study/tutorials/20260622164035_Let's Build a Quant Trading Strategy, MemLabs/part1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0ba75c87",
   "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": "code",
   "execution_count": 8,
   "id": "ee4d8884",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CUDA Available: True\n",
      "CUDA Version: 13.2\n"
     ]
    }
   ],
   "source": [
    "print('CUDA Available:', torch.cuda.is_available())\n",
    "print('CUDA Version:', torch.version.cuda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "390e1b3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "research.set_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3849b967",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "polars.config.Config"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pl.Config.set_tbl_width_chars(200)\n",
    "pl.Config.set_fmt_str_lengths(100)\n",
    "pl.Config.set_tbl_cols(-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0f64bf2",
   "metadata": {},
   "source": [
    "# Part 1: ML Model in PyTorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f60cc47e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "annualized_rate=np.float64(93.59487165438073)\n"
     ]
    }
   ],
   "source": [
    "# Trading pair symbol\n",
    "sym = 'BTCUSDT'\n",
    "# time horizon of time series (time interval)\n",
    "time_interval = '1h'\n",
    "# Max number of auto-regressive lags\n",
    "max_lags = 4\n",
    "# Forecast horizon in steps \n",
    "forecast_horizon = 1\n",
    "# Sharpe annualized rate (so it's independent of time frequency)\n",
    "annualized_rate = research.sharpe_annualization_factor(time_interval, 365, 24)\n",
    "\n",
    "print(f\"{annualized_rate=}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a935f32c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading BTCUSDT: 100%|██████████| 346/346 [00:11<00:00, 29.86it/s]\n"
     ]
    }
   ],
   "source": [
    "start_date = datetime(2024, 10, 29, 0, 0)\n",
    "end_date = datetime(2025, 10, 9, 0, 0)\n",
    "binance.download_date_range(sym, start_date, end_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "282ade73",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading BTCUSDT: 100%|██████████| 346/346 [00:18<00:00, 18.69day/s]\n"
     ]
    },
    {
     "data": {
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       ".dataframe > thead > tr,\n",
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       "</style>\n",
       "<small>shape: (8_304, 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>69998.4</td><td>69733.0</td><td>69939.5</td></tr><tr><td>2024-10-29 01:00:00</td><td>69939.5</td><td>70399.4</td><td>69810.8</td><td>70287.0</td></tr><tr><td>2024-10-29 02:00:00</td><td>70286.9</td><td>71607.0</td><td>70198.1</td><td>71173.9</td></tr><tr><td>2024-10-29 03:00:00</td><td>71173.8</td><td>71409.0</td><td>70870.2</td><td>70927.5</td></tr><tr><td>2024-10-29 04:00:00</td><td>70927.4</td><td>71123.4</td><td>70906.1</td><td>71062.6</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>2025-10-09 19:00:00</td><td>120600.0</td><td>121060.1</td><td>120600.0</td><td>120897.6</td></tr><tr><td>2025-10-09 20:00:00</td><td>120897.6</td><td>121147.2</td><td>120787.9</td><td>121045.4</td></tr><tr><td>2025-10-09 21:00:00</td><td>121045.3</td><td>121457.2</td><td>120998.3</td><td>121407.8</td></tr><tr><td>2025-10-09 22:00:00</td><td>121407.9</td><td>121712.4</td><td>121342.0</td><td>121619.5</td></tr><tr><td>2025-10-09 23:00:00</td><td>121619.6</td><td>121671.1</td><td>121271.3</td><td>121579.3</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (8_304, 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  ┆ 69998.4  ┆ 69733.0  ┆ 69939.5  │\n",
       "│ 2024-10-29 01:00:00 ┆ 69939.5  ┆ 70399.4  ┆ 69810.8  ┆ 70287.0  │\n",
       "│ 2024-10-29 02:00:00 ┆ 70286.9  ┆ 71607.0  ┆ 70198.1  ┆ 71173.9  │\n",
       "│ 2024-10-29 03:00:00 ┆ 71173.8  ┆ 71409.0  ┆ 70870.2  ┆ 70927.5  │\n",
       "│ 2024-10-29 04:00:00 ┆ 70927.4  ┆ 71123.4  ┆ 70906.1  ┆ 71062.6  │\n",
       "│ …                   ┆ …        ┆ …        ┆ …        ┆ …        │\n",
       "│ 2025-10-09 19:00:00 ┆ 120600.0 ┆ 121060.1 ┆ 120600.0 ┆ 120897.6 │\n",
       "│ 2025-10-09 20:00:00 ┆ 120897.6 ┆ 121147.2 ┆ 120787.9 ┆ 121045.4 │\n",
       "│ 2025-10-09 21:00:00 ┆ 121045.3 ┆ 121457.2 ┆ 120998.3 ┆ 121407.8 │\n",
       "│ 2025-10-09 22:00:00 ┆ 121407.9 ┆ 121712.4 ┆ 121342.0 ┆ 121619.5 │\n",
       "│ 2025-10-09 23:00:00 ┆ 121619.6 ┆ 121671.1 ┆ 121271.3 ┆ 121579.3 │\n",
       "└─────────────────────┴──────────┴──────────┴──────────┴──────────┘"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = research.load_ohlc_timeseries_range(sym, time_interval, start_date, end_date)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2901fe88",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading BTCUSDT: 100%|██████████| 346/346 [00:19<00:00, 17.69day/s]\n"
     ]
    },
    {
     "data": {
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       "<div><style>\n",
       ".dataframe > thead > tr,\n",
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       "}\n",
       "</style>\n",
       "<small>shape: (8_304, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>datetime</th><th>price_median</th></tr><tr><td>datetime[μs]</td><td>f64</td></tr></thead><tbody><tr><td>2024-10-29 00:00:00</td><td>69848.5</td></tr><tr><td>2024-10-29 01:00:00</td><td>69969.0</td></tr><tr><td>2024-10-29 02:00:00</td><td>71180.1</td></tr><tr><td>2024-10-29 03:00:00</td><td>71170.5</td></tr><tr><td>2024-10-29 04:00:00</td><td>71029.3</td></tr><tr><td>&hellip;</td><td>&hellip;</td></tr><tr><td>2025-10-09 19:00:00</td><td>120839.8</td></tr><tr><td>2025-10-09 20:00:00</td><td>120942.8</td></tr><tr><td>2025-10-09 21:00:00</td><td>121300.7</td></tr><tr><td>2025-10-09 22:00:00</td><td>121500.7</td></tr><tr><td>2025-10-09 23:00:00</td><td>121448.7</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (8_304, 2)\n",
       "┌─────────────────────┬──────────────┐\n",
       "│ datetime            ┆ price_median │\n",
       "│ ---                 ┆ ---          │\n",
       "│ datetime[μs]        ┆ f64          │\n",
       "╞═════════════════════╪══════════════╡\n",
       "│ 2024-10-29 00:00:00 ┆ 69848.5      │\n",
       "│ 2024-10-29 01:00:00 ┆ 69969.0      │\n",
       "│ 2024-10-29 02:00:00 ┆ 71180.1      │\n",
       "│ 2024-10-29 03:00:00 ┆ 71170.5      │\n",
       "│ 2024-10-29 04:00:00 ┆ 71029.3      │\n",
       "│ …                   ┆ …            │\n",
       "│ 2025-10-09 19:00:00 ┆ 120839.8     │\n",
       "│ 2025-10-09 20:00:00 ┆ 120942.8     │\n",
       "│ 2025-10-09 21:00:00 ┆ 121300.7     │\n",
       "│ 2025-10-09 22:00:00 ┆ 121500.7     │\n",
       "│ 2025-10-09 23:00:00 ┆ 121448.7     │\n",
       "└─────────────────────┴──────────────┘"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "research.load_timeseries_range(sym, time_interval, start_date, end_date, pl.col('price').quantile(0.5).alias('price_median'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f8d34868",
   "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(ts, sym, 'close', time_interval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ee7b4018",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "  #altair-viz-6922a6bd859041a5a0a8f007c66bb6fe.vega-embed {\n",
       "    width: 100%;\n",
       "    display: flex;\n",
       "  }\n",
       "\n",
       "  #altair-viz-6922a6bd859041a5a0a8f007c66bb6fe.vega-embed details,\n",
       "  #altair-viz-6922a6bd859041a5a0a8f007c66bb6fe.vega-embed details summary {\n",
       "    position: relative;\n",
       "  }\n",
       "</style>\n",
       "<div id=\"altair-viz-6922a6bd859041a5a0a8f007c66bb6fe\"></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-6922a6bd859041a5a0a8f007c66bb6fe\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-6922a6bd859041a5a0a8f007c66bb6fe\");\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: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"6\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"6.4.1\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"7\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v6.json\", \"data\": [{\"name\": \"param_db82be42624d4632_store\"}, {\"name\": \"param_e9b61157fc812227_store\"}, {\"name\": \"source_0\", \"values\": [{\"close\": 69939.5, \"datetime\": \"2024-10-29T00:00:00.000\"}, {\"close\": 70287.0, \"datetime\": \"2024-10-29T01:00:00.000\"}, {\"close\": 71173.9, \"datetime\": \"2024-10-29T02:00:00.000\"}, {\"close\": 70927.5, \"datetime\": \"2024-10-29T03:00:00.000\"}, {\"close\": 71062.6, \"datetime\": \"2024-10-29T04:00:00.000\"}, {\"close\": 71010.9, \"datetime\": \"2024-10-29T05:00:00.000\"}, {\"close\": 71226.6, \"datetime\": \"2024-10-29T06:00:00.000\"}, {\"close\": 71004.2, \"datetime\": \"2024-10-29T07:00:00.000\"}, {\"close\": 71122.1, \"datetime\": \"2024-10-29T08:00:00.000\"}, {\"close\": 71280.0, \"datetime\": \"2024-10-29T09:00:00.000\"}, {\"close\": 71146.6, \"datetime\": \"2024-10-29T10:00:00.000\"}, {\"close\": 71440.1, \"datetime\": \"2024-10-29T11:00:00.000\"}, 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\"domainRaw\": {\"signal\": \"param_e9b61157fc812227[\\\"close\\\"]\"}}], \"axes\": [{\"scale\": \"x\", \"gridScale\": \"y\", \"grid\": true, \"labels\": false, \"aria\": false, \"minExtent\": 0, \"orient\": \"bottom\", \"zindex\": 0, \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"maxExtent\": 0, \"domain\": false, \"ticks\": false}, {\"scale\": \"y\", \"zindex\": 0, \"grid\": true, \"domain\": false, \"labels\": false, \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"maxExtent\": 0, \"gridScale\": \"x\", \"ticks\": false, \"orient\": \"left\", \"minExtent\": 0, \"aria\": false}, {\"scale\": \"x\", \"title\": \"datetime\", \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"grid\": false, \"orient\": \"bottom\", \"labelFlush\": true, \"labelOverlap\": true, \"zindex\": 0}, {\"scale\": \"y\", \"labelOverlap\": true, \"zindex\": 0, \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"title\": \"close\", \"orient\": \"left\", \"grid\": false}], \"title\": {\"text\": \"BTCUSDT 1h close\", \"frame\": \"group\"}, \"style\": \"cell\", \"background\": \"white\", \"height\": 400, \"width\": 800, \"padding\": 5}, {\"mode\": \"vega\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.data_transformers.enable(\"vegafusion\")\n",
    "research.plot_dyn_timeseries(ts, sym, 'close', time_interval)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2b4417c",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fe1ac560",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "  #altair-viz-a919d4e55b4442d5b2b612267114813a.vega-embed {\n",
       "    width: 100%;\n",
       "    display: flex;\n",
       "  }\n",
       "\n",
       "  #altair-viz-a919d4e55b4442d5b2b612267114813a.vega-embed details,\n",
       "  #altair-viz-a919d4e55b4442d5b2b612267114813a.vega-embed details summary {\n",
       "    position: relative;\n",
       "  }\n",
       "</style>\n",
       "<div id=\"altair-viz-a919d4e55b4442d5b2b612267114813a\"></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-a919d4e55b4442d5b2b612267114813a\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-a919d4e55b4442d5b2b612267114813a\");\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: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"6\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"6.4.1\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"7\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"$schema\": \"https://vega.github.io/schema/vega/v6.json\", \"data\": [{\"name\": \"param_1e9efca18e7a2868_store\"}, {\"name\": \"source_0\", \"values\": [{\"index\": 0, \"price\": 100.0}, {\"index\": 1, \"price\": 120.0}, {\"index\": 2, \"price\": 100.0}]}, {\"name\": \"source_0_x_domain_index\", \"values\": [{\"min\": 0, \"max\": 2}]}, {\"name\": \"source_0_y_domain_price\", \"values\": [{\"min\": 100.0, \"max\": 120.0}]}], \"signals\": [{\"name\": \"unit\", \"value\": {}, \"on\": [{\"events\": \"pointermove\", \"update\": \"isTuple(group()) ? group() : unit\"}]}, {\"name\": \"param_1e9efca18e7a2868\", \"update\": \"vlSelectionResolve(\\\"param_1e9efca18e7a2868_store\\\", \\\"union\\\")\"}, {\"name\": \"param_1e9efca18e7a2868_index\", \"on\": [{\"events\": [{\"source\": \"view\", \"type\": \"dblclick\"}], \"update\": \"null\"}, {\"events\": {\"signal\": \"param_1e9efca18e7a2868_translate_delta\"}, \"update\": \"panLinear(param_1e9efca18e7a2868_translate_anchor.extent_x, -param_1e9efca18e7a2868_translate_delta.x / width)\"}, {\"events\": {\"signal\": \"param_1e9efca18e7a2868_zoom_delta\"}, \"update\": \"zoomLinear(domain(\\\"x\\\"), param_1e9efca18e7a2868_zoom_anchor.x, param_1e9efca18e7a2868_zoom_delta)\"}]}, {\"name\": \"param_1e9efca18e7a2868_price\", \"on\": [{\"events\": [{\"source\": \"view\", \"type\": \"dblclick\"}], \"update\": \"null\"}, {\"events\": {\"signal\": \"param_1e9efca18e7a2868_translate_delta\"}, \"update\": \"panLinear(param_1e9efca18e7a2868_translate_anchor.extent_y, param_1e9efca18e7a2868_translate_delta.y / height)\"}, {\"events\": {\"signal\": \"param_1e9efca18e7a2868_zoom_delta\"}, \"update\": \"zoomLinear(domain(\\\"y\\\"), param_1e9efca18e7a2868_zoom_anchor.y, param_1e9efca18e7a2868_zoom_delta)\"}]}, {\"name\": \"param_1e9efca18e7a2868_tuple\", \"on\": [{\"events\": [{\"signal\": \"param_1e9efca18e7a2868_index || param_1e9efca18e7a2868_price\"}], \"update\": \"param_1e9efca18e7a2868_index && param_1e9efca18e7a2868_price ? {unit: \\\"\\\", fields: param_1e9efca18e7a2868_tuple_fields, values: [param_1e9efca18e7a2868_index,param_1e9efca18e7a2868_price]} : null\"}]}, {\"name\": \"param_1e9efca18e7a2868_tuple_fields\", \"value\": [{\"field\": \"index\", \"channel\": \"x\", \"type\": \"R\"}, {\"field\": \"price\", \"channel\": \"y\", \"type\": \"R\"}]}, {\"name\": \"param_1e9efca18e7a2868_translate_anchor\", \"value\": {}, \"on\": [{\"events\": [{\"source\": \"scope\", \"type\": \"pointerdown\"}], \"update\": \"{x: x(unit), y: y(unit), extent_x: domain(\\\"x\\\"), extent_y: domain(\\\"y\\\")}\"}]}, {\"name\": \"param_1e9efca18e7a2868_translate_delta\", \"value\": {}, \"on\": [{\"events\": [{\"source\": \"window\", \"between\": [{\"source\": \"scope\", \"type\": \"pointerdown\"}, {\"source\": \"window\", \"type\": \"pointerup\"}], \"type\": \"pointermove\", \"consume\": true}], \"update\": \"{x: param_1e9efca18e7a2868_translate_anchor.x - x(unit), y: param_1e9efca18e7a2868_translate_anchor.y - y(unit)}\"}]}, {\"name\": \"param_1e9efca18e7a2868_zoom_anchor\", \"on\": [{\"events\": [{\"source\": \"scope\", \"consume\": true, \"type\": \"wheel\"}], \"update\": \"{x: invert(\\\"x\\\", x(unit)), y: invert(\\\"y\\\", y(unit))}\"}]}, {\"name\": \"param_1e9efca18e7a2868_zoom_delta\", \"on\": [{\"events\": [{\"source\": \"scope\", \"consume\": true, \"type\": \"wheel\"}], \"update\": \"pow(1.001, event.deltaY * pow(16, event.deltaMode))\", \"force\": true}]}, {\"name\": \"param_1e9efca18e7a2868_modify\", \"on\": [{\"events\": {\"signal\": \"param_1e9efca18e7a2868_tuple\"}, \"update\": \"modify(\\\"param_1e9efca18e7a2868_store\\\", param_1e9efca18e7a2868_tuple, true)\"}]}], \"marks\": [{\"type\": \"line\", \"name\": \"marks\", \"from\": {\"data\": \"source_0\"}, \"sort\": {\"field\": \"x\"}, \"encode\": {\"update\": {\"stroke\": {\"value\": \"#4c78a8\"}, \"tooltip\": {\"signal\": \"{\\\"index\\\": format(datum[\\\"index\\\"], \\\"\\\"), \\\"price\\\": format(datum[\\\"price\\\"], \\\"\\\")}\"}, \"x\": {\"field\": \"index\", \"scale\": \"x\"}, \"defined\": {\"signal\": \"isValid(datum[\\\"index\\\"]) && isFinite(+datum[\\\"index\\\"]) && isValid(datum[\\\"price\\\"]) && isFinite(+datum[\\\"price\\\"])\"}, \"y\": {\"field\": \"price\", \"scale\": \"y\"}}}, \"interactive\": true, \"clip\": true, \"style\": [\"line\"]}], \"scales\": [{\"name\": \"x\", \"type\": \"linear\", \"domain\": [{\"signal\": \"(data(\\\"source_0_x_domain_index\\\")[0] || {}).min\"}, {\"signal\": \"(data(\\\"source_0_x_domain_index\\\")[0] || {}).max\"}], \"range\": [0, {\"signal\": \"width\"}], \"zero\": false, \"nice\": true, \"domainRaw\": {\"signal\": \"param_1e9efca18e7a2868[\\\"index\\\"]\"}}, {\"name\": \"y\", \"type\": \"linear\", \"domain\": [{\"signal\": \"(data(\\\"source_0_y_domain_price\\\")[0] || {}).min\"}, {\"signal\": \"(data(\\\"source_0_y_domain_price\\\")[0] || {}).max\"}], \"range\": [{\"signal\": \"height\"}, 0], \"nice\": true, \"zero\": true, \"domainRaw\": {\"signal\": \"param_1e9efca18e7a2868[\\\"price\\\"]\"}}], \"axes\": [{\"scale\": \"x\", \"aria\": false, \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"domain\": false, \"labels\": false, \"grid\": true, \"ticks\": false, \"gridScale\": \"y\", \"minExtent\": 0, \"maxExtent\": 0, \"zindex\": 0, \"orient\": \"bottom\"}, {\"scale\": \"y\", \"minExtent\": 0, \"orient\": \"left\", \"domain\": false, \"labels\": false, \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"maxExtent\": 0, \"grid\": true, \"aria\": false, \"ticks\": false, \"zindex\": 0, \"gridScale\": \"x\"}, {\"scale\": \"x\", \"orient\": \"bottom\", \"tickCount\": {\"signal\": \"ceil(width/40)\"}, \"labelOverlap\": true, \"title\": \"index\", \"labelFlush\": true, \"zindex\": 0, \"grid\": false}, {\"scale\": \"y\", \"orient\": \"left\", \"title\": \"price\", \"labelOverlap\": true, \"tickCount\": {\"signal\": \"ceil(height/40)\"}, \"zindex\": 0, \"grid\": false}], \"title\": {\"text\": \"price\", \"frame\": \"group\"}, \"style\": \"cell\", \"height\": 400, \"width\": 800, \"padding\": 5, \"background\": \"white\"}, {\"mode\": \"vega\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_time_series = pl.DataFrame({'price':[100.0,120.0,100.0]})\n",
    "research.plot_column(price_time_series, 'price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "497659ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (3, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>price</th><th>delta</th><th>return</th><th>log_return</th><th>test</th></tr><tr><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>100.0</td><td>null</td><td>null</td><td>null</td><td>null</td></tr><tr><td>120.0</td><td>20.0</td><td>0.2</td><td>0.182322</td><td>100.0</td></tr><tr><td>100.0</td><td>-20.0</td><td>-0.166667</td><td>-0.182322</td><td>120.0</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (3, 5)\n",
       "┌───────┬───────┬───────────┬────────────┬───────┐\n",
       "│ price ┆ delta ┆ return    ┆ log_return ┆ test  │\n",
       "│ ---   ┆ ---   ┆ ---       ┆ ---        ┆ ---   │\n",
       "│ f64   ┆ f64   ┆ f64       ┆ f64        ┆ f64   │\n",
       "╞═══════╪═══════╪═══════════╪════════════╪═══════╡\n",
       "│ 100.0 ┆ null  ┆ null      ┆ null       ┆ null  │\n",
       "│ 120.0 ┆ 20.0  ┆ 0.2       ┆ 0.182322   ┆ 100.0 │\n",
       "│ 100.0 ┆ -20.0 ┆ -0.166667 ┆ -0.182322  ┆ 120.0 │\n",
       "└───────┴───────┴───────────┴────────────┴───────┘"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_time_series.with_columns(\n",
    "    pl.col('price').diff().alias('delta'),\n",
    "    ((pl.col('price')-pl.col('price').shift())/pl.col('price').shift()).alias('return'),\n",
    "    (pl.col('price')/pl.col('price').shift()).log().alias('log_return'),\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "study (3.14.5.final.0)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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