sklego_group_timeseries_split.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b926afe8-b8bd-46eb-9b02-d298d2f3c301",
   "metadata": {},
   "source": [
    "# Cross-Validation in Financial Machine Learning\n",
    "\n",
    "The purpose of this notebook is twofold:\n",
    "\n",
    "- Firstly, we outline the need for Time Series Cross Validation in the field of Financial Machine Learning.\n",
    "- Then, we test the sklego scikit-learn-compatible [GroupTimesSeriesSplit](https://scikit-lego.netlify.app/crossvalidation.html#GroupTimeSeriesSplit) on the ADIA Lab Market Prediction Competition dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b5e402f-53e9-4e97-9a74-277b47582ac6",
   "metadata": {},
   "source": [
    "## The need for Time Series Cross Validation in Financial Machine Learning \n",
    "\n",
    "Tabular time series cross-validation is a specialized technique used in machine learning and data analysis to evaluate the performance of predictive models on time series data organized in a tabular format. Unlike traditional cross-validation methods that randomly split the data into training and testing sets, tabular time series cross-validation takes into account the temporal ordering of the data. \n",
    "\n",
    "The importance of tabular time series cross-validation arises from the unique characteristics of time series data, where the order and timing of observations are crucial. In many real-world applications, such as finance, economics, and weather forecasting, the historical patterns and trends in the data play a significant role in making accurate predictions for the future. Traditional cross-validation methods may not be suitable for evaluating time series models as they can lead to data leakage and unrealistic performance estimates."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d13b49e-3d7c-4e63-9d1e-40be2e22c0ab",
   "metadata": {},
   "source": [
    "## Cross Validation in the ADIA Lab Market Prediction Competition\n",
    "\n",
    "### Overview\n",
    "\n",
    "This notebook focuses on GroupTimeSeriesSplit from sklego one of the already existing module dealing with time series cross validation.\n",
    "\n",
    "GroupTimeSeriesSplit from sklego is a time series cross-validator that supports non-overlapping groups. Time series tasks in machine learning require special types of validation, because the time order of the objects is important for a fairer evaluation of an ML model’s quality.\n",
    "Also, there can be different time units for splitting the data for different tasks - hours, days, months, ...\n",
    "\n",
    "The onyl parameter that can me modified is *n_splits*, an integer which represents the number of splits. The calculation of the train and validation sets are done in such a way that the amount of observations per fold are as similar as possible. GroupTimeSeriesSplit from sklego only handles a rolling window.\n",
    "\n",
    "See the [full documentation](https://scikit-lego.netlify.app/crossvalidation.html#GroupTimeSeriesSplit) for more details."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af06480e-6c0d-473d-bfbc-552abec8e991",
   "metadata": {},
   "source": [
    "### Implementation\n",
    "\n",
    "Below, we should a Cross Validation quickstarted on the ADIA Lab Market Prediction Competition dataset. In this section, the goal is to explain how GroupTimeSeriesSplit works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fa5649f3-7ac9-4acc-9472-1ede92449d61",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "from tqdm.notebook import tqdm\n",
    "tqdm.pandas()\n",
    "from sklego.model_selection import GroupTimeSeriesSplit\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from scipy.stats import norm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27cf45fe-ff18-4795-befc-3d956d846609",
   "metadata": {},
   "source": [
    "### Loading the data\n",
    "Let's take a look at the data. The data is from the first phase of ADIA Lab market prediction competition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d204e9fd-168d-4c8d-b91d-f81ec4e30264",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip3 install crunch-cli --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "012b392f-ee43-4039-af6d-7966468505a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import and instantiante the crunch package in this notebook\n",
    "import crunch\n",
    "crunch = crunch.load_notebook(__name__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89885090-ddd9-41d5-8dac-53fa4002cab4",
   "metadata": {},
   "outputs": [],
   "source": [
    "!crunch --notebook setup curly-cedric --token dNC6peKScPoVCTssI27DJ8zzRHpLtu7otHINZa0uv5T12MiYjvWcYAnNJ27XkkHI\n",
    "%cd curly-cedric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07c46c05-782e-43f7-930d-d5ea96181c19",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the data\n",
    "X, y, _ = crunch.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35e2bf7b-8bc4-4b7c-90e0-05d2c2114949",
   "metadata": {},
   "source": [
    "Let's merge X and y to have the complete dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "61e74d61-7cbd-4c7c-9349-35e74b8bb10e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = X.merge(y, on=['date', 'id'], how='inner')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85881b64-e328-43f1-81a8-a5c323f62231",
   "metadata": {},
   "source": [
    "Let's say we want to split the data into four folds.\n",
    "\n",
    "The only parameter GroupTimeSeriesSplit expects is *n_splits* which controls the number of folds wanted for cross validation.\n",
    "\n",
    "Another important parameter to specify is called *groups*. This is the parameter on which GroupTimesSeriesSplit will perform the split. For the split to take place correctly, *groups* must correspond to the column in the dataframe used as time unit. For ADIA Lab dataset, *groups* will be the column *date*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "837f09cd-a568-4b3d-8fa1-f460a13e3b73",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cc4498459ff4491880034497a7dd9ff1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CV = GroupTimeSeriesSplit(n_splits=4)\n",
    "\n",
    "features = df.columns.drop(['y'])\n",
    "target = ['date', 'id', 'y']\n",
    "\n",
    "groups = X['date'].to_numpy()\n",
    "\n",
    "for train_index, val_index in tqdm(CV.split(X, y, groups)):\n",
    "    train_set = df.iloc[train_index]\n",
    "    val_set = df.iloc[val_index]\n",
    "    X_train, y_train = train_set[features], train_set[target]\n",
    "    X_val, y_val = val_set[features], val_set[target]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "570ecf82-85f4-4afc-a8be-7ca9881b2978",
   "metadata": {},
   "source": [
    "For the sake of readability, we will only print the features of the 5th fold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1f3aaafa-89b1-4204-9132-70b317c8a091",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>id</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>...</th>\n",
       "      <th>451</th>\n",
       "      <th>452</th>\n",
       "      <th>453</th>\n",
       "      <th>454</th>\n",
       "      <th>455</th>\n",
       "      <th>456</th>\n",
       "      <th>457</th>\n",
       "      <th>458</th>\n",
       "      <th>459</th>\n",
       "      <th>460</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>595406</th>\n",
       "      <td>230</td>\n",
       "      <td>0977f5a47ccac048220f6b034ba82c6507bd2c68d1f12e...</td>\n",
       "      <td>0.915736</td>\n",
       "      <td>-0.009873</td>\n",
       "      <td>0.227885</td>\n",
       "      <td>0.794135</td>\n",
       "      <td>1.398273</td>\n",
       "      <td>-0.522382</td>\n",
       "      <td>-0.446472</td>\n",
       "      <td>-0.002898</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.202822</td>\n",
       "      <td>-1.440828</td>\n",
       "      <td>1.183228</td>\n",
       "      <td>1.823373</td>\n",
       "      <td>0.646298</td>\n",
       "      <td>-0.248723</td>\n",
       "      <td>0.601144</td>\n",
       "      <td>0.956986</td>\n",
       "      <td>-0.890538</td>\n",
       "      <td>0.810928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595407</th>\n",
       "      <td>230</td>\n",
       "      <td>34c2668acf702b2a14703c348bd26096114b1829fddbd2...</td>\n",
       "      <td>-0.057720</td>\n",
       "      <td>-0.783656</td>\n",
       "      <td>-0.290305</td>\n",
       "      <td>-0.740137</td>\n",
       "      <td>-0.941091</td>\n",
       "      <td>-0.299490</td>\n",
       "      <td>-1.190424</td>\n",
       "      <td>-1.086950</td>\n",
       "      <td>...</td>\n",
       "      <td>0.921004</td>\n",
       "      <td>-1.058998</td>\n",
       "      <td>0.317589</td>\n",
       "      <td>-0.146035</td>\n",
       "      <td>-0.277556</td>\n",
       "      <td>-1.151363</td>\n",
       "      <td>0.019373</td>\n",
       "      <td>0.142905</td>\n",
       "      <td>-1.511336</td>\n",
       "      <td>0.086593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595408</th>\n",
       "      <td>230</td>\n",
       "      <td>5ff27db43cd97e5b15af0e0647a89e8b5f8307a00ef334...</td>\n",
       "      <td>-0.488367</td>\n",
       "      <td>-0.410409</td>\n",
       "      <td>-0.850352</td>\n",
       "      <td>0.187171</td>\n",
       "      <td>-0.952577</td>\n",
       "      <td>0.194464</td>\n",
       "      <td>0.246688</td>\n",
       "      <td>0.758820</td>\n",
       "      <td>...</td>\n",
       "      <td>1.550771</td>\n",
       "      <td>-0.237884</td>\n",
       "      <td>-0.605789</td>\n",
       "      <td>1.193946</td>\n",
       "      <td>-0.452425</td>\n",
       "      <td>-0.234220</td>\n",
       "      <td>-0.177900</td>\n",
       "      <td>-0.535949</td>\n",
       "      <td>-0.977012</td>\n",
       "      <td>0.745278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595409</th>\n",
       "      <td>230</td>\n",
       "      <td>fadd62182f8ba0d77295e62043232792c66641a73afb64...</td>\n",
       "      <td>0.093536</td>\n",
       "      <td>-0.364950</td>\n",
       "      <td>0.831907</td>\n",
       "      <td>0.596220</td>\n",
       "      <td>1.584107</td>\n",
       "      <td>1.145510</td>\n",
       "      <td>-0.344829</td>\n",
       "      <td>-1.564130</td>\n",
       "      <td>...</td>\n",
       "      <td>1.143580</td>\n",
       "      <td>-0.108078</td>\n",
       "      <td>1.571716</td>\n",
       "      <td>0.905618</td>\n",
       "      <td>-0.526750</td>\n",
       "      <td>0.477192</td>\n",
       "      <td>0.632056</td>\n",
       "      <td>-0.078048</td>\n",
       "      <td>0.492667</td>\n",
       "      <td>-0.482480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595410</th>\n",
       "      <td>230</td>\n",
       "      <td>bbd324fe9ae9d15ce4ae38fe4d3088cf7f4b58df394af3...</td>\n",
       "      <td>-1.088501</td>\n",
       "      <td>1.188931</td>\n",
       "      <td>1.353126</td>\n",
       "      <td>-0.069721</td>\n",
       "      <td>0.646174</td>\n",
       "      <td>-0.330662</td>\n",
       "      <td>-0.413437</td>\n",
       "      <td>0.488905</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.749659</td>\n",
       "      <td>-0.854367</td>\n",
       "      <td>1.434475</td>\n",
       "      <td>0.260351</td>\n",
       "      <td>-0.349109</td>\n",
       "      <td>-0.830712</td>\n",
       "      <td>0.187419</td>\n",
       "      <td>0.446949</td>\n",
       "      <td>-1.126225</td>\n",
       "      <td>1.590658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 463 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        date                                                 id         0  \\\n",
       "595406   230  0977f5a47ccac048220f6b034ba82c6507bd2c68d1f12e...  0.915736   \n",
       "595407   230  34c2668acf702b2a14703c348bd26096114b1829fddbd2... -0.057720   \n",
       "595408   230  5ff27db43cd97e5b15af0e0647a89e8b5f8307a00ef334... -0.488367   \n",
       "595409   230  fadd62182f8ba0d77295e62043232792c66641a73afb64...  0.093536   \n",
       "595410   230  bbd324fe9ae9d15ce4ae38fe4d3088cf7f4b58df394af3... -1.088501   \n",
       "\n",
       "               1         2         3         4         5         6         7  \\\n",
       "595406 -0.009873  0.227885  0.794135  1.398273 -0.522382 -0.446472 -0.002898   \n",
       "595407 -0.783656 -0.290305 -0.740137 -0.941091 -0.299490 -1.190424 -1.086950   \n",
       "595408 -0.410409 -0.850352  0.187171 -0.952577  0.194464  0.246688  0.758820   \n",
       "595409 -0.364950  0.831907  0.596220  1.584107  1.145510 -0.344829 -1.564130   \n",
       "595410  1.188931  1.353126 -0.069721  0.646174 -0.330662 -0.413437  0.488905   \n",
       "\n",
       "        ...       451       452       453       454       455       456  \\\n",
       "595406  ... -2.202822 -1.440828  1.183228  1.823373  0.646298 -0.248723   \n",
       "595407  ...  0.921004 -1.058998  0.317589 -0.146035 -0.277556 -1.151363   \n",
       "595408  ...  1.550771 -0.237884 -0.605789  1.193946 -0.452425 -0.234220   \n",
       "595409  ...  1.143580 -0.108078  1.571716  0.905618 -0.526750  0.477192   \n",
       "595410  ... -0.749659 -0.854367  1.434475  0.260351 -0.349109 -0.830712   \n",
       "\n",
       "             457       458       459       460  \n",
       "595406  0.601144  0.956986 -0.890538  0.810928  \n",
       "595407  0.019373  0.142905 -1.511336  0.086593  \n",
       "595408 -0.177900 -0.535949 -0.977012  0.745278  \n",
       "595409  0.632056 -0.078048  0.492667 -0.482480  \n",
       "595410  0.187419  0.446949 -1.126225  1.590658  \n",
       "\n",
       "[5 rows x 463 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "93a3bced-6e22-4714-96a0-4e498042ca33",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>id</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>...</th>\n",
       "      <th>451</th>\n",
       "      <th>452</th>\n",
       "      <th>453</th>\n",
       "      <th>454</th>\n",
       "      <th>455</th>\n",
       "      <th>456</th>\n",
       "      <th>457</th>\n",
       "      <th>458</th>\n",
       "      <th>459</th>\n",
       "      <th>460</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>595411</th>\n",
       "      <td>231</td>\n",
       "      <td>a6809fee20392224e54b8450cd8b0e437d0cef77bd5da4...</td>\n",
       "      <td>-0.680147</td>\n",
       "      <td>1.260474</td>\n",
       "      <td>-0.453504</td>\n",
       "      <td>0.177937</td>\n",
       "      <td>-1.600160</td>\n",
       "      <td>1.314191</td>\n",
       "      <td>0.116122</td>\n",
       "      <td>-0.135393</td>\n",
       "      <td>...</td>\n",
       "      <td>0.540786</td>\n",
       "      <td>1.614841</td>\n",
       "      <td>-0.703755</td>\n",
       "      <td>0.745201</td>\n",
       "      <td>0.242272</td>\n",
       "      <td>-1.067609</td>\n",
       "      <td>-0.660520</td>\n",
       "      <td>-0.933815</td>\n",
       "      <td>-1.087864</td>\n",
       "      <td>0.375882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595412</th>\n",
       "      <td>231</td>\n",
       "      <td>c75e2ca85a6d30ba71523b686a82c117ba12c70da8348d...</td>\n",
       "      <td>0.424848</td>\n",
       "      <td>-2.181913</td>\n",
       "      <td>-1.124953</td>\n",
       "      <td>0.518796</td>\n",
       "      <td>-1.130703</td>\n",
       "      <td>-0.008386</td>\n",
       "      <td>1.129705</td>\n",
       "      <td>-0.214017</td>\n",
       "      <td>...</td>\n",
       "      <td>0.714138</td>\n",
       "      <td>2.942027</td>\n",
       "      <td>0.697394</td>\n",
       "      <td>-1.232427</td>\n",
       "      <td>-2.354766</td>\n",
       "      <td>-0.523435</td>\n",
       "      <td>0.136194</td>\n",
       "      <td>0.209052</td>\n",
       "      <td>0.073544</td>\n",
       "      <td>-0.355840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595413</th>\n",
       "      <td>231</td>\n",
       "      <td>a93992138e25bb217b65a99a5f651bdbc2ba26b0266cb2...</td>\n",
       "      <td>-0.254230</td>\n",
       "      <td>1.600029</td>\n",
       "      <td>-0.453175</td>\n",
       "      <td>-2.003238</td>\n",
       "      <td>0.256290</td>\n",
       "      <td>-0.426001</td>\n",
       "      <td>-0.175929</td>\n",
       "      <td>-0.820087</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.935556</td>\n",
       "      <td>0.154621</td>\n",
       "      <td>-0.402665</td>\n",
       "      <td>-0.200069</td>\n",
       "      <td>0.028470</td>\n",
       "      <td>0.681284</td>\n",
       "      <td>0.096306</td>\n",
       "      <td>-1.471745</td>\n",
       "      <td>0.024057</td>\n",
       "      <td>1.164956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595414</th>\n",
       "      <td>231</td>\n",
       "      <td>583c4cce7934e652c01ca43600cdc54ae6695d1d71f342...</td>\n",
       "      <td>1.870631</td>\n",
       "      <td>1.084600</td>\n",
       "      <td>-0.445278</td>\n",
       "      <td>-0.949804</td>\n",
       "      <td>1.103963</td>\n",
       "      <td>-0.198156</td>\n",
       "      <td>-0.662312</td>\n",
       "      <td>-0.672722</td>\n",
       "      <td>...</td>\n",
       "      <td>1.150978</td>\n",
       "      <td>-1.040806</td>\n",
       "      <td>-1.171546</td>\n",
       "      <td>-0.387898</td>\n",
       "      <td>-0.142381</td>\n",
       "      <td>1.273763</td>\n",
       "      <td>0.107711</td>\n",
       "      <td>1.183794</td>\n",
       "      <td>-0.427609</td>\n",
       "      <td>-1.204504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595415</th>\n",
       "      <td>231</td>\n",
       "      <td>781f7c7897da7fb2db24b44a9735ab3dca227a9e49998b...</td>\n",
       "      <td>-0.135822</td>\n",
       "      <td>-1.043441</td>\n",
       "      <td>0.304029</td>\n",
       "      <td>0.609359</td>\n",
       "      <td>-0.325438</td>\n",
       "      <td>1.527977</td>\n",
       "      <td>-0.839068</td>\n",
       "      <td>0.931433</td>\n",
       "      <td>...</td>\n",
       "      <td>2.382727</td>\n",
       "      <td>0.855549</td>\n",
       "      <td>0.537741</td>\n",
       "      <td>1.068949</td>\n",
       "      <td>0.401399</td>\n",
       "      <td>-0.764428</td>\n",
       "      <td>1.069790</td>\n",
       "      <td>1.497228</td>\n",
       "      <td>-0.616554</td>\n",
       "      <td>-0.385112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 463 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        date                                                 id         0  \\\n",
       "595411   231  a6809fee20392224e54b8450cd8b0e437d0cef77bd5da4... -0.680147   \n",
       "595412   231  c75e2ca85a6d30ba71523b686a82c117ba12c70da8348d...  0.424848   \n",
       "595413   231  a93992138e25bb217b65a99a5f651bdbc2ba26b0266cb2... -0.254230   \n",
       "595414   231  583c4cce7934e652c01ca43600cdc54ae6695d1d71f342...  1.870631   \n",
       "595415   231  781f7c7897da7fb2db24b44a9735ab3dca227a9e49998b... -0.135822   \n",
       "\n",
       "               1         2         3         4         5         6         7  \\\n",
       "595411  1.260474 -0.453504  0.177937 -1.600160  1.314191  0.116122 -0.135393   \n",
       "595412 -2.181913 -1.124953  0.518796 -1.130703 -0.008386  1.129705 -0.214017   \n",
       "595413  1.600029 -0.453175 -2.003238  0.256290 -0.426001 -0.175929 -0.820087   \n",
       "595414  1.084600 -0.445278 -0.949804  1.103963 -0.198156 -0.662312 -0.672722   \n",
       "595415 -1.043441  0.304029  0.609359 -0.325438  1.527977 -0.839068  0.931433   \n",
       "\n",
       "        ...       451       452       453       454       455       456  \\\n",
       "595411  ...  0.540786  1.614841 -0.703755  0.745201  0.242272 -1.067609   \n",
       "595412  ...  0.714138  2.942027  0.697394 -1.232427 -2.354766 -0.523435   \n",
       "595413  ... -0.935556  0.154621 -0.402665 -0.200069  0.028470  0.681284   \n",
       "595414  ...  1.150978 -1.040806 -1.171546 -0.387898 -0.142381  1.273763   \n",
       "595415  ...  2.382727  0.855549  0.537741  1.068949  0.401399 -0.764428   \n",
       "\n",
       "             457       458       459       460  \n",
       "595411 -0.660520 -0.933815 -1.087864  0.375882  \n",
       "595412  0.136194  0.209052  0.073544 -0.355840  \n",
       "595413  0.096306 -1.471745  0.024057  1.164956  \n",
       "595414  0.107711  1.183794 -0.427609 -1.204504  \n",
       "595415  1.069790  1.497228 -0.616554 -0.385112  \n",
       "\n",
       "[5 rows x 463 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_val.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6c6e4309-c4b2-449f-95b5-2febe6c694f9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199,\n",
       "       200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212,\n",
       "       213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225,\n",
       "       226, 227, 228, 229, 230])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train['date'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "939082f1-ace5-4381-8b38-3d912d8de443",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243,\n",
       "       244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256,\n",
       "       257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_val['date'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5ec27e4-e341-4148-90b7-02ca6e442730",
   "metadata": {},
   "source": [
    "We can see that the splitting is correctly done, indeed the training and the validation set do not overlap. However it is impossible to put an embargo with GroupTimeSeriesSPlit form sklego."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6578fe0a-4268-4992-978f-d6bb60185aeb",
   "metadata": {},
   "source": [
    "### Application of GroupTimeSeriesSplit in Supervised Learning\n",
    "\n",
    "In this section, we will use GroupTimeSeriesSplit to perform Cross Validation on the ADIA Lab Market Prediction Competition data.\n",
    "\n",
    "The model to generate the predictions is described as follows:\n",
    "- preprocessing: as we are using a linear model with an mse loss function, models benefit from data being identically distributed: data are cross-sectionally Gaussianised.\n",
    "- modeling: we perform a linear regression using scikit-learn for the training and the inference.\n",
    "\n",
    "The performance of our model will nevertheless be assessed using the spearman rank correlation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "943193b4-057f-419a-96fa-27c5f91bd617",
   "metadata": {},
   "source": [
    "#### Preprocessing step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "97cd7631-413b-4d5b-8dec-e6fd27aa82a7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def gaussianise_df(x):\n",
    "    nstocks = x.shape[0]\n",
    "    x0 = np.linspace(-3, 3, nstocks)\n",
    "    cdf_vector = norm.cdf(x0)\n",
    "    xx = np.linspace(cdf_vector[0], cdf_vector[-1], nstocks)\n",
    "    gx = norm.ppf(xx)\n",
    "    gauss_xc = np.empty_like(gx)\n",
    "    for c in x.columns[2:]:\n",
    "        indices = np.argsort(x[c].values)\n",
    "        gauss_xc[indices] = gx\n",
    "        x[c] = gauss_xc\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "23099325-5257-4175-bcdb-c5d12bef885a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "45cef4fb5f4f4ce98089e025c6cd7a52",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/269 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gauss_df = df.groupby('date', group_keys=False).progress_apply(lambda x: gaussianise_df(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed7d7177-4084-4de4-ba12-f9ae1aa9dc0d",
   "metadata": {},
   "source": [
    "Let's compare the data before and after the transformation. The choice of the plotted features is purely random."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1ec78d6e-28a7-447f-99d5-952c1375c425",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x1200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(14,12))\n",
    "ax1 = fig.add_subplot(321)\n",
    "ax2 = fig.add_subplot(322)\n",
    "ax3 = fig.add_subplot(323)\n",
    "ax4 = fig.add_subplot(324)\n",
    "ax5 = fig.add_subplot(325)\n",
    "ax6 = fig.add_subplot(326)\n",
    "\n",
    "df['55'].plot(kind='hist', bins=100, ax=ax1, title='feature 55')\n",
    "gauss_df['55'].plot(kind='hist', bins=100, ax=ax2, title='Gaussian feature 55')\n",
    "df['400'].plot(kind='hist', bins=100, ax=ax3, title='feature 400')\n",
    "gauss_df['400'].plot(kind='hist', bins=100, ax=ax4, title='Gaussian feature 400')\n",
    "df['y'].plot(kind='hist', bins=100, ax=ax5, title='target')\n",
    "gauss_df['y'].plot(kind='hist', bins=100, ax=ax6, title='Gaussian target')\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abdd2024-68ee-47c7-80bd-b582e5d27970",
   "metadata": {},
   "source": [
    "#### Prediction step\n",
    "The following function takes as an input the predictions of the target, the actual target, val_refs which contains the *date* and *id* columns of the validation sets. It returns the spearman rank correlation for each date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4f8ae5df-a754-4b6c-8fe7-d3316b6bc8e5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_spearman_results(preds, y_test, val_refs):\n",
    "    preds.rename({f'{target}':f'pred_{target}' for target in preds.columns}, axis=1, inplace=True)\n",
    "    preds_ref = pd.concat([preds.reset_index(drop=True), val_refs.reset_index(drop=True), y_test.reset_index(drop=True)], axis=1)\n",
    "    spearman = pd.DataFrame()\n",
    "    \n",
    "    target_suffixes = [col.split('_')[-1] for col in preds_ref.columns if 'pred' in col]\n",
    "    for suffix in target_suffixes:\n",
    "        spearman[f'target_{suffix}'] = preds_ref.groupby('date')[[f'pred_{suffix}', f'{suffix}']].corr(method='spearman').unstack().iloc[:,1]\n",
    "\n",
    "    return spearman"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dc038dc-0f2d-4f33-b3c0-30e741a5c36f",
   "metadata": {},
   "source": [
    "The following function defines the linear model that we use to predict the target. It takes as input X_train, y_train, X_val, y_val, val_refs and the target. It returns the average spearman rank correlation over the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3cda54c3-8fdf-4577-9995-a001ff7579d1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def linear_model(X_train, y_train, X_val, y_val, val_refs, target):\n",
    "    model = LinearRegression(n_jobs=10, positive=False)\n",
    "    model.fit(X_train, y_train[target])\n",
    "    importance = model.coef_\n",
    "    preds = pd.DataFrame(model.predict(X_val), columns=[target])\n",
    "    spearman_results = get_spearman_results(preds, y_val, val_refs)\n",
    "\n",
    "    return spearman_results.mean().values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "569a2625-7dff-44bc-9341-66124c1af091",
   "metadata": {},
   "source": [
    "The following function sets up the cross validation. It takes as input the arguments of GroupTimeSeriesSplit, the dataset, the features and the target. It returns the spearman rank correlation for each fold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9b151a71-fc20-4640-a1b0-9b8f19c06ae8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def cross_validation(n_splits, df, features, target):\n",
    "    val_score = []\n",
    "    CV = GroupTimeSeriesSplit(n_splits=n_splits)\n",
    "    refs = df.iloc[:, :2]\n",
    "    X = df[features]\n",
    "    y = df[target]\n",
    "    X = pd.concat([refs, X], axis=1)\n",
    "    y = pd.concat([refs, y], axis=1)\n",
    "    \n",
    "    # creating the folds\n",
    "    for train_index, val_index in tqdm(CV.split(X, y, groups)):\n",
    "        train_set = df.iloc[train_index]\n",
    "        val_set = df.iloc[val_index]\n",
    "        X_train, y_train = train_set[features], train_set[target]\n",
    "        X_val, y_val = val_set[features], val_set[target]\n",
    "        val_refs = val_set.iloc[:, :2]\n",
    "        \n",
    "    # training and validation steps\n",
    "        spearman_score = linear_model(X_train, y_train, X_val, y_val, val_refs, 'y')\n",
    "        val_score.append(spearman_score*100)\n",
    "    return val_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2136307-5ca1-4285-a209-85928bba1d6f",
   "metadata": {},
   "source": [
    "We are going to run the cross validation with 4 splits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "79bbcf93-fe8e-4d7d-a1be-bc7e7a0d18ae",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1b7669aecf7842d8815422bfff83f92f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_split = 4\n",
    "features = gauss_df.columns.drop(['id', 'date', 'y'])\n",
    "target = ['y']\n",
    "\n",
    "gauss_val_score = cross_validation(n_split, gauss_df, features, target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "735924bf-a4ab-48c7-9520-7b11b84189bf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "n_split = len(gauss_val_score)\n",
    "x = [i for i in range(1, n_split+1)]\n",
    "xticks = ['Fold {}'.format(i) for i in range(1, n_split+1)]\n",
    "ax.plot(x, gauss_val_score, '-o')\n",
    "ax.set_xticks(x, xticks)\n",
    "ax.set_title('Spearman rank correlation (%) for each fold')\n",
    "ax.set_ylabel('Spearman rank correlation (%)')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "13fe169b-57c3-4168-9639-79dfab87effe",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The spearman rank correlation mean over the folds is equal to 3.41\n"
     ]
    }
   ],
   "source": [
    "print(\"The spearman rank correlation mean over the folds is equal to {:.2f}\".format(np.mean(gauss_val_score)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26d2d7e6-4ee0-4724-b241-56136f8bb5dd",
   "metadata": {},
   "source": [
    "The obtained results are quite good for such a simple model that does not take into account non-linearities."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcf90aa4-48c0-4fe5-ab48-65bf59194b08",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "The purpose of this notebook is to demonstrate the need for a time series cross validator in Financial Machine Learning and to show how to apply it via an already existing library: GroupTimeSeriesSplit.\n",
    "\n",
    "Even if GroupTimeSeriesSplit from sklego is an interesting module allowing its user to perform cross validation in a rolling window fashion, it possesses several flaws :\n",
    "- The cross validation methodology is limited to a rolling window. It would be interesting to add an expanding window setting.\n",
    "- The performance of the module needs to be improved. The splits are correctly made, but it takes a long time to create them.\n",
    "- Some parameters are missing from the module. It would be interesting to be able to control the size of the test size and to be able to set an embargo [1] between the train and the validation set. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "104a052b-30a0-450e-aba6-d9ab4406d987",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "[1] López de Prado, Marcos. Advances in Financial Machine Learning. Wiley, 2018.\n",
    "\n",
    "[2] Cross Validation — scikit-lego latest documentation. https://scikit-lego.netlify.app/crossvalidation.html#GroupTimeSeriesSplit. Last visited, 18th july 2023."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}