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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+BANKNIFTY_Option.csv filter=lfs diff=lfs merge=lfs -text

BANKNIFTY_Option.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8bad0f8d39a100144c444ac8640545c2bc1a51a19322482e26c6095dac7b5288
+size 35508486

app.py

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+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.metrics import mean_squared_error, r2_score
+from tensorflow.keras.models import load_model
+import streamlit as st
+
+# Step 1: Load the datasets
+def load_data():
+    options_path = 'BANKNIFTY_Option.csv'
+    futures_path = 'BANKNIFTY_Future.csv'
+    options_data = pd.read_csv(options_path)
+    futures_data = pd.read_csv(futures_path)
+    return options_data, futures_data
+
+# Step 2: Preprocessing and Merging
+def preprocess_data(options_data, futures_data):
+    options_data['lasttradetime'] = pd.to_datetime(options_data['lasttradetime'], unit='s')
+    futures_data['lasttradetime'] = pd.to_datetime(futures_data['lasttradetime'], unit='s')
+
+    # Merge datasets on lasttradetime
+    merged_data = pd.merge(options_data, futures_data, on='lasttradetime', how='inner', suffixes=('_options', '_futures'))
+
+    # Create new features
+    merged_data['price_diff'] = merged_data['close_options'] - merged_data['close_futures']
+    merged_data['volume_ratio'] = merged_data['tradedqty'] / merged_data['volume']
+    merged_data['openinterest_diff'] = merged_data['openinterest_options'] - merged_data['openinterest_futures']
+
+    # Drop unnecessary columns
+    merged_data = merged_data[['lasttradetime', 'close_options', 'price_diff', 'volume_ratio', 'openinterest_diff']]
+    merged_data = merged_data.set_index('lasttradetime')
+
+    return merged_data
+
+# Step 3: Load Pre-Trained Model and Scaler
+def load_trained_model_and_scaler():
+    model = load_model('banknifty_model.h5')
+    scaler = np.load('scaler.npy', allow_pickle=True).item()
+    return model, scaler
+
+# Step 4: Predict Future Prices
+def predict_future_prices(model, scaler, X, last_date, steps=5):
+    last_sequence = X[-1:, :, :]
+    future_forecast = []
+    future_dates = pd.date_range(start=last_date, periods=steps + 1, freq='5min')[1:]
+
+    for _ in range(steps):
+        next_pred = model.predict(last_sequence)[0, 0]
+        future_forecast.append(next_pred)
+        next_input = np.concatenate([last_sequence[:, 1:, :], np.array([[[next_pred, 0, 0, 0]]])], axis=1)
+        last_sequence = next_input
+
+    future_forecast_rescaled = scaler.inverse_transform(
+        np.hstack((np.array(future_forecast).reshape(-1, 1), np.zeros((steps, X.shape[2] - 1))))
+    )[:, 0]
+    return future_forecast_rescaled, future_dates
+
+# Step 5: Evaluate Model
+def evaluate_model(model, X_test, y_test, scaler):
+    y_pred = model.predict(X_test)
+    y_pred_rescaled = scaler.inverse_transform(
+        np.hstack((y_pred, np.zeros((y_pred.shape[0], X_test.shape[2] - 1))))
+    )[:, 0]
+    y_test_rescaled = scaler.inverse_transform(
+        np.hstack((y_test.reshape(-1, 1), np.zeros((y_test.shape[0], X_test.shape[2] - 1))))
+    )[:, 0]
+    
+    mse = mean_squared_error(y_test_rescaled, y_pred_rescaled)
+    r2 = r2_score(y_test_rescaled, y_pred_rescaled)
+    
+    # Calculate accuracy
+    accuracy = 1 - (np.abs(y_test_rescaled - y_pred_rescaled).mean() / y_test_rescaled.mean())
+    return mse, r2, accuracy
+
+# Streamlit App
+def main():
+    st.title("Bank Nifty Options & Futures Forecasting")
+
+    # Load and preprocess data
+    options_data, futures_data = load_data()
+    merged_data = preprocess_data(options_data, futures_data)
+
+    st.write("### Merged Dataset")
+    st.dataframe(merged_data.head(10))
+
+    # Feature Scaling
+    model, scaler = load_trained_model_and_scaler()
+    scaled_data = scaler.transform(merged_data)
+
+    # Create Sequences
+    time_steps = 72
+    X = np.array([scaled_data[i:i + time_steps] for i in range(len(scaled_data) - time_steps)])
+    y = scaled_data[time_steps:, 0]
+
+    # Evaluate Model
+    mse, r2, accuracy = evaluate_model(model, X, y, scaler)
+    st.write(f"### Model Evaluation")
+    st.write(f"Mean Squared Error (MSE): {mse:.2f}")
+    st.write(f"R² Score: {r2:.2f}")
+    st.write(f"Accuracy: {accuracy * 100:.2f}%")
+
+    # Predict Future Prices
+    st.write("### Predicting Future Prices...")
+    last_date = merged_data.index[-1]
+    steps = st.slider("Select Number of Future Steps to Predict", min_value=1, max_value=20, value=5)
+    future_prices, future_dates = predict_future_prices(model, scaler, X, last_date, steps)
+
+    st.write("### Predicted Future Prices")
+    for date, price in zip(future_dates, future_prices):
+        st.write(f"{date}: {price:.2f}")
+
+    # Plot Results
+    st.write("### Predicted Prices Plot")
+    fig, ax = plt.subplots(figsize=(12, 6))
+    ax.plot(future_dates, future_prices, marker='o', label="Predicted Prices")
+    ax.set_title("Future Predicted Prices")
+    ax.set_xlabel("Date")
+    ax.set_ylabel("Price")
+    ax.legend()
+    st.pyplot(fig)
+
+if __name__ == "__main__":
+    main()

banknifty_model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e3baa682d1b1fd7c8911331cd30a5b7ad0cd0c9d475a197206cd634fc69fbf69
+size 4277752

requirements.txt

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+pandas
+numpy
+matplotlib
+scikit-learn
+tensorflow
+streamlit

scaler.npy

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1d3b0aa02fd64ca0a63a88eb47366e80bd00444a2835531d4449aa95e318d94f
+size 1069