durbin-notebook-rnn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optiver Trading At The Close\n",
    "\n",
    "[Competition](https://www.kaggle.com/competitions/optiver-trading-at-the-close)\n",
    "\n",
    "[GitHub Repository](https://github.com/cvaisnor/DNN-Final-Project)\n",
    "\n",
    "[Google Slides Presentation](https://docs.google.com/presentation/d/1Xc5F1_NveFi1il3GqHej2aqVmklR_jZU4kx6ZJhiDEM/edit?usp=sharing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# General Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip show tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import GRU, Dropout, Dense\n",
    "from tensorflow.keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import importlib\n",
    "\n",
    "import helpers\n",
    "\n",
    "kaggle_environment = False # True if running on Kaggle, don't forget to add the dataset!\n",
    "\n",
    "from helpers.common import load_data, plot_predictions, submit_dummy_predictions\n",
    "from helpers.pipeline import LogFeatures, LagFeatures, RollingMeanFeatures, DiffFeatures, ExpandingMeanFeatures, \\\n",
    "ForwardFillValues, FillZero, DataFrameWrapper, PolynomialFeaturesWrapper, MissingValueImputer, DropColumns, FillOne\n",
    "\n",
    "if not kaggle_environment:\n",
    "    # Reload the modules\n",
    "    importlib.reload(helpers.common)\n",
    "    importlib.reload(helpers.pipeline)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "original_train, test, revealed_targets, sample_submission = load_data(kaggle_environment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "split_ratio = 0.8  # 80% for training, 20% for testing\n",
    "split_idx = int(len(original_train) * split_ratio)\n",
    "\n",
    "train = original_train.iloc[:split_idx]\n",
    "test = original_train.iloc[split_idx:]\n",
    "\n",
    "train = train.dropna(subset=['target'])\n",
    "test = test.dropna(subset=['target'])\n",
    "\n",
    "# Subsample\n",
    "max_samples = 100000\n",
    "if train.shape[0] > max_samples:\n",
    "    train = train[-max_samples:]\n",
    "\n",
    "if test.shape[0] > max_samples:\n",
    "    test = test[-max_samples:]\n",
    "    \n",
    "# train = train.dropna()\n",
    "# test = test.dropna()\n",
    "\n",
    "# y = train['target'].values\n",
    "# y_test = test['target'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100000, 17)\n",
      "(100000, 17)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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>stock_id</th>\n",
       "      <th>date_id</th>\n",
       "      <th>seconds_in_bucket</th>\n",
       "      <th>imbalance_size</th>\n",
       "      <th>imbalance_buy_sell_flag</th>\n",
       "      <th>reference_price</th>\n",
       "      <th>matched_size</th>\n",
       "      <th>far_price</th>\n",
       "      <th>near_price</th>\n",
       "      <th>bid_price</th>\n",
       "      <th>bid_size</th>\n",
       "      <th>ask_price</th>\n",
       "      <th>ask_size</th>\n",
       "      <th>wap</th>\n",
       "      <th>target</th>\n",
       "      <th>time_id</th>\n",
       "      <th>row_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5137980</th>\n",
       "      <td>0</td>\n",
       "      <td>471</td>\n",
       "      <td>500</td>\n",
       "      <td>1467081.79</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.997344</td>\n",
       "      <td>36059637.12</td>\n",
       "      <td>0.997002</td>\n",
       "      <td>0.997002</td>\n",
       "      <td>0.997344</td>\n",
       "      <td>40845.00</td>\n",
       "      <td>0.997515</td>\n",
       "      <td>41785.76</td>\n",
       "      <td>0.997428</td>\n",
       "      <td>1.230240</td>\n",
       "      <td>25955</td>\n",
       "      <td>471_500_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5137981</th>\n",
       "      <td>1</td>\n",
       "      <td>471</td>\n",
       "      <td>500</td>\n",
       "      <td>479742.36</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.998503</td>\n",
       "      <td>15727852.06</td>\n",
       "      <td>0.997559</td>\n",
       "      <td>0.997821</td>\n",
       "      <td>0.998188</td>\n",
       "      <td>67894.26</td>\n",
       "      <td>0.999186</td>\n",
       "      <td>70246.53</td>\n",
       "      <td>0.998679</td>\n",
       "      <td>-23.149849</td>\n",
       "      <td>25955</td>\n",
       "      <td>471_500_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5137982</th>\n",
       "      <td>2</td>\n",
       "      <td>471</td>\n",
       "      <td>500</td>\n",
       "      <td>199076.21</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000145</td>\n",
       "      <td>39893400.17</td>\n",
       "      <td>1.000291</td>\n",
       "      <td>1.000242</td>\n",
       "      <td>0.999900</td>\n",
       "      <td>354946.30</td>\n",
       "      <td>1.000340</td>\n",
       "      <td>27630.45</td>\n",
       "      <td>1.000308</td>\n",
       "      <td>2.269745</td>\n",
       "      <td>25955</td>\n",
       "      <td>471_500_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5137983</th>\n",
       "      <td>3</td>\n",
       "      <td>471</td>\n",
       "      <td>500</td>\n",
       "      <td>7322026.95</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.997682</td>\n",
       "      <td>92111805.96</td>\n",
       "      <td>0.997035</td>\n",
       "      <td>0.997284</td>\n",
       "      <td>0.997682</td>\n",
       "      <td>6011.40</td>\n",
       "      <td>0.997732</td>\n",
       "      <td>4007.80</td>\n",
       "      <td>0.997712</td>\n",
       "      <td>0.849962</td>\n",
       "      <td>25955</td>\n",
       "      <td>471_500_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5137984</th>\n",
       "      <td>4</td>\n",
       "      <td>471</td>\n",
       "      <td>500</td>\n",
       "      <td>1811912.82</td>\n",
       "      <td>-1</td>\n",
       "      <td>0.999935</td>\n",
       "      <td>61508071.65</td>\n",
       "      <td>0.999705</td>\n",
       "      <td>0.999705</td>\n",
       "      <td>0.999935</td>\n",
       "      <td>173.76</td>\n",
       "      <td>1.000108</td>\n",
       "      <td>120957.84</td>\n",
       "      <td>0.999936</td>\n",
       "      <td>-0.680089</td>\n",
       "      <td>25955</td>\n",
       "      <td>471_500_4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5237975</th>\n",
       "      <td>195</td>\n",
       "      <td>480</td>\n",
       "      <td>540</td>\n",
       "      <td>2440722.89</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.000317</td>\n",
       "      <td>28280361.74</td>\n",
       "      <td>0.999734</td>\n",
       "      <td>0.999734</td>\n",
       "      <td>1.000317</td>\n",
       "      <td>32257.04</td>\n",
       "      <td>1.000434</td>\n",
       "      <td>319862.40</td>\n",
       "      <td>1.000328</td>\n",
       "      <td>2.310276</td>\n",
       "      <td>26454</td>\n",
       "      <td>480_540_195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5237976</th>\n",
       "      <td>196</td>\n",
       "      <td>480</td>\n",
       "      <td>540</td>\n",
       "      <td>349510.47</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.000643</td>\n",
       "      <td>9187699.11</td>\n",
       "      <td>1.000129</td>\n",
       "      <td>1.000386</td>\n",
       "      <td>1.000643</td>\n",
       "      <td>205108.40</td>\n",
       "      <td>1.000900</td>\n",
       "      <td>93393.07</td>\n",
       "      <td>1.000819</td>\n",
       "      <td>-8.220077</td>\n",
       "      <td>26454</td>\n",
       "      <td>480_540_196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5237977</th>\n",
       "      <td>197</td>\n",
       "      <td>480</td>\n",
       "      <td>540</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.995789</td>\n",
       "      <td>12725436.10</td>\n",
       "      <td>0.995789</td>\n",
       "      <td>0.995789</td>\n",
       "      <td>0.995789</td>\n",
       "      <td>16790.66</td>\n",
       "      <td>0.995883</td>\n",
       "      <td>180038.32</td>\n",
       "      <td>0.995797</td>\n",
       "      <td>1.169443</td>\n",
       "      <td>26454</td>\n",
       "      <td>480_540_197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5237978</th>\n",
       "      <td>198</td>\n",
       "      <td>480</td>\n",
       "      <td>540</td>\n",
       "      <td>1000898.84</td>\n",
       "      <td>1</td>\n",
       "      <td>0.999210</td>\n",
       "      <td>94773271.05</td>\n",
       "      <td>0.999210</td>\n",
       "      <td>0.999210</td>\n",
       "      <td>0.998970</td>\n",
       "      <td>125631.72</td>\n",
       "      <td>0.999210</td>\n",
       "      <td>669893.00</td>\n",
       "      <td>0.999008</td>\n",
       "      <td>-1.540184</td>\n",
       "      <td>26454</td>\n",
       "      <td>480_540_198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5237979</th>\n",
       "      <td>199</td>\n",
       "      <td>480</td>\n",
       "      <td>540</td>\n",
       "      <td>1884285.71</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.002129</td>\n",
       "      <td>24073677.32</td>\n",
       "      <td>1.000859</td>\n",
       "      <td>1.001494</td>\n",
       "      <td>1.002129</td>\n",
       "      <td>250081.44</td>\n",
       "      <td>1.002447</td>\n",
       "      <td>300167.56</td>\n",
       "      <td>1.002274</td>\n",
       "      <td>-6.530285</td>\n",
       "      <td>26454</td>\n",
       "      <td>480_540_199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         stock_id  date_id  seconds_in_bucket  imbalance_size  \\\n",
       "5137980         0      471                500      1467081.79   \n",
       "5137981         1      471                500       479742.36   \n",
       "5137982         2      471                500       199076.21   \n",
       "5137983         3      471                500      7322026.95   \n",
       "5137984         4      471                500      1811912.82   \n",
       "...           ...      ...                ...             ...   \n",
       "5237975       195      480                540      2440722.89   \n",
       "5237976       196      480                540       349510.47   \n",
       "5237977       197      480                540            0.00   \n",
       "5237978       198      480                540      1000898.84   \n",
       "5237979       199      480                540      1884285.71   \n",
       "\n",
       "         imbalance_buy_sell_flag  reference_price  matched_size  far_price  \\\n",
       "5137980                       -1         0.997344   36059637.12   0.997002   \n",
       "5137981                       -1         0.998503   15727852.06   0.997559   \n",
       "5137982                        1         1.000145   39893400.17   1.000291   \n",
       "5137983                       -1         0.997682   92111805.96   0.997035   \n",
       "5137984                       -1         0.999935   61508071.65   0.999705   \n",
       "...                          ...              ...           ...        ...   \n",
       "5237975                       -1         1.000317   28280361.74   0.999734   \n",
       "5237976                       -1         1.000643    9187699.11   1.000129   \n",
       "5237977                        0         0.995789   12725436.10   0.995789   \n",
       "5237978                        1         0.999210   94773271.05   0.999210   \n",
       "5237979                       -1         1.002129   24073677.32   1.000859   \n",
       "\n",
       "         near_price  bid_price   bid_size  ask_price   ask_size       wap  \\\n",
       "5137980    0.997002   0.997344   40845.00   0.997515   41785.76  0.997428   \n",
       "5137981    0.997821   0.998188   67894.26   0.999186   70246.53  0.998679   \n",
       "5137982    1.000242   0.999900  354946.30   1.000340   27630.45  1.000308   \n",
       "5137983    0.997284   0.997682    6011.40   0.997732    4007.80  0.997712   \n",
       "5137984    0.999705   0.999935     173.76   1.000108  120957.84  0.999936   \n",
       "...             ...        ...        ...        ...        ...       ...   \n",
       "5237975    0.999734   1.000317   32257.04   1.000434  319862.40  1.000328   \n",
       "5237976    1.000386   1.000643  205108.40   1.000900   93393.07  1.000819   \n",
       "5237977    0.995789   0.995789   16790.66   0.995883  180038.32  0.995797   \n",
       "5237978    0.999210   0.998970  125631.72   0.999210  669893.00  0.999008   \n",
       "5237979    1.001494   1.002129  250081.44   1.002447  300167.56  1.002274   \n",
       "\n",
       "            target  time_id       row_id  \n",
       "5137980   1.230240    25955    471_500_0  \n",
       "5137981 -23.149849    25955    471_500_1  \n",
       "5137982   2.269745    25955    471_500_2  \n",
       "5137983   0.849962    25955    471_500_3  \n",
       "5137984  -0.680089    25955    471_500_4  \n",
       "...            ...      ...          ...  \n",
       "5237975   2.310276    26454  480_540_195  \n",
       "5237976  -8.220077    26454  480_540_196  \n",
       "5237977   1.169443    26454  480_540_197  \n",
       "5237978  -1.540184    26454  480_540_198  \n",
       "5237979  -6.530285    26454  480_540_199  \n",
       "\n",
       "[100000 rows x 17 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Restructure training and test sets into RNN compatible training sequences\n",
    "# # Assign Variables\n",
    "\n",
    "# def generate_sequences(data, sequence_size, prediction_size, step_size, num_sequences):\n",
    "#     past_sequences = []\n",
    "#     future_prices = []\n",
    "#     total_possible_sequences = (len(data) - prediction_size - sequence_size) // step_size\n",
    "#     starting_sequence = total_possible_sequences - num_sequences\n",
    "\n",
    "#     print (f'Data length: {len(data)}, starting_sequence: {starting_sequence}')\n",
    "\n",
    "#     for i in range(starting_sequence, total_possible_sequences):\n",
    "#         start_index = i * step_size\n",
    "#         end_index = start_index + sequence_size\n",
    "#         prediction_end_index = end_index + prediction_size\n",
    "\n",
    "#         if prediction_end_index < len(data):\n",
    "#           past_sequence = data.iloc[start_index:end_index, :].values\n",
    "#           future_price_sequence = data.iloc[end_index:prediction_end_index, data.columns.get_loc('Close')].values\n",
    "\n",
    "#           past_sequences.append(past_sequence)\n",
    "#           future_prices.append(future_price_sequence)\n",
    "\n",
    "#         else:\n",
    "#             print(f'ERROR: Calculations were incorrect start index {start_index}, end index {end_index}, prediction end index {prediction_end_index}')\n",
    "\n",
    "#     return np.array(past_sequences), np.array(future_prices)\n",
    "\n",
    "# num_train_samples = train.shape[0]\n",
    "# num_test_samples = train.shape[0]\n",
    "# num_training_sequences = 3500\n",
    "# num_test_sequences = 200\n",
    "# prediction_size = 1\n",
    "# step_size = 1\n",
    "\n",
    "# print('Creating training data')\n",
    "# train_inputs, train_outputs = generate_sequences(\n",
    "#     train,\n",
    "#     num_train_samples,\n",
    "#     prediction_size,\n",
    "#     step_size,\n",
    "#     num_training_sequences\n",
    "# )\n",
    "\n",
    "# print('Creating test data')\n",
    "# test_inputs, test_outputs = generate_sequences(\n",
    "#     test,\n",
    "#     num_test_samples,\n",
    "#     prediction_size,\n",
    "#     step_size,\n",
    "#     num_test_sequences\n",
    "# )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering Section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.26 s, sys: 137 ms, total: 1.4 s\n",
      "Wall time: 1.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "\n",
    "# drop_columns=['target', 'bid_size', 'ask_size', 'imbalance_size', 'matched_size', 'time_id', 'row_id', 'currently_scored']\n",
    "# drop_columns=['target', 'bid_size', 'ask_size', 'imbalance_size', 'matched_size', 'time_id', 'row_id', 'currently_scored']\n",
    "drop_columns=['bid_size', 'ask_size', 'imbalance_size', 'matched_size', 'time_id', 'row_id', 'currently_scored']\n",
    "\n",
    "# Main Pipeline\n",
    "pipeline = Pipeline([\n",
    "    ('logs', LogFeatures(['imbalance_size', 'matched_size', 'bid_size', 'ask_size'])),\n",
    "    ('wap_lags', LagFeatures(['wap'], [1, 6], 1.0)),\n",
    "    ('matched_size_lags', LagFeatures(['matched_size'], [1, 6], 0.0)),\n",
    "    ('wap_rolling_means', RollingMeanFeatures(['wap'], [2])),\n",
    "    ('diff_features', DiffFeatures(['wap', 'imbalance_buy_sell_flag', 'matched_size_log', 'ask_price', 'reference_price'])),\n",
    "    ('diff_features_2', DiffFeatures(['wap_diff', 'matched_size_log_diff', 'ask_price_diff', 'reference_price_diff'])),\n",
    "    ('diff_features_3', DiffFeatures(['wap_diff_diff', 'matched_size_log_diff_diff', 'ask_price_diff_diff', 'reference_price_diff_diff'])),\n",
    "    ('wap_expanding_mean', ExpandingMeanFeatures(['wap'])),\n",
    "    ('drop_columns', DropColumns(drop_columns)),\n",
    "#     ('missing_value_imputer', MissingValueImputer(['far_price', 'near_price'])),\n",
    "    ('forward_fill', ForwardFillValues()),\n",
    "    # ('fill_zero', FillZero()),\n",
    "    ('fill_one', FillOne()),\n",
    "    # ('preprocessor', DataFrameWrapper(preprocessor, columns_to_keep)),\n",
    "    # ('drop_grouping_columns', DropColumns(['stock_id', 'date_id'])),\n",
    "    ('scaler', DataFrameWrapper(StandardScaler())),\n",
    "    # ('poly', PolynomialFeaturesWrapper(2)),\n",
    "])\n",
    "\n",
    "import warnings\n",
    "\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\", category=pd.errors.SettingWithCopyWarning)\n",
    "    train_preprocessed = pipeline.fit_transform(train)\n",
    "    test_preprocessed = pipeline.transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X = train_transformed\n",
    "# X_test = test_transformed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "stock_id                           float64\n",
       "date_id                            float64\n",
       "seconds_in_bucket                  float64\n",
       "imbalance_buy_sell_flag            float64\n",
       "reference_price                    float64\n",
       "far_price                          float64\n",
       "near_price                         float64\n",
       "bid_price                          float64\n",
       "ask_price                          float64\n",
       "wap                                float64\n",
       "target                             float64\n",
       "imbalance_size_log                 float64\n",
       "matched_size_log                   float64\n",
       "bid_size_log                       float64\n",
       "ask_size_log                       float64\n",
       "wap_lag_1                          float64\n",
       "wap_lag_6                          float64\n",
       "matched_size_lag_1                 float64\n",
       "matched_size_lag_6                 float64\n",
       "wap_rolling_mean2                  float64\n",
       "wap_diff                           float64\n",
       "imbalance_buy_sell_flag_diff       float64\n",
       "matched_size_log_diff              float64\n",
       "ask_price_diff                     float64\n",
       "reference_price_diff               float64\n",
       "wap_diff_diff                      float64\n",
       "matched_size_log_diff_diff         float64\n",
       "ask_price_diff_diff                float64\n",
       "reference_price_diff_diff          float64\n",
       "wap_diff_diff_diff                 float64\n",
       "matched_size_log_diff_diff_diff    float64\n",
       "ask_price_diff_diff_diff           float64\n",
       "reference_price_diff_diff_diff     float64\n",
       "wap_expanding_mean                 float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_preprocessed.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X['wap_lag_1']\n",
    "# X[seconds_in_bucket]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "import numpy as np\n",
    "\n",
    "def create_predict_sequences(df, feature_columns, max_sequence_length=60):\n",
    "    \"\"\"\n",
    "    Create sequences for RNN predictions.\n",
    "\n",
    "    :param df: Pandas DataFrame containing the data.\n",
    "    :param feature_columns: List of column names to be used as features.\n",
    "    :param max_sequence_length: Maximum length of sequences.\n",
    "    :return: Tuple (X, y) of sequences and their corresponding targets.\n",
    "    \"\"\"\n",
    "    X = []\n",
    "\n",
    "    for _, group in df.groupby(['stock_id', 'date_id']):\n",
    "        sequences = group[feature_columns].values\n",
    "        padded_sequences = pad_sequences([sequences], maxlen=max_sequence_length, padding='pre', dtype='float32')[0]\n",
    "\n",
    "        X.append(padded_sequences)\n",
    "\n",
    "    return np.array(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "import numpy as np\n",
    "\n",
    "def create_sequences(df, feature_columns, max_sequence_length=60, step_size=3):\n",
    "    \"\"\"\n",
    "    Create sequences for RNN training, aiming to predict all values for each day.\n",
    "\n",
    "    :param df: Pandas DataFrame containing the data.\n",
    "    :param feature_columns: List of column names to be used as features.\n",
    "    :param max_sequence_length: Maximum length of sequences.\n",
    "    :return: Tuple (X, y) of sequences and their corresponding targets.\n",
    "    \"\"\"\n",
    "    X = []  # Features\n",
    "    y = []  # Targets\n",
    "\n",
    "    # Group by 'stock_id' and 'date_id'\n",
    "    for _, group in df.groupby(['stock_id', 'date_id']):\n",
    "        # Extract features and targets\n",
    "        group_features = group[feature_columns].values\n",
    "        group_targets = group['target'].values\n",
    "\n",
    "        # Generate multiple sequences for each group\n",
    "        for i in range(0, len(group), step_size):\n",
    "            # Define the end of the sequence\n",
    "            end_idx = i + 5\n",
    "\n",
    "            # If there are not enough previous timesteps for a full sequence,\n",
    "            # pad the sequence at the beginning\n",
    "            if end_idx < max_sequence_length:\n",
    "                sequence = group_features[:end_idx]\n",
    "                sequence = pad_sequences([sequence], maxlen=max_sequence_length, padding='pre', dtype='float32')[0]\n",
    "            else:\n",
    "                start_idx = end_idx - max_sequence_length\n",
    "                sequence = group_features[start_idx:end_idx]\n",
    "\n",
    "            X.append(sequence)\n",
    "            y.append(group_targets[i])\n",
    "\n",
    "    return np.array(X), np.array(y)\n",
    "\n",
    "feature_columns = train_preprocessed.columns.difference(['stock_id', 'date_id', 'target'])\n",
    "X_train, y_train = create_sequences(train_preprocessed, feature_columns)\n",
    "X_test, y_test = create_sequences(test_preprocessed, feature_columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequences: 34404, Samples per sequence: 60, number of features per sample 31\n"
     ]
    }
   ],
   "source": [
    "print(f'Sequences: {len(X_train)}, Samples per sequence: {len(X_train[0])}, number of features per sample {len(X_train[0][0])}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All feature transformations should be combined into a generate_feature() function. This can then be applied to the test data will in the for loop for the Optiver API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_features(cumulative_test_df, current_test, pipeline):\n",
    "    transformed_df = pipeline.transform(cumulative_test_df)\n",
    "\n",
    "    predict_sequences, _ = create_predict_sequences(transformed_df, feature_columns)\n",
    "\n",
    "    return predict_sequences    \n",
    "    # Only return rows corresponding to the current test dataframe\n",
    "    # return transformed_df.iloc[-len(current_test):]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Slicing\n",
    "\n",
    "Generally, don't change the cell below. It is used to slice the data into training and validation sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "# from sklearn.model_selection import train_test_split\n",
    "\n",
    "# # X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "# X_train = X\n",
    "# y_train = y "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Development"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1076/1076 [==============================] - 149s 137ms/step - loss: 0.6017\n",
      "CPU times: user 2min 42s, sys: 1min 34s, total: 4min 16s\n",
      "Wall time: 2min 28s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from tensorflow.keras.layers import GRU, Dropout, Dense\n",
    "from tensorflow.keras.models import Sequential\n",
    "\n",
    "tf.random.set_seed(0)\n",
    "Init = tf.keras.initializers.GlorotUniform(seed=0)\n",
    "\n",
    "num_features = 31\n",
    "num_predictions = 1\n",
    "gru_size = 200\n",
    "\n",
    "\n",
    "# Example: Reshape X_train to have 10 timesteps\n",
    "\n",
    "# num_timesteps = 600\n",
    "# subset_size = 20000\n",
    "\n",
    "# X_train_subset = X_train.iloc[-subset_size:]\n",
    "\n",
    "# # Adjust num_samples based on the subset size\n",
    "# num_samples = X_train_subset.shape[0] - num_timesteps + 1\n",
    "\n",
    "# # Initialize the reshaped training data array\n",
    "# X_train_reshaped = np.zeros((num_samples, num_timesteps, num_features))\n",
    "# y_train_subset = y_train[-X_train_reshaped.shape[0]:]\n",
    "\n",
    "\n",
    "# # Populate the reshaped array\n",
    "# for i in range(num_samples):\n",
    "#     X_train_reshaped[i] = X_train_subset.values[i:i+num_timesteps]\n",
    "\n",
    "# Now use the reshaped array for training\n",
    "# hist = rnn.fit(X_train_reshaped, y_train, epochs=5, batch_size=16, callbacks=callbacks)\n",
    "\n",
    "\n",
    "rnn = Sequential(\n",
    "    [\n",
    "        GRU(gru_size, return_sequences=True, input_shape=[None, num_features], name='GRU1', kernel_initializer=Init, recurrent_initializer=Init),\n",
    "        Dropout(.2, name='d1'),\n",
    "        GRU(gru_size, name='GRU2', kernel_initializer=Init, recurrent_initializer=Init),\n",
    "        Dropout(.2, name='d2'),\n",
    "        Dense(num_predictions, name='out', kernel_initializer=Init)\n",
    "    ],\n",
    "    name='RNN_model'\n",
    ")\n",
    "\n",
    "rnn.compile(optimizer='adam', loss='mean_absolute_error')\n",
    "callbacks = []\n",
    "hist = rnn.fit(X_train, y_train, epochs=1, batch_size=32, callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "# import lightgbm as lgb\n",
    "\n",
    "\n",
    "# # lgbm = lgb.LGBMRegressor(n_jobs=-1, random_state=0, force_col_wise=True,\n",
    "# #                          verbose=-1, boosting_type='gbdt', num_leaves=10,\n",
    "# #                          reg_alpha=0, reg_lambda=0.2, objective='regression_l1')\n",
    "\n",
    "# # lgbm = lgb.LGBMRegressor(n_jobs=-1, random_state=0, objective='regression_l1', num_leaves=100)\n",
    "# lgbm = lgb.LGBMRegressor(n_jobs=-1, reg_lambda=0.2, random_state=0, num_leaves=650, boosting_type='gbdt', reg_alpha=0.2, objective='regression_l1')\n",
    "# lgbm.fit(X_train, y_train)\n",
    "# # lgbm.fit(X_train.drop(columns=['row_id', 'time_id', 'target']), y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = rnn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1082/1082 [==============================] - 51s 47ms/step\n",
      "Mean Absolute Error on the test set: 0.4668\n",
      "1076/1076 [==============================] - 49s 46ms/step\n",
      "Mean Absolute Error on the training set: 0.5636\n"
     ]
    }
   ],
   "source": [
    "# lgbm.score(X_test, y_test)\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "# predictions = model.predict(X_test.drop(columns=['row_id', 'time_id', 'target']))\n",
    "\n",
    "# X_test_subset = X_test.iloc[-subset_size:]\n",
    "\n",
    "# # Adjust num_samples based on the subset size\n",
    "# num_samples = X_test_subset.shape[0] - num_timesteps + 1\n",
    "\n",
    "# # Initialize the reshaped testing data array\n",
    "# X_test_reshaped = np.zeros((num_samples, num_timesteps, num_features))\n",
    "# y_test_subset = y_test[-X_test_reshaped.shape[0]:]\n",
    "\n",
    "\n",
    "# # Populate the reshaped array\n",
    "# for i in range(num_samples):\n",
    "#     X_test_reshaped[i] = X_test_subset.values[i:i+num_timesteps]\n",
    "\n",
    "\n",
    "predictions = model.predict(X_test)\n",
    "\n",
    "mae = mean_absolute_error(y_test, predictions)\n",
    "print(f\"Mean Absolute Error on the test set: {mae:.4f}\")\n",
    "# predictions2 = model.predict(X_train.drop(columns=['row_id', 'time_id', 'target']))\n",
    "predictions2 = model.predict(X_train)\n",
    "mae2 = mean_absolute_error(y_train, predictions2)\n",
    "print(f\"Mean Absolute Error on the training set: {mae2:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_predictions(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_predictions(y_train, predictions2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-3.841069 3.864067\n"
     ]
    }
   ],
   "source": [
    "print(predictions.min(), predictions.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert to numpy arrays\n",
    "# X_train = X_train.values\n",
    "# X_test = X_test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Uncomment to use NN as the model instead\n",
    "# # convert to numpy arrays\n",
    "# X_train = X_train.values\n",
    "# X_test = X_test.values\n",
    "# # creating the model and fitting it on the data from the original sklearn train test split\n",
    "# model = PyTorchMLP(input_size=X_train.shape[1], hidden_size=128)\n",
    "# model.fit(X_train, y_train, X_test, y_test, epochs=2, batch_size=256, learning_rate=0.001, device=device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating Predictions\n",
    "\n",
    "Only for Kaggle:\n",
    "When uploading notebook, make sure to add the Optiver data on the right side of the screen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if kaggle_environment:\n",
    "    import optiver2023\n",
    "    env = optiver2023.make_env()\n",
    "    iter_test = env.iter_test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment this cell to try to get unstuck from having to restart and run the whole notebook again\n",
    "# submit_dummy_predictions(env, sample_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if kaggle_environment:\n",
    "#     # To count how many time the \"for loop\" runs.\n",
    "#     counter = 0\n",
    "\n",
    "#     # init 3 empty lists\n",
    "#     # test_ls, revealed_targets_ls, sample_prediction_ls = [], [], []\n",
    "#     cumulative_test_df = pd.DataFrame()\n",
    "\n",
    "#     for (test_in, revealed_targets, sample_prediction) in iter_test:\n",
    "#         # Append the dataframe that API return into the list.\n",
    "#         # test_ls.append(test_in.copy())\n",
    "#         # revealed_targets_ls.append(revealed_targets.copy())\n",
    "#         # sample_prediction_ls.append(sample_prediction.copy())\n",
    "\n",
    "#         current_date_id = test_in.iloc[0]['date_id']\n",
    "#         current_date_rows = cumulative_test_df[cumulative_test_df['current_date_id'] == date_id_to_keep]\n",
    "#         cumulative_test_df = pd.concat([current_date_rows, test_in], axis=0, ignore_index=True)\n",
    "\n",
    "#         # Generate features\n",
    "#         test_transformed = generate_features(cumulative_test_df, test_in, pipeline)\n",
    "\n",
    "#         # Writes our predictions \n",
    "#         sample_prediction[\"target\"] = model.predict(test_transformed)\n",
    "        \n",
    "#         # This line submit our predictions.\n",
    "#         env.predict(sample_prediction)\n",
    "#         counter += 1\n",
    "\n",
    "#     print('\\n', '=' * 50, sep=\"\")\n",
    "#     print(f\"counter: {counter}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if kaggle_environment:\n",
    "    # To count how many time the \"for loop\" runs.\n",
    "    counter = 0\n",
    "\n",
    "    # init 3 empty lists\n",
    "    # test_ls, revealed_targets_ls, sample_prediction_ls = [], [], []\n",
    "    cumulative_test_df = pd.DataFrame()\n",
    "\n",
    "    for (test_in, revealed_targets, sample_prediction) in iter_test:\n",
    "        # Append the dataframe that API return into the list.\n",
    "        # test_ls.append(test_in.copy())\n",
    "        # revealed_targets_ls.append(revealed_targets.copy())\n",
    "        # sample_prediction_ls.append(sample_prediction.copy())\n",
    "\n",
    "        test_in['target'] = 0\n",
    "        current_date_id = test_in.iloc[0]['date_id']\n",
    "        cumulative_test_df = pd.concat([cumulative_test_df, test_in], axis=0, ignore_index=True)\n",
    "#         if len(cumulative_test_df.columns) > 0:\n",
    "        cumulative_test_df = cumulative_test_df[cumulative_test_df['date_id'] == current_date_id]\n",
    "#             cumulative_test_df = pd.concat([current_date_rows, test_in], axis=0, ignore_index=True)\n",
    "        print(f'{counter}: {cumulative_test_df.shape[0]}')\n",
    "\n",
    "        # Generate features\n",
    "        test_transformed = generate_features(cumulative_test_df, test_in, pipeline)\n",
    "\n",
    "        # Writes our predictions \n",
    "        sample_prediction[\"target\"] = model.predict(test_transformed)\n",
    "        \n",
    "        # This line submit our predictions.\n",
    "        env.predict(sample_prediction)\n",
    "        counter += 1\n",
    "\n",
    "    print('\\n', '=' * 50, sep=\"\")\n",
    "    print(f\"counter: {counter}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cumulative_test_df.to_csv('file.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test.var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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