forecasting.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VqQZ2rltGxId"
      },
      "source": [
        "# Stock price forecasting system\n",
        "Authors: Leonardo Emili, Alessio Luciani"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "72KweISRGt2c"
      },
      "source": [
        "## Project setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Y7Ko8G0EN5i",
        "outputId": "428b775c-bb40-423c-dcc5-4c617eda6329"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 1.8MB 32.9MB/s \n",
            "\u001b[K     |████████████████████████████████| 819kB 40.8MB/s \n",
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            "\u001b[K     |████████████████████████████████| 1.3MB 50.6MB/s \n",
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            "\u001b[K     |████████████████████████████████| 143kB 47.0MB/s \n",
            "\u001b[?25h  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for subprocess32 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for future (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: tensorflow 2.5.0 has requirement tensorboard~=2.5, but you'll have tensorboard 2.4.1 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: pytorch-lightning 1.3.6 has requirement PyYAML<=5.4.1,>=5.1, but you'll have pyyaml 3.13 which is incompatible.\u001b[0m\n",
            "Mounted at /content/gdrive/\n"
          ]
        }
      ],
      "source": [
        "try:\n",
        "    # Setup the project directory if running on Colab\n",
        "    from google.colab import drive\n",
        "    !pip install -q wandb pytorch-lightning\n",
        "    drive.mount('/content/gdrive/')\n",
        "    !cp gdrive/MyDrive/stock-forecasting-proj/data.zip .\n",
        "    !unzip -qo data.zip\n",
        "    !rm -f data.zip\n",
        "except:\n",
        "    pass"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J4U0QPnb1ZAy",
        "outputId": "5442175f-7de4-42f3-96d1-0989fe7b211d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Global seed set to 42\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: W&B API key is configured (use `wandb login --relogin` to force relogin)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m If you're specifying your api key in code, ensure this code is not shared publically.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Consider setting the WANDB_API_KEY environment variable, or running `wandb login` from the command line.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 2,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%matplotlib inline\n",
        "\n",
        "# Data\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from dataclasses import dataclass, field, asdict\n",
        "import plotly.express as px\n",
        "import missingno as msno\n",
        "from sklearn.preprocessing import scale\n",
        "\n",
        "# Torch\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import Dataset, DataLoader, random_split\n",
        "import pytorch_lightning as pl\n",
        "\n",
        "pl.seed_everything(42)\n",
        "\n",
        "# General\n",
        "import glob\n",
        "from tqdm.notebook import tqdm\n",
        "from typing import *\n",
        "import datetime\n",
        "\n",
        "import wandb\n",
        "from pytorch_lightning.loggers import WandbLogger\n",
        "wandb.login(key='<YOUR_WANDB_KEY>')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "atGCQIQt1ZA7"
      },
      "source": [
        "## Dataset loading\n",
        "We load the initial datasets into Pandas dataframes: one for fundamentals data and one for prices of every stock."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "4ad10fbe79084916b1bb5858b2ff2960",
            "ff04d6f25a024afda4acedc4ab4397bf",
            "5555fbb17ae64f02b805fb4ecb1321a3",
            "bf4032f71a0a4cf0bf2b19f4379e0961",
            "b163ddc689fd4b00ab5da916e7a8ca4e",
            "200750d4a1634d9fa5c1cbb8e36ad08d",
            "dffc247b98d84f42af72f05729706bb2",
            "6d756eaf2c8c4e88b12cc5ca997eb388"
          ]
        },
        "id": "KGZ4Tru01ZA7",
        "outputId": "24db6393-3fbe-4d17-9b30-81b373aa15d9"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4ad10fbe79084916b1bb5858b2ff2960",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Reading stock price data', max=429.0, style=ProgressStyle…"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# Key stats dataframe\n",
        "key_stats_df = pd.read_csv(\"data/key_stats_yahoo.csv\")\n",
        "key_stats_df.drop(key_stats_df.columns[0], axis=1, inplace=True)\n",
        "key_stats_df.Date = pd.to_datetime(key_stats_df.Date)\n",
        "\n",
        "# Prices dataframes for every stock\n",
        "prices_files = glob.glob(\"data/prices/*.csv\")\n",
        "dfs_names = [f.rsplit('/', 1)[1][:-len('.csv')] for f in prices_files]\n",
        "prices_dfs = []\n",
        "for f in tqdm(prices_files, desc='Reading stock price data', total=len(prices_files)):\n",
        "    df = pd.read_csv(f)\n",
        "    df.Date = pd.to_datetime(df.Date)\n",
        "    prices_dfs.append(df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oXn_lZEMSCp1"
      },
      "source": [
        "## Dataset analysis\n",
        "Visualize our input data, showing only the top 5 rows of each dataframe."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "id": "tORhSJQqHpa_",
        "outputId": "6569c3d1-cc24-4944-f6d6-706c9b4ee8ad"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Key stats dataframe format:\n"
          ]
        },
        {
          "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>Ticker</th>\n",
              "      <th>Price</th>\n",
              "      <th>DE Ratio</th>\n",
              "      <th>Trailing P/E</th>\n",
              "      <th>Price/Sales</th>\n",
              "      <th>Price/Book</th>\n",
              "      <th>Profit Margin</th>\n",
              "      <th>Operating Margin</th>\n",
              "      <th>Return on Assets</th>\n",
              "      <th>Return on Equity</th>\n",
              "      <th>Revenue Per Share</th>\n",
              "      <th>Market Cap</th>\n",
              "      <th>Enterprise Value</th>\n",
              "      <th>Forward P/E</th>\n",
              "      <th>PEG Ratio</th>\n",
              "      <th>Enterprise Value/Revenue</th>\n",
              "      <th>Enterprise Value/EBITDA</th>\n",
              "      <th>Revenue</th>\n",
              "      <th>Gross Profit</th>\n",
              "      <th>EBITDA</th>\n",
              "      <th>Net Income Avl to Common</th>\n",
              "      <th>Diluted EPS</th>\n",
              "      <th>Earnings Growth</th>\n",
              "      <th>Revenue Growth</th>\n",
              "      <th>Total Cash</th>\n",
              "      <th>Total Cash Per Share</th>\n",
              "      <th>Total Debt</th>\n",
              "      <th>Current Ratio</th>\n",
              "      <th>Book Value Per Share</th>\n",
              "      <th>Cash Flow</th>\n",
              "      <th>Beta</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2004-01-30 19:01:00</td>\n",
              "      <td>a</td>\n",
              "      <td>33.8320</td>\n",
              "      <td>0.407</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.80</td>\n",
              "      <td>6.01</td>\n",
              "      <td>29.56</td>\n",
              "      <td>11.97</td>\n",
              "      <td>24.95</td>\n",
              "      <td>47.81</td>\n",
              "      <td>12.803</td>\n",
              "      <td>1.745000e+10</td>\n",
              "      <td>1.652000e+10</td>\n",
              "      <td>25.83</td>\n",
              "      <td>3.84</td>\n",
              "      <td>2.73</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.73</td>\n",
              "      <td>2.290000e+09</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.790000e+09</td>\n",
              "      <td>3.774</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1.610000e+09</td>\n",
              "      <td>3.370</td>\n",
              "      <td>1.150000e+09</td>\n",
              "      <td>2.040</td>\n",
              "      <td>5.930</td>\n",
              "      <td>144000000.0</td>\n",
              "      <td>2.474</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2004-04-13 04:07:00</td>\n",
              "      <td>a</td>\n",
              "      <td>28.4626</td>\n",
              "      <td>0.375</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.43</td>\n",
              "      <td>4.98</td>\n",
              "      <td>25.56</td>\n",
              "      <td>6.20</td>\n",
              "      <td>23.59</td>\n",
              "      <td>46.82</td>\n",
              "      <td>13.160</td>\n",
              "      <td>1.532000e+10</td>\n",
              "      <td>1.477000e+10</td>\n",
              "      <td>20.83</td>\n",
              "      <td>2.89</td>\n",
              "      <td>2.35</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.35</td>\n",
              "      <td>2.290000e+09</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.610000e+09</td>\n",
              "      <td>3.391</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1.680000e+09</td>\n",
              "      <td>3.490</td>\n",
              "      <td>1.150000e+09</td>\n",
              "      <td>2.284</td>\n",
              "      <td>6.394</td>\n",
              "      <td>144000000.0</td>\n",
              "      <td>2.496</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2004-12-13 21:37:00</td>\n",
              "      <td>a</td>\n",
              "      <td>21.8816</td>\n",
              "      <td>0.323</td>\n",
              "      <td>33.58</td>\n",
              "      <td>1.61</td>\n",
              "      <td>3.24</td>\n",
              "      <td>4.86</td>\n",
              "      <td>5.38</td>\n",
              "      <td>5.26</td>\n",
              "      <td>10.92</td>\n",
              "      <td>14.610</td>\n",
              "      <td>1.161000e+10</td>\n",
              "      <td>1.037000e+10</td>\n",
              "      <td>15.79</td>\n",
              "      <td>1.62</td>\n",
              "      <td>1.44</td>\n",
              "      <td>15.300</td>\n",
              "      <td>1.44</td>\n",
              "      <td>2.290000e+09</td>\n",
              "      <td>15.300</td>\n",
              "      <td>3.490000e+08</td>\n",
              "      <td>0.710</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2.320000e+09</td>\n",
              "      <td>4.750</td>\n",
              "      <td>1.150000e+09</td>\n",
              "      <td>2.441</td>\n",
              "      <td>7.320</td>\n",
              "      <td>483000000.0</td>\n",
              "      <td>2.547</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2005-01-22 05:17:00</td>\n",
              "      <td>a</td>\n",
              "      <td>20.2478</td>\n",
              "      <td>0.322</td>\n",
              "      <td>30.70</td>\n",
              "      <td>1.52</td>\n",
              "      <td>3.04</td>\n",
              "      <td>4.86</td>\n",
              "      <td>5.38</td>\n",
              "      <td>5.26</td>\n",
              "      <td>10.92</td>\n",
              "      <td>14.610</td>\n",
              "      <td>1.071000e+10</td>\n",
              "      <td>9.760000e+09</td>\n",
              "      <td>14.34</td>\n",
              "      <td>1.51</td>\n",
              "      <td>1.36</td>\n",
              "      <td>14.400</td>\n",
              "      <td>1.36</td>\n",
              "      <td>3.120000e+09</td>\n",
              "      <td>14.400</td>\n",
              "      <td>3.490000e+08</td>\n",
              "      <td>0.710</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18.6</td>\n",
              "      <td>2.320000e+09</td>\n",
              "      <td>4.710</td>\n",
              "      <td>1.150000e+09</td>\n",
              "      <td>2.446</td>\n",
              "      <td>7.329</td>\n",
              "      <td>663000000.0</td>\n",
              "      <td>2.166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2006-01-09 02:35:00</td>\n",
              "      <td>a</td>\n",
              "      <td>31.7118</td>\n",
              "      <td>2.169</td>\n",
              "      <td>52.82</td>\n",
              "      <td>3.31</td>\n",
              "      <td>4.26</td>\n",
              "      <td>6.38</td>\n",
              "      <td>3.58</td>\n",
              "      <td>1.68</td>\n",
              "      <td>3.69</td>\n",
              "      <td>10.403</td>\n",
              "      <td>1.712000e+10</td>\n",
              "      <td>1.487000e+10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.81</td>\n",
              "      <td>2.89</td>\n",
              "      <td>35.665</td>\n",
              "      <td>2.89</td>\n",
              "      <td>3.120000e+09</td>\n",
              "      <td>35.665</td>\n",
              "      <td>1.410000e+08</td>\n",
              "      <td>0.660</td>\n",
              "      <td>64.9</td>\n",
              "      <td>22.8</td>\n",
              "      <td>2.250000e+09</td>\n",
              "      <td>4.555</td>\n",
              "      <td>2.169000e+00</td>\n",
              "      <td>2.169</td>\n",
              "      <td>8.095</td>\n",
              "      <td>890000000.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Date Ticker    Price  ...  Book Value Per Share    Cash Flow   Beta\n",
              "0 2004-01-30 19:01:00      a  33.8320  ...                 5.930  144000000.0  2.474\n",
              "1 2004-04-13 04:07:00      a  28.4626  ...                 6.394  144000000.0  2.496\n",
              "2 2004-12-13 21:37:00      a  21.8816  ...                 7.320  483000000.0  2.547\n",
              "3 2005-01-22 05:17:00      a  20.2478  ...                 7.329  663000000.0  2.166\n",
              "4 2006-01-09 02:35:00      a  31.7118  ...                 8.095  890000000.0    NaN\n",
              "\n",
              "[5 rows x 32 columns]"
            ]
          },
          "execution_count": 4,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(\"Key stats dataframe format:\")\n",
        "key_stats_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "id": "_95C-hBq1ZA9",
        "outputId": "3785ea65-08b5-41f5-95d1-c82c049b80d6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Prices dataframe format:\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "        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>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Volume</th>\n",
              "      <th>High</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adjusted Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1990-12-06</td>\n",
              "      <td>1.083333</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>28022400</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>1.166667</td>\n",
              "      <td>0.825420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1990-06-13</td>\n",
              "      <td>1.125000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>10702800</td>\n",
              "      <td>1.312500</td>\n",
              "      <td>1.291667</td>\n",
              "      <td>0.913858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1990-06-14</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>5994000</td>\n",
              "      <td>1.354167</td>\n",
              "      <td>1.333333</td>\n",
              "      <td>0.943337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1990-06-15</td>\n",
              "      <td>1.270833</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>3896400</td>\n",
              "      <td>1.333333</td>\n",
              "      <td>1.291667</td>\n",
              "      <td>0.913858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1990-06-18</td>\n",
              "      <td>1.229167</td>\n",
              "      <td>1.3125</td>\n",
              "      <td>2242800</td>\n",
              "      <td>1.312500</td>\n",
              "      <td>1.239583</td>\n",
              "      <td>0.877009</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Date       Low    Open    Volume      High     Close  Adjusted Close\n",
              "0 1990-12-06  1.083333  0.0000  28022400  1.250000  1.166667        0.825420\n",
              "1 1990-06-13  1.125000  0.0000  10702800  1.312500  1.291667        0.913858\n",
              "2 1990-06-14  1.250000  0.0000   5994000  1.354167  1.333333        0.943337\n",
              "3 1990-06-15  1.270833  0.0000   3896400  1.333333  1.291667        0.913858\n",
              "4 1990-06-18  1.229167  1.3125   2242800  1.312500  1.239583        0.877009"
            ]
          },
          "execution_count": 5,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(\"Prices dataframe format:\")\n",
        "prices_dfs[0].head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "la4nlCg-Jgfd"
      },
      "source": [
        "### Utility functions\n",
        "Here are a some utility functions that are used across the notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ShX2IDrGJibL"
      },
      "outputs": [],
      "source": [
        "def prices_df_nan_summary(prices_dfs, names):\n",
        "    ''' Utility function to summarize columns that have missing values. '''\n",
        "    nan_dfs = []\n",
        "    for i, (prices_df, name) in enumerate(zip(prices_dfs, names)):\n",
        "        nan_cols = []\n",
        "        nan_values = []\n",
        "        for column in prices_df.columns:\n",
        "            nan_absolute = prices_df[column].isnull().sum()\n",
        "            if nan_absolute > 0:\n",
        "                # Only consider columns that cointain NaN values\n",
        "                nan_cols.append(column)\n",
        "                nan_values.append(nan_absolute)\n",
        "        if len(nan_cols):\n",
        "            # Either we have all the data for a given day or we don't have any data for it\n",
        "            assert len(set(nan_values)) == 1\n",
        "            count = nan_values[0]\n",
        "            nan_dfs.append((name, round(100*count/len(prices_df.index), 3), count))\n",
        "    return pd.DataFrame(nan_dfs, columns=['Stock name', 'Missing data (%)', 'Count'])\n",
        "\n",
        "def remove_trailing_nan(df: pd.DataFrame, ticker: str, col: str = 'Low') -> pd.DataFrame:\n",
        "    '''\n",
        "    A trick to detect if the input DataFrame ends with a contiguous collection of NaN rows.\n",
        "    It is mainly an adaptation from: https://stackoverflow.com/questions/29007830/identifying-consecutive-nans-with-pandas\n",
        "    '''\n",
        "    if ticker == 'CTQ':\n",
        "        # This special case refers to a stock that is not active for a long period but contains two days of activity:\n",
        "        # up ..... up down ..... down [up down ... down up] down ..... down\n",
        "        # We consider the period comprised in square brackets as an inactivity period. Otherwise feature imputation\n",
        "        # would be harder and less reliable, hence dropping a few rows (i.e. only 2) increases the quality of our data.\n",
        "        df.iloc[1397] = np.nan\n",
        "        df.iloc[1400] = np.nan\n",
        "    group_by_nan = df[col].isnull().astype(int).groupby(df[col].notnull().astype(int).cumsum()).cumsum()\n",
        "    training_nans = group_by_nan.iloc[-1]\n",
        "    return df[:-training_nans] if training_nans > 0 else df\n",
        "\n",
        "def merge_prices_fundamentals(\n",
        "    prices_dfs: List[pd.DataFrame],\n",
        "    key_stats_df: pd.DataFrame,\n",
        "    dfs_names: List[str],\n",
        "    drop_cols: List[str] = ['Date', 'Price']\n",
        "    ) -> List[pd.DataFrame]:\n",
        "\n",
        "    # Define the target list of dataframes\n",
        "    prices_dfs_new = []\n",
        "    names_new = []\n",
        "    for ticker, ticker_df in tqdm(key_stats_df.groupby('Ticker'), desc='Merging the datasets ...'):\n",
        "        # Consider only stocks for which we have fundamental data\n",
        "        if ticker.upper() not in dfs_names: continue\n",
        "\n",
        "        # The current financial report\n",
        "        ticker_iterator = list(ticker_df.iterrows())\n",
        "        ticker_iter_idx = 0\n",
        "        names_new.append(ticker.upper())\n",
        "\n",
        "        # Merge the two dataset according to the value of ticker_date\n",
        "        ticker_row = ticker_iterator[ticker_iter_idx][1]\n",
        "        ticker_date = ticker_row.Date\n",
        "        ticker_row.drop(drop_cols, inplace=True)\n",
        "        fundamental_cols, financial_values = ticker_row.index, ticker_row.values\n",
        "\n",
        "        # Consider the relative stock ticker dataframe\n",
        "        prices_df_idx = dfs_names.index(ticker.upper())\n",
        "        prices_df = prices_dfs[prices_df_idx]\n",
        "        # Dropping unused price value columns (we are using adjusted close)\n",
        "        prices_df = prices_df.drop(['High', 'Low', 'Open', 'Close'], axis=1)\n",
        "        fundamental_rows = []\n",
        "\n",
        "        # Discard events that occurred before the first financial report\n",
        "        begin_idx = prices_df[prices_df.Date >= ticker_date].index[0]\n",
        "        for i, row in prices_df[begin_idx:].iterrows():\n",
        "            # Use the last updated financial report if available (otherwise fast-forward the latest)\n",
        "            if ticker_date < row.Date and ticker_iter_idx + 1 < len(ticker_iterator):\n",
        "                # Update info about fundamental data\n",
        "                ticker_iter_idx += 1\n",
        "                ticker_row = ticker_iterator[ticker_iter_idx][1]\n",
        "                ticker_date = ticker_row.Date\n",
        "                ticker_row.drop(drop_cols, inplace=True)\n",
        "                financial_values = ticker_row.values\n",
        "            \n",
        "            fundamental_rows.append(financial_values)\n",
        "\n",
        "        # Finally merge the two datasets into one that has the same rows and the union of columns\n",
        "        fundamental_df = pd.DataFrame(fundamental_rows, columns=fundamental_cols)\n",
        "        valid_prices_df = prices_df[begin_idx:].reset_index(drop=True)\n",
        "        merged_df = pd.concat((fundamental_df, valid_prices_df), axis=1)\n",
        "\n",
        "        assert merged_df.shape == (fundamental_df.shape[0], fundamental_df.shape[1] + valid_prices_df.shape[1])\n",
        "        prices_dfs_new.append(merged_df)\n",
        "    \n",
        "    return prices_dfs_new, names_new\n",
        "\n",
        "def fill_missing_days(\n",
        "    aggregate_dfs: List[pd.DataFrame],\n",
        "    ticker_names: List[str],\n",
        "    remove_weekends: bool = True,\n",
        "    end_year: int = 2013\n",
        "    ) -> List[pd.DataFrame]:\n",
        "    '''\n",
        "    Returns the aggregated dataframes where gaps have been filled using fast-forward techniques.\n",
        "    If remove_weekends is specified, only prices for weekdays are retained.\n",
        "    '''\n",
        "\n",
        "    def is_weekday(date: datetime) -> bool:\n",
        "        ''' Returns true if the provided date corresponds to a weekday. '''\n",
        "        return date.weekday() < 5\n",
        "\n",
        "    result_dfs = []\n",
        "    names_new = []\n",
        "    for df, ticker_name in tqdm(zip(aggregate_dfs, ticker_names), desc='Filling missing days ...'):\n",
        "        rows = list(df.iterrows())\n",
        "        _, last_row = rows[0]\n",
        "        \n",
        "        # Skip datasets (less than 2%) whose available data starts after the end_year\n",
        "        if end_year is not None and last_row.Date.year > end_year: continue\n",
        "        date_idx = last_row.index.tolist().index(\"Date\")\n",
        "        names_new.append(ticker_name)\n",
        "\n",
        "        output_rows = [last_row.values.tolist()]\n",
        "        for _, row in rows[1:]:\n",
        "            # Skip rows that exceed the end_year\n",
        "            if end_year is not None and row.Date.year > end_year: break\n",
        "\n",
        "            # Compute the current time gap in our dataset\n",
        "            missing_days = (row.Date - last_row.Date).days - 1\n",
        "            for day_offset in range(1, missing_days+1):\n",
        "                # Fill the gap adding [day_offset] days with the last observed values\n",
        "                delta = datetime.timedelta(days=day_offset)\n",
        "                output_rows.append(last_row.values.tolist())\n",
        "                output_rows[-1][date_idx] = last_row.Date + delta\n",
        "\n",
        "            # Add and update the latest observed values\n",
        "            output_rows.append(row.values.tolist())\n",
        "            last_row = row\n",
        "    \n",
        "        # Skip weekends\n",
        "        if remove_weekends:\n",
        "            output_rows = [row for row in output_rows if is_weekday(row[date_idx])]\n",
        "        result_dfs.append(pd.DataFrame(output_rows, columns=aggregate_dfs[0].columns))\n",
        "    return result_dfs, names_new\n",
        "\n",
        "def train_val_test_split(ds, train_size=0.75, val_size=0.15, test_size=0.10, seed=42):\n",
        "    ''' Create random splits of the original [DataFrame], nmanually specify the seed for reproducible results. '''\n",
        "    if train_size + val_size + test_size != 1:\n",
        "        print('Input proportions do not sum up to 1! Using default values.', file=sys.stderr)\n",
        "    n, m  = int(len(ds)*train_size), int(len(ds)*val_size)\n",
        "    lengths = [n, m, len(ds)-n-m]\n",
        "    return random_split(ds, lengths, generator=torch.Generator().manual_seed(seed))\n",
        "\n",
        "def r2_score(y_hat: np.ndarray, y: np.ndarray) -> float:\n",
        "    ''' Computes the R Squared coefficient between y_hat and y. '''\n",
        "    rss = torch.sum((y - y_hat) ** 2)\n",
        "    tss = torch.sum((y - y.mean()) ** 2)\n",
        "    return 1 - rss / tss\n",
        "\n",
        "def adjusted_r2_score(y_hat: np.ndarray, y: np.ndarray, p: int) -> float:\n",
        "    ''' Computes the Adjusted R Squared coefficient between y_hat and y. '''\n",
        "    rss = torch.sum((y - y_hat) ** 2)\n",
        "    tss = torch.sum((y - y.mean()) ** 2)\n",
        "    df_e = y.shape[0] - p - 1\n",
        "    df_t = y.shape[0] - 1\n",
        "    return 1 - (rss / tss) * (df_t / df_e)\n",
        "\n",
        "@torch.no_grad()\n",
        "def evaluate_trading_strategy(\n",
        "    model: nn.Module,\n",
        "    ds: pl.LightningDataModule,\n",
        "    stock_number: int,\n",
        "    price_idx: int = -5,\n",
        "    ticker_num_idx: int = -7,\n",
        "    target_ticker = 'msft',\n",
        "    ) -> None:\n",
        "    '''\n",
        "    Evaluates the performance of a model evaluating the profit of a trading\n",
        "    trategy that acts according to model's predictions.\n",
        "    '''\n",
        "    print('Evaluating trading strategy ...')\n",
        "    pnl = 0.0\n",
        "    tries = 0\n",
        "    correct = 0\n",
        "    def should_buy(x, y):\n",
        "        return x < y\n",
        "    for (X, y), (X_real, y_real) in zip(ds.test_dataloader(target_ticker), ds._test_dataloader(target_ticker)):\n",
        "        y_pred = model(X)\n",
        "        for i in range(X.shape[0]):\n",
        "            # Adding profit/loss due to new long/short order\n",
        "            current_price = X_real[i, -1, price_idx]\n",
        "            ret = 0.0\n",
        "            if should_buy(X[i, -1, price_idx], y_pred[i]):\n",
        "                ret = (y_real[i]-current_price)/current_price\n",
        "                pnl += ret\n",
        "                tries += 1\n",
        "                correct += 1 if ret >= 0 else 0\n",
        "\n",
        "    print(f\"1-year profit: {pnl.item() * 100}%\")\n",
        "    print(f\"Operation accuracy: {(correct / tries) * 100}%\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "85jJ5qtMpq4M"
      },
      "source": [
        "#### Technical indicators\n",
        "Use some well-known [technical indicators](https://en.wikipedia.org/wiki/Technical_indicator) to boost the performances of our models."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EjqW2-4BpoQN"
      },
      "outputs": [],
      "source": [
        "def add_sma(dfs: List[pd.DataFrame], period: int = 10) -> None:\n",
        "    ''' Computes the Simple Moving Average from a given dataframe. '''\n",
        "    for i in tqdm(range(len(dfs)), desc='Adding SMA ...'):\n",
        "        ret = np.cumsum(dfs[i][\"Adjusted Close\"]).to_numpy()\n",
        "        ret[period:] = ret[period:] - ret[:-period]\n",
        "        sma = ret[period - 1:] / period\n",
        "        missing_values = dfs[i].shape[0] - sma.shape[0]\n",
        "        half_missing_values = missing_values//2\n",
        "        sma_points = np.empty(dfs[i].shape[0], dtype=float)\n",
        "        sma_points[half_missing_values:(half_missing_values)+sma.shape[0]] = sma\n",
        "        sma_points[:half_missing_values] = dfs[i][\"Adjusted Close\"][:half_missing_values]\n",
        "        sma_points[half_missing_values+sma.shape[0]:] = dfs[i][\"Adjusted Close\"][half_missing_values+sma.shape[0]:]\n",
        "        dfs[i][\"SMA\"] = sma_points\n",
        "\n",
        "def add_rsi(dfs: List[pd.DataFrame], period: int = 14) -> None:\n",
        "    ''' Computes the Relative Strength Index from a given dataframe. \n",
        "        Formula available at https://en.wikipedia.org/wiki/Relative_strength_index.\n",
        "        Also adds overbought and oversold when the RSI index hits 70 or 30.'''\n",
        "    for j in tqdm(range(len(dfs)), desc='Adding RSI ...'):\n",
        "        rsi_points = np.empty(dfs[j].shape[0], dtype=float)\n",
        "        overbought_points = np.zeros(dfs[j].shape[0], dtype=float)\n",
        "        oversold_points = np.zeros(dfs[j].shape[0], dtype=float)\n",
        "        smmau = 0\n",
        "        smmad = 0\n",
        "\n",
        "        rsi_points[0] = 50\n",
        "        for i in range(1, dfs[j].shape[0]):\n",
        "            u = max(0, dfs[j][\"Adjusted Close\"][i] - dfs[j][\"Adjusted Close\"][i-1])\n",
        "            d = max(0, dfs[j][\"Adjusted Close\"][i-1] - dfs[j][\"Adjusted Close\"][i])\n",
        "            smmau = (smmau * (period - 1) + u) / period\n",
        "            smmad = (smmad * (period - 1) + d) / period\n",
        "            if smmad == 0 or smmau == 0:\n",
        "                rsi_points[i] = 50\n",
        "            else:    \n",
        "                rs = smmau / smmad\n",
        "                rsi = 100 - (100/(1+rs))\n",
        "                rsi_points[i] = rsi\n",
        "                if rsi >= 70: overbought_points[i] = 1\n",
        "                if rsi <= 30: oversold_points[i] = 1\n",
        "        \n",
        "        dfs[j][\"RSI\"] = rsi_points\n",
        "        dfs[j][\"Overbought\"] = overbought_points\n",
        "        dfs[j][\"Oversold\"] = oversold_points"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NaJalO_rSO2S"
      },
      "source": [
        "### Missing values imputation\n",
        "Another visualization of the input data from the [Missingno](https://github.com/ResidentMario/missingno) library, that allows us to have an overview of the distribution of missing values (i.e. in the form of NaN values)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "EkN5eXNURMF3",
        "outputId": "a419147a-3cb6-49ec-f841-32f6d9e0b950"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1152x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "_ = msno.matrix(key_stats_df, figsize=(16,6), fontsize=12)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OOzxh7E5Us4B",
        "outputId": "c0994459-eb82-4153-bba8-94bb1fd8305a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Overview of the missing values in the key_stats dataframe\n",
            "\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "Forward P/E                  41.251%\n",
              "DE Ratio                     16.886%\n",
              "Earnings Growth              15.966%\n",
              "Enterprise Value/EBITDA      10.633%\n",
              "EBITDA                        10.37%\n",
              "Current Ratio                 10.37%\n",
              "Cash Flow                     9.801%\n",
              "Trailing P/E                  8.432%\n",
              "Beta                          4.413%\n",
              "PEG Ratio                     4.325%\n",
              "Gross Profit                   3.92%\n",
              "Total Debt                    3.844%\n",
              "Price                         3.657%\n",
              "Return on Equity              3.011%\n",
              "Return on Assets              2.508%\n",
              "Price/Book                    2.354%\n",
              "Revenue Growth                2.146%\n",
              "Operating Margin              1.391%\n",
              "Enterprise Value/Revenue      0.942%\n",
              "Revenue                       0.909%\n",
              "Total Cash                    0.854%\n",
              "Enterprise Value              0.723%\n",
              "Total Cash Per Share          0.712%\n",
              "Profit Margin                 0.712%\n",
              "Price/Sales                   0.548%\n",
              "Book Value Per Share          0.449%\n",
              "Diluted EPS                   0.296%\n",
              "Market Cap                    0.285%\n",
              "Revenue Per Share             0.241%\n",
              "Net Income Avl to Common      0.055%\n",
              "Ticker                          0.0%\n",
              "Date                            0.0%\n",
              "dtype: object"
            ]
          },
          "execution_count": 10,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(\"Overview of the missing values in the key_stats dataframe\\n\")\n",
        "key_stats_summary = key_stats_df.isna().sum()           \\\n",
        "                        .sort_values(ascending=False)   \\\n",
        "                        .apply(lambda x: f\"{round(100*x/len(key_stats_df.index), 3)}%\")\n",
        "key_stats_summary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "NXUCmdbSPnXN",
        "outputId": "4d015d43-8981-4a71-e0ba-cbc44fae3ba0"
      },
      "outputs": [
        {
          "data": {
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          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
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      ],
      "source": [
        "summary = prices_df_nan_summary(prices_dfs, dfs_names)\n",
        "px.bar(summary, x='Stock name', y='Missing data (%)', hover_data=['Count'], title=\"Stock price dataset before preprocessing (only columns with missing values are displayed)\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hBbElSVujmB7"
      },
      "source": [
        "For most of the above stocks with missing values, we noticed that they indeed exist up to a given time and after that no more data is available. It may due to a business failure, hence no more stocks will be exchanged from that moment on."
      ]
    },
    {
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      "execution_count": null,
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        "id": "wtGO1GP96Fh6",
        "outputId": "8a2beb75-17a4-44b2-b482-ffe340bb0357"
      },
      "outputs": [
        {
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\"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Stock price dataset after preprocessing (only columns with missing values are displayed)\"}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Stock name\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Missing data (%)\"}}},\n",
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              "\n",
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              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Clear our input data from training NaN values\n",
        "prices_dfs_new = [remove_trailing_nan(df,name) for df,name in zip(prices_dfs, dfs_names)]\n",
        "\n",
        "# Remove INTH stock from our dataset since it contains many inactivity periods\n",
        "if 'INTH' in dfs_names:\n",
        "    inth_idx = dfs_names.index('INTH')\n",
        "    del dfs_names[inth_idx]\n",
        "    del prices_dfs_new[inth_idx]\n",
        "\n",
        "summary = prices_df_nan_summary(prices_dfs_new, dfs_names)\n",
        "px.bar(summary, x='Stock name', y='Missing data (%)', hover_data=['Count'], title=\"Stock price dataset after preprocessing (only columns with missing values are displayed)\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YGK20OSHYHXQ"
      },
      "source": [
        "At this point we use the fast forward imputation technique to fill-in missing values. Please note that in this case missing values are mostly due to holidays or periods when stocks are not exchanged."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ykn_a6mU171e"
      },
      "source": [
        "## Building the new dataset\n",
        "We impute missing values, merge the two datasets, fill missing days and compute additional features (e.g. RSI)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        {
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              "HBox(children=(FloatProgress(value=0.0, description='Merging the datasets ...', max=534.0, style=ProgressStyle…"
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        {
          "name": "stdout",
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          "text": [
            "\n"
          ]
        },
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Filling missing days ...', max=1.0, sty…"
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        {
          "name": "stdout",
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          "text": [
            "\n"
          ]
        },
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# Impute missing values in the prices dataset (i.e. fast-forward last valid values)\n",
        "prices_dfs_new = [df.fillna(method='ffill') for df in prices_dfs_new]\n",
        "assert pd.concat(prices_dfs_new).isnull().sum().sum() == 0\n",
        "\n",
        "# In this case this dataframe contains financial reports that may contain NaN values either because that\n",
        "# metric was not available at that time OR because it was monitoring an initial stage of a company growth.\n",
        "# What we do is to apply the classic fast-forward, and fill initial missing values with zeroes.\n",
        "# Please note: we also discard the 'Forward P/E' column since the imputation here would introduce too much noise.\n",
        "key_stats_df_new = key_stats_df.drop('Forward P/E', axis=1).fillna(method='ffill').fillna(0.)\n",
        "assert key_stats_df_new.isnull().sum().sum() == 0\n",
        "\n",
        "# Before merging the two datasets we need to sort them\n",
        "prices_dfs_new = [df.sort_values(by=['Date']).reset_index(drop=True) for df in prices_dfs_new]\n",
        "key_stats_df_new = key_stats_df_new.sort_values(by=['Date']).reset_index(drop=True)\n",
        "\n",
        "# Merge the stock price dataset with fundamental data of the relative company\n",
        "aggregate_dfs, ticker_names = merge_prices_fundamentals(prices_dfs_new, key_stats_df_new, dfs_names)\n",
        "assert pd.concat(aggregate_dfs).isnull().sum().sum() == 0\n",
        "\n",
        "# Fill gaps from the original dataset\n",
        "dfs, ticker_names = fill_missing_days(aggregate_dfs, ticker_names)\n",
        "assert pd.concat(dfs).isnull().sum().sum() == 0\n",
        "\n",
        "# Add SMA indicator to each dataframe\n",
        "add_sma(dfs)\n",
        "\n",
        "# Add RSI indicator to each dataframe\n",
        "add_rsi(dfs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3zIsjn_9ncMh"
      },
      "source": [
        "## Creating sliding window dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G2WPrPXovR49"
      },
      "outputs": [],
      "source": [
        "class SlidingWindowDataset(Dataset):\n",
        "    '''\n",
        "    A Dataset structure that shapes the input data according to the sliding window approach.\n",
        "    Prices data consist of (x,y) window pairs of the following format:\n",
        "        x: (window_size, feature_size)\n",
        "        y: (1,target_cols)\n",
        "    '''\n",
        "    def __init__(\n",
        "        self,\n",
        "        prices: np.ndarray,\n",
        "        indexes: np.ndarray,\n",
        "        target_idx: int,\n",
        "        columns: Optional[List[str]] = None\n",
        "        ) -> None:\n",
        "        self.prices = prices\n",
        "        self.indexes = indexes\n",
        "        self.target_idx = target_idx\n",
        "        self.columns = columns\n",
        "\n",
        "    @property\n",
        "    def shape(self):\n",
        "        return (self.indexes.shape[0], self.indexes.shape[1]-1, self.prices.shape[1])\n",
        "\n",
        "    def __getitem__(self, index: int) -> Tuple[np.ndarray, np.ndarray]:\n",
        "        x_idxs, y_idx = self.indexes[index, :-1], self.indexes[index, -1]\n",
        "        return self.prices[x_idxs], self.prices[y_idx, self.target_idx]\n",
        "\n",
        "    def __len__(self) -> int:\n",
        "        return self.indexes.shape[0]\n",
        "\n",
        "    def store(\n",
        "        self,\n",
        "        idx_path: str = 'data/preprocessed/indexes.npy',\n",
        "        prices_path: str ='data/preprocessed/prices.npy'\n",
        "        ) -> None:\n",
        "        ''' Stores the collection of price values without the window size constraint, hence reducing output size. '''\n",
        "        dir_path, _ = os.path.split(idx_path)\n",
        "        os.makedirs(dir_path, exist_ok=True)\n",
        "        dir_path, _ = os.path.split(prices_path)\n",
        "        os.makedirs(dir_path, exist_ok=True)\n",
        "        with open(idx_path, 'wb') as f1, open(prices_path, 'wb') as f2:\n",
        "            np.save(f1, self.prices, allow_pickle=allow_pickle)\n",
        "            np.save(f2, self.indexes, allow_pickle=allow_pickle)\n",
        "    \n",
        "    @classmethod\n",
        "    def from_files(\n",
        "        self,\n",
        "        idx_path: str = 'data/preprocessed/indexes.npy',\n",
        "        prices_path: str = 'data/preprocessed/prices.npy',\n",
        "        target_col_idx: int = 30    # Index for adjusted close\n",
        "        ) -> None:\n",
        "        with open(idx_path, 'wb') as f1, open(prices_path, 'wb') as f2:\n",
        "            prices = np.load(f1)\n",
        "            indexes = np.load(f2)\n",
        "        return cls(prices, indexes, target_col_idx)\n",
        "\n",
        "    @classmethod\n",
        "    def from_dfs(\n",
        "        cls,\n",
        "        dfs: List[pd.DataFrame],\n",
        "        window_size: int = 20,\n",
        "        target_col: str = 'Adjusted Close',\n",
        "        val_date: str = '2012-01-01',\n",
        "        test_date: str = '2013-01-01'\n",
        "        ):\n",
        "\n",
        "        # Split the dataset into train/val/test using these dates as boundaries\n",
        "        val_begin = pd.Timestamp(val_date)\n",
        "        test_begin = pd.Timestamp(test_date)\n",
        "\n",
        "        # Init general variables\n",
        "        columns = dfs[0].columns.values\n",
        "        date_col_idx = np.where(columns == 'Date')[0][0]\n",
        "        target_col_idx = np.where(columns == target_col)[0]\n",
        "\n",
        "        # ... same\n",
        "        train_stack, val_stack, test_stack = [], [], []\n",
        "        train_split, val_split, test_split = [], [], []\n",
        "        train_offset, val_offset, test_offset = 0, 0, 0\n",
        "        \n",
        "        for df in tqdm(dfs, desc='Loading datasets from dataframes ...'):\n",
        "            df_np = df.to_numpy()\n",
        "\n",
        "            # Compute the indexes on which to split each dataframe\n",
        "            train_idxs = np.where(df_np[:, date_col_idx] < val_begin)[0]\n",
        "            val_idxs = np.where((df_np[:, date_col_idx] >= val_begin) & (df_np[:, date_col_idx] < test_begin))[0]\n",
        "            test_idxs = np.where(df_np[:, date_col_idx] >= test_begin)[0]\n",
        "\n",
        "            # If we have enough data to form a window ... (of size n+1 for the target row)\n",
        "            if len(train_idxs) > window_size + 1:\n",
        "                # Unroll a window over training indexes and select training rows\n",
        "                train_windows = train_offset + torch.from_numpy(train_idxs).unfold(0, window_size+1, 1)\n",
        "                train_stack.append(train_windows)\n",
        "                train_split.append(df.iloc[train_idxs])\n",
        "                train_offset += len(train_idxs)\n",
        "\n",
        "            # ... same\n",
        "            if len(val_idxs) > window_size + 1:\n",
        "                # ... same, but we are interested in relative indexes (hence substract the first element)\n",
        "                val_windows = val_offset + torch.from_numpy(val_idxs - val_idxs[0]).unfold(0, window_size+1, 1)\n",
        "                val_stack.append(val_windows)\n",
        "                val_split.append(df.iloc[val_idxs])\n",
        "                val_offset += len(val_idxs)\n",
        "            \n",
        "            # ... same\n",
        "            if len(test_idxs) > window_size + 1:\n",
        "                # ... same, but we are interested in relative indexes (hence substract the first element)\n",
        "                test_windows = test_offset + torch.from_numpy(test_idxs - test_idxs[0]).unfold(0, window_size+1, 1)\n",
        "                test_stack.append(test_windows)\n",
        "                test_split.append(df.iloc[test_idxs])\n",
        "                test_offset += len(test_idxs)\n",
        "\n",
        "        # The list of features used for training (without dropped cols)\n",
        "        drop_cols = ['Date', 'Overbought', 'Oversold']\n",
        "        columns = dfs[0].columns.drop(drop_cols + ['Ticker']).tolist()\n",
        "        _columns = dfs[0].columns.drop(drop_cols).tolist()\n",
        "\n",
        "        scalable_features = ['DE Ratio', 'Trailing P/E', 'Price/Sales', 'Price/Book',\n",
        "            'Profit Margin', 'Operating Margin', 'Return on Assets',\n",
        "            'Return on Equity', 'Revenue Per Share', 'Market Cap',\n",
        "            'Enterprise Value', 'PEG Ratio', 'Enterprise Value/Revenue',\n",
        "            'Enterprise Value/EBITDA', 'Revenue', 'Gross Profit', 'EBITDA',\n",
        "            'Net Income Avl to Common ', 'Diluted EPS', 'Earnings Growth',\n",
        "            'Revenue Growth', 'Total Cash', 'Total Cash Per Share', 'Total Debt',\n",
        "            'Current Ratio', 'Book Value Per Share', 'Cash Flow', 'Beta', 'Volume',\n",
        "            'Adjusted Close', 'SMA', 'RSI']\n",
        "\n",
        "        # Compute dataset statistics from the train split\n",
        "        train_data = pd.concat(train_split).drop(drop_cols + ['Ticker'], axis=1)\n",
        "        mean_vec = train_data[scalable_features].mean()\n",
        "        std_vec = train_data[scalable_features].std()\n",
        "        target_idx = scalable_features.index(target_col)\n",
        "\n",
        "        # Create train/val/test splits with sliding windows, dropping unused columns\n",
        "        # ... scaling train/val/test to have zero mean and unit variance,\n",
        "        # ... keeping _test unaltered and using it as a parallel test split (i.e. to evaluate the trading strategy)\n",
        "        train_data[scalable_features] = (train_data[scalable_features] - mean_vec) / std_vec\n",
        "        train_idxs = torch.cat(train_stack)\n",
        "        train_ds = cls(train_data.to_numpy(), train_idxs, target_col_idx-1, columns)\n",
        "\n",
        "        val_data = pd.concat(val_split).drop(drop_cols + ['Ticker'], axis=1)\n",
        "        val_data[scalable_features] = (val_data[scalable_features] - mean_vec) / std_vec\n",
        "        val_idxs = torch.cat(val_stack)\n",
        "        val_ds = cls(val_data.to_numpy(), val_idxs, target_col_idx-1, columns)\n",
        "\n",
        "        test_idxs = torch.cat(test_stack)\n",
        "        test_data = pd.concat(test_split).drop(drop_cols, axis=1)\n",
        "        _test_ds = cls(test_data.copy().to_numpy(), test_idxs, target_col_idx-1, _columns)\n",
        "        test_data[scalable_features] = (test_data[scalable_features] - mean_vec) / std_vec\n",
        "        test_ds = cls(test_data.to_numpy(), test_idxs, target_col_idx-1, _columns)\n",
        "\n",
        "        price_idx = columns.index(target_col)\n",
        "        return train_ds, val_ds, test_ds, _test_ds, (mean_vec[target_idx], std_vec[target_idx], price_idx)\n",
        "\n",
        "class SlidingWindowDataModule(pl.LightningDataModule):\n",
        "    def __init__(\n",
        "        self,\n",
        "        ds_splits: List[SlidingWindowDataset],\n",
        "        batch_size: int = 128\n",
        "        ) -> None:\n",
        "        super().__init__()\n",
        "        self.train_ds, self.val_ds, self.test_ds, self._test_ds = ds_splits\n",
        "        self.batch_size = batch_size        \n",
        "\n",
        "    def train_dataloader(self) -> DataLoader:\n",
        "        return DataLoader(self.train_ds, batch_size=self.batch_size, drop_last=True, pin_memory=True, shuffle=True)\n",
        "    \n",
        "    def val_dataloader(self) -> DataLoader:\n",
        "        return DataLoader(self.val_ds, batch_size=self.batch_size, drop_last=True, pin_memory=True)\n",
        "\n",
        "    def compute_test_mask(self, ticker: str) -> Tuple[int, np.ndarray, List[str]]:\n",
        "        ''' Compute the mask on the test split to test on a given stock. '''\n",
        "        ticker_idx = self._test_ds.columns.index('Ticker')\n",
        "        mask = self._test_ds.prices[:, ticker_idx] == ticker\n",
        "        mask_idxs = np.where(mask)[0]\n",
        "        start, end = mask_idxs[0], mask_idxs[-1]\n",
        "        new_idxs = np.where((self._test_ds.indexes[:, 0] >= start) & (self._test_ds.indexes[:, -1] <= end))[0]\n",
        "        ticker_idxs = self._test_ds.indexes[new_idxs]\n",
        "        ticker_prices = self._test_ds.prices[:, ticker_idx+1:].astype(float)\n",
        "        new_columns = self._test_ds.columns[ticker_idx+1:]\n",
        "        return ticker_idx, ticker_idxs, new_columns\n",
        "\n",
        "    def test_dataloader(self, ticker: str) -> DataLoader:\n",
        "        ticker_idx, ticker_idxs, new_columns = self.compute_test_mask(ticker)\n",
        "        ticker_prices = self.test_ds.prices[:, ticker_idx+1:].astype(float)\n",
        "        ticker_ds = SlidingWindowDataset(ticker_prices, ticker_idxs, self.test_ds.target_idx, new_columns)\n",
        "        return DataLoader(ticker_ds, batch_size=self.batch_size, pin_memory=True)\n",
        "    \n",
        "    def _test_dataloader(self, ticker: str) -> DataLoader:\n",
        "        ticker_idx, ticker_idxs, new_columns = self.compute_test_mask(ticker)\n",
        "        ticker_prices = self._test_ds.prices[:, ticker_idx+1:].astype(float)\n",
        "        ticker_ds = SlidingWindowDataset(ticker_prices, ticker_idxs, self._test_ds.target_idx, new_columns)\n",
        "        return DataLoader(ticker_ds, batch_size=self.batch_size, pin_memory=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p3Duv4yjoYry"
      },
      "source": [
        "The dataset can be created either locally on your pc from the dataframes. You can do it calling the ````from_dfs```` method, or loading a preprocessed dataset calling the ```from_files``` method and specifying the required paths."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "3b01e1d6b00f4365bc996d8b0fe9ed0f",
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          ]
        },
        "id": "iAKO2c_X2poi",
        "outputId": "03f607c4-da5b-4cad-e6e6-7658ef6e5f62"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3b01e1d6b00f4365bc996d8b0fe9ed0f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Loading datasets from dataframes ...', max=266.0, style=P…"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# Preprocess the dataset using DataFrames\n",
        "*ds_splits, (price_mean, price_std, price_idx) = SlidingWindowDataset.from_dfs(dfs, window_size=20)\n",
        "\n",
        "# Persist out the preprocessed dataset to reuse it later\n",
        "#ds.store()\n",
        "\n",
        "# Load preprocessed dataset from files\n",
        "#ds = SlidingWindowDataset.from_files()\n",
        "\n",
        "ds = SlidingWindowDataModule(ds_splits, batch_size=1024)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ttR0_-i_owtM"
      },
      "source": [
        "## Hyperparameters\n",
        "Here we define the list of hyperparameters that are used to define the shape of the dataset and models' behaviours."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XpvCs7WwpMvs"
      },
      "outputs": [],
      "source": [
        "@dataclass\n",
        "class HParams:\n",
        "  input_dim: int = ds_splits[0].shape[2]  # Denoted by data transformations\n",
        "  window_size: int = ds_splits[0].shape[1]\n",
        "  batch_size: int = ds.batch_size\n",
        "  features: int = field(default_factory=list)\n",
        "  hidden_dim: int = 128\n",
        "  num_layers: int = 1\n",
        "  dropout: int = 0.5\n",
        "  lr: int = 0.1\n",
        "  num_heads: int = -1\n",
        "  epochs: int = 5\n",
        "  test_stock: str = 't'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D0z18zIo6A6Z"
      },
      "source": [
        "## Models definition"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0oVsYJsSeyC7"
      },
      "outputs": [],
      "source": [
        "class SimpleLSTM(nn.Module):\n",
        "    def __init__(self, hparams):\n",
        "        super().__init__()\n",
        "        self.hparams = hparams\n",
        "        self.lstm = nn.LSTM(hparams.input_dim, hparams.hidden_dim, num_layers=hparams.num_layers, batch_first=True)\n",
        "        self.fc1 = nn.Linear(hparams.hidden_dim, hparams.hidden_dim//2)\n",
        "        self.fc2 = nn.Linear(hparams.hidden_dim//2, 1)\n",
        "        self.dropout = nn.Dropout(hparams.dropout)\n",
        "\n",
        "    def forward(self, x: np.ndarray):\n",
        "        x, (h,c) = self.lstm(x)\n",
        "        h = self.dropout(h)\n",
        "        h = h.view(self.hparams.num_layers, -1, self.hparams.hidden_dim)[-1]\n",
        "        x = torch.relu(self.fc1(h))\n",
        "        x = self.fc2(x)\n",
        "        return x\n",
        "\n",
        "class SimpleGRU(nn.Module):\n",
        "    def __init__(self, hparams):\n",
        "        super().__init__()\n",
        "        self.hparams = hparams\n",
        "        self.gru = nn.GRU(hparams.input_dim, hparams.hidden_dim, num_layers=hparams.num_layers, batch_first=True)\n",
        "        self.fc1 = nn.Linear(hparams.hidden_dim, hparams.hidden_dim//2)\n",
        "        self.fc2 = nn.Linear(hparams.hidden_dim//2, 1)\n",
        "        self.dropout = nn.Dropout(hparams.dropout)\n",
        "\n",
        "    def forward(self, x: np.ndarray):\n",
        "        x, h = self.gru(x)\n",
        "        h = self.dropout(h)\n",
        "        h = h.view(self.hparams.num_layers, -1, self.hparams.hidden_dim)[-1]\n",
        "        x = torch.relu(self.fc1(h))\n",
        "        x = self.fc2(x)\n",
        "        return x\n",
        "\n",
        "class ConvolutionalGRU(nn.Module):\n",
        "    def __init__(self, hparams):\n",
        "        super().__init__()\n",
        "        self.hparams = hparams\n",
        "        self.cnn = nn.Conv1d(hparams.input_dim, hparams.input_dim*2, kernel_size=5)\n",
        "        self.gru = nn.GRU(hparams.input_dim*2, hparams.hidden_dim, num_layers=hparams.num_layers, batch_first=True)\n",
        "        self.fc1 = nn.Linear(hparams.hidden_dim, hparams.hidden_dim//2)\n",
        "        self.fc2 = nn.Linear(hparams.hidden_dim//2, 1)\n",
        "        self.dropout = nn.Dropout(hparams.dropout)\n",
        "\n",
        "    def forward(self, x: np.ndarray):\n",
        "        x = torch.einsum('bwn->bnw', x)\n",
        "        x = torch.relu(self.cnn(x))\n",
        "        x = torch.einsum('bnw->bwn', x)\n",
        "        x, h = self.gru(x)\n",
        "        h = self.dropout(h)\n",
        "        h = h.view(self.hparams.num_layers, -1, self.hparams.hidden_dim)[-1]\n",
        "        x = torch.relu(self.fc1(h))\n",
        "        x = self.fc2(x)\n",
        "        return x\n",
        "\n",
        "class AttentionGRU(nn.Module):\n",
        "    def __init__(self, hparams):\n",
        "        super().__init__()\n",
        "        self.hparams = hparams\n",
        "        self.att = nn.MultiheadAttention(hparams.input_dim, num_heads=hparams.num_heads)\n",
        "        self.gru = nn.GRU(hparams.input_dim, hparams.hidden_dim, num_layers=hparams.num_layers, batch_first=True)\n",
        "        self.fc1 = nn.Linear(hparams.hidden_dim, hparams.hidden_dim//2)\n",
        "        self.fc2 = nn.Linear(hparams.hidden_dim//2, 1)\n",
        "        self.dropout = nn.Dropout(hparams.dropout)\n",
        "\n",
        "    def forward(self, x: np.ndarray):\n",
        "        # Workaround since currently nn.MultiheadAttention does not support batch_first\n",
        "        x = torch.einsum('bwn->wbn', x)\n",
        "        x, _ = self.att(x, x, x)\n",
        "        x = torch.einsum('wbn->bwn', x)\n",
        "        x, h = self.gru(x)\n",
        "        h = self.dropout(h)\n",
        "        h = h.view(self.hparams.num_layers, -1, self.hparams.hidden_dim)[-1]\n",
        "        x = torch.relu(self.fc1(h))\n",
        "        x = self.fc2(x)\n",
        "        return x\n",
        "\n",
        "class StockPriceRegressor(pl.LightningModule):\n",
        "    def __init__(self, model: nn.Module, hparams: Dict[str, any]):\n",
        "        super().__init__()\n",
        "        self.save_hyperparameters(asdict(hparams))\n",
        "        self.loss_fn = nn.MSELoss()\n",
        "        self.model = model\n",
        "\n",
        "    def training_step(self, batch: Tuple[np.ndarray, np.ndarray], batch_idx: int):\n",
        "        x, y = batch\n",
        "        y_hat = self.model(x)\n",
        "        loss = self.loss_fn(y_hat, y)\n",
        "        return loss\n",
        "\n",
        "    @torch.no_grad()\n",
        "    def evaluation(self, batch: Tuple[np.ndarray, np.ndarray]) -> float:\n",
        "        x, y = batch\n",
        "        y_hat = self.model(x)\n",
        "        loss = self.loss_fn(y_hat, y)\n",
        "        r2 = r2_score(y_hat, y)\n",
        "        # p := no. of explanatory variables in the model (i.e window_size * feature_size)\n",
        "        r2_adj = adjusted_r2_score(y_hat, y, x.shape[1] * x.shape[2])\n",
        "        return loss, r2, r2_adj\n",
        "\n",
        "    def validation_step(self, batch: Tuple[np.ndarray, np.ndarray], batch_idx: int) -> None:\n",
        "        loss, r2, r2_adj = self.evaluation(batch)\n",
        "        metrics = {'val_loss': loss, 'val_r2': r2, 'val_r2_adj': r2_adj}\n",
        "        self.log_dict(metrics)\n",
        "\n",
        "    def test_step(self, batch: Tuple[np.ndarray, np.ndarray], batch_idx: int) -> None:\n",
        "        loss, r2, r2_adj = self.evaluation(batch)\n",
        "        metrics = {'test_loss': loss, 'test_r2': r2, 'test_r2_adj': r2_adj}\n",
        "        self.log_dict(metrics)\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        return torch.optim.SGD(self.parameters(), lr=self.hparams.lr)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sW7eqETf8JVi"
      },
      "source": [
        "## Experiments\n",
        "In this section we show a collection of experiments that we did to test RNN architectures on our task."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SO6LFoNr8Ldy"
      },
      "source": [
        "### Naive GRU"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        "outputId": "ecbd032b-6e88-41c6-94f7-b06181a65a79"
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      "outputs": [
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              "                Tracking run with wandb version 0.10.32<br/>\n",
              "                Syncing run <strong style=\"color:#cdcd00\">lucky-universe-187</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://wandb.ai/leonardoemili/spf\" target=\"_blank\">https://wandb.ai/leonardoemili/spf</a><br/>\n",
              "                Run page: <a href=\"https://wandb.ai/leonardoemili/spf/runs/1ohfz1au\" target=\"_blank\">https://wandb.ai/leonardoemili/spf/runs/1ohfz1au</a><br/>\n",
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            "1 | model   | SimpleGRU | 70.5 K\n",
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            "\n",
            "--------------------------------------------------------------------------------\n",
            "DATALOADER:0 TEST RESULTS\n",
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              "<tr><td>val_loss</td><td>0.00827</td></tr><tr><td>val_r2</td><td>0.98995</td></tr><tr><td>val_r2_adj</td><td>0.97316</td></tr><tr><td>epoch</td><td>9</td></tr><tr><td>trainer/global_step</td><td>4830</td></tr><tr><td>_runtime</td><td>629</td></tr><tr><td>_timestamp</td><td>1624181631</td></tr><tr><td>_step</td><td>10</td></tr><tr><td>test_loss</td><td>0.00138</td></tr><tr><td>test_r2</td><td>-5.08612</td></tr><tr><td>test_r2_adj</td><td>4.65167</td></tr></table>"
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              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
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              "<tr><td>val_loss</td><td>█▃▂▂▂▁▂▄▁▁</td></tr><tr><td>val_r2</td><td>▁▆▅▇▇▇▇▅██</td></tr><tr><td>val_r2_adj</td><td>▁▆▅▇▇▇▇▅██</td></tr><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇██</td></tr><tr><td>trainer/global_step</td><td>▁▂▃▃▄▅▆▆▇██</td></tr><tr><td>_runtime</td><td>▁▂▃▃▄▅▆▆▇██</td></tr><tr><td>_timestamp</td><td>▁▂▃▃▄▅▆▆▇██</td></tr><tr><td>_step</td><td>▁▂▂▃▄▅▅▆▇▇█</td></tr><tr><td>test_loss</td><td>▁</td></tr><tr><td>test_r2</td><td>▁</td></tr><tr><td>test_r2_adj</td><td>▁</td></tr></table><br/>"
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              "\n",
              "                    <br/>Synced <strong style=\"color:#cdcd00\">lucky-universe-187</strong>: <a href=\"https://wandb.ai/leonardoemili/spf/runs/1ohfz1au\" target=\"_blank\">https://wandb.ai/leonardoemili/spf/runs/1ohfz1au</a><br/>\n",
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          "text": [
            "Evaluating trading strategy ...\n",
            "1-year profit: 83.82514246947896%\n",
            "Operation accuracy: 61.44578313253012%\n"
          ]
        }
      ],
      "source": [
        "hparams = HParams()\n",
        "hparams.features = ds_splits[0].columns\n",
        "hparams.epochs = 10\n",
        "\n",
        "run = wandb.init(reinit=True, project='spf')\n",
        "gru = SimpleGRU(hparams).double()\n",
        "model = StockPriceRegressor(gru, hparams)\n",
        "trainer = pl.Trainer(\n",
        "    logger = WandbLogger(),\n",
        "    log_every_n_steps = 1,\n",
        "    gpus = 1 if torch.cuda.is_available() else 0,\n",
        "    max_epochs = hparams.epochs,\n",
        "    deterministic=True\n",
        ")\n",
        "trainer.fit(model, ds)\n",
        "trainer.test(model, ds.test_dataloader(hparams.test_stock))\n",
        "run.finish()\n",
        "\n",
        "# A practical evaluation strategy based on profit estimation\n",
        "evaluate_trading_strategy(model.model, ds, len(dfs), price_idx=price_idx, target_ticker=hparams.test_stock)"
      ]
    },
    {
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        "id": "nkHGabjB8Zw0"
      },
      "source": [
        "### Naive LSTM"
      ]
    },
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              "                Tracking run with wandb version 0.10.32<br/>\n",
              "                Syncing run <strong style=\"color:#cdcd00\">soft-dew-188</strong> to <a href=\"https://wandb.ai\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
              "                Project page: <a href=\"https://wandb.ai/leonardoemili/spf\" target=\"_blank\">https://wandb.ai/leonardoemili/spf</a><br/>\n",
              "                Run page: <a href=\"https://wandb.ai/leonardoemili/spf/runs/2j72fujm\" target=\"_blank\">https://wandb.ai/leonardoemili/spf/runs/2j72fujm</a><br/>\n",
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            "\n",
            "  | Name    | Type       | Params\n",
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              "<tr><td>val_loss</td><td>█▄▄▃▂▁▂▄▂▂</td></tr><tr><td>val_r2</td><td>▁▆▆▅▄▅▆▅▇█</td></tr><tr><td>val_r2_adj</td><td>▁▆▆▅▄▅▆▅▇█</td></tr><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇██</td></tr><tr><td>trainer/global_step</td><td>▁▂▃▃▄▅▆▆▇██</td></tr><tr><td>_runtime</td><td>▁▂▃▃▄▅▆▆▇██</td></tr><tr><td>_timestamp</td><td>▁▂▃▃▄▅▆▆▇██</td></tr><tr><td>_step</td><td>▁▂▂▃▄▅▅▆▇▇█</td></tr><tr><td>test_loss</td><td>▁</td></tr><tr><td>test_r2</td><td>▁</td></tr><tr><td>test_r2_adj</td><td>▁</td></tr></table><br/>"
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              "\n",
              "                    <br/>Synced <strong style=\"color:#cdcd00\">soft-dew-188</strong>: <a href=\"https://wandb.ai/leonardoemili/spf/runs/2j72fujm\" target=\"_blank\">https://wandb.ai/leonardoemili/spf/runs/2j72fujm</a><br/>\n",
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          "text": [
            "Evaluating trading strategy ...\n",
            "1-year profit: 49.806095554526536%\n",
            "Operation accuracy: 59.210526315789465%\n"
          ]
        }
      ],
      "source": [
        "hparams = HParams()\n",
        "hparams.features = ds_splits[0].columns\n",
        "hparams.epochs = 10\n",
        "\n",
        "run = wandb.init(reinit=True, project='spf')\n",
        "lstm = SimpleLSTM(hparams).double()\n",
        "model = StockPriceRegressor(lstm, hparams)\n",
        "trainer = pl.Trainer(\n",
        "    logger = WandbLogger(),\n",
        "    log_every_n_steps = 1,\n",
        "    gpus = 1 if torch.cuda.is_available() else 0,\n",
        "    max_epochs = hparams.epochs,\n",
        "    deterministic=True\n",
        ")\n",
        "trainer.fit(model, ds)\n",
        "trainer.test(model, ds.test_dataloader(hparams.test_stock))\n",
        "run.finish()\n",
        "\n",
        "# A practical evaluation strategy based on profit estimation\n",
        "evaluate_trading_strategy(model.model, ds, len(dfs), price_idx=price_idx, target_ticker=hparams.test_stock)"
      ]
    },
    {
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        "id": "tnUcPMYi8bey"
      },
      "source": [
        "### Attention-based GRU"
      ]
    },
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        "id": "NxQgwvBk6phv",
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mleonardoemili\u001b[0m (use `wandb login --relogin` to force relogin)\n"
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              "                Project page: <a href=\"https://wandb.ai/leonardoemili/spf\" target=\"_blank\">https://wandb.ai/leonardoemili/spf</a><br/>\n",
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              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: right }\n",
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              "<tr><td>val_loss</td><td>█▂▂▁▂▁▄▄▂▁</td></tr><tr><td>val_r2</td><td>▁▇▇█▆▇▅▆█▇</td></tr><tr><td>val_r2_adj</td><td>▁▇▇█▆▇▅▆█▇</td></tr><tr><td>epoch</td><td>▁▂▃▃▄▅▆▆▇██</td></tr><tr><td>trainer/global_step</td><td>▁▂▃▃▄▅▆▆▇██</td></tr><tr><td>_runtime</td><td>▁▂▃▃▄▅▆▆▇██</td></tr><tr><td>_timestamp</td><td>▁▂▃▃▄▅▆▆▇██</td></tr><tr><td>_step</td><td>▁▂▂▃▄▅▅▆▇▇█</td></tr><tr><td>test_loss</td><td>▁</td></tr><tr><td>test_r2</td><td>▁</td></tr><tr><td>test_r2_adj</td><td>▁</td></tr></table><br/>"
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              "\n",
              "                    <br/>Synced <strong style=\"color:#cdcd00\">graceful-eon-185</strong>: <a href=\"https://wandb.ai/leonardoemili/spf/runs/2irmqvuf\" target=\"_blank\">https://wandb.ai/leonardoemili/spf/runs/2irmqvuf</a><br/>\n",
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          "text": [
            "Evaluating trading strategy ...\n",
            "1-year profit: 66.85811905534707%\n",
            "Operation accuracy: 54.347826086956516%\n"
          ]
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      ],
      "source": [
        "hparams = HParams()\n",
        "hparams.features = ds_splits[0].columns\n",
        "hparams.epochs = 10\n",
        "hparams.num_heads = 4\n",
        "\n",
        "run = wandb.init(reinit=True, project='spf')\n",
        "gru = AttentionGRU(hparams).double()\n",
        "model = StockPriceRegressor(gru, hparams)\n",
        "trainer = pl.Trainer(\n",
        "    logger = WandbLogger(),\n",
        "    log_every_n_steps = 1,\n",
        "    gpus = 1 if torch.cuda.is_available() else 0,\n",
        "    max_epochs = hparams.epochs,\n",
        "    deterministic=True\n",
        ")\n",
        "trainer.fit(model, ds)\n",
        "trainer.test(model, ds.test_dataloader(hparams.test_stock))\n",
        "run.finish()\n",
        "\n",
        "# A practical evaluation strategy based on profit estimation\n",
        "evaluate_trading_strategy(model.model, ds, len(dfs), price_idx=price_idx, target_ticker=hparams.test_stock)"
      ]
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    {
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      "source": [
        "### CNN-based GRU"
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    },
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              "                    <br/>Synced <strong style=\"color:#cdcd00\">vocal-monkey-186</strong>: <a href=\"https://wandb.ai/leonardoemili/spf/runs/1u7jflmu\" target=\"_blank\">https://wandb.ai/leonardoemili/spf/runs/1u7jflmu</a><br/>\n",
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            "Evaluating trading strategy ...\n",
            "1-year profit: 85.21573768402295%\n",
            "Operation accuracy: 63.52941176470588%\n"
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      "source": [
        "hparams = HParams()\n",
        "hparams.features = ds_splits[0].columns\n",
        "hparams.epochs = 10\n",
        "\n",
        "run = wandb.init(reinit=True, project='spf')\n",
        "lstm = ConvolutionalGRU(hparams).double()\n",
        "model = StockPriceRegressor(gru, hparams)\n",
        "trainer = pl.Trainer(\n",
        "    logger = WandbLogger(),\n",
        "    log_every_n_steps = 1,\n",
        "    gpus = 1 if torch.cuda.is_available() else 0,\n",
        "    max_epochs = hparams.epochs,\n",
        "    deterministic=True\n",
        ")\n",
        "trainer.fit(model, ds)\n",
        "trainer.test(model, ds.test_dataloader(hparams.test_stock))\n",
        "run.finish()\n",
        "\n",
        "# A practical evaluation strategy based on profit estimation\n",
        "evaluate_trading_strategy(model.model, ds, len(dfs), price_idx=price_idx, target_ticker=hparams.test_stock)"
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