thoutam/nse-lstm-model

NSE LSTM Model - Indian Stock Market Prediction

Overview

This is a comprehensive LSTM (Long Short-Term Memory) neural network model trained on 6.8 million records across 3,622 symbols from the National Stock Exchange (NSE) of India. The model covers data from 2004-2025 and provides stock price predictions based on technical indicators and historical patterns.

Model Details

• Architecture: LSTM with Dropout layers
• Input Shape: (batch_size, 5, 25) - 5 days × 25 features
• Output: Single prediction value for next day's price
• Training Data: 6,795,445 records across 3,622 symbols
• Features: OHLCV data + 20 technical indicators
• Model Size: 0.23 MB
• Parameters: 16,289

Features

• Price Data: OPEN, HIGH, LOW, CLOSE, VOLUME
• Technical Indicators:
  • Moving Averages (5, 10, 20, 50 day)
  • Bollinger Bands (20 day)
  • RSI (14 day)
  • MACD
  • Volume indicators (OBV, VPT)

Usage

Python

import tensorflow as tf
import pickle
import numpy as np

# Load model and scaler
model = tf.keras.models.load_model("nse_lstm_model.keras")
with open("nse_lstm_scaler.pkl", "rb") as f:
    scaler = pickle.load(f)

# Prepare input data (5 days × 25 features)
input_data = np.random.randn(1, 5, 25)  # Your normalized features here

# Make prediction
prediction = model.predict(input_data)
print(f"Predicted price change: {prediction[0][0]}")

Input Data Format

Your input should be normalized data with shape (batch_size, 5, 25):

• 5: Number of days (lookback period)
• 25: Number of features (OHLCV + technical indicators)

Output

The model outputs a single value representing the predicted price change/movement for the next day.

Data Sources

• NSE Bhavcopy: Daily equity data from 2004-2025
• Symbols: 3,622 unique equity symbols
• Frequency: Daily data points
• Coverage: All major Indian stocks

Performance

• Training MAE: 0.0216
• Validation MAE: 0.0217
• Memory Efficient: Processes large datasets with minimal memory usage
• Fast Inference: Optimized for real-time predictions

License

MIT License - Free for commercial and research use.

Citation

If you use this model in your research, please cite:

@software{nse_lstm_model,
  title={NSE LSTM Model - Indian Stock Market Prediction},
  author={Your Name},
  year={2025},
  url={https://huggingface.co/thoutam/nse-lstm-model}
}

Support

For questions or issues, please open an issue on the Hugging Face repository.