{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "77e01b58-afc9-4f43-ab3b-0ad337887c83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [10/200], Loss: 0.000509\n",
      "Epoch [20/200], Loss: 0.000356\n",
      "Epoch [30/200], Loss: 0.000368\n",
      "Epoch [40/200], Loss: 0.000402\n",
      "Epoch [50/200], Loss: 0.000538\n",
      "Epoch [60/200], Loss: 0.000405\n",
      "Epoch [70/200], Loss: 0.000425\n",
      "Epoch [80/200], Loss: 0.000558\n",
      "Epoch [90/200], Loss: 0.000572\n",
      "Epoch [100/200], Loss: 0.000512\n",
      "Epoch [110/200], Loss: 0.000549\n",
      "Epoch [120/200], Loss: 0.000596\n",
      "Epoch [130/200], Loss: 0.000553\n",
      "Epoch [140/200], Loss: 0.000764\n",
      "Epoch [150/200], Loss: 0.000707\n",
      "Epoch [160/200], Loss: 0.000598\n",
      "Epoch [170/200], Loss: 0.000684\n",
      "Epoch [180/200], Loss: 0.000750\n",
      "Epoch [190/200], Loss: 0.000750\n",
      "Epoch [200/200], Loss: 0.000812\n",
      "MAE: 9.1560\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "import matplotlib.pyplot as plt\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "\n",
    "# =========================\n",
    "# 1. HYPERPARAMETERS BLOCK\n",
    "# =========================\n",
    "seq_length = 4           # Number of quarters in each sequence\n",
    "hidden_size = 32         # LSTM hidden units\n",
    "num_layers = 2            # LSTM layers\n",
    "dropout = 0.4              # Dropout rate\n",
    "input_size = 1             # Features per time step\n",
    "lr = 0.0005               # Learning rate\n",
    "EPOCHS = 200              # Training epochs\n",
    "BATCH_SIZE = 16           # Mini-batch size\n",
    "TARGET_COL = \"Stock Return\"  # Target column name\n",
    "# =========================\n",
    "\n",
    "# =========================\n",
    "# 2. LOAD & PREPROCESS DATA\n",
    "# =========================\n",
    "df = pd.read_csv(\"datasetTF.csv\")\n",
    "\n",
    "# Combine Year + Quarter into datetime\n",
    "df[\"Date\"] = pd.PeriodIndex(\n",
    "    df[\"Year\"].astype(str) + \"Q\" + df[\"Quarter\"].astype(str),\n",
    "    freq=\"Q\"\n",
    ").to_timestamp()\n",
    "\n",
    "# Sort by date\n",
    "df = df.sort_values(\"Date\").reset_index(drop=True)\n",
    "\n",
    "# Scale target values\n",
    "scaler = MinMaxScaler()\n",
    "scaled_values = scaler.fit_transform(df[[TARGET_COL]])\n",
    "\n",
    "# Create sequences\n",
    "def create_sequences(data, seq_length):\n",
    "    xs, ys = [], []\n",
    "    for i in range(len(data) - seq_length):\n",
    "        x = data[i:(i + seq_length)]\n",
    "        y = data[i + seq_length]\n",
    "        xs.append(x)\n",
    "        ys.append(y)\n",
    "    return np.array(xs), np.array(ys)\n",
    "\n",
    "X, y = create_sequences(scaled_values, seq_length)\n",
    "\n",
    "# Convert to tensors\n",
    "X_tensor = torch.tensor(X, dtype=torch.float32)\n",
    "y_tensor = torch.tensor(y, dtype=torch.float32)\n",
    "\n",
    "# Train-test split (time-series safe)\n",
    "train_size = int(len(X_tensor) * 0.8)\n",
    "X_train, X_test = X_tensor[:train_size], X_tensor[train_size:]\n",
    "y_train, y_test = y_tensor[:train_size], y_tensor[train_size:]\n",
    "\n",
    "# DataLoader for training\n",
    "train_dataset = TensorDataset(X_train, y_train)\n",
    "train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=False)\n",
    "\n",
    "# =========================\n",
    "# 3. DEFINE LSTM MODEL\n",
    "# =========================\n",
    "class LSTMModel(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, num_layers, dropout):\n",
    "        super(LSTMModel, self).__init__()\n",
    "        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, dropout=dropout)\n",
    "        self.fc = nn.Linear(hidden_size, 1)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        out, _ = self.lstm(x)\n",
    "        out = self.fc(out[:, -1, :])  # Last time step\n",
    "        return out\n",
    "\n",
    "model = LSTMModel(input_size, hidden_size, num_layers, dropout)\n",
    "criterion = nn.HuberLoss(delta=1.0)  # robust to outliers\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr)\n",
    "\n",
    "# =========================\n",
    "# 4. TRAIN MODEL\n",
    "# =========================\n",
    "for epoch in range(EPOCHS):\n",
    "    model.train()\n",
    "    for X_batch, y_batch in train_loader:\n",
    "        optimizer.zero_grad()\n",
    "        output = model(X_batch)\n",
    "        loss = criterion(output, y_batch)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    if (epoch+1) % 10 == 0:\n",
    "        print(f\"Epoch [{epoch+1}/{EPOCHS}], Loss: {loss.item():.6f}\")\n",
    "\n",
    "# =========================\n",
    "# 5. PREDICTION & METRICS\n",
    "# =========================\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    y_pred = model(X_test)\n",
    "\n",
    "# Inverse scale\n",
    "y_test_inv = scaler.inverse_transform(y_test.numpy())\n",
    "y_pred_inv = scaler.inverse_transform(y_pred.numpy())\n",
    "\n",
    "# Metrics\n",
    "mae = mean_absolute_error(y_test_inv, y_pred_inv)\n",
    "#direction_acc = np.mean(np.sign(y_test_inv) == np.sign(y_pred_inv))\n",
    "print(f\"MAE: {mae:.4f}\")\n",
    "#print(f\"Directional Accuracy: {direction_acc*100:.2f}%\")\n",
    "\n",
    "# Predicted vs Actual index plot\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(y_test_inv, label='Actual')\n",
    "plt.plot(y_pred_inv, label='Predicted')\n",
    "plt.title('LSTM Evaluation')\n",
    "plt.xlabel('Sample Index')\n",
    "plt.ylabel('Stock Return')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bb374387-25bb-43fe-8488-9f11391de30e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100 | Val Loss: 0.0035 | Val Corr: -0.0123 | Val DirAcc: 100.00%\n",
      "Epoch 2/100 | Val Loss: 0.0041 | Val Corr: -0.0124 | Val DirAcc: 100.00%\n",
      "Epoch 3/100 | Val Loss: 0.0040 | Val Corr: -0.0135 | Val DirAcc: 100.00%\n",
      "Epoch 4/100 | Val Loss: 0.0040 | Val Corr: -0.0144 | Val DirAcc: 100.00%\n",
      "Epoch 5/100 | Val Loss: 0.0038 | Val Corr: -0.0152 | Val DirAcc: 100.00%\n",
      "Epoch 6/100 | Val Loss: 0.0037 | Val Corr: -0.0160 | Val DirAcc: 100.00%\n",
      "Epoch 7/100 | Val Loss: 0.0037 | Val Corr: -0.0165 | Val DirAcc: 100.00%\n",
      "Epoch 8/100 | Val Loss: 0.0036 | Val Corr: -0.0170 | Val DirAcc: 100.00%\n",
      "Epoch 9/100 | Val Loss: 0.0035 | Val Corr: -0.0172 | Val DirAcc: 100.00%\n",
      "Epoch 10/100 | Val Loss: 0.0036 | Val Corr: -0.0173 | Val DirAcc: 100.00%\n",
      "Epoch 11/100 | Val Loss: 0.0038 | Val Corr: -0.0173 | Val DirAcc: 100.00%\n",
      "Epoch 12/100 | Val Loss: 0.0035 | Val Corr: -0.0171 | Val DirAcc: 100.00%\n",
      "Epoch 13/100 | Val Loss: 0.0039 | Val Corr: -0.0170 | Val DirAcc: 100.00%\n",
      "Epoch 14/100 | Val Loss: 0.0036 | Val Corr: -0.0170 | Val DirAcc: 100.00%\n",
      "Epoch 15/100 | Val Loss: 0.0036 | Val Corr: -0.0170 | Val DirAcc: 100.00%\n",
      "Epoch 16/100 | Val Loss: 0.0035 | Val Corr: -0.0169 | Val DirAcc: 100.00%\n",
      "Epoch 17/100 | Val Loss: 0.0038 | Val Corr: -0.0170 | Val DirAcc: 100.00%\n",
      "Epoch 18/100 | Val Loss: 0.0035 | Val Corr: -0.0170 | Val DirAcc: 100.00%\n",
      "Epoch 19/100 | Val Loss: 0.0036 | Val Corr: -0.0171 | Val DirAcc: 100.00%\n",
      "Epoch 20/100 | Val Loss: 0.0035 | Val Corr: -0.0171 | Val DirAcc: 100.00%\n",
      "Epoch 21/100 | Val Loss: 0.0037 | Val Corr: -0.0172 | Val DirAcc: 100.00%\n",
      "Epoch 22/100 | Val Loss: 0.0036 | Val Corr: -0.0171 | Val DirAcc: 100.00%\n",
      "Epoch 23/100 | Val Loss: 0.0037 | Val Corr: -0.0172 | Val DirAcc: 100.00%\n",
      "Epoch 24/100 | Val Loss: 0.0035 | Val Corr: -0.0172 | Val DirAcc: 100.00%\n",
      "Epoch 25/100 | Val Loss: 0.0035 | Val Corr: -0.0172 | Val DirAcc: 100.00%\n",
      "Epoch 26/100 | Val Loss: 0.0035 | Val Corr: -0.0172 | Val DirAcc: 100.00%\n",
      "Epoch 27/100 | Val Loss: 0.0037 | Val Corr: -0.0173 | Val DirAcc: 100.00%\n",
      "Early stopping triggered\n",
      "Test MAE: 8.1125\n",
      "Test Pearson Corr: 0.1443\n",
      "Test Directional Accuracy: 50.48%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "import matplotlib.pyplot as plt\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "\n",
    "# =========================\n",
    "# 1. HYPERPARAMETERS BLOCK\n",
    "# =========================\n",
    "seq_length = 4\n",
    "hidden_size = 64      # smaller to avoid overfitting\n",
    "num_layers = 2        # simpler model for stability\n",
    "dropout = 0.1\n",
    "input_size = 1\n",
    "lr = 0.0003\n",
    "EPOCHS = 100\n",
    "BATCH_SIZE = 32\n",
    "PATIENCE = 15  # early stopping patience\n",
    "TARGET_COL = \"Stock Return\"\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "# =========================\n",
    "\n",
    "# =========================\n",
    "# 2. LOAD & PREPROCESS DATA\n",
    "# =========================\n",
    "df = pd.read_csv(\"datasetTF.csv\")\n",
    "\n",
    "# Combine Year + Quarter into datetime\n",
    "df[\"Date\"] = pd.PeriodIndex(\n",
    "    df[\"Year\"].astype(str) + \"Q\" + df[\"Quarter\"].astype(str),\n",
    "    freq=\"Q\"\n",
    ").to_timestamp()\n",
    "\n",
    "# Sort by date\n",
    "df = df.sort_values(\"Date\").reset_index(drop=True)\n",
    "\n",
    "# Scale target values\n",
    "scaler = MinMaxScaler()\n",
    "scaled_values = scaler.fit_transform(df[[TARGET_COL]])\n",
    "\n",
    "# Sequence creation function\n",
    "def create_sequences(data, seq_length):\n",
    "    xs, ys = [], []\n",
    "    for i in range(len(data) - seq_length):\n",
    "        xs.append(data[i:(i + seq_length)])\n",
    "        ys.append(data[i + seq_length])\n",
    "    return np.array(xs), np.array(ys)\n",
    "\n",
    "X, y = create_sequences(scaled_values, seq_length)\n",
    "\n",
    "# Convert to tensors\n",
    "X_tensor = torch.tensor(X, dtype=torch.float32)\n",
    "y_tensor = torch.tensor(y, dtype=torch.float32)\n",
    "\n",
    "# =========================\n",
    "# 3. SPLIT DATA (train/val/test)\n",
    "# =========================\n",
    "train_size = int(len(X_tensor) * 0.8)\n",
    "val_size = int(len(X_tensor) * 0.1)\n",
    "\n",
    "X_train, y_train = X_tensor[:train_size], y_tensor[:train_size]\n",
    "X_val, y_val = (\n",
    "    X_tensor[train_size:train_size+val_size],\n",
    "    y_tensor[train_size:train_size+val_size]\n",
    ")\n",
    "X_test, y_test = X_tensor[train_size+val_size:], y_tensor[train_size+val_size:]\n",
    "\n",
    "# DataLoaders\n",
    "train_dataset = TensorDataset(X_train, y_train)\n",
    "val_dataset = TensorDataset(X_val, y_val)\n",
    "test_dataset = TensorDataset(X_test, y_test)\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
    "val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False)\n",
    "test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False)\n",
    "\n",
    "# =========================\n",
    "# 4. DEFINE LSTM MODEL\n",
    "# =========================\n",
    "class LSTMModel(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, num_layers, dropout):\n",
    "        super().__init__()\n",
    "        self.lstm = nn.LSTM(input_size, hidden_size, num_layers,\n",
    "                            batch_first=True, dropout=dropout)\n",
    "        self.fc = nn.Linear(hidden_size, 1)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        out, _ = self.lstm(x)\n",
    "        return self.fc(out[:, -1, :])\n",
    "\n",
    "# Metrics\n",
    "def pearson_corr(y_true, y_pred):\n",
    "    vx = y_true - torch.mean(y_true)\n",
    "    vy = y_pred - torch.mean(y_pred)\n",
    "    corr = torch.sum(vx * vy) / (\n",
    "        torch.sqrt(torch.sum(vx ** 2)) * torch.sqrt(torch.sum(vy ** 2)) + 1e-8\n",
    "    )\n",
    "    return corr.item()\n",
    "\n",
    "def directional_accuracy(y_true, y_pred):\n",
    "    return (torch.sign(y_true) == torch.sign(y_pred)).float().mean().item()\n",
    "\n",
    "# =========================\n",
    "# 5. TRAINING LOOP WITH EARLY STOPPING\n",
    "# =========================\n",
    "model = LSTMModel(input_size, hidden_size, num_layers, dropout).to(device)\n",
    "criterion = nn.HuberLoss(delta=1.0)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
    "\n",
    "best_val_loss = float(\"inf\")\n",
    "patience_counter = 0\n",
    "\n",
    "for epoch in range(EPOCHS):\n",
    "    # Training\n",
    "    model.train()\n",
    "    for X_batch, y_batch in train_loader:\n",
    "        X_batch, y_batch = X_batch.to(device), y_batch.to(device)\n",
    "        optimizer.zero_grad()\n",
    "        output = model(X_batch)\n",
    "        loss = criterion(output, y_batch)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    \n",
    "    # Validation\n",
    "    model.eval()\n",
    "    val_losses, val_corrs, val_dirs = [], [], []\n",
    "    with torch.no_grad():\n",
    "        for X_batch, y_batch in val_loader:\n",
    "            X_batch, y_batch = X_batch.to(device), y_batch.to(device)\n",
    "            preds = model(X_batch)\n",
    "            val_losses.append(criterion(preds, y_batch).item())\n",
    "            val_corrs.append(pearson_corr(y_batch, preds))\n",
    "            val_dirs.append(directional_accuracy(y_batch, preds))\n",
    "    \n",
    "    avg_val_loss = np.mean(val_losses)\n",
    "    avg_val_corr = np.mean(val_corrs)\n",
    "    avg_val_dir = np.mean(val_dirs)\n",
    "    \n",
    "    print(f\"Epoch {epoch+1}/{EPOCHS} | Val Loss: {avg_val_loss:.4f} | \"\n",
    "          f\"Val Corr: {avg_val_corr:.4f} | Val DirAcc: {avg_val_dir*100:.2f}%\")\n",
    "    \n",
    "    # Early stopping check\n",
    "    if avg_val_loss < best_val_loss:\n",
    "        best_val_loss = avg_val_loss\n",
    "        torch.save(model.state_dict(), \"best_model.pth\")\n",
    "        patience_counter = 0\n",
    "    else:\n",
    "        patience_counter += 1\n",
    "        if patience_counter >= PATIENCE:\n",
    "            print(\"Early stopping triggered\")\n",
    "            break\n",
    "\n",
    "# =========================\n",
    "# 6. TEST EVALUATION\n",
    "# =========================\n",
    "model.load_state_dict(torch.load(\"best_model.pth\"))\n",
    "model.eval()\n",
    "test_preds, test_targets = [], []\n",
    "with torch.no_grad():\n",
    "    for X_batch, y_batch in test_loader:\n",
    "        X_batch, y_batch = X_batch.to(device), y_batch.to(device)\n",
    "        preds = model(X_batch)\n",
    "        test_preds.extend(preds.cpu().numpy())\n",
    "        test_targets.extend(y_batch.cpu().numpy())\n",
    "\n",
    "# Inverse scaling\n",
    "y_test_inv = scaler.inverse_transform(test_targets)\n",
    "y_pred_inv = scaler.inverse_transform(test_preds)\n",
    "\n",
    "# Metrics\n",
    "mae = mean_absolute_error(y_test_inv, y_pred_inv)\n",
    "corr = np.corrcoef(y_test_inv.flatten(), y_pred_inv.flatten())[0, 1]\n",
    "dir_acc = np.mean(np.sign(y_test_inv) == np.sign(y_pred_inv))\n",
    "\n",
    "print(f\"Test MAE: {mae:.4f}\")\n",
    "print(f\"Test Pearson Corr: {corr:.4f}\")\n",
    "print(f\"Test Directional Accuracy: {dir_acc*100:.2f}%\")\n",
    "\n",
    "# Predicted vs Actual index plot\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(y_test_inv, label='Actual')\n",
    "plt.plot(y_pred_inv, label='Predicted')\n",
    "plt.title('LSTM Evaluation')\n",
    "plt.xlabel('Sample Index')\n",
    "plt.ylabel('Stock Return')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0e9260a3-40f9-4477-8906-527a18cdd90d",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'dataset13.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mFileNotFoundError\u001b[39m                         Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[24]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msklearn\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpreprocessing\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m StandardScaler\n\u001b[32m      5\u001b[39m \u001b[38;5;66;03m#dataset\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m df = pd.read_csv(\u001b[33m\"\u001b[39m\u001b[33mdataset13.csv\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m      8\u001b[39m \u001b[38;5;66;03m#Fix column names\u001b[39;00m\n\u001b[32m      9\u001b[39m df.columns = df.columns.str.strip().str.replace(\u001b[33m'\u001b[39m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33m'\u001b[39m)\n",
      "\u001b[36mFile \u001b[39m\u001b[32mC:\\Anaconda3\\envs\\Assignment2\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1026\u001b[39m, in \u001b[36mread_csv\u001b[39m\u001b[34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[39m\n\u001b[32m   1013\u001b[39m kwds_defaults = _refine_defaults_read(\n\u001b[32m   1014\u001b[39m     dialect,\n\u001b[32m   1015\u001b[39m     delimiter,\n\u001b[32m   (...)\u001b[39m\u001b[32m   1022\u001b[39m     dtype_backend=dtype_backend,\n\u001b[32m   1023\u001b[39m )\n\u001b[32m   1024\u001b[39m kwds.update(kwds_defaults)\n\u001b[32m-> \u001b[39m\u001b[32m1026\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m _read(filepath_or_buffer, kwds)\n",
      "\u001b[36mFile \u001b[39m\u001b[32mC:\\Anaconda3\\envs\\Assignment2\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:620\u001b[39m, in \u001b[36m_read\u001b[39m\u001b[34m(filepath_or_buffer, kwds)\u001b[39m\n\u001b[32m    617\u001b[39m _validate_names(kwds.get(\u001b[33m\"\u001b[39m\u001b[33mnames\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[32m    619\u001b[39m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m620\u001b[39m parser = TextFileReader(filepath_or_buffer, **kwds)\n\u001b[32m    622\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[32m    623\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
      "\u001b[36mFile \u001b[39m\u001b[32mC:\\Anaconda3\\envs\\Assignment2\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1620\u001b[39m, in \u001b[36mTextFileReader.__init__\u001b[39m\u001b[34m(self, f, engine, **kwds)\u001b[39m\n\u001b[32m   1617\u001b[39m     \u001b[38;5;28mself\u001b[39m.options[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m] = kwds[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m   1619\u001b[39m \u001b[38;5;28mself\u001b[39m.handles: IOHandles | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1620\u001b[39m \u001b[38;5;28mself\u001b[39m._engine = \u001b[38;5;28mself\u001b[39m._make_engine(f, \u001b[38;5;28mself\u001b[39m.engine)\n",
      "\u001b[36mFile \u001b[39m\u001b[32mC:\\Anaconda3\\envs\\Assignment2\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1880\u001b[39m, in \u001b[36mTextFileReader._make_engine\u001b[39m\u001b[34m(self, f, engine)\u001b[39m\n\u001b[32m   1878\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[32m   1879\u001b[39m         mode += \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m-> \u001b[39m\u001b[32m1880\u001b[39m \u001b[38;5;28mself\u001b[39m.handles = get_handle(\n\u001b[32m   1881\u001b[39m     f,\n\u001b[32m   1882\u001b[39m     mode,\n\u001b[32m   1883\u001b[39m     encoding=\u001b[38;5;28mself\u001b[39m.options.get(\u001b[33m\"\u001b[39m\u001b[33mencoding\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[32m   1884\u001b[39m     compression=\u001b[38;5;28mself\u001b[39m.options.get(\u001b[33m\"\u001b[39m\u001b[33mcompression\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[32m   1885\u001b[39m     memory_map=\u001b[38;5;28mself\u001b[39m.options.get(\u001b[33m\"\u001b[39m\u001b[33mmemory_map\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m),\n\u001b[32m   1886\u001b[39m     is_text=is_text,\n\u001b[32m   1887\u001b[39m     errors=\u001b[38;5;28mself\u001b[39m.options.get(\u001b[33m\"\u001b[39m\u001b[33mencoding_errors\u001b[39m\u001b[33m\"\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33mstrict\u001b[39m\u001b[33m\"\u001b[39m),\n\u001b[32m   1888\u001b[39m     storage_options=\u001b[38;5;28mself\u001b[39m.options.get(\u001b[33m\"\u001b[39m\u001b[33mstorage_options\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[32m   1889\u001b[39m )\n\u001b[32m   1890\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m.handles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m   1891\u001b[39m f = \u001b[38;5;28mself\u001b[39m.handles.handle\n",
      "\u001b[36mFile \u001b[39m\u001b[32mC:\\Anaconda3\\envs\\Assignment2\\Lib\\site-packages\\pandas\\io\\common.py:873\u001b[39m, in \u001b[36mget_handle\u001b[39m\u001b[34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[39m\n\u001b[32m    868\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[32m    869\u001b[39m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[32m    870\u001b[39m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[32m    871\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m ioargs.encoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs.mode:\n\u001b[32m    872\u001b[39m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m873\u001b[39m         handle = \u001b[38;5;28mopen\u001b[39m(\n\u001b[32m    874\u001b[39m             handle,\n\u001b[32m    875\u001b[39m             ioargs.mode,\n\u001b[32m    876\u001b[39m             encoding=ioargs.encoding,\n\u001b[32m    877\u001b[39m             errors=errors,\n\u001b[32m    878\u001b[39m             newline=\u001b[33m\"\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m    879\u001b[39m         )\n\u001b[32m    880\u001b[39m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    881\u001b[39m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[32m    882\u001b[39m         handle = \u001b[38;5;28mopen\u001b[39m(handle, ioargs.mode)\n",
      "\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: 'dataset13.csv'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "#dataset\n",
    "df = pd.read_csv(\"datasetTF.csv\")\n",
    "\n",
    "#Fix column names\n",
    "df.columns = df.columns.str.strip().str.replace('\\t', '')\n",
    "\n",
    "#Convert types\n",
    "df['Return on Equity'] = pd.to_numeric(df['Return on Equity'], errors='coerce')\n",
    "\n",
    "#Sort by company and time\n",
    "df = df.sort_values(by=['CO_ID', 'Year', 'Quarter'])\n",
    "\n",
    "#Select features and target\n",
    "features = [\n",
    "    'Return on Assets', 'Return on Capital', 'Return on Equity',\n",
    "    'Return on Common Equity', 'Gross Profit Margin', 'EBITA Margin',\n",
    "    'EBIT Margin', 'Net Income Margin', 'Asset Turnover', 'Current Ratio',\n",
    "    'Debt / Equity', 'Long Term Debt / Captial', 'Book Value Per Share'\n",
    "]\n",
    "\n",
    "target = 'Stock Return'\n",
    "\n",
    "#Fill missing values per company\n",
    "df[features] = df.groupby('CO_ID')[features].transform(lambda g: g.ffill().bfill())\n",
    "\n",
    "# Normalize\n",
    "scaler = StandardScaler()\n",
    "df[features] = scaler.fit_transform(df[features])\n",
    "\n",
    "# Check if missing values remain\n",
    "df[features].isnull().sum()\n",
    "\n",
    "X_all, y_all = [], []\n",
    "window_size = 20\n",
    "target_offset = 1\n",
    "\n",
    "for _, group in df.groupby('CO_ID'):\n",
    "    X = group[features].values\n",
    "    y = group[target].values\n",
    "    for i in range(len(X) - window_size - target_offset + 1):\n",
    "        X_all.append(X[i:i+window_size])\n",
    "        y_all.append(y[i+window_size+target_offset-1])\n",
    "\n",
    "import torch\n",
    "X_tensor = torch.tensor(np.array(X_all), dtype=torch.float32)\n",
    "y_tensor = torch.tensor(np.array(y_all), dtype=torch.float32).view(-1, 1)\n",
    "\n",
    "\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "class FinancialDataset(Dataset):\n",
    "    def __init__(self, X, y):\n",
    "        self.X = X\n",
    "        self.y = y\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.X)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.X[idx], self.y[idx]\n",
    "\n",
    "dataset = FinancialDataset(X_tensor, y_tensor)\n",
    "train_loader = DataLoader(dataset, batch_size=16, shuffle=True)\n",
    "\n",
    "\n",
    "from torch.utils.data import random_split, DataLoader\n",
    "\n",
    "# Step 1: Split into training and validation sets\n",
    "total_size = len(dataset)\n",
    "train_size = int(0.8 * total_size)\n",
    "val_size = total_size - train_size\n",
    "\n",
    "train_dataset, val_dataset = random_split(dataset, [train_size, val_size])\n",
    "\n",
    "# Step 2: Create DataLoaders\n",
    "train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)\n",
    "val_loader = DataLoader(val_dataset, batch_size=16, shuffle=False)\n",
    "\n",
    "# --------------------------\n",
    "# HYPERPARAMETERS\n",
    "# --------------------------\n",
    "input_dim = 13         # Number of input features\n",
    "hidden_dim = 64        # LSTM hidden size\n",
    "num_layers = 2         # Number of LSTM layers\n",
    "dropout = 0.4          # Dropout rate\n",
    "learning_rate = 0.0005 # ← Try reducing from 0.001\n",
    "batch_size = 128        # Batch size for training\n",
    "num_epochs = 50        # Number of training epochs\n",
    "\n",
    "\n",
    "import torch.nn as nn\n",
    "\n",
    "class LSTMRegressor(nn.Module):\n",
    "    def __init__(self, input_dim, hidden_dim, num_layers, dropout):\n",
    "        super().__init__()\n",
    "        self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, dropout=dropout, batch_first=True)\n",
    "        self.fc = nn.Linear(hidden_dim, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        out, _ = self.lstm(x)\n",
    "        return self.fc(out[:, -1, :])  # last time step\n",
    "\n",
    "\n",
    "\n",
    "model = LSTMRegressor(input_dim, hidden_dim, num_layers, dropout)\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "629fc650-3253-4fa2-b2a4-e5fc2df39d55",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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