walk_forward_backtest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## Download data from yfinance"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "68f6ff202a740b4d"
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n"
     ]
    },
    {
     "data": {
      "text/plain": "                  Open        High         Low       Close   Adj Close  \\\nDate                                                                     \n1980-12-12    0.128348    0.128906    0.128348    0.128348    0.099319   \n1980-12-15    0.122210    0.122210    0.121652    0.121652    0.094137   \n1980-12-16    0.113281    0.113281    0.112723    0.112723    0.087228   \n1980-12-17    0.115513    0.116071    0.115513    0.115513    0.089387   \n1980-12-18    0.118862    0.119420    0.118862    0.118862    0.091978   \n...                ...         ...         ...         ...         ...   \n2023-11-20  189.889999  191.910004  189.880005  191.449997  191.449997   \n2023-11-21  191.410004  191.520004  189.740005  190.639999  190.639999   \n2023-11-22  191.490005  192.929993  190.830002  191.309998  191.309998   \n2023-11-24  190.869995  190.899994  189.250000  189.970001  189.970001   \n2023-11-27  189.919998  190.669998  188.899994  189.789993  189.789993   \n\n               Volume  \nDate                   \n1980-12-12  469033600  \n1980-12-15  175884800  \n1980-12-16  105728000  \n1980-12-17   86441600  \n1980-12-18   73449600  \n...               ...  \n2023-11-20   46505100  \n2023-11-21   38134500  \n2023-11-22   39617700  \n2023-11-24   24048300  \n2023-11-27   40500500  \n\n[10830 rows x 6 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1980-12-12</th>\n      <td>0.128348</td>\n      <td>0.128906</td>\n      <td>0.128348</td>\n      <td>0.128348</td>\n      <td>0.099319</td>\n      <td>469033600</td>\n    </tr>\n    <tr>\n      <th>1980-12-15</th>\n      <td>0.122210</td>\n      <td>0.122210</td>\n      <td>0.121652</td>\n      <td>0.121652</td>\n      <td>0.094137</td>\n      <td>175884800</td>\n    </tr>\n    <tr>\n      <th>1980-12-16</th>\n      <td>0.113281</td>\n      <td>0.113281</td>\n      <td>0.112723</td>\n      <td>0.112723</td>\n      <td>0.087228</td>\n      <td>105728000</td>\n    </tr>\n    <tr>\n      <th>1980-12-17</th>\n      <td>0.115513</td>\n      <td>0.116071</td>\n      <td>0.115513</td>\n      <td>0.115513</td>\n      <td>0.089387</td>\n      <td>86441600</td>\n    </tr>\n    <tr>\n      <th>1980-12-18</th>\n      <td>0.118862</td>\n      <td>0.119420</td>\n      <td>0.118862</td>\n      <td>0.118862</td>\n      <td>0.091978</td>\n      <td>73449600</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2023-11-20</th>\n      <td>189.889999</td>\n      <td>191.910004</td>\n      <td>189.880005</td>\n      <td>191.449997</td>\n      <td>191.449997</td>\n      <td>46505100</td>\n    </tr>\n    <tr>\n      <th>2023-11-21</th>\n      <td>191.410004</td>\n      <td>191.520004</td>\n      <td>189.740005</td>\n      <td>190.639999</td>\n      <td>190.639999</td>\n      <td>38134500</td>\n    </tr>\n    <tr>\n      <th>2023-11-22</th>\n      <td>191.490005</td>\n      <td>192.929993</td>\n      <td>190.830002</td>\n      <td>191.309998</td>\n      <td>191.309998</td>\n      <td>39617700</td>\n    </tr>\n    <tr>\n      <th>2023-11-24</th>\n      <td>190.869995</td>\n      <td>190.899994</td>\n      <td>189.250000</td>\n      <td>189.970001</td>\n      <td>189.970001</td>\n      <td>24048300</td>\n    </tr>\n    <tr>\n      <th>2023-11-27</th>\n      <td>189.919998</td>\n      <td>190.669998</td>\n      <td>188.899994</td>\n      <td>189.789993</td>\n      <td>189.789993</td>\n      <td>40500500</td>\n    </tr>\n  </tbody>\n</table>\n<p>10830 rows × 6 columns</p>\n</div>"
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import yfinance as yf\n",
    "\n",
    "ticker = 'AAPL'\n",
    "df = yf.download(ticker)\n",
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-28T08:51:24.131224Z",
     "start_time": "2023-11-28T08:51:21.949684Z"
    }
   },
   "id": "dc300ead46521083"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Preprocess the data\n",
    "### Filter the data range"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "d35758c0ee30b287"
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "df = df.loc['2018-01-01':].copy()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-28T08:51:24.135660Z",
     "start_time": "2023-11-28T08:51:24.131815Z"
    }
   },
   "id": "85255ac55a7a7377"
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Create the target variable\n",
    "\n",
    "#### Percentage change\n",
    "- Percentage change on Adj Close for tomorrow"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "bb3d9ee544f6d21"
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "df['change_tomorrow'] = df['Adj Close'].pct_change(-1)\n",
    "df.change_tomorrow = df.change_tomorrow * -1\n",
    "df.change_tomorrow = df.change_tomorrow * 100"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-28T08:51:24.140532Z",
     "start_time": "2023-11-28T08:51:24.136777Z"
    }
   },
   "id": "2ddd16d3b98fd8eb"
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Remove rows with any missing values"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "9f086b99f74a95e5"
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "                  Open        High         Low       Close   Adj Close  \\\nDate                                                                     \n2018-01-02   42.540001   43.075001   42.314999   43.064999   40.722881   \n2018-01-03   43.132500   43.637501   42.990002   43.057499   40.715778   \n2018-01-04   43.134998   43.367500   43.020000   43.257500   40.904900   \n2018-01-05   43.360001   43.842499   43.262501   43.750000   41.370625   \n2018-01-08   43.587502   43.902500   43.482498   43.587502   41.216965   \n...                ...         ...         ...         ...         ...   \n2023-11-17  190.250000  190.380005  188.570007  189.690002  189.690002   \n2023-11-20  189.889999  191.910004  189.880005  191.449997  191.449997   \n2023-11-21  191.410004  191.520004  189.740005  190.639999  190.639999   \n2023-11-22  191.490005  192.929993  190.830002  191.309998  191.309998   \n2023-11-24  190.869995  190.899994  189.250000  189.970001  189.970001   \n\n               Volume  change_tomorrow  \nDate                                    \n2018-01-02  102223600        -0.017445  \n2018-01-03  118071600         0.462344  \n2018-01-04   89738400         1.125738  \n2018-01-05   94640000        -0.372807  \n2018-01-08   82271200        -0.011496  \n...               ...              ...  \n2023-11-17   50922700         0.919297  \n2023-11-20   46505100        -0.424883  \n2023-11-21   38134500         0.350216  \n2023-11-22   39617700        -0.705373  \n2023-11-24   24048300        -0.094846  \n\n[1485 rows x 7 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n      <th>change_tomorrow</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2018-01-02</th>\n      <td>42.540001</td>\n      <td>43.075001</td>\n      <td>42.314999</td>\n      <td>43.064999</td>\n      <td>40.722881</td>\n      <td>102223600</td>\n      <td>-0.017445</td>\n    </tr>\n    <tr>\n      <th>2018-01-03</th>\n      <td>43.132500</td>\n      <td>43.637501</td>\n      <td>42.990002</td>\n      <td>43.057499</td>\n      <td>40.715778</td>\n      <td>118071600</td>\n      <td>0.462344</td>\n    </tr>\n    <tr>\n      <th>2018-01-04</th>\n      <td>43.134998</td>\n      <td>43.367500</td>\n      <td>43.020000</td>\n      <td>43.257500</td>\n      <td>40.904900</td>\n      <td>89738400</td>\n      <td>1.125738</td>\n    </tr>\n    <tr>\n      <th>2018-01-05</th>\n      <td>43.360001</td>\n      <td>43.842499</td>\n      <td>43.262501</td>\n      <td>43.750000</td>\n      <td>41.370625</td>\n      <td>94640000</td>\n      <td>-0.372807</td>\n    </tr>\n    <tr>\n      <th>2018-01-08</th>\n      <td>43.587502</td>\n      <td>43.902500</td>\n      <td>43.482498</td>\n      <td>43.587502</td>\n      <td>41.216965</td>\n      <td>82271200</td>\n      <td>-0.011496</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2023-11-17</th>\n      <td>190.250000</td>\n      <td>190.380005</td>\n      <td>188.570007</td>\n      <td>189.690002</td>\n      <td>189.690002</td>\n      <td>50922700</td>\n      <td>0.919297</td>\n    </tr>\n    <tr>\n      <th>2023-11-20</th>\n      <td>189.889999</td>\n      <td>191.910004</td>\n      <td>189.880005</td>\n      <td>191.449997</td>\n      <td>191.449997</td>\n      <td>46505100</td>\n      <td>-0.424883</td>\n    </tr>\n    <tr>\n      <th>2023-11-21</th>\n      <td>191.410004</td>\n      <td>191.520004</td>\n      <td>189.740005</td>\n      <td>190.639999</td>\n      <td>190.639999</td>\n      <td>38134500</td>\n      <td>0.350216</td>\n    </tr>\n    <tr>\n      <th>2023-11-22</th>\n      <td>191.490005</td>\n      <td>192.929993</td>\n      <td>190.830002</td>\n      <td>191.309998</td>\n      <td>191.309998</td>\n      <td>39617700</td>\n      <td>-0.705373</td>\n    </tr>\n    <tr>\n      <th>2023-11-24</th>\n      <td>190.869995</td>\n      <td>190.899994</td>\n      <td>189.250000</td>\n      <td>189.970001</td>\n      <td>189.970001</td>\n      <td>24048300</td>\n      <td>-0.094846</td>\n    </tr>\n  </tbody>\n</table>\n<p>1485 rows × 7 columns</p>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.dropna().copy()\n",
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-28T08:51:24.163932Z",
     "start_time": "2023-11-28T08:51:24.141397Z"
    }
   },
   "id": "f712c8c7c114c26e"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Machine Learning modelling\n",
    "\n",
    "### Split the data\n",
    "1. Target variable: which variable do you want to predict?\n",
    "2. Explanatory variables: which variables do you want to use to predict the target variable?"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "2957a878ed2c0608"
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "y = df.change_tomorrow\n",
    "X = df[['Open','High','Low','Close','Volume']]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-28T08:51:24.164383Z",
     "start_time": "2023-11-28T08:51:24.150031Z"
    }
   },
   "id": "86d1192ab9f95325"
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Time Series Split"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "84b10b4a2605ff4d"
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "\n",
    "ts = TimeSeriesSplit(test_size=200)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-28T08:51:24.938212Z",
     "start_time": "2023-11-28T08:51:24.151594Z"
    }
   },
   "id": "163c92e9c34c2f9c"
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Compute and evaluate model in a for loop\n",
    "\n",
    "1. Separate the data in train and test\n",
    "2. Compute the model on the train set\n",
    "3. Evaluate the model (mse) on the test set\n",
    "4. Append the errors (mse) in an empty list"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "47c27f8b00d1d4ed"
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "model_dt = GradientBoostingRegressor(max_depth=15, random_state=42)\n",
    "\n",
    "error_mse_list = []\n",
    "\n",
    "for index_train, index_test in ts.split(df):\n",
    "    X_train, y_train = X.iloc[index_train], y.iloc[index_train]\n",
    "    X_test, y_test = X.iloc[index_test], y.iloc[index_test]\n",
    "    \n",
    "    model_dt.fit(X_train, y_train)\n",
    "    \n",
    "    y_pred = model_dt.predict(X_test)\n",
    "    error_mse = mean_squared_error(y_test, y_pred)\n",
    "    \n",
    "    error_mse_list.append(error_mse)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-28T08:51:27.225427Z",
     "start_time": "2023-11-28T08:51:24.940960Z"
    }
   },
   "id": "fb56af228aa247f1"
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "[11.791461602776133,\n 12.94395453784437,\n 3.5222440511007824,\n 6.593685188246979,\n 3.109191599649687]"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_mse_list"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-28T08:51:27.231320Z",
     "start_time": "2023-11-28T08:51:27.226343Z"
    }
   },
   "id": "1b67a32971017528"
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Anchored Walk Forward evaluation in backtesting\n",
    "\n",
    "### Create a new strategy"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "5d0a1daa129ca951"
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/py312/lib/python3.12/site-packages/backtesting/_plotting.py:50: UserWarning: Jupyter Notebook detected. Setting Bokeh output to notebook. This may not work in Jupyter clients without JavaScript support (e.g. PyCharm, Spyder IDE). Reset with `backtesting.set_bokeh_output(notebook=False)`.\n",
      "  warnings.warn('Jupyter Notebook detected. '\n"
     ]
    },
    {
     "data": {
      "text/html": "    <style>\n        .bk-notebook-logo {\n            display: block;\n            width: 20px;\n            height: 20px;\n            background-image: url(data:image/png;base64,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);\n        }\n    </style>\n    <div>\n        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-notebook-logo\"></a>\n        <span id=\"eecaef34-5027-44b1-ac88-b77bb86c9ce9\">Loading BokehJS ...</span>\n    </div>\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\nconst JS_MIME_TYPE = 'application/javascript';\n  const HTML_MIME_TYPE = 'text/html';\n  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n  const CLASS_NAME = 'output_bokeh rendered_html';\n\n  /**\n   * Render data to the DOM node\n   */\n  function render(props, node) {\n    const script = document.createElement(\"script\");\n    node.appendChild(script);\n  }\n\n  /**\n   * Handle when an output is cleared or removed\n   */\n  function handleClearOutput(event, handle) {\n    function drop(id) {\n      const view = Bokeh.index.get_by_id(id)\n      if (view != null) {\n        view.model.document.clear()\n        Bokeh.index.delete(view)\n      }\n    }\n\n    const cell = handle.cell;\n\n    const id = cell.output_area._bokeh_element_id;\n    const server_id = cell.output_area._bokeh_server_id;\n\n    // Clean up Bokeh references\n    if (id != null) {\n      drop(id)\n    }\n\n    if (server_id !== undefined) {\n      // Clean up Bokeh references\n      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n      cell.notebook.kernel.execute(cmd_clean, {\n        iopub: {\n          output: function(msg) {\n            const id = msg.content.text.trim()\n            drop(id)\n          }\n        }\n      });\n      // Destroy server and session\n      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n      cell.notebook.kernel.execute(cmd_destroy);\n    }\n  }\n\n  /**\n   * Handle when a new output is added\n   */\n  function handleAddOutput(event, handle) {\n    const output_area = handle.output_area;\n    const output = handle.output;\n\n    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n      return\n    }\n\n    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n      // store reference to embed id on output_area\n      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n    }\n    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n      const bk_div = document.createElement(\"div\");\n      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n      const script_attrs = bk_div.children[0].attributes;\n      for (let i = 0; i < script_attrs.length; i++) {\n        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n      }\n      // store reference to server id on output_area\n      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n    }\n  }\n\n  function register_renderer(events, OutputArea) {\n\n    function append_mime(data, metadata, element) {\n      // create a DOM node to render to\n      const toinsert = this.create_output_subarea(\n        metadata,\n        CLASS_NAME,\n        EXEC_MIME_TYPE\n      );\n      this.keyboard_manager.register_events(toinsert);\n      // Render to node\n      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n      render(props, toinsert[toinsert.length - 1]);\n      element.append(toinsert);\n      return toinsert\n    }\n\n    /* Handle when an output is cleared or removed */\n    events.on('clear_output.CodeCell', handleClearOutput);\n    events.on('delete.Cell', handleClearOutput);\n\n    /* Handle when a new output is added */\n    events.on('output_added.OutputArea', handleAddOutput);\n\n    /**\n     * Register the mime type and append_mime function with output_area\n     */\n    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n      /* Is output safe? */\n      safe: true,\n      /* Index of renderer in `output_area.display_order` */\n      index: 0\n    });\n  }\n\n  // register the mime type if in Jupyter Notebook environment and previously unregistered\n  if (root.Jupyter !== undefined) {\n    const events = require('base/js/events');\n    const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  }\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"eecaef34-5027-44b1-ac88-b77bb86c9ce9\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.1.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"eecaef34-5027-44b1-ac88-b77bb86c9ce9\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));",
      "application/vnd.bokehjs_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"eecaef34-5027-44b1-ac88-b77bb86c9ce9\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.1.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"eecaef34-5027-44b1-ac88-b77bb86c9ce9\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from backtesting import Backtest, Strategy"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-28T08:51:27.536485Z",
     "start_time": "2023-11-28T08:51:27.229430Z"
    }
   },
   "id": "874f4b380d745a5"
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "class Regression(Strategy):\n",
    "    limit_buy = 1\n",
    "    limit_sell = -1\n",
    "    \n",
    "    n_train = 600\n",
    "    coef_retrain = 200\n",
    "    \n",
    "    def init(self):\n",
    "        self.model = GradientBoostingRegressor(max_depth=15, random_state=42)\n",
    "        self.already_bought = False\n",
    "        \n",
    "        X_train = self.data.df.iloc[:self.n_train, :-1]\n",
    "        y_train = self.data.df.iloc[:self.n_train, -1]\n",
    "        \n",
    "        self.model.fit(X=X_train, y=y_train)\n",
    "\n",
    "    def next(self):\n",
    "        explanatory_today = self.data.df.iloc[[-1], :-1]\n",
    "        forecast_tomorrow = self.model.predict(explanatory_today)[0]\n",
    "        \n",
    "        if forecast_tomorrow > self.limit_buy and self.already_bought == False:\n",
    "            self.buy()\n",
    "            self.already_bought = True\n",
    "        elif forecast_tomorrow < self.limit_sell and self.already_bought == True:\n",
    "            self.sell()\n",
    "            self.already_bought = False\n",
    "        else:\n",
    "            pass"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-28T09:03:35.607702Z",
     "start_time": "2023-11-28T09:03:35.596535Z"
    }
   },
   "id": "39c08c14e78dcd53"
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [],
   "source": [
    "class WalkForwardAnchored(Regression):\n",
    "    def next(self):\n",
    "        \n",
    "        # we don't take any action and move on to the following day\n",
    "        if len(self.data) < self.n_train:\n",
    "            return\n",
    "        \n",
    "        # we retrain the model each 200 days\n",
    "        if len(self.data) % self.coef_retrain == 0:\n",
    "            X_train = self.data.df.iloc[:, :-1]\n",
    "            y_train = self.data.df.iloc[:, -1]\n",
    "\n",
    "            self.model.fit(X_train, y_train)\n",
    "\n",
    "            super().next()\n",
    "            \n",
    "        else:\n",
    "            \n",
    "            super().next()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-28T09:03:36.320183Z",
     "start_time": "2023-11-28T09:03:36.315309Z"
    }
   },
   "id": "506a7d39a6f39cd9"
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Run the backtest with optimization"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "ce29cbe2bc2edd37"
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [],
   "source": [
    "from backtesting import Backtest\n",
    "bt = Backtest(df, WalkForwardAnchored, cash=10000, commission=.002, exclusive_orders=True)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-28T09:03:38.147883Z",
     "start_time": "2023-11-28T09:03:38.143790Z"
    }
   },
   "id": "d32b1a2845217d0c"
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "Start                     2018-01-02 00:00:00\nEnd                       2023-11-24 00:00:00\nDuration                   2152 days 00:00:00\nExposure Time [%]                    59.52862\nEquity Final [$]                  6942.352582\nEquity Peak [$]                  10078.387298\nReturn [%]                         -30.576474\nBuy & Hold Return [%]              341.123899\nReturn (Ann.) [%]                   -6.005128\nVolatility (Ann.) [%]               24.899604\nSharpe Ratio                              0.0\nSortino Ratio                             0.0\nCalmar Ratio                              0.0\nMax. Drawdown [%]                  -59.788814\nAvg. Drawdown [%]                  -59.788814\nMax. Drawdown Duration     1281 days 00:00:00\nAvg. Drawdown Duration     1281 days 00:00:00\n# Trades                                   71\nWin Rate [%]                        54.929577\nBest Trade [%]                      14.117335\nWorst Trade [%]                    -38.357939\nAvg. Trade [%]                      -0.515429\nMax. Trade Duration         168 days 00:00:00\nAvg. Trade Duration          19 days 00:00:00\nProfit Factor                        0.920256\nExpectancy [%]                      -0.189369\nSQN                                 -0.618595\n_strategy                 WalkForwardAnchored\n_equity_curve                             ...\n_trades                       Size  EntryB...\ndtype: object"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats = bt.run()\n",
    "stats"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-28T09:03:43.242674Z",
     "start_time": "2023-11-28T09:03:39.684152Z"
    }
   },
   "id": "61f7db45879832b3"
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "import multiprocessing as mp\n",
    "mp.set_start_method('fork')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-28T08:51:27.550480Z",
     "start_time": "2023-11-28T08:51:27.545357Z"
    }
   },
   "id": "8bf0918cf45f30fe"
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "ename": "InvalidParameterError",
     "evalue": "The 'criterion' parameter of ExtraTreesRegressor must be a str among {'absolute_error', 'squared_error', 'friedman_mse', 'poisson'}. Got 'mse' instead.",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mInvalidParameterError\u001B[0m                     Traceback (most recent call last)",
      "Cell \u001B[0;32mIn[14], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m stats_skopt, heatmap, optimize_result \u001B[38;5;241m=\u001B[39m \u001B[43mbt\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43moptimize\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m      2\u001B[0m \u001B[43m    \u001B[49m\u001B[43mlimit_buy\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m \u001B[49m\u001B[38;5;28;43mrange\u001B[39;49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m0\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m6\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mlimit_sell\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m \u001B[49m\u001B[38;5;28;43mrange\u001B[39;49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m-\u001B[39;49m\u001B[38;5;241;43m6\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m0\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m      3\u001B[0m \u001B[43m    \u001B[49m\u001B[43mmaximize\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mReturn [\u001B[39;49m\u001B[38;5;124;43m%\u001B[39;49m\u001B[38;5;124;43m]\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m      4\u001B[0m \u001B[43m    \u001B[49m\u001B[43mmax_tries\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m500\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m      5\u001B[0m \u001B[43m    \u001B[49m\u001B[43mrandom_state\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m42\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m      6\u001B[0m \u001B[43m    \u001B[49m\u001B[43mreturn_heatmap\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[1;32m      7\u001B[0m \u001B[43m    \u001B[49m\u001B[43mreturn_optimization\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[1;32m      8\u001B[0m \u001B[43m    \u001B[49m\u001B[43mmethod\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mskopt\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m      9\u001B[0m \u001B[43m    \u001B[49m\u001B[38;5;66;43;03m# criterion='squared_error'\u001B[39;49;00m\n\u001B[1;32m     10\u001B[0m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m     12\u001B[0m dff \u001B[38;5;241m=\u001B[39m heatmap\u001B[38;5;241m.\u001B[39mreset_index()\n\u001B[1;32m     13\u001B[0m dff \u001B[38;5;241m=\u001B[39m dff\u001B[38;5;241m.\u001B[39msort_values(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mReturn [\u001B[39m\u001B[38;5;124m%\u001B[39m\u001B[38;5;124m]\u001B[39m\u001B[38;5;124m'\u001B[39m, ascending\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m)\n",
      "File \u001B[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/py312/lib/python3.12/site-packages/backtesting/backtesting.py:1490\u001B[0m, in \u001B[0;36mBacktest.optimize\u001B[0;34m(self, maximize, method, max_tries, constraint, return_heatmap, return_optimization, random_state, **kwargs)\u001B[0m\n\u001B[1;32m   1488\u001B[0m     output \u001B[38;5;241m=\u001B[39m _optimize_grid()\n\u001B[1;32m   1489\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m method \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mskopt\u001B[39m\u001B[38;5;124m'\u001B[39m:\n\u001B[0;32m-> 1490\u001B[0m     output \u001B[38;5;241m=\u001B[39m \u001B[43m_optimize_skopt\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m   1491\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m   1492\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mMethod should be \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mgrid\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m or \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mskopt\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m, not \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mmethod\u001B[38;5;132;01m!r}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m)\n",
      "File \u001B[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/py312/lib/python3.12/site-packages/backtesting/backtesting.py:1456\u001B[0m, in \u001B[0;36mBacktest.optimize.<locals>._optimize_skopt\u001B[0;34m()\u001B[0m\n\u001B[1;32m   1452\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m warnings\u001B[38;5;241m.\u001B[39mcatch_warnings():\n\u001B[1;32m   1453\u001B[0m     warnings\u001B[38;5;241m.\u001B[39mfilterwarnings(\n\u001B[1;32m   1454\u001B[0m         \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mignore\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mThe objective has been evaluated at this point before.\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m-> 1456\u001B[0m     res \u001B[38;5;241m=\u001B[39m \u001B[43mforest_minimize\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m   1457\u001B[0m \u001B[43m        \u001B[49m\u001B[43mfunc\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mobjective_function\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m   1458\u001B[0m \u001B[43m        \u001B[49m\u001B[43mdimensions\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdimensions\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m   1459\u001B[0m \u001B[43m        \u001B[49m\u001B[43mn_calls\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mmax_tries\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m   1460\u001B[0m \u001B[43m        \u001B[49m\u001B[43mbase_estimator\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mExtraTreesRegressor\u001B[49m\u001B[43m(\u001B[49m\u001B[43mn_estimators\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m20\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmin_samples_leaf\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m2\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m   1461\u001B[0m \u001B[43m        \u001B[49m\u001B[43macq_func\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mLCB\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m   1462\u001B[0m \u001B[43m        \u001B[49m\u001B[43mkappa\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m3\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m   1463\u001B[0m \u001B[43m        \u001B[49m\u001B[43mn_initial_points\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mmin\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mmax_tries\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m20\u001B[39;49m\u001B[43m \u001B[49m\u001B[38;5;241;43m+\u001B[39;49m\u001B[43m \u001B[49m\u001B[38;5;241;43m3\u001B[39;49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43m \u001B[49m\u001B[38;5;28;43mlen\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m   1464\u001B[0m \u001B[43m        \u001B[49m\u001B[43minitial_point_generator\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mlhs\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m  \u001B[49m\u001B[38;5;66;43;03m# 'sobel' requires n_initial_points ~ 2**N\u001B[39;49;00m\n\u001B[1;32m   1465\u001B[0m \u001B[43m        \u001B[49m\u001B[43mcallback\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mDeltaXStopper\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m9e-7\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m   1466\u001B[0m \u001B[43m        \u001B[49m\u001B[43mrandom_state\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mrandom_state\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m   1468\u001B[0m stats \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mrun(\u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39m\u001B[38;5;28mdict\u001B[39m(\u001B[38;5;28mzip\u001B[39m(kwargs\u001B[38;5;241m.\u001B[39mkeys(), res\u001B[38;5;241m.\u001B[39mx)))\n\u001B[1;32m   1469\u001B[0m output \u001B[38;5;241m=\u001B[39m [stats]\n",
      "File \u001B[0;32m~/Library/CloudStorage/OneDrive-NielsenIQ/PycharmProjects/quant_analysis/src/scikit-optimize/skopt/optimizer/forest.py:186\u001B[0m, in \u001B[0;36mforest_minimize\u001B[0;34m(func, dimensions, base_estimator, n_calls, n_random_starts, n_initial_points, acq_func, initial_point_generator, x0, y0, random_state, verbose, callback, n_points, xi, kappa, n_jobs, model_queue_size)\u001B[0m\n\u001B[1;32m     10\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mforest_minimize\u001B[39m(func, dimensions, base_estimator\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mET\u001B[39m\u001B[38;5;124m\"\u001B[39m, n_calls\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m100\u001B[39m,\n\u001B[1;32m     11\u001B[0m                     n_random_starts\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, n_initial_points\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m10\u001B[39m, acq_func\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mEI\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m     12\u001B[0m                     initial_point_generator\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mrandom\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[1;32m     13\u001B[0m                     x0\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, y0\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, random_state\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, verbose\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m,\n\u001B[1;32m     14\u001B[0m                     callback\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, n_points\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m10000\u001B[39m, xi\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m0.01\u001B[39m, kappa\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1.96\u001B[39m,\n\u001B[1;32m     15\u001B[0m                     n_jobs\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m, model_queue_size\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m):\n\u001B[1;32m     16\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124;03m\"\"\"Sequential optimisation using decision trees.\u001B[39;00m\n\u001B[1;32m     17\u001B[0m \n\u001B[1;32m     18\u001B[0m \u001B[38;5;124;03m    A tree based regression model is used to model the expensive to evaluate\u001B[39;00m\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    184\u001B[0m \u001B[38;5;124;03m        :class:`skopt.dummy_minimize`, :class:`skopt.gbrt_minimize`\u001B[39;00m\n\u001B[1;32m    185\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m\n\u001B[0;32m--> 186\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mbase_minimize\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfunc\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdimensions\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbase_estimator\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    187\u001B[0m \u001B[43m                         \u001B[49m\u001B[43mn_calls\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mn_calls\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mn_points\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mn_points\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    188\u001B[0m \u001B[43m                         \u001B[49m\u001B[43mn_random_starts\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mn_random_starts\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    189\u001B[0m \u001B[43m                         \u001B[49m\u001B[43mn_initial_points\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mn_initial_points\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    190\u001B[0m \u001B[43m                         \u001B[49m\u001B[43minitial_point_generator\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43minitial_point_generator\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    191\u001B[0m \u001B[43m                         \u001B[49m\u001B[43mx0\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mx0\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my0\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43my0\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mrandom_state\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mrandom_state\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    192\u001B[0m \u001B[43m                         \u001B[49m\u001B[43mn_jobs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mn_jobs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    193\u001B[0m \u001B[43m                         \u001B[49m\u001B[43macq_func\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43macq_func\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    194\u001B[0m \u001B[43m                         \u001B[49m\u001B[43mxi\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mxi\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mkappa\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mkappa\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mverbose\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mverbose\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    195\u001B[0m \u001B[43m                         \u001B[49m\u001B[43mcallback\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcallback\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43macq_optimizer\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43msampling\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m    196\u001B[0m \u001B[43m                         \u001B[49m\u001B[43mmodel_queue_size\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mmodel_queue_size\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m~/Library/CloudStorage/OneDrive-NielsenIQ/PycharmProjects/quant_analysis/src/scikit-optimize/skopt/optimizer/base.py:300\u001B[0m, in \u001B[0;36mbase_minimize\u001B[0;34m(func, dimensions, base_estimator, n_calls, n_random_starts, n_initial_points, initial_point_generator, acq_func, acq_optimizer, x0, y0, random_state, verbose, callback, n_points, n_restarts_optimizer, xi, kappa, n_jobs, model_queue_size)\u001B[0m\n\u001B[1;32m    298\u001B[0m next_x \u001B[38;5;241m=\u001B[39m optimizer\u001B[38;5;241m.\u001B[39mask()\n\u001B[1;32m    299\u001B[0m next_y \u001B[38;5;241m=\u001B[39m func(next_x)\n\u001B[0;32m--> 300\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[43moptimizer\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtell\u001B[49m\u001B[43m(\u001B[49m\u001B[43mnext_x\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnext_y\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    301\u001B[0m result\u001B[38;5;241m.\u001B[39mspecs \u001B[38;5;241m=\u001B[39m specs\n\u001B[1;32m    302\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m eval_callbacks(callbacks, result):\n",
      "File \u001B[0;32m~/Library/CloudStorage/OneDrive-NielsenIQ/PycharmProjects/quant_analysis/src/scikit-optimize/skopt/optimizer/optimizer.py:493\u001B[0m, in \u001B[0;36mOptimizer.tell\u001B[0;34m(self, x, y, fit)\u001B[0m\n\u001B[1;32m    490\u001B[0m         y \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(y)\n\u001B[1;32m    491\u001B[0m         y[\u001B[38;5;241m1\u001B[39m] \u001B[38;5;241m=\u001B[39m log(y[\u001B[38;5;241m1\u001B[39m])\n\u001B[0;32m--> 493\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_tell\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfit\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m~/Library/CloudStorage/OneDrive-NielsenIQ/PycharmProjects/quant_analysis/src/scikit-optimize/skopt/optimizer/optimizer.py:536\u001B[0m, in \u001B[0;36mOptimizer._tell\u001B[0;34m(self, x, y, fit)\u001B[0m\n\u001B[1;32m    534\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m warnings\u001B[38;5;241m.\u001B[39mcatch_warnings():\n\u001B[1;32m    535\u001B[0m     warnings\u001B[38;5;241m.\u001B[39msimplefilter(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mignore\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m--> 536\u001B[0m     \u001B[43mest\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mspace\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtransform\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mXi\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43myi\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    538\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mhasattr\u001B[39m(\u001B[38;5;28mself\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnext_xs_\u001B[39m\u001B[38;5;124m\"\u001B[39m) \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39macq_func \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mgp_hedge\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n\u001B[1;32m    539\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mgains_ \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m=\u001B[39m est\u001B[38;5;241m.\u001B[39mpredict(np\u001B[38;5;241m.\u001B[39mvstack(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mnext_xs_))\n",
      "File \u001B[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/py312/lib/python3.12/site-packages/sklearn/base.py:1145\u001B[0m, in \u001B[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001B[0;34m(estimator, *args, **kwargs)\u001B[0m\n\u001B[1;32m   1140\u001B[0m partial_fit_and_fitted \u001B[38;5;241m=\u001B[39m (\n\u001B[1;32m   1141\u001B[0m     fit_method\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__name__\u001B[39m \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mpartial_fit\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01mand\u001B[39;00m _is_fitted(estimator)\n\u001B[1;32m   1142\u001B[0m )\n\u001B[1;32m   1144\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m global_skip_validation \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m partial_fit_and_fitted:\n\u001B[0;32m-> 1145\u001B[0m     \u001B[43mestimator\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_validate_params\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m   1147\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m config_context(\n\u001B[1;32m   1148\u001B[0m     skip_parameter_validation\u001B[38;5;241m=\u001B[39m(\n\u001B[1;32m   1149\u001B[0m         prefer_skip_nested_validation \u001B[38;5;129;01mor\u001B[39;00m global_skip_validation\n\u001B[1;32m   1150\u001B[0m     )\n\u001B[1;32m   1151\u001B[0m ):\n\u001B[1;32m   1152\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m fit_method(estimator, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n",
      "File \u001B[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/py312/lib/python3.12/site-packages/sklearn/base.py:638\u001B[0m, in \u001B[0;36mBaseEstimator._validate_params\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m    630\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_validate_params\u001B[39m(\u001B[38;5;28mself\u001B[39m):\n\u001B[1;32m    631\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124;03m\"\"\"Validate types and values of constructor parameters\u001B[39;00m\n\u001B[1;32m    632\u001B[0m \n\u001B[1;32m    633\u001B[0m \u001B[38;5;124;03m    The expected type and values must be defined in the `_parameter_constraints`\u001B[39;00m\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    636\u001B[0m \u001B[38;5;124;03m    accepted constraints.\u001B[39;00m\n\u001B[1;32m    637\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m\n\u001B[0;32m--> 638\u001B[0m     \u001B[43mvalidate_parameter_constraints\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m    639\u001B[0m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_parameter_constraints\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    640\u001B[0m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_params\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdeep\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    641\u001B[0m \u001B[43m        \u001B[49m\u001B[43mcaller_name\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[38;5;18;43m__class__\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[38;5;18;43m__name__\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m    642\u001B[0m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m/opt/homebrew/Caskroom/miniforge/base/envs/py312/lib/python3.12/site-packages/sklearn/utils/_param_validation.py:96\u001B[0m, in \u001B[0;36mvalidate_parameter_constraints\u001B[0;34m(parameter_constraints, params, caller_name)\u001B[0m\n\u001B[1;32m     90\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m     91\u001B[0m     constraints_str \u001B[38;5;241m=\u001B[39m (\n\u001B[1;32m     92\u001B[0m         \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m, \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;241m.\u001B[39mjoin([\u001B[38;5;28mstr\u001B[39m(c)\u001B[38;5;250m \u001B[39m\u001B[38;5;28;01mfor\u001B[39;00m\u001B[38;5;250m \u001B[39mc\u001B[38;5;250m \u001B[39m\u001B[38;5;129;01min\u001B[39;00m\u001B[38;5;250m \u001B[39mconstraints[:\u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1\u001B[39m]])\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m or\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m     93\u001B[0m         \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mconstraints[\u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1\u001B[39m]\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m     94\u001B[0m     )\n\u001B[0;32m---> 96\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m InvalidParameterError(\n\u001B[1;32m     97\u001B[0m     \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mThe \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mparam_name\u001B[38;5;132;01m!r}\u001B[39;00m\u001B[38;5;124m parameter of \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcaller_name\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m must be\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m     98\u001B[0m     \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mconstraints_str\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m. Got \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mparam_val\u001B[38;5;132;01m!r}\u001B[39;00m\u001B[38;5;124m instead.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m     99\u001B[0m )\n",
      "\u001B[0;31mInvalidParameterError\u001B[0m: The 'criterion' parameter of ExtraTreesRegressor must be a str among {'absolute_error', 'squared_error', 'friedman_mse', 'poisson'}. Got 'mse' instead."
     ]
    }
   ],
   "source": [
    "stats_skopt, heatmap, optimize_result = bt.optimize(\n",
    "    limit_buy = range(0, 6), limit_sell = range(-6, 0),\n",
    "    maximize='Return [%]',\n",
    "    max_tries=500,\n",
    "    random_state=42,\n",
    "    return_heatmap=True,\n",
    "    return_optimization=True,\n",
    "    method='skopt',\n",
    "    # criterion='squared_error'\n",
    "    )\n",
    "\n",
    "dff = heatmap.reset_index()\n",
    "dff = dff.sort_values('Return [%]', ascending=False)\n",
    "dff"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-28T08:52:51.910996Z",
     "start_time": "2023-11-28T08:51:27.551567Z"
    }
   },
   "id": "c2a2b2d2d0a484e4"
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Unanchored Walk Forward\n",
    "#### Create library of strategy"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "2f2e74ab62b7689d"
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [],
   "source": [
    "from backtesting import Strategy\n",
    "# from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "class Regression(Strategy):\n",
    "    limit_buy = 1\n",
    "    limit_sell = -1\n",
    "    \n",
    "    n_train = 600\n",
    "    coef_retrain = 200\n",
    "    \n",
    "    def init(self):\n",
    "        self.model = GradientBoostingRegressor(max_depth=15, random_state=42)\n",
    "        self.already_bought = False\n",
    "        \n",
    "        X_train = self.data.df.iloc[:self.n_train, :-1]\n",
    "        y_train = self.data.df.iloc[:self.n_train, -1]\n",
    "        \n",
    "        self.model.fit(X=X_train, y=y_train)\n",
    "\n",
    "    def next(self):\n",
    "        explanatory_today = self.data.df.iloc[[-1], :-1]\n",
    "        forecast_tomorrow = self.model.predict(explanatory_today)[0]\n",
    "        \n",
    "        if forecast_tomorrow > self.limit_buy and self.already_bought == False:\n",
    "            self.buy()\n",
    "            self.already_bought = True\n",
    "        elif forecast_tomorrow < self.limit_sell and self.already_bought == True:\n",
    "            self.sell()\n",
    "            self.already_bought = False\n",
    "        else:\n",
    "            pass\n",
    "\n",
    "\n",
    "class WalkForwardAnchored(Regression):\n",
    "    def next(self):\n",
    "        \n",
    "        # we don't take any action and move on to the following day\n",
    "        if len(self.data) < self.n_train:\n",
    "            return\n",
    "        \n",
    "        # we retrain the model each 200 days\n",
    "        if len(self.data) % self.coef_retrain == 0:\n",
    "            X_train = self.data.df.iloc[:, :-1]\n",
    "            y_train = self.data.df.iloc[:, -1]\n",
    "\n",
    "            self.model.fit(X_train, y_train)\n",
    "\n",
    "            super().next()\n",
    "            \n",
    "        else:\n",
    "            \n",
    "            super().next()\n",
    "            \n",
    "            \n",
    "class WalkForwardUnanchored(Regression):\n",
    "    def next(self):\n",
    "        \n",
    "        # we don't take any action and move on to the following day\n",
    "        if len(self.data) < self.n_train:\n",
    "            return\n",
    "        \n",
    "        # we retrain the model each 200 days\n",
    "        # we only use the last 600 days to train the model\n",
    "        if len(self.data) % self.coef_retrain == 0:\n",
    "            X_train = self.data.df.iloc[-self.n_train:, :-1]\n",
    "            y_train = self.data.df.iloc[-self.n_train:, -1]\n",
    "\n",
    "            self.model.fit(X_train, y_train)\n",
    "\n",
    "            super().next()\n",
    "            \n",
    "        else:\n",
    "            \n",
    "            super().next()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-28T09:04:06.514635Z",
     "start_time": "2023-11-28T09:04:06.504847Z"
    }
   },
   "id": "d17eb99700f2e758"
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Import the strategy and perform the backtest with optimization"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "23886ab9558b27a"
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "data": {
      "text/plain": "Start                     2018-01-02 00:00:00\nEnd                       2023-11-24 00:00:00\nDuration                   2152 days 00:00:00\nExposure Time [%]                    59.52862\nEquity Final [$]                  5444.072049\nEquity Peak [$]                  10078.387298\nReturn [%]                          -45.55928\nBuy & Hold Return [%]              341.123899\nReturn (Ann.) [%]                   -9.804041\nVolatility (Ann.) [%]               23.552217\nSharpe Ratio                              0.0\nSortino Ratio                             0.0\nCalmar Ratio                              0.0\nMax. Drawdown [%]                  -56.274404\nAvg. Drawdown [%]                  -56.274404\nMax. Drawdown Duration     1281 days 00:00:00\nAvg. Drawdown Duration     1281 days 00:00:00\n# Trades                                   91\nWin Rate [%]                        50.549451\nBest Trade [%]                      13.472373\nWorst Trade [%]                    -38.357939\nAvg. Trade [%]                      -0.670525\nMax. Trade Duration         168 days 00:00:00\nAvg. Trade Duration          15 days 00:00:00\nProfit Factor                        0.791782\nExpectancy [%]                      -0.428627\nSQN                                 -0.961696\n_strategy                 WalkForwardUnanc...\n_equity_curve                             ...\n_trades                       Size  EntryB...\ndtype: object"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bt_unanchored = Backtest(df, WalkForwardUnanchored, cash=10000, commission=.002, exclusive_orders=True)\n",
    "stats_unanchored = bt_unanchored.run()\n",
    "stats_unanchored\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-28T09:04:10.810964Z",
     "start_time": "2023-11-28T09:04:08.497282Z"
    }
   },
   "id": "bc6b38562e310354"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "stats_skopt, heatmap, optimize_result = bt_unanchored.optimize(\n",
    "    limit_buy = range(0, 6), limit_sell = range(-6, 0),\n",
    "    maximize='Return [%]',\n",
    "    max_tries=500,\n",
    "    random_state=42,\n",
    "    return_heatmap=True,\n",
    "    return_optimization=True,\n",
    "    method='skopt'\n",
    "    )\n",
    "\n",
    "dff = heatmap.reset_index()\n",
    "dff = dff.sort_values('Return [%]', ascending=False)\n",
    "dff"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-28T08:52:51.918063Z",
     "start_time": "2023-11-28T08:52:51.913291Z"
    }
   },
   "id": "b7443b47a35bcb79"
  },
  {
   "cell_type": "markdown",
   "source": [
    "### interpret the strategy performance"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "c2af008c81a36d1e"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "bt.plot()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T08:52:51.914861Z"
    }
   },
   "id": "3d58bce25f269aa5"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "bt_unanchored.plot()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-11-28T08:52:51.915951Z"
    }
   },
   "id": "67b6dac574f28d57"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}