VWAP_included_stock_price_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fa438f6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Linear Regression Model:\n",
      "RMSE: 1.6880694911329877, MAE: 0.9432271009469612, MAPE: 0.006084051219316342, Accuracy: 0.8282685512367491, Precision: 0.8607594936708861, Confusion Matrix:\n",
      "[[560  99]\n",
      " [144 612]], Recall: 0.8095238095238095, F1 Score: 0.834355828220859\n",
      "\n",
      "SVR Model:\n",
      "RMSE: 139.76497837705972, MAE: 80.8494314698498, MAPE: 0.5135323618594273, Accuracy: 0.9992932862190813, Precision: 1.0, Confusion Matrix:\n",
      "[[738   0]\n",
      " [  1 676]], Recall: 0.9985228951255539, F1 Score: 0.999260901699926\n",
      "\n",
      "Random Forest Model:\n",
      "RMSE: 2.2696019969365957, MAE: 1.2959525326831525, MAPE: 0.008225147954228209, Accuracy: 0.8600706713780919, Precision: 0.8626292466765141, Confusion Matrix:\n",
      "[[633  93]\n",
      " [105 584]], Recall: 0.8476052249637155, F1 Score: 0.8550512445095169\n",
      "\n",
      "Gradient Boosting Model:\n",
      "RMSE: 2.685918356233534, MAE: 1.69115479259762, MAPE: 0.011694869673820272, Accuracy: 0.8989399293286219, Precision: 0.890961262553802, Confusion Matrix:\n",
      "[[651  76]\n",
      " [ 67 621]], Recall: 0.9026162790697675, F1 Score: 0.8967509025270758\n",
      "\n",
      "XGBoost Model:\n",
      "RMSE: 2.804661454159519, MAE: 1.5301211794416443, MAPE: 0.010211801526489423, Accuracy: 0.9003533568904594, Precision: 0.9134751773049645, Confusion Matrix:\n",
      "[[630  61]\n",
      " [ 80 644]], Recall: 0.8895027624309392, F1 Score: 0.9013296011196641\n",
      "\n",
      "AdaBoost Model:\n",
      "RMSE: 9.866286038032165, MAE: 8.043651625035126, MAPE: 0.19044334186374637, Accuracy: 0.995053003533569, Precision: 0.9920948616600791, Confusion Matrix:\n",
      "[[906   4]\n",
      " [  3 502]], Recall: 0.994059405940594, F1 Score: 0.9930761622156281\n",
      "\n",
      "Decision Tree Model:\n",
      "RMSE: 3.090476936764667, MAE: 1.6747161107176924, MAPE: 0.010524837872635435, Accuracy: 0.8678445229681979, Precision: 0.874826147426982, Confusion Matrix:\n",
      "[[599  90]\n",
      " [ 97 629]], Recall: 0.8663911845730028, F1 Score: 0.8705882352941178\n",
      "\n",
      "KNeighbors Model:\n",
      "RMSE: 3.567538180054403, MAE: 2.177264935594977, MAPE: 0.017010749895821846, Accuracy: 0.9215547703180212, Precision: 0.9369369369369369, Confusion Matrix:\n",
      "[[680  42]\n",
      " [ 69 624]], Recall: 0.9004329004329005, F1 Score: 0.9183222958057395\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import dependencies\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, accuracy_score, precision_score, confusion_matrix, recall_score, f1_score\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import xgboost as xgb\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "# Load the dataset\n",
    "df = pd.read_csv('SBIN.csv')\n",
    "\n",
    "# Data preprocessing\n",
    "df.drop(['Date', 'Adj Close'], axis=1, inplace=True)  # Drop irrelevant columns\n",
    "\n",
    "# Handle missing values\n",
    "imputer = SimpleImputer(strategy='mean')\n",
    "df = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)\n",
    "\n",
    "# Calculate VWAP\n",
    "df['VWAP'] = (df['Close'] * df['Volume']).cumsum() / df['Volume'].cumsum()\n",
    "\n",
    "# Select features including VWAP and target variable\n",
    "X = df[['Open', 'High', 'Low', 'Volume', 'VWAP']]\n",
    "y = df['Close']\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Scale the features using Min-Max scaling\n",
    "scaler = MinMaxScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Function to train and evaluate models\n",
    "def train_and_evaluate_model(model, X_train, y_train, X_test, y_test, params=None):\n",
    "    if params:\n",
    "        model = GridSearchCV(model, params, scoring='neg_mean_squared_error', cv=5, n_jobs=-1)\n",
    "    model.fit(X_train, y_train)\n",
    "    predictions = model.predict(X_test)\n",
    "    \n",
    "    # Calculate evaluation metrics\n",
    "    rmse = np.sqrt(mean_squared_error(y_test, predictions))\n",
    "    mae = mean_absolute_error(y_test, predictions)\n",
    "    mape = mean_absolute_percentage_error(y_test, predictions)\n",
    "    accuracy = accuracy_score(y_test > predictions, y_test > predictions.round())\n",
    "    precision = precision_score(y_test > predictions, y_test > predictions.round())\n",
    "    confusion = confusion_matrix(y_test > predictions, y_test > predictions.round())\n",
    "    recall = recall_score(y_test > predictions, y_test > predictions.round())\n",
    "    f1 = f1_score(y_test > predictions, y_test > predictions.round())\n",
    "\n",
    "    # Print the evaluation metrics\n",
    "    print(f\"RMSE: {rmse}, MAE: {mae}, MAPE: {mape}, Accuracy: {accuracy}, Precision: {precision}, Confusion Matrix:\\n{confusion}, Recall: {recall}, F1 Score: {f1}\")\n",
    "\n",
    "    return model\n",
    "\n",
    "# Define models and their hyperparameters\n",
    "models_params = {\n",
    "    'Linear Regression': (LinearRegression(), None),\n",
    "    'SVR': (SVR(), {'C': [0.1, 1], 'epsilon': [0.01, 0.1, 0.5], 'kernel': ['sigmoid']}),\n",
    "    'Random Forest': (RandomForestRegressor(), None),\n",
    "    'Gradient Boosting': (GradientBoostingRegressor(), None),\n",
    "    'XGBoost': (xgb.XGBRegressor(), None),\n",
    "    'AdaBoost': (AdaBoostRegressor(), None),\n",
    "    'Decision Tree': (DecisionTreeRegressor(), None),\n",
    "    'KNeighbors': (KNeighborsRegressor(), {'n_neighbors': [3, 5, 7, 9, 11, 15, 20, 23, 25, 30, 60, 70, 150]})\n",
    "}\n",
    "\n",
    "# Train and evaluate all models\n",
    "for name, (model, params) in models_params.items():\n",
    "    print(f\"\\n{name} Model:\")\n",
    "    train_and_evaluate_model(model, X_train_scaled, y_train, X_test_scaled, y_test, params)\n",
    "\n",
    "# KNN Hyperparameter Tuning\n",
    "def knn_hyperparameter_tuning(X_train, y_train):\n",
    "    k_values = range(1, 31)  # Example range for k\n",
    "    mse_values = []\n",
    "    \n",
    "    for k in k_values:\n",
    "        knn_model = KNeighborsRegressor(n_neighbors=k)\n",
    "        mse = -cross_val_score(knn_model, X_train, y_train, cv=5, scoring='neg_mean_squared_error').mean()\n",
    "        mse_values.append(mse)\n",
    "    \n",
    "    return k_values, mse_values\n",
    "\n",
    "# Perform KNN Hyperparameter Tuning\n",
    "k_values, mse_values = knn_hyperparameter_tuning(X_train_scaled, y_train)\n",
    "\n",
    "# Plotting the results of KNN Hyperparameter Tuning\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(k_values, mse_values, marker='o', linestyle='-')\n",
    "plt.title('KNN Hyperparameter Tuning: MSE vs. Number of Neighbors')\n",
    "plt.xlabel('Number of Neighbors (k)')\n",
    "plt.ylabel('Mean Squared Error (MSE)')\n",
    "plt.xticks(k_values)  # Show all k values on x-axis\n",
    "plt.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1adb6df",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
   "name": "python3"
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   "file_extension": ".py",
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