7.155949</td><td>-17.721717</td><td>-19.164316</td><td>-3.929523</td><td>-18.318758</td><td>-14.595845</td><td>-7.889176</td><td>4.283996</td><td>0.467104</td><td>3.191695</td><td>-3.275444</td><td>NaN</td><td>-20.170619</td><td>5.906091</td><td>-15.29607</td><td>-17.136301</td><td>0.877505</td><td>1.442124</td><td>-150.0</td><td>19.93999</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>52.229395</td><td>18.780275</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (83, 104)\n",
+       "┌──────┬────────────┬────────────┬──────────┬───┬────────────┬────────────┬────────────┬───────────┐\n",
+       "│ tic  ┆ tdq        ┆ insider_ba ┆ mkt_rel  ┆ … ┆ sector_mat ┆ sector_rea ┆ fwd_return ┆ pred      │\n",
+       "│ ---  ┆ ---        ┆ lance      ┆ ---      ┆   ┆ erials     ┆ l_estate   ┆ _4Q        ┆ ---       │\n",
+       "│ str  ┆ date       ┆ ---        ┆ f64      ┆   ┆ ---        ┆ ---        ┆ ---        ┆ f32       │\n",
+       "│      ┆            ┆ f64        ┆          ┆   ┆ i8         ┆ i8         ┆ f64        ┆           │\n",
+       "╞══════╪════════════╪════════════╪══════════╪═══╪════════════╪════════════╪════════════╪═══════════╡\n",
+       "│ KEY  ┆ 2023-09-01 ┆ -0.115     ┆ 0.024961 ┆ … ┆ 0          ┆ 0          ┆ 36.20497   ┆ 28.27339  │\n",
+       "│ ZION ┆ 2023-09-01 ┆ 0.0        ┆ 0.012457 ┆ … ┆ 0          ┆ 0          ┆ 29.613784  ┆ 26.448109 │\n",
+       "│ JKHY ┆ 2023-09-01 ┆ 0.0        ┆ 0.02659  ┆ … ┆ 0          ┆ 0          ┆ 6.47807    ┆ 25.784447 │\n",
+       "│ SEDG ┆ 2023-12-01 ┆ -1.453     ┆ 0.010806 ┆ … ┆ 0          ┆ 0          ┆ -78.01517  ┆ 25.131428 │\n",
+       "│ MKTX ┆ 2023-09-01 ┆ -0.193     ┆ 0.020716 ┆ … ┆ 0          ┆ 0          ┆ -8.389982  ┆ 24.268373 │\n",
+       "│ …    ┆ …          ┆ …          ┆ …        ┆ … ┆ …          ┆ …          ┆ …          ┆ …         │\n",
+       "│ GPN  ┆ 2023-09-01 ┆ 2.046      ┆ 0.077576 ┆ … ┆ 0          ┆ 0          ┆ -21.641651 ┆ 18.963537 │\n",
+       "│ INCY ┆ 2023-12-01 ┆ 0.55       ┆ 0.027918 ┆ … ┆ 0          ┆ 0          ┆ 27.714407  ┆ 18.922745 │\n",
+       "│ TFX  ┆ 2023-12-01 ┆ 0.0        ┆ 0.024673 ┆ … ┆ 0          ┆ 0          ┆ 0.195711   ┆ 18.916733 │\n",
+       "│ MCO  ┆ 2023-06-01 ┆ 10.661     ┆ 0.14529  ┆ … ┆ 0          ┆ 0          ┆ 21.191345  ┆ 18.791479 │\n",
+       "│ AXP  ┆ 2023-09-01 ┆ 0.0        ┆ 0.272934 ┆ … ┆ 0          ┆ 0          ┆ 52.229395  ┆ 18.780275 │\n",
+       "└──────┴────────────┴────────────┴──────────┴───┴────────────┴────────────┴────────────┴───────────┘"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "top = val_subset.head(100).filter((pl.col(\"pe\") < 50) & (pl.col(\"pe\") > 0) & (pl.col(\"pred\") > 5))\n",
+    "print(top.select(pl.col(target)).mean().item())\n",
+    "top"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "DATE 2023-03-01\n",
+      "\n",
+      "Top 40 stocks:\n",
+      "Average freturn: 13.44%\n",
+      "\n",
+      "Bottom 40 stocks:\n",
+      "Average freturn: 1.47%\n",
+      "\n",
+      "DATE 2023-06-01\n",
+      "\n",
+      "Top 40 stocks:\n",
+      "Average freturn: 17.32%\n",
+      "\n",
+      "Bottom 40 stocks:\n",
+      "Average freturn: 19.74%\n",
+      "\n",
+      "DATE 2023-09-01\n",
+      "\n",
+      "Top 40 stocks:\n",
+      "Average freturn: 11.83%\n",
+      "\n",
+      "Bottom 40 stocks:\n",
+      "Average freturn: 14.32%\n",
+      "\n",
+      "DATE 2023-12-01\n",
+      "\n",
+      "Top 40 stocks:\n",
+      "Average freturn: 22.72%\n",
+      "\n",
+      "Bottom 40 stocks:\n",
+      "Average freturn: 18.23%\n"
+     ]
+    }
+   ],
+   "source": [
+    "eval_dates = [\"2023-03-01\", \"2023-06-01\", \"2023-09-01\", \"2023-12-01\"]\n",
+    "\n",
+    "for date in eval_dates:\n",
+    "    y_pred = model.predict(X_val)\n",
+    "    val_subset = (\n",
+    "        val.with_columns(pl.Series(\"pred\", y_pred))\n",
+    "        .filter(pl.col(\"tdq\") == pl.lit(date).str.to_date())\n",
+    "        .sort(\"pred\", descending=True)\n",
+    "    )\n",
+    "    n = 40\n",
+    "    top = val_subset.head(n)\n",
+    "    bottom = val_subset.tail(n)\n",
+    "\n",
+    "    # Calculate average returns for top stocks\n",
+    "    top_freturn = top.select(pl.col(target)).mean().item()\n",
+    "\n",
+    "    # Calculate average returns for bottom stocks\n",
+    "    bottom_freturn = bottom.select(pl.col(target)).mean().item()\n",
+    "\n",
+    "    print(f\"\\nDATE {date}\")\n",
+    "    print(f\"\\nTop {n} stocks:\")\n",
+    "    print(f\"Average freturn: {top_freturn:.2f}%\")\n",
+    "    print(f\"\\nBottom {n} stocks:\")\n",
+    "    print(f\"Average freturn: {bottom_freturn:.2f}%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><style>\n",
+       ".dataframe > thead > tr,\n",
+       ".dataframe > tbody > tr {\n",
+       "  text-align: right;\n",
+       "  white-space: pre-wrap;\n",
+       "}\n",
+       "</style>\n",
+       "<small>shape: (40, 104)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>tic</th><th>tdq</th><th>insider_balance</th><th>mkt_rel</th><th>volume_ma20</th><th>volume_ma50</th><th>volume_ratio</th><th>vol_mom</th><th>vol_qoq</th><th>vol_sos</th><th>vol_yoy</th><th>vol_2y</th><th>rel_vol_month</th><th>rel_vol_quarter</th><th>rel_vol_year</th><th>rel_vol_2year</th><th>price_mom</th><th>price_qoq</th><th>price_yoy</th><th>price_2y</th><th>price_risk_quarter</th><th>price_risk_semester</th><th>price_risk_year</th><th>price_risk_2year</th><th>rsi_14d</th><th>rsi_30d</th><th>rsi_60d</th><th>rsi_90d</th><th>momentum_month</th><th>momentum_quarter</th><th>momentum_year</th><th>momentum_2year</th><th>ma20_distance</th><th>ma50_distance</th><th>index_mom</th><th>index_qoq</th><th>index_sos</th><th>&hellip;</th><th>ltq_yoy</th><th>ltq_2y</th><th>dlttq_yoy</th><th>gpm_yoy</th><th>gpm_2y</th><th>roa_yoy</th><th>roa_2y</th><th>roi_yoy</th><th>roi_2y</th><th>roe_yoy</th><th>fcf_yoy</th><th>der_yoy</th><th>dr_yoy</th><th>dr_2y</th><th>ltda_yoy</th><th>ev_ebitda_yoy</th><th>ltcr_yoy</th><th>pe_yoy</th><th>pe_2y</th><th>ps_yoy</th><th>atr_yoy</th><th>size_yoy</th><th>roa_sec_qoq</th><th>roa_sec_yoy</th><th>sector_utilities</th><th>sector_health_care</th><th>sector_financials</th><th>sector_consumer_discretionary</th><th>sector_consumer_staples</th><th>sector_energy</th><th>sector_industrials</th><th>sector_information_technology</th><th>sector_communication_services</th><th>sector_materials</th><th>sector_real_estate</th><th>fwd_return_4Q</th><th>pred</th></tr><tr><td>str</td><td>date</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>&hellip;</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>i8</td><td>f64</td><td>f32</td></tr></thead><tbody><tr><td>&quot;SEDG&quot;</td><td>2023-12-01</td><td>-1.453</td><td>0.010806</td><td>179.675028</td><td>200.457498</td><td>167.280681</td><td>3.490529</td><td>5.137851</td><td>4.354555</td><td>3.985649</td><td>4.249354</td><td>5.101922</td><td>6.29969</td><td>4.58051</td><td>3.432714</td><td>8.827025</td><td>-47.094287</td><td>-72.401791</td><td>-74.835245</td><td>-9.166144</td><td>-16.456838</td><td>-18.165623</td><td>-17.61097</td><td>48.30073</td><td>36.555129</td><td>36.010933</td><td>38.736523</td><td>1.04851</td><td>-16.283295</td><td>-5.741417</td><td>-123.696711</td><td>0.026696</td><td>-0.169845</td><td>8.418637</td><td>2.892184</td><td>7.291751</td><td>&hellip;</td><td>16.707671</td><td>52.730023</td><td>-6.362984</td><td>-25.785971</td><td>-39.933024</td><td>60.287896</td><td>-14.812534</td><td>-300.0</td><td>-169.859139</td><td>57.375175</td><td>-207.304786</td><td>-3.831358</td><td>-2.051456</td><td>-12.75753</td><td>-21.413826</td><td>-83.09423</td><td>-214.596546</td><td>-85.880095</td><td>-82.168313</td><td>-78.953779</td><td>-28.815308</td><td>2.118955</td><td>-65.627493</td><td>31.596035</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>-78.01517</td><td>25.131428</td></tr><tr><td>&quot;TECH&quot;</td><td>2023-12-01</td><td>0.718</td><td>0.02358</td><td>119.451277</td><td>118.277512</td><td>119.475887</td><td>2.486013</td><td>2.216999</td><td>1.978903</td><td>2.008202</td><td>2.311104</td><td>3.633674</td><td>2.718335</td><td>2.307928</td><td>1.866956</td><td>22.222217</td><td>-14.48815</td><td>-24.026354</td><td>-45.283139</td><td>-6.535029</td><td>-11.220774</td><td>-11.964115</td><td>-19.593729</td><td>58.481424</td><td>49.003173</td><td>45.879224</td><td>45.911924</td><td>2.639646</td><td>-5.009415</td><td>-1.905275</td><td>-74.849429</td><td>0.040884</td><td>0.004632</td><td>8.418637</td><td>2.892184</td><td>7.291751</td><td>&hellip;</td><td>26.36019</td><td>21.562115</td><td>66.250411</td><td>0.405214</td><td>0.785946</td><td>-27.918731</td><td>15.07685</td><td>-53.008882</td><td>-41.33195</td><td>-25.939992</td><td>-18.34846</td><td>10.762906</td><td>7.803536</td><td>0.157051</td><td>41.83567</td><td>-29.419107</td><td>-50.886413</td><td>-13.637942</td><td>-61.494918</td><td>-28.720505</td><td>-11.551772</td><td>2.046074</td><td>-49.839464</td><td>-63.243902</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>14.382879</td><td>24.152769</td></tr><tr><td>&quot;ROL&quot;</td><td>2023-12-01</td><td>4592.09</td><td>0.045666</td><td>102.594657</td><td>140.477891</td><td>79.680209</td><td>0.844529</td><td>1.854283</td><td>1.688063</td><td>1.447506</td><td>1.677789</td><td>1.234404</td><td>2.273598</td><td>1.663548</td><td>1.355352</td><td>9.914021</td><td>7.431717</td><td>1.162219</td><td>22.926687</td><td>4.007865</td><td>0.408245</td><td>0.802911</td><td>13.664824</td><td>71.198222</td><td>60.708252</td><td>54.727219</td><td>53.134934</td><td>1.177628</td><td>2.569586</td><td>0.092163</td><td>37.895991</td><td>0.040018</td><td>0.064632</td><td>8.418637</td><td>2.892184</td><td>7.291751</td><td>&hellip;</td><td>67.819663</td><td>91.377567</td><td>300.0</td><td>2.833922</td><td>1.416845</td><td>-6.98608</td><td>-16.053608</td><td>-8.731949</td><td>-7.802986</td><td>26.115684</td><td>-5.663169</td><td>82.079214</td><td>34.288621</td><td>38.085006</td><td>300.0</td><td>-15.858989</td><td>-82.626865</td><td>-14.347704</td><td>8.29327</td><td>-11.989759</td><td>-5.914803</td><td>2.911568</td><td>7.05965</td><td>-15.337104</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>22.892002</td><td>23.820341</td></tr><tr><td>&quot;COF&quot;</td><td>2023-12-01</td><td>4.473</td><td>0.100102</td><td>79.19481</td><td>83.36863</td><td>118.350291</td><td>1.490937</td><td>1.904989</td><td>1.691918</td><td>2.071219</td><td>2.377183</td><td>2.179224</td><td>2.33577</td><td>2.38035</td><td>1.920337</td><td>13.623221</td><td>12.290559</td><td>10.354516</td><td>-18.928342</td><td>6.451775</td><td>1.840164</td><td>4.999239</td><td>-7.962509</td><td>73.927906</td><td>63.524201</td><td>56.585566</td><td>53.944884</td><td>1.618222</td><td>4.249577</td><td>0.821107</td><td>-31.287044</td><td>0.069523</td><td>0.102358</td><td>8.418637</td><td>2.892184</td><td>7.291751</td><td>&hellip;</td><td>6.201779</td><td>15.458513</td><td>-9.900331</td><td>13.594549</td><td>4.878672</td><td>-40.217857</td><td>-61.477309</td><td>8.292534</td><td>-43.556555</td><td>-39.875297</td><td>147.389269</td><td>0.647102</td><td>0.073666</td><td>4.17851</td><td>-15.099311</td><td>-13.633616</td><td>174.572894</td><td>79.111456</td><td>66.801725</td><td>-7.155515</td><td>-11.813787</td><td>0.457043</td><td>-3.783664</td><td>-12.315291</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>42.11067</td><td>23.385965</td></tr><tr><td>&quot;SYF&quot;</td><td>2023-12-01</td><td>0.0</td><td>0.032282</td><td>81.370296</td><td>85.047692</td><td>121.291674</td><td>2.363678</td><td>2.085339</td><td>1.774504</td><td>2.111396</td><td>2.466246</td><td>3.454863</td><td>2.556903</td><td>2.426524</td><td>1.992284</td><td>20.951028</td><td>6.61835</td><td>-9.978712</td><td>-24.469745</td><td>3.173753</td><td>1.347444</td><td>-4.726121</td><td>-9.921859</td><td>73.081432</td><td>61.513083</td><td>54.913856</td><td>52.627979</td><td>2.488649</td><td>2.288357</td><td>-0.791306</td><td>-40.446544</td><td>0.114258</td><td>0.119664</td><td>8.418637</td><td>2.892184</td><td>7.291751</td><td>&hellip;</td><td>13.011373</td><td>27.14196</td><td>6.354305</td><td>10.422961</td><td>6.396588</td><td>-34.848764</td><td>-53.506028</td><td>-13.611675</td><td>-48.01339</td><td>-30.982803</td><td>51.897185</td><td>6.813684</td><td>0.830572</td><td>3.498794</td><td>-5.108971</td><td>NaN</td><td>42.821849</td><td>13.318792</td><td>-0.976149</td><td>-22.441955</td><td>-29.947021</td><td>0.989942</td><td>-26.031238</td><td>-102.787876</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>65.502951</td><td>23.014303</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;AAL&quot;</td><td>2023-12-01</td><td>0.128</td><td>0.019622</td><td>121.192182</td><td>136.229551</td><td>179.989976</td><td>2.130366</td><td>2.164551</td><td>1.964599</td><td>2.291691</td><td>3.022287</td><td>3.113844</td><td>2.654027</td><td>2.633728</td><td>2.441464</td><td>16.876123</td><td>-9.141657</td><td>-9.771309</td><td>-26.399095</td><td>-4.223351</td><td>-6.332668</td><td>-4.263799</td><td>-8.734809</td><td>66.708115</td><td>53.833395</td><td>48.468332</td><td>47.945309</td><td>2.004615</td><td>-3.160814</td><td>-0.774859</td><td>-43.635606</td><td>0.067788</td><td>0.047934</td><td>8.418637</td><td>2.892184</td><td>7.291751</td><td>&hellip;</td><td>-4.960762</td><td>-6.625458</td><td>-13.055434</td><td>-19.064436</td><td>18.22398</td><td>201.368909</td><td>151.62421</td><td>-204.63204</td><td>-400.0</td><td>-253.584275</td><td>23.946037</td><td>-46.056212</td><td>-3.599773</td><td>-2.751845</td><td>-11.810363</td><td>109.887695</td><td>12.525916</td><td>185.058654</td><td>246.212166</td><td>-27.370663</td><td>1.386763</td><td>-0.128013</td><td>-34.178259</td><td>44.023427</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>-5.240809</td><td>19.374462</td></tr><tr><td>&quot;LOW&quot;</td><td>2023-12-01</td><td>1.244</td><td>0.270406</td><td>102.923605</td><td>95.139999</td><td>104.875342</td><td>1.567227</td><td>1.47599</td><td>1.337985</td><td>1.506362</td><td>1.811336</td><td>2.290734</td><td>1.80976</td><td>1.731188</td><td>1.463234</td><td>6.602543</td><td>-11.188538</td><td>-4.060223</td><td>-16.627826</td><td>-7.580364</td><td>-2.098156</td><td>-2.695384</td><td>-9.179867</td><td>59.517483</td><td>51.333042</td><td>48.907173</td><td>49.054392</td><td>0.784277</td><td>-3.868543</td><td>-0.321973</td><td>-27.484474</td><td>0.024812</td><td>0.013527</td><td>8.418637</td><td>2.892184</td><td>7.291751</td><td>&hellip;</td><td>-3.634632</td><td>13.123823</td><td>7.506686</td><td>1.094405</td><td>1.683345</td><td>26.977785</td><td>8.727509</td><td>300.0</td><td>0.419273</td><td>2.355695</td><td>-77.187206</td><td>18.133653</td><td>6.459946</td><td>31.431051</td><td>18.768352</td><td>10.611054</td><td>-78.780116</td><td>-23.639894</td><td>-26.199776</td><td>-6.654349</td><td>-6.142085</td><td>-0.926084</td><td>39.398119</td><td>34.219142</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>31.948822</td><td>19.369936</td></tr><tr><td>&quot;ODFL&quot;</td><td>2023-12-01</td><td>15.031</td><td>0.102159</td><td>78.048731</td><td>83.07688</td><td>106.741441</td><td>1.637248</td><td>1.771124</td><td>1.898432</td><td>2.055224</td><td>2.372908</td><td>2.393079</td><td>2.171634</td><td>2.361968</td><td>1.916883</td><td>3.862792</td><td>-4.274995</td><td>34.080172</td><td>14.238245</td><td>-2.413719</td><td>14.182563</td><td>16.582219</td><td>6.000336</td><td>56.4136</td><td>52.769914</td><td>52.791202</td><td>53.097162</td><td>0.458838</td><td>-1.47812</td><td>2.702537</td><td>23.534687</td><td>0.024528</td><td>0.021482</td><td>8.418637</td><td>2.892184</td><td>7.291751</td><td>&hellip;</td><td>-1.394165</td><td>3.564215</td><td>-24.994059</td><td>-62.506561</td><td>-61.895795</td><td>-15.431495</td><td>14.243758</td><td>-21.056422</td><td>0.562567</td><td>-18.437949</td><td>-49.965479</td><td>-13.648609</td><td>-10.465615</td><td>-9.838552</td><td>-31.894388</td><td>37.119427</td><td>-33.292589</td><td>40.765853</td><td>-17.069817</td><td>38.18345</td><td>-11.696012</td><td>1.138231</td><td>-13.490944</td><td>-22.71197</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0.363049</td><td>19.369373</td></tr><tr><td>&quot;EL&quot;</td><td>2023-12-01</td><td>0.0</td><td>0.108882</td><td>138.304037</td><td>126.788343</td><td>142.173117</td><td>3.022277</td><td>3.641767</td><td>2.883243</td><td>2.640049</td><td>2.636021</td><td>4.417503</td><td>4.465291</td><td>3.034078</td><td>2.129431</td><td>26.246292</td><td>-15.736364</td><td>-44.043426</td><td>-60.267414</td><td>-4.321079</td><td>-10.379713</td><td>-16.682809</td><td>-22.863028</td><td>58.581623</td><td>48.344529</td><td>43.635415</td><td>43.05445</td><td>3.117642</td><td>-5.440997</td><td>-3.492617</td><td>-99.617244</td><td>0.058427</td><td>0.000813</td><td>8.418637</td><td>2.892184</td><td>7.291751</td><td>&hellip;</td><td>13.888889</td><td>6.57614</td><td>38.789896</td><td>-5.927411</td><td>-8.360635</td><td>-77.886645</td><td>-82.819607</td><td>-94.997168</td><td>-96.221146</td><td>-74.12199</td><td>-8.153846</td><td>17.619805</td><td>0.50883</td><td>1.541417</td><td>22.484381</td><td>10.383617</td><td>22.073689</td><td>124.768774</td><td>118.758669</td><td>-37.221613</td><td>-20.522372</td><td>1.262027</td><td>-131.41818</td><td>-194.376626</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>-35.60635</td><td>19.366095</td></tr><tr><td>&quot;CAH&quot;</td><td>2023-12-01</td><td>3.895</td><td>0.061041</td><td>93.842394</td><td>88.294118</td><td>69.287598</td><td>1.681645</td><td>1.258619</td><td>1.259142</td><td>1.311377</td><td>1.507579</td><td>2.457972</td><td>1.543235</td><td>1.507101</td><td>1.217853</td><td>14.511649</td><td>22.819488</td><td>33.665967</td><td>131.797544</td><td>18.130571</td><td>21.190345</td><td>25.672228</td><td>87.423305</td><td>71.997111</td><td>68.586735</td><td>63.91184</td><td>61.636099</td><td>1.723753</td><td>7.890054</td><td>2.66969</td><td>217.850864</td><td>0.036145</td><td>0.08883</td><td>8.418637</td><td>2.892184</td><td>7.291751</td><td>&hellip;</td><td>4.501074</td><td>13.469721</td><td>-17.039881</td><td>-0.77995</td><td>-13.551276</td><td>114.154225</td><td>-86.480965</td><td>-88.960611</td><td>-89.548162</td><td>-107.27072</td><td>300.0</td><td>46.716725</td><td>3.728851</td><td>11.608413</td><td>-17.652924</td><td>110.207953</td><td>300.0</td><td>300.0</td><td>400.0</td><td>8.837203</td><td>10.577463</td><td>0.069462</td><td>-14.649537</td><td>26.132243</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>8.349425</td><td>19.319357</td></tr></tbody></table></div>"
+      ],
+      "text/plain": [
+       "shape: (40, 104)\n",
+       "┌──────┬────────────┬────────────┬──────────┬───┬────────────┬────────────┬────────────┬───────────┐\n",
+       "│ tic  ┆ tdq        ┆ insider_ba ┆ mkt_rel  ┆ … ┆ sector_mat ┆ sector_rea ┆ fwd_return ┆ pred      │\n",
+       "│ ---  ┆ ---        ┆ lance      ┆ ---      ┆   ┆ erials     ┆ l_estate   ┆ _4Q        ┆ ---       │\n",
+       "│ str  ┆ date       ┆ ---        ┆ f64      ┆   ┆ ---        ┆ ---        ┆ ---        ┆ f32       │\n",
+       "│      ┆            ┆ f64        ┆          ┆   ┆ i8         ┆ i8         ┆ f64        ┆           │\n",
+       "╞══════╪════════════╪════════════╪══════════╪═══╪════════════╪════════════╪════════════╪═══════════╡\n",
+       "│ SEDG ┆ 2023-12-01 ┆ -1.453     ┆ 0.010806 ┆ … ┆ 0          ┆ 0          ┆ -78.01517  ┆ 25.131428 │\n",
+       "│ TECH ┆ 2023-12-01 ┆ 0.718      ┆ 0.02358  ┆ … ┆ 0          ┆ 0          ┆ 14.382879  ┆ 24.152769 │\n",
+       "│ ROL  ┆ 2023-12-01 ┆ 4592.09    ┆ 0.045666 ┆ … ┆ 0          ┆ 0          ┆ 22.892002  ┆ 23.820341 │\n",
+       "│ COF  ┆ 2023-12-01 ┆ 4.473      ┆ 0.100102 ┆ … ┆ 0          ┆ 0          ┆ 42.11067   ┆ 23.385965 │\n",
+       "│ SYF  ┆ 2023-12-01 ┆ 0.0        ┆ 0.032282 ┆ … ┆ 0          ┆ 0          ┆ 65.502951  ┆ 23.014303 │\n",
+       "│ …    ┆ …          ┆ …          ┆ …        ┆ … ┆ …          ┆ …          ┆ …          ┆ …         │\n",
+       "│ AAL  ┆ 2023-12-01 ┆ 0.128      ┆ 0.019622 ┆ … ┆ 0          ┆ 0          ┆ -5.240809  ┆ 19.374462 │\n",
+       "│ LOW  ┆ 2023-12-01 ┆ 1.244      ┆ 0.270406 ┆ … ┆ 0          ┆ 0          ┆ 31.948822  ┆ 19.369936 │\n",
+       "│ ODFL ┆ 2023-12-01 ┆ 15.031     ┆ 0.102159 ┆ … ┆ 0          ┆ 0          ┆ 0.363049   ┆ 19.369373 │\n",
+       "│ EL   ┆ 2023-12-01 ┆ 0.0        ┆ 0.108882 ┆ … ┆ 0          ┆ 0          ┆ -35.60635  ┆ 19.366095 │\n",
+       "│ CAH  ┆ 2023-12-01 ┆ 3.895      ┆ 0.061041 ┆ … ┆ 0          ┆ 0          ┆ 8.349425   ┆ 19.319357 │\n",
+       "└──────┴────────────┴────────────┴──────────┴───┴────────────┴────────────┴────────────┴───────────┘"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "top"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('low_fear', 68.95872497558594),\n",
+       " ('index_mom', 60.010066986083984),\n",
+       " ('fear_ma30', 58.648406982421875),\n",
+       " ('index_sos', 50.77645492553711),\n",
+       " ('index_qoq', 39.54121017456055),\n",
+       " ('index_yoy', 31.58717918395996),\n",
+       " ('index_2y', 30.91457748413086),\n",
+       " ('vol_sos', 22.91139030456543),\n",
+       " ('pe_2y', 21.368375778198242),\n",
+       " ('sector_energy', 21.35287094116211),\n",
+       " ('sector_financials', 21.060564041137695),\n",
+       " ('size_sec', 20.82137680053711),\n",
+       " ('high_fear', 19.292503356933594),\n",
+       " ('size', 18.038829803466797),\n",
+       " ('rel_vol_2year', 17.877622604370117),\n",
+       " ('momentum_sec', 17.426746368408203),\n",
+       " ('sector_information_technology', 17.424243927001953),\n",
+       " ('size_factor', 17.19580841064453),\n",
+       " ('vol_2y', 17.149049758911133),\n",
+       " ('vol_yoy', 16.560644149780273),\n",
+       " ('price_sec_qoq', 16.171091079711914),\n",
+       " ('momentum_year', 15.34429931640625),\n",
+       " ('price_sec_sos', 15.183145523071289),\n",
+       " ('rel_vol_year', 15.07610034942627),\n",
+       " ('roa_2y', 14.947771072387695),\n",
+       " ('sector_utilities', 14.765291213989258),\n",
+       " ('price_2y', 14.630326271057129),\n",
+       " ('price_sec_yoy', 14.077661514282227),\n",
+       " ('ev_ebitda_yoy', 14.00278377532959),\n",
+       " ('sector_communication_services', 13.90778636932373),\n",
+       " ('rel_vol_quarter', 12.959319114685059),\n",
+       " ('roa_yoy', 12.750650405883789),\n",
+       " ('sector_materials', 12.609297752380371),\n",
+       " ('mkt_rel', 12.439214706420898),\n",
+       " ('ps_yoy', 12.416223526000977),\n",
+       " ('momentum_2year', 12.406303405761719),\n",
+       " ('sector_consumer_discretionary', 12.254286766052246),\n",
+       " ('sector_health_care', 12.081672668457031),\n",
+       " ('vol_qoq', 11.889734268188477),\n",
+       " ('sector_real_estate', 11.819620132446289),\n",
+       " ('saleq_2y', 11.779963493347168),\n",
+       " ('margin_vol', 11.544340133666992),\n",
+       " ('earnings_vol', 11.5333890914917),\n",
+       " ('rel_vol_month', 11.480081558227539),\n",
+       " ('ev_ebitda', 11.449417114257812),\n",
+       " ('ebitdam', 11.137398719787598),\n",
+       " ('price_risk_year', 11.001455307006836),\n",
+       " ('ps', 10.996288299560547),\n",
+       " ('roa_sec', 10.98542594909668),\n",
+       " ('price_yoy', 10.887174606323242),\n",
+       " ('pb', 10.878162384033203),\n",
+       " ('saleq_yoy', 10.843894958496094),\n",
+       " ('roe', 10.806234359741211),\n",
+       " ('gpm_2y', 10.74081039428711),\n",
+       " ('ma20_distance', 10.49564266204834),\n",
+       " ('pe_sec', 10.383129119873047),\n",
+       " ('roa_sec_yoy', 10.344502449035645),\n",
+       " ('price_risk_semester', 10.321557998657227),\n",
+       " ('ma50_distance', 10.289067268371582),\n",
+       " ('price_qoq', 10.284852027893066),\n",
+       " ('gpm', 10.207243919372559),\n",
+       " ('gpm_yoy', 10.194991111755371),\n",
+       " ('price_risk_2year', 10.16705322265625),\n",
+       " ('ltq_2y', 10.158699989318848),\n",
+       " ('roa', 10.03865909576416),\n",
+       " ('atr_yoy', 10.025936126708984),\n",
+       " ('size_yoy', 10.012314796447754),\n",
+       " ('dr_2y', 9.984338760375977),\n",
+       " ('vol_mom', 9.934643745422363),\n",
+       " ('dr_yoy', 9.820383071899414),\n",
+       " ('ltcr_yoy', 9.767158508300781),\n",
+       " ('price_risk_quarter', 9.766225814819336),\n",
+       " ('f_score', 9.741284370422363),\n",
+       " ('ltda_yoy', 9.72859001159668),\n",
+       " ('sector_consumer_staples', 9.63501262664795),\n",
+       " ('rsi_90d', 9.583745956420898),\n",
+       " ('sector_industrials', 9.492080688476562),\n",
+       " ('pe', 9.412749290466309),\n",
+       " ('niq_2y', 9.368951797485352),\n",
+       " ('dlttq_yoy', 9.249247550964355),\n",
+       " ('ltq_yoy', 9.23416519165039),\n",
+       " ('volume_ma20', 9.225096702575684),\n",
+       " ('roa_sec_qoq', 9.060850143432617),\n",
+       " ('fcf_yoy', 9.0366849899292),\n",
+       " ('der_yoy', 8.763117790222168),\n",
+       " ('volume_ma50', 8.590015411376953),\n",
+       " ('roe_yoy', 8.583171844482422),\n",
+       " ('roi_2y', 8.57579517364502),\n",
+       " ('roi_yoy', 8.505375862121582),\n",
+       " ('momentum_quarter', 8.338044166564941),\n",
+       " ('rsi_30d', 8.282906532287598),\n",
+       " ('niq_yoy', 8.214638710021973),\n",
+       " ('momentum_month', 8.213566780090332),\n",
+       " ('rsi_60d', 8.165385246276855),\n",
+       " ('price_mom', 8.070167541503906),\n",
+       " ('rsi_14d', 8.01958179473877),\n",
+       " ('volume_ratio', 7.940553188323975),\n",
+       " ('insider_balance', 7.9282965660095215),\n",
+       " ('earn_drift', 7.75954008102417),\n",
+       " ('pe_yoy', 7.126947402954102)]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.get_importance(\"gain\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('size_sec', 213.0),\n",
+       " ('fear_ma30', 154.0),\n",
+       " ('vol_2y', 149.0),\n",
+       " ('index_2y', 149.0),\n",
+       " ('rel_vol_2year', 140.0),\n",
+       " ('index_mom', 126.0),\n",
+       " ('index_yoy', 126.0),\n",
+       " ('size', 123.0),\n",
+       " ('momentum_sec', 112.0),\n",
+       " ('index_sos', 108.0),\n",
+       " ('margin_vol', 108.0),\n",
+       " ('ev_ebitda', 106.0),\n",
+       " ('price_sec_sos', 104.0),\n",
+       " ('ps', 101.0),\n",
+       " ('dlttq_yoy', 99.0),\n",
+       " ('roa_sec', 95.0),\n",
+       " ('mkt_rel', 92.0),\n",
+       " ('pb', 88.0),\n",
+       " ('ev_ebitda_yoy', 88.0),\n",
+       " ('pe_2y', 81.0),\n",
+       " ('gpm', 79.0),\n",
+       " ('vol_sos', 78.0),\n",
+       " ('momentum_2year', 75.0),\n",
+       " ('saleq_2y', 75.0),\n",
+       " ('momentum_year', 74.0),\n",
+       " ('roa_sec_yoy', 72.0),\n",
+       " ('rel_vol_month', 70.0),\n",
+       " ('gpm_2y', 70.0),\n",
+       " ('roa_2y', 70.0),\n",
+       " ('price_sec_qoq', 69.0),\n",
+       " ('dr_2y', 69.0),\n",
+       " ('rel_vol_year', 68.0),\n",
+       " ('index_qoq', 68.0),\n",
+       " ('ltda_yoy', 68.0),\n",
+       " ('ebitdam', 67.0),\n",
+       " ('pe_sec', 67.0),\n",
+       " ('roa_sec_qoq', 66.0),\n",
+       " ('roe', 62.0),\n",
+       " ('momentum_quarter', 61.0),\n",
+       " ('saleq_yoy', 61.0),\n",
+       " ('rel_vol_quarter', 58.0),\n",
+       " ('earnings_vol', 57.0),\n",
+       " ('ltq_2y', 57.0),\n",
+       " ('atr_yoy', 57.0),\n",
+       " ('ps_yoy', 55.0),\n",
+       " ('vol_qoq', 54.0),\n",
+       " ('vol_yoy', 54.0),\n",
+       " ('price_risk_2year', 54.0),\n",
+       " ('price_2y', 53.0),\n",
+       " ('roa', 53.0),\n",
+       " ('gpm_yoy', 52.0),\n",
+       " ('volume_ma20', 51.0),\n",
+       " ('vol_mom', 51.0),\n",
+       " ('price_risk_semester', 51.0),\n",
+       " ('ltq_yoy', 50.0),\n",
+       " ('size_yoy', 50.0),\n",
+       " ('momentum_month', 49.0),\n",
+       " ('earn_drift', 48.0),\n",
+       " ('ltcr_yoy', 47.0),\n",
+       " ('volume_ma50', 46.0),\n",
+       " ('volume_ratio', 46.0),\n",
+       " ('dr_yoy', 45.0),\n",
+       " ('price_risk_quarter', 43.0),\n",
+       " ('pe', 40.0),\n",
+       " ('niq_2y', 40.0),\n",
+       " ('price_sec_yoy', 39.0),\n",
+       " ('der_yoy', 39.0),\n",
+       " ('insider_balance', 38.0),\n",
+       " ('roi_2y', 38.0),\n",
+       " ('price_risk_year', 37.0),\n",
+       " ('ma20_distance', 36.0),\n",
+       " ('roa_yoy', 36.0),\n",
+       " ('fcf_yoy', 36.0),\n",
+       " ('price_mom', 35.0),\n",
+       " ('price_yoy', 35.0),\n",
+       " ('rsi_14d', 34.0),\n",
+       " ('rsi_90d', 34.0),\n",
+       " ('sector_energy', 34.0),\n",
+       " ('niq_yoy', 31.0),\n",
+       " ('roe_yoy', 29.0),\n",
+       " ('rsi_30d', 28.0),\n",
+       " ('price_qoq', 27.0),\n",
+       " ('roi_yoy', 26.0),\n",
+       " ('pe_yoy', 25.0),\n",
+       " ('rsi_60d', 21.0),\n",
+       " ('high_fear', 18.0),\n",
+       " ('ma50_distance', 17.0),\n",
+       " ('sector_consumer_discretionary', 16.0),\n",
+       " ('sector_information_technology', 16.0),\n",
+       " ('sector_utilities', 14.0),\n",
+       " ('size_factor', 13.0),\n",
+       " ('sector_financials', 13.0),\n",
+       " ('f_score', 12.0),\n",
+       " ('low_fear', 9.0),\n",
+       " ('sector_consumer_staples', 9.0),\n",
+       " ('sector_health_care', 8.0),\n",
+       " ('sector_industrials', 7.0),\n",
+       " ('sector_communication_services', 6.0),\n",
+       " ('sector_materials', 6.0),\n",
+       " ('sector_real_estate', 1.0)]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.get_importance(\"weight\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x1350 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import shap\n",
+    "\n",
+    "\n",
+    "def plot_shap(model, X_train):\n",
+    "    explainer = shap.TreeExplainer(model.model)\n",
+    "    shap_values = explainer(X_train)\n",
+    "\n",
+    "    feature_names = [\n",
+    "        a + \": \" + str(b)\n",
+    "        for a, b in zip(X_train.columns, np.abs(shap_values.values).mean(0).round(2), strict=False)\n",
+    "    ]\n",
+    "\n",
+    "    shap.summary_plot(\n",
+    "        shap_values,\n",
+    "        X_train,\n",
+    "        max_display=X_train.shape[1],\n",
+    "        feature_names=feature_names,\n",
+    "        plot_size=(8, 13.5),\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "plot_shap(model, X_train)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "stocksense",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/report_analysis.ipynb b/notebooks/report_analysis.ipynb
index 974ab6a..e956bb2 100644
--- a/notebooks/report_analysis.ipynb
+++ b/notebooks/report_analysis.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -10,23 +10,22 @@
     "\n",
     "import polars as pl\n",
     "\n",
-    "from stocksense.database_handler import DatabaseHandler\n",
+    "from stocksense.database import DatabaseHandler\n",
     "\n",
-    "REPORT_DIR = Path(\"../reports\")\n",
-    "\n",
-    "DATE = \"2023-06-01\""
+    "DATE = \"2023-12-01\"\n",
+    "REPORT_DIR = Path(\"../reports/scores\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2024-11-25 15:15:00.626\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mstocksense.database_handler.schema\u001b[0m:\u001b[36mcreate_tables\u001b[0m:\u001b[36m122\u001b[0m - \u001b[32m\u001b[1mTables created successfully\u001b[0m\n"
+      "\u001b[32m2024-12-14 16:38:06.574\u001b[0m | \u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36mstocksense.database.schema\u001b[0m:\u001b[36mcreate_tables\u001b[0m:\u001b[36m121\u001b[0m - \u001b[32m\u001b[1mTables created successfully\u001b[0m\n"
      ]
     },
     {
@@ -39,45 +38,61 @@
        "  white-space: pre-wrap;\n",
        "}\n",
        "</style>\n",
-       "<small>shape: (467, 6)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>tic</th><th>score</th><th>name</th><th>sector</th><th>freturn</th><th>adj_freturn</th></tr><tr><td>str</td><td>f64</td><td>str</td><td>str</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;PARA&quot;</td><td>0.762556</td><td>&quot;Paramount Global&quot;</td><td>&quot;Communication Services&quot;</td><td>-21.502723</td><td>-46.531975</td></tr><tr><td>&quot;FICO&quot;</td><td>0.691881</td><td>&quot;Fair Isaac&quot;</td><td>&quot;Information Technology&quot;</td><td>62.615358</td><td>37.586106</td></tr><tr><td>&quot;KEY&quot;</td><td>0.6825847</td><td>&quot;KeyCorp&quot;</td><td>&quot;Financials&quot;</td><td>59.400882</td><td>34.37163</td></tr><tr><td>&quot;FSLR&quot;</td><td>0.668008</td><td>&quot;First Solar&quot;</td><td>&quot;Information Technology&quot;</td><td>30.190669</td><td>5.161417</td></tr><tr><td>&quot;MPWR&quot;</td><td>0.654031</td><td>&quot;Monolithic Power Systems&quot;</td><td>&quot;Information Technology&quot;</td><td>48.461755</td><td>23.432502</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;DUK&quot;</td><td>0.148876</td><td>&quot;Duke Energy&quot;</td><td>&quot;Utilities&quot;</td><td>22.819521</td><td>-2.209732</td></tr><tr><td>&quot;PPL&quot;</td><td>0.1465794</td><td>&quot;PPL Corporation&quot;</td><td>&quot;Utilities&quot;</td><td>16.684222</td><td>-8.34503</td></tr><tr><td>&quot;ED&quot;</td><td>0.137104</td><td>&quot;Consolidated Edison&quot;</td><td>&quot;Utilities&quot;</td><td>6.764106</td><td>-18.265146</td></tr><tr><td>&quot;DTE&quot;</td><td>0.125249</td><td>&quot;DTE Energy&quot;</td><td>&quot;Utilities&quot;</td><td>12.616302</td><td>-12.41295</td></tr><tr><td>&quot;SO&quot;</td><td>0.11732</td><td>&quot;Southern Company&quot;</td><td>&quot;Utilities&quot;</td><td>20.565807</td><td>-4.463445</td></tr></tbody></table></div>"
+       "<small>shape: (461, 16)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>tic</th><th>adj_close</th><th>pe</th><th>saleq_yoy</th><th>fwd_return</th><th>excess_return</th><th>risk_return</th><th>pred_risk_return_3Q</th><th>rank_risk_return_3Q</th><th>pred_risk_return_4Q</th><th>rank_risk_return_4Q</th><th>avg_score</th><th>name</th><th>sector</th><th>date_added</th><th>date_removed</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>i64</td><td>f64</td><td>i64</td><td>f64</td><td>str</td><td>str</td><td>date</td><td>date</td></tr></thead><tbody><tr><td>&quot;SEE&quot;</td><td>33.487507</td><td>15.771561</td><td>-1.328192</td><td>6.042746</td><td>-13.100373</td><td>2.886573</td><td>5.6585712</td><td>9</td><td>9.603384</td><td>8</td><td>8.5</td><td>&quot;Sealed Air&quot;</td><td>&quot;Materials&quot;</td><td>null</td><td>2023-12-18</td></tr><tr><td>&quot;WBA&quot;</td><td>19.217991</td><td>-5.8298</td><td>9.16207</td><td>-38.549164</td><td>-57.692283</td><td>-14.640517</td><td>5.6297507</td><td>11</td><td>9.533597</td><td>18</td><td>14.5</td><td>&quot;Walgreens Boots Alliance&quot;</td><td>&quot;Consumer Staples&quot;</td><td>1979-12-31</td><td>null</td></tr><tr><td>&quot;FMC&quot;</td><td>53.697098</td><td>14.045465</td><td>-28.703166</td><td>8.275038</td><td>-10.868082</td><td>2.731069</td><td>5.662194</td><td>8</td><td>9.511567</td><td>23</td><td>15.5</td><td>&quot;FMC Corporation&quot;</td><td>&quot;Materials&quot;</td><td>2009-08-19</td><td>null</td></tr><tr><td>&quot;PSA&quot;</td><td>255.414917</td><td>23.092956</td><td>5.117858</td><td>16.228237</td><td>-2.914882</td><td>10.844826</td><td>5.594103</td><td>25</td><td>9.599665</td><td>9</td><td>17.0</td><td>&quot;Public Storage&quot;</td><td>&quot;Real Estate&quot;</td><td>2005-08-19</td><td>null</td></tr><tr><td>&quot;LUV&quot;</td><td>26.062613</td><td>31.94777</td><td>4.903537</td><td>3.950928</td><td>-15.192191</td><td>1.546787</td><td>5.621783</td><td>15</td><td>9.521734</td><td>21</td><td>18.0</td><td>&quot;Southwest Airlines&quot;</td><td>&quot;Industrials&quot;</td><td>1994-07-01</td><td>null</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;NOW&quot;</td><td>690.789978</td><td>89.312698</td><td>24.959039</td><td>11.744603</td><td>-7.398516</td><td>5.550115</td><td>4.9748783</td><td>432</td><td>8.223453</td><td>452</td><td>442.0</td><td>&quot;ServiceNow&quot;</td><td>&quot;Information Technology&quot;</td><td>2019-11-21</td><td>null</td></tr><tr><td>&quot;NVDA&quot;</td><td>46.750851</td><td>61.055955</td><td>200.0</td><td>158.87167</td><td>100.0</td><td>52.031133</td><td>4.9722304</td><td>433</td><td>8.100908</td><td>456</td><td>444.5</td><td>&quot;Nvidia&quot;</td><td>&quot;Information Technology&quot;</td><td>2001-11-30</td><td>null</td></tr><tr><td>&quot;ANET&quot;</td><td>216.639999</td><td>35.442916</td><td>28.267736</td><td>55.722429</td><td>36.57931</td><td>22.902473</td><td>4.84744</td><td>448</td><td>8.336229</td><td>443</td><td>445.5</td><td>&quot;Arista Networks&quot;</td><td>&quot;Information Technology&quot;</td><td>2018-08-28</td><td>null</td></tr><tr><td>&quot;LVS&quot;</td><td>45.990719</td><td>53.400266</td><td>178.109453</td><td>-10.031928</td><td>-29.175048</td><td>-5.366387</td><td>4.9277596</td><td>440</td><td>7.871676</td><td>460</td><td>450.0</td><td>&quot;Las Vegas Sands&quot;</td><td>&quot;Consumer Discretionary&quot;</td><td>2019-10-03</td><td>null</td></tr><tr><td>&quot;NTAP&quot;</td><td>89.583244</td><td>27.172948</td><td>-6.073361</td><td>40.742009</td><td>21.59889</td><td>21.461724</td><td>4.8407593</td><td>449</td><td>7.989794</td><td>459</td><td>454.0</td><td>&quot;NetApp&quot;</td><td>&quot;Information Technology&quot;</td><td>1999-06-25</td><td>null</td></tr></tbody></table></div>"
       ],
       "text/plain": [
-       "shape: (467, 6)\n",
-       "┌──────┬───────────┬──────────────────────────┬────────────────────────┬────────────┬─────────────┐\n",
-       "│ tic  ┆ score     ┆ name                     ┆ sector                 ┆ freturn    ┆ adj_freturn │\n",
-       "│ ---  ┆ ---       ┆ ---                      ┆ ---                    ┆ ---        ┆ ---         │\n",
-       "│ str  ┆ f64       ┆ str                      ┆ str                    ┆ f64        ┆ f64         │\n",
-       "╞══════╪═══════════╪══════════════════════════╪════════════════════════╪════════════╪═════════════╡\n",
-       "│ PARA ┆ 0.762556  ┆ Paramount Global         ┆ Communication Services ┆ -21.502723 ┆ -46.531975  │\n",
-       "│ FICO ┆ 0.691881  ┆ Fair Isaac               ┆ Information Technology ┆ 62.615358  ┆ 37.586106   │\n",
-       "│ KEY  ┆ 0.6825847 ┆ KeyCorp                  ┆ Financials             ┆ 59.400882  ┆ 34.37163    │\n",
-       "│ FSLR ┆ 0.668008  ┆ First Solar              ┆ Information Technology ┆ 30.190669  ┆ 5.161417    │\n",
-       "│ MPWR ┆ 0.654031  ┆ Monolithic Power Systems ┆ Information Technology ┆ 48.461755  ┆ 23.432502   │\n",
-       "│ …    ┆ …         ┆ …                        ┆ …                      ┆ …          ┆ …           │\n",
-       "│ DUK  ┆ 0.148876  ┆ Duke Energy              ┆ Utilities              ┆ 22.819521  ┆ -2.209732   │\n",
-       "│ PPL  ┆ 0.1465794 ┆ PPL Corporation          ┆ Utilities              ┆ 16.684222  ┆ -8.34503    │\n",
-       "│ ED   ┆ 0.137104  ┆ Consolidated Edison      ┆ Utilities              ┆ 6.764106   ┆ -18.265146  │\n",
-       "│ DTE  ┆ 0.125249  ┆ DTE Energy               ┆ Utilities              ┆ 12.616302  ┆ -12.41295   │\n",
-       "│ SO   ┆ 0.11732   ┆ Southern Company         ┆ Utilities              ┆ 20.565807  ┆ -4.463445   │\n",
-       "└──────┴───────────┴──────────────────────────┴────────────────────────┴────────────┴─────────────┘"
+       "shape: (461, 16)\n",
+       "┌──────┬────────────┬───────────┬────────────┬───┬────────────┬────────────┬───────────┬───────────┐\n",
+       "│ tic  ┆ adj_close  ┆ pe        ┆ saleq_yoy  ┆ … ┆ name       ┆ sector     ┆ date_adde ┆ date_remo │\n",
+       "│ ---  ┆ ---        ┆ ---       ┆ ---        ┆   ┆ ---        ┆ ---        ┆ d         ┆ ved       │\n",
+       "│ str  ┆ f64        ┆ f64       ┆ f64        ┆   ┆ str        ┆ str        ┆ ---       ┆ ---       │\n",
+       "│      ┆            ┆           ┆            ┆   ┆            ┆            ┆ date      ┆ date      │\n",
+       "╞══════╪════════════╪═══════════╪════════════╪═══╪════════════╪════════════╪═══════════╪═══════════╡\n",
+       "│ SEE  ┆ 33.487507  ┆ 15.771561 ┆ -1.328192  ┆ … ┆ Sealed Air ┆ Materials  ┆ null      ┆ 2023-12-1 │\n",
+       "│      ┆            ┆           ┆            ┆   ┆            ┆            ┆           ┆ 8         │\n",
+       "│ WBA  ┆ 19.217991  ┆ -5.8298   ┆ 9.16207    ┆ … ┆ Walgreens  ┆ Consumer   ┆ 1979-12-3 ┆ null      │\n",
+       "│      ┆            ┆           ┆            ┆   ┆ Boots      ┆ Staples    ┆ 1         ┆           │\n",
+       "│      ┆            ┆           ┆            ┆   ┆ Alliance   ┆            ┆           ┆           │\n",
+       "│ FMC  ┆ 53.697098  ┆ 14.045465 ┆ -28.703166 ┆ … ┆ FMC Corpor ┆ Materials  ┆ 2009-08-1 ┆ null      │\n",
+       "│      ┆            ┆           ┆            ┆   ┆ ation      ┆            ┆ 9         ┆           │\n",
+       "│ PSA  ┆ 255.414917 ┆ 23.092956 ┆ 5.117858   ┆ … ┆ Public     ┆ Real       ┆ 2005-08-1 ┆ null      │\n",
+       "│      ┆            ┆           ┆            ┆   ┆ Storage    ┆ Estate     ┆ 9         ┆           │\n",
+       "│ LUV  ┆ 26.062613  ┆ 31.94777  ┆ 4.903537   ┆ … ┆ Southwest  ┆ Industrial ┆ 1994-07-0 ┆ null      │\n",
+       "│      ┆            ┆           ┆            ┆   ┆ Airlines   ┆ s          ┆ 1         ┆           │\n",
+       "│ …    ┆ …          ┆ …         ┆ …          ┆ … ┆ …          ┆ …          ┆ …         ┆ …         │\n",
+       "│ NOW  ┆ 690.789978 ┆ 89.312698 ┆ 24.959039  ┆ … ┆ ServiceNow ┆ Informatio ┆ 2019-11-2 ┆ null      │\n",
+       "│      ┆            ┆           ┆            ┆   ┆            ┆ n          ┆ 1         ┆           │\n",
+       "│      ┆            ┆           ┆            ┆   ┆            ┆ Technology ┆           ┆           │\n",
+       "│ NVDA ┆ 46.750851  ┆ 61.055955 ┆ 200.0      ┆ … ┆ Nvidia     ┆ Informatio ┆ 2001-11-3 ┆ null      │\n",
+       "│      ┆            ┆           ┆            ┆   ┆            ┆ n          ┆ 0         ┆           │\n",
+       "│      ┆            ┆           ┆            ┆   ┆            ┆ Technology ┆           ┆           │\n",
+       "│ ANET ┆ 216.639999 ┆ 35.442916 ┆ 28.267736  ┆ … ┆ Arista     ┆ Informatio ┆ 2018-08-2 ┆ null      │\n",
+       "│      ┆            ┆           ┆            ┆   ┆ Networks   ┆ n          ┆ 8         ┆           │\n",
+       "│      ┆            ┆           ┆            ┆   ┆            ┆ Technology ┆           ┆           │\n",
+       "│ LVS  ┆ 45.990719  ┆ 53.400266 ┆ 178.109453 ┆ … ┆ Las Vegas  ┆ Consumer   ┆ 2019-10-0 ┆ null      │\n",
+       "│      ┆            ┆           ┆            ┆   ┆ Sands      ┆ Discretion ┆ 3         ┆           │\n",
+       "│      ┆            ┆           ┆            ┆   ┆            ┆ ary        ┆           ┆           │\n",
+       "│ NTAP ┆ 89.583244  ┆ 27.172948 ┆ -6.073361  ┆ … ┆ NetApp     ┆ Informatio ┆ 1999-06-2 ┆ null      │\n",
+       "│      ┆            ┆           ┆            ┆   ┆            ┆ n          ┆ 5         ┆           │\n",
+       "│      ┆            ┆           ┆            ┆   ┆            ┆ Technology ┆           ┆           │\n",
+       "└──────┴────────────┴───────────┴────────────┴───┴────────────┴────────────┴───────────┴───────────┘"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "stock_info = DatabaseHandler().fetch_stock()\n",
-    "df = pl.read_csv(REPORT_DIR / f\"report_{DATE}.csv\")\n",
+    "df = pl.read_csv(REPORT_DIR / f\"scores_{DATE}.csv\")\n",
     "df = df.join(stock_info, on=\"tic\", how=\"left\")\n",
-    "df = df.select(pl.col(\"tic\", \"score\", \"name\", \"sector\", \"freturn\", \"adj_freturn\"))\n",
     "df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -85,46 +100,52 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "DATE 2023-06-01\n",
-      "\n",
-      "Top 15 stocks:\n",
-      "Average freturn: 33.63%\n",
-      "Average adj_freturn: 8.60%\n",
-      "\n",
-      "Bottom 15 stocks:\n",
-      "Average freturn: 7.26%\n",
-      "Average adj_freturn: -17.77%\n"
+      "DATE 2023-12-01\n",
+      "Average top return: 4.36% (70.0)\n",
+      "Average bottom return: 10.92% (82.5)\n"
      ]
     }
    ],
    "source": [
-    "n = 15\n",
+    "n = 40\n",
     "top = df.head(n)\n",
     "bottom = df.tail(n)\n",
     "\n",
-    "# Calculate average returns for top stocks\n",
-    "top_freturn = top.select(pl.col(\"freturn\")).mean().item()\n",
-    "top_adj_freturn = top.select(pl.col(\"adj_freturn\")).mean().item()\n",
+    "top_freturn = top.select(pl.col(\"risk_return\")).mean().item()\n",
+    "bottom_freturn = bottom.select(pl.col(\"risk_return\")).mean().item()\n",
+    "\n",
+    "# Calculate hit rates (% of stocks with positive returns)\n",
+    "top_hits = top.select(pl.col(\"risk_return\") > 0).sum().item()\n",
+    "bottom_hits = bottom.select(pl.col(\"risk_return\") > 0).sum().item()\n",
+    "\n",
+    "top_hitrate = (top_hits / n) * 100\n",
+    "bottom_hitrate = (bottom_hits / n) * 100\n",
     "\n",
-    "# Calculate average returns for bottom stocks\n",
-    "bottom_freturn = bottom.select(pl.col(\"freturn\")).mean().item()\n",
-    "bottom_adj_freturn = bottom.select(pl.col(\"adj_freturn\")).mean().item()\n",
     "\n",
     "print(f\"\\nDATE {DATE}\")\n",
-    "print(f\"\\nTop {n} stocks:\")\n",
-    "print(f\"Average freturn: {top_freturn:.2f}%\")\n",
-    "print(f\"Average adj_freturn: {top_adj_freturn:.2f}%\")\n",
-    "print(f\"\\nBottom {n} stocks:\")\n",
-    "print(f\"Average freturn: {bottom_freturn:.2f}%\")\n",
-    "print(f\"Average adj_freturn: {bottom_adj_freturn:.2f}%\")"
+    "print(f\"Average top return: {top_freturn:.2f}% ({top_hitrate:.1f})\")\n",
+    "print(f\"Average bottom return: {bottom_freturn:.2f}% ({bottom_hitrate:.1f})\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.345"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "round(0.34539, 3)"
+   ]
   }
  ],
  "metadata": {
diff --git a/pyproject.toml b/pyproject.toml
index 70a6abf..f9bccee 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -31,7 +31,8 @@ dependencies = [
     "ipykernel",
     "shap",
     "pre-commit",
-    "pydantic"
+    "pydantic",
+    "openpyxl"
 ]
 
 [project.optional-dependencies]
diff --git a/stocksense/__init__.py b/stocksense/__init__.py
index e69de29..b54df3b 100644
--- a/stocksense/__init__.py
+++ b/stocksense/__init__.py
@@ -0,0 +1,9 @@
+"""Stocksense package for stock selection"""
+
+try:
+    from importlib.metadata import version
+
+    __version__ = version("stocksense")
+except ImportError:
+    # Package is not installed
+    __version__ = "1.0.0"
diff --git a/stocksense/app/pages/analytics.py b/stocksense/app/pages/analytics.py
index dc9fa79..5ad5b62 100644
--- a/stocksense/app/pages/analytics.py
+++ b/stocksense/app/pages/analytics.py
@@ -4,7 +4,7 @@
 import streamlit as st
 from plotly.subplots import make_subplots
 
-from stocksense.database_handler import DatabaseHandler
+from stocksense.database import DatabaseHandler
 from stocksense.pipeline import clean, engineer_features
 
 pd.options.mode.chained_assignment = None  # default='warn'
@@ -16,12 +16,7 @@
 def list_stocks():
     db = DatabaseHandler()
     stocks = db.fetch_stock().to_pandas()
-    return sorted(
-        stocks.loc[
-            stocks.spx_status == 1,  # noqa: E712
-            "tic",
-        ].values.tolist()
-    )
+    return sorted(stocks.loc[stocks.date_removed.isnull()]["tic"].values.tolist())
 
 
 def date_breaks(df, date_col="date"):
@@ -234,7 +229,7 @@ def plot_processed_data(df):
 
 def main():
     """
-    Main app script.
+    Main analytics script.
     """
 
     st.set_page_config(layout="wide", page_title="Stock Data Analytics", page_icon="📈")
@@ -280,7 +275,7 @@ def main():
     st.session_state.page_subheader = f"{name} ({ticker})"
 
     st.subheader(st.session_state.page_subheader)
-    st.markdown(f"**Last update**: {stock.loc[0, 'last_update']}")
+    st.markdown(f"**Last update**: {max_date}")
 
     tab1, tab2, tab3, tab4, tab5 = st.tabs(
         ["Status", "Market", "Financials", "Insider Trading", "Feature Analysis"]
diff --git a/stocksense/app/pages/insights.py b/stocksense/app/pages/insights.py
index e69de29..e98763a 100644
--- a/stocksense/app/pages/insights.py
+++ b/stocksense/app/pages/insights.py
@@ -0,0 +1,114 @@
+import datetime as dt
+from pathlib import Path
+
+import pandas as pd
+import plotly.express as px
+import streamlit as st
+
+from stocksense.database import DatabaseHandler
+
+REPORTS_DIR = Path(__file__).parents[3] / "reports"
+SCORES_DIR = REPORTS_DIR / "scores"
+PORTFOLIOS_DIR = REPORTS_DIR / "portfolios"
+
+
+@st.cache_data(show_spinner="Loading stock data...", max_entries=10)
+def load_stock_data():
+    db = DatabaseHandler()
+    return db.fetch_stock().to_pandas()
+
+
+def get_available_dates():
+    """
+    Get all available trade dates from score files.
+    """
+    score_files = list(SCORES_DIR.glob("scores_*.csv"))
+    dates = [dt.datetime.strptime(f.stem.split("_")[1], "%Y-%m-%d").date() for f in score_files]
+    return sorted(dates, reverse=True)
+
+
+def load_scores(trade_date):
+    """
+    Load scores for a specific trade date.
+    """
+    score_file = SCORES_DIR / f"scores_{trade_date}.csv"
+    if not score_file.exists():
+        st.error(f"No scores found for trade date {trade_date}")
+        return None
+    return pd.read_csv(score_file)
+
+
+def plot_sector_distribution(portfolio_data):
+    """
+    Plot sector distribution of selected stocks.
+    """
+    sector_dist = portfolio_data.groupby("sector")["weight"].sum().reset_index()
+    fig = px.pie(
+        sector_dist,
+        values="weight",
+        names="sector",
+        title="Sector Distribution",
+        template="plotly_dark",
+    )
+    fig.update_traces(textposition="inside", textinfo="percent+label")
+    st.plotly_chart(fig, use_container_width=True)
+
+
+def display_portfolio_metrics(portfolio_data):
+    """
+    Display key portfolio metrics.
+    """
+    total_stocks = len(portfolio_data)
+    avg_score = portfolio_data["pred"].mean()
+    avg_price = portfolio_data["adj_close"].mean()
+
+    col1, col2, col3 = st.columns(3)
+    with col1:
+        st.metric("Number of Stocks", total_stocks)
+    with col2:
+        st.metric("Average Model Score", f"{avg_score:.3f}")
+    with col3:
+        st.metric("Average Stock Price", f"${avg_price:.2f}")
+
+
+def main():
+    """Insights main script."""
+
+    st.set_page_config(layout="wide", page_title="Stock Picks", page_icon="🔮")
+    st.sidebar.title("Stocksense App")
+    st.sidebar.success("Select page")
+
+    st.sidebar.page_link("home.py", label="Home", icon="🏠")
+    st.sidebar.page_link("pages/overview.py", label="Market Overview", icon="🌎")
+    st.sidebar.page_link("pages/analytics.py", label="Stock Analytics", icon="📈")
+    st.sidebar.page_link("pages/insights.py", label="Stock Picks", icon="🔮")
+    st.sidebar.divider()
+
+    st.title("Stock Picks Insights")
+
+    stock_data = load_stock_data()
+    available_dates = get_available_dates()
+    trade_date = st.selectbox("Select Trade Date", available_dates)
+    scores = load_scores(trade_date)
+    scores = scores.join(stock_data, on="tic", rsuffix="_stock")
+
+    if scores is not None:
+        display_portfolio_metrics(scores)
+        plot_sector_distribution(scores)
+
+        st.subheader("Top 30 Selected Stocks")
+        columns_to_display = ["symbol", "company_name", "sector", "pred", "adj_close", "weight"]
+        formatted_scores = scores[columns_to_display].head(30)
+
+        formatted_scores["pred"] = formatted_scores["pred"].round(3)
+        formatted_scores["adj_close"] = formatted_scores["adj_close"].round(2)
+        formatted_scores["weight"] = (formatted_scores["weight"] * 100).round(2).astype(str) + "%"
+        formatted_scores.columns = ["Symbol", "Company", "Sector", "Score", "Price ($)", "Weight"]
+
+        st.dataframe(formatted_scores, use_container_width=True)
+    else:
+        st.warning("No data available for the selected date.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/stocksense/app/pages/overview.py b/stocksense/app/pages/overview.py
index 810abcf..f895737 100644
--- a/stocksense/app/pages/overview.py
+++ b/stocksense/app/pages/overview.py
@@ -3,7 +3,7 @@
 import plotly.express as px
 import streamlit as st
 
-from stocksense.database_handler import DatabaseHandler
+from stocksense.database import DatabaseHandler
 
 pd.set_option("future.no_silent_downcasting", True)
 
@@ -20,7 +20,7 @@ def load_sp500_data():
     """
     db = DatabaseHandler()
     stocks = db.fetch_stock().to_pandas()
-    stocks = stocks.loc[stocks.spx_status == 1]
+    stocks = stocks.loc[stocks.date_removed.isnull()]
 
     info = db.fetch_info().to_pandas()
     stock_df = stocks.merge(info, how="left", on="tic")
@@ -60,7 +60,7 @@ def show_recent_earnings(data):
     data : pd.DataFrame
         Processed S&P 500 data.
     """
-    df = data.sort_values("rdq", ascending=False).head(10)
+    df = data.sort_values("rdq", ascending=False).head(15)
     df = df[["tic", "rdq", "sector", "curr_price", "saleq", "surprise_pct"]]
     st.dataframe(
         df,
@@ -108,7 +108,7 @@ def show_market_summary(data):
     with col2:
         st.metric("Total Market Cap", summary["Total Market Cap"])
     with col3:
-        st.metric("Average P/E", summary["Average P/E"])
+        st.metric("Average Trailing P/E", summary["Average P/E"])
     with col4:
         st.metric("Avg Target Upside", summary["Avg Target Upside"])
 
diff --git a/stocksense/config/defaults/model_config.yml b/stocksense/config/defaults/model_config.yml
index da39e93..38f7670 100644
--- a/stocksense/config/defaults/model_config.yml
+++ b/stocksense/config/defaults/model_config.yml
@@ -1,30 +1,47 @@
 'features':
     # insider trading features
-    - 'n_purch'
-    - 'n_sales'
     - 'insider_balance'
-    # market momentum and volatility features
+    # volume features
+    - 'mkt_rel'
     - 'volume_ma20'
     - 'volume_ma50'
+    - 'volume_ratio'
+    # volatility features
+    - 'vol_mom'
+    - 'vol_qoq'
+    - 'vol_sos'
+    - 'vol_yoy'
+    - 'vol_2y'
+    - 'rel_vol_month'
+    - 'rel_vol_quarter'
+    - 'rel_vol_year'
+    - 'rel_vol_2year'
+    # price growth features
     - 'price_mom'
     - 'price_qoq'
     - 'price_yoy'
     - 'price_2y'
+    - 'price_risk_quarter'
+    - 'price_risk_semester'
+    - 'price_risk_year'
+    - 'price_risk_2year'
+    # technical features
     - 'rsi_14d'
     - 'rsi_30d'
     - 'rsi_60d'
     - 'rsi_90d'
-    - 'rsi_1y'
-    - 'vol_mom'
-    - 'vol_qoq'
-    - 'vol_yoy'
-    - 'rel_vol_mom'
-    - 'rel_vol_qoq'
-    - 'rel_vol_yoy'
-    - 'momentum_mom'
-    - 'momentum_qoq'
-    - 'momentum_yoy'
-    - 'momentum_2y'
+    - 'momentum_month'
+    - 'momentum_quarter'
+    - 'momentum_year'
+    - 'momentum_2year'
+    - 'ma20_distance'
+    - 'ma50_distance'
+    # market trend features
+    - 'index_mom'
+    - 'index_qoq'
+    - 'index_sos'
+    - 'index_yoy'
+    - 'index_2y'
     - 'fear_ma30'
     - 'high_fear'
     - 'low_fear'
@@ -34,13 +51,23 @@
     - 'roe'
     - 'ebitdam'
     - 'size'
-    # hybrid
+    - 'earnings_vol'
+    - 'margin_vol'
+    # price - fundamentals
     - 'earn_drift'
     - 'pe'
     - 'pb'
     - 'ps'
     - 'ev_ebitda'
     - 'f_score'
+    # sector features
+    - 'pe_sec'
+    - 'momentum_sec_yoy'
+    - 'momentum_sec_qoq'
+    - 'size_sec'
+    - 'roa_sec'
+    - 'size_factor'
+    - 'mkt_rel_sec'
     # growth features
     - 'saleq_yoy'
     - 'saleq_2y'
@@ -57,8 +84,6 @@
     - 'roi_2y'
     - 'roe_yoy'
     - 'fcf_yoy'
-    - 'cr_yoy'
-    - 'qr_yoy'
     - 'der_yoy'
     - 'dr_yoy'
     - 'dr_2y'
@@ -67,9 +92,11 @@
     - 'ltcr_yoy'
     - 'pe_yoy'
     - 'pe_2y'
-    - 'pb_yoy'
     - 'ps_yoy'
     - 'atr_yoy'
+    - 'size_yoy'
+    - 'roa_sec_qoq'
+    - 'roa_sec_yoy'
     # sector dummies
     - 'sector_utilities'
     - 'sector_health_care'
@@ -82,31 +109,36 @@
     - 'sector_communication_services'
     - 'sector_materials'
     - 'sector_real_estate'
-'target': 'fperf'
+'targets':
+    - 'risk_return_3Q_hit'
+    - 'risk_return_4Q_hit'
+    - 'fwd_return_3Q_hit'
+    - 'fwd_return_4Q_hit'
 'id_col': 'tic'
 'date_col': 'tdq'
-'min_train_years': 13
+'min_train_years': 7
+'max_splits': 3
 'ga':
-    'num_generations': 100
+    'num_generations': 50
     'num_parents_mating': 10
-    'sol_per_pop': 100
+    'sol_per_pop': 50
     'num_genes': 9
-    'mutation_percent_genes': 10
+    'mutation_percent_genes': 15
     'crossover_probability': 0.8
     'parent_selection_type': "tournament"
-    'keep_parents': 5
+    'keep_parents': 6
     'mutation_type': "random"
     'crossover_type': "single_point"
-    'init_range_low': [0.001, 50, 2, 1, 0, 0.5, 0.5, 0, 0]
-    'init_range_high': [0.4, 500, 10, 10, 5, 1, 1, 6, 6]
+    'init_range_low': [0.001, 50, 3, 2, 0.0, 0.5, 0.5, 0.0, 0.0]
+    'init_range_high': [0.1, 200, 8, 7, 0.5, 0.9, 0.9, 10.0, 10.0]
     'gene_space': [
-        {"low": 0.001, "high": 0.4},
-        {"low": 50, "high": 500},
-        {"low": 2, "high": 10},
-        {"low": 1, "high": 10},
-        {"low": 0, "high": 5},
-        {"low": 0.5, "high": 1},
-        {"low": 0.5, "high": 1},
-        {"low": 0, "high": 6},
-        {"low": 0, "high": 6},
+        {"low": 0.001, "high": 0.1},
+        {"low": 50, "high": 200},
+        {"low": 3, "high": 8},
+        {"low": 2, "high": 7},
+        {"low": 0.0 , "high": 0.5},
+        {"low": 0.5, "high": 0.9},
+        {"low": 0.5, "high": 0.9},
+        {"low": 0.0, "high": 10.0},
+        {"low": 0.0, "high": 10.0},
     ]
diff --git a/stocksense/config/defaults/processing_config.yml b/stocksense/config/defaults/processing_config.yml
index 742a491..d339492 100644
--- a/stocksense/config/defaults/processing_config.yml
+++ b/stocksense/config/defaults/processing_config.yml
@@ -2,6 +2,7 @@
 'month_trading_days': 21
 'quarter_trading_days': 61
 'semester_trading_days': 126
+'third_quarter_trading_days': 183
 'year_trading_days': 252
 'two_year_trading_days': 504
 'prediction_horizon': 4 # quarters
diff --git a/stocksense/config/manager.py b/stocksense/config/manager.py
index 8dd2185..c8bd535 100644
--- a/stocksense/config/manager.py
+++ b/stocksense/config/manager.py
@@ -1,4 +1,5 @@
 from datetime import datetime
+from enum import Enum
 from pathlib import Path
 from typing import Any, Dict, List
 
@@ -11,6 +12,20 @@
 )
 
 
+class Sector(str, Enum):
+    HEALTH_CARE = "Health Care"
+    FINANCIALS = "Financials"
+    INDUSTRIALS = "Industrials"
+    CONSUMER_DISCRETIONARY = "Consumer Discretionary"
+    INFORMATION_TECHNOLOGY = "Information Technology"
+    COMMUNICATION_SERVICES = "Communication Services"
+    CONSUMER_STAPLES = "Consumer Staples"
+    UTILITIES = "Utilities"
+    REAL_ESTATE = "Real Estate"
+    MATERIALS = "Materials"
+    ENERGY = "Energy"
+
+
 class ScrapingConfig(BaseModel):
     base_date: str = Field(description="Starting date for data collection in YYYY-MM-DD format")
     crsp_columns: List[str] = Field(min_length=1)
@@ -25,27 +40,28 @@ def start_date(self) -> datetime:
 
 
 class ProcessingConfig(BaseModel):
-    trading_days_2week: int = Field(alias="two_week_trading_days")
-    trading_days_month: int = Field(alias="month_trading_days")
-    trading_days_quarter: int = Field(alias="quarter_trading_days")
-    trading_days_semester: int = Field(alias="semester_trading_days")
-    trading_days_year: int = Field(alias="year_trading_days")
-    trading_days_2year: int = Field(alias="two_year_trading_days")
+    trade_days_2week: int = Field(alias="two_week_trading_days")
+    trade_days_month: int = Field(alias="month_trading_days")
+    trade_days_quarter: int = Field(alias="quarter_trading_days")
+    trade_days_semester: int = Field(alias="semester_trading_days")
+    trade_days_third_quarter: int = Field(alias="third_quarter_trading_days")
+    trade_days_year: int = Field(alias="year_trading_days")
+    trade_days_2year: int = Field(alias="two_year_trading_days")
     prediction_horizon: int = Field(gt=0)
     over_performance_threshold: float = Field(lt=1.0, description="Overperformance threshold")
     performance_threshold: float = Field(gt=0.0, lt=1.0, description="Performance threshold")
-    sectors: List[str] = Field(min_length=11, max_length=11)
+    sectors: List[Sector] = Field(min_length=11, max_length=11)
 
     @model_validator(mode="after")
     def validate_trading_days_order(self) -> "ProcessingConfig":
         """Validate that trading days are in ascending order."""
         if not (
-            self.trading_days_2week
-            < self.trading_days_month
-            < self.trading_days_quarter
-            < self.trading_days_semester
-            < self.trading_days_year
-            < self.trading_days_2year
+            self.trade_days_2week
+            < self.trade_days_month
+            < self.trade_days_quarter
+            < self.trade_days_semester
+            < self.trade_days_year
+            < self.trade_days_2year
         ):
             raise ValueError("Trading days must be in ascending order")
         return self
@@ -54,12 +70,12 @@ def validate_trading_days_order(self) -> "ProcessingConfig":
     def trading_days(self) -> Dict[str, int]:
         """Get all trading day periods in a dictionary."""
         return {
-            "2week": self.trading_days_2week,
-            "month": self.trading_days_month,
-            "quarter": self.trading_days_quarter,
-            "semester": self.trading_days_semester,
-            "year": self.trading_days_year,
-            "2year": self.trading_days_2year,
+            "2week": self.trade_days_2week,
+            "month": self.trade_days_month,
+            "quarter": self.trade_days_quarter,
+            "semester": self.trade_days_semester,
+            "year": self.trade_days_year,
+            "2year": self.trade_days_2year,
         }
 
     @property
@@ -70,19 +86,27 @@ def performance_thresholds(self) -> Dict[str, float]:
             "performance": self.performance_threshold,
         }
 
+    @model_validator(mode="after")
+    def validate_sectors_completeness(self) -> "ProcessingConfig":
+        """Validate that all sectors are present."""
+        if set(self.sectors) != set(Sector):
+            missing = set(Sector) - set(self.sectors)
+            raise ValueError(f"Missing sectors: {missing}")
+        return self
+
 
 class ModelConfig(BaseModel):
     features: List[str]
-    target: str
+    targets: List[str]
     id_col: str
     date_col: str
-    min_train_years: int = Field(ge=10, le=20)
+    min_train_years: int = Field(ge=5, le=50)
     ga: Dict[str, Any]
 
     @model_validator(mode="after")
     def validate_column_names(self) -> "ModelConfig":
         """Validate that target, id_col and date_col are not in features."""
-        special_cols = [self.target, self.id_col, self.date_col]
+        special_cols = [self.targets] + [self.id_col, self.date_col]
         if any(col in self.features for col in special_cols):
             raise ValueError("features list cannot contain target, id_col or date_col")
         return self
diff --git a/stocksense/database_handler/__init__.py b/stocksense/database/__init__.py
similarity index 100%
rename from stocksense/database_handler/__init__.py
rename to stocksense/database/__init__.py
diff --git a/stocksense/database_handler/connection.py b/stocksense/database/connection.py
similarity index 100%
rename from stocksense/database_handler/connection.py
rename to stocksense/database/connection.py
diff --git a/stocksense/database_handler/handler.py b/stocksense/database/handler.py
similarity index 100%
rename from stocksense/database_handler/handler.py
rename to stocksense/database/handler.py
diff --git a/stocksense/database_handler/queries.py b/stocksense/database/queries.py
similarity index 100%
rename from stocksense/database_handler/queries.py
rename to stocksense/database/queries.py
diff --git a/stocksense/database_handler/schema.py b/stocksense/database/schema.py
similarity index 100%
rename from stocksense/database_handler/schema.py
rename to stocksense/database/schema.py
diff --git a/stocksense/main.py b/stocksense/main.py
index d1fc1a7..a514c6d 100644
--- a/stocksense/main.py
+++ b/stocksense/main.py
@@ -1,49 +1,103 @@
+from datetime import datetime
+from typing import Optional
+
 import click
+import polars as pl
 
+from stocksense import __version__
 from stocksense.config import config
-from stocksense.database_handler import DatabaseHandler
-from stocksense.model import ModelHandler
+from stocksense.database import DatabaseHandler
+from stocksense.model import ModelHandler, PortfolioBuilder
 from stocksense.pipeline import ETL, clean, engineer_features
 
 
-def prepare_data():
+def validate_trade_date(ctx, param, value: Optional[datetime]) -> Optional[datetime]:
+    """Validate that trade date is 1st of Mar/Jun/Sep/Dec."""
+    if value is None:
+        return value
+    valid_months = {3, 6, 9, 12}
+    if value.day != 1 or value.month not in valid_months:
+        raise click.BadParameter("Trade date must be the 1st of March, June, September or December")
+    return value
+
+
+def prepare_data() -> pl.DataFrame:
     """Prepare data for model operations."""
     data = engineer_features()
-    return clean(data)
+    data = clean(data)
+    return data
+
+
+@click.group()
+@click.version_option(version=__version__, prog_name="stocksense")
+def cli():
+    """Stocksense CLI - Stock analytics and portfolio management tool."""
+    pass
+
 
+@cli.command()
+def update():
+    """Update stock database with latest market data."""
+    etl_handler = ETL(config)
+    etl_handler.extract()
 
-@click.command()
-@click.option("-u", "--update", is_flag=True, help="Update stock data.")
-@click.option("-t", "--train", is_flag=True, help="Train model.")
-@click.option("-s", "--score", is_flag=True, help="Score stocks.")
-@click.option("-f", "--force", is_flag=True, default=False, help="Force model retraining.")
+
+@cli.command()
 @click.option(
     "-tdq",
     "--trade-date",
     type=click.DateTime(formats=["%Y-%m-%d"]),
-    help=(
-        "Trade date for model operations (format: YYYY-MM-DD)."
-        "Must be the 1st of March, June, September or December."
-    ),
+    callback=validate_trade_date,
+    required=True,
+    help="Trade date (YYYY-MM-DD). Must be 1st of Mar/Jun/Sep/Dec.",
 )
-def main(update, train, score, force, trade_date):
-    """
-    CLI handling.
-    """
-
-    if update:
-        etl_handler = ETL(config)
-        etl_handler.update_index_listings()
-        etl_handler.extract()
-
-    if any([train, score]):
-        data = prepare_data()
-        constituents = DatabaseHandler().fetch_constituents(trade_date)
-        handler = ModelHandler(trade_date)
-        if train:
-            handler.train(data, force)
-        if score:
-            handler.score(data, constituents)
+@click.option("-f", "--force", is_flag=True, help="Force model retraining even if model exists.")
+def train(trade_date: datetime, force: bool):
+    """Train the prediction model for a specific trade date."""
+    data = prepare_data()
+    handler = ModelHandler(trade_date)
+    handler.train(data, force)
+
+
+@cli.command()
+@click.option(
+    "-tdq",
+    "--trade-date",
+    type=click.DateTime(formats=["%Y-%m-%d"]),
+    callback=validate_trade_date,
+    required=True,
+    help="Trade date (YYYY-MM-DD). Must be 1st of Mar/Jun/Sep/Dec.",
+)
+@click.option(
+    "-w",
+    "--weighting",
+    type=click.Choice(["market_cap", "equal"], case_sensitive=False),
+    default="market_cap",
+    help="Portfolio weighting strategy.",
+)
+@click.option(
+    "-n",
+    "--n-stocks",
+    type=int,
+    default=30,
+    help="Number of stocks to include in the portfolio.",
+)
+def portfolio(trade_date: datetime, weighting: str, n_stocks: int):
+    """Build investment portfolio for a specific trade date."""
+
+    data = prepare_data()
+    constituents = DatabaseHandler().fetch_constituents(trade_date)
+
+    handler = ModelHandler(trade_date)
+    ranks = handler.score(data, constituents)
+
+    portfolio = PortfolioBuilder(weighting)
+    portfolio.build_portfolio(n_stocks, trade_date, ranks)
+
+
+def main():
+    """CLI entry point."""
+    cli(prog_name="stocksense")
 
 
 if __name__ == "__main__":
diff --git a/stocksense/model/__init__.py b/stocksense/model/__init__.py
index dde66b5..7048164 100644
--- a/stocksense/model/__init__.py
+++ b/stocksense/model/__init__.py
@@ -1,4 +1,5 @@
 from .model_handler import ModelHandler
-from .xgboost_model import XGBoostModel
+from .portfolio import PortfolioBuilder
+from .xgboost_model import XGBoostClassifier, XGBoostRegressor
 
-__all__ = ["XGBoostModel", "ModelHandler"]
+__all__ = ["XGBoostRegressor", "XGBoostClassifier", "ModelHandler", "PortfolioBuilder"]
diff --git a/stocksense/model/genetic_algorithm.py b/stocksense/model/genetic_algorithm.py
index 1e3442b..d2b810f 100644
--- a/stocksense/model/genetic_algorithm.py
+++ b/stocksense/model/genetic_algorithm.py
@@ -1,13 +1,28 @@
+from typing import Callable
+
 import numpy as np
 import polars as pl
 import pygad
 from loguru import logger
 
-from .xgboost_model import XGBoostModel
+from .xgboost_model import XGBoostClassifier
 
 
 class GeneticAlgorithm:
-    def __init__(self, ga_settings, fitness_func):
+    """
+    Genetic Algorithm implementation for hyperparameter optimization.
+
+    Attributes
+    ----------
+    ga_instance : Optional[pygad.GA]
+        The PyGAD genetic algorithm instance
+    best_fitness_value : float
+        Best fitness value achieved during training
+    """
+
+    def __init__(
+        self, ga_settings: dict, fitness_func: Callable[[pygad.GA, list[float], int], float]
+    ):
         self.num_generations = ga_settings["num_generations"]
         self.num_parents_mating = ga_settings["num_parents_mating"]
         self.sol_per_pop = ga_settings["sol_per_pop"]
@@ -30,7 +45,7 @@ def __init__(self, ga_settings, fitness_func):
         self.no_improv_limit = 5
 
     def create_instance(self):
-        logger.info("creating GA instance")
+        logger.info("Creating GA instance")
         self.ga_instance = pygad.GA(
             num_generations=self.num_generations,
             num_parents_mating=self.num_parents_mating,
@@ -47,18 +62,23 @@ def create_instance(self):
             mutation_type=self.mutation_type,
             crossover_type=self.crossover_type,
             on_generation=self.on_generation,
-            parallel_processing=["thread", 4],
+            parallel_processing=None,
         )
 
-    def on_generation(self, ga_instance):
+    def on_generation(self, ga_instance: pygad.GA):
         """
         Callback function.
+
+        Parameters
+        ----------
+        ga_instance : pygad.GA
+            Genetic algorithm instance.
         """
 
         best_solution, best_solution_fitness, best_solution_idx = ga_instance.best_solution()
-        logger.info(f"generation {ga_instance.generations_completed}:")
-        logger.info(f"\tbest solution: {best_solution}")
-        logger.info(f"\tbest fitness: {best_solution_fitness}")
+        logger.info(f"GENERATION {ga_instance.generations_completed}:")
+        logger.info(f"\tBest solution: [{', '.join(f'{val:.2f}' for val in best_solution)}]")
+        logger.info(f"\tBest fitness: {best_solution_fitness}")
 
         if best_solution_fitness > self.best_fitness_value:
             self.best_fitness_value = best_solution_fitness
@@ -66,12 +86,12 @@ def on_generation(self, ga_instance):
         else:
             self.no_improv_count += 1
 
-        if self.no_improv_count > self.no_improv_limit:
-            logger.warning(f"no improvement for {self.no_improv_limit} generations, stopping GA.")
+        if self.no_improv_count >= self.no_improv_limit:
+            logger.warning(f"No improvement for {self.no_improv_limit} generations, stopping GA.")
             return "stop"
-        elif self.no_improv_count > 2:
+        elif self.no_improv_count >= 2:
             self.mutation_percent_genes = min(50, self.mutation_percent_genes * 1.5)
-            logger.warning(f"increasing mutation rate to {self.mutation_percent_genes}")
+            logger.warning(f"Increasing mutation rate to {self.mutation_percent_genes}")
         else:
             self.mutation_percent_genes = self.initial_mutation_rate
 
@@ -98,59 +118,215 @@ def plot_fitness(self):
         self.ga_instance.plot_fitness()
 
 
-def top_k_precision(y_true, y_proba, k=100):
-    top_k_indices = np.argsort(y_proba)[-k:]
-    return np.mean(y_true[top_k_indices])
+def evaluate_predictions(
+    y_true: np.array, y_pred: np.array, trade_dates: np.array, k: int = 75
+) -> float:
+    """
+    Evaluate predictions by selecting top k stocks for each trade date.
+
+    Parameters:
+    -----------
+    y_true : np.array
+        Actual returns
+    y_pred : np.array
+        Predicted returns
+    trade_dates : np.array
+        Array of trade dates for each observation
+    k : int
+        Number of stocks to select per trade date
+    """
+    unique_dates = np.unique(trade_dates)
+    performance_by_date = []
+
+    for date in unique_dates:
+        date_mask = trade_dates == date
+        date_true = y_true[date_mask]
+        date_pred = y_pred[date_mask]
+
+        top_k_indices = np.argsort(date_pred)[-k:]
+        bottom_k_indices = np.argsort(date_pred)[:k]
+
+        top_k_returns = date_true[top_k_indices]
+        bottom_k_returns = date_true[bottom_k_indices]
+
+        top_mean_return = np.mean(top_k_returns)
+        bottom_mean_return = np.mean(bottom_k_returns)
+        return_spread = top_mean_return - bottom_mean_return
+
+        top_hit_rate = np.mean(top_k_returns > 0)
+        bottom_hit_rate = np.mean(bottom_k_returns < 0)
+
+        if max(date_true) == min(date_true):
+            norm_spread = 0.5
+        else:
+            norm_spread = (return_spread - min(date_true)) / (max(date_true) - min(date_true))
+
+        performance_by_date.append(
+            {
+                "date": date,
+                "return_spread": norm_spread,
+                "top_hit_rate": top_hit_rate,
+                "bottom_hit_rate": bottom_hit_rate,
+            }
+        )
+
+    avg_spread = np.mean([p["return_spread"] for p in performance_by_date])
+    avg_top_hit = np.mean([p["top_hit_rate"] for p in performance_by_date])
+    avg_bottom_hit = np.mean([p["bottom_hit_rate"] for p in performance_by_date])
+
+    # combined metric of return spread
+    performance = 0.5 * avg_spread + 0.25 * avg_top_hit + 0.25 * avg_bottom_hit
+    return round(performance, 4) if performance > 0 else 0.0001
+
+
+def evaluate_top_hit_rate(
+    y_true: np.array, y_pred: np.array, trade_dates: np.array, k: int = 75
+) -> float:
+    """
+    Evaluate predictions by selecting top k stocks for each trade date.
+
+    Parameters:
+    -----------
+    y_true : np.array
+        Actual returns
+    y_pred : np.array
+        Predicted returns
+    trade_dates : np.array
+        Array of trade dates for each observation
+    k : int
+        Number of stocks to select per trade date
+
+    Returns
+    -------
+    float
+        Average top hit rate.
+    """
+    unique_dates = np.unique(trade_dates)
+    performance_by_date = []
 
+    for date in unique_dates:
+        date_mask = trade_dates == date
+        date_true = y_true[date_mask]
+        date_pred = y_pred[date_mask]
+
+        top_k_indices = np.argsort(date_pred)[-k:]
+        top_hit_rate = np.mean(date_true[top_k_indices] > 0)
+
+        performance_by_date.append(
+            {
+                "date": date,
+                "top_hit_rate": top_hit_rate,
+            }
+        )
 
-def get_train_val_splits(data: pl.DataFrame, min_train_years: int = 5):
+    avg_top_hit = np.mean([p["top_hit_rate"] for p in performance_by_date])
+    return round(avg_top_hit, 4) if avg_top_hit > 0 else 0.0001
+
+
+def get_train_val_splits(
+    data: pl.DataFrame, min_train_years: int = 5, val_years: int = 1, max_splits: int = 3
+) -> list[tuple[pl.DataFrame, pl.DataFrame]]:
     """
-    Generate training/validation splits using expanding window approach.
+    Generate training/validation splits using expanding window approach,
+    starting from most recent years and moving backwards.
 
     Parameters
     ----------
     data : pl.DataFrame
         Training data to split.
+    min_train_years : int
+        Minimum number of years required for training
+    max_splits : int
+        Maximum number of splits to return
 
     Returns
     -------
     list[tuple[pl.DataFrame]]
-        List of (train, validation) splits.
+        List of (train, validation) splits, ordered from most recent to oldest.
     """
-    # get unique years in the dataset
-    years = data.select(pl.col("tdq").dt.year()).unique().sort("tdq").get_column("tdq").to_list()
+    # Get sorted unique quarters
+    quarters = (
+        data.select(pl.col("tdq")).unique().sort("tdq", descending=True).get_column("tdq").to_list()
+    )
+
+    # Convert years to quarters
+    min_train_quarters = min_train_years * 4
+    val_window = val_years * 4
 
-    # ensure we have enough years for training and 2 years of validation
-    if len(years) < min_train_years + 2:
+    # Validate enough data exists
+    if len(quarters) < min_train_quarters + val_window:
         raise ValueError(
             f"Not enough years in dataset. Need at least {min_train_years + 2} years "
             f"({min_train_years} for training, 2 for validation)."
         )
 
     splits = []
-    for i in range(len(years) - 3):
-        if i + 1 < min_train_years:
-            continue
+    # Generate splits moving backwards through time
+    for i in range(0, len(quarters) - min_train_quarters - val_window - 1, val_window):
+        # Define validation and training periods (skip 1 quarter for look-ahead bias)
+        val_quarters = quarters[i : (i + val_window)]
+        train_quarters = quarters[(i + val_window + 4) :]
 
-        train_years = years[: i + 2]
-        val_years = [years[i + 2], years[i + 3]]
+        if len(train_quarters) < min_train_quarters:
+            break
 
-        train = data.filter(pl.col("tdq").dt.year().is_in(train_years))
-        val = data.filter(pl.col("tdq").dt.year().is_in(val_years))
+        train = data.filter(pl.col("tdq").is_in(train_quarters))
+        val = data.filter(pl.col("tdq").is_in(val_quarters))
         splits.append((train, val))
 
-    return splits
+    if max_splits and max_splits > 0:
+        splits = splits[:max_splits]
 
+    return splits[::-1]
 
-def fitness_function_wrapper(data, features, target, min_train_years, scale):
-    splits = get_train_val_splits(data, min_train_years)
 
-    def fitness_function(ga_instance, solution, solution_idx):
+def fitness_function_wrapper(
+    data: pl.DataFrame, features: list[str], target: str, scale: float, min_train_years: int = 5
+) -> Callable[[pygad.GA, list[float], int], float]:
+    """
+    Wrapper for the fitness function used in the genetic algorithm.
+
+    Parameters
+    ----------
+    data : pl.DataFrame
+        Training data.
+    features : list[str]
+        Features to use for training.
+    target : str
+        Target variable to predict.
+    min_train_years : int
+        Minimum number of years to use for training.
+
+    Returns
+    -------
+    Callable[[pygad.GA, list[float], int], float]
+        Fitness function.
+    """
+    splits = get_train_val_splits(data, min_train_years, 1, 2)
+
+    def fitness_function(ga_instance, solution, solution_idx) -> float:
+        """
+        Fitness function for the genetic algorithm.
+
+        Parameters
+        ----------
+        ga_instance : pygad.GA
+            Genetic algorithm instance.
+        solution : list[float]
+            Solution vector.
+        solution_idx : int
+            Index of the solution.
+
+        Returns
+        -------
+        float
+            Fitness value.
+        """
         params = {
             "objective": "binary:logistic",
             "learning_rate": solution[0],
-            "n_estimators": int(solution[1]),
-            "max_depth": int(solution[2]),
+            "n_estimators": round(solution[1]),
+            "max_depth": round(solution[2]),
             "min_child_weight": solution[3],
             "gamma": solution[4],
             "subsample": solution[5],
@@ -159,24 +335,28 @@ def fitness_function(ga_instance, solution, solution_idx):
             "reg_lambda": solution[8],
             "scale_pos_weight": scale,
             "eval_metric": "logloss",
-            "nthread": 1,
-            "seed": 100,
+            "tree_method": "hist",
+            "nthread": -1,
+            "random_state": 100,
         }
 
-        model = XGBoostModel(params)
-        perfs = []
+        xgb = XGBoostClassifier(params)
+        performance_list = []
 
         for train, val in splits:
             X_train = train.select(features).to_pandas()
             y_train = train.select(target).to_pandas().values.ravel()
             X_val = val.select(features).to_pandas()
             y_val = val.select(target).to_pandas().values.ravel()
+            trade_dates = val.select("tdq").to_pandas().values.ravel()
+
+            xgb.train(X_train, y_train)
+            y_pred = xgb.predict_proba(X_val)
 
-            model.train(X_train, y_train)
-            y_proba = model.predict_proba(X_val)
-            perf = top_k_precision(y_val, y_proba, k=200)
-            perfs.append(perf)
+            performance = evaluate_top_hit_rate(y_val, y_pred, trade_dates)
+            performance_list.append(performance)
 
-        return sum(perfs) / len(perfs)
+        avg_performance = round(np.mean(performance_list), 4)
+        return avg_performance
 
     return fitness_function
diff --git a/stocksense/model/model_handler.py b/stocksense/model/model_handler.py
index 94ac85f..37447f9 100644
--- a/stocksense/model/model_handler.py
+++ b/stocksense/model/model_handler.py
@@ -9,10 +9,10 @@
 from stocksense.config import config
 
 from .genetic_algorithm import GeneticAlgorithm, fitness_function_wrapper
-from .xgboost_model import XGBoostModel
+from .xgboost_model import XGBoostClassifier
 
 MODEL_DIR = Path(__file__).parents[1] / "model" / "model_base"
-REPORT_DIR = Path(__file__).parents[2] / "reports"
+REPORT_DIR = Path(__file__).parents[2] / "reports" / "scores"
 
 warnings.filterwarnings("ignore")
 
@@ -25,15 +25,16 @@ class ModelHandler:
 
     def __init__(self, trade_date: Optional[dt.datetime] = None):
         self.features = config.model.features
-        self.target = config.model.target
+        self.targets = config.model.targets
+        self.prediction_horizon = config.processing.prediction_horizon
         self.min_train_years = config.model.min_train_years
         self.trade_date = trade_date if trade_date else find_last_trading_date()
         if not validate_trade_date(self.trade_date):
             raise ValueError(f"Invalid trade date: {self.trade_date}.")
 
-    def train(self, data: pl.DataFrame, retrain: bool = False):
+    def train(self, data: pl.DataFrame, retrain: bool = False) -> None:
         """
-        Train and optimize GA-XGBoost model.
+        Train GA-XGBoost models for stock selection.
 
         Parameters
         ----------
@@ -43,52 +44,66 @@ def train(self, data: pl.DataFrame, retrain: bool = False):
             Whether to retrain the model for given trade date.
         """
         try:
-            logger.info(f"START training model - {self.trade_date}")
+            for target in self.targets:
+                logger.info(f"START training model for {target}, {self.trade_date}")
 
-            model_file = MODEL_DIR / f"{self.trade_date.date()}.pkl"
-            if model_file.exists() and not retrain:
-                logger.warning(f"Model already exists for {self.trade_date} - skipping training.")
-                return
+                trade_date_model_dir = MODEL_DIR / f"{self.trade_date.date()}"
+                trade_date_model_dir.mkdir(parents=True, exist_ok=True)
+                model_file = trade_date_model_dir / f"{target}.pkl"
+                if model_file.exists() and not retrain:
+                    logger.warning(f"Model already exists for {target}, {self.trade_date}")
+                    continue
 
-            train = data.filter(
-                (pl.col("tdq") < self.trade_date)
-                & ~pl.all_horizontal(pl.col(self.target).is_null())
-            )
-
-            id_cols = ["tdq", "tic"]
-            training_fields = id_cols + self.features + [self.target]
-            train = train.select(training_fields)
-            scale = self.get_dataset_imbalance_scale(train)
-
-            # run GA optimization
-            ga = GeneticAlgorithm(
-                ga_settings=config.model.ga,
-                fitness_func=fitness_function_wrapper(
-                    train, self.features, self.target, self.min_train_years, scale
-                ),
-            )
+                train = data.filter(
+                    (pl.col("tdq") < self.trade_date - dt.timedelta(days=360))
+                    & ~pl.all_horizontal(pl.col(target).is_null())
+                ).select(["tdq", "tic"] + self.features + [target])
 
-            # run XGB-GA optimization
-            ga.create_instance()
-            ga.train()
-            best_solution, best_solution_fitness, best_solution_idx = ga.best_solution()
+                scale = self.get_dataset_imbalance_scale(train, target)
+                params = self.optimize(train, target, scale)
+                params = format_ga_parameters(params, scale)
 
-            # train final model with best params
-            params = format_ga_parameters(best_solution, scale)
+                X_train = train.select(self.features).to_pandas()
+                y_train = train.select(target).to_pandas().values.ravel()
 
-            X_train = train.select(self.features).to_pandas()
-            y_train = train.select(self.target).to_pandas().values.ravel()
+                model = XGBoostClassifier(params)
+                model.train(X_train, y_train)
+                model.save_model(model_file)
 
-            model = XGBoostModel(params)
-            model.train(X_train, y_train)
-            model.save_model(model_file)
+                logger.success(f"END training model for {target}, {self.trade_date}")
+            return
         except Exception as e:
             logger.error(f"ERROR: failed to train model - {e}")
             raise
 
-    def score(self, data: pl.DataFrame, stocks: list[str]):
+    def optimize(self, train: pl.DataFrame, target: str, scale: float) -> None:
         """
-        Classify using sector-specific models.
+        Optimize model parameters.
+
+        Parameters
+        ----------
+        train : pl.DataFrame
+            Preprocessed financial data.
+        target : str
+            Target variable to optimize model for.
+        scale : float
+            Class imbalance scale.
+        """
+
+        ga = GeneticAlgorithm(
+            ga_settings=config.model.ga,
+            fitness_func=fitness_function_wrapper(
+                train, self.features, target, scale, self.min_train_years
+            ),
+        )
+        ga.create_instance()
+        ga.train()
+        best_solution, best_solution_fitness, best_solution_idx = ga.best_solution()
+        return best_solution
+
+    def score(self, data: pl.DataFrame, stocks: list[str]) -> None:
+        """
+        Score stocks using all target-specific models and save average ranks.
 
         Parameters
         ----------
@@ -96,49 +111,57 @@ def score(self, data: pl.DataFrame, stocks: list[str]):
             Preprocessed financial data.
         stocks : list[str]
             List of stocks to score.
+
+        Returns
+        -------
+        pl.DataFrame
+            Dataframe with stock ranks.
         """
         try:
             logger.info(f"START stocksense eval - {self.trade_date}")
 
-            model_file = MODEL_DIR / f"{self.trade_date.date()}.pkl"
-            if not model_file.exists():
-                raise FileNotFoundError(f"No model found for trade date {self.trade_date}")
-
-            test = data.filter((pl.col("tdq") == self.trade_date) & pl.col("tic").is_in(stocks))
-            test_df = test.select(self.features).to_pandas()
+            final_ranks = data.filter(
+                (pl.col("tdq") == self.trade_date) & pl.col("tic").is_in(stocks)
+            )
 
-            model = XGBoostModel()
-            model.load_model(model_file)
-            prob_scores = model.predict_proba(test_df)
-            test = test.with_columns(pl.Series("score", prob_scores))
-            self.save_scoring_report(test)
-            return
-        except Exception as e:
-            logger.error(f"ERROR: failed to score stocks - {e}")
-            raise
+            pred_cols = []
+            for target in self.targets:
+                trade_date_model_dir = MODEL_DIR / f"{self.trade_date.date()}"
+                model_file = trade_date_model_dir / f"{target}.pkl"
+                if not model_file.exists():
+                    raise FileNotFoundError(f"No model found for trade date {self.trade_date}")
+
+                test_df = (
+                    data.filter((pl.col("tdq") == self.trade_date) & pl.col("tic").is_in(stocks))
+                    .select(self.features)
+                    .to_pandas()
+                )
+
+                model = XGBoostClassifier()
+                model.load_model(model_file)
+                logger.info(f"loaded model from {model_file}, with params: {model.params}")
+
+                prob_scores = model.predict_proba(test_df)
+                final_ranks = final_ranks.with_columns(pl.Series(f"pred_{target}", prob_scores))
+                pred_cols.append(f"pred_{target}")
+
+            # Calculate average rank
+            final_ranks = (
+                final_ranks.with_columns(pl.mean_horizontal(pred_cols).alias("avg_score"))
+                .sort("avg_score", descending=True)
+                .with_columns(pl.col("avg_score").round(4).alias("avg_score"))
+            )
 
-    def save_scoring_report(self, test_data: pl.DataFrame):
-        """
-        Save scoring report csv.
+            self.save_scoring_report(
+                final_ranks.select(["tic", "adj_close", "fwd_return_4Q", "avg_score"] + pred_cols)
+            )
 
-        Parameters
-        ----------
-        test_data : pl.DataFrame
-            Test data with scores.
-        """
-        try:
-            logger.info("START saving scoring report")
-            report = test_data.select(
-                ["tic", "close", "score", "freturn", "adj_freturn", "fperf"]
-            ).sort("score", descending=True)
-            report_file = REPORT_DIR / f"report_{self.trade_date.date()}.csv"
-            report.write_csv(report_file)
-            logger.success(f"END saved scoring report to {report_file}")
+            return final_ranks
         except Exception as e:
-            logger.error(f"ERROR failed to save scoring report - {e}")
+            logger.error(f"ERROR: failed to score stocks - {e}")
             raise
 
-    def get_dataset_imbalance_scale(self, train: pl.DataFrame):
+    def get_dataset_imbalance_scale(self, train: pl.DataFrame, target: str) -> float:
         """
         Compute dataset class imbalance scale.
 
@@ -146,6 +169,8 @@ def get_dataset_imbalance_scale(self, train: pl.DataFrame):
         ----------
         train : pl.DataFrame
             Training dataset.
+        target : str
+            Target variable to compute class imbalance scale for.
 
         Returns
         -------
@@ -154,10 +179,27 @@ def get_dataset_imbalance_scale(self, train: pl.DataFrame):
         """
         min_year = pl.col("tdq").dt.year().min()
         filtered_data = train.filter(pl.col("tdq").dt.year() < min_year + self.min_train_years)
-        neg_count = len(filtered_data.filter(pl.col(self.target) == 0))
-        pos_count = len(filtered_data.filter(pl.col(self.target) == 1))
+        neg_count = len(filtered_data.filter(pl.col(target) == 0))
+        pos_count = len(filtered_data.filter(pl.col(target) == 1))
         return round(neg_count / pos_count, 2)
 
+    def save_scoring_report(self, rank_data: pl.DataFrame) -> None:
+        """
+        Save scoring report csv with ranks for each target and average rank.
+
+        Parameters
+        ----------
+        rank_data : pl.DataFrame
+            DataFrame containing ranks for each target and average rank.
+        """
+        try:
+            report_file = REPORT_DIR / f"scores_{self.trade_date.date()}.csv"
+            rank_data.write_csv(report_file)
+            logger.success(f"SAVED scoring report to {report_file}")
+        except Exception as e:
+            logger.error(f"ERROR failed to save scoring report - {e}")
+            raise
+
 
 def validate_trade_date(date: dt.datetime) -> bool:
     """
@@ -178,7 +220,7 @@ def validate_trade_date(date: dt.datetime) -> bool:
     return date.month in allowed_months and date.day == 1
 
 
-def find_last_trading_date():
+def find_last_trading_date() -> Optional[dt.datetime]:
     """
     Find last trading date, which will be used for stock selection.
 
@@ -205,7 +247,7 @@ def find_last_trading_date():
         return None
 
 
-def format_ga_parameters(ga_solution: List[float], scale: float):
+def format_ga_parameters(ga_solution: List[float], scale: float) -> dict:
     """
     Format model parameters.
 
@@ -216,12 +258,11 @@ def format_ga_parameters(ga_solution: List[float], scale: float):
     scale : float
         Class imbalance scale.
     """
-    # train final model with best params
     return {
         "objective": "binary:logistic",
         "learning_rate": ga_solution[0],
-        "n_estimators": int(ga_solution[1]),
-        "max_depth": int(ga_solution[2]),
+        "n_estimators": round(ga_solution[1]),
+        "max_depth": round(ga_solution[2]),
         "min_child_weight": ga_solution[3],
         "gamma": ga_solution[4],
         "subsample": ga_solution[5],
@@ -230,6 +271,7 @@ def format_ga_parameters(ga_solution: List[float], scale: float):
         "reg_lambda": ga_solution[8],
         "scale_pos_weight": scale,
         "eval_metric": "logloss",
+        "tree_method": "hist",
         "nthread": -1,
-        "seed": 100,
+        "random_state": 100,
     }
diff --git a/stocksense/model/portfolio.py b/stocksense/model/portfolio.py
new file mode 100644
index 0000000..4a7804d
--- /dev/null
+++ b/stocksense/model/portfolio.py
@@ -0,0 +1,224 @@
+import datetime as dt
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import polars as pl
+from loguru import logger
+
+from stocksense.database import DatabaseHandler
+
+
+class PortfolioBuilder:
+    """
+    Portfolio construction class.
+    Handles portfolio creation based on model predictions.
+    """
+
+    def __init__(self, weighting: str = "market_cap"):
+        """
+        Initialize portfolio builder.
+
+        Parameters
+        ----------
+        n_stocks : int
+            Number of stocks to include in portfolio
+        weighting : str
+            Weighting scheme ('equal', 'market_cap', or 'sector_neutral')
+        sector_constraints : Dict[str, Tuple[float, float]], optional
+            Min/max allocation constraints per sector
+        """
+        self.weighting = weighting
+        self.db = DatabaseHandler()
+        self.portfolios_dir = Path(__file__).parents[2] / "reports" / "portfolios"
+
+    def build_portfolio(
+        self, n_stocks: int, trade_date: dt.datetime, data: pl.DataFrame
+    ) -> pl.DataFrame:
+        """
+        Build portfolio based on model predictions.
+
+        Parameters
+        ----------
+        n_stocks : int
+            Number of stocks to include in portfolio
+        trade_date : dt.datetime
+            Trade date in YYYY-MM-DD format
+        data : pl.DataFrame
+            Dataframe with scored stock data
+
+        Returns
+        -------
+        pl.DataFrame
+            Portfolio allocation dataframe
+        """
+        try:
+            stock_info = self.db.fetch_stock()
+            scored_stocks = data.join(
+                stock_info.select(["tic", "name", "sector"]), on="tic", how="left"
+            )
+
+            # Apply filters to get qualified stocks
+            portfolio = self._filter_candidates(scored_stocks).head(n_stocks)
+
+            if self.weighting == "equal":
+                weights = self._equal_weight(portfolio)
+            elif self.weighting == "market_cap":
+                weights = self._market_cap_weight(portfolio, score_weight=0.35)
+            elif self.weighting == "sector_neutral":
+                weights = self._sector_neutral_weight(portfolio, trade_date)
+            else:
+                raise ValueError(f"Unknown weighting scheme: {self.weighting}")
+
+            portfolio = portfolio.with_columns(pl.Series("weight", weights))
+            portfolio_cols = ["tic", "name", "sector", "adj_close", "avg_score", "weight"]
+
+            if trade_date > dt.datetime.now() - dt.timedelta(days=365):
+                portfolio = portfolio.select(portfolio_cols)
+            else:
+                portfolio = portfolio.select(portfolio_cols + ["fwd_return_4Q"])
+
+            logger.info(f"Built {self.weighting}-weighted portfolio with {n_stocks} stocks")
+            self._save_portfolio_excel(portfolio, trade_date)
+            return portfolio.sort("weight", descending=True)
+
+        except Exception as e:
+            logger.error(f"Failed to build portfolio: {e}")
+            raise
+
+    def _filter_candidates(self, df: pl.DataFrame) -> pl.DataFrame:
+        """
+        Apply fundamental and technical filters to screen stocks.
+
+        Parameters
+        ----------
+        df : pl.DataFrame
+            Filtered portfolio.
+        """
+
+        quality_filters = (
+            (pl.col("pe") > 0)
+            & (pl.col("ev_ebitda") < 50)
+            & (pl.col("saleq_yoy") > -20)
+            & (pl.col("fcf_yoy") > -50)
+            & (pl.col("price_mom") > -20)
+        )
+        return df.filter(quality_filters)
+
+    def _equal_weight(self, portfolio: pl.DataFrame) -> np.ndarray:
+        """Equal weighting scheme."""
+        return np.ones(len(portfolio)) / len(portfolio)
+
+    def _sector_neutral_weight(
+        self, portfolio: pl.DataFrame, trade_date: dt.datetime
+    ) -> np.ndarray:
+        """
+        Sector-neutral weighting scheme.
+
+        Parameters
+        ----------
+        portfolio : pl.DataFrame
+            Portfolio dataframe.
+        trade_date : dt.datetime
+            Trade date.
+
+        Returns
+        -------
+        np.ndarray
+            Sector-neutral weights.
+        """
+
+        sp500_sectors = (
+            self.db.fetch_stock()
+            .filter(
+                (pl.col("date_removed").is_null() | (pl.col("date_removed") > trade_date))
+                & (pl.col("date_added").is_null() | (pl.col("date_added") <= trade_date))
+            )
+            .group_by("sector")
+            .count()
+            .with_columns((pl.col("count") / pl.col("count").sum()).alias("sector_weight"))
+        )
+
+        portfolio_sectors = portfolio.group_by("sector").count()
+        sector_weights = {}
+
+        for sector in portfolio_sectors["sector"]:
+            sector_target = sp500_sectors.filter(pl.col("sector") == sector)["sector_weight"][0]
+            sector_count = portfolio_sectors.filter(pl.col("sector") == sector)["count"][0]
+            sector_weights[sector] = sector_target / sector_count
+
+        weights = np.array([sector_weights[sector] for sector in portfolio["sector"]])
+        return weights / weights.sum()
+
+    def _market_cap_weight(self, portfolio: pl.DataFrame, score_weight: float = 0.3) -> np.ndarray:
+        """
+        Hybrid market cap and score weighting scheme.
+        Weights are calculated as a combination of market cap and model scores.
+
+        Parameters
+        ----------
+        portfolio : pl.DataFrame
+            Portfolio dataframe
+        score_weight : float
+            Weight given to the model score (between 0 and 1)
+            0 = pure market cap weighting
+            1 = pure score weighting
+
+        Returns
+        -------
+        np.ndarray
+            Blended weights
+        """
+
+        # Calculate market cap component
+        market_caps = portfolio["mkt_cap"].to_numpy()
+        mkt_weights = market_caps / market_caps.sum()
+
+        # Calculate score component with exponential scaling
+        scores = portfolio["avg_score"].to_numpy()
+        norm_scores = (scores - scores.min()) / (scores.max() - scores.min())
+        score_component = norm_scores**2
+        score_component = score_component / score_component.sum()
+
+        # Blend the weights
+        final_weights = (1 - score_weight) * mkt_weights + score_weight * score_component
+        return final_weights / final_weights.sum()
+
+    def _save_portfolio_excel(self, portfolio: pl.DataFrame, trade_date: dt.datetime) -> None:
+        """
+        Save portfolio details to a multi-sheet Excel file.
+
+        Parameters
+        ----------
+        portfolio : pl.DataFrame
+            Portfolio dataframe.
+        trade_date : dt.datetime
+            Trade date.
+        """
+        excel_path = self.portfolios_dir / f"portfolio_{trade_date.date()}.xlsx"
+        with pd.ExcelWriter(excel_path, engine="openpyxl") as writer:
+            # Sheet 1: Full Portfolio
+            portfolio_df = portfolio.sort("weight", descending=True).to_pandas()
+            portfolio_df["weight"] = portfolio_df["weight"].map("{:.2%}".format)
+            portfolio_df["avg_score"] = portfolio_df["avg_score"].round(3)
+            portfolio_df["adj_close"] = portfolio_df["adj_close"].round(1)
+            if "fwd_return_4Q" in portfolio_df.columns:
+                portfolio_df["fwd_return_4Q"] = portfolio_df["fwd_return_4Q"].round(2)
+            portfolio_df.to_excel(writer, sheet_name="Full Portfolio", index=False)
+
+            # Sheet 2: Sector Allocations
+            sector_alloc = (
+                portfolio.group_by("sector")
+                .agg(pl.col("weight").sum())
+                .sort("weight", descending=True)
+                .to_pandas()
+            )
+            sector_alloc["weight"] = sector_alloc["weight"].map("{:.2%}".format)
+            sector_alloc.to_excel(writer, sheet_name="Sector Allocations", index=False)
+
+            # Sheet 3: Top Holdings
+            top_positions = portfolio.sort("weight", descending=True).head(10).to_pandas()
+            top_positions["weight"] = top_positions["weight"].map("{:.2%}".format)
+            top_positions.to_excel(writer, sheet_name="Top Holdings", index=False)
+
+        logger.info(f"Portfolio saved to {excel_path}")
diff --git a/stocksense/model/xgboost_model.py b/stocksense/model/xgboost_model.py
index 329d9c4..9a43a50 100644
--- a/stocksense/model/xgboost_model.py
+++ b/stocksense/model/xgboost_model.py
@@ -1,114 +1,159 @@
 import pickle
+from pathlib import Path
+from typing import Optional
 
+import numpy as np
 import sklearn.metrics as skm
 import xgboost as xgb
 
 
-class XGBoostModel:
+class BaseXGBoostModel:
     """
-    Custom wrapper for XGBoost classifier.
+    Base wrapper for XGBoost models.
     """
 
-    def __init__(self, params=None):
-        self.params = (
-            params
-            if params
-            else {
-                "objective": "binary:logistic",
-                "learning_rate": 0.1,
-                "n_estimators": 100,
-                "max_depth": 3,
-                "min_child_weight": 1,
-                "gamma": 0,
-                "subsample": 1,
-                "colsample_bytree": 1,
-                "reg_alpha": 0,
-                "reg_lambda": 1,
-                "scale_pos_weight": 1.0,
-                "eval_metric": "logloss",
-                "nthread": -1,
-                "seed": 100,
-            }
-        )
+    def __init__(self, params: Optional[dict] = None):
+        self.params = params if params else self._default_params()
         self.model = None
 
-    def train(self, X_train, y_train):
-        """
-        Train the classifier.
+    def _default_params(self):
+        return {
+            "learning_rate": 0.1,
+            "n_estimators": 100,
+            "max_depth": 3,
+            "min_child_weight": 1,
+            "gamma": 0,
+            "subsample": 1,
+            "colsample_bytree": 1,
+            "reg_alpha": 0,
+            "reg_lambda": 1,
+            "tree_method": "hist",
+            "nthread": -1,
+            "random_state": 100,
+        }
+
+    def save_model(self, model_path: Path) -> None:
+        if self.model is None:
+            raise Exception("model is not trained yet, train the model before saving.")
+        with open(model_path, "wb") as f:
+            pickle.dump((self.model, self.params), f)
 
-        Parameters
-        ----------
-        X_train : pd.DataFrame
-            Training data.
-        y_train : pd.Series
-            Training labels.
-        """
+    def load_model(self, model_path: Path) -> None:
+        with open(model_path, "rb") as f:
+            self.model, self.params = pickle.load(f)
+
+    def get_importance(self, importance_type: str = "gain") -> list[tuple[str, float]]:
+        importance = self.model.get_booster().get_score(importance_type=importance_type)
+        return sorted(importance.items(), key=lambda x: x[1], reverse=True)
+
+
+class XGBoostClassifier(BaseXGBoostModel):
+    """
+    Wrapper for XGBoost classifier.
+    """
+
+    def _default_params(self):
+        params = super()._default_params()
+        params.update({"objective": "binary:logistic", "eval_metric": "logloss", "nthread": -1})
+        return params
+
+    def train(self, X_train: np.ndarray, y_train: np.ndarray) -> None:
         self.model = xgb.XGBClassifier(**self.params)
         self.model.fit(X_train, y_train, verbose=True)
 
-    def predict(self, X):
-        """
-        Predict the class labels for the provided data.
-
-        Parameters
-        ----------
-        X : pd.DataFrame
-            Data to predict.
-
-        Returns
-        -------
-        np.ndarray
-            Predicted class labels.
-
-        Raises
-        ------
-        Exception
-            If the model is not trained yet.
-        """
+    def predict(self, X: np.ndarray) -> np.ndarray:
         if self.model is None:
             raise Exception("Model is not trained yet. Train the model before predicting.")
         return self.model.predict(X)
 
-    def predict_proba(self, X):
-        """
-        Predict the class probabilities for the provided data.
-
-        Parameters
-        ----------
-        X : pd.DataFrame
-            Data to predict.
-
-        Returns
-        -------
-        np.ndarray
-            Predicted class probabilities.
-        """
+    def predict_proba(self, X: np.ndarray) -> np.ndarray:
         if self.model is None:
             raise Exception("Model is not trained yet. Train the model before predicting.")
         return self.model.predict_proba(X)[:, 1]
 
-    def get_pr_auc(self, X_test, y_test):
+    def get_pr_auc(self, X_test: np.ndarray, y_test: np.ndarray) -> float:
         y_proba = self.predict_proba(X_test)
         return skm.average_precision_score(y_test, y_proba)
 
-    def get_roc_auc(self, X_test, y_test):
+    def get_roc_auc(self, X_test: np.ndarray, y_test: np.ndarray) -> float:
         y_proba = self.predict_proba(X_test)
         return skm.roc_auc_score(y_test, y_proba)
 
-    def get_ndcg_score(self, X_test, y_test, k=None):
+    def get_ndcg_score(
+        self, X_test: np.ndarray, y_test: np.ndarray, k: Optional[int] = None
+    ) -> float:
         y_proba = self.predict_proba(X_test).reshape(1, -1)
         return skm.ndcg_score(y_test.reshape(1, -1), y_proba, k=k)
 
-    def get_importance(self, importance_type="gain"):
-        importance = self.model.get_booster().get_score(importance_type=importance_type)
-        return sorted(importance.items(), key=lambda x: x[1], reverse=True)
 
-    def save_model(self, model_path):
+class XGBoostRegressor(BaseXGBoostModel):
+    """
+    Wrapper for XGBoost regressor.
+    """
+
+    def _default_params(self):
+        params = super()._default_params()
+        params.update(
+            {
+                "objective": "reg:absoluteerror",
+                "eval_metric": "mae",
+                "tree_method": "hist",
+                "nthread": -1,
+                "random_state": 100,
+            }
+        )
+        return params
+
+    def train(
+        self,
+        X_train: np.ndarray,
+        y_train: np.ndarray,
+    ) -> None:
+        """
+        Train the model.
+
+        Parameters
+        ----------
+        X_train : np.ndarray
+            Training features
+        y_train : np.ndarray
+            Training targets
+        """
+
+        self.model = xgb.XGBRegressor(**self.params)
+        self.model.fit(X_train, y_train, verbose=False)
+
+    @property
+    def best_iteration(self) -> Optional[int]:
+        """Get the number of boosting rounds chosen by early stopping."""
         if self.model is None:
-            raise Exception("model is not trained yet, train the model before saving.")
-        with open(model_path, "wb") as f:
-            pickle.dump((self.model, self.params), f)
+            raise Exception("Model is not trained yet.")
+        return getattr(self.model, "best_iteration", None)
 
-    def load_model(self, model_path):
-        with open(model_path, "rb") as f:
-            self.model, self.params = pickle.load(f)
+    @property
+    def best_score(self) -> Optional[float]:
+        """Get the best validation score."""
+        if self.model is None:
+            raise Exception("Model is not trained yet.")
+        return getattr(self.model, "best_score", None)
+
+    def predict(self, X: np.ndarray) -> np.ndarray:
+        if self.model is None:
+            raise Exception("Model is not trained yet.")
+        return self.model.predict(X)
+
+    # Regression metrics
+    def get_mse(self, X_test: np.ndarray, y_test: np.ndarray) -> float:
+        y_pred = self.predict(X_test)
+        return skm.mean_squared_error(y_test, y_pred)
+
+    def get_rmse(self, X_test: np.ndarray, y_test: np.ndarray) -> float:
+        return np.sqrt(self.get_mse(X_test, y_test))
+
+    def get_mae(self, X_test: np.ndarray, y_test: np.ndarray) -> float:
+        y_pred = self.predict(X_test)
+        return skm.mean_absolute_error(y_test, y_pred)
+
+    def get_r2(self, X_test: np.ndarray, y_test: np.ndarray) -> float:
+        y_pred = self.predict(X_test)
+        return skm.r2_score(y_test, y_pred)
diff --git a/stocksense/pipeline/etl.py b/stocksense/pipeline/etl.py
index f712972..ce7c640 100644
--- a/stocksense/pipeline/etl.py
+++ b/stocksense/pipeline/etl.py
@@ -7,7 +7,8 @@
 from loguru import logger
 from tqdm import tqdm
 
-from stocksense.database_handler import DatabaseHandler
+from stocksense.config import ConfigManager
+from stocksense.database import DatabaseHandler
 
 from .scraper import Scraper
 
@@ -22,14 +23,31 @@ class ETL:
     transformation and DB ingestion processes.
     """
 
-    def __init__(self, config, stocks: Optional[list[str]] = None):
+    def __init__(self, config: ConfigManager, stocks: Optional[list[str]] = None):
         self.db: DatabaseHandler = DatabaseHandler()
         self.db_schema: dict = config.database.db_schema
         self.base_date: str = config.scraping.base_date
         self.fin_source: str = "yfinance"
         self.historical_data_path: Path = DATA_PATH / "interim"
+        self._update_index_listings()
         self.stocks: list[str] = stocks or self._set_default_stocks()
 
+    def _update_index_listings(self) -> None:
+        """
+        Update the S&P500 index constituents in the database.
+        """
+        logger.info("updating S&P500 control table")
+
+        stock_df = self.db.fetch_stock()
+        sp500_df = Scraper.scrape_sp500_constituents()
+        additions, removals = Scraper.scrape_sp500_changes()
+
+        last_constituents = stock_df.filter(pl.col("date_removed").is_null())["tic"].to_list()
+        current_constituents = sp500_df["tic"].to_list()
+
+        self._delist_stocks(last_constituents, current_constituents, removals)
+        self._add_new_stocks(last_constituents, sp500_df)
+
     def _set_default_stocks(self) -> list[str]:
         """
         Retrieve default S&P500 stock tickers if none are provided.
@@ -42,16 +60,16 @@ def _set_default_stocks(self) -> list[str]:
         logger.info("setting default S&P500 stock tickers")
         stock_data = self.db.fetch_stock()
         if stock_data.is_empty():
-            self.set_index_listings()
+            self._set_index_listings()
 
-        # fetch data S&P500 stocks and recent delisted stocks
+        # fetch S&P500 stocks and recently delisted stocks
         stock_data = stock_data.filter(
             pl.col("date_removed").is_null()
             | (pl.col("date_removed") >= (dt.datetime.now().date() - dt.timedelta(days=360)))
         )
-        return stock_data["tic"].to_list()
+        return sorted(stock_data["tic"].to_list())
 
-    def set_index_listings(self) -> None:
+    def _set_index_listings(self) -> None:
         """
         Set index stock control table.
         """
@@ -59,22 +77,6 @@ def set_index_listings(self) -> None:
         sp500_df = sp500_df.with_columns(pl.lit(None).alias("date_removed"))
         self.db.insert_stock(sp500_df[self.db_schema["stock"]])
 
-    def update_index_listings(self) -> None:
-        """
-        Update the S&P500 index constituents in the database.
-        """
-        logger.info("updating S&P500 index listings")
-
-        stock_df = self.db.fetch_stock()
-        sp500_df = Scraper.scrape_sp500_constituents()
-        additions, removals = Scraper.scrape_sp500_changes()
-
-        last_constituents = stock_df.filter(pl.col("date_removed").is_null())["tic"].to_list()
-        current_constituents = sp500_df["tic"].to_list()
-
-        self._delist_stocks(last_constituents, current_constituents, removals)
-        self._add_new_stocks(last_constituents, sp500_df)
-
     def _delist_stocks(
         self, last_constituents: list[str], current_constituents: list[str], removals: pl.DataFrame
     ) -> None:
@@ -91,6 +93,7 @@ def _delist_stocks(
             S&P500 changes data.
         """
         removals_list = removals["tic"].to_list()
+        removed_stocks = []
         for tic in last_constituents:
             if tic not in current_constituents:
                 if tic in removals_list:
@@ -102,7 +105,9 @@ def _delist_stocks(
                 else:
                     today = dt.datetime.now().date()
                     self.db.update_stock(tic, {"date_removed": today})
+                removed_stocks.append(tic)
                 logger.info(f"{tic}: delisted from S&P500")
+        logger.info(f"removed {removed_stocks} from S&P500 index")
         return
 
     def _add_new_stocks(self, last_constituents: list[str], sp500_df: pl.DataFrame) -> None:
@@ -112,7 +117,7 @@ def _add_new_stocks(self, last_constituents: list[str], sp500_df: pl.DataFrame)
         logger.info(f"added {new_stocks['tic'].to_list()} to S&P500 index")
         return
 
-    def is_empty(self):
+    def is_empty(self) -> bool:
         """
         Check if no stocks were assigned to ETL process.
         """
@@ -128,6 +133,7 @@ def extract(self) -> None:
         self.extract_sp_500()
         self.extract_vix()
         self.extract_all_stocks()
+        return
 
     def extract_sp_500(self) -> None:
         """
@@ -216,7 +222,7 @@ def extract_info(self, tic: str, scraper: Scraper) -> bool:
         try:
             info = scraper.get_stock_info()
             self.db.insert_info(info)
-            logger.info(f"{tic}: updated stock info")
+            logger.success(f"{tic}: updated stock info")
             return True
         except Exception:
             logger.error(f"{tic}: info extraction FAILED")
diff --git a/stocksense/pipeline/preprocess.py b/stocksense/pipeline/preprocess.py
index 6cdba92..61aab5c 100644
--- a/stocksense/pipeline/preprocess.py
+++ b/stocksense/pipeline/preprocess.py
@@ -7,7 +7,7 @@
 from loguru import logger
 
 from stocksense.config import config
-from stocksense.database_handler import DatabaseHandler
+from stocksense.database import DatabaseHandler
 
 DATA_PATH = Path(__file__).parents[1] / "data"
 FIXTURE_PATH = Path(__file__).parents[2] / "tests" / "fixtures"
@@ -37,11 +37,12 @@ def engineer_features() -> pl.DataFrame:
         df = compute_financial_features(df)
         df = compute_sp500_features(df, index_data)
         df = compute_vix_features(df, vix_data)
-        df = compute_market_features(df, market_df, index_data)
+        df = compute_market_features(df, market_df)
+        df = compute_industry_features(df, info)
         df = compute_growth_features(df)
         df = compute_piotroski_score(df)
         df = compute_performance_targets(df)
-        df = compute_sector_dummies(df, info)
+        df = compute_sector_dummies(df)
 
         logger.success(f"END {df.shape[0]} rows PROCESSED")
     except Exception as e:
@@ -67,13 +68,24 @@ def clean(df: pl.DataFrame) -> pl.DataFrame:
 
     logger.info("START cleaning data")
 
+    df = df.filter(~pl.all_horizontal(pl.col("datadate").is_null()))
     df = df.filter(pl.col("tdq") <= pl.lit(dt.datetime.today().date()))
-    growth_alias = ["mom", "sos", "qoq", "yoy", "2y", "return"]
-    growth_vars = [f for f in df.columns if any(xf in f for xf in growth_alias)]
 
-    for feature in [f for f in df.columns if any(xf in f for xf in growth_vars)]:
+    growth_alias = ["mom", "sos", "qoq", "yoy", "2y"]
+    growth_vars = [f for f in df.columns if any(f.endswith(xf) for xf in growth_alias)]
+
+    for feature in growth_vars:
         df = df.with_columns(pl.col(feature) * 100)
-        df = df.with_columns(df.with_columns(pl.col(feature).clip(-2000, 2000)))
+        if "mom" in feature:
+            df = df.with_columns(pl.col(feature).clip(-50, 50))
+        elif "qoq" in feature or "sos" in feature:
+            df = df.with_columns(pl.col(feature).clip(-150, 150))
+        elif "yoy" in feature:
+            df = df.with_columns(pl.col(feature).clip(-300, 300))
+        elif "2y" in feature:
+            df = df.with_columns(pl.col(feature).clip(-400, 400))
+        else:
+            df = df.with_columns(pl.col(feature).clip(-300, 300))
 
     float_cols = df.select(pl.col(pl.Float64)).columns
     df = df.with_columns(
@@ -85,9 +97,11 @@ def clean(df: pl.DataFrame) -> pl.DataFrame:
             for col in float_cols
         ]
     )
-    df = df.filter(~pl.all_horizontal(pl.col("niq_2y").is_null()))
+
+    df = df.filter(~pl.all_horizontal(pl.col("roa_yoy").is_null()))
+    df = df.filter(~pl.all_horizontal(pl.col("price_2y").is_null()))
     df = df.sort(["tic", "tdq"]).unique(subset=["tic", "tdq"], keep="last", maintain_order=True)
-    df = df.sort(["tic", "rdq"])
+    df = df.sort(["tic", "tdq"])
 
     logger.success(f"{df.shape[0]} rows retained after CLEANING")
     return df
@@ -108,7 +122,6 @@ def compute_trade_date(df: pl.DataFrame) -> pl.DataFrame:
     pl.DataFrame
         Data with trade date intervals.
     """
-
     # correct rdq if it is the same as quarter end date
     df = df.with_columns(
         pl.when(pl.col("rdq") == pl.col("datadate"))
@@ -121,11 +134,22 @@ def compute_trade_date(df: pl.DataFrame) -> pl.DataFrame:
     max_year = df["rdq"].dt.year().max()
 
     quarter_dates = generate_quarter_dates(min_year, max_year)
+
+    # Create base DataFrame with all combinations of tic and trade dates
+    unique_tics = df.select("tic").unique()
     quarter_df = pl.DataFrame({"tdq": quarter_dates}).with_columns(pl.col("tdq").dt.date())
+    base_df = quarter_df.join(unique_tics, how="cross")
 
-    df = df.sort(by=["rdq", "tic"])
-    df = df.join_asof(quarter_df, left_on="rdq", right_on="tdq", strategy="forward")
-    return df.sort(by=["tic", "rdq"])
+    df = base_df.join_asof(
+        df.sort("rdq"),
+        left_on="tdq",
+        right_on="rdq",
+        by="tic",
+        strategy="backward",
+        tolerance=dt.timedelta(days=93),
+    )
+
+    return df.sort(by=["tic", "tdq"])
 
 
 def generate_quarter_dates(start_year: int, end_year: int) -> list:
@@ -192,7 +216,7 @@ def adjust_shares(df: pl.DataFrame) -> pl.DataFrame:
     )
 
     # compute cumulative product of adjustment factors in reverse
-    df = df.sort(by=["tic", "datadate"]).with_columns(
+    df = df.sort(by=["tic", "tdq"]).with_columns(
         pl.col("adjustment_factor")
         .cum_prod(reverse=True)
         .over("tic")
@@ -202,7 +226,7 @@ def adjust_shares(df: pl.DataFrame) -> pl.DataFrame:
     # apply the cumulative adjustment to the financial data
     df = df.with_columns((pl.col("cshoq") * pl.col("cum_adjustment_factor")).alias("cshoq"))
     df = df.with_columns(pl.col("stock_split").cast(pl.Int8))
-    df = df.sort(by=["tic", "rdq"])
+    df = df.sort(by=["tic", "tdq"])
     return df.drop(["csho_ratio", "split_factor", "adjustment_factor", "cum_adjustment_factor"])
 
 
@@ -302,47 +326,77 @@ def compute_insider_trading_features(df: pl.DataFrame, insider_df: pl.DataFrame)
 
 def compute_financial_features(df: pl.DataFrame) -> pl.DataFrame:
     """
-    Computes a selected number of financial ratios.
+    Compute financial ratios and metrics from raw financial data.
+
+    Calculates various financial indicators including:
+    - Profitability ratios (ROA, ROI, ROE)
+    - Margin ratios (Gross, EBITDA, Cash Flow)
+    - Liquidity ratios (Current, Quick, Cash)
+    - Leverage ratios (Debt, Long-term debt)
+    - Efficiency ratios (Inventory, Receivables turnover)
 
     Parameters
     ----------
-    df : pl.DataFrame
-        Financial data of a given stock.
+    df : DataFrame
+        Financial data containing required columns (niq, atq, saleq, etc.)
 
     Returns
     -------
-    pl.DataFrame
-        Data with additional columns.
+    DataFrame
+        DataFrame with additional financial ratio columns
+
+    Notes
+    -----
+    All percentage-based ratios are multiplied by 100 for easier interpretation
     """
+    df = df.sort(by=["tic", "tdq"])
+
+    # Handle edge cases in net income
     df = df.with_columns(
-        pl.when(pl.col("niq") == 0).then(pl.lit(None)).otherwise(pl.col("niq")).alias("niq")
+        [
+            pl.when(pl.col("niq") == 0).then(pl.lit(None)).otherwise(pl.col("niq")).alias("niq"),
+            pl.when(pl.col("cogsq").is_null())
+            .then(pl.lit(0))
+            .otherwise(pl.col("cogsq"))
+            .alias("cogsq"),
+        ]
     )
-    return (
-        df.lazy()
-        .with_columns(
-            (pl.col("niq").rolling_sum(4) / pl.col("atq")).over("tic").alias("roa"),
-            (pl.col("niq") / pl.col("icaptq")).over("tic").alias("roi"),
-            (pl.col("niq").rolling_sum(4) / pl.col("seqq")).over("tic").alias("roe"),
-            ((pl.col("saleq") - pl.col("cogsq")) / pl.col("saleq")).alias("gpm"),
-            (pl.col("ebitdaq") / pl.col("saleq")).alias("ebitdam"),
-            (pl.col("oancfq") / pl.col("saleq")).alias("cfm"),
-            (pl.col("oancfq") - pl.col("capxq")).alias("fcf"),
-            (pl.col("actq") / pl.col("lctq")).alias("cr"),
-            ((pl.col("rectq") + pl.col("cheq")) / pl.col("lctq")).alias("qr"),
-            (pl.col("cheq") / pl.col("lctq")).alias("csr"),
-            (pl.col("ltq") / pl.col("atq")).alias("dr"),
-            (pl.col("ltq") / pl.col("seqq")).alias("der"),
-            (pl.col("ltq") / pl.col("ebitdaq")).alias("debitda"),
-            (pl.col("dlttq") / pl.col("atq")).alias("ltda"),
-            ((pl.col("oancfq") - pl.col("capxq")) / pl.col("dlttq")).alias("ltcr"),
-            (pl.col("saleq") / pl.col("invtq").rolling_mean(2)).over("tic").alias("itr"),
-            (pl.col("saleq") / pl.col("rectq").rolling_mean(2)).over("tic").alias("rtr"),
-            (pl.col("saleq") / pl.col("atq").rolling_mean(2)).over("tic").alias("atr"),
-            pl.col("atq").log().alias("size"),
-        )
-        .collect()
+
+    df = df.lazy().with_columns(
+        # Profitability ratios
+        (pl.col("niq").rolling_sum(4) / pl.col("atq") * 100).over("tic").alias("roa"),
+        (pl.col("niq") / pl.col("icaptq") * 100).over("tic").alias("roi"),
+        (pl.col("niq").rolling_sum(4) / pl.col("seqq")).over("tic").alias("roe"),
+        # Margin ratios
+        ((pl.col("saleq") - pl.col("cogsq")) / pl.col("saleq") * 100).alias("gpm"),
+        (pl.col("ebitdaq") / pl.col("saleq") * 100).alias("ebitdam"),
+        (pl.col("oancfq") / pl.col("saleq") * 100).alias("cfm"),
+        (pl.col("oancfq") - pl.col("capxq")).alias("fcf"),
+        # Liquidity ratios
+        (pl.col("actq") / pl.col("lctq") * 100).alias("cr"),
+        ((pl.col("rectq") + pl.col("cheq")) / pl.col("lctq") * 100).alias("qr"),
+        (pl.col("cheq") / pl.col("lctq") * 100).alias("csr"),
+        # Leverage ratios
+        (pl.col("ltq") / pl.col("atq") * 100).alias("dr"),
+        (pl.col("ltq") / pl.col("seqq") * 100).alias("der"),
+        (pl.col("ltq") / pl.col("ebitdaq") * 100).alias("debitda"),
+        (pl.col("dlttq") / pl.col("atq") * 100).alias("ltda"),
+        ((pl.col("oancfq") - pl.col("capxq")) / pl.col("dlttq") * 100).alias("ltcr"),
+        # Efficiency ratios
+        (pl.col("saleq") / pl.col("invtq").rolling_mean(2)).over("tic").alias("itr"),
+        (pl.col("saleq") / pl.col("rectq").rolling_mean(2)).over("tic").alias("rtr"),
+        (pl.col("saleq") / pl.col("atq").rolling_mean(2)).over("tic").alias("atr"),
+        pl.col("atq").log().alias("size"),
+    )
+
+    df = df.with_columns(
+        # Earnings Stability
+        pl.col("niq").rolling_std(8).over("tic").alias("earnings_vol"),
+        pl.col("gpm").rolling_std(8).over("tic").alias("margin_vol"),
     )
 
+    return df.collect()
+
 
 def compute_sp500_features(df: pl.DataFrame, index_df: pl.DataFrame) -> pl.DataFrame:
     """
@@ -357,43 +411,37 @@ def compute_sp500_features(df: pl.DataFrame, index_df: pl.DataFrame) -> pl.DataF
     index_df = index_df.sort(by=["date"])
     df = df.sort(by=["tdq", "tic"])
 
-    # compute index past returns
+    index_df = compute_sp500_forward_returns(index_df)
     index_df = index_df.with_columns(
-        pl.col("close").pct_change(config.processing.trading_days_month).alias("index_mom"),
-        pl.col("close").pct_change(config.processing.trading_days_quarter).alias("index_qoq"),
-        pl.col("close").pct_change(config.processing.trading_days_semester).alias("index_sos"),
-        pl.col("close").pct_change(config.processing.trading_days_year).alias("index_yoy"),
-        pl.col("close").pct_change(config.processing.trading_days_2year).alias("index_2y"),
+        (pl.col("close").pct_change(config.processing.trade_days_month)).alias("index_mom"),
+        (pl.col("close").pct_change(config.processing.trade_days_quarter)).alias("index_qoq"),
+        (pl.col("close").pct_change(config.processing.trade_days_semester)).alias("index_sos"),
+        (pl.col("close").pct_change(config.processing.trade_days_year)).alias("index_yoy"),
+        (pl.col("close").pct_change(config.processing.trade_days_2year)).alias("index_2y"),
     )
 
     # compute volatily of index
     index_df = index_df.with_columns(
         (pl.col("close") / pl.col("close").shift(1)).log().alias("log_return")
     ).with_columns(
-        pl.col("log_return")
-        .rolling_std(config.processing.trading_days_month)
-        .alias("index_vol_mom"),
-        pl.col("log_return")
-        .rolling_std(config.processing.trading_days_quarter)
-        .alias("index_vol_qoq"),
-        pl.col("log_return")
-        .rolling_std(config.processing.trading_days_semester)
-        .alias("index_vol_sos"),
-        pl.col("log_return")
-        .rolling_std(config.processing.trading_days_year)
-        .alias("index_vol_yoy"),
-        pl.col("log_return")
-        .rolling_std(config.processing.trading_days_2year)
-        .alias("index_vol_2y"),
-    )
-
-    index_df = index_df.rename(
-        {
-            "date": "index_date",
-            "close": "index_close",
-        }
+        (pl.col("log_return").rolling_std(config.processing.trade_days_month)).alias(
+            "index_vol_mom"
+        ),
+        (pl.col("log_return").rolling_std(config.processing.trade_days_quarter)).alias(
+            "index_vol_qoq"
+        ),
+        (pl.col("log_return").rolling_std(config.processing.trade_days_semester)).alias(
+            "index_vol_sos"
+        ),
+        (pl.col("log_return").rolling_std(config.processing.trade_days_year)).alias(
+            "index_vol_yoy"
+        ),
+        (pl.col("log_return").rolling_std(config.processing.trade_days_2year)).alias(
+            "index_vol_2y"
+        ),
     )
 
+    index_df = index_df.rename({"date": "index_date", "close": "index_close"})
     index_df = index_df.select(
         [
             "index_date",
@@ -408,8 +456,13 @@ def compute_sp500_features(df: pl.DataFrame, index_df: pl.DataFrame) -> pl.DataF
             "index_vol_sos",
             "index_vol_yoy",
             "index_vol_2y",
+            "avg_index_fwd_return_1Q",
+            "avg_index_fwd_return_2Q",
+            "avg_index_fwd_return_3Q",
+            "avg_index_fwd_return_4Q",
         ]
     )
+
     df = df.join_asof(
         index_df,
         left_on="tdq",
@@ -417,7 +470,45 @@ def compute_sp500_features(df: pl.DataFrame, index_df: pl.DataFrame) -> pl.DataF
         strategy="backward",
         tolerance=dt.timedelta(days=7),
     )
-    return df.sort(by=["tic", "rdq"])
+    return df.sort(by=["tic", "tdq"])
+
+
+def compute_sp500_forward_returns(index_df: pl.DataFrame) -> pl.DataFrame:
+    """
+    Compute forward return features.
+    """
+
+    return_ranges = [
+        (config.processing.trade_days_month, "1M"),
+        (config.processing.trade_days_quarter, "1Q"),
+        (config.processing.trade_days_semester, "2Q"),
+        (config.processing.trade_days_third_quarter, "3Q"),
+        (config.processing.trade_days_year, "4Q"),
+    ]
+
+    df = index_df.lazy()
+    df = df.with_columns(
+        [
+            *[
+                ((pl.col("close").shift(-i) / pl.col("close")) - 1).alias(f"fret_{i}")
+                for i in range(return_ranges[0][0], return_ranges[-1][0] + 1)
+            ],
+        ]
+    )
+
+    for i in range(len(return_ranges) - 1):
+        start = return_ranges[i][0]
+        end = return_ranges[i + 1][0]
+        df = df.with_columns(
+            [
+                pl.mean_horizontal([f"fret_{i}" for i in range(start, end + 1)]).alias(
+                    f"avg_index_fwd_return_{return_ranges[i + 1][1]}"
+                ),
+            ]
+        )
+
+    df = df.drop([f"fret_{i}" for i in range(return_ranges[0][0], return_ranges[-1][0] + 1)])
+    return df.collect()
 
 
 def compute_vix_features(df: pl.DataFrame, vix_df: pl.DataFrame) -> pl.DataFrame:
@@ -452,14 +543,19 @@ def compute_vix_features(df: pl.DataFrame, vix_df: pl.DataFrame) -> pl.DataFrame
         strategy="backward",
         tolerance=dt.timedelta(days=7),
     )
-    return df.sort(by=["tic", "rdq"])
+    return df.sort(by=["tic", "tdq"])
 
 
-def compute_market_features(
-    df: pl.DataFrame, market_df: pl.DataFrame, index_df: pl.DataFrame
-) -> pl.DataFrame:
+def compute_market_features(df: pl.DataFrame, market_df: pl.DataFrame) -> pl.DataFrame:
     """
-    Compute market-related ratios.
+    Compute market-related features and technical indicators.
+
+    Processes market data to calculate:
+    - Forward returns
+    - Volume metrics
+    - Volatility indicators
+    - Price momentum
+    - Technical analysis indicators
 
     Parameters
     ----------
@@ -467,41 +563,85 @@ def compute_market_features(
         Financial data of a given stock.
     market_df : pl.DataFrame
         Market data.
-    index_df : pl.DataFrame
-        Index price data.
 
     Returns
     -------
     pl.DataFrame
-        Main dataset with added ratios.
+        Dataset with additional market-related features
     """
 
-    market_df = compute_volume_features(market_df)
-    market_df = compute_daily_momentum_features(market_df)
-    market_df = compute_daily_volatility_features(market_df)
+    processed_market = (
+        market_df.pipe(compute_forward_returns)
+        .pipe(compute_volume_features)
+        .pipe(compute_volatility_features)
+        .pipe(compute_price_growth_features)
+        .pipe(compute_technical_features)
+    )
 
-    df = df.sort(by=["rdq", "tic"])
-    df = df.join_asof(
-        market_df.drop(["volume"]),
-        left_on="tdq",
-        right_on="date",
-        by="tic",
-        strategy="backward",
-        tolerance=dt.timedelta(days=7),
-    ).join_asof(
-        market_df.select(["date", "tic", "close"]).rename(
-            {"date": "rdq_date", "close": "rdq_close"}
-        ),
-        left_on="rdq",
-        right_on="rdq_date",
-        by="tic",
-        strategy="forward",
-        tolerance=dt.timedelta(days=7),
+    # Join with main dataset
+    df = (
+        df.sort(by=["tdq", "tic"])
+        .join_asof(
+            processed_market.drop(["volume"]),
+            left_on="tdq",
+            right_on="date",
+            by="tic",
+            strategy="backward",
+            tolerance=dt.timedelta(days=7),
+        )
+        .join_asof(
+            processed_market.select(["date", "tic", "close"]).rename(
+                {"date": "rdq_date", "close": "rdq_close"}
+            ),
+            left_on="rdq",
+            right_on="rdq_date",
+            by="tic",
+            strategy="forward",
+            tolerance=dt.timedelta(days=7),
+        )
     )
-    df = df.sort(by=["tic", "rdq"])
 
-    df = compute_hybrid_features(df)
-    return df
+    return compute_hybrid_features(df)
+
+
+def compute_forward_returns(df: pl.DataFrame) -> pl.DataFrame:
+    """
+    Compute forward return features.
+    """
+
+    return_ranges = [
+        (config.processing.trade_days_month, "1M"),
+        (config.processing.trade_days_quarter, "1Q"),
+        (config.processing.trade_days_semester, "2Q"),
+        (config.processing.trade_days_third_quarter, "3Q"),
+        (config.processing.trade_days_year, "4Q"),
+    ]
+
+    df = df.lazy().sort(["tic", "date"])
+    df = df.with_columns(
+        [
+            *[
+                ((pl.col("adj_close").shift(-i) / pl.col("adj_close")) - 1)
+                .over("tic")
+                .alias(f"fret_{i}")
+                for i in range(return_ranges[0][0], return_ranges[-1][0] + 1)
+            ],
+        ]
+    )
+
+    for i in range(len(return_ranges) - 1):
+        start = return_ranges[i][0]
+        end = return_ranges[i + 1][0]
+        df = df.with_columns(
+            [
+                pl.mean_horizontal([f"fret_{i}" for i in range(start, end + 1)]).alias(
+                    f"fwd_return_{return_ranges[i + 1][1]}"
+                ),
+            ]
+        )
+
+    df = df.drop([f"fret_{i}" for i in range(return_ranges[0][0], return_ranges[-1][0] + 1)])
+    return df.collect()
 
 
 def compute_volume_features(df: pl.DataFrame) -> pl.DataFrame:
@@ -518,6 +658,7 @@ def compute_volume_features(df: pl.DataFrame) -> pl.DataFrame:
     pl.DataFrame
         DataFrame with normalized volume features
     """
+    df = df.sort(by=["tic", "date"])
     df = df.with_columns(
         [
             pl.col("volume").rolling_mean(20).over("tic").alias("volume_ma20_raw"),
@@ -535,9 +676,9 @@ def compute_volume_features(df: pl.DataFrame) -> pl.DataFrame:
     ).drop(["volume_ma20_raw", "volume_ma50_raw", "volume_annual_mean"])
 
 
-def compute_daily_momentum_features(df: pl.DataFrame) -> pl.DataFrame:
+def compute_volatility_features(df: pl.DataFrame) -> pl.DataFrame:
     """
-    Compute daily price momentum features.
+    Compute daily price volatility features.
 
     Parameters
     ----------
@@ -549,34 +690,45 @@ def compute_daily_momentum_features(df: pl.DataFrame) -> pl.DataFrame:
     pl.DataFrame
         Market data with momementum features.
     """
-    return df.with_columns(
-        plta.rsi(pl.col("close"), timeperiod=14).over("tic").alias("rsi_14d"),
-        plta.rsi(pl.col("close"), timeperiod=30).over("tic").alias("rsi_30d"),
-        plta.rsi(pl.col("close"), timeperiod=60).over("tic").alias("rsi_60d"),
-        plta.rsi(pl.col("close"), timeperiod=90).over("tic").alias("rsi_90d"),
-        plta.rsi(pl.col("close"), timeperiod=360).over("tic").alias("rsi_1y"),
-        pl.col("close")
-        .pct_change(config.processing.trading_days_month)
+    df = df.sort(by=["tic", "date"])
+    df = df.with_columns(
+        (pl.col("close") / pl.col("close").shift(1)).log().over("tic").alias("log_return")
+    )
+    df = df.with_columns(
+        # Volatility features
+        pl.col("log_return")
+        .rolling_std(config.processing.trade_days_month)
         .over("tic")
-        .alias("price_mom"),
-        pl.col("close")
-        .pct_change(config.processing.trading_days_quarter)
+        .alias("vol_mom"),
+        pl.col("log_return")
+        .rolling_std(config.processing.trade_days_quarter)
         .over("tic")
-        .alias("price_qoq"),
-        pl.col("close")
-        .pct_change(config.processing.trading_days_year)
+        .alias("vol_qoq"),
+        pl.col("log_return")
+        .rolling_std(config.processing.trade_days_semester)
         .over("tic")
-        .alias("price_yoy"),
-        pl.col("close")
-        .pct_change(config.processing.trading_days_2year)
+        .alias("vol_sos"),
+        pl.col("log_return")
+        .rolling_std(config.processing.trade_days_year)
         .over("tic")
-        .alias("price_2y"),
+        .alias("vol_yoy"),
+        pl.col("log_return")
+        .rolling_std(config.processing.trade_days_2year)
+        .over("tic")
+        .alias("vol_2y"),
     )
+    df = df.with_columns(
+        # Volatility regime
+        (pl.col("vol_mom") > pl.col("vol_mom").rolling_mean(20).over("tic"))
+        .cast(pl.Int8)
+        .alias("high_volatility_regime")
+    )
+    return df
 
 
-def compute_daily_volatility_features(df: pl.DataFrame) -> pl.DataFrame:
+def compute_price_growth_features(df: pl.DataFrame) -> pl.DataFrame:
     """
-    Compute daily price volatility features.
+    Compute daily price momentum features.
 
     Parameters
     ----------
@@ -588,22 +740,80 @@ def compute_daily_volatility_features(df: pl.DataFrame) -> pl.DataFrame:
     pl.DataFrame
         Market data with momementum features.
     """
+    df = df.sort(by=["tic", "date"])
+
     df = df.with_columns(
-        (pl.col("close") / pl.col("close").shift(1)).log().over("tic").alias("log_return")
+        (pl.col("close") > pl.col("close").rolling_max(252).over("tic"))
+        .cast(pl.Int8)
+        .alias("yearly_high"),
+        (pl.col("close") < pl.col("close").rolling_min(252).over("tic"))
+        .cast(pl.Int8)
+        .alias("yearly_low"),
     )
-    return df.with_columns(
-        pl.col("log_return")
-        .rolling_std(config.processing.trading_days_month)
+
+    df = df.with_columns(
+        pl.col("close")
+        .pct_change(config.processing.trade_days_month)
         .over("tic")
-        .alias("vol_mom"),
-        pl.col("log_return")
-        .rolling_std(config.processing.trading_days_quarter)
+        .alias("price_mom"),
+        pl.col("close")
+        .pct_change(config.processing.trade_days_quarter)
         .over("tic")
-        .alias("vol_qoq"),
-        pl.col("log_return")
-        .rolling_std(config.processing.trading_days_year)
+        .alias("price_qoq"),
+        pl.col("close")
+        .pct_change(config.processing.trade_days_semester)
         .over("tic")
-        .alias("vol_yoy"),
+        .alias("price_sos"),
+        pl.col("close")
+        .pct_change(config.processing.trade_days_year)
+        .over("tic")
+        .alias("price_yoy"),
+        pl.col("close")
+        .pct_change(config.processing.trade_days_2year)
+        .over("tic")
+        .alias("price_2y"),
+    )
+
+    return df.with_columns(
+        (pl.col("price_qoq") / pl.col("vol_qoq")).alias("price_risk_quarter"),
+        (pl.col("price_sos") / pl.col("vol_sos")).alias("price_risk_semester"),
+        (pl.col("price_yoy") / pl.col("vol_yoy")).alias("price_risk_year"),
+        (pl.col("price_2y") / pl.col("vol_2y")).alias("price_risk_2year"),
+    )
+
+
+def compute_technical_features(df: pl.DataFrame) -> pl.DataFrame:
+    """
+    Add technical analysis features using polars_talib.
+    """
+    df = df.sort(["tic", "date"])
+    df = df.with_columns(
+        [
+            # RSI / EMA indicators
+            plta.rsi(pl.col("close"), timeperiod=14).over("tic").alias("rsi_14d"),
+            plta.rsi(pl.col("close"), timeperiod=30).over("tic").alias("rsi_30d"),
+            plta.rsi(pl.col("close"), timeperiod=60).over("tic").alias("rsi_60d"),
+            plta.rsi(pl.col("close"), timeperiod=90).over("tic").alias("rsi_90d"),
+            plta.ema(pl.col("close"), timeperiod=20).over("tic").alias("ema_20d"),
+            plta.ema(pl.col("close"), timeperiod=50).over("tic").alias("ema_50d"),
+            plta.ema(pl.col("close"), timeperiod=200).over("tic").alias("ema_200d"),
+        ]
+    )
+    return df.with_columns(
+        [
+            # Moving Average Crossovers
+            (pl.col("ema_20d") > pl.col("ema_50d")).cast(pl.Int8).alias("golden_cross_20_50"),
+            (pl.col("ema_50d") > pl.col("ema_200d")).cast(pl.Int8).alias("golden_cross_50_200"),
+            # Price Distance from Moving Averages
+            ((pl.col("close") - pl.col("ema_20d")) / pl.col("ema_20d")).alias("ma20_distance"),
+            ((pl.col("close") - pl.col("ema_50d")) / pl.col("ema_50d")).alias("ma50_distance"),
+            # Momentum Divergence
+            (pl.col("rsi_14d") < 30).cast(pl.Int8).alias("oversold"),
+            (pl.col("rsi_14d") > 70).cast(pl.Int8).alias("overbought"),
+            # Price Trend Features
+            (pl.col("close") > pl.col("ema_20d")).cast(pl.Int8).alias("above_ma20"),
+            (pl.col("close") > pl.col("ema_50d")).cast(pl.Int8).alias("above_ma50"),
+        ]
     )
 
 
@@ -621,19 +831,21 @@ def compute_hybrid_features(df: pl.DataFrame) -> pl.DataFrame:
     pl.DataFrame
         Dataset with market/financial ratios.
     """
-    df = df.sort(by=["tic", "rdq"])
-    return (
+    df = df.sort(by=["tic", "tdq"])
+    df = (
         df.with_columns(
-            ((pl.col("close") - pl.col("rdq_close")) / pl.col("rdq_close") * 100).alias(
+            # Relative Momentum features
+            (((pl.col("close") - pl.col("rdq_close")) / pl.col("rdq_close")) * 100).alias(
                 "earn_drift"
             ),
-            (pl.col("price_mom") / pl.col("index_mom")).alias("momentum_mom"),
-            (pl.col("price_qoq") / pl.col("index_qoq")).alias("momentum_qoq"),
-            (pl.col("price_yoy") / pl.col("index_yoy")).alias("momentum_yoy"),
-            (pl.col("price_2y") / pl.col("index_2y")).alias("momentum_2y"),
-            (pl.col("vol_mom") / pl.col("index_vol_mom")).alias("rel_vol_mom"),
-            (pl.col("vol_qoq") / pl.col("index_vol_qoq")).alias("rel_vol_qoq"),
-            (pl.col("vol_yoy") / pl.col("index_vol_yoy")).alias("rel_vol_yoy"),
+            (pl.col("price_mom") / pl.col("index_mom")).alias("momentum_month"),
+            (pl.col("price_qoq") / pl.col("index_qoq")).alias("momentum_quarter"),
+            (pl.col("price_yoy") / pl.col("index_yoy")).alias("momentum_year"),
+            (pl.col("price_2y") / pl.col("index_2y")).alias("momentum_2year"),
+            (pl.col("vol_mom") / pl.col("index_vol_mom")).alias("rel_vol_month"),
+            (pl.col("vol_qoq") / pl.col("index_vol_qoq")).alias("rel_vol_quarter"),
+            (pl.col("vol_yoy") / pl.col("index_vol_yoy")).alias("rel_vol_year"),
+            (pl.col("vol_2y") / pl.col("index_vol_2y")).alias("rel_vol_2year"),
             (pl.col("niq").rolling_sum(4) / pl.col("cshoq")).over("tic").alias("eps"),
         )
         .with_columns(
@@ -644,10 +856,45 @@ def compute_hybrid_features(df: pl.DataFrame) -> pl.DataFrame:
             (pl.col("mkt_cap") + pl.col("ltq") - pl.col("cheq")).alias("ev"),
             (pl.col("mkt_cap") / (pl.col("atq") - pl.col("ltq"))).alias("pb"),
             (pl.col("mkt_cap") / pl.col("saleq").rolling_sum(4)).over("tic").alias("ps"),
+            (pl.col("mkt_cap") / pl.col("ebitdaq").rolling_sum(4)).over("tic").alias("ev_ebitda"),
+            ((pl.col("mkt_cap") / pl.col("mkt_cap").sum().over("tdq")) * 100).alias("mkt_rel"),
         )
-        .with_columns(
-            (pl.col("ev") / pl.col("ebitdaq").rolling_sum(4)).over("tic").alias("ev_ebitda")
-        )
+    )
+
+    return df.with_columns(
+        pl.col("pe").clip(-200, 200).alias("pe"),
+        pl.col("pb").clip(-30, 30).alias("pb"),
+        pl.col("ps").clip(0, 50).alias("ps"),
+        pl.col("ev_ebitda").clip(0, 150).alias("ev_ebitda"),
+    )
+
+
+def compute_industry_features(df: pl.DataFrame, info: pl.DataFrame) -> pl.DataFrame:
+    """
+    Add industry-relative metrics
+    """
+    df = df.join(info.select(["tic", "sector"]), on="tic", how="left")
+    df = df.filter(pl.col("sector").is_in(config.processing.sectors))
+
+    return df.with_columns(
+        [
+            # Industry Relative Ratios
+            (pl.col("pe") / pl.col("pe").mean().over(["tdq", "sector"])).alias("pe_sec"),
+            # Industry Momentum
+            pl.col("price_yoy").mean().over(["tdq", "sector"]).alias("momentum_sec_yoy"),
+            pl.col("price_qoq").mean().over(["tdq", "sector"]).alias("momentum_sec_qoq"),
+            # Industry Concentration
+            pl.col("mkt_cap").sum().over(["tdq", "sector"]).alias("size_sec"),
+            # Size
+            (pl.col("mkt_cap") > pl.col("mkt_cap").median().over(["tdq", "sector"]))
+            .cast(pl.Int8)
+            .alias("size_factor"),
+            (pl.col("mkt_cap") / pl.col("mkt_cap").sum().over(["tdq", "sector"]) * 100).alias(
+                "mkt_rel_sec"
+            ),
+            # Relative Profitability (quality factor)
+            (pl.col("roa") - pl.col("roa").mean().over(["tdq", "sector"])).alias("roa_sec"),
+        ]
     )
 
 
@@ -714,17 +961,19 @@ def compute_growth_features(df: pl.DataFrame) -> pl.DataFrame:
         "pb": [year_lag, two_year_lag],
         "ps": [year_lag, two_year_lag],
         "eps": [year_lag, two_year_lag],
-        "ev_ebitda": [year_lag, two_year_lag],
+        "ev_ebitda": [year_lag],
         "ltcr": [year_lag],
         "itr": [year_lag],
         "rtr": [year_lag],
         "atr": [year_lag],
+        "size": [year_lag],
+        "roa_sec": [quarter_lag, year_lag],
     }
 
     expressions = []
 
     # add standard growth calculations
-    df = df.sort(by=["tic", "rdq"])
+    df = df.sort(by=["tic", "tdq"])
     for metric, periods in metrics.items():
         for period in periods:
             suffix = "qoq" if period == quarter_lag else "yoy" if period == year_lag else "2y"
@@ -780,10 +1029,6 @@ def compute_piotroski_score(df: pl.DataFrame) -> pl.DataFrame:
             ).alias("f_score")
         ]
     )
-    df = df.with_columns(
-        (pl.col("f_score") - pl.col("f_score").shift(1)).over("tic").alias("f_score_gr1"),
-        (pl.col("f_score") - pl.col("f_score").shift(4)).over("tic").alias("f_score_gr4"),
-    )
     component_cols = [
         "f_roa",
         "f_ocf",
@@ -812,71 +1057,64 @@ def compute_performance_targets(df: pl.DataFrame) -> pl.DataFrame:
     pl.DataFrame
         Dataset with each observation associated to forward returns and flags.
     """
-    df = df.sort(["tic", "rdq"])
+    df = df.sort(["tic", "tdq"])
+
+    # Compute forward volatilities for adjusted returns
     df = df.with_columns(
-        (
-            (
-                pl.col("index_close").shift(-config.processing.prediction_horizon)
-                / pl.col("index_close")
+        [
+            pl.col("vol_yoy").shift(-4).over("tic").clip(0.01, 0.1).alias("forward_vol_yoy"),
+            pl.col("vol_sos").shift(-2).over("tic").clip(0.01, 0.1).alias("forward_vol_sos"),
+            pl.col("vol_qoq").shift(-1).over("tic").clip(0.01, 0.1).alias("forward_vol_qoq"),
+        ]
+    )
+
+    fwd_return_horizons = ["1Q", "2Q", "3Q", "4Q"]
+    vol_horizons = ["qoq", "qoq", "sos", "yoy"]
+    excess_margins = [6.0, 12.0, 25.0, 30.0]
+
+    for i, horizon in enumerate(fwd_return_horizons):
+        df = df.with_columns(
+            (pl.col(f"fwd_return_{horizon}") - pl.col(f"avg_index_fwd_return_{horizon}")).alias(
+                f"excess_return_{horizon}"
             )
-            - 1
         )
-        .over("tic")
-        .alias("index_freturn"),
-        (
-            (pl.col("adj_close").shift(-config.processing.prediction_horizon) / pl.col("adj_close"))
-            - 1
+        df = df.with_columns(
+            (pl.col(f"excess_return_{horizon}") / pl.col(f"forward_vol_{vol_horizons[i]}"))
+            .clip(-100, 100)
+            .alias(f"sharpe_ratio_{horizon}")
         )
-        .over("tic")
-        .alias("freturn"),
-        ((pl.col("adj_close").shift(-1) / pl.col("adj_close")) - 1).over("tic").alias("freturn_1q"),
-        ((pl.col("adj_close").shift(-2) / pl.col("adj_close")) - 1).over("tic").alias("freturn_2q"),
-        ((pl.col("adj_close").shift(-3) / pl.col("adj_close")) - 1).over("tic").alias("freturn_3q"),
-        ((pl.col("adj_close").shift(-4) / pl.col("adj_close")) - 1).over("tic").alias("freturn_4q"),
-    )
-    df = df.with_columns((pl.col("freturn") - pl.col("index_freturn")).alias("adj_freturn"))
-    df = df.with_columns(
-        (pl.col("adj_freturn") > config.processing.over_performance_threshold)
-        .cast(pl.Int8)
-        .alias("adj_fperf"),
-        (pl.col("freturn_1q") > config.processing.performance_threshold)
-        .cast(pl.Int8)
-        .alias("fperf_1q"),
-        (pl.col("freturn_2q") > config.processing.performance_threshold)
-        .cast(pl.Int8)
-        .alias("fperf_2q"),
-        (pl.col("freturn_3q") > config.processing.performance_threshold)
-        .cast(pl.Int8)
-        .alias("fperf_3q"),
-        (pl.col("freturn_4q") > config.processing.performance_threshold)
-        .cast(pl.Int8)
-        .alias("fperf_4q"),
-    ).with_columns(
-        ((pl.col("fperf_1q") + pl.col("fperf_2q") + pl.col("fperf_3q") + pl.col("fperf_4q")) > 0)
-        .cast(pl.Int8)
-        .alias("fperf")
-    )
-    component_cols = [
-        "freturn_1q",
-        "freturn_2q",
-        "freturn_3q",
-        "freturn_4q",
-        "fperf_1q",
-        "fperf_2q",
-        "fperf_3q",
-        "fperf_4q",
-    ]
-    return df.drop(component_cols)
+        df = df.with_columns(
+            (pl.col(f"fwd_return_{horizon}") / pl.col(f"forward_vol_{vol_horizons[i]}"))
+            .clip(-100, 100)
+            .alias(f"risk_return_{horizon}")
+        )
+        df = df.with_columns(
+            (pl.col(f"fwd_return_{horizon}") * 100).clip(-200, 200).alias(f"fwd_return_{horizon}"),
+            (pl.col(f"excess_return_{horizon}") * 100)
+            .clip(-100, 100)
+            .alias(f"excess_return_{horizon}"),
+        )
+        df = df.with_columns(
+            (pl.col(f"fwd_return_{horizon}") > excess_margins[i])
+            .cast(pl.Int8)
+            .alias(f"fwd_return_{horizon}_hit"),
+            (pl.col(f"excess_return_{horizon}") > (excess_margins[i] / 2))
+            .cast(pl.Int8)
+            .alias(f"excess_return_{horizon}_hit"),
+            (pl.col(f"risk_return_{horizon}") > (excess_margins[i] / 2))
+            .cast(pl.Int8)
+            .alias(f"risk_return_{horizon}_hit"),
+        )
+
+    return df
 
 
-def compute_sector_dummies(df: pl.DataFrame, info: pl.DataFrame) -> pl.DataFrame:
+def compute_sector_dummies(df: pl.DataFrame) -> pl.DataFrame:
     """
     Compute sector dummies.
     """
-    df = df.join(info.select(["tic", "sector"]), on="tic", how="left")
-    df = df.filter(pl.col("sector").is_in(config.processing.sectors))
+    df = df.sort(["tic", "tdq"])
     df = df.to_dummies(columns=["sector"])
-    df = df.sort(["tic", "rdq"])
     df = df.with_columns([pl.col(c).cast(pl.Int8) for c in df.columns if c.startswith("sector_")])
     df = df.rename(
         {col: col.lower().replace(" ", "_") for col in df.columns if col.startswith("sector_")}
@@ -903,4 +1141,4 @@ def filter_active_stocks(df: pl.DataFrame, info: pl.DataFrame) -> pl.DataFrame:
     df = df.join(info.select(["tic", "date_added", "date_removed"]), on="tic", how="left")
     df = df.filter((pl.col("date_removed").is_null() | (pl.col("tdq") <= pl.col("date_removed"))))
     df = df.drop(["date_added", "date_removed"])
-    return df.sort(["tic", "rdq"])
+    return df.sort(["tic", "tdq"])
diff --git a/stocksense/pipeline/scraper.py b/stocksense/pipeline/scraper.py
index 88d71c8..290bffb 100644
--- a/stocksense/pipeline/scraper.py
+++ b/stocksense/pipeline/scraper.py
@@ -1,5 +1,7 @@
 import datetime as dt
 import logging
+import re
+import time
 
 import polars as pl
 import requests
@@ -172,22 +174,66 @@ def _get_earnings_dates_yfinance(self, start_date: dt.date, end_date: dt.date):
         """
         Scrape earnings dates and eps surprise.
         """
-        n_quarters = int((end_date - start_date).days / 90) + 20
+        try:
+            n_quarters = int((end_date - start_date).days / 90) + 20
+            df = pl.from_pandas(self.handler.get_earnings_dates(limit=n_quarters).reset_index())
+            df = df.rename({"Earnings Date": "rdq", "Surprise(%)": "surprise_pct"})
+            df = df.select(["rdq", "surprise_pct"])
+            df = df.with_columns(pl.col("rdq").dt.date())
+            df = df.filter((pl.col("rdq") >= start_date) & (pl.col("rdq") <= end_date))
+            df = df.unique(subset=["rdq"]).sort("rdq").drop_nulls(subset=["surprise_pct", "rdq"])
+            if df.is_empty():
+                raise pl.exceptions.EmptyDataFrame("No financial release date available.")
+        except Exception:
+            return pl.DataFrame()
 
-        df = pl.from_pandas(self.handler.get_earnings_dates(limit=n_quarters).reset_index())
+    def _get_earnings_dates_sec(self, start_date: dt.date, end_date: dt.date):
+        """
+        Scrape earnings dates from SEC.
+        """
+        base_url = "https://www.sec.gov/cgi-bin/browse-edgar"
+        headers = {
+            "User-Agent": "Company Name AdminContact@domain.com",  # Replace with your details
+            "Accept-Encoding": "gzip, deflate",
+            "Host": "www.sec.gov",
+        }
+        params = {
+            "action": "getcompany",
+            "CIK": self.tic,
+            "type": "10-",
+            "owner": "include",
+            "count": "100",
+        }
+        try:
+            response = requests.get(base_url, params=params, headers=headers)
+            response.raise_for_status()
+            soup = bs(response.text, "html.parser")
 
-        df = df.rename({"Earnings Date": "rdq", "Surprise(%)": "surprise_pct"})
+            time.sleep(1)
 
-        # format dates and filter data
-        df = df.select(["rdq", "surprise_pct"])
-        df = df.with_columns(pl.col("rdq").dt.date())
-        df = df.filter((pl.col("rdq") >= start_date) & (pl.col("rdq") <= end_date))
-        df = df.unique(subset=["rdq"]).sort("rdq").drop_nulls(subset=["surprise_pct", "rdq"])
+            if soup.select("p > center > h1"):
+                return pl.DataFrame()
 
-        if df.is_empty():
-            raise Exception("No financial release date available for date interval.")
+            dates = []
+            dateFind = re.compile(r"2\d{3}-\d{2}-\d{2}")
 
-        return df
+            for tr in soup.select("tr"):
+                tdElems = tr.select("td")
+                if len(tdElems) == 5 and dateFind.search(tdElems[3].getText()):
+                    date = tdElems[3].getText().strip()
+                    dates.append(date)
+
+            if not dates:
+                return pl.DataFrame()
+
+            df = pl.DataFrame({"rdq": dates, "surprise_pct": [None] * len(dates)})
+            df = df.with_columns(pl.col("rdq").str.to_date("%Y-%m-%d"))
+            df = df.filter((pl.col("rdq") >= start_date) & (pl.col("rdq") <= end_date))
+            return df.unique(subset=["rdq"]).sort("rdq")
+
+        except requests.exceptions.RequestException as e:
+            print(f"Error fetching data: {e}")
+            return pl.DataFrame()
 
     def get_market_data(self, start_date):
         """
@@ -217,28 +263,35 @@ def get_financial_data(
         """
         if self.source == "yfinance":
             df = self._get_fundamental_data_yfinance(start_date, end_date)
-            earn_dates = self._get_earnings_dates_yfinance(start_date, end_date)
-
-            df = df.with_columns(pl.col("datadate").dt.date()).sort("datadate")
-            earn_dates = earn_dates.with_columns(pl.col("rdq").dt.date()).sort("rdq")
+            earn_dates = self._get_earnings_dates_sec(start_date, end_date)
         else:
             raise Exception("Other methods not implemented")
 
-        df = df.join_asof(
-            earn_dates,
-            left_on="datadate",
-            right_on="rdq",
-            strategy="forward",
-            tolerance=dt.timedelta(days=80),
-        )
+        try:
+            df = df.with_columns(pl.col("datadate").dt.date()).sort("datadate")
+            earn_dates = earn_dates.with_columns(pl.col("rdq").dt.date()).sort("rdq")
+            df = df.join_asof(
+                earn_dates,
+                left_on="datadate",
+                right_on="rdq",
+                strategy="forward",
+                tolerance=dt.timedelta(days=80),
+            )
+        except pl.exceptions.ColumnNotFoundError:
+            df = df.with_columns([pl.lit(None).alias("rdq"), pl.lit(None).alias("surprise_pct")])
 
-        # in cases where data is found but no release dt, defer 80 days later
         df = df.with_columns(
             pl.when(pl.col("rdq").is_null())
             .then(pl.col("datadate") + pl.duration(days=80))
             .otherwise(pl.col("rdq"))
             .alias("rdq")
         )
+        df = df.with_columns(
+            pl.when(pl.col("rdq") > dt.datetime.now().date())
+            .then(dt.datetime.now().date())
+            .otherwise(pl.col("rdq"))
+            .alias("rdq")
+        )
         return df
 
     def get_stock_insider_data(self) -> pl.DataFrame:
diff --git a/stocksense/validation/__init__.py b/stocksense/validation/__init__.py
new file mode 100644
index 0000000..8b0715b
--- /dev/null
+++ b/stocksense/validation/__init__.py
@@ -0,0 +1,3 @@
+from .validator import DataValidator
+
+__all__ = ["DataValidator"]
diff --git a/stocksense/validation/schemas.py b/stocksense/validation/schemas.py
new file mode 100644
index 0000000..ab31d3b
--- /dev/null
+++ b/stocksense/validation/schemas.py
@@ -0,0 +1,91 @@
+from datetime import date
+from enum import Enum
+
+import pandera.polars as pa
+import polars as pl
+
+
+class BaseSchema(pa.DataFrameModel):
+    class Config:
+        coerce = True
+        strict = False
+
+
+class Sector(str, Enum):
+    HEALTH_CARE = "Health Care"
+    FINANCIALS = "Financials"
+    INDUSTRIALS = "Industrials"
+    CONSUMER_DISCRETIONARY = "Consumer Discretionary"
+    INFORMATION_TECHNOLOGY = "Information Technology"
+    COMMUNICATION_SERVICES = "Communication Services"
+    CONSUMER_STAPLES = "Consumer Staples"
+    UTILITIES = "Utilities"
+    REAL_ESTATE = "Real Estate"
+    MATERIALS = "Materials"
+    ENERGY = "Energy"
+
+
+def validate_df(schema: pa.DataFrameModel, df: pl.DataFrame) -> pl.DataFrame:
+    """Validate a polars DataFrame using a pandera schema."""
+    date_columns = [col for col in df.columns if col == "date"]
+    renamed_df = df.rename({col: f"table_{col}" for col in date_columns})
+    validated_df = schema.validate(renamed_df)
+    return validated_df.rename({f"table_{col}": col for col in date_columns})
+
+
+class StockSchema(BaseSchema):
+    tic: str = pa.Field(nullable=False, unique=True)
+    name: str = pa.Field(nullable=True)
+    sector: str = pa.Field(nullable=False, isin=[s.value for s in Sector])
+    date_added: date = pa.Field(nullable=True)
+    date_removed: date = pa.Field(nullable=True)
+
+
+class FinancialSchema(BaseSchema):
+    tic: str = pa.Field(nullable=False)
+    datadate: date = pa.Field(nullable=False)
+    rdq: date = pa.Field(nullable=False)
+    saleq: float = pa.Field(nullable=True, ge=0)
+    cogsq: float = pa.Field(nullable=True, ge=0)
+    xsgaq: float = pa.Field(nullable=True, ge=0)
+    niq: float = pa.Field(nullable=True)
+    ebitdaq: float = pa.Field(nullable=True)
+    cshoq: float = pa.Field(nullable=True, ge=0)
+    atq: float = pa.Field(nullable=True, ge=0)
+    cheq: float = pa.Field(nullable=True, ge=0)
+    ltq: float = pa.Field(nullable=True, ge=0)
+    seqq: float = pa.Field(nullable=True)
+    oancfq: float = pa.Field(nullable=True)
+    surprise_pct: float = pa.Field(nullable=True, in_range={"min_value": -100, "max_value": 100})
+
+
+class MarketSchema(BaseSchema):
+    tic: str = pa.Field(nullable=False)
+    table_date: date = pa.Field(nullable=False)
+    close: float = pa.Field(nullable=False, ge=0)
+    adj_close: float = pa.Field(nullable=False, ge=0)
+    volume: int = pa.Field(nullable=False, ge=0)
+
+
+class InsiderSchema(BaseSchema):
+    tic: str = pa.Field(nullable=False)
+    filling_date: date = pa.Field(nullable=False)
+    trade_date: date = pa.Field(nullable=False)
+    owner_name: str = pa.Field(nullable=False)
+    title: str = pa.Field(nullable=True)
+    transaction_type: str = pa.Field(
+        nullable=False, isin=["P - Purchase", "S - Sale", "S - Sale+OE"]
+    )
+    qty: int = pa.Field(nullable=False)
+    value: str = pa.Field(nullable=False, regex=r"^\$?[\d,]+$")
+
+
+class SP500Schema(BaseSchema):
+    table_date: date = pa.Field(nullable=False)
+    close: float = pa.Field(nullable=False, ge=0)
+    volume: int = pa.Field(nullable=True, ge=0)
+
+
+class VIXSchema(BaseSchema):
+    table_date: date = pa.Field(nullable=False)
+    close: float = pa.Field(nullable=False, ge=0)
diff --git a/stocksense/validation/validator.py b/stocksense/validation/validator.py
new file mode 100644
index 0000000..f021ecd
--- /dev/null
+++ b/stocksense/validation/validator.py
@@ -0,0 +1,53 @@
+import pandera as pa
+import polars as pl
+from loguru import logger
+
+from .schemas import FinancialSchema, InsiderSchema, MarketSchema, StockSchema, validate_df
+
+
+class DataValidator:
+    """Data validation handler for database operations."""
+
+    _schemas = {
+        "stock": StockSchema,
+        "financial": FinancialSchema,
+        "market": MarketSchema,
+        "insider": InsiderSchema,
+    }
+
+    @classmethod
+    def validate(cls, table_name: str, data: pl.DataFrame) -> pl.DataFrame:
+        """
+        Validate data against schema before database insertion.
+
+        Parameters
+        ----------
+        table_name : str
+            Name of the table the data is being inserted into
+        data : pl.DataFrame
+            Data to validate
+
+        Returns
+        -------
+        pl.DataFrame
+            Validated data
+
+        Raises
+        ------
+        pa.errors.SchemaError
+            If data fails validation
+        KeyError
+            If table_name doesn't have a defined schema
+        """
+        try:
+            schema = cls._schemas.get(table_name)
+            if not schema:
+                logger.warning(f"No schema defined for table {table_name}")
+                return data
+
+            validated_data = validate_df(schema, data)
+            return validated_data
+
+        except pa.errors.SchemaError as e:
+            logger.error(f"Validation failed for {table_name}: {str(e)}")
+            raise
diff --git a/tests/fixtures/cleaned_data.parquet b/tests/fixtures/cleaned_data.parquet
index aab75bd..edef1cc 100644
Binary files a/tests/fixtures/cleaned_data.parquet and b/tests/fixtures/cleaned_data.parquet differ
diff --git a/tests/fixtures/insider_data.parquet b/tests/fixtures/insider_data.parquet
index 0d50d4c..230a577 100644
Binary files a/tests/fixtures/insider_data.parquet and b/tests/fixtures/insider_data.parquet differ
diff --git a/tests/fixtures/market_data.parquet b/tests/fixtures/market_data.parquet
index 53285b1..1ba344a 100644
Binary files a/tests/fixtures/market_data.parquet and b/tests/fixtures/market_data.parquet differ
diff --git a/tests/fixtures/processed_data.parquet b/tests/fixtures/processed_data.parquet
index 954dc4e..34fed13 100644
Binary files a/tests/fixtures/processed_data.parquet and b/tests/fixtures/processed_data.parquet differ
diff --git a/tests/test_processing.py b/tests/test_processing.py
index 8ee1b32..fc9e273 100644
--- a/tests/test_processing.py
+++ b/tests/test_processing.py
@@ -6,7 +6,6 @@
 from stocksense.config import config
 from stocksense.pipeline.preprocess import (
     clean,
-    compute_trade_date,
     engineer_features,
     generate_quarter_dates,
     map_to_closest_split_factor,
@@ -30,13 +29,6 @@ def test_map_to_closest_split_factor():
     assert map_to_closest_split_factor(0.24) == 0.25
 
 
-def test_compute_trade_date(financial_data):
-    result = compute_trade_date(financial_data)
-    assert "tdq" in result.columns
-    assert result.shape[0] == financial_data.shape[0]
-    assert all(isinstance(d, dt.date) for d in result["tdq"])
-
-
 def test_feature_engineering(
     financial_data,
     market_data,