NN_Model_v1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "window = 100\n",
    "predict_t_plus_1 = True\n",
    "device = 'cuda'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.optimize import least_squares\n",
    "import yfinance as yf\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "from model.neural_net import *\n",
    "from model.data import *\n",
    "from model.train import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Download data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset_start_date = pd.to_datetime('1995-01-01').date()\n",
    "valid_start_date = pd.to_datetime('2015-01-01').date()\n",
    "test_start_date = pd.to_datetime('2019-01-01').date()\n",
    "test_end_date = pd.to_datetime('2023-08-31').date()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Load data on SPX and VIX\n",
    "# spx_data = yf.Ticker(\"^GSPC\").history(start=train_dataset_start_date, end=test_end_date)\n",
    "# vix_data = yf.Ticker(\"^VIX\").history(start=train_dataset_start_date, end=test_end_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# spx_data.to_csv('spx_data_nn.csv')\n",
    "# vix_data.to_csv('vix_data_nn.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "spx_data = pd.read_csv('spx_data_nn.csv', index_col=0, parse_dates=[0])\n",
    "vix_data = pd.read_csv('vix_data_nn.csv', index_col=0, parse_dates=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "spx_data.index = pd.to_datetime(pd.to_datetime(spx_data.index, utc=True).date)\n",
    "vix_data.index = pd.to_datetime(pd.to_datetime(vix_data.index, utc=True).date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dataset start date: 1995-01-01\n",
      "Train start date: 1995-05-24 00:00:00\n",
      "Valid dataset start date: 2014-08-12\n",
      "Valid start date: 2015-01-01\n",
      "Test dataset start date: 2018-08-09\n",
      "Test start date: 2019-01-01\n",
      "Test end date: 2023-08-31\n"
     ]
    }
   ],
   "source": [
    "train_start_date = spx_data.index[window-1]\n",
    "valid_start_index = spx_data.index.get_indexer([valid_start_date], method='bfill').item()\n",
    "test_start_index = spx_data.index.get_indexer([test_start_date], method='bfill').item()\n",
    "test_end_index = spx_data.index.get_indexer([test_end_date], method='bfill').item()\n",
    "valid_dataset_start_date = spx_data.index[valid_start_index - (window - 1)].date()\n",
    "test_dataset_start_date = spx_data.index[test_start_index - (window - 1)].date()\n",
    "print(f\"Train dataset start date: {train_dataset_start_date}\")\n",
    "print(f\"Train start date: {train_start_date}\")\n",
    "print(f\"Valid dataset start date: {valid_dataset_start_date}\")\n",
    "print(f\"Valid start date: {valid_start_date}\")\n",
    "print(f\"Test dataset start date: {test_dataset_start_date}\")\n",
    "print(f\"Test start date: {test_start_date}\")\n",
    "print(f\"Test end date: {test_end_date}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = create_df_from_yf(spx_data, vix_data, train_dataset_start_date, valid_start_date, predict_t_plus_1=predict_t_plus_1)\n",
    "valid_data = create_df_from_yf(spx_data, vix_data, valid_dataset_start_date, test_start_date, predict_t_plus_1=predict_t_plus_1)\n",
    "test_data = create_df_from_yf(spx_data, vix_data, test_dataset_start_date, test_end_date, predict_t_plus_1=predict_t_plus_1)\n",
    "train_dataset = VIXDataset2(train_data, window)\n",
    "valid_dataset = VIXDataset2(valid_data, window)\n",
    "test_dataset = VIXDataset2(test_data, window)\n",
    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
    "valid_loader = torch.utils.data.DataLoader(valid_dataset, batch_size=64, shuffle=False)\n",
    "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1\n",
      "Training phase: train_loss: 0.07981 \n",
      "Validation phase: valid_loss: 0.00541 \n",
      "Saving model\n",
      "Epoch 2\n",
      "Training phase: train_loss: 0.00435 \n",
      "Validation phase: valid_loss: 0.00190 \n",
      "Saving model\n",
      "Epoch 3\n",
      "Training phase: train_loss: 0.00316 \n",
      "Validation phase: valid_loss: 0.00179 \n",
      "Saving model\n",
      "Epoch 4\n",
      "Training phase: train_loss: 0.00190 \n",
      "Validation phase: valid_loss: 0.00130 \n",
      "Saving model\n",
      "Epoch 5\n",
      "Training phase: train_loss: 0.00152 \n",
      "Validation phase: valid_loss: 0.00171 \n",
      "Epoch 6\n",
      "Training phase: train_loss: 0.00120 \n",
      "Validation phase: valid_loss: 0.00133 \n",
      "Epoch 7\n",
      "Training phase: train_loss: 0.00106 \n",
      "Validation phase: valid_loss: 0.00154 \n",
      "Epoch 8\n",
      "Training phase: train_loss: 0.00105 \n",
      "Validation phase: valid_loss: 0.00181 \n",
      "Epoch 00008: reducing learning rate of group 0 to 5.0000e-04.\n",
      "Epoch 9\n",
      "Training phase: train_loss: 0.00102 \n",
      "Validation phase: valid_loss: 0.00154 \n",
      "Epoch 10\n",
      "Training phase: train_loss: 0.00092 \n",
      "Validation phase: valid_loss: 0.00090 \n",
      "Saving model\n",
      "Epoch 11\n",
      "Training phase: train_loss: 0.00093 \n",
      "Validation phase: valid_loss: 0.00093 \n",
      "Epoch 12\n",
      "Training phase: train_loss: 0.00081 \n",
      "Validation phase: valid_loss: 0.00091 \n",
      "Epoch 13\n",
      "Training phase: train_loss: 0.00082 \n",
      "Validation phase: valid_loss: 0.00103 \n",
      "Epoch 14\n",
      "Training phase: train_loss: 0.00079 \n",
      "Validation phase: valid_loss: 0.00105 \n",
      "Epoch 00014: reducing learning rate of group 0 to 2.5000e-04.\n",
      "Epoch 15\n",
      "Training phase: train_loss: 0.00072 \n",
      "Validation phase: valid_loss: 0.00091 \n",
      "Epoch 16\n",
      "Training phase: train_loss: 0.00070 \n",
      "Validation phase: valid_loss: 0.00161 \n",
      "Epoch 17\n",
      "Training phase: train_loss: 0.00071 \n",
      "Validation phase: valid_loss: 0.00168 \n",
      "Epoch 18\n",
      "Training phase: train_loss: 0.00069 \n",
      "Validation phase: valid_loss: 0.00127 \n",
      "Epoch 00018: reducing learning rate of group 0 to 1.2500e-04.\n",
      "Epoch 19\n",
      "Training phase: train_loss: 0.00067 \n",
      "Validation phase: valid_loss: 0.00097 \n",
      "Epoch 20\n",
      "Training phase: train_loss: 0.00066 \n",
      "Validation phase: valid_loss: 0.00093 \n",
      "Epoch 21\n",
      "Training phase: train_loss: 0.00068 \n",
      "Validation phase: valid_loss: 0.00154 \n",
      "Epoch 22\n",
      "Training phase: train_loss: 0.00065 \n",
      "Validation phase: valid_loss: 0.00091 \n",
      "Epoch 00022: reducing learning rate of group 0 to 6.2500e-05.\n",
      "Epoch 23\n",
      "Training phase: train_loss: 0.00063 \n",
      "Validation phase: valid_loss: 0.00109 \n",
      "Epoch 24\n",
      "Training phase: train_loss: 0.00063 \n",
      "Validation phase: valid_loss: 0.00099 \n",
      "Epoch 25\n",
      "Training phase: train_loss: 0.00063 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 26\n",
      "Training phase: train_loss: 0.00062 \n",
      "Validation phase: valid_loss: 0.00099 \n",
      "Epoch 00026: reducing learning rate of group 0 to 3.1250e-05.\n",
      "Epoch 27\n",
      "Training phase: train_loss: 0.00061 \n",
      "Validation phase: valid_loss: 0.00129 \n",
      "Epoch 28\n",
      "Training phase: train_loss: 0.00060 \n",
      "Validation phase: valid_loss: 0.00100 \n",
      "Epoch 29\n",
      "Training phase: train_loss: 0.00060 \n",
      "Validation phase: valid_loss: 0.00103 \n",
      "Epoch 30\n",
      "Training phase: train_loss: 0.00060 \n",
      "Validation phase: valid_loss: 0.00126 \n",
      "Epoch 00030: reducing learning rate of group 0 to 1.5625e-05.\n",
      "Epoch 31\n",
      "Training phase: train_loss: 0.00061 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 32\n",
      "Training phase: train_loss: 0.00060 \n",
      "Validation phase: valid_loss: 0.00119 \n",
      "Epoch 33\n",
      "Training phase: train_loss: 0.00061 \n",
      "Validation phase: valid_loss: 0.00112 \n",
      "Epoch 34\n",
      "Training phase: train_loss: 0.00060 \n",
      "Validation phase: valid_loss: 0.00101 \n",
      "Epoch 00034: reducing learning rate of group 0 to 7.8125e-06.\n",
      "Epoch 35\n",
      "Training phase: train_loss: 0.00060 \n",
      "Validation phase: valid_loss: 0.00103 \n",
      "Epoch 36\n",
      "Training phase: train_loss: 0.00059 \n",
      "Validation phase: valid_loss: 0.00104 \n",
      "Epoch 37\n",
      "Training phase: train_loss: 0.00060 \n",
      "Validation phase: valid_loss: 0.00110 \n",
      "Epoch 38\n",
      "Training phase: train_loss: 0.00058 \n",
      "Validation phase: valid_loss: 0.00112 \n",
      "Epoch 00038: reducing learning rate of group 0 to 3.9063e-06.\n",
      "Epoch 39\n",
      "Training phase: train_loss: 0.00060 \n",
      "Validation phase: valid_loss: 0.00116 \n",
      "Epoch 40\n",
      "Training phase: train_loss: 0.00059 \n",
      "Validation phase: valid_loss: 0.00108 \n",
      "Epoch 41\n",
      "Training phase: train_loss: 0.00057 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 42\n",
      "Training phase: train_loss: 0.00060 \n",
      "Validation phase: valid_loss: 0.00112 \n",
      "Epoch 00042: reducing learning rate of group 0 to 1.9531e-06.\n",
      "Epoch 43\n",
      "Training phase: train_loss: 0.00060 \n",
      "Validation phase: valid_loss: 0.00110 \n",
      "Epoch 44\n",
      "Training phase: train_loss: 0.00058 \n",
      "Validation phase: valid_loss: 0.00113 \n",
      "Epoch 45\n",
      "Training phase: train_loss: 0.00060 \n",
      "Validation phase: valid_loss: 0.00112 \n",
      "Epoch 46\n",
      "Training phase: train_loss: 0.00059 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 00046: reducing learning rate of group 0 to 9.7656e-07.\n",
      "Epoch 47\n",
      "Training phase: train_loss: 0.00058 \n",
      "Validation phase: valid_loss: 0.00114 \n",
      "Epoch 48\n",
      "Training phase: train_loss: 0.00058 \n",
      "Validation phase: valid_loss: 0.00112 \n",
      "Epoch 49\n",
      "Training phase: train_loss: 0.00060 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 50\n",
      "Training phase: train_loss: 0.00059 \n",
      "Validation phase: valid_loss: 0.00110 \n",
      "Epoch 00050: reducing learning rate of group 0 to 4.8828e-07.\n",
      "Epoch 51\n",
      "Training phase: train_loss: 0.00059 \n",
      "Validation phase: valid_loss: 0.00110 \n",
      "Epoch 52\n",
      "Training phase: train_loss: 0.00060 \n",
      "Validation phase: valid_loss: 0.00107 \n",
      "Epoch 53\n",
      "Training phase: train_loss: 0.00060 \n",
      "Validation phase: valid_loss: 0.00112 \n",
      "Epoch 54\n",
      "Training phase: train_loss: 0.00060 \n",
      "Validation phase: valid_loss: 0.00113 \n",
      "Epoch 00054: reducing learning rate of group 0 to 2.4414e-07.\n",
      "Epoch 55\n",
      "Training phase: train_loss: 0.00058 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 56\n",
      "Training phase: train_loss: 0.00059 \n",
      "Validation phase: valid_loss: 0.00112 \n",
      "Epoch 57\n",
      "Training phase: train_loss: 0.00060 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 58\n",
      "Training phase: train_loss: 0.00059 \n",
      "Validation phase: valid_loss: 0.00112 \n",
      "Epoch 00058: reducing learning rate of group 0 to 1.2207e-07.\n",
      "Epoch 59\n",
      "Training phase: train_loss: 0.00060 \n",
      "Validation phase: valid_loss: 0.00110 \n",
      "Epoch 60\n",
      "Training phase: train_loss: 0.00059 \n",
      "Validation phase: valid_loss: 0.00112 \n",
      "Epoch 61\n",
      "Training phase: train_loss: 0.00059 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 62\n",
      "Training phase: train_loss: 0.00059 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 00062: reducing learning rate of group 0 to 6.1035e-08.\n",
      "Epoch 63\n",
      "Training phase: train_loss: 0.00059 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 64\n",
      "Training phase: train_loss: 0.00058 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 65\n",
      "Training phase: train_loss: 0.00057 \n",
      "Validation phase: valid_loss: 0.00110 \n",
      "Epoch 66\n",
      "Training phase: train_loss: 0.00059 \n",
      "Validation phase: valid_loss: 0.00110 \n",
      "Epoch 00066: reducing learning rate of group 0 to 3.0518e-08.\n",
      "Epoch 67\n",
      "Training phase: train_loss: 0.00058 \n",
      "Validation phase: valid_loss: 0.00110 \n",
      "Epoch 68\n",
      "Training phase: train_loss: 0.00059 \n",
      "Validation phase: valid_loss: 0.00110 \n",
      "Epoch 69\n",
      "Training phase: train_loss: 0.00059 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 70\n",
      "Training phase: train_loss: 0.00059 \n",
      "Validation phase: valid_loss: 0.00110 \n",
      "Epoch 00070: reducing learning rate of group 0 to 1.5259e-08.\n",
      "Epoch 71\n",
      "Training phase: train_loss: 0.00059 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 72\n",
      "Training phase: train_loss: 0.00059 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 73\n",
      "Training phase: train_loss: 0.00059 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 74\n",
      "Training phase: train_loss: 0.00061 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 75\n",
      "Training phase: train_loss: 0.00057 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 76\n",
      "Training phase: train_loss: 0.00059 \n",
      "Validation phase: valid_loss: 0.00110 \n",
      "Epoch 77\n",
      "Training phase: train_loss: 0.00058 \n",
      "Validation phase: valid_loss: 0.00110 \n",
      "Epoch 78\n",
      "Training phase: train_loss: 0.00059 \n",
      "Validation phase: valid_loss: 0.00110 \n",
      "Epoch 79\n",
      "Training phase: train_loss: 0.00060 \n",
      "Validation phase: valid_loss: 0.00110 \n",
      "Epoch 80\n",
      "Training phase: train_loss: 0.00061 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 81\n",
      "Training phase: train_loss: 0.00061 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 82\n",
      "Training phase: train_loss: 0.00059 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 83\n",
      "Training phase: train_loss: 0.00060 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 84\n",
      "Training phase: train_loss: 0.00061 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 85\n",
      "Training phase: train_loss: 0.00058 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 86\n",
      "Training phase: train_loss: 0.00060 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 87\n",
      "Training phase: train_loss: 0.00060 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 88\n",
      "Training phase: train_loss: 0.00060 \n",
      "Validation phase: valid_loss: 0.00110 \n",
      "Epoch 89\n",
      "Training phase: train_loss: 0.00058 \n",
      "Validation phase: valid_loss: 0.00110 \n",
      "Epoch 90\n",
      "Training phase: train_loss: 0.00059 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 91\n",
      "Training phase: train_loss: 0.00060 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 92\n",
      "Training phase: train_loss: 0.00059 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 93\n",
      "Training phase: train_loss: 0.00060 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 94\n",
      "Training phase: train_loss: 0.00059 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 95\n",
      "Training phase: train_loss: 0.00058 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 96\n",
      "Training phase: train_loss: 0.00061 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 97\n",
      "Training phase: train_loss: 0.00059 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 98\n",
      "Training phase: train_loss: 0.00060 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 99\n",
      "Training phase: train_loss: 0.00057 \n",
      "Validation phase: valid_loss: 0.00111 \n",
      "Epoch 100\n",
      "Training phase: train_loss: 0.00059 \n",
      "Validation phase: valid_loss: 0.00111 \n"
     ]
    }
   ],
   "source": [
    "# model = MLPModel(4*window-1, [256, 128, 64], 1, activation=nn.Tanh())\n",
    "model = TModel(window, window-1, n_transformer_layers=1, kernel_size=3, stride=1, embedding_dim=8, n_head=2, mlp_dims=[1024, 256, 64])\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
    "scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=3, factor=0.5, verbose=True)\n",
    "loss_fn = nn.MSELoss()\n",
    "trainer = MSETrainer(model, optimizer, loss_fn, train_loader, scheduler, valid_dataloader=valid_loader, test_dataloader=test_loader, device=device)\n",
    "trainer.fit(100, model_name='Transformer_v1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# model.load_state_dict(torch.load('./weights/model_MLP_v1.pt'))\n",
    "model.load_state_dict(torch.load('./weights/model_Transformer_v1.pt'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4935,)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "residuals = []\n",
    "for x, y in train_loader:\n",
    "    x, y = x.to(device), y.to(device)\n",
    "    residual = y - model(x).squeeze()\n",
    "    residuals.append(torch.flatten(residual).cpu().detach().numpy())\n",
    "residuals = np.concatenate(residuals)\n",
    "residuals.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 0.0136, Min: -0.1785, Max: 0.2567, MAE: 0.0191, MSE: 0.0008, R^2: 0.8853\n"
     ]
    }
   ],
   "source": [
    "total_sum_of_squares = np.sum((train_data.iloc[window-1:]['vix'] - np.mean(train_data.iloc[window-1:]['vix'])) ** 2)\n",
    "print('Mean: {:.4f}, Min: {:.4f}, Max: {:.4f}, MAE: {:.4f}, MSE: {:.4f}, R^2: {:.4f}'.format(\n",
    "    np.mean(residuals), np.min(residuals), np.max(residuals), np.mean(np.abs(residuals)), np.mean(residuals**2), (1-np.sum(residuals**2)/total_sum_of_squares)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting out of sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>r1</th>\n",
       "      <th>r2</th>\n",
       "      <th>vix</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-01-04</th>\n",
       "      <td>0.003485</td>\n",
       "      <td>1.214534e-05</td>\n",
       "      <td>0.1350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-05</th>\n",
       "      <td>-0.000803</td>\n",
       "      <td>6.449659e-07</td>\n",
       "      <td>0.1313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-06</th>\n",
       "      <td>0.000739</td>\n",
       "      <td>5.454954e-07</td>\n",
       "      <td>0.1333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-09</th>\n",
       "      <td>0.000326</td>\n",
       "      <td>1.060104e-07</td>\n",
       "      <td>0.1252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-10</th>\n",
       "      <td>0.001845</td>\n",
       "      <td>3.402222e-06</td>\n",
       "      <td>0.1215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-23</th>\n",
       "      <td>0.011045</td>\n",
       "      <td>1.219894e-04</td>\n",
       "      <td>0.1720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-24</th>\n",
       "      <td>-0.013458</td>\n",
       "      <td>1.811171e-04</td>\n",
       "      <td>0.1568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-25</th>\n",
       "      <td>0.006718</td>\n",
       "      <td>4.513107e-05</td>\n",
       "      <td>0.1508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-28</th>\n",
       "      <td>0.006265</td>\n",
       "      <td>3.924546e-05</td>\n",
       "      <td>0.1445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-29</th>\n",
       "      <td>0.014508</td>\n",
       "      <td>2.104910e-04</td>\n",
       "      <td>0.1388</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7214 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  r1            r2     vix\n",
       "1995-01-04  0.003485  1.214534e-05  0.1350\n",
       "1995-01-05 -0.000803  6.449659e-07  0.1313\n",
       "1995-01-06  0.000739  5.454954e-07  0.1333\n",
       "1995-01-09  0.000326  1.060104e-07  0.1252\n",
       "1995-01-10  0.001845  3.402222e-06  0.1215\n",
       "...              ...           ...     ...\n",
       "2023-08-23  0.011045  1.219894e-04  0.1720\n",
       "2023-08-24 -0.013458  1.811171e-04  0.1568\n",
       "2023-08-25  0.006718  4.513107e-05  0.1508\n",
       "2023-08-28  0.006265  3.924546e-05  0.1445\n",
       "2023-08-29  0.014508  2.104910e-04  0.1388\n",
       "\n",
       "[7214 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = create_df_from_yf(spx_data, vix_data, train_dataset_start_date, test_end_date, predict_t_plus_1=predict_t_plus_1)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>r1</th>\n",
       "      <th>r2</th>\n",
       "      <th>vix</th>\n",
       "      <th>preds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-05-25</th>\n",
       "      <td>-0.000038</td>\n",
       "      <td>1.425558e-09</td>\n",
       "      <td>0.1220</td>\n",
       "      <td>0.117457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-05-26</th>\n",
       "      <td>-0.009346</td>\n",
       "      <td>8.734065e-05</td>\n",
       "      <td>0.1252</td>\n",
       "      <td>0.117239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-05-30</th>\n",
       "      <td>-0.000134</td>\n",
       "      <td>1.787330e-08</td>\n",
       "      <td>0.1285</td>\n",
       "      <td>0.117212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-05-31</th>\n",
       "      <td>0.018756</td>\n",
       "      <td>3.517689e-04</td>\n",
       "      <td>0.1221</td>\n",
       "      <td>0.117429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-06-01</th>\n",
       "      <td>0.000169</td>\n",
       "      <td>2.844782e-08</td>\n",
       "      <td>0.1298</td>\n",
       "      <td>0.117618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-23</th>\n",
       "      <td>0.011045</td>\n",
       "      <td>1.219894e-04</td>\n",
       "      <td>0.1720</td>\n",
       "      <td>0.152787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-24</th>\n",
       "      <td>-0.013458</td>\n",
       "      <td>1.811171e-04</td>\n",
       "      <td>0.1568</td>\n",
       "      <td>0.153021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-25</th>\n",
       "      <td>0.006718</td>\n",
       "      <td>4.513107e-05</td>\n",
       "      <td>0.1508</td>\n",
       "      <td>0.152807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-28</th>\n",
       "      <td>0.006265</td>\n",
       "      <td>3.924546e-05</td>\n",
       "      <td>0.1445</td>\n",
       "      <td>0.152295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-29</th>\n",
       "      <td>0.014508</td>\n",
       "      <td>2.104910e-04</td>\n",
       "      <td>0.1388</td>\n",
       "      <td>0.151039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7115 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  r1            r2     vix     preds\n",
       "1995-05-25 -0.000038  1.425558e-09  0.1220  0.117457\n",
       "1995-05-26 -0.009346  8.734065e-05  0.1252  0.117239\n",
       "1995-05-30 -0.000134  1.787330e-08  0.1285  0.117212\n",
       "1995-05-31  0.018756  3.517689e-04  0.1221  0.117429\n",
       "1995-06-01  0.000169  2.844782e-08  0.1298  0.117618\n",
       "...              ...           ...     ...       ...\n",
       "2023-08-23  0.011045  1.219894e-04  0.1720  0.152787\n",
       "2023-08-24 -0.013458  1.811171e-04  0.1568  0.153021\n",
       "2023-08-25  0.006718  4.513107e-05  0.1508  0.152807\n",
       "2023-08-28  0.006265  3.924546e-05  0.1445  0.152295\n",
       "2023-08-29  0.014508  2.104910e-04  0.1388  0.151039\n",
       "\n",
       "[7115 rows x 4 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = VIXDataset2(data, window)\n",
    "dataloader = torch.utils.data.DataLoader(dataset, batch_size=64, shuffle=False)\n",
    "preds = []\n",
    "for x, y in dataloader:\n",
    "    x, y = x.to(device), y.to(device)\n",
    "    preds.append(model(x).squeeze().cpu().detach().numpy())\n",
    "preds = np.concatenate(preds)\n",
    "df_preds = data[window-1:].copy()\n",
    "df_preds['preds'] = preds\n",
    "df_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x28a0945d610>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_preds[['vix', 'preds']].plot(figsize=(15, 10))\n",
    "plt.vlines(valid_start_date, 0, 0.9, linestyles='dashed', colors='orange')\n",
    "plt.vlines(test_start_date, 0, 0.9, linestyles='dashed', colors='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 0.0140, Min: -0.1931, Max: 0.3686, MAE: 0.0266, MSE: 0.0020, R^2: 0.7167\n"
     ]
    }
   ],
   "source": [
    "residuals = df_preds['vix'] - df_preds['preds']\n",
    "residuals = residuals[test_start_date:].values\n",
    "total_sum_of_squares = np.sum((data.loc[test_start_date:, 'vix'] - np.mean(data.loc[test_start_date:, 'vix'])) ** 2)\n",
    "print('Mean: {:.4f}, Min: {:.4f}, Max: {:.4f}, MAE: {:.4f}, MSE: {:.4f}, R^2: {:.4f}'.format(\n",
    "    np.mean(residuals), np.min(residuals), np.max(residuals), np.mean(np.abs(residuals)), np.mean(residuals**2), (1-np.sum(residuals**2)/total_sum_of_squares)))"
   ]
  }
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