study/tutorials/20260622164035_Let's Build a Quant Trading Strategy, MemLabs/part2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "60ff9942",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data and analysis libraries\n",
    "import polars as pl                         # Fast dataframes for financial data\n",
    "import numpy as np                          # Numerical computing library\n",
    "from datetime import datetime, timedelta    # Date and time operations\n",
    "import random\n",
    "\n",
    "# Machine learning libraries  \n",
    "import torch                                # PyTorch framework\n",
    "import torch.nn as nn                       # Neural network modules\n",
    "import torch.optim as optim                 # Optimization algorithms\n",
    "import research                             # Model building and training utilities\n",
    "\n",
    "# Visualization and \n",
    "import altair as alt                        # Interactive visualization library\n",
    "\n",
    "# data sources\n",
    "import binance                              # Binance market data utilities"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c0ea8fb",
   "metadata": {},
   "source": [
    "# Part 2 - Strategy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d66d9e5d",
   "metadata": {},
   "source": [
    "### Strategy Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "5d1e173d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1 Maker strategies => they are providing liquidity => adding liquidity to a market and aim to be compensated for it\n",
    "# 2 Taker strategies => they taking away liquidity => market orders that consume liquidity => what we are going to focus on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "c65ba3e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# I've got another video — 'Introduction to Quantitative Trading' — that goes way deeper into taker and maker strategies. \n",
    "# If you want the full breakdown, definitely watch that one. Link's below.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "14ea08df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Key Questions for our taking strategy\n",
    "\n",
    "# 1. Entry/Exit\n",
    "# 2. Trade Sizing\n",
    "# 3. Leverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "a947ef52",
   "metadata": {},
   "outputs": [],
   "source": [
    "## the key goal is we want to create a strategy that maximises profits from the model's statistical edge"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95bcd44c",
   "metadata": {},
   "source": [
    "###  Load Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "e11b032a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# I changed the model as it not predicts 12 hours ahead and not 8h. The reason why ito increase our time horizon is because the fees ate too much into profits.\n",
    "# Also increased the model's features to 3 from 1 - so uses the 3 most recent lags,\n",
    "# We will go into more detail later\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "4d3d7863",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearModel(\n",
       "  (linear): Linear(in_features=3, out_features=1, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import models\n",
    "model = models.LinearModel(3)\n",
    "# security alert \n",
    "model.load_state_dict(torch.load('model_weights.pth', weights_only=True))\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5181d82",
   "metadata": {},
   "source": [
    "### Model Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "02c5c2f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear.weight:\n",
      "[[-0.02849757 -0.08180149 -0.05941094]]\n",
      "linear.bias:\n",
      "[0.00058626]\n"
     ]
    }
   ],
   "source": [
    "research.print_model_params(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a2fdd7f",
   "metadata": {},
   "source": [
    "### What is Mean Reversion?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "0d1cf90f",
   "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: (6, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>log_return</th><th>mean</th></tr><tr><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>-0.014</td><td>0.000501</td></tr><tr><td>0.011399</td><td>0.000501</td></tr><tr><td>-0.012212</td><td>0.000501</td></tr><tr><td>0.01997</td><td>0.000501</td></tr><tr><td>-0.01442</td><td>0.000501</td></tr><tr><td>0.01227</td><td>0.000501</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (6, 2)\n",
       "┌────────────┬──────────┐\n",
       "│ log_return ┆ mean     │\n",
       "│ ---        ┆ ---      │\n",
       "│ f64        ┆ f64      │\n",
       "╞════════════╪══════════╡\n",
       "│ -0.014     ┆ 0.000501 │\n",
       "│ 0.011399   ┆ 0.000501 │\n",
       "│ -0.012212  ┆ 0.000501 │\n",
       "│ 0.01997    ┆ 0.000501 │\n",
       "│ -0.01442   ┆ 0.000501 │\n",
       "│ 0.01227    ┆ 0.000501 │\n",
       "└────────────┴──────────┘"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series = [-0.014000, 0.011399, -0.012212, 0.01997, -0.01442, 0.01227]\n",
    "mu = np.mean(series)\n",
    "mean_reversion_df = pl.DataFrame({'log_return': series, 'mean': mu})\n",
    "mean_reversion_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "1d538da5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "research.plot_multiple_lines(mean_reversion_df, ['log_return','mean'], 'Mean Reversion')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27ad3f39",
   "metadata": {},
   "source": [
    "### Interpretability - Linear Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "fc7ddcbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00208506255"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def linear_model(x):\n",
    "    w, b = -0.09911217, 0.00059838\n",
    "    return w * x + b\n",
    "\n",
    "linear_model(-0.015) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "0354a4cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.0013838634"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model(0.02) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "57c7ab22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00059818177566"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model(0.000002)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "2c39cad4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00059838"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model(0.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12071b88",
   "metadata": {},
   "source": [
    "### Interpretability - Non Linear Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "12461990",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.1509])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def nn_model(x):\n",
    "    x = torch.tensor([x])\n",
    "    W = torch.tensor([0.08035, -0.01478, -0.03523,  0.06777,  0.03789,  0.0013991, -0.13303,  0.8045])\n",
    "    b = torch.tensor([0.16421])\n",
    "    return torch.tanh(torch.sum(x * W) + b)\n",
    "\n",
    "nn_model(-0.015)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "59632102",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.1785])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# no interability\n",
    "nn_model(0.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "e88101ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.1627])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn_model(0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "09c1d8ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# not disparaging neural networks as my background is neural networks \n",
    "# but for trading, neural networks can increase your expected value but at the expense of complexity\n",
    "# i tend to favour linear models because they are interpretible, are not sensitive to noise, less prone to overfit (generalization)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9705c87",
   "metadata": {},
   "source": [
    "## Strategy Development!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "930ad380",
   "metadata": {},
   "source": [
    "### Load Time Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "64e6cbcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# binance.download_trades(sym, download_window)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "1b580a43",
   "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: (692, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>open</th><th>high</th><th>low</th><th>close</th></tr><tr><td>datetime[μs]</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2024-10-29 00:00:00</td><td>69939.9</td><td>71607.0</td><td>69733.0</td><td>71440.1</td></tr><tr><td>2024-10-29 12:00:00</td><td>71440.0</td><td>73660.0</td><td>70900.0</td><td>72739.5</td></tr><tr><td>2024-10-30 00:00:00</td><td>72739.5</td><td>72797.4</td><td>71931.1</td><td>71995.0</td></tr><tr><td>2024-10-30 12:00:00</td><td>71994.9</td><td>72984.9</td><td>71444.2</td><td>72349.0</td></tr><tr><td>2024-10-31 00:00:00</td><td>72349.0</td><td>72720.3</td><td>72030.5</td><td>72213.3</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>2025-10-07 12:00:00</td><td>124397.1</td><td>125098.0</td><td>120516.0</td><td>121286.5</td></tr><tr><td>2025-10-08 00:00:00</td><td>121286.6</td><td>123150.0</td><td>121005.3</td><td>122825.7</td></tr><tr><td>2025-10-08 12:00:00</td><td>122825.8</td><td>124170.6</td><td>121607.8</td><td>123237.5</td></tr><tr><td>2025-10-09 00:00:00</td><td>123237.4</td><td>123279.7</td><td>121081.5</td><td>122672.9</td></tr><tr><td>2025-10-09 12:00:00</td><td>122673.0</td><td>123740.1</td><td>119572.8</td><td>121579.3</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (692, 5)\n",
       "┌─────────────────────┬──────────┬──────────┬──────────┬──────────┐\n",
       "│ datetime            ┆ open     ┆ high     ┆ low      ┆ close    │\n",
       "│ ---                 ┆ ---      ┆ ---      ┆ ---      ┆ ---      │\n",
       "│ datetime[μs]        ┆ f64      ┆ f64      ┆ f64      ┆ f64      │\n",
       "╞═════════════════════╪══════════╪══════════╪══════════╪══════════╡\n",
       "│ 2024-10-29 00:00:00 ┆ 69939.9  ┆ 71607.0  ┆ 69733.0  ┆ 71440.1  │\n",
       "│ 2024-10-29 12:00:00 ┆ 71440.0  ┆ 73660.0  ┆ 70900.0  ┆ 72739.5  │\n",
       "│ 2024-10-30 00:00:00 ┆ 72739.5  ┆ 72797.4  ┆ 71931.1  ┆ 71995.0  │\n",
       "│ 2024-10-30 12:00:00 ┆ 71994.9  ┆ 72984.9  ┆ 71444.2  ┆ 72349.0  │\n",
       "│ 2024-10-31 00:00:00 ┆ 72349.0  ┆ 72720.3  ┆ 72030.5  ┆ 72213.3  │\n",
       "│ …                   ┆ …        ┆ …        ┆ …        ┆ …        │\n",
       "│ 2025-10-07 12:00:00 ┆ 124397.1 ┆ 125098.0 ┆ 120516.0 ┆ 121286.5 │\n",
       "│ 2025-10-08 00:00:00 ┆ 121286.6 ┆ 123150.0 ┆ 121005.3 ┆ 122825.7 │\n",
       "│ 2025-10-08 12:00:00 ┆ 122825.8 ┆ 124170.6 ┆ 121607.8 ┆ 123237.5 │\n",
       "│ 2025-10-09 00:00:00 ┆ 123237.4 ┆ 123279.7 ┆ 121081.5 ┆ 122672.9 │\n",
       "│ 2025-10-09 12:00:00 ┆ 122673.0 ┆ 123740.1 ┆ 119572.8 ┆ 121579.3 │\n",
       "└─────────────────────┴──────────┴──────────┴──────────┴──────────┘"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sym = 'BTCUSDT'\n",
    "time_interval = '12h'\n",
    "\n",
    "ts = pl.read_csv(f\"{sym}_{time_interval}_ohlc.csv\", try_parse_dates=True).sort('datetime')\n",
    "ts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68aab98d",
   "metadata": {},
   "source": [
    "### Add Target and Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "1b257aca",
   "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: (692, 9)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>open</th><th>high</th><th>low</th><th>close</th><th>close_log_return</th><th>close_log_return_lag_1</th><th>close_log_return_lag_2</th><th>close_log_return_lag_3</th></tr><tr><td>datetime[μs]</td><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>2024-10-29 00:00:00</td><td>69939.9</td><td>71607.0</td><td>69733.0</td><td>71440.1</td><td>null</td><td>null</td><td>null</td><td>null</td></tr><tr><td>2024-10-29 12:00:00</td><td>71440.0</td><td>73660.0</td><td>70900.0</td><td>72739.5</td><td>0.018025</td><td>null</td><td>null</td><td>null</td></tr><tr><td>2024-10-30 00:00:00</td><td>72739.5</td><td>72797.4</td><td>71931.1</td><td>71995.0</td><td>-0.010288</td><td>0.018025</td><td>null</td><td>null</td></tr><tr><td>2024-10-30 12:00:00</td><td>71994.9</td><td>72984.9</td><td>71444.2</td><td>72349.0</td><td>0.004905</td><td>-0.010288</td><td>0.018025</td><td>null</td></tr><tr><td>2024-10-31 00:00:00</td><td>72349.0</td><td>72720.3</td><td>72030.5</td><td>72213.3</td><td>-0.001877</td><td>0.004905</td><td>-0.010288</td><td>0.018025</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></tr><tr><td>2025-10-07 12:00:00</td><td>124397.1</td><td>125098.0</td><td>120516.0</td><td>121286.5</td><td>-0.025324</td><td>-0.001858</td><td>0.003757</td><td>0.005933</td></tr><tr><td>2025-10-08 00:00:00</td><td>121286.6</td><td>123150.0</td><td>121005.3</td><td>122825.7</td><td>0.012611</td><td>-0.025324</td><td>-0.001858</td><td>0.003757</td></tr><tr><td>2025-10-08 12:00:00</td><td>122825.8</td><td>124170.6</td><td>121607.8</td><td>123237.5</td><td>0.003347</td><td>0.012611</td><td>-0.025324</td><td>-0.001858</td></tr><tr><td>2025-10-09 00:00:00</td><td>123237.4</td><td>123279.7</td><td>121081.5</td><td>122672.9</td><td>-0.004592</td><td>0.003347</td><td>0.012611</td><td>-0.025324</td></tr><tr><td>2025-10-09 12:00:00</td><td>122673.0</td><td>123740.1</td><td>119572.8</td><td>121579.3</td><td>-0.008955</td><td>-0.004592</td><td>0.003347</td><td>0.012611</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (692, 9)\n",
       "┌────────────┬──────────┬──────────┬──────────┬───┬────────────┬───────────┬───────────┬───────────┐\n",
       "│ datetime   ┆ open     ┆ high     ┆ low      ┆ … ┆ close_log_ ┆ close_log ┆ close_log ┆ close_log │\n",
       "│ ---        ┆ ---      ┆ ---      ┆ ---      ┆   ┆ return     ┆ _return_l ┆ _return_l ┆ _return_l │\n",
       "│ datetime[μ ┆ f64      ┆ f64      ┆ f64      ┆   ┆ ---        ┆ ag_1      ┆ ag_2      ┆ ag_3      │\n",
       "│ s]         ┆          ┆          ┆          ┆   ┆ f64        ┆ ---       ┆ ---       ┆ ---       │\n",
       "│            ┆          ┆          ┆          ┆   ┆            ┆ f64       ┆ f64       ┆ f64       │\n",
       "╞════════════╪══════════╪══════════╪══════════╪═══╪════════════╪═══════════╪═══════════╪═══════════╡\n",
       "│ 2024-10-29 ┆ 69939.9  ┆ 71607.0  ┆ 69733.0  ┆ … ┆ null       ┆ null      ┆ null      ┆ null      │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2024-10-29 ┆ 71440.0  ┆ 73660.0  ┆ 70900.0  ┆ … ┆ 0.018025   ┆ null      ┆ null      ┆ null      │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2024-10-30 ┆ 72739.5  ┆ 72797.4  ┆ 71931.1  ┆ … ┆ -0.010288  ┆ 0.018025  ┆ null      ┆ null      │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2024-10-30 ┆ 71994.9  ┆ 72984.9  ┆ 71444.2  ┆ … ┆ 0.004905   ┆ -0.010288 ┆ 0.018025  ┆ null      │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2024-10-31 ┆ 72349.0  ┆ 72720.3  ┆ 72030.5  ┆ … ┆ -0.001877  ┆ 0.004905  ┆ -0.010288 ┆ 0.018025  │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ …          ┆ …        ┆ …        ┆ …        ┆ … ┆ …          ┆ …         ┆ …         ┆ …         │\n",
       "│ 2025-10-07 ┆ 124397.1 ┆ 125098.0 ┆ 120516.0 ┆ … ┆ -0.025324  ┆ -0.001858 ┆ 0.003757  ┆ 0.005933  │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-10-08 ┆ 121286.6 ┆ 123150.0 ┆ 121005.3 ┆ … ┆ 0.012611   ┆ -0.025324 ┆ -0.001858 ┆ 0.003757  │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-10-08 ┆ 122825.8 ┆ 124170.6 ┆ 121607.8 ┆ … ┆ 0.003347   ┆ 0.012611  ┆ -0.025324 ┆ -0.001858 │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-10-09 ┆ 123237.4 ┆ 123279.7 ┆ 121081.5 ┆ … ┆ -0.004592  ┆ 0.003347  ┆ 0.012611  ┆ -0.025324 │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-10-09 ┆ 122673.0 ┆ 123740.1 ┆ 119572.8 ┆ … ┆ -0.008955  ┆ -0.004592 ┆ 0.003347  ┆ 0.012611  │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "└────────────┴──────────┴──────────┴──────────┴───┴────────────┴───────────┴───────────┴───────────┘"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sym = 'BTCUSDT'\n",
    "forecast_horizon = 1\n",
    "ts = research.add_log_return_features(ts, 'close', forecast_horizon, max_no_lags=3)\n",
    "ts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d65ef912",
   "metadata": {},
   "source": [
    "### Time Split "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "85e01c4c",
   "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: (173, 9)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>open</th><th>high</th><th>low</th><th>close</th><th>close_log_return</th><th>close_log_return_lag_1</th><th>close_log_return_lag_2</th><th>close_log_return_lag_3</th></tr><tr><td>datetime[μs]</td><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>2025-07-15 12:00:00</td><td>117110.4</td><td>118499.8</td><td>115678.1</td><td>117738.5</td><td>0.005349</td><td>-0.022844</td><td>-0.013743</td><td>0.020259</td></tr><tr><td>2025-07-16 00:00:00</td><td>117738.6</td><td>119299.9</td><td>117017.1</td><td>118755.1</td><td>0.008597</td><td>0.005349</td><td>-0.022844</td><td>-0.013743</td></tr><tr><td>2025-07-16 12:00:00</td><td>118755.1</td><td>120100.0</td><td>118156.0</td><td>118590.7</td><td>-0.001385</td><td>0.008597</td><td>0.005349</td><td>-0.022844</td></tr><tr><td>2025-07-17 00:00:00</td><td>118590.7</td><td>119216.4</td><td>117663.6</td><td>117968.9</td><td>-0.005257</td><td>-0.001385</td><td>0.008597</td><td>0.005349</td></tr><tr><td>2025-07-17 12:00:00</td><td>117968.8</td><td>120951.5</td><td>117412.8</td><td>119176.6</td><td>0.010185</td><td>-0.005257</td><td>-0.001385</td><td>0.008597</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></tr><tr><td>2025-10-07 12:00:00</td><td>124397.1</td><td>125098.0</td><td>120516.0</td><td>121286.5</td><td>-0.025324</td><td>-0.001858</td><td>0.003757</td><td>0.005933</td></tr><tr><td>2025-10-08 00:00:00</td><td>121286.6</td><td>123150.0</td><td>121005.3</td><td>122825.7</td><td>0.012611</td><td>-0.025324</td><td>-0.001858</td><td>0.003757</td></tr><tr><td>2025-10-08 12:00:00</td><td>122825.8</td><td>124170.6</td><td>121607.8</td><td>123237.5</td><td>0.003347</td><td>0.012611</td><td>-0.025324</td><td>-0.001858</td></tr><tr><td>2025-10-09 00:00:00</td><td>123237.4</td><td>123279.7</td><td>121081.5</td><td>122672.9</td><td>-0.004592</td><td>0.003347</td><td>0.012611</td><td>-0.025324</td></tr><tr><td>2025-10-09 12:00:00</td><td>122673.0</td><td>123740.1</td><td>119572.8</td><td>121579.3</td><td>-0.008955</td><td>-0.004592</td><td>0.003347</td><td>0.012611</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (173, 9)\n",
       "┌────────────┬──────────┬──────────┬──────────┬───┬────────────┬───────────┬───────────┬───────────┐\n",
       "│ datetime   ┆ open     ┆ high     ┆ low      ┆ … ┆ close_log_ ┆ close_log ┆ close_log ┆ close_log │\n",
       "│ ---        ┆ ---      ┆ ---      ┆ ---      ┆   ┆ return     ┆ _return_l ┆ _return_l ┆ _return_l │\n",
       "│ datetime[μ ┆ f64      ┆ f64      ┆ f64      ┆   ┆ ---        ┆ ag_1      ┆ ag_2      ┆ ag_3      │\n",
       "│ s]         ┆          ┆          ┆          ┆   ┆ f64        ┆ ---       ┆ ---       ┆ ---       │\n",
       "│            ┆          ┆          ┆          ┆   ┆            ┆ f64       ┆ f64       ┆ f64       │\n",
       "╞════════════╪══════════╪══════════╪══════════╪═══╪════════════╪═══════════╪═══════════╪═══════════╡\n",
       "│ 2025-07-15 ┆ 117110.4 ┆ 118499.8 ┆ 115678.1 ┆ … ┆ 0.005349   ┆ -0.022844 ┆ -0.013743 ┆ 0.020259  │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-07-16 ┆ 117738.6 ┆ 119299.9 ┆ 117017.1 ┆ … ┆ 0.008597   ┆ 0.005349  ┆ -0.022844 ┆ -0.013743 │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-07-16 ┆ 118755.1 ┆ 120100.0 ┆ 118156.0 ┆ … ┆ -0.001385  ┆ 0.008597  ┆ 0.005349  ┆ -0.022844 │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-07-17 ┆ 118590.7 ┆ 119216.4 ┆ 117663.6 ┆ … ┆ -0.005257  ┆ -0.001385 ┆ 0.008597  ┆ 0.005349  │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-07-17 ┆ 117968.8 ┆ 120951.5 ┆ 117412.8 ┆ … ┆ 0.010185   ┆ -0.005257 ┆ -0.001385 ┆ 0.008597  │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ …          ┆ …        ┆ …        ┆ …        ┆ … ┆ …          ┆ …         ┆ …         ┆ …         │\n",
       "│ 2025-10-07 ┆ 124397.1 ┆ 125098.0 ┆ 120516.0 ┆ … ┆ -0.025324  ┆ -0.001858 ┆ 0.003757  ┆ 0.005933  │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-10-08 ┆ 121286.6 ┆ 123150.0 ┆ 121005.3 ┆ … ┆ 0.012611   ┆ -0.025324 ┆ -0.001858 ┆ 0.003757  │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-10-08 ┆ 122825.8 ┆ 124170.6 ┆ 121607.8 ┆ … ┆ 0.003347   ┆ 0.012611  ┆ -0.025324 ┆ -0.001858 │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-10-09 ┆ 123237.4 ┆ 123279.7 ┆ 121081.5 ┆ … ┆ -0.004592  ┆ 0.003347  ┆ 0.012611  ┆ -0.025324 │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-10-09 ┆ 122673.0 ┆ 123740.1 ┆ 119572.8 ┆ … ┆ -0.008955  ┆ -0.004592 ┆ 0.003347  ┆ 0.012611  │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "└────────────┴──────────┴──────────┴──────────┴───┴────────────┴───────────┴───────────┴───────────┘"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_size = 0.25\n",
    "_, trades = research.timeseries_split(ts, test_size)\n",
    "trades"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e267a5e",
   "metadata": {},
   "source": [
    "## Strategy Decision # 1: Entry / Exit Signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "b9e9b6a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# q1: when do we get in? entry signal\n",
    "# q2: when do we get out? exit signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "2ca8f40b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Time Based \n",
    "# 2. Predicate Based "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "cf27f866",
   "metadata": {},
   "outputs": [],
   "source": [
    "# predicate example => we only want to trade if our y_hat is above or below a certain threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "b279941f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# time based => each row represents a roundtrip trade. trade to open position at start of interval, trade to close the position at end of interval. each row = 2 trades. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "b6987f16",
   "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: (173, 9)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>open</th><th>high</th><th>low</th><th>close</th><th>close_log_return</th><th>close_log_return_lag_1</th><th>close_log_return_lag_2</th><th>close_log_return_lag_3</th></tr><tr><td>datetime[μs]</td><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>2025-07-15 12:00:00</td><td>117110.4</td><td>118499.8</td><td>115678.1</td><td>117738.5</td><td>0.005349</td><td>-0.022844</td><td>-0.013743</td><td>0.020259</td></tr><tr><td>2025-07-16 00:00:00</td><td>117738.6</td><td>119299.9</td><td>117017.1</td><td>118755.1</td><td>0.008597</td><td>0.005349</td><td>-0.022844</td><td>-0.013743</td></tr><tr><td>2025-07-16 12:00:00</td><td>118755.1</td><td>120100.0</td><td>118156.0</td><td>118590.7</td><td>-0.001385</td><td>0.008597</td><td>0.005349</td><td>-0.022844</td></tr><tr><td>2025-07-17 00:00:00</td><td>118590.7</td><td>119216.4</td><td>117663.6</td><td>117968.9</td><td>-0.005257</td><td>-0.001385</td><td>0.008597</td><td>0.005349</td></tr><tr><td>2025-07-17 12:00:00</td><td>117968.8</td><td>120951.5</td><td>117412.8</td><td>119176.6</td><td>0.010185</td><td>-0.005257</td><td>-0.001385</td><td>0.008597</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></tr><tr><td>2025-10-07 12:00:00</td><td>124397.1</td><td>125098.0</td><td>120516.0</td><td>121286.5</td><td>-0.025324</td><td>-0.001858</td><td>0.003757</td><td>0.005933</td></tr><tr><td>2025-10-08 00:00:00</td><td>121286.6</td><td>123150.0</td><td>121005.3</td><td>122825.7</td><td>0.012611</td><td>-0.025324</td><td>-0.001858</td><td>0.003757</td></tr><tr><td>2025-10-08 12:00:00</td><td>122825.8</td><td>124170.6</td><td>121607.8</td><td>123237.5</td><td>0.003347</td><td>0.012611</td><td>-0.025324</td><td>-0.001858</td></tr><tr><td>2025-10-09 00:00:00</td><td>123237.4</td><td>123279.7</td><td>121081.5</td><td>122672.9</td><td>-0.004592</td><td>0.003347</td><td>0.012611</td><td>-0.025324</td></tr><tr><td>2025-10-09 12:00:00</td><td>122673.0</td><td>123740.1</td><td>119572.8</td><td>121579.3</td><td>-0.008955</td><td>-0.004592</td><td>0.003347</td><td>0.012611</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (173, 9)\n",
       "┌────────────┬──────────┬──────────┬──────────┬───┬────────────┬───────────┬───────────┬───────────┐\n",
       "│ datetime   ┆ open     ┆ high     ┆ low      ┆ … ┆ close_log_ ┆ close_log ┆ close_log ┆ close_log │\n",
       "│ ---        ┆ ---      ┆ ---      ┆ ---      ┆   ┆ return     ┆ _return_l ┆ _return_l ┆ _return_l │\n",
       "│ datetime[μ ┆ f64      ┆ f64      ┆ f64      ┆   ┆ ---        ┆ ag_1      ┆ ag_2      ┆ ag_3      │\n",
       "│ s]         ┆          ┆          ┆          ┆   ┆ f64        ┆ ---       ┆ ---       ┆ ---       │\n",
       "│            ┆          ┆          ┆          ┆   ┆            ┆ f64       ┆ f64       ┆ f64       │\n",
       "╞════════════╪══════════╪══════════╪══════════╪═══╪════════════╪═══════════╪═══════════╪═══════════╡\n",
       "│ 2025-07-15 ┆ 117110.4 ┆ 118499.8 ┆ 115678.1 ┆ … ┆ 0.005349   ┆ -0.022844 ┆ -0.013743 ┆ 0.020259  │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-07-16 ┆ 117738.6 ┆ 119299.9 ┆ 117017.1 ┆ … ┆ 0.008597   ┆ 0.005349  ┆ -0.022844 ┆ -0.013743 │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-07-16 ┆ 118755.1 ┆ 120100.0 ┆ 118156.0 ┆ … ┆ -0.001385  ┆ 0.008597  ┆ 0.005349  ┆ -0.022844 │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-07-17 ┆ 118590.7 ┆ 119216.4 ┆ 117663.6 ┆ … ┆ -0.005257  ┆ -0.001385 ┆ 0.008597  ┆ 0.005349  │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-07-17 ┆ 117968.8 ┆ 120951.5 ┆ 117412.8 ┆ … ┆ 0.010185   ┆ -0.005257 ┆ -0.001385 ┆ 0.008597  │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ …          ┆ …        ┆ …        ┆ …        ┆ … ┆ …          ┆ …         ┆ …         ┆ …         │\n",
       "│ 2025-10-07 ┆ 124397.1 ┆ 125098.0 ┆ 120516.0 ┆ … ┆ -0.025324  ┆ -0.001858 ┆ 0.003757  ┆ 0.005933  │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-10-08 ┆ 121286.6 ┆ 123150.0 ┆ 121005.3 ┆ … ┆ 0.012611   ┆ -0.025324 ┆ -0.001858 ┆ 0.003757  │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-10-08 ┆ 122825.8 ┆ 124170.6 ┆ 121607.8 ┆ … ┆ 0.003347   ┆ 0.012611  ┆ -0.025324 ┆ -0.001858 │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-10-09 ┆ 123237.4 ┆ 123279.7 ┆ 121081.5 ┆ … ┆ -0.004592  ┆ 0.003347  ┆ 0.012611  ┆ -0.025324 │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-10-09 ┆ 122673.0 ┆ 123740.1 ┆ 119572.8 ┆ … ┆ -0.008955  ┆ -0.004592 ┆ 0.003347  ┆ 0.012611  │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "└────────────┴──────────┴──────────┴──────────┴───┴────────────┴───────────┴───────────┴───────────┘"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trades"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35b672ee",
   "metadata": {},
   "source": [
    "### Add Model's Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "f2d29dcf",
   "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: (173, 10)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>open</th><th>high</th><th>low</th><th>close</th><th>close_log_return</th><th>close_log_return_lag_1</th><th>close_log_return_lag_2</th><th>close_log_return_lag_3</th><th>y_hat</th></tr><tr><td>datetime[μs]</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f32</td></tr></thead><tbody><tr><td>2025-07-15 12:00:00</td><td>117110.4</td><td>118499.8</td><td>115678.1</td><td>117738.5</td><td>0.005349</td><td>-0.022844</td><td>-0.013743</td><td>0.020259</td><td>0.001158</td></tr><tr><td>2025-07-16 00:00:00</td><td>117738.6</td><td>119299.9</td><td>117017.1</td><td>118755.1</td><td>0.008597</td><td>0.005349</td><td>-0.022844</td><td>-0.013743</td><td>0.003119</td></tr><tr><td>2025-07-16 12:00:00</td><td>118755.1</td><td>120100.0</td><td>118156.0</td><td>118590.7</td><td>-0.001385</td><td>0.008597</td><td>0.005349</td><td>-0.022844</td><td>0.001261</td></tr><tr><td>2025-07-17 00:00:00</td><td>118590.7</td><td>119216.4</td><td>117663.6</td><td>117968.9</td><td>-0.005257</td><td>-0.001385</td><td>0.008597</td><td>0.005349</td><td>-0.000395</td></tr><tr><td>2025-07-17 12:00:00</td><td>117968.8</td><td>120951.5</td><td>117412.8</td><td>119176.6</td><td>0.010185</td><td>-0.005257</td><td>-0.001385</td><td>0.008597</td><td>0.000339</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></tr><tr><td>2025-10-07 12:00:00</td><td>124397.1</td><td>125098.0</td><td>120516.0</td><td>121286.5</td><td>-0.025324</td><td>-0.001858</td><td>0.003757</td><td>0.005933</td><td>-0.000021</td></tr><tr><td>2025-10-08 00:00:00</td><td>121286.6</td><td>123150.0</td><td>121005.3</td><td>122825.7</td><td>0.012611</td><td>-0.025324</td><td>-0.001858</td><td>0.003757</td><td>0.001237</td></tr><tr><td>2025-10-08 12:00:00</td><td>122825.8</td><td>124170.6</td><td>121607.8</td><td>123237.5</td><td>0.003347</td><td>0.012611</td><td>-0.025324</td><td>-0.001858</td><td>0.002409</td></tr><tr><td>2025-10-09 00:00:00</td><td>123237.4</td><td>123279.7</td><td>121081.5</td><td>122672.9</td><td>-0.004592</td><td>0.003347</td><td>0.012611</td><td>-0.025324</td><td>0.000964</td></tr><tr><td>2025-10-09 12:00:00</td><td>122673.0</td><td>123740.1</td><td>119572.8</td><td>121579.3</td><td>-0.008955</td><td>-0.004592</td><td>0.003347</td><td>0.012611</td><td>-0.000306</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (173, 10)\n",
       "┌────────────┬──────────┬──────────┬──────────┬───┬────────────┬───────────┬───────────┬───────────┐\n",
       "│ datetime   ┆ open     ┆ high     ┆ low      ┆ … ┆ close_log_ ┆ close_log ┆ close_log ┆ y_hat     │\n",
       "│ ---        ┆ ---      ┆ ---      ┆ ---      ┆   ┆ return_lag ┆ _return_l ┆ _return_l ┆ ---       │\n",
       "│ datetime[μ ┆ f64      ┆ f64      ┆ f64      ┆   ┆ _1         ┆ ag_2      ┆ ag_3      ┆ f32       │\n",
       "│ s]         ┆          ┆          ┆          ┆   ┆ ---        ┆ ---       ┆ ---       ┆           │\n",
       "│            ┆          ┆          ┆          ┆   ┆ f64        ┆ f64       ┆ f64       ┆           │\n",
       "╞════════════╪══════════╪══════════╪══════════╪═══╪════════════╪═══════════╪═══════════╪═══════════╡\n",
       "│ 2025-07-15 ┆ 117110.4 ┆ 118499.8 ┆ 115678.1 ┆ … ┆ -0.022844  ┆ -0.013743 ┆ 0.020259  ┆ 0.001158  │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-07-16 ┆ 117738.6 ┆ 119299.9 ┆ 117017.1 ┆ … ┆ 0.005349   ┆ -0.022844 ┆ -0.013743 ┆ 0.003119  │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-07-16 ┆ 118755.1 ┆ 120100.0 ┆ 118156.0 ┆ … ┆ 0.008597   ┆ 0.005349  ┆ -0.022844 ┆ 0.001261  │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-07-17 ┆ 118590.7 ┆ 119216.4 ┆ 117663.6 ┆ … ┆ -0.001385  ┆ 0.008597  ┆ 0.005349  ┆ -0.000395 │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-07-17 ┆ 117968.8 ┆ 120951.5 ┆ 117412.8 ┆ … ┆ -0.005257  ┆ -0.001385 ┆ 0.008597  ┆ 0.000339  │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ …          ┆ …        ┆ …        ┆ …        ┆ … ┆ …          ┆ …         ┆ …         ┆ …         │\n",
       "│ 2025-10-07 ┆ 124397.1 ┆ 125098.0 ┆ 120516.0 ┆ … ┆ -0.001858  ┆ 0.003757  ┆ 0.005933  ┆ -0.000021 │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-10-08 ┆ 121286.6 ┆ 123150.0 ┆ 121005.3 ┆ … ┆ -0.025324  ┆ -0.001858 ┆ 0.003757  ┆ 0.001237  │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-10-08 ┆ 122825.8 ┆ 124170.6 ┆ 121607.8 ┆ … ┆ 0.012611   ┆ -0.025324 ┆ -0.001858 ┆ 0.002409  │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-10-09 ┆ 123237.4 ┆ 123279.7 ┆ 121081.5 ┆ … ┆ 0.003347   ┆ 0.012611  ┆ -0.025324 ┆ 0.000964  │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-10-09 ┆ 122673.0 ┆ 123740.1 ┆ 119572.8 ┆ … ┆ -0.004592  ┆ 0.003347  ┆ 0.012611  ┆ -0.000306 │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "└────────────┴──────────┴──────────┴──────────┴───┴────────────┴───────────┴───────────┴───────────┘"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target = 'close_log_return'\n",
    "features = [f'{target}_lag_1',f'{target}_lag_2',f'{target}_lag_3']\n",
    "trades = research.add_model_predictions(trades, model, features)\n",
    "trades"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3cf64ab",
   "metadata": {},
   "source": [
    "### Add Directional Signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "71f913c7",
   "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: (173, 11)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>open</th><th>high</th><th>low</th><th>close</th><th>close_log_return</th><th>close_log_return_lag_1</th><th>close_log_return_lag_2</th><th>close_log_return_lag_3</th><th>y_hat</th><th>dir_signal</th></tr><tr><td>datetime[μs]</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f32</td><td>f32</td></tr></thead><tbody><tr><td>2025-07-15 12:00:00</td><td>117110.4</td><td>118499.8</td><td>115678.1</td><td>117738.5</td><td>0.005349</td><td>-0.022844</td><td>-0.013743</td><td>0.020259</td><td>0.001158</td><td>1.0</td></tr><tr><td>2025-07-16 00:00:00</td><td>117738.6</td><td>119299.9</td><td>117017.1</td><td>118755.1</td><td>0.008597</td><td>0.005349</td><td>-0.022844</td><td>-0.013743</td><td>0.003119</td><td>1.0</td></tr><tr><td>2025-07-16 12:00:00</td><td>118755.1</td><td>120100.0</td><td>118156.0</td><td>118590.7</td><td>-0.001385</td><td>0.008597</td><td>0.005349</td><td>-0.022844</td><td>0.001261</td><td>1.0</td></tr><tr><td>2025-07-17 00:00:00</td><td>118590.7</td><td>119216.4</td><td>117663.6</td><td>117968.9</td><td>-0.005257</td><td>-0.001385</td><td>0.008597</td><td>0.005349</td><td>-0.000395</td><td>-1.0</td></tr><tr><td>2025-07-17 12:00:00</td><td>117968.8</td><td>120951.5</td><td>117412.8</td><td>119176.6</td><td>0.010185</td><td>-0.005257</td><td>-0.001385</td><td>0.008597</td><td>0.000339</td><td>1.0</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></tr><tr><td>2025-10-07 12:00:00</td><td>124397.1</td><td>125098.0</td><td>120516.0</td><td>121286.5</td><td>-0.025324</td><td>-0.001858</td><td>0.003757</td><td>0.005933</td><td>-0.000021</td><td>-1.0</td></tr><tr><td>2025-10-08 00:00:00</td><td>121286.6</td><td>123150.0</td><td>121005.3</td><td>122825.7</td><td>0.012611</td><td>-0.025324</td><td>-0.001858</td><td>0.003757</td><td>0.001237</td><td>1.0</td></tr><tr><td>2025-10-08 12:00:00</td><td>122825.8</td><td>124170.6</td><td>121607.8</td><td>123237.5</td><td>0.003347</td><td>0.012611</td><td>-0.025324</td><td>-0.001858</td><td>0.002409</td><td>1.0</td></tr><tr><td>2025-10-09 00:00:00</td><td>123237.4</td><td>123279.7</td><td>121081.5</td><td>122672.9</td><td>-0.004592</td><td>0.003347</td><td>0.012611</td><td>-0.025324</td><td>0.000964</td><td>1.0</td></tr><tr><td>2025-10-09 12:00:00</td><td>122673.0</td><td>123740.1</td><td>119572.8</td><td>121579.3</td><td>-0.008955</td><td>-0.004592</td><td>0.003347</td><td>0.012611</td><td>-0.000306</td><td>-1.0</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (173, 11)\n",
       "┌────────────┬──────────┬──────────┬──────────┬───┬────────────┬───────────┬───────────┬───────────┐\n",
       "│ datetime   ┆ open     ┆ high     ┆ low      ┆ … ┆ close_log_ ┆ close_log ┆ y_hat     ┆ dir_signa │\n",
       "│ ---        ┆ ---      ┆ ---      ┆ ---      ┆   ┆ return_lag ┆ _return_l ┆ ---       ┆ l         │\n",
       "│ datetime[μ ┆ f64      ┆ f64      ┆ f64      ┆   ┆ _2         ┆ ag_3      ┆ f32       ┆ ---       │\n",
       "│ s]         ┆          ┆          ┆          ┆   ┆ ---        ┆ ---       ┆           ┆ f32       │\n",
       "│            ┆          ┆          ┆          ┆   ┆ f64        ┆ f64       ┆           ┆           │\n",
       "╞════════════╪══════════╪══════════╪══════════╪═══╪════════════╪═══════════╪═══════════╪═══════════╡\n",
       "│ 2025-07-15 ┆ 117110.4 ┆ 118499.8 ┆ 115678.1 ┆ … ┆ -0.013743  ┆ 0.020259  ┆ 0.001158  ┆ 1.0       │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-07-16 ┆ 117738.6 ┆ 119299.9 ┆ 117017.1 ┆ … ┆ -0.022844  ┆ -0.013743 ┆ 0.003119  ┆ 1.0       │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-07-16 ┆ 118755.1 ┆ 120100.0 ┆ 118156.0 ┆ … ┆ 0.005349   ┆ -0.022844 ┆ 0.001261  ┆ 1.0       │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-07-17 ┆ 118590.7 ┆ 119216.4 ┆ 117663.6 ┆ … ┆ 0.008597   ┆ 0.005349  ┆ -0.000395 ┆ -1.0      │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-07-17 ┆ 117968.8 ┆ 120951.5 ┆ 117412.8 ┆ … ┆ -0.001385  ┆ 0.008597  ┆ 0.000339  ┆ 1.0       │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ …          ┆ …        ┆ …        ┆ …        ┆ … ┆ …          ┆ …         ┆ …         ┆ …         │\n",
       "│ 2025-10-07 ┆ 124397.1 ┆ 125098.0 ┆ 120516.0 ┆ … ┆ 0.003757   ┆ 0.005933  ┆ -0.000021 ┆ -1.0      │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-10-08 ┆ 121286.6 ┆ 123150.0 ┆ 121005.3 ┆ … ┆ -0.001858  ┆ 0.003757  ┆ 0.001237  ┆ 1.0       │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-10-08 ┆ 122825.8 ┆ 124170.6 ┆ 121607.8 ┆ … ┆ -0.025324  ┆ -0.001858 ┆ 0.002409  ┆ 1.0       │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-10-09 ┆ 123237.4 ┆ 123279.7 ┆ 121081.5 ┆ … ┆ 0.012611   ┆ -0.025324 ┆ 0.000964  ┆ 1.0       │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-10-09 ┆ 122673.0 ┆ 123740.1 ┆ 119572.8 ┆ … ┆ 0.003347   ┆ 0.012611  ┆ -0.000306 ┆ -1.0      │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "└────────────┴──────────┴──────────┴──────────┴───┴────────────┴───────────┴───────────┴───────────┘"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trades = trades.with_columns(pl.col('y_hat').sign().alias('dir_signal'))\n",
    "trades"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e45b351",
   "metadata": {},
   "source": [
    "### Calculate Trade Log Return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "3ff95e7b",
   "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: (173, 12)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>open</th><th>high</th><th>low</th><th>close</th><th>close_log_return</th><th>close_log_return_lag_1</th><th>close_log_return_lag_2</th><th>close_log_return_lag_3</th><th>y_hat</th><th>dir_signal</th><th>trade_log_return</th></tr><tr><td>datetime[μs]</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f32</td><td>f32</td><td>f64</td></tr></thead><tbody><tr><td>2025-07-15 12:00:00</td><td>117110.4</td><td>118499.8</td><td>115678.1</td><td>117738.5</td><td>0.005349</td><td>-0.022844</td><td>-0.013743</td><td>0.020259</td><td>0.001158</td><td>1.0</td><td>0.005349</td></tr><tr><td>2025-07-16 00:00:00</td><td>117738.6</td><td>119299.9</td><td>117017.1</td><td>118755.1</td><td>0.008597</td><td>0.005349</td><td>-0.022844</td><td>-0.013743</td><td>0.003119</td><td>1.0</td><td>0.008597</td></tr><tr><td>2025-07-16 12:00:00</td><td>118755.1</td><td>120100.0</td><td>118156.0</td><td>118590.7</td><td>-0.001385</td><td>0.008597</td><td>0.005349</td><td>-0.022844</td><td>0.001261</td><td>1.0</td><td>-0.001385</td></tr><tr><td>2025-07-17 00:00:00</td><td>118590.7</td><td>119216.4</td><td>117663.6</td><td>117968.9</td><td>-0.005257</td><td>-0.001385</td><td>0.008597</td><td>0.005349</td><td>-0.000395</td><td>-1.0</td><td>0.005257</td></tr><tr><td>2025-07-17 12:00:00</td><td>117968.8</td><td>120951.5</td><td>117412.8</td><td>119176.6</td><td>0.010185</td><td>-0.005257</td><td>-0.001385</td><td>0.008597</td><td>0.000339</td><td>1.0</td><td>0.010185</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></tr><tr><td>2025-10-07 12:00:00</td><td>124397.1</td><td>125098.0</td><td>120516.0</td><td>121286.5</td><td>-0.025324</td><td>-0.001858</td><td>0.003757</td><td>0.005933</td><td>-0.000021</td><td>-1.0</td><td>0.025324</td></tr><tr><td>2025-10-08 00:00:00</td><td>121286.6</td><td>123150.0</td><td>121005.3</td><td>122825.7</td><td>0.012611</td><td>-0.025324</td><td>-0.001858</td><td>0.003757</td><td>0.001237</td><td>1.0</td><td>0.012611</td></tr><tr><td>2025-10-08 12:00:00</td><td>122825.8</td><td>124170.6</td><td>121607.8</td><td>123237.5</td><td>0.003347</td><td>0.012611</td><td>-0.025324</td><td>-0.001858</td><td>0.002409</td><td>1.0</td><td>0.003347</td></tr><tr><td>2025-10-09 00:00:00</td><td>123237.4</td><td>123279.7</td><td>121081.5</td><td>122672.9</td><td>-0.004592</td><td>0.003347</td><td>0.012611</td><td>-0.025324</td><td>0.000964</td><td>1.0</td><td>-0.004592</td></tr><tr><td>2025-10-09 12:00:00</td><td>122673.0</td><td>123740.1</td><td>119572.8</td><td>121579.3</td><td>-0.008955</td><td>-0.004592</td><td>0.003347</td><td>0.012611</td><td>-0.000306</td><td>-1.0</td><td>0.008955</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (173, 12)\n",
       "┌────────────┬──────────┬──────────┬──────────┬───┬────────────┬───────────┬───────────┬───────────┐\n",
       "│ datetime   ┆ open     ┆ high     ┆ low      ┆ … ┆ close_log_ ┆ y_hat     ┆ dir_signa ┆ trade_log │\n",
       "│ ---        ┆ ---      ┆ ---      ┆ ---      ┆   ┆ return_lag ┆ ---       ┆ l         ┆ _return   │\n",
       "│ datetime[μ ┆ f64      ┆ f64      ┆ f64      ┆   ┆ _3         ┆ f32       ┆ ---       ┆ ---       │\n",
       "│ s]         ┆          ┆          ┆          ┆   ┆ ---        ┆           ┆ f32       ┆ f64       │\n",
       "│            ┆          ┆          ┆          ┆   ┆ f64        ┆           ┆           ┆           │\n",
       "╞════════════╪══════════╪══════════╪══════════╪═══╪════════════╪═══════════╪═══════════╪═══════════╡\n",
       "│ 2025-07-15 ┆ 117110.4 ┆ 118499.8 ┆ 115678.1 ┆ … ┆ 0.020259   ┆ 0.001158  ┆ 1.0       ┆ 0.005349  │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-07-16 ┆ 117738.6 ┆ 119299.9 ┆ 117017.1 ┆ … ┆ -0.013743  ┆ 0.003119  ┆ 1.0       ┆ 0.008597  │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-07-16 ┆ 118755.1 ┆ 120100.0 ┆ 118156.0 ┆ … ┆ -0.022844  ┆ 0.001261  ┆ 1.0       ┆ -0.001385 │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-07-17 ┆ 118590.7 ┆ 119216.4 ┆ 117663.6 ┆ … ┆ 0.005349   ┆ -0.000395 ┆ -1.0      ┆ 0.005257  │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-07-17 ┆ 117968.8 ┆ 120951.5 ┆ 117412.8 ┆ … ┆ 0.008597   ┆ 0.000339  ┆ 1.0       ┆ 0.010185  │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ …          ┆ …        ┆ …        ┆ …        ┆ … ┆ …          ┆ …         ┆ …         ┆ …         │\n",
       "│ 2025-10-07 ┆ 124397.1 ┆ 125098.0 ┆ 120516.0 ┆ … ┆ 0.005933   ┆ -0.000021 ┆ -1.0      ┆ 0.025324  │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-10-08 ┆ 121286.6 ┆ 123150.0 ┆ 121005.3 ┆ … ┆ 0.003757   ┆ 0.001237  ┆ 1.0       ┆ 0.012611  │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-10-08 ┆ 122825.8 ┆ 124170.6 ┆ 121607.8 ┆ … ┆ -0.001858  ┆ 0.002409  ┆ 1.0       ┆ 0.003347  │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-10-09 ┆ 123237.4 ┆ 123279.7 ┆ 121081.5 ┆ … ┆ -0.025324  ┆ 0.000964  ┆ 1.0       ┆ -0.004592 │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-10-09 ┆ 122673.0 ┆ 123740.1 ┆ 119572.8 ┆ … ┆ 0.012611   ┆ -0.000306 ┆ -1.0      ┆ 0.008955  │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "└────────────┴──────────┴──────────┴──────────┴───┴────────────┴───────────┴───────────┴───────────┘"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trades = trades.with_columns((pl.col('close_log_return')*pl.col('dir_signal')).alias('trade_log_return'))\n",
    "trades"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42c72f51",
   "metadata": {},
   "source": [
    "### Calculate Cumulative Trade Log Return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "c7ab2f3d",
   "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: (173, 13)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>open</th><th>high</th><th>low</th><th>close</th><th>close_log_return</th><th>close_log_return_lag_1</th><th>close_log_return_lag_2</th><th>close_log_return_lag_3</th><th>y_hat</th><th>dir_signal</th><th>trade_log_return</th><th>cum_trade_log_return</th></tr><tr><td>datetime[μs]</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f32</td><td>f32</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2025-07-15 12:00:00</td><td>117110.4</td><td>118499.8</td><td>115678.1</td><td>117738.5</td><td>0.005349</td><td>-0.022844</td><td>-0.013743</td><td>0.020259</td><td>0.001158</td><td>1.0</td><td>0.005349</td><td>0.005349</td></tr><tr><td>2025-07-16 00:00:00</td><td>117738.6</td><td>119299.9</td><td>117017.1</td><td>118755.1</td><td>0.008597</td><td>0.005349</td><td>-0.022844</td><td>-0.013743</td><td>0.003119</td><td>1.0</td><td>0.008597</td><td>0.013946</td></tr><tr><td>2025-07-16 12:00:00</td><td>118755.1</td><td>120100.0</td><td>118156.0</td><td>118590.7</td><td>-0.001385</td><td>0.008597</td><td>0.005349</td><td>-0.022844</td><td>0.001261</td><td>1.0</td><td>-0.001385</td><td>0.012561</td></tr><tr><td>2025-07-17 00:00:00</td><td>118590.7</td><td>119216.4</td><td>117663.6</td><td>117968.9</td><td>-0.005257</td><td>-0.001385</td><td>0.008597</td><td>0.005349</td><td>-0.000395</td><td>-1.0</td><td>0.005257</td><td>0.017818</td></tr><tr><td>2025-07-17 12:00:00</td><td>117968.8</td><td>120951.5</td><td>117412.8</td><td>119176.6</td><td>0.010185</td><td>-0.005257</td><td>-0.001385</td><td>0.008597</td><td>0.000339</td><td>1.0</td><td>0.010185</td><td>0.028003</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>2025-10-07 12:00:00</td><td>124397.1</td><td>125098.0</td><td>120516.0</td><td>121286.5</td><td>-0.025324</td><td>-0.001858</td><td>0.003757</td><td>0.005933</td><td>-0.000021</td><td>-1.0</td><td>0.025324</td><td>0.011273</td></tr><tr><td>2025-10-08 00:00:00</td><td>121286.6</td><td>123150.0</td><td>121005.3</td><td>122825.7</td><td>0.012611</td><td>-0.025324</td><td>-0.001858</td><td>0.003757</td><td>0.001237</td><td>1.0</td><td>0.012611</td><td>0.023884</td></tr><tr><td>2025-10-08 12:00:00</td><td>122825.8</td><td>124170.6</td><td>121607.8</td><td>123237.5</td><td>0.003347</td><td>0.012611</td><td>-0.025324</td><td>-0.001858</td><td>0.002409</td><td>1.0</td><td>0.003347</td><td>0.027231</td></tr><tr><td>2025-10-09 00:00:00</td><td>123237.4</td><td>123279.7</td><td>121081.5</td><td>122672.9</td><td>-0.004592</td><td>0.003347</td><td>0.012611</td><td>-0.025324</td><td>0.000964</td><td>1.0</td><td>-0.004592</td><td>0.022639</td></tr><tr><td>2025-10-09 12:00:00</td><td>122673.0</td><td>123740.1</td><td>119572.8</td><td>121579.3</td><td>-0.008955</td><td>-0.004592</td><td>0.003347</td><td>0.012611</td><td>-0.000306</td><td>-1.0</td><td>0.008955</td><td>0.031594</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (173, 13)\n",
       "┌────────────┬──────────┬──────────┬──────────┬───┬───────────┬────────────┬───────────┬───────────┐\n",
       "│ datetime   ┆ open     ┆ high     ┆ low      ┆ … ┆ y_hat     ┆ dir_signal ┆ trade_log ┆ cum_trade │\n",
       "│ ---        ┆ ---      ┆ ---      ┆ ---      ┆   ┆ ---       ┆ ---        ┆ _return   ┆ _log_retu │\n",
       "│ datetime[μ ┆ f64      ┆ f64      ┆ f64      ┆   ┆ f32       ┆ f32        ┆ ---       ┆ rn        │\n",
       "│ s]         ┆          ┆          ┆          ┆   ┆           ┆            ┆ f64       ┆ ---       │\n",
       "│            ┆          ┆          ┆          ┆   ┆           ┆            ┆           ┆ f64       │\n",
       "╞════════════╪══════════╪══════════╪══════════╪═══╪═══════════╪════════════╪═══════════╪═══════════╡\n",
       "│ 2025-07-15 ┆ 117110.4 ┆ 118499.8 ┆ 115678.1 ┆ … ┆ 0.001158  ┆ 1.0        ┆ 0.005349  ┆ 0.005349  │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆           ┆            ┆           ┆           │\n",
       "│ 2025-07-16 ┆ 117738.6 ┆ 119299.9 ┆ 117017.1 ┆ … ┆ 0.003119  ┆ 1.0        ┆ 0.008597  ┆ 0.013946  │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆           ┆            ┆           ┆           │\n",
       "│ 2025-07-16 ┆ 118755.1 ┆ 120100.0 ┆ 118156.0 ┆ … ┆ 0.001261  ┆ 1.0        ┆ -0.001385 ┆ 0.012561  │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆           ┆            ┆           ┆           │\n",
       "│ 2025-07-17 ┆ 118590.7 ┆ 119216.4 ┆ 117663.6 ┆ … ┆ -0.000395 ┆ -1.0       ┆ 0.005257  ┆ 0.017818  │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆           ┆            ┆           ┆           │\n",
       "│ 2025-07-17 ┆ 117968.8 ┆ 120951.5 ┆ 117412.8 ┆ … ┆ 0.000339  ┆ 1.0        ┆ 0.010185  ┆ 0.028003  │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆           ┆            ┆           ┆           │\n",
       "│ …          ┆ …        ┆ …        ┆ …        ┆ … ┆ …         ┆ …          ┆ …         ┆ …         │\n",
       "│ 2025-10-07 ┆ 124397.1 ┆ 125098.0 ┆ 120516.0 ┆ … ┆ -0.000021 ┆ -1.0       ┆ 0.025324  ┆ 0.011273  │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆           ┆            ┆           ┆           │\n",
       "│ 2025-10-08 ┆ 121286.6 ┆ 123150.0 ┆ 121005.3 ┆ … ┆ 0.001237  ┆ 1.0        ┆ 0.012611  ┆ 0.023884  │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆           ┆            ┆           ┆           │\n",
       "│ 2025-10-08 ┆ 122825.8 ┆ 124170.6 ┆ 121607.8 ┆ … ┆ 0.002409  ┆ 1.0        ┆ 0.003347  ┆ 0.027231  │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆           ┆            ┆           ┆           │\n",
       "│ 2025-10-09 ┆ 123237.4 ┆ 123279.7 ┆ 121081.5 ┆ … ┆ 0.000964  ┆ 1.0        ┆ -0.004592 ┆ 0.022639  │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆           ┆            ┆           ┆           │\n",
       "│ 2025-10-09 ┆ 122673.0 ┆ 123740.1 ┆ 119572.8 ┆ … ┆ -0.000306 ┆ -1.0       ┆ 0.008955  ┆ 0.031594  │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆           ┆            ┆           ┆           │\n",
       "└────────────┴──────────┴──────────┴──────────┴───┴───────────┴────────────┴───────────┴───────────┘"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trades = trades.with_columns(pl.col('trade_log_return').cum_sum().alias('cum_trade_log_return'))\n",
    "trades"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1c67bcb",
   "metadata": {},
   "source": [
    "### Display Equity Curve (Log Space)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "8952e3b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "  #altair-viz-688acfcd33a64cae83c2939ddcc5247b.vega-embed {\n",
       "    width: 100%;\n",
       "    display: flex;\n",
       "  }\n",
       "\n",
       "  #altair-viz-688acfcd33a64cae83c2939ddcc5247b.vega-embed details,\n",
       "  #altair-viz-688acfcd33a64cae83c2939ddcc5247b.vega-embed details summary {\n",
       "    position: relative;\n",
       "  }\n",
       "</style>\n",
       "<div id=\"altair-viz-688acfcd33a64cae83c2939ddcc5247b\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-688acfcd33a64cae83c2939ddcc5247b\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-688acfcd33a64cae83c2939ddcc5247b\");\n",
       "    }\n",
       "\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@6?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@6.4.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@7?noext\",\n",
       "    };\n",
       "\n",
       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
       "      return (VEGA_DEBUG[key] == version) ?\n",
       "        Promise.resolve(paths[lib]) :\n",
       "        new Promise(function(resolve, reject) {\n",
       "          var s = document.createElement('script');\n",
       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "          s.async = true;\n",
       "          s.onload = () => {\n",
       "            VEGA_DEBUG[key] = version;\n",
       "            return resolve(paths[lib]);\n",
       "          };\n",
       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      let deps = [\"vega-embed\"];\n",
       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"6\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"6.4.1\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"7\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-cf7c83dfdbf398e5e99c62267ce75e02\"}, \"mark\": {\"type\": \"line\", \"tooltip\": true}, \"encoding\": {\"x\": {\"field\": \"index\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"cum_trade_log_return\", \"type\": \"quantitative\"}}, \"height\": 400, \"params\": [{\"name\": \"param_1e9efca18e7a2868\", \"select\": {\"type\": \"interval\", \"encodings\": [\"x\", \"y\"]}, \"bind\": \"scales\"}], \"title\": \"cum_trade_log_return\", \"width\": 800, \"$schema\": \"https://vega.github.io/schema/vega-lite/v6.4.1.json\", \"datasets\": {\"data-cf7c83dfdbf398e5e99c62267ce75e02\": [{\"index\": 0, \"cum_trade_log_return\": 0.005348983943119523}, {\"index\": 1, \"cum_trade_log_return\": 0.013946309720568758}, {\"index\": 2, \"cum_trade_log_return\": 0.012560989008832017}, {\"index\": 3, \"cum_trade_log_return\": 0.017818027252286206}, {\"index\": 4, \"cum_trade_log_return\": 0.02800342348185465}, {\"index\": 5, \"cum_trade_log_return\": 0.027994193439408958}, {\"index\": 6, \"cum_trade_log_return\": 0.01720326957251459}, {\"index\": 7, \"cum_trade_log_return\": 0.020620767830858325}, {\"index\": 8, \"cum_trade_log_return\": 0.01650750167975812}, {\"index\": 9, \"cum_trade_log_return\": 0.01697253192358974}, {\"index\": 10, \"cum_trade_log_return\": 0.011415383442921}, {\"index\": 11, \"cum_trade_log_return\": 0.018462904546693696}, {\"index\": 12, \"cum_trade_log_return\": 0.012389178874477255}, {\"index\": 13, \"cum_trade_log_return\": 0.027714734547209584}, {\"index\": 14, \"cum_trade_log_return\": 0.034836238250389694}, {\"index\": 15, \"cum_trade_log_return\": 0.04749079734857599}, {\"index\": 16, \"cum_trade_log_return\": 0.045562452407919306}, {\"index\": 17, \"cum_trade_log_return\": 0.04425171747125176}, {\"index\": 18, \"cum_trade_log_return\": 0.042025012398455186}, {\"index\": 19, \"cum_trade_log_return\": 0.026780182366361087}, {\"index\": 20, \"cum_trade_log_return\": 0.03599814465982896}, {\"index\": 21, \"cum_trade_log_return\": 0.03748109630226544}, {\"index\": 22, \"cum_trade_log_return\": 0.03859462545565035}, {\"index\": 23, \"cum_trade_log_return\": 0.03720276127759571}, {\"index\": 24, \"cum_trade_log_return\": 0.04874085846191427}, {\"index\": 25, \"cum_trade_log_return\": 0.04361905032971036}, {\"index\": 26, \"cum_trade_log_return\": 0.05013699346323931}, {\"index\": 27, \"cum_trade_log_return\": 0.05452074462030153}, {\"index\": 28, \"cum_trade_log_return\": 0.0489129449994922}, {\"index\": 29, \"cum_trade_log_return\": 0.04576712488554693}, {\"index\": 30, \"cum_trade_log_return\": 0.04784364401670497}, {\"index\": 31, \"cum_trade_log_return\": 0.052586520288119984}, {\"index\": 32, \"cum_trade_log_return\": 0.03007888078551455}, {\"index\": 33, \"cum_trade_log_return\": 0.026175763429518443}, {\"index\": 34, \"cum_trade_log_return\": 0.008653430266700825}, {\"index\": 35, \"cum_trade_log_return\": 0.01295078291420498}, {\"index\": 36, \"cum_trade_log_return\": 0.002133023880023309}, {\"index\": 37, \"cum_trade_log_return\": 0.013632853023348672}, {\"index\": 38, \"cum_trade_log_return\": 0.01662196434615386}, {\"index\": 39, \"cum_trade_log_return\": 0.01923962947110035}, {\"index\": 40, \"cum_trade_log_return\": 0.014427286803588426}, {\"index\": 41, \"cum_trade_log_return\": 0.012511574904348795}, {\"index\": 42, \"cum_trade_log_return\": 0.00628568615241631}, {\"index\": 43, \"cum_trade_log_return\": 0.007323110629139001}, {\"index\": 44, \"cum_trade_log_return\": 0.013895850970654027}, {\"index\": 45, \"cum_trade_log_return\": 0.027102899510451327}, {\"index\": 46, \"cum_trade_log_return\": 0.018972502291396964}, {\"index\": 47, \"cum_trade_log_return\": 0.0239191444166333}, {\"index\": 48, \"cum_trade_log_return\": 0.025737804121472474}, {\"index\": 49, \"cum_trade_log_return\": 0.029475188822349827}, {\"index\": 50, \"cum_trade_log_return\": 0.023969086778224423}, {\"index\": 51, \"cum_trade_log_return\": 0.039935819693251794}, {\"index\": 52, \"cum_trade_log_return\": 0.04801328018481317}, {\"index\": 53, \"cum_trade_log_return\": 0.037256726208820035}, {\"index\": 54, \"cum_trade_log_return\": 0.053170140873848346}, {\"index\": 55, \"cum_trade_log_return\": 0.0546698923211035}, {\"index\": 56, \"cum_trade_log_return\": 0.06822644630413364}, {\"index\": 57, \"cum_trade_log_return\": 0.07202913520760364}, {\"index\": 58, \"cum_trade_log_return\": 0.049184111624649876}, {\"index\": 59, \"cum_trade_log_return\": 0.06863846482428468}, {\"index\": 60, \"cum_trade_log_return\": 0.09126327593165752}, {\"index\": 61, \"cum_trade_log_return\": 0.0972314494291983}, {\"index\": 62, \"cum_trade_log_return\": 0.0830856295129068}, {\"index\": 63, \"cum_trade_log_return\": 0.08360732506935695}, {\"index\": 64, \"cum_trade_log_return\": 0.0835161332216745}, {\"index\": 65, \"cum_trade_log_return\": 0.09136222048092947}, {\"index\": 66, \"cum_trade_log_return\": 0.0837215162169401}, {\"index\": 67, \"cum_trade_log_return\": 0.06259039753494158}, {\"index\": 68, \"cum_trade_log_return\": 0.07369991116496266}, {\"index\": 69, \"cum_trade_log_return\": 0.06746102328365412}, {\"index\": 70, \"cum_trade_log_return\": 0.04435906774108046}, {\"index\": 71, \"cum_trade_log_return\": 0.05145376713604966}, {\"index\": 72, \"cum_trade_log_return\": 0.056659003424439455}, {\"index\": 73, \"cum_trade_log_return\": 0.046802364147338696}, {\"index\": 74, \"cum_trade_log_return\": 0.04114537786405878}, {\"index\": 75, \"cum_trade_log_return\": 0.039459836717008076}, {\"index\": 76, \"cum_trade_log_return\": 0.07982569512974586}, {\"index\": 77, \"cum_trade_log_return\": 0.09387460381372191}, {\"index\": 78, \"cum_trade_log_return\": 0.09291728340509647}, {\"index\": 79, \"cum_trade_log_return\": 0.09878571680432396}, {\"index\": 80, \"cum_trade_log_return\": 0.0877778532372899}, {\"index\": 81, \"cum_trade_log_return\": 0.06763204436775777}, {\"index\": 82, \"cum_trade_log_return\": 0.057510270813045966}, {\"index\": 83, \"cum_trade_log_return\": 0.0554930464218076}, {\"index\": 84, \"cum_trade_log_return\": 0.07235358843673716}, {\"index\": 85, \"cum_trade_log_return\": 0.06841512589185314}, {\"index\": 86, \"cum_trade_log_return\": 0.0690974782989429}, {\"index\": 87, \"cum_trade_log_return\": 0.05408769506892268}, {\"index\": 88, \"cum_trade_log_return\": 0.05081150281169768}, {\"index\": 89, \"cum_trade_log_return\": 0.07291479438044002}, {\"index\": 90, \"cum_trade_log_return\": 0.0572042557609113}, {\"index\": 91, \"cum_trade_log_return\": 0.060026754650426555}, {\"index\": 92, \"cum_trade_log_return\": 0.061124288529541645}, {\"index\": 93, \"cum_trade_log_return\": 0.05736670550337957}, {\"index\": 94, \"cum_trade_log_return\": 0.056058974931119276}, {\"index\": 95, \"cum_trade_log_return\": 0.06079322329655161}, {\"index\": 96, \"cum_trade_log_return\": 0.06507022883158724}, {\"index\": 97, \"cum_trade_log_return\": 0.06973658264236429}, {\"index\": 98, \"cum_trade_log_return\": 0.05625072543939889}, {\"index\": 99, \"cum_trade_log_return\": 0.05442126706328499}, {\"index\": 100, \"cum_trade_log_return\": 0.0520164390348174}, {\"index\": 101, \"cum_trade_log_return\": 0.06007227881325261}, {\"index\": 102, \"cum_trade_log_return\": 0.0593091037332089}, {\"index\": 103, \"cum_trade_log_return\": 0.07384166971304158}, {\"index\": 104, \"cum_trade_log_return\": 0.058726169587806264}, {\"index\": 105, \"cum_trade_log_return\": 0.05765997411463442}, {\"index\": 106, \"cum_trade_log_return\": 0.05224858887857805}, {\"index\": 107, \"cum_trade_log_return\": 0.06231440557067174}, {\"index\": 108, \"cum_trade_log_return\": 0.06075363062384135}, {\"index\": 109, \"cum_trade_log_return\": 0.06923425949340163}, {\"index\": 110, \"cum_trade_log_return\": 0.06931817510291476}, {\"index\": 111, \"cum_trade_log_return\": 0.06588873455090732}, {\"index\": 112, \"cum_trade_log_return\": 0.07390584603157418}, {\"index\": 113, \"cum_trade_log_return\": 0.08075677039311835}, {\"index\": 114, \"cum_trade_log_return\": 0.09517492723124155}, {\"index\": 115, \"cum_trade_log_return\": 0.0957375590992094}, {\"index\": 116, \"cum_trade_log_return\": 0.0829546706939028}, {\"index\": 117, \"cum_trade_log_return\": 0.08785285235730264}, {\"index\": 118, \"cum_trade_log_return\": 0.07818899933447294}, {\"index\": 119, \"cum_trade_log_return\": 0.07875391920634343}, {\"index\": 120, \"cum_trade_log_return\": 0.07832074159157322}, {\"index\": 121, \"cum_trade_log_return\": 0.07734756517340109}, {\"index\": 122, \"cum_trade_log_return\": 0.07267142225701985}, {\"index\": 123, \"cum_trade_log_return\": 0.07092525837765089}, {\"index\": 124, \"cum_trade_log_return\": 0.07345750328466308}, {\"index\": 125, \"cum_trade_log_return\": 0.07277731023631616}, {\"index\": 126, \"cum_trade_log_return\": 0.08592664215031687}, {\"index\": 127, \"cum_trade_log_return\": 0.08122857720969638}, {\"index\": 128, \"cum_trade_log_return\": 0.07948826948240885}, {\"index\": 129, \"cum_trade_log_return\": 0.08473638846086909}, {\"index\": 130, \"cum_trade_log_return\": 0.08475433471696023}, {\"index\": 131, \"cum_trade_log_return\": 0.07897784091757552}, {\"index\": 132, \"cum_trade_log_return\": 0.07230633734819497}, {\"index\": 133, \"cum_trade_log_return\": 0.07460015817761169}, {\"index\": 134, \"cum_trade_log_return\": 0.07278558771983332}, {\"index\": 135, \"cum_trade_log_return\": 0.07221807487284253}, {\"index\": 136, \"cum_trade_log_return\": 0.0689642383029176}, {\"index\": 137, \"cum_trade_log_return\": 0.048175588004592526}, {\"index\": 138, \"cum_trade_log_return\": 0.04631771226191084}, {\"index\": 139, \"cum_trade_log_return\": 0.049278564957885226}, {\"index\": 140, \"cum_trade_log_return\": 0.040459801760648145}, {\"index\": 141, \"cum_trade_log_return\": 0.04966277074110182}, {\"index\": 142, \"cum_trade_log_return\": 0.05206734058018717}, {\"index\": 143, \"cum_trade_log_return\": 0.03480815866063894}, {\"index\": 144, \"cum_trade_log_return\": 0.01318104092719128}, {\"index\": 145, \"cum_trade_log_return\": 0.014542400368402309}, {\"index\": 146, \"cum_trade_log_return\": 0.019166698753875534}, {\"index\": 147, \"cum_trade_log_return\": 0.016217702696997172}, {\"index\": 148, \"cum_trade_log_return\": 0.019064493026433052}, {\"index\": 149, \"cum_trade_log_return\": 0.016712703066231186}, {\"index\": 150, \"cum_trade_log_return\": 0.04200041543657036}, {\"index\": 151, \"cum_trade_log_return\": 0.04260352485854063}, {\"index\": 152, \"cum_trade_log_return\": 0.023115401857096814}, {\"index\": 153, \"cum_trade_log_return\": 0.034146012581537516}, {\"index\": 154, \"cum_trade_log_return\": 0.0254663761666137}, {\"index\": 155, \"cum_trade_log_return\": 0.04928800627107452}, {\"index\": 156, \"cum_trade_log_return\": 0.03385477740056224}, {\"index\": 157, \"cum_trade_log_return\": 0.03247574820049335}, {\"index\": 158, \"cum_trade_log_return\": 0.0177152801635665}, {\"index\": 159, \"cum_trade_log_return\": 0.019161362765843393}, {\"index\": 160, \"cum_trade_log_return\": 0.0036923688089944024}, {\"index\": 161, \"cum_trade_log_return\": 0.005807013444196025}, {\"index\": 162, \"cum_trade_log_return\": 0.0023796242445200764}, {\"index\": 163, \"cum_trade_log_return\": -0.006328240626745356}, {\"index\": 164, \"cum_trade_log_return\": -0.006218857986309415}, {\"index\": 165, \"cum_trade_log_return\": -0.012152121726205896}, {\"index\": 166, \"cum_trade_log_return\": -0.015908712529954388}, {\"index\": 167, \"cum_trade_log_return\": -0.014051072402733036}, {\"index\": 168, \"cum_trade_log_return\": 0.01127308416574219}, {\"index\": 169, \"cum_trade_log_return\": 0.02388384592498432}, {\"index\": 170, \"cum_trade_log_return\": 0.027230956622193003}, {\"index\": 171, \"cum_trade_log_return\": 0.02263903214819637}, {\"index\": 172, \"cum_trade_log_return\": 0.03159377079726644}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "research.plot_column(trades, 'cum_trade_log_return')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c9162f9",
   "metadata": {},
   "source": [
    "## Key Strategy Decision #2: Trade Sizing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "58f72eef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Constant Trade Size\n",
    "# 2. Compounding Trade Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "0e90b0db",
   "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: (173, 8)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>open</th><th>close</th><th>trade_log_return</th><th>y_hat</th><th>entry_trade_value</th><th>exit_trade_value</th><th>signed_trade_qty</th></tr><tr><td>datetime[μs]</td><td>f64</td><td>f64</td><td>f64</td><td>f32</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2025-07-15 12:00:00</td><td>117110.4</td><td>117738.5</td><td>0.005349</td><td>0.001158</td><td>100.0</td><td>100.536332</td><td>0.000854</td></tr><tr><td>2025-07-16 00:00:00</td><td>117738.6</td><td>118755.1</td><td>0.008597</td><td>0.003119</td><td>100.0</td><td>100.863439</td><td>0.000849</td></tr><tr><td>2025-07-16 12:00:00</td><td>118755.1</td><td>118590.7</td><td>-0.001385</td><td>0.001261</td><td>100.0</td><td>99.861564</td><td>0.000842</td></tr><tr><td>2025-07-17 00:00:00</td><td>118590.7</td><td>117968.9</td><td>0.005257</td><td>-0.000395</td><td>100.0</td><td>100.527088</td><td>-0.000843</td></tr><tr><td>2025-07-17 12:00:00</td><td>117968.8</td><td>119176.6</td><td>0.010185</td><td>0.000339</td><td>100.0</td><td>101.023744</td><td>0.000848</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></tr><tr><td>2025-10-07 12:00:00</td><td>124397.1</td><td>121286.5</td><td>0.025324</td><td>-0.000021</td><td>100.0</td><td>102.564754</td><td>-0.000804</td></tr><tr><td>2025-10-08 00:00:00</td><td>121286.6</td><td>122825.7</td><td>0.012611</td><td>0.001237</td><td>100.0</td><td>101.269061</td><td>0.000824</td></tr><tr><td>2025-10-08 12:00:00</td><td>122825.8</td><td>123237.5</td><td>0.003347</td><td>0.002409</td><td>100.0</td><td>100.335272</td><td>0.000814</td></tr><tr><td>2025-10-09 00:00:00</td><td>123237.4</td><td>122672.9</td><td>-0.004592</td><td>0.000964</td><td>100.0</td><td>99.54186</td><td>0.000811</td></tr><tr><td>2025-10-09 12:00:00</td><td>122673.0</td><td>121579.3</td><td>0.008955</td><td>-0.000306</td><td>100.0</td><td>100.899495</td><td>-0.000815</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (173, 8)\n",
       "┌────────────┬──────────┬──────────┬────────────┬───────────┬────────────┬────────────┬────────────┐\n",
       "│ datetime   ┆ open     ┆ close    ┆ trade_log_ ┆ y_hat     ┆ entry_trad ┆ exit_trade ┆ signed_tra │\n",
       "│ ---        ┆ ---      ┆ ---      ┆ return     ┆ ---       ┆ e_value    ┆ _value     ┆ de_qty     │\n",
       "│ datetime[μ ┆ f64      ┆ f64      ┆ ---        ┆ f32       ┆ ---        ┆ ---        ┆ ---        │\n",
       "│ s]         ┆          ┆          ┆ f64        ┆           ┆ f64        ┆ f64        ┆ f64        │\n",
       "╞════════════╪══════════╪══════════╪════════════╪═══════════╪════════════╪════════════╪════════════╡\n",
       "│ 2025-07-15 ┆ 117110.4 ┆ 117738.5 ┆ 0.005349   ┆ 0.001158  ┆ 100.0      ┆ 100.536332 ┆ 0.000854   │\n",
       "│ 12:00:00   ┆          ┆          ┆            ┆           ┆            ┆            ┆            │\n",
       "│ 2025-07-16 ┆ 117738.6 ┆ 118755.1 ┆ 0.008597   ┆ 0.003119  ┆ 100.0      ┆ 100.863439 ┆ 0.000849   │\n",
       "│ 00:00:00   ┆          ┆          ┆            ┆           ┆            ┆            ┆            │\n",
       "│ 2025-07-16 ┆ 118755.1 ┆ 118590.7 ┆ -0.001385  ┆ 0.001261  ┆ 100.0      ┆ 99.861564  ┆ 0.000842   │\n",
       "│ 12:00:00   ┆          ┆          ┆            ┆           ┆            ┆            ┆            │\n",
       "│ 2025-07-17 ┆ 118590.7 ┆ 117968.9 ┆ 0.005257   ┆ -0.000395 ┆ 100.0      ┆ 100.527088 ┆ -0.000843  │\n",
       "│ 00:00:00   ┆          ┆          ┆            ┆           ┆            ┆            ┆            │\n",
       "│ 2025-07-17 ┆ 117968.8 ┆ 119176.6 ┆ 0.010185   ┆ 0.000339  ┆ 100.0      ┆ 101.023744 ┆ 0.000848   │\n",
       "│ 12:00:00   ┆          ┆          ┆            ┆           ┆            ┆            ┆            │\n",
       "│ …          ┆ …        ┆ …        ┆ …          ┆ …         ┆ …          ┆ …          ┆ …          │\n",
       "│ 2025-10-07 ┆ 124397.1 ┆ 121286.5 ┆ 0.025324   ┆ -0.000021 ┆ 100.0      ┆ 102.564754 ┆ -0.000804  │\n",
       "│ 12:00:00   ┆          ┆          ┆            ┆           ┆            ┆            ┆            │\n",
       "│ 2025-10-08 ┆ 121286.6 ┆ 122825.7 ┆ 0.012611   ┆ 0.001237  ┆ 100.0      ┆ 101.269061 ┆ 0.000824   │\n",
       "│ 00:00:00   ┆          ┆          ┆            ┆           ┆            ┆            ┆            │\n",
       "│ 2025-10-08 ┆ 122825.8 ┆ 123237.5 ┆ 0.003347   ┆ 0.002409  ┆ 100.0      ┆ 100.335272 ┆ 0.000814   │\n",
       "│ 12:00:00   ┆          ┆          ┆            ┆           ┆            ┆            ┆            │\n",
       "│ 2025-10-09 ┆ 123237.4 ┆ 122672.9 ┆ -0.004592  ┆ 0.000964  ┆ 100.0      ┆ 99.54186   ┆ 0.000811   │\n",
       "│ 00:00:00   ┆          ┆          ┆            ┆           ┆            ┆            ┆            │\n",
       "│ 2025-10-09 ┆ 122673.0 ┆ 121579.3 ┆ 0.008955   ┆ -0.000306 ┆ 100.0      ┆ 100.899495 ┆ -0.000815  │\n",
       "│ 12:00:00   ┆          ┆          ┆            ┆           ┆            ┆            ┆            │\n",
       "└────────────┴──────────┴──────────┴────────────┴───────────┴────────────┴────────────┴────────────┘"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "capital = 100\n",
    "ratio = 1.0 # you could experiment with different ratios like kelly criterion\n",
    "trade_value = ratio * capital\n",
    "\n",
    "# entry trade value and exit trade value\n",
    "trades = trades.with_columns(\n",
    "    pl.lit(trade_value).alias('entry_trade_value'),\n",
    "    (trade_value * pl.col('trade_log_return').exp()).alias('exit_trade_value'),\n",
    "    (trade_value / pl.col('open')).alias('trade_qty'),\n",
    ").with_columns(\n",
    "    (pl.col('trade_qty') * pl.col('dir_signal')).alias('signed_trade_qty'),\n",
    ")\n",
    "\n",
    "trades.select('datetime','open','close', 'trade_log_return','y_hat','entry_trade_value','exit_trade_value', 'signed_trade_qty')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6032680f",
   "metadata": {},
   "source": [
    "### Add Trade Gross PnL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "97baddc3",
   "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: (173, 8)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>open</th><th>close</th><th>trade_log_return</th><th>y_hat</th><th>entry_trade_value</th><th>exit_trade_value</th><th>signed_trade_qty</th></tr><tr><td>datetime[μs]</td><td>f64</td><td>f64</td><td>f64</td><td>f32</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2025-07-15 12:00:00</td><td>117110.4</td><td>117738.5</td><td>0.005349</td><td>0.001158</td><td>100.0</td><td>100.536332</td><td>0.000854</td></tr><tr><td>2025-07-16 00:00:00</td><td>117738.6</td><td>118755.1</td><td>0.008597</td><td>0.003119</td><td>100.0</td><td>100.863439</td><td>0.000849</td></tr><tr><td>2025-07-16 12:00:00</td><td>118755.1</td><td>118590.7</td><td>-0.001385</td><td>0.001261</td><td>100.0</td><td>99.861564</td><td>0.000842</td></tr><tr><td>2025-07-17 00:00:00</td><td>118590.7</td><td>117968.9</td><td>0.005257</td><td>-0.000395</td><td>100.0</td><td>100.527088</td><td>-0.000843</td></tr><tr><td>2025-07-17 12:00:00</td><td>117968.8</td><td>119176.6</td><td>0.010185</td><td>0.000339</td><td>100.0</td><td>101.023744</td><td>0.000848</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></tr><tr><td>2025-10-07 12:00:00</td><td>124397.1</td><td>121286.5</td><td>0.025324</td><td>-0.000021</td><td>100.0</td><td>102.564754</td><td>-0.000804</td></tr><tr><td>2025-10-08 00:00:00</td><td>121286.6</td><td>122825.7</td><td>0.012611</td><td>0.001237</td><td>100.0</td><td>101.269061</td><td>0.000824</td></tr><tr><td>2025-10-08 12:00:00</td><td>122825.8</td><td>123237.5</td><td>0.003347</td><td>0.002409</td><td>100.0</td><td>100.335272</td><td>0.000814</td></tr><tr><td>2025-10-09 00:00:00</td><td>123237.4</td><td>122672.9</td><td>-0.004592</td><td>0.000964</td><td>100.0</td><td>99.54186</td><td>0.000811</td></tr><tr><td>2025-10-09 12:00:00</td><td>122673.0</td><td>121579.3</td><td>0.008955</td><td>-0.000306</td><td>100.0</td><td>100.899495</td><td>-0.000815</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (173, 8)\n",
       "┌────────────┬──────────┬──────────┬────────────┬───────────┬────────────┬────────────┬────────────┐\n",
       "│ datetime   ┆ open     ┆ close    ┆ trade_log_ ┆ y_hat     ┆ entry_trad ┆ exit_trade ┆ signed_tra │\n",
       "│ ---        ┆ ---      ┆ ---      ┆ return     ┆ ---       ┆ e_value    ┆ _value     ┆ de_qty     │\n",
       "│ datetime[μ ┆ f64      ┆ f64      ┆ ---        ┆ f32       ┆ ---        ┆ ---        ┆ ---        │\n",
       "│ s]         ┆          ┆          ┆ f64        ┆           ┆ f64        ┆ f64        ┆ f64        │\n",
       "╞════════════╪══════════╪══════════╪════════════╪═══════════╪════════════╪════════════╪════════════╡\n",
       "│ 2025-07-15 ┆ 117110.4 ┆ 117738.5 ┆ 0.005349   ┆ 0.001158  ┆ 100.0      ┆ 100.536332 ┆ 0.000854   │\n",
       "│ 12:00:00   ┆          ┆          ┆            ┆           ┆            ┆            ┆            │\n",
       "│ 2025-07-16 ┆ 117738.6 ┆ 118755.1 ┆ 0.008597   ┆ 0.003119  ┆ 100.0      ┆ 100.863439 ┆ 0.000849   │\n",
       "│ 00:00:00   ┆          ┆          ┆            ┆           ┆            ┆            ┆            │\n",
       "│ 2025-07-16 ┆ 118755.1 ┆ 118590.7 ┆ -0.001385  ┆ 0.001261  ┆ 100.0      ┆ 99.861564  ┆ 0.000842   │\n",
       "│ 12:00:00   ┆          ┆          ┆            ┆           ┆            ┆            ┆            │\n",
       "│ 2025-07-17 ┆ 118590.7 ┆ 117968.9 ┆ 0.005257   ┆ -0.000395 ┆ 100.0      ┆ 100.527088 ┆ -0.000843  │\n",
       "│ 00:00:00   ┆          ┆          ┆            ┆           ┆            ┆            ┆            │\n",
       "│ 2025-07-17 ┆ 117968.8 ┆ 119176.6 ┆ 0.010185   ┆ 0.000339  ┆ 100.0      ┆ 101.023744 ┆ 0.000848   │\n",
       "│ 12:00:00   ┆          ┆          ┆            ┆           ┆            ┆            ┆            │\n",
       "│ …          ┆ …        ┆ …        ┆ …          ┆ …         ┆ …          ┆ …          ┆ …          │\n",
       "│ 2025-10-07 ┆ 124397.1 ┆ 121286.5 ┆ 0.025324   ┆ -0.000021 ┆ 100.0      ┆ 102.564754 ┆ -0.000804  │\n",
       "│ 12:00:00   ┆          ┆          ┆            ┆           ┆            ┆            ┆            │\n",
       "│ 2025-10-08 ┆ 121286.6 ┆ 122825.7 ┆ 0.012611   ┆ 0.001237  ┆ 100.0      ┆ 101.269061 ┆ 0.000824   │\n",
       "│ 00:00:00   ┆          ┆          ┆            ┆           ┆            ┆            ┆            │\n",
       "│ 2025-10-08 ┆ 122825.8 ┆ 123237.5 ┆ 0.003347   ┆ 0.002409  ┆ 100.0      ┆ 100.335272 ┆ 0.000814   │\n",
       "│ 12:00:00   ┆          ┆          ┆            ┆           ┆            ┆            ┆            │\n",
       "│ 2025-10-09 ┆ 123237.4 ┆ 122672.9 ┆ -0.004592  ┆ 0.000964  ┆ 100.0      ┆ 99.54186   ┆ 0.000811   │\n",
       "│ 00:00:00   ┆          ┆          ┆            ┆           ┆            ┆            ┆            │\n",
       "│ 2025-10-09 ┆ 122673.0 ┆ 121579.3 ┆ 0.008955   ┆ -0.000306 ┆ 100.0      ┆ 100.899495 ┆ -0.000815  │\n",
       "│ 12:00:00   ┆          ┆          ┆            ┆           ┆            ┆            ┆            │\n",
       "└────────────┴──────────┴──────────┴────────────┴───────────┴────────────┴────────────┴────────────┘"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trades = trades.with_columns((pl.col('exit_trade_value') - pl.col('entry_trade_value')).alias('trade_gross_pnl'))\n",
    "trades.select('datetime','open','close', 'trade_log_return','y_hat','entry_trade_value','exit_trade_value', 'signed_trade_qty', )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d68c2f6",
   "metadata": {},
   "source": [
    "### Add Transaction Fees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "3531bd9f",
   "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: (173, 11)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>open</th><th>close</th><th>trade_log_return</th><th>y_hat</th><th>entry_trade_value</th><th>exit_trade_value</th><th>signed_trade_qty</th><th>trade_gross_pnl</th><th>maker_fee</th><th>taker_fee</th></tr><tr><td>datetime[μs]</td><td>f64</td><td>f64</td><td>f64</td><td>f32</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2025-07-15 12:00:00</td><td>117110.4</td><td>117738.5</td><td>0.005349</td><td>0.001158</td><td>100.0</td><td>100.536332</td><td>0.000854</td><td>0.536332</td><td>0.090241</td><td>0.090241</td></tr><tr><td>2025-07-16 00:00:00</td><td>117738.6</td><td>118755.1</td><td>0.008597</td><td>0.003119</td><td>100.0</td><td>100.863439</td><td>0.000849</td><td>0.863439</td><td>0.090389</td><td>0.090389</td></tr><tr><td>2025-07-16 12:00:00</td><td>118755.1</td><td>118590.7</td><td>-0.001385</td><td>0.001261</td><td>100.0</td><td>99.861564</td><td>0.000842</td><td>-0.138436</td><td>0.089938</td><td>0.089938</td></tr><tr><td>2025-07-17 00:00:00</td><td>118590.7</td><td>117968.9</td><td>0.005257</td><td>-0.000395</td><td>100.0</td><td>100.527088</td><td>-0.000843</td><td>0.527088</td><td>0.090237</td><td>0.090237</td></tr><tr><td>2025-07-17 12:00:00</td><td>117968.8</td><td>119176.6</td><td>0.010185</td><td>0.000339</td><td>100.0</td><td>101.023744</td><td>0.000848</td><td>1.023744</td><td>0.090461</td><td>0.090461</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></tr><tr><td>2025-10-07 12:00:00</td><td>124397.1</td><td>121286.5</td><td>0.025324</td><td>-0.000021</td><td>100.0</td><td>102.564754</td><td>-0.000804</td><td>2.564754</td><td>0.091154</td><td>0.091154</td></tr><tr><td>2025-10-08 00:00:00</td><td>121286.6</td><td>122825.7</td><td>0.012611</td><td>0.001237</td><td>100.0</td><td>101.269061</td><td>0.000824</td><td>1.269061</td><td>0.090571</td><td>0.090571</td></tr><tr><td>2025-10-08 12:00:00</td><td>122825.8</td><td>123237.5</td><td>0.003347</td><td>0.002409</td><td>100.0</td><td>100.335272</td><td>0.000814</td><td>0.335272</td><td>0.090151</td><td>0.090151</td></tr><tr><td>2025-10-09 00:00:00</td><td>123237.4</td><td>122672.9</td><td>-0.004592</td><td>0.000964</td><td>100.0</td><td>99.54186</td><td>0.000811</td><td>-0.45814</td><td>0.089794</td><td>0.089794</td></tr><tr><td>2025-10-09 12:00:00</td><td>122673.0</td><td>121579.3</td><td>0.008955</td><td>-0.000306</td><td>100.0</td><td>100.899495</td><td>-0.000815</td><td>0.899495</td><td>0.090405</td><td>0.090405</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (173, 11)\n",
       "┌────────────┬──────────┬──────────┬───────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
       "│ datetime   ┆ open     ┆ close    ┆ trade_log ┆ … ┆ signed_tr ┆ trade_gro ┆ maker_fee ┆ taker_fee │\n",
       "│ ---        ┆ ---      ┆ ---      ┆ _return   ┆   ┆ ade_qty   ┆ ss_pnl    ┆ ---       ┆ ---       │\n",
       "│ datetime[μ ┆ f64      ┆ f64      ┆ ---       ┆   ┆ ---       ┆ ---       ┆ f64       ┆ f64       │\n",
       "│ s]         ┆          ┆          ┆ f64       ┆   ┆ f64       ┆ f64       ┆           ┆           │\n",
       "╞════════════╪══════════╪══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
       "│ 2025-07-15 ┆ 117110.4 ┆ 117738.5 ┆ 0.005349  ┆ … ┆ 0.000854  ┆ 0.536332  ┆ 0.090241  ┆ 0.090241  │\n",
       "│ 12:00:00   ┆          ┆          ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "│ 2025-07-16 ┆ 117738.6 ┆ 118755.1 ┆ 0.008597  ┆ … ┆ 0.000849  ┆ 0.863439  ┆ 0.090389  ┆ 0.090389  │\n",
       "│ 00:00:00   ┆          ┆          ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "│ 2025-07-16 ┆ 118755.1 ┆ 118590.7 ┆ -0.001385 ┆ … ┆ 0.000842  ┆ -0.138436 ┆ 0.089938  ┆ 0.089938  │\n",
       "│ 12:00:00   ┆          ┆          ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "│ 2025-07-17 ┆ 118590.7 ┆ 117968.9 ┆ 0.005257  ┆ … ┆ -0.000843 ┆ 0.527088  ┆ 0.090237  ┆ 0.090237  │\n",
       "│ 00:00:00   ┆          ┆          ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "│ 2025-07-17 ┆ 117968.8 ┆ 119176.6 ┆ 0.010185  ┆ … ┆ 0.000848  ┆ 1.023744  ┆ 0.090461  ┆ 0.090461  │\n",
       "│ 12:00:00   ┆          ┆          ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "│ …          ┆ …        ┆ …        ┆ …         ┆ … ┆ …         ┆ …         ┆ …         ┆ …         │\n",
       "│ 2025-10-07 ┆ 124397.1 ┆ 121286.5 ┆ 0.025324  ┆ … ┆ -0.000804 ┆ 2.564754  ┆ 0.091154  ┆ 0.091154  │\n",
       "│ 12:00:00   ┆          ┆          ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "│ 2025-10-08 ┆ 121286.6 ┆ 122825.7 ┆ 0.012611  ┆ … ┆ 0.000824  ┆ 1.269061  ┆ 0.090571  ┆ 0.090571  │\n",
       "│ 00:00:00   ┆          ┆          ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "│ 2025-10-08 ┆ 122825.8 ┆ 123237.5 ┆ 0.003347  ┆ … ┆ 0.000814  ┆ 0.335272  ┆ 0.090151  ┆ 0.090151  │\n",
       "│ 12:00:00   ┆          ┆          ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "│ 2025-10-09 ┆ 123237.4 ┆ 122672.9 ┆ -0.004592 ┆ … ┆ 0.000811  ┆ -0.45814  ┆ 0.089794  ┆ 0.089794  │\n",
       "│ 00:00:00   ┆          ┆          ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "│ 2025-10-09 ┆ 122673.0 ┆ 121579.3 ┆ 0.008955  ┆ … ┆ -0.000815 ┆ 0.899495  ┆ 0.090405  ┆ 0.090405  │\n",
       "│ 12:00:00   ┆          ┆          ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "└────────────┴──────────┴──────────┴───────────┴───┴───────────┴───────────┴───────────┴───────────┘"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "taker_fee = binance.TAKER_FEE\n",
    "maker_fee = binance.MAKER_FEE\n",
    "\n",
    "trades = trades.with_columns(\n",
    "    (pl.col('entry_trade_value') * taker_fee + pl.col('exit_trade_value') * taker_fee).alias('taker_fee'),\n",
    "    (pl.col('entry_trade_value') * maker_fee + pl.col('exit_trade_value') * maker_fee).alias('maker_fee')\n",
    ")\n",
    "\n",
    "trades.select('datetime','open','close', 'trade_log_return','y_hat','entry_trade_value','exit_trade_value', 'signed_trade_qty', 'trade_gross_pnl','maker_fee','taker_fee')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73b29f48",
   "metadata": {},
   "source": [
    "### Calculate Trade Net PnL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "4fc0eba3",
   "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: (173, 11)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>open</th><th>close</th><th>trade_log_return</th><th>y_hat</th><th>entry_trade_value</th><th>exit_trade_value</th><th>signed_trade_qty</th><th>trade_gross_pnl</th><th>trade_net_taker_pnl</th><th>trade_net_maker_pnl</th></tr><tr><td>datetime[μs]</td><td>f64</td><td>f64</td><td>f64</td><td>f32</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2025-07-15 12:00:00</td><td>117110.4</td><td>117738.5</td><td>0.005349</td><td>0.001158</td><td>100.0</td><td>100.536332</td><td>0.000854</td><td>0.536332</td><td>0.44609</td><td>0.44609</td></tr><tr><td>2025-07-16 00:00:00</td><td>117738.6</td><td>118755.1</td><td>0.008597</td><td>0.003119</td><td>100.0</td><td>100.863439</td><td>0.000849</td><td>0.863439</td><td>0.77305</td><td>0.77305</td></tr><tr><td>2025-07-16 12:00:00</td><td>118755.1</td><td>118590.7</td><td>-0.001385</td><td>0.001261</td><td>100.0</td><td>99.861564</td><td>0.000842</td><td>-0.138436</td><td>-0.228374</td><td>-0.228374</td></tr><tr><td>2025-07-17 00:00:00</td><td>118590.7</td><td>117968.9</td><td>0.005257</td><td>-0.000395</td><td>100.0</td><td>100.527088</td><td>-0.000843</td><td>0.527088</td><td>0.436851</td><td>0.436851</td></tr><tr><td>2025-07-17 12:00:00</td><td>117968.8</td><td>119176.6</td><td>0.010185</td><td>0.000339</td><td>100.0</td><td>101.023744</td><td>0.000848</td><td>1.023744</td><td>0.933284</td><td>0.933284</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></tr><tr><td>2025-10-07 12:00:00</td><td>124397.1</td><td>121286.5</td><td>0.025324</td><td>-0.000021</td><td>100.0</td><td>102.564754</td><td>-0.000804</td><td>2.564754</td><td>2.4736</td><td>2.4736</td></tr><tr><td>2025-10-08 00:00:00</td><td>121286.6</td><td>122825.7</td><td>0.012611</td><td>0.001237</td><td>100.0</td><td>101.269061</td><td>0.000824</td><td>1.269061</td><td>1.17849</td><td>1.17849</td></tr><tr><td>2025-10-08 12:00:00</td><td>122825.8</td><td>123237.5</td><td>0.003347</td><td>0.002409</td><td>100.0</td><td>100.335272</td><td>0.000814</td><td>0.335272</td><td>0.245121</td><td>0.245121</td></tr><tr><td>2025-10-09 00:00:00</td><td>123237.4</td><td>122672.9</td><td>-0.004592</td><td>0.000964</td><td>100.0</td><td>99.54186</td><td>0.000811</td><td>-0.45814</td><td>-0.547934</td><td>-0.547934</td></tr><tr><td>2025-10-09 12:00:00</td><td>122673.0</td><td>121579.3</td><td>0.008955</td><td>-0.000306</td><td>100.0</td><td>100.899495</td><td>-0.000815</td><td>0.899495</td><td>0.80909</td><td>0.80909</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (173, 11)\n",
       "┌────────────┬──────────┬──────────┬───────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
       "│ datetime   ┆ open     ┆ close    ┆ trade_log ┆ … ┆ signed_tr ┆ trade_gro ┆ trade_net ┆ trade_net │\n",
       "│ ---        ┆ ---      ┆ ---      ┆ _return   ┆   ┆ ade_qty   ┆ ss_pnl    ┆ _taker_pn ┆ _maker_pn │\n",
       "│ datetime[μ ┆ f64      ┆ f64      ┆ ---       ┆   ┆ ---       ┆ ---       ┆ l         ┆ l         │\n",
       "│ s]         ┆          ┆          ┆ f64       ┆   ┆ f64       ┆ f64       ┆ ---       ┆ ---       │\n",
       "│            ┆          ┆          ┆           ┆   ┆           ┆           ┆ f64       ┆ f64       │\n",
       "╞════════════╪══════════╪══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
       "│ 2025-07-15 ┆ 117110.4 ┆ 117738.5 ┆ 0.005349  ┆ … ┆ 0.000854  ┆ 0.536332  ┆ 0.44609   ┆ 0.44609   │\n",
       "│ 12:00:00   ┆          ┆          ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "│ 2025-07-16 ┆ 117738.6 ┆ 118755.1 ┆ 0.008597  ┆ … ┆ 0.000849  ┆ 0.863439  ┆ 0.77305   ┆ 0.77305   │\n",
       "│ 00:00:00   ┆          ┆          ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "│ 2025-07-16 ┆ 118755.1 ┆ 118590.7 ┆ -0.001385 ┆ … ┆ 0.000842  ┆ -0.138436 ┆ -0.228374 ┆ -0.228374 │\n",
       "│ 12:00:00   ┆          ┆          ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "│ 2025-07-17 ┆ 118590.7 ┆ 117968.9 ┆ 0.005257  ┆ … ┆ -0.000843 ┆ 0.527088  ┆ 0.436851  ┆ 0.436851  │\n",
       "│ 00:00:00   ┆          ┆          ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "│ 2025-07-17 ┆ 117968.8 ┆ 119176.6 ┆ 0.010185  ┆ … ┆ 0.000848  ┆ 1.023744  ┆ 0.933284  ┆ 0.933284  │\n",
       "│ 12:00:00   ┆          ┆          ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "│ …          ┆ …        ┆ …        ┆ …         ┆ … ┆ …         ┆ …         ┆ …         ┆ …         │\n",
       "│ 2025-10-07 ┆ 124397.1 ┆ 121286.5 ┆ 0.025324  ┆ … ┆ -0.000804 ┆ 2.564754  ┆ 2.4736    ┆ 2.4736    │\n",
       "│ 12:00:00   ┆          ┆          ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "│ 2025-10-08 ┆ 121286.6 ┆ 122825.7 ┆ 0.012611  ┆ … ┆ 0.000824  ┆ 1.269061  ┆ 1.17849   ┆ 1.17849   │\n",
       "│ 00:00:00   ┆          ┆          ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "│ 2025-10-08 ┆ 122825.8 ┆ 123237.5 ┆ 0.003347  ┆ … ┆ 0.000814  ┆ 0.335272  ┆ 0.245121  ┆ 0.245121  │\n",
       "│ 12:00:00   ┆          ┆          ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "│ 2025-10-09 ┆ 123237.4 ┆ 122672.9 ┆ -0.004592 ┆ … ┆ 0.000811  ┆ -0.45814  ┆ -0.547934 ┆ -0.547934 │\n",
       "│ 00:00:00   ┆          ┆          ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "│ 2025-10-09 ┆ 122673.0 ┆ 121579.3 ┆ 0.008955  ┆ … ┆ -0.000815 ┆ 0.899495  ┆ 0.80909   ┆ 0.80909   │\n",
       "│ 12:00:00   ┆          ┆          ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "└────────────┴──────────┴──────────┴───────────┴───┴───────────┴───────────┴───────────┴───────────┘"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trades = trades.with_columns(\n",
    "    (pl.col('trade_gross_pnl')-pl.col('taker_fee')).alias('trade_net_taker_pnl'),\n",
    "    (pl.col('trade_gross_pnl')-pl.col('maker_fee')).alias('trade_net_maker_pnl'),\n",
    ")\n",
    "\n",
    "trades.select('datetime','open','close', 'trade_log_return','y_hat','entry_trade_value','exit_trade_value', 'signed_trade_qty', 'trade_gross_pnl','trade_net_taker_pnl','trade_net_maker_pnl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dacafd73",
   "metadata": {},
   "source": [
    "### Display Equity Curves for Constant Sizing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "b03ae4d6",
   "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: (173, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>equity_curve_gross</th><th>equity_curve_taker</th><th>equity_curve_maker</th></tr><tr><td>datetime[μs]</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2025-07-15 12:00:00</td><td>100.536332</td><td>100.44609</td><td>100.44609</td></tr><tr><td>2025-07-16 00:00:00</td><td>101.39977</td><td>101.219141</td><td>101.219141</td></tr><tr><td>2025-07-16 12:00:00</td><td>101.261334</td><td>100.990767</td><td>100.990767</td></tr><tr><td>2025-07-17 00:00:00</td><td>101.788422</td><td>101.427618</td><td>101.427618</td></tr><tr><td>2025-07-17 12:00:00</td><td>102.812167</td><td>102.360901</td><td>102.360901</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>2025-10-07 12:00:00</td><td>102.004763</td><td>86.793861</td><td>86.793861</td></tr><tr><td>2025-10-08 00:00:00</td><td>103.273824</td><td>87.972351</td><td>87.972351</td></tr><tr><td>2025-10-08 12:00:00</td><td>103.609096</td><td>88.217472</td><td>88.217472</td></tr><tr><td>2025-10-09 00:00:00</td><td>103.150956</td><td>87.669538</td><td>87.669538</td></tr><tr><td>2025-10-09 12:00:00</td><td>104.050451</td><td>88.478629</td><td>88.478629</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (173, 4)\n",
       "┌─────────────────────┬────────────────────┬────────────────────┬────────────────────┐\n",
       "│ datetime            ┆ equity_curve_gross ┆ equity_curve_taker ┆ equity_curve_maker │\n",
       "│ ---                 ┆ ---                ┆ ---                ┆ ---                │\n",
       "│ datetime[μs]        ┆ f64                ┆ f64                ┆ f64                │\n",
       "╞═════════════════════╪════════════════════╪════════════════════╪════════════════════╡\n",
       "│ 2025-07-15 12:00:00 ┆ 100.536332         ┆ 100.44609          ┆ 100.44609          │\n",
       "│ 2025-07-16 00:00:00 ┆ 101.39977          ┆ 101.219141         ┆ 101.219141         │\n",
       "│ 2025-07-16 12:00:00 ┆ 101.261334         ┆ 100.990767         ┆ 100.990767         │\n",
       "│ 2025-07-17 00:00:00 ┆ 101.788422         ┆ 101.427618         ┆ 101.427618         │\n",
       "│ 2025-07-17 12:00:00 ┆ 102.812167         ┆ 102.360901         ┆ 102.360901         │\n",
       "│ …                   ┆ …                  ┆ …                  ┆ …                  │\n",
       "│ 2025-10-07 12:00:00 ┆ 102.004763         ┆ 86.793861          ┆ 86.793861          │\n",
       "│ 2025-10-08 00:00:00 ┆ 103.273824         ┆ 87.972351          ┆ 87.972351          │\n",
       "│ 2025-10-08 12:00:00 ┆ 103.609096         ┆ 88.217472          ┆ 88.217472          │\n",
       "│ 2025-10-09 00:00:00 ┆ 103.150956         ┆ 87.669538          ┆ 87.669538          │\n",
       "│ 2025-10-09 12:00:00 ┆ 104.050451         ┆ 88.478629          ┆ 88.478629          │\n",
       "└─────────────────────┴────────────────────┴────────────────────┴────────────────────┘"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def equity_curve(capital, col_name, suffix):\n",
    "    return (capital + (pl.col(col_name).cum_sum())).alias(f'equity_curve_{suffix}')\n",
    "\n",
    "trades = trades.with_columns(\n",
    "    equity_curve(capital, 'trade_net_taker_pnl', 'taker'),\n",
    "    equity_curve(capital, 'trade_net_maker_pnl', 'maker'),\n",
    "    equity_curve(capital, 'trade_gross_pnl', 'gross'),\n",
    ")\n",
    "trades.select('datetime','equity_curve_gross','equity_curve_taker','equity_curve_maker')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "75c25737",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "research.plot_static_timeseries(trades, sym, 'equity_curve_taker', time_interval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "f1d54a1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "research.plot_static_timeseries(trades, sym, 'equity_curve_maker', time_interval)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45e6441c",
   "metadata": {},
   "source": [
    "### Calculate Total Net Return using Constant Sizing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "3e631e9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.11521371381389589"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "constant_sizing_net_return = trades['equity_curve_taker'][-1] / capital - 1\n",
    "constant_sizing_net_return"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89629612",
   "metadata": {},
   "source": [
    "## experiment with compounding trade sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "cfc11bda",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2 => 102 (100 + 2)\n",
    "# 1 => 103 (100 + 2 + 1)\n",
    "# -1 => 102 (100 + 2 + 1 - 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "11c12200",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(100.54538182137402)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_return_1 = 0.005439\n",
    "pnl1 = capital * np.exp(log_return_1)\n",
    "pnl1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "fd6fd875",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(101.41349671401576)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_return_2 = 0.008597\n",
    "pnl2 = pnl1 * np.exp(log_return_2)\n",
    "pnl2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "262b324e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(101.55405171883751)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_return_3 = 0.001385\n",
    "pnl3 = pnl2 * np.exp(log_return_3)\n",
    "pnl3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "95a895f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(101.5449122654644)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((capital * np.exp(0.005349)) * np.exp(0.008597)) * np.exp(0.001385)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a832d90e",
   "metadata": {},
   "source": [
    "### Time Addivitivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "55e897fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(101.55405171883751)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "capital * np.exp(log_return_1 + log_return_2 + log_return_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fb0eff3",
   "metadata": {},
   "source": [
    "### Add Compounding Trade Sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "1dda3e89",
   "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: (173, 8)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>open</th><th>close</th><th>trade_log_return</th><th>entry_trade_value</th><th>exit_trade_value</th><th>signed_trade_qty</th><th>trade_gross_pnl</th></tr><tr><td>datetime[μs]</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>2025-07-15 12:00:00</td><td>117110.4</td><td>117738.5</td><td>0.005349</td><td>100.0</td><td>100.536332</td><td>0.000854</td><td>0.536332</td></tr><tr><td>2025-07-16 00:00:00</td><td>117738.6</td><td>118755.1</td><td>0.008597</td><td>100.536332</td><td>101.404401</td><td>0.000854</td><td>0.86807</td></tr><tr><td>2025-07-16 12:00:00</td><td>118755.1</td><td>118590.7</td><td>-0.001385</td><td>101.404401</td><td>101.264021</td><td>0.000854</td><td>-0.14038</td></tr><tr><td>2025-07-17 00:00:00</td><td>118590.7</td><td>117968.9</td><td>0.005257</td><td>101.264021</td><td>101.797772</td><td>-0.000854</td><td>0.533751</td></tr><tr><td>2025-07-17 12:00:00</td><td>117968.8</td><td>119176.6</td><td>0.010185</td><td>101.797772</td><td>102.839921</td><td>0.000863</td><td>1.042149</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></tr><tr><td>2025-10-07 12:00:00</td><td>124397.1</td><td>121286.5</td><td>0.025324</td><td>98.604718</td><td>101.133686</td><td>-0.000793</td><td>2.528968</td></tr><tr><td>2025-10-08 00:00:00</td><td>121286.6</td><td>122825.7</td><td>0.012611</td><td>101.133686</td><td>102.417135</td><td>0.000834</td><td>1.283448</td></tr><tr><td>2025-10-08 12:00:00</td><td>122825.8</td><td>123237.5</td><td>0.003347</td><td>102.417135</td><td>102.760511</td><td>0.000834</td><td>0.343376</td></tr><tr><td>2025-10-09 00:00:00</td><td>123237.4</td><td>122672.9</td><td>-0.004592</td><td>102.760511</td><td>102.289724</td><td>0.000834</td><td>-0.470787</td></tr><tr><td>2025-10-09 12:00:00</td><td>122673.0</td><td>121579.3</td><td>0.008955</td><td>102.289724</td><td>103.209815</td><td>-0.000834</td><td>0.920091</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (173, 8)\n",
       "┌────────────┬──────────┬──────────┬────────────┬────────────┬────────────┬────────────┬───────────┐\n",
       "│ datetime   ┆ open     ┆ close    ┆ trade_log_ ┆ entry_trad ┆ exit_trade ┆ signed_tra ┆ trade_gro │\n",
       "│ ---        ┆ ---      ┆ ---      ┆ return     ┆ e_value    ┆ _value     ┆ de_qty     ┆ ss_pnl    │\n",
       "│ datetime[μ ┆ f64      ┆ f64      ┆ ---        ┆ ---        ┆ ---        ┆ ---        ┆ ---       │\n",
       "│ s]         ┆          ┆          ┆ f64        ┆ f64        ┆ f64        ┆ f64        ┆ f64       │\n",
       "╞════════════╪══════════╪══════════╪════════════╪════════════╪════════════╪════════════╪═══════════╡\n",
       "│ 2025-07-15 ┆ 117110.4 ┆ 117738.5 ┆ 0.005349   ┆ 100.0      ┆ 100.536332 ┆ 0.000854   ┆ 0.536332  │\n",
       "│ 12:00:00   ┆          ┆          ┆            ┆            ┆            ┆            ┆           │\n",
       "│ 2025-07-16 ┆ 117738.6 ┆ 118755.1 ┆ 0.008597   ┆ 100.536332 ┆ 101.404401 ┆ 0.000854   ┆ 0.86807   │\n",
       "│ 00:00:00   ┆          ┆          ┆            ┆            ┆            ┆            ┆           │\n",
       "│ 2025-07-16 ┆ 118755.1 ┆ 118590.7 ┆ -0.001385  ┆ 101.404401 ┆ 101.264021 ┆ 0.000854   ┆ -0.14038  │\n",
       "│ 12:00:00   ┆          ┆          ┆            ┆            ┆            ┆            ┆           │\n",
       "│ 2025-07-17 ┆ 118590.7 ┆ 117968.9 ┆ 0.005257   ┆ 101.264021 ┆ 101.797772 ┆ -0.000854  ┆ 0.533751  │\n",
       "│ 00:00:00   ┆          ┆          ┆            ┆            ┆            ┆            ┆           │\n",
       "│ 2025-07-17 ┆ 117968.8 ┆ 119176.6 ┆ 0.010185   ┆ 101.797772 ┆ 102.839921 ┆ 0.000863   ┆ 1.042149  │\n",
       "│ 12:00:00   ┆          ┆          ┆            ┆            ┆            ┆            ┆           │\n",
       "│ …          ┆ …        ┆ …        ┆ …          ┆ …          ┆ …          ┆ …          ┆ …         │\n",
       "│ 2025-10-07 ┆ 124397.1 ┆ 121286.5 ┆ 0.025324   ┆ 98.604718  ┆ 101.133686 ┆ -0.000793  ┆ 2.528968  │\n",
       "│ 12:00:00   ┆          ┆          ┆            ┆            ┆            ┆            ┆           │\n",
       "│ 2025-10-08 ┆ 121286.6 ┆ 122825.7 ┆ 0.012611   ┆ 101.133686 ┆ 102.417135 ┆ 0.000834   ┆ 1.283448  │\n",
       "│ 00:00:00   ┆          ┆          ┆            ┆            ┆            ┆            ┆           │\n",
       "│ 2025-10-08 ┆ 122825.8 ┆ 123237.5 ┆ 0.003347   ┆ 102.417135 ┆ 102.760511 ┆ 0.000834   ┆ 0.343376  │\n",
       "│ 12:00:00   ┆          ┆          ┆            ┆            ┆            ┆            ┆           │\n",
       "│ 2025-10-09 ┆ 123237.4 ┆ 122672.9 ┆ -0.004592  ┆ 102.760511 ┆ 102.289724 ┆ 0.000834   ┆ -0.470787 │\n",
       "│ 00:00:00   ┆          ┆          ┆            ┆            ┆            ┆            ┆           │\n",
       "│ 2025-10-09 ┆ 122673.0 ┆ 121579.3 ┆ 0.008955   ┆ 102.289724 ┆ 103.209815 ┆ -0.000834  ┆ 0.920091  │\n",
       "│ 12:00:00   ┆          ┆          ┆            ┆            ┆            ┆            ┆           │\n",
       "└────────────┴──────────┴──────────┴────────────┴────────────┴────────────┴────────────┴───────────┘"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trades = trades.with_columns(\n",
    "    ((pl.col('cum_trade_log_return').exp()) * capital).shift().fill_null(capital).alias('entry_trade_value'),\n",
    "    ((pl.col('cum_trade_log_return').exp()) * capital).alias('exit_trade_value'),\n",
    ").with_columns(\n",
    "    (pl.col('entry_trade_value') / pl.col('open') * pl.col('dir_signal')).alias('signed_trade_qty'),\n",
    "    (pl.col('exit_trade_value')-pl.col('entry_trade_value')).alias('trade_gross_pnl'),\n",
    ")\n",
    "trades.select('datetime','open','close','trade_log_return','entry_trade_value','exit_trade_value','signed_trade_qty','trade_gross_pnl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2e8917a",
   "metadata": {},
   "source": [
    "### Add Transaction Fees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "0afc0a76",
   "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: (173, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>entry_trade_value</th><th>exit_trade_value</th><th>tx_fee_maker</th><th>tx_fee_taker</th></tr><tr><td>datetime[μs]</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2025-07-15 12:00:00</td><td>100.0</td><td>100.536332</td><td>0.090241</td><td>0.090241</td></tr><tr><td>2025-07-16 00:00:00</td><td>100.536332</td><td>101.404401</td><td>0.090873</td><td>0.090873</td></tr><tr><td>2025-07-16 12:00:00</td><td>101.404401</td><td>101.264021</td><td>0.091201</td><td>0.091201</td></tr><tr><td>2025-07-17 00:00:00</td><td>101.264021</td><td>101.797772</td><td>0.091378</td><td>0.091378</td></tr><tr><td>2025-07-17 12:00:00</td><td>101.797772</td><td>102.839921</td><td>0.092087</td><td>0.092087</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>2025-10-07 12:00:00</td><td>98.604718</td><td>101.133686</td><td>0.089882</td><td>0.089882</td></tr><tr><td>2025-10-08 00:00:00</td><td>101.133686</td><td>102.417135</td><td>0.091598</td><td>0.091598</td></tr><tr><td>2025-10-08 12:00:00</td><td>102.417135</td><td>102.760511</td><td>0.09233</td><td>0.09233</td></tr><tr><td>2025-10-09 00:00:00</td><td>102.760511</td><td>102.289724</td><td>0.092273</td><td>0.092273</td></tr><tr><td>2025-10-09 12:00:00</td><td>102.289724</td><td>103.209815</td><td>0.092475</td><td>0.092475</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (173, 5)\n",
       "┌─────────────────────┬───────────────────┬──────────────────┬──────────────┬──────────────┐\n",
       "│ datetime            ┆ entry_trade_value ┆ exit_trade_value ┆ tx_fee_maker ┆ tx_fee_taker │\n",
       "│ ---                 ┆ ---               ┆ ---              ┆ ---          ┆ ---          │\n",
       "│ datetime[μs]        ┆ f64               ┆ f64              ┆ f64          ┆ f64          │\n",
       "╞═════════════════════╪═══════════════════╪══════════════════╪══════════════╪══════════════╡\n",
       "│ 2025-07-15 12:00:00 ┆ 100.0             ┆ 100.536332       ┆ 0.090241     ┆ 0.090241     │\n",
       "│ 2025-07-16 00:00:00 ┆ 100.536332        ┆ 101.404401       ┆ 0.090873     ┆ 0.090873     │\n",
       "│ 2025-07-16 12:00:00 ┆ 101.404401        ┆ 101.264021       ┆ 0.091201     ┆ 0.091201     │\n",
       "│ 2025-07-17 00:00:00 ┆ 101.264021        ┆ 101.797772       ┆ 0.091378     ┆ 0.091378     │\n",
       "│ 2025-07-17 12:00:00 ┆ 101.797772        ┆ 102.839921       ┆ 0.092087     ┆ 0.092087     │\n",
       "│ …                   ┆ …                 ┆ …                ┆ …            ┆ …            │\n",
       "│ 2025-10-07 12:00:00 ┆ 98.604718         ┆ 101.133686       ┆ 0.089882     ┆ 0.089882     │\n",
       "│ 2025-10-08 00:00:00 ┆ 101.133686        ┆ 102.417135       ┆ 0.091598     ┆ 0.091598     │\n",
       "│ 2025-10-08 12:00:00 ┆ 102.417135        ┆ 102.760511       ┆ 0.09233      ┆ 0.09233      │\n",
       "│ 2025-10-09 00:00:00 ┆ 102.760511        ┆ 102.289724       ┆ 0.092273     ┆ 0.092273     │\n",
       "│ 2025-10-09 12:00:00 ┆ 102.289724        ┆ 103.209815       ┆ 0.092475     ┆ 0.092475     │\n",
       "└─────────────────────┴───────────────────┴──────────────────┴──────────────┴──────────────┘"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trades = research.add_tx_fees(trades, binance.MAKER_FEE, binance.TAKER_FEE)\n",
    "trades.select('datetime','entry_trade_value','exit_trade_value','tx_fee_maker','tx_fee_taker')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65d0c2cd",
   "metadata": {},
   "source": [
    "### Add Trade Net PnL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "1140e515",
   "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: (173, 9)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>open</th><th>close</th><th>close_log_return</th><th>y_hat</th><th>entry_trade_value</th><th>exit_trade_value</th><th>trade_gross_pnl</th><th>trade_net_taker_pnl</th></tr><tr><td>datetime[μs]</td><td>f64</td><td>f64</td><td>f64</td><td>f32</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2025-07-15 12:00:00</td><td>117110.4</td><td>117738.5</td><td>0.005349</td><td>0.001158</td><td>100.0</td><td>100.536332</td><td>0.536332</td><td>0.44609</td></tr><tr><td>2025-07-16 00:00:00</td><td>117738.6</td><td>118755.1</td><td>0.008597</td><td>0.003119</td><td>100.536332</td><td>101.404401</td><td>0.86807</td><td>0.777196</td></tr><tr><td>2025-07-16 12:00:00</td><td>118755.1</td><td>118590.7</td><td>-0.001385</td><td>0.001261</td><td>101.404401</td><td>101.264021</td><td>-0.14038</td><td>-0.231581</td></tr><tr><td>2025-07-17 00:00:00</td><td>118590.7</td><td>117968.9</td><td>-0.005257</td><td>-0.000395</td><td>101.264021</td><td>101.797772</td><td>0.533751</td><td>0.442373</td></tr><tr><td>2025-07-17 12:00:00</td><td>117968.8</td><td>119176.6</td><td>0.010185</td><td>0.000339</td><td>101.797772</td><td>102.839921</td><td>1.042149</td><td>0.950062</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></tr><tr><td>2025-10-07 12:00:00</td><td>124397.1</td><td>121286.5</td><td>-0.025324</td><td>-0.000021</td><td>98.604718</td><td>101.133686</td><td>2.528968</td><td>2.439086</td></tr><tr><td>2025-10-08 00:00:00</td><td>121286.6</td><td>122825.7</td><td>0.012611</td><td>0.001237</td><td>101.133686</td><td>102.417135</td><td>1.283448</td><td>1.191851</td></tr><tr><td>2025-10-08 12:00:00</td><td>122825.8</td><td>123237.5</td><td>0.003347</td><td>0.002409</td><td>102.417135</td><td>102.760511</td><td>0.343376</td><td>0.251046</td></tr><tr><td>2025-10-09 00:00:00</td><td>123237.4</td><td>122672.9</td><td>-0.004592</td><td>0.000964</td><td>102.760511</td><td>102.289724</td><td>-0.470787</td><td>-0.563059</td></tr><tr><td>2025-10-09 12:00:00</td><td>122673.0</td><td>121579.3</td><td>-0.008955</td><td>-0.000306</td><td>102.289724</td><td>103.209815</td><td>0.920091</td><td>0.827616</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (173, 9)\n",
       "┌────────────┬──────────┬──────────┬───────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
       "│ datetime   ┆ open     ┆ close    ┆ close_log ┆ … ┆ entry_tra ┆ exit_trad ┆ trade_gro ┆ trade_net │\n",
       "│ ---        ┆ ---      ┆ ---      ┆ _return   ┆   ┆ de_value  ┆ e_value   ┆ ss_pnl    ┆ _taker_pn │\n",
       "│ datetime[μ ┆ f64      ┆ f64      ┆ ---       ┆   ┆ ---       ┆ ---       ┆ ---       ┆ l         │\n",
       "│ s]         ┆          ┆          ┆ f64       ┆   ┆ f64       ┆ f64       ┆ f64       ┆ ---       │\n",
       "│            ┆          ┆          ┆           ┆   ┆           ┆           ┆           ┆ f64       │\n",
       "╞════════════╪══════════╪══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
       "│ 2025-07-15 ┆ 117110.4 ┆ 117738.5 ┆ 0.005349  ┆ … ┆ 100.0     ┆ 100.53633 ┆ 0.536332  ┆ 0.44609   │\n",
       "│ 12:00:00   ┆          ┆          ┆           ┆   ┆           ┆ 2         ┆           ┆           │\n",
       "│ 2025-07-16 ┆ 117738.6 ┆ 118755.1 ┆ 0.008597  ┆ … ┆ 100.53633 ┆ 101.40440 ┆ 0.86807   ┆ 0.777196  │\n",
       "│ 00:00:00   ┆          ┆          ┆           ┆   ┆ 2         ┆ 1         ┆           ┆           │\n",
       "│ 2025-07-16 ┆ 118755.1 ┆ 118590.7 ┆ -0.001385 ┆ … ┆ 101.40440 ┆ 101.26402 ┆ -0.14038  ┆ -0.231581 │\n",
       "│ 12:00:00   ┆          ┆          ┆           ┆   ┆ 1         ┆ 1         ┆           ┆           │\n",
       "│ 2025-07-17 ┆ 118590.7 ┆ 117968.9 ┆ -0.005257 ┆ … ┆ 101.26402 ┆ 101.79777 ┆ 0.533751  ┆ 0.442373  │\n",
       "│ 00:00:00   ┆          ┆          ┆           ┆   ┆ 1         ┆ 2         ┆           ┆           │\n",
       "│ 2025-07-17 ┆ 117968.8 ┆ 119176.6 ┆ 0.010185  ┆ … ┆ 101.79777 ┆ 102.83992 ┆ 1.042149  ┆ 0.950062  │\n",
       "│ 12:00:00   ┆          ┆          ┆           ┆   ┆ 2         ┆ 1         ┆           ┆           │\n",
       "│ …          ┆ …        ┆ …        ┆ …         ┆ … ┆ …         ┆ …         ┆ …         ┆ …         │\n",
       "│ 2025-10-07 ┆ 124397.1 ┆ 121286.5 ┆ -0.025324 ┆ … ┆ 98.604718 ┆ 101.13368 ┆ 2.528968  ┆ 2.439086  │\n",
       "│ 12:00:00   ┆          ┆          ┆           ┆   ┆           ┆ 6         ┆           ┆           │\n",
       "│ 2025-10-08 ┆ 121286.6 ┆ 122825.7 ┆ 0.012611  ┆ … ┆ 101.13368 ┆ 102.41713 ┆ 1.283448  ┆ 1.191851  │\n",
       "│ 00:00:00   ┆          ┆          ┆           ┆   ┆ 6         ┆ 5         ┆           ┆           │\n",
       "│ 2025-10-08 ┆ 122825.8 ┆ 123237.5 ┆ 0.003347  ┆ … ┆ 102.41713 ┆ 102.76051 ┆ 0.343376  ┆ 0.251046  │\n",
       "│ 12:00:00   ┆          ┆          ┆           ┆   ┆ 5         ┆ 1         ┆           ┆           │\n",
       "│ 2025-10-09 ┆ 123237.4 ┆ 122672.9 ┆ -0.004592 ┆ … ┆ 102.76051 ┆ 102.28972 ┆ -0.470787 ┆ -0.563059 │\n",
       "│ 00:00:00   ┆          ┆          ┆           ┆   ┆ 1         ┆ 4         ┆           ┆           │\n",
       "│ 2025-10-09 ┆ 122673.0 ┆ 121579.3 ┆ -0.008955 ┆ … ┆ 102.28972 ┆ 103.20981 ┆ 0.920091  ┆ 0.827616  │\n",
       "│ 12:00:00   ┆          ┆          ┆           ┆   ┆ 4         ┆ 5         ┆           ┆           │\n",
       "└────────────┴──────────┴──────────┴───────────┴───┴───────────┴───────────┴───────────┴───────────┘"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trades = trades.with_columns(\n",
    "    (pl.col('trade_gross_pnl') - pl.col('tx_fee_taker')).alias('trade_net_taker_pnl')\n",
    ")\n",
    "trades.select('datetime','open','close','close_log_return','y_hat','entry_trade_value','exit_trade_value','trade_gross_pnl','trade_net_taker_pnl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abfd648c",
   "metadata": {},
   "source": [
    "### Display Equity Curve (Compounding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "f67648aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trades = research.add_equity_curve(trades, capital, 'trade_net_taker_pnl', 'taker')\n",
    "research.plot_static_timeseries(trades, sym, 'equity_curve_taker', time_interval)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36821709",
   "metadata": {},
   "source": [
    "### Calculate Total Net Return for Compounding Trade Sizing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "eacbd1f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.13110997061524476"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compound_total_net_return = trades['equity_curve_taker'][-1] / capital - 1\n",
    "compound_total_net_return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "b24f6391",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.11521371381389589"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "constant_sizing_net_return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "c73d6a21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(-0.02)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.round(compound_total_net_return - constant_sizing_net_return, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28a29908",
   "metadata": {},
   "source": [
    "## Key Strategy Decision #3: Leverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "4a6dd3a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# leverage is borrowing money to increase our profits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "187097dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "400"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leverage = 4\n",
    "leverage * capital"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "47972ce9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# in theory, we are multiplying our trade size to increase our returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "73936bd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we are not multiplying our trade returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "8badc6a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# just remember, it amplifies BOTH your profit and losses so it's important to have a postive expected value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "d882bc60",
   "metadata": {},
   "outputs": [],
   "source": [
    "# leverage only works when you have a high Sharpe model. The more risk you reduce, the more you reduce drawdowns and more leverage you can use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "c4a4f64a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# key decision 3: should we use leverage? if so, how much? What's the sweet spot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "9556db2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# It's not a golden utopia to multiplying profits, because it can easily wipe you out "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "f8271aed",
   "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: (173, 27)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>open</th><th>high</th><th>low</th><th>close</th><th>close_log_return</th><th>close_log_return_lag_1</th><th>close_log_return_lag_2</th><th>close_log_return_lag_3</th><th>y_hat</th><th>dir_signal</th><th>trade_log_return</th><th>cum_trade_log_return</th><th>entry_trade_value</th><th>exit_trade_value</th><th>trade_qty</th><th>signed_trade_qty</th><th>trade_gross_pnl</th><th>taker_fee</th><th>maker_fee</th><th>trade_net_taker_pnl</th><th>trade_net_maker_pnl</th><th>equity_curve_taker</th><th>equity_curve_maker</th><th>equity_curve_gross</th><th>tx_fee_maker</th><th>tx_fee_taker</th></tr><tr><td>datetime[μs]</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f32</td><td>f32</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><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>2025-07-15 12:00:00</td><td>117110.4</td><td>118499.8</td><td>115678.1</td><td>117738.5</td><td>0.005349</td><td>-0.022844</td><td>-0.013743</td><td>0.020259</td><td>0.001158</td><td>1.0</td><td>0.005349</td><td>0.005349</td><td>400.0</td><td>402.145326</td><td>0.000854</td><td>0.003416</td><td>2.145326</td><td>0.090241</td><td>0.090241</td><td>1.784361</td><td>1.784361</td><td>101.784361</td><td>101.784361</td><td>102.145326</td><td>0.360965</td><td>0.360965</td></tr><tr><td>2025-07-16 00:00:00</td><td>117738.6</td><td>119299.9</td><td>117017.1</td><td>118755.1</td><td>0.008597</td><td>0.005349</td><td>-0.022844</td><td>-0.013743</td><td>0.003119</td><td>1.0</td><td>0.008597</td><td>0.013946</td><td>402.145326</td><td>405.617605</td><td>0.000849</td><td>0.003416</td><td>3.472279</td><td>0.090389</td><td>0.090389</td><td>3.108786</td><td>3.108786</td><td>104.893147</td><td>104.893147</td><td>105.617605</td><td>0.363493</td><td>0.363493</td></tr><tr><td>2025-07-16 12:00:00</td><td>118755.1</td><td>120100.0</td><td>118156.0</td><td>118590.7</td><td>-0.001385</td><td>0.008597</td><td>0.005349</td><td>-0.022844</td><td>0.001261</td><td>1.0</td><td>-0.001385</td><td>0.012561</td><td>405.617605</td><td>405.056084</td><td>0.000842</td><td>0.003416</td><td>-0.561521</td><td>0.089938</td><td>0.089938</td><td>-0.926325</td><td>-0.926325</td><td>103.966822</td><td>103.966822</td><td>105.056084</td><td>0.364803</td><td>0.364803</td></tr><tr><td>2025-07-17 00:00:00</td><td>118590.7</td><td>119216.4</td><td>117663.6</td><td>117968.9</td><td>-0.005257</td><td>-0.001385</td><td>0.008597</td><td>0.005349</td><td>-0.000395</td><td>-1.0</td><td>0.005257</td><td>0.017818</td><td>405.056084</td><td>407.191086</td><td>0.000843</td><td>-0.003416</td><td>2.135002</td><td>0.090237</td><td>0.090237</td><td>1.769491</td><td>1.769491</td><td>105.736313</td><td>105.736313</td><td>107.191086</td><td>0.365511</td><td>0.365511</td></tr><tr><td>2025-07-17 12:00:00</td><td>117968.8</td><td>120951.5</td><td>117412.8</td><td>119176.6</td><td>0.010185</td><td>-0.005257</td><td>-0.001385</td><td>0.008597</td><td>0.000339</td><td>1.0</td><td>0.010185</td><td>0.028003</td><td>407.191086</td><td>411.359682</td><td>0.000848</td><td>0.003452</td><td>4.168596</td><td>0.090461</td><td>0.090461</td><td>3.800248</td><td>3.800248</td><td>109.536561</td><td>109.536561</td><td>111.359682</td><td>0.368348</td><td>0.368348</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>2025-10-07 12:00:00</td><td>124397.1</td><td>125098.0</td><td>120516.0</td><td>121286.5</td><td>-0.025324</td><td>-0.001858</td><td>0.003757</td><td>0.005933</td><td>-0.000021</td><td>-1.0</td><td>0.025324</td><td>0.011273</td><td>394.418873</td><td>404.534746</td><td>0.000804</td><td>-0.003171</td><td>10.115873</td><td>0.091154</td><td>0.091154</td><td>9.756344</td><td>9.756344</td><td>40.726198</td><td>40.726198</td><td>104.534746</td><td>0.359529</td><td>0.359529</td></tr><tr><td>2025-10-08 00:00:00</td><td>121286.6</td><td>123150.0</td><td>121005.3</td><td>122825.7</td><td>0.012611</td><td>-0.025324</td><td>-0.001858</td><td>0.003757</td><td>0.001237</td><td>1.0</td><td>0.012611</td><td>0.023884</td><td>404.534746</td><td>409.66854</td><td>0.000824</td><td>0.003335</td><td>5.133794</td><td>0.090571</td><td>0.090571</td><td>4.767402</td><td>4.767402</td><td>45.4936</td><td>45.4936</td><td>109.66854</td><td>0.366391</td><td>0.366391</td></tr><tr><td>2025-10-08 12:00:00</td><td>122825.8</td><td>124170.6</td><td>121607.8</td><td>123237.5</td><td>0.003347</td><td>0.012611</td><td>-0.025324</td><td>-0.001858</td><td>0.002409</td><td>1.0</td><td>0.003347</td><td>0.027231</td><td>409.66854</td><td>411.042043</td><td>0.000814</td><td>0.003335</td><td>1.373503</td><td>0.090151</td><td>0.090151</td><td>1.004184</td><td>1.004184</td><td>46.497784</td><td>46.497784</td><td>111.042043</td><td>0.36932</td><td>0.36932</td></tr><tr><td>2025-10-09 00:00:00</td><td>123237.4</td><td>123279.7</td><td>121081.5</td><td>122672.9</td><td>-0.004592</td><td>0.003347</td><td>0.012611</td><td>-0.025324</td><td>0.000964</td><td>1.0</td><td>-0.004592</td><td>0.022639</td><td>411.042043</td><td>409.158896</td><td>0.000811</td><td>0.003335</td><td>-1.883147</td><td>0.089794</td><td>0.089794</td><td>-2.252237</td><td>-2.252237</td><td>44.245546</td><td>44.245546</td><td>109.158896</td><td>0.36909</td><td>0.36909</td></tr><tr><td>2025-10-09 12:00:00</td><td>122673.0</td><td>123740.1</td><td>119572.8</td><td>121579.3</td><td>-0.008955</td><td>-0.004592</td><td>0.003347</td><td>0.012611</td><td>-0.000306</td><td>-1.0</td><td>0.008955</td><td>0.031594</td><td>409.158896</td><td>412.839261</td><td>0.000815</td><td>-0.003335</td><td>3.680365</td><td>0.090405</td><td>0.090405</td><td>3.310466</td><td>3.310466</td><td>47.556012</td><td>47.556012</td><td>112.839261</td><td>0.369899</td><td>0.369899</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (173, 27)\n",
       "┌────────────┬──────────┬──────────┬──────────┬───┬────────────┬───────────┬───────────┬───────────┐\n",
       "│ datetime   ┆ open     ┆ high     ┆ low      ┆ … ┆ equity_cur ┆ equity_cu ┆ tx_fee_ma ┆ tx_fee_ta │\n",
       "│ ---        ┆ ---      ┆ ---      ┆ ---      ┆   ┆ ve_maker   ┆ rve_gross ┆ ker       ┆ ker       │\n",
       "│ datetime[μ ┆ f64      ┆ f64      ┆ f64      ┆   ┆ ---        ┆ ---       ┆ ---       ┆ ---       │\n",
       "│ s]         ┆          ┆          ┆          ┆   ┆ f64        ┆ f64       ┆ f64       ┆ f64       │\n",
       "╞════════════╪══════════╪══════════╪══════════╪═══╪════════════╪═══════════╪═══════════╪═══════════╡\n",
       "│ 2025-07-15 ┆ 117110.4 ┆ 118499.8 ┆ 115678.1 ┆ … ┆ 101.784361 ┆ 102.14532 ┆ 0.360965  ┆ 0.360965  │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆ 6         ┆           ┆           │\n",
       "│ 2025-07-16 ┆ 117738.6 ┆ 119299.9 ┆ 117017.1 ┆ … ┆ 104.893147 ┆ 105.61760 ┆ 0.363493  ┆ 0.363493  │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆ 5         ┆           ┆           │\n",
       "│ 2025-07-16 ┆ 118755.1 ┆ 120100.0 ┆ 118156.0 ┆ … ┆ 103.966822 ┆ 105.05608 ┆ 0.364803  ┆ 0.364803  │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆ 4         ┆           ┆           │\n",
       "│ 2025-07-17 ┆ 118590.7 ┆ 119216.4 ┆ 117663.6 ┆ … ┆ 105.736313 ┆ 107.19108 ┆ 0.365511  ┆ 0.365511  │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆ 6         ┆           ┆           │\n",
       "│ 2025-07-17 ┆ 117968.8 ┆ 120951.5 ┆ 117412.8 ┆ … ┆ 109.536561 ┆ 111.35968 ┆ 0.368348  ┆ 0.368348  │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆ 2         ┆           ┆           │\n",
       "│ …          ┆ …        ┆ …        ┆ …        ┆ … ┆ …          ┆ …         ┆ …         ┆ …         │\n",
       "│ 2025-10-07 ┆ 124397.1 ┆ 125098.0 ┆ 120516.0 ┆ … ┆ 40.726198  ┆ 104.53474 ┆ 0.359529  ┆ 0.359529  │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆ 6         ┆           ┆           │\n",
       "│ 2025-10-08 ┆ 121286.6 ┆ 123150.0 ┆ 121005.3 ┆ … ┆ 45.4936    ┆ 109.66854 ┆ 0.366391  ┆ 0.366391  │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆           ┆           │\n",
       "│ 2025-10-08 ┆ 122825.8 ┆ 124170.6 ┆ 121607.8 ┆ … ┆ 46.497784  ┆ 111.04204 ┆ 0.36932   ┆ 0.36932   │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆ 3         ┆           ┆           │\n",
       "│ 2025-10-09 ┆ 123237.4 ┆ 123279.7 ┆ 121081.5 ┆ … ┆ 44.245546  ┆ 109.15889 ┆ 0.36909   ┆ 0.36909   │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆ 6         ┆           ┆           │\n",
       "│ 2025-10-09 ┆ 122673.0 ┆ 123740.1 ┆ 119572.8 ┆ … ┆ 47.556012  ┆ 112.83926 ┆ 0.369899  ┆ 0.369899  │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆ 1         ┆           ┆           │\n",
       "└────────────┴──────────┴──────────┴──────────┴───┴────────────┴───────────┴───────────┴───────────┘"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trades = research.add_compounding_trades(trades, capital, leverage, maker_fee, taker_fee)\n",
    "trades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "7d6c0443",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.5244398824609791"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trades['equity_curve_taker'][-1] / capital - 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6f64069",
   "metadata": {},
   "source": [
    "## What if we increase to 8x leverage?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "5beb455f",
   "metadata": {},
   "outputs": [],
   "source": [
    "trades = research.add_compounding_trades(trades, capital, 8, maker_fee, taker_fee)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "082db735",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.0488797649219581"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trades['equity_curve_taker'][-1] / capital - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "2977672c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reason for this huge return is the combination of model's edge, compounding and leverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "7ddef57a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is possible on a small scale because we trading such small size that we are not moving markets around"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "5cd558d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# show win rate to show there's no manipulation to make the PnL look good"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8312f837",
   "metadata": {},
   "source": [
    "### Win Rate hasn't changed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "0f3c07d9",
   "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, 1)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>trade_log_return</th></tr><tr><td>f64</td></tr></thead><tbody><tr><td>0.50289</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (1, 1)\n",
       "┌──────────────────┐\n",
       "│ trade_log_return │\n",
       "│ ---              │\n",
       "│ f64              │\n",
       "╞══════════════════╡\n",
       "│ 0.50289          │\n",
       "└──────────────────┘"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trades.select((pl.col(\"trade_log_return\") > 0).mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e3d0f65",
   "metadata": {},
   "source": [
    "### Factor Liquidation (price moves against us too much)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "42146ef6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Liquidation is when we go bust. If use too much leverage, then a small price change can wipe us out. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "b9f83c1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# leverage is a double edged sword. you can amplify profits, but too much leverage and you can wipe out all your money"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "23c991d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Equity = Maintenance Margin\n",
    "# calulcation differs from different exchanges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "a9c44066",
   "metadata": {},
   "outputs": [],
   "source": [
    "maintenance_margin = 0.005\n",
    "\n",
    "def long_liquidation_price(p, l, mmr):\n",
    "    return (p * l) / (l + 1 - mmr * l)\n",
    "\n",
    "def short_liquidation_price(p, l, mmr):\n",
    "    return (p * l) / (l - 1 + mmr * l)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bf692b7",
   "metadata": {},
   "source": [
    "### show how leverage affects long positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "10931d9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "133.7792642140468"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "long_liquidation_price(200, 2, maintenance_margin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "c6603ae3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "160.64257028112448"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "long_liquidation_price(200, 4, maintenance_margin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "1a819e9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "182.64840182648402"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "long_liquidation_price(200, 10, maintenance_margin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "eb433108",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "197.04433497536945"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "long_liquidation_price(200, 50, maintenance_margin)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fef048f3",
   "metadata": {},
   "source": [
    "### show how leverage affects short positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "830a4cb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "396.03960396039605"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "short_liquidation_price(200, 2, maintenance_margin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "9c9552df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "264.9006622516556"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "short_liquidation_price(200, 4, maintenance_margin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "740debd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "220.99447513812152"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "short_liquidation_price(200, 10, maintenance_margin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "4318d4b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "202.36087689713324"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "short_liquidation_price(200, 60, maintenance_margin)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83028cab",
   "metadata": {},
   "source": [
    "### Add Liquidation Prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "56c8fe70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "091d3f46",
   "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: (173, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>open</th><th>high</th><th>low</th><th>close</th><th>liquidation_price</th><th>dir_signal</th></tr><tr><td>datetime[μs]</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f32</td></tr></thead><tbody><tr><td>2025-07-15 12:00:00</td><td>117110.4</td><td>118499.8</td><td>115678.1</td><td>117738.5</td><td>94064.578313</td><td>1.0</td></tr><tr><td>2025-07-16 00:00:00</td><td>117738.6</td><td>119299.9</td><td>117017.1</td><td>118755.1</td><td>94569.156627</td><td>1.0</td></tr><tr><td>2025-07-16 12:00:00</td><td>118755.1</td><td>120100.0</td><td>118156.0</td><td>118590.7</td><td>95385.62249</td><td>1.0</td></tr><tr><td>2025-07-17 00:00:00</td><td>118590.7</td><td>119216.4</td><td>117663.6</td><td>117968.9</td><td>157073.774834</td><td>-1.0</td></tr><tr><td>2025-07-17 12:00:00</td><td>117968.8</td><td>120951.5</td><td>117412.8</td><td>119176.6</td><td>94754.056225</td><td>1.0</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>2025-10-07 12:00:00</td><td>124397.1</td><td>125098.0</td><td>120516.0</td><td>121286.5</td><td>164764.370861</td><td>-1.0</td></tr><tr><td>2025-10-08 00:00:00</td><td>121286.6</td><td>123150.0</td><td>121005.3</td><td>122825.7</td><td>97418.955823</td><td>1.0</td></tr><tr><td>2025-10-08 12:00:00</td><td>122825.8</td><td>124170.6</td><td>121607.8</td><td>123237.5</td><td>98655.261044</td><td>1.0</td></tr><tr><td>2025-10-09 00:00:00</td><td>123237.4</td><td>123279.7</td><td>121081.5</td><td>122672.9</td><td>98985.863454</td><td>1.0</td></tr><tr><td>2025-10-09 12:00:00</td><td>122673.0</td><td>123740.1</td><td>119572.8</td><td>121579.3</td><td>162480.794702</td><td>-1.0</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (173, 7)\n",
       "┌─────────────────────┬──────────┬──────────┬──────────┬──────────┬───────────────────┬────────────┐\n",
       "│ datetime            ┆ open     ┆ high     ┆ low      ┆ close    ┆ liquidation_price ┆ dir_signal │\n",
       "│ ---                 ┆ ---      ┆ ---      ┆ ---      ┆ ---      ┆ ---               ┆ ---        │\n",
       "│ datetime[μs]        ┆ f64      ┆ f64      ┆ f64      ┆ f64      ┆ f64               ┆ f32        │\n",
       "╞═════════════════════╪══════════╪══════════╪══════════╪══════════╪═══════════════════╪════════════╡\n",
       "│ 2025-07-15 12:00:00 ┆ 117110.4 ┆ 118499.8 ┆ 115678.1 ┆ 117738.5 ┆ 94064.578313      ┆ 1.0        │\n",
       "│ 2025-07-16 00:00:00 ┆ 117738.6 ┆ 119299.9 ┆ 117017.1 ┆ 118755.1 ┆ 94569.156627      ┆ 1.0        │\n",
       "│ 2025-07-16 12:00:00 ┆ 118755.1 ┆ 120100.0 ┆ 118156.0 ┆ 118590.7 ┆ 95385.62249       ┆ 1.0        │\n",
       "│ 2025-07-17 00:00:00 ┆ 118590.7 ┆ 119216.4 ┆ 117663.6 ┆ 117968.9 ┆ 157073.774834     ┆ -1.0       │\n",
       "│ 2025-07-17 12:00:00 ┆ 117968.8 ┆ 120951.5 ┆ 117412.8 ┆ 119176.6 ┆ 94754.056225      ┆ 1.0        │\n",
       "│ …                   ┆ …        ┆ …        ┆ …        ┆ …        ┆ …                 ┆ …          │\n",
       "│ 2025-10-07 12:00:00 ┆ 124397.1 ┆ 125098.0 ┆ 120516.0 ┆ 121286.5 ┆ 164764.370861     ┆ -1.0       │\n",
       "│ 2025-10-08 00:00:00 ┆ 121286.6 ┆ 123150.0 ┆ 121005.3 ┆ 122825.7 ┆ 97418.955823      ┆ 1.0        │\n",
       "│ 2025-10-08 12:00:00 ┆ 122825.8 ┆ 124170.6 ┆ 121607.8 ┆ 123237.5 ┆ 98655.261044      ┆ 1.0        │\n",
       "│ 2025-10-09 00:00:00 ┆ 123237.4 ┆ 123279.7 ┆ 121081.5 ┆ 122672.9 ┆ 98985.863454      ┆ 1.0        │\n",
       "│ 2025-10-09 12:00:00 ┆ 122673.0 ┆ 123740.1 ┆ 119572.8 ┆ 121579.3 ┆ 162480.794702     ┆ -1.0       │\n",
       "└─────────────────────┴──────────┴──────────┴──────────┴──────────┴───────────────────┴────────────┘"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "trades = trades.with_columns(\n",
    "    pl.when(pl.col(\"dir_signal\") == 1)  # long position\n",
    "      .then(\n",
    "          (pl.col(\"open\") * leverage)\n",
    "          / (leverage + 1 - maintenance_margin * leverage)\n",
    "      )\n",
    "      .when(pl.col(\"dir_signal\") == -1)  # short position\n",
    "      .then(\n",
    "          (pl.col(\"open\") * leverage)\n",
    "          / (leverage - 1 + maintenance_margin * leverage)\n",
    "      )\n",
    "      .otherwise(None)\n",
    "      .alias(\"liquidation_price\")\n",
    ")\n",
    "trades.select('datetime','open','high','low','close','liquidation_price','dir_signal')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68f72677",
   "metadata": {},
   "source": [
    "### Add Liquidation Flag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "dd40542f",
   "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: (173, 9)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>open</th><th>low</th><th>high</th><th>close</th><th>dir_signal</th><th>worst_price</th><th>liquidation_price</th><th>liquidated</th></tr><tr><td>datetime[μs]</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f32</td><td>f64</td><td>f64</td><td>bool</td></tr></thead><tbody><tr><td>2025-07-15 12:00:00</td><td>117110.4</td><td>115678.1</td><td>118499.8</td><td>117738.5</td><td>1.0</td><td>115678.1</td><td>94064.578313</td><td>false</td></tr><tr><td>2025-07-16 00:00:00</td><td>117738.6</td><td>117017.1</td><td>119299.9</td><td>118755.1</td><td>1.0</td><td>117017.1</td><td>94569.156627</td><td>false</td></tr><tr><td>2025-07-16 12:00:00</td><td>118755.1</td><td>118156.0</td><td>120100.0</td><td>118590.7</td><td>1.0</td><td>118156.0</td><td>95385.62249</td><td>false</td></tr><tr><td>2025-07-17 00:00:00</td><td>118590.7</td><td>117663.6</td><td>119216.4</td><td>117968.9</td><td>-1.0</td><td>119216.4</td><td>157073.774834</td><td>false</td></tr><tr><td>2025-07-17 12:00:00</td><td>117968.8</td><td>117412.8</td><td>120951.5</td><td>119176.6</td><td>1.0</td><td>117412.8</td><td>94754.056225</td><td>false</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></tr><tr><td>2025-10-07 12:00:00</td><td>124397.1</td><td>120516.0</td><td>125098.0</td><td>121286.5</td><td>-1.0</td><td>125098.0</td><td>164764.370861</td><td>false</td></tr><tr><td>2025-10-08 00:00:00</td><td>121286.6</td><td>121005.3</td><td>123150.0</td><td>122825.7</td><td>1.0</td><td>121005.3</td><td>97418.955823</td><td>false</td></tr><tr><td>2025-10-08 12:00:00</td><td>122825.8</td><td>121607.8</td><td>124170.6</td><td>123237.5</td><td>1.0</td><td>121607.8</td><td>98655.261044</td><td>false</td></tr><tr><td>2025-10-09 00:00:00</td><td>123237.4</td><td>121081.5</td><td>123279.7</td><td>122672.9</td><td>1.0</td><td>121081.5</td><td>98985.863454</td><td>false</td></tr><tr><td>2025-10-09 12:00:00</td><td>122673.0</td><td>119572.8</td><td>123740.1</td><td>121579.3</td><td>-1.0</td><td>123740.1</td><td>162480.794702</td><td>false</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (173, 9)\n",
       "┌────────────┬──────────┬──────────┬──────────┬───┬────────────┬───────────┬───────────┬───────────┐\n",
       "│ datetime   ┆ open     ┆ low      ┆ high     ┆ … ┆ dir_signal ┆ worst_pri ┆ liquidati ┆ liquidate │\n",
       "│ ---        ┆ ---      ┆ ---      ┆ ---      ┆   ┆ ---        ┆ ce        ┆ on_price  ┆ d         │\n",
       "│ datetime[μ ┆ f64      ┆ f64      ┆ f64      ┆   ┆ f32        ┆ ---       ┆ ---       ┆ ---       │\n",
       "│ s]         ┆          ┆          ┆          ┆   ┆            ┆ f64       ┆ f64       ┆ bool      │\n",
       "╞════════════╪══════════╪══════════╪══════════╪═══╪════════════╪═══════════╪═══════════╪═══════════╡\n",
       "│ 2025-07-15 ┆ 117110.4 ┆ 115678.1 ┆ 118499.8 ┆ … ┆ 1.0        ┆ 115678.1  ┆ 94064.578 ┆ false     │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆ 313       ┆           │\n",
       "│ 2025-07-16 ┆ 117738.6 ┆ 117017.1 ┆ 119299.9 ┆ … ┆ 1.0        ┆ 117017.1  ┆ 94569.156 ┆ false     │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆ 627       ┆           │\n",
       "│ 2025-07-16 ┆ 118755.1 ┆ 118156.0 ┆ 120100.0 ┆ … ┆ 1.0        ┆ 118156.0  ┆ 95385.622 ┆ false     │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆ 49        ┆           │\n",
       "│ 2025-07-17 ┆ 118590.7 ┆ 117663.6 ┆ 119216.4 ┆ … ┆ -1.0       ┆ 119216.4  ┆ 157073.77 ┆ false     │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆ 4834      ┆           │\n",
       "│ 2025-07-17 ┆ 117968.8 ┆ 117412.8 ┆ 120951.5 ┆ … ┆ 1.0        ┆ 117412.8  ┆ 94754.056 ┆ false     │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆ 225       ┆           │\n",
       "│ …          ┆ …        ┆ …        ┆ …        ┆ … ┆ …          ┆ …         ┆ …         ┆ …         │\n",
       "│ 2025-10-07 ┆ 124397.1 ┆ 120516.0 ┆ 125098.0 ┆ … ┆ -1.0       ┆ 125098.0  ┆ 164764.37 ┆ false     │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆ 0861      ┆           │\n",
       "│ 2025-10-08 ┆ 121286.6 ┆ 121005.3 ┆ 123150.0 ┆ … ┆ 1.0        ┆ 121005.3  ┆ 97418.955 ┆ false     │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆ 823       ┆           │\n",
       "│ 2025-10-08 ┆ 122825.8 ┆ 121607.8 ┆ 124170.6 ┆ … ┆ 1.0        ┆ 121607.8  ┆ 98655.261 ┆ false     │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆ 044       ┆           │\n",
       "│ 2025-10-09 ┆ 123237.4 ┆ 121081.5 ┆ 123279.7 ┆ … ┆ 1.0        ┆ 121081.5  ┆ 98985.863 ┆ false     │\n",
       "│ 00:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆ 454       ┆           │\n",
       "│ 2025-10-09 ┆ 122673.0 ┆ 119572.8 ┆ 123740.1 ┆ … ┆ -1.0       ┆ 123740.1  ┆ 162480.79 ┆ false     │\n",
       "│ 12:00:00   ┆          ┆          ┆          ┆   ┆            ┆           ┆ 4702      ┆           │\n",
       "└────────────┴──────────┴──────────┴──────────┴───┴────────────┴───────────┴───────────┴───────────┘"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trades = trades.with_columns([\n",
    "    # Worst price based on direction\n",
    "    pl.when(pl.col(\"dir_signal\") == 1)\n",
    "      .then(pl.col(\"low\"))\n",
    "      .otherwise(pl.col(\"high\"))\n",
    "      .alias(\"worst_price\"),\n",
    "\n",
    "    # Liquidation flag\n",
    "    pl.when(\n",
    "        (pl.col(\"dir_signal\") == 1) & (pl.col(\"low\") <= pl.col(\"liquidation_price\"))\n",
    "    )\n",
    "    .then(True)\n",
    "    .when(\n",
    "        (pl.col(\"dir_signal\") == -1) & (pl.col(\"high\") >= pl.col(\"liquidation_price\"))\n",
    "    )\n",
    "    .then(True)\n",
    "    .otherwise(False)\n",
    "    .alias(\"liquidated\")\n",
    "])\n",
    "trades.select('datetime','open','low','high','close','dir_signal','worst_price','liquidation_price','liquidated')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18cab73e",
   "metadata": {},
   "source": [
    "### Find Liquidated Trades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "16c82ae9",
   "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: (0, 30)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>open</th><th>high</th><th>low</th><th>close</th><th>close_log_return</th><th>close_log_return_lag_1</th><th>close_log_return_lag_2</th><th>close_log_return_lag_3</th><th>y_hat</th><th>dir_signal</th><th>trade_log_return</th><th>cum_trade_log_return</th><th>entry_trade_value</th><th>exit_trade_value</th><th>trade_qty</th><th>signed_trade_qty</th><th>trade_gross_pnl</th><th>taker_fee</th><th>maker_fee</th><th>trade_net_taker_pnl</th><th>trade_net_maker_pnl</th><th>equity_curve_taker</th><th>equity_curve_maker</th><th>equity_curve_gross</th><th>tx_fee_maker</th><th>tx_fee_taker</th><th>liquidation_price</th><th>worst_price</th><th>liquidated</th></tr><tr><td>datetime[μs]</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f32</td><td>f32</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>bool</td></tr></thead><tbody></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (0, 30)\n",
       "┌──────────────┬──────┬──────┬─────┬───┬──────────────┬─────────────────┬─────────────┬────────────┐\n",
       "│ datetime     ┆ open ┆ high ┆ low ┆ … ┆ tx_fee_taker ┆ liquidation_pri ┆ worst_price ┆ liquidated │\n",
       "│ ---          ┆ ---  ┆ ---  ┆ --- ┆   ┆ ---          ┆ ce              ┆ ---         ┆ ---        │\n",
       "│ datetime[μs] ┆ f64  ┆ f64  ┆ f64 ┆   ┆ f64          ┆ ---             ┆ f64         ┆ bool       │\n",
       "│              ┆      ┆      ┆     ┆   ┆              ┆ f64             ┆             ┆            │\n",
       "╞══════════════╪══════╪══════╪═════╪═══╪══════════════╪═════════════════╪═════════════╪════════════╡\n",
       "└──────────────┴──────┴──────┴─────┴───┴──────────────┴─────────────────┴─────────────┴────────────┘"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trades.filter(pl.col(\"liquidated\") == True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "8345403f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# topics we haven't covered - this is just a foundation to build upon\n",
    "# alpha decay (also known as model drift) => where the prediction performance drifts => \n",
    "# market impact => we are not trading big sizes => if we were, we could potentially move markets against us\n",
    "# funding fees/rebates\n",
    "# slippage => we may not always get the best price, we may get executed at prices below top of the book (best bid/ask)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "study (3.14.5.final.0)",
   "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": "3.14.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}