Results.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "from models import *\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sqlite3\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import plotly.express as px\n",
    "\n",
    "import dgl\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tqdm import tqdm\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading the Event Data and the Stock Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Connect to the SQLite database\n",
    "conn = sqlite3.connect('./data-src/final_data.db')\n",
    "\n",
    "# Replace 'your_table' with the actual table name and 'conn' with your database connection\n",
    "query = 'SELECT * FROM ALL_FEATURES'\n",
    "\n",
    "# Use the pandas read_sql function to read the data into a DataFrame\n",
    "events_df = pd.read_sql(query, conn)\n",
    "\n",
    "# Close the database connection\n",
    "conn.close()\n",
    "\n",
    "# Now, you can work with the DataFrame 'df'\n",
    "events_df['date'] = pd.to_datetime(events_df['date'])\n",
    "events_df = events_df.sort_values(by='date')\n",
    "stock_df = pd.read_csv('./data-src/stock_data.zip', compression='zip', header=[0, 1], index_col=0, parse_dates=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "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",
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       "    }\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>ticker</th>\n",
       "      <th>disease</th>\n",
       "      <th>stage</th>\n",
       "      <th>date</th>\n",
       "      <th>catalyst</th>\n",
       "      <th>avg_5_10</th>\n",
       "      <th>avg_5_30</th>\n",
       "      <th>std_5_10</th>\n",
       "      <th>std_5_30</th>\n",
       "      <th>mkt_cap</th>\n",
       "      <th>...</th>\n",
       "      <th>VOL_20</th>\n",
       "      <th>VOL_BVOL_20</th>\n",
       "      <th>avg_5_day_ret</th>\n",
       "      <th>std_5_day_ret</th>\n",
       "      <th>atr_10</th>\n",
       "      <th>volume_std_10</th>\n",
       "      <th>trans_std_10</th>\n",
       "      <th>prev_vol</th>\n",
       "      <th>prev_trans</th>\n",
       "      <th>NCAR_20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PCRX</td>\n",
       "      <td>Postsurgical pain</td>\n",
       "      <td>Approved</td>\n",
       "      <td>2011-10-31</td>\n",
       "      <td>Approved October 31, 2011.</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.461667</td>\n",
       "      <td>-0.256693</td>\n",
       "      <td>-0.271868</td>\n",
       "      <td>4.356451e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.057309</td>\n",
       "      <td>0.038122</td>\n",
       "      <td>-0.000519</td>\n",
       "      <td>0.037962</td>\n",
       "      <td>0.75990</td>\n",
       "      <td>167259.498603</td>\n",
       "      <td>382.814054</td>\n",
       "      <td>610058.00</td>\n",
       "      <td>1105.0</td>\n",
       "      <td>-1.041054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>INCY</td>\n",
       "      <td>Myelofibrosis</td>\n",
       "      <td>Approved</td>\n",
       "      <td>2011-11-16</td>\n",
       "      <td>Approved November 16, 2011.</td>\n",
       "      <td>-0.349</td>\n",
       "      <td>-1.483000</td>\n",
       "      <td>-0.260818</td>\n",
       "      <td>-1.000006</td>\n",
       "      <td>2.858325e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>0.039642</td>\n",
       "      <td>0.020804</td>\n",
       "      <td>-0.003871</td>\n",
       "      <td>0.010732</td>\n",
       "      <td>0.52645</td>\n",
       "      <td>556849.398882</td>\n",
       "      <td>1825.197219</td>\n",
       "      <td>702536.00</td>\n",
       "      <td>4864.0</td>\n",
       "      <td>0.754430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HALO</td>\n",
       "      <td>Overactive bladder</td>\n",
       "      <td>Approved</td>\n",
       "      <td>2011-12-08</td>\n",
       "      <td>Approved December 8, 2011.</td>\n",
       "      <td>0.341</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>-0.465475</td>\n",
       "      <td>-0.425801</td>\n",
       "      <td>1.283978e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025458</td>\n",
       "      <td>0.015331</td>\n",
       "      <td>-0.001556</td>\n",
       "      <td>0.010211</td>\n",
       "      <td>0.40365</td>\n",
       "      <td>388706.415339</td>\n",
       "      <td>1365.249193</td>\n",
       "      <td>441988.00</td>\n",
       "      <td>2404.0</td>\n",
       "      <td>3.427405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CRIS</td>\n",
       "      <td>Basal Cell Carcinoma</td>\n",
       "      <td>Approved</td>\n",
       "      <td>2012-01-30</td>\n",
       "      <td>Approved January 30, 2012.</td>\n",
       "      <td>16.200</td>\n",
       "      <td>43.900000</td>\n",
       "      <td>-4.322212</td>\n",
       "      <td>-12.690005</td>\n",
       "      <td>4.351435e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.031528</td>\n",
       "      <td>0.019181</td>\n",
       "      <td>0.024642</td>\n",
       "      <td>0.008263</td>\n",
       "      <td>17.53000</td>\n",
       "      <td>2242.228571</td>\n",
       "      <td>842.861436</td>\n",
       "      <td>5517.65</td>\n",
       "      <td>2349.0</td>\n",
       "      <td>-2.355933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CORT</td>\n",
       "      <td>Cushing's Syndrome</td>\n",
       "      <td>Approved</td>\n",
       "      <td>2012-02-17</td>\n",
       "      <td>Approved February 17, 2012.</td>\n",
       "      <td>-0.252</td>\n",
       "      <td>-0.435667</td>\n",
       "      <td>-0.199273</td>\n",
       "      <td>-0.173276</td>\n",
       "      <td>3.498192e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.112342</td>\n",
       "      <td>0.101775</td>\n",
       "      <td>0.011668</td>\n",
       "      <td>0.044609</td>\n",
       "      <td>0.25200</td>\n",
       "      <td>419064.493780</td>\n",
       "      <td>1502.998882</td>\n",
       "      <td>1456485.00</td>\n",
       "      <td>4305.0</td>\n",
       "      <td>-0.955556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  ticker               disease     stage       date  \\\n",
       "0   PCRX     Postsurgical pain  Approved 2011-10-31   \n",
       "1   INCY         Myelofibrosis  Approved 2011-11-16   \n",
       "2   HALO    Overactive bladder  Approved 2011-12-08   \n",
       "3   CRIS  Basal Cell Carcinoma  Approved 2012-01-30   \n",
       "4   CORT    Cushing's Syndrome  Approved 2012-02-17   \n",
       "\n",
       "                      catalyst  avg_5_10   avg_5_30  std_5_10   std_5_30  \\\n",
       "0   Approved October 31, 2011.     0.455   0.461667 -0.256693  -0.271868   \n",
       "1  Approved November 16, 2011.    -0.349  -1.483000 -0.260818  -1.000006   \n",
       "2   Approved December 8, 2011.     0.341   0.791667 -0.465475  -0.425801   \n",
       "3   Approved January 30, 2012.    16.200  43.900000 -4.322212 -12.690005   \n",
       "4  Approved February 17, 2012.    -0.252  -0.435667 -0.199273  -0.173276   \n",
       "\n",
       "        mkt_cap  ...    VOL_20  VOL_BVOL_20  avg_5_day_ret  std_5_day_ret  \\\n",
       "0  4.356451e+08  ...  0.057309     0.038122      -0.000519       0.037962   \n",
       "1  2.858325e+09  ...  0.039642     0.020804      -0.003871       0.010732   \n",
       "2  1.283978e+09  ...  0.025458     0.015331      -0.001556       0.010211   \n",
       "3  4.351435e+08  ...  0.031528     0.019181       0.024642       0.008263   \n",
       "4  3.498192e+08  ...  0.112342     0.101775       0.011668       0.044609   \n",
       "\n",
       "     atr_10  volume_std_10  trans_std_10    prev_vol  prev_trans   NCAR_20  \n",
       "0   0.75990  167259.498603    382.814054   610058.00      1105.0 -1.041054  \n",
       "1   0.52645  556849.398882   1825.197219   702536.00      4864.0  0.754430  \n",
       "2   0.40365  388706.415339   1365.249193   441988.00      2404.0  3.427405  \n",
       "3  17.53000    2242.228571    842.861436     5517.65      2349.0 -2.355933  \n",
       "4   0.25200  419064.493780   1502.998882  1456485.00      4305.0 -0.955556  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the Return Classes and Train and Test Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scale_X(train_X, test_X, special_exclude_cols=['catalyst', 'date', 'ticker', 'stage', 'disease']):\n",
    "    sc = MinMaxScaler()\n",
    "    tmp_train = train_X.loc[:, ~train_X.columns.isin(special_exclude_cols)]\n",
    "    tmp_cols = train_X.columns[~train_X.columns.isin(special_exclude_cols)]\n",
    "\n",
    "    tmp_test = test_X.loc[:, ~test_X.columns.isin(special_exclude_cols)]\n",
    "\n",
    "    train_features_scaled = sc.fit_transform(tmp_train)\n",
    "    test_features_scaled = sc.transform(tmp_test)\n",
    "\n",
    "    train_features_scaled_df = pd.DataFrame(train_features_scaled, columns = tmp_cols)\n",
    "    X_train_scaled = pd.concat([train_features_scaled_df, train_X[special_exclude_cols].reset_index(drop=True)], axis=1)\n",
    "\n",
    "    test_features_scaled_df = pd.DataFrame(test_features_scaled, columns = tmp_cols)\n",
    "    X_test_scaled = pd.concat([test_features_scaled_df, test_X[special_exclude_cols].reset_index(drop=True)], axis=1)\n",
    "\n",
    "    return X_train_scaled, X_test_scaled\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data handling, target_col is VOL_20, NCAR_20, RET_20, VOL_BVOL_20\n",
    "target_col = 'NCAR_20'\n",
    "exclude_cols = ['quintiles', 'NCAR_20', 'RET_20', 'VOL_20','VOL_BVOL_20']\n",
    "test_size = 0.2\n",
    "num_cuts = 3\n",
    "dh = Data_Handler(events_df)\n",
    "quintiles = dh.qcut_data(target_col, num_cuts)\n",
    "dh.df['quintiles'] = quintiles\n",
    "labels = [str(label) for label in quintiles.unique()].sort()\n",
    "X_train, X_test, y_train, y_test = dh.train_test_split('quintiles', cols2exclude=exclude_cols, test_size=test_size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_scaled, X_test_scaled = scale_X(train_X=X_train, test_X=X_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transferring tabular data into graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_graph(X, y):\n",
    "    # Extract features and labels from the DataFrame\n",
    "    feature_col = [i for i in X.columns if i not in exclude_cols + ['catalyst', 'date', 'ticker', 'stage', 'disease']]\n",
    "    features = torch.tensor(X[feature_col].values, dtype=torch.float32)\n",
    "    labels = torch.tensor(y.values, dtype=torch.float32).view(-1, 1)\n",
    "\n",
    "    # Map 'categorical' cols to indices\n",
    "    # Create a DGL graph from edges and node features using vectorized operations\n",
    "    catalyst_matrix = X['catalyst'].astype('category').cat.codes.values[:, None]\n",
    "    ticker_matrix = X['ticker'].astype('category').cat.codes.values[:, None]\n",
    "    disease_matrix = X['disease'].astype('category').cat.codes.values[:, None]\n",
    "    stage_matrix = X['stage'].astype('category').cat.codes.values[:, None]\n",
    "    date_matrix = pd.to_datetime(X['date']).values[:, None]\n",
    "\n",
    "    # Create condition matrices\n",
    "    ticker_condition = ticker_matrix == ticker_matrix.T\n",
    "    date_condition = np.abs(date_matrix - date_matrix.T) < np.timedelta64(365, 'D')\n",
    "    disease_condition = disease_matrix == disease_matrix.T\n",
    "    stage_condition = stage_matrix == stage_matrix.T\n",
    "\n",
    "    # Combine conditions to form the final condition matrix\n",
    "    condition_matrix = np.logical_or(np.logical_and(ticker_condition, date_condition),\n",
    "                                      np.logical_or(disease_condition, stage_condition))\n",
    "\n",
    "    # Create edges between nodes based on shared 'ticker', 'date', 'disease', or 'stage'\n",
    "    src_nodes, dst_nodes = np.where(condition_matrix & (catalyst_matrix != catalyst_matrix.T))\n",
    "\n",
    "    src_nodes = torch.tensor(src_nodes, dtype=torch.long)\n",
    "    dst_nodes = torch.tensor(dst_nodes, dtype=torch.long)\n",
    "\n",
    "    g = dgl.graph((src_nodes, dst_nodes))\n",
    "    g.ndata['features'] = features\n",
    "    g.ndata['labels'] = labels\n",
    "\n",
    "    return g\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_graph_network(g, filename):\n",
    "    # Convert DGL graph to networkx graph\n",
    "    nx_graph = g.to_networkx()\n",
    "\n",
    "    # Visualize the networkx graph\n",
    "    plt.figure(figsize=(16, 9))\n",
    "    pos = nx.spring_layout(nx_graph)  # You can choose a different layout if needed\n",
    "    nx.draw(nx_graph, pos, with_labels=True, node_color='skyblue', node_size=800, font_size=8, font_color='black', font_weight='bold', edge_color='gray', linewidths=0.5)\n",
    "\n",
    "    # Display the plot\n",
    "    plt.savefig(filename)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert data to DGL format\n",
    "train_graph = create_graph(X=X_train_scaled, y=y_train)\n",
    "test_graph = create_graph(X=X_test_scaled, y=y_test)\n",
    "example10_graph = create_graph(X=X_train_scaled.head(10), y=y_train.head(10))\n",
    "example100_graph = create_graph(X=X_train_scaled.head(100), y=y_train.head(100))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph(num_nodes=1832, num_edges=735974,\n",
      "      ndata_schemes={'features': Scheme(shape=(18,), dtype=torch.float32), 'labels': Scheme(shape=(1,), dtype=torch.float32)}\n",
      "      edata_schemes={})\n",
      "Graph(num_nodes=459, num_edges=36084,\n",
      "      ndata_schemes={'features': Scheme(shape=(18,), dtype=torch.float32), 'labels': Scheme(shape=(1,), dtype=torch.float32)}\n",
      "      edata_schemes={})\n",
      "Graph(num_nodes=10, num_edges=90,\n",
      "      ndata_schemes={'features': Scheme(shape=(18,), dtype=torch.float32), 'labels': Scheme(shape=(1,), dtype=torch.float32)}\n",
      "      edata_schemes={})\n",
      "Graph(num_nodes=100, num_edges=5382,\n",
      "      ndata_schemes={'features': Scheme(shape=(18,), dtype=torch.float32), 'labels': Scheme(shape=(1,), dtype=torch.float32)}\n",
      "      edata_schemes={})\n"
     ]
    }
   ],
   "source": [
    "print(train_graph)\n",
    "print(test_graph)\n",
    "print(example10_graph)\n",
    "print(example100_graph)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GCN(nn.Module):\n",
    "    def __init__(self, input_dim, hidden_dim, output_dim):\n",
    "        super(GCN, self).__init__()\n",
    "        self.layer1 = dgl.nn.GraphConv(input_dim, hidden_dim)\n",
    "        self.layer2 = dgl.nn.GraphConv(hidden_dim, hidden_dim)\n",
    "        self.fc1 = nn.Linear(hidden_dim, hidden_dim)\n",
    "        self.fc2 = nn.Linear(hidden_dim, output_dim)\n",
    "\n",
    "    def forward(self, g, features):\n",
    "        x = F.relu(self.layer1(g, features))\n",
    "        x = F.relu(self.layer2(g, x))\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = self.fc2(x)\n",
    "        return x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GCN(\n",
      "  (layer1): GraphConv(in=18, out=256, normalization=both, activation=None)\n",
      "  (layer2): GraphConv(in=256, out=256, normalization=both, activation=None)\n",
      "  (fc1): Linear(in_features=256, out_features=256, bias=True)\n",
      "  (fc2): Linear(in_features=256, out_features=3, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "#  Instantiate the model\n",
    "input_dim = X_train_scaled.shape[1] - 5  # Number of input features\n",
    "hidden_dim = 256\n",
    "output_dim = 3  # Binary classification\n",
    "# model = GCN(input_dim=input_dim, hidden_dim=hidden_dim, output_dim=output_dim)\n",
    "model = GCN(input_dim=input_dim, hidden_dim=hidden_dim, output_dim=output_dim)\n",
    "print(model)\n",
    "\n",
    "# Set up optimizer and loss function\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)\n",
    "criterion = nn.CrossEntropyLoss()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training:   0%|          | 1/1000 [00:00<09:39,  1.73epoch/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000, Loss: 1.0964733362197876\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training:  10%|█         | 101/1000 [00:35<04:06,  3.65epoch/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 101/1000, Loss: 1.086601972579956\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training:  20%|██        | 201/1000 [01:02<04:38,  2.87epoch/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 201/1000, Loss: 1.076674461364746\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training:  30%|███       | 301/1000 [01:35<04:02,  2.88epoch/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 301/1000, Loss: 1.0762220621109009\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training:  40%|████      | 401/1000 [02:04<02:35,  3.86epoch/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 401/1000, Loss: 1.0757590532302856\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training:  50%|█████     | 501/1000 [02:29<02:07,  3.92epoch/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 501/1000, Loss: 1.0752125978469849\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training:  60%|██████    | 601/1000 [02:55<01:42,  3.87epoch/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 601/1000, Loss: 1.0746238231658936\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training:  70%|███████   | 701/1000 [03:21<01:18,  3.79epoch/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 701/1000, Loss: 1.0740629434585571\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training:  80%|████████  | 801/1000 [03:47<00:50,  3.91epoch/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 801/1000, Loss: 1.0736074447631836\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training:  90%|█████████ | 901/1000 [04:13<00:26,  3.81epoch/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 901/1000, Loss: 1.0732157230377197\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: 100%|██████████| 1000/1000 [04:39<00:00,  3.58epoch/s]\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 1_000\n",
    "losses = []\n",
    "\n",
    "for epoch in tqdm(range(num_epochs), desc=\"Training\", unit=\"epoch\"):\n",
    "    model.train()\n",
    "    logits = model(train_graph, train_graph.ndata['features'])\n",
    "\n",
    "    # Use the appropriate format for multi-class labels\n",
    "    labels = torch.tensor(y_train.values, dtype=torch.long)  # Assuming y_train contains class indices\n",
    "\n",
    "    # Calculate the loss using CrossEntropyLoss\n",
    "    loss = criterion(logits, labels)\n",
    "\n",
    "    losses.append(loss.item())\n",
    "\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    if epoch % 100 == 0:\n",
    "        tqdm.write(f'Epoch {epoch + 1}/{num_epochs}, Loss: {loss.item()}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(losses, label='Cross Entropy Loss')\n",
    "plt.title(\"Cross Entropy Loss during Training\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluate the model on the test set\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    try:\n",
    "        test_logits = model(test_graph, test_graph.ndata['features'])\n",
    "    except:\n",
    "        test_graph_added_self_node = dgl.add_self_loop(test_graph)\n",
    "        test_logits = model(test_graph_added_self_node, test_graph_added_self_node.ndata['features'])\n",
    "\n",
    "    # For multi-class classification, use the argmax to get the predicted class\n",
    "    predicted_labels = torch.argmax(test_logits, dim=1).numpy()\n",
    "\n",
    "    # Optionally, you can calculate probabilities using softmax\n",
    "    probabilities = F.softmax(test_logits, dim=1).numpy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score, accuracy_score, precision_score, recall_score, f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_metrics(y_test, y_prob, y_pred, model_name):\n",
    "   auc = roc_auc_score(y_test, y_prob, multi_class='ovr')\n",
    "   accuracy = accuracy_score(y_test, y_pred)\n",
    "   precision = precision_score(y_test, y_pred, average='macro')\n",
    "   recall = recall_score(y_test, y_pred, average='macro')\n",
    "   f1 = f1_score(y_test, y_pred, average='macro')\n",
    "\n",
    "   results = {\n",
    "         'Model': model_name,\n",
    "         'AUC': auc,\n",
    "         'Accuracy': accuracy,\n",
    "         'Precision': precision,\n",
    "         'Recall': recall,\n",
    "         'F1 Score': f1\n",
    "   }\n",
    "\n",
    "   return pd.Series(results)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model             GNN\n",
      "AUC          0.604229\n",
      "Accuracy     0.420479\n",
      "Precision    0.396026\n",
      "Recall       0.402894\n",
      "F1 Score     0.395811\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(calculate_metrics(y_test=y_test, y_prob=probabilities, y_pred=predicted_labels, model_name='GNN'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extract Returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "actual_ret = events_df.iloc[-len(y_test):,16]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trading Strategy - GCN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_return(y_test,actual_ret):\n",
    "    positions = []\n",
    "    for i in range(len(y_test)):\n",
    "        if y_test[i] == 2:\n",
    "            positions.append(1)\n",
    "        if y_test[i] == 1:\n",
    "            positions.append(0)\n",
    "        if y_test[i] == 0:\n",
    "            positions.append(-1)\n",
    "    \n",
    "    return (1 + np.array(positions)*np.array(actual_ret)).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average return of GCN trading strategy with NCAR prediction: 0.9976688189972315\n"
     ]
    }
   ],
   "source": [
    "print(f'Average return of GCN trading strategy with NCAR prediction: {extract_return(predicted_labels,actual_ret)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the correlation matrix\n",
    "corr = events_df.loc[:,['RET_20','VOL_20', 'VOL_BVOL_20', 'NCAR_20',\n",
    "                        'bio_score', 'sec_score'\n",
    "                       ]].corr()\n",
    "\n",
    "# Generate a mask for the upper triangle\n",
    "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "plt.figure(figsize=(14, 12))\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(corr, mask=mask, cmap='coolwarm', vmax=.3, center=0,\n",
    "            square=True, linewidths=.5, annot=True, cbar_kws={\"shrink\": .5})\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the correlation matrix\n",
    "corr = events_df.iloc[:,5:-1].corr()\n",
    "\n",
    "# Generate a mask for the upper triangle\n",
    "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "plt.figure(figsize=(14, 12))\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(corr, mask=mask, cmap='coolwarm', vmax=.3, center=0,\n",
    "            square=True, linewidths=.5, annot=True, cbar_kws={\"shrink\": .5})\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/richi/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning:\n",
      "\n",
      "lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Create and fit the logistic regression model\n",
    "model = LR()\n",
    "model.train(X_train_scaled.iloc[:, :-5], y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = model.predict(X_test_scaled.iloc[:, :-5])\n",
    "y_prob = model.predict_prob(X_test_scaled.iloc[:, :-5])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model        Logistic Regression\n",
      "AUC                     0.598038\n",
      "Accuracy                0.442266\n",
      "Precision               0.436652\n",
      "Recall                  0.433393\n",
      "F1 Score                 0.43479\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(calculate_metrics(y_test=y_test, y_prob=y_prob, y_pred=y_pred, model_name='Logistic Regression'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lr_fit_metrics = model.results.calculate_metrics(y_test, y_prob, y_pred, \"Logistic Regression\")\n",
    "# model.results.print_confusion_matrix(y_test, y_pred, labels)\n",
    "# model.results.plot_roc_curve(y_test, y_prob, \"Logistic Regression\")\n",
    "# print(lr_fit_metrics)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average return of LR trading strategy with NCAR prediction: 1.0008788318311341\n"
     ]
    }
   ],
   "source": [
    "print(f'Average return of LR trading strategy with NCAR prediction: {extract_return(y_pred,actual_ret)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "trading_data = events_df.loc[X_test.index].reset_index(drop=True)\n",
    "trading_data['lr_preds'] = y_pred\n",
    "trading_data['lr_signal'] = np.where(y_pred > 2, 1, np.where(y_pred < 2, -1, 0))\n",
    "ts = Trading_Strategy()\n",
    "lr_portfolio = ts.buy_short('lr_signal', trading_data, stock_df, 20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
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   ],
   "source": [
    "px.line(np.array(lr_portfolio))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create and fit the XGBoost model\n",
    "model = GB()\n",
    "model.train(X_train_scaled.iloc[:, :-5], y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = model.predict(X_test_scaled.iloc[:, :-5])\n",
    "y_prob = model.predict_prob(X_test_scaled.iloc[:, :-5])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model         XGBoost\n",
      "AUC          0.599901\n",
      "Accuracy     0.420479\n",
      "Precision    0.404459\n",
      "Recall       0.403824\n",
      "F1 Score     0.402239\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(calculate_metrics(y_test=y_test, y_prob=y_prob, y_pred=y_pred, model_name='XGBoost'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average return of XGB trading strategy with NCAR prediction: 0.9940802408950986\n"
     ]
    }
   ],
   "source": [
    "print(f'Average return of XGB trading strategy with NCAR prediction: {extract_return(y_pred,actual_ret)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gb_fit_metrics = model.results.calculate_metrics(y_test, y_prob, y_pred, \"XGBoost Classifier\")\n",
    "# model.results.print_confusion_matrix(y_test, y_pred, labels)\n",
    "# model.results.plot_roc_curve(y_test, y_prob, \"XGBoost Classifier\")\n",
    "# print(gb_fit_metrics)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "trading_data['gb_preds'] = y_pred\n",
    "trading_data['gb_signal'] = np.where(y_pred > 2, 1, np.where(y_pred < 2, -1, 0))\n",
    "ts = Trading_Strategy()\n",
    "gb_portfolio = ts.buy_short('gb_signal', trading_data, stock_df, 20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
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   "source": [
    "px.line(np.array(gb_portfolio))\n"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MLP Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/richi/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning:\n",
      "\n",
      "Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Create and fit the Multi Layer Perceptron model\n",
    "model = MLP()\n",
    "model.train(X_train_scaled.iloc[:, :-5], y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = model.predict(X_test_scaled.iloc[:, :-5])\n",
    "y_prob = model.predict_prob(X_test_scaled.iloc[:, :-5])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model             MLP\n",
      "AUC          0.598933\n",
      "Accuracy     0.442266\n",
      "Precision    0.417798\n",
      "Recall       0.420959\n",
      "F1 Score       0.4189\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(calculate_metrics(y_test=y_test, y_prob=y_prob, y_pred=y_pred, model_name='MLP'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average return of MLP trading strategy with NCAR prediction: 0.9965528102056327\n"
     ]
    }
   ],
   "source": [
    "print(f'Average return of MLP trading strategy with NCAR prediction: {extract_return(y_pred,actual_ret)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp_fit_metrics = model.results.calculate_metrics(y_test, y_prob, y_pred, \"MLP Classifier\")\n",
    "model.results.print_confusion_matrix(y_test, y_pred, labels)\n",
    "model.results.plot_roc_curve(y_test, y_prob, \"MLP Classifier\")\n",
    "print(mlp_fit_metrics)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "trading_data['mlp_preds'] = y_pred\n",
    "trading_data['mlp_signal'] = np.where(y_pred > 2, 1, np.where(y_pred < 2, -1, 0))\n",
    "ts = Trading_Strategy()\n",
    "mlp_portfolio = ts.buy_short('mlp_signal', trading_data, stock_df, 20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
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     "metadata": {},
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   ],
   "source": [
    "px.line(np.array(mlp_portfolio))\n"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'RET_20 Prediction':['1.06%','1.07%','1.5%','0.3%'],\n",
    "       'NCAR_20 Prediction':['-0.03%','0.08%','-0.06%','-0.04%']}\n",
    "ret_df = pd.DataFrame(data)\n",
    "\n",
    "new_index = ['GCN', 'LogReg', 'XGB', 'MLP']\n",
    "ret_df.index = new_index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>LogReg</th>\n",
       "      <td>1.07%</td>\n",
       "      <td>0.08%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGB</th>\n",
       "      <td>1.5%</td>\n",
       "      <td>-0.06%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLP</th>\n",
       "      <td>0.3%</td>\n",
       "      <td>-0.04%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       RET_20 Prediction NCAR_20 Prediction\n",
       "GCN                1.06%             -0.03%\n",
       "LogReg             1.07%              0.08%\n",
       "XGB                 1.5%             -0.06%\n",
       "MLP                 0.3%             -0.04%"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ret_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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