metadata
language:
- en
tags:
- finance
- stock-market
- lstm
- time-series
- prediction
- nse
- india
- technical-analysis
license: mit
datasets:
- nse-bhavcopy
metrics:
- mae
- mse
library_name: tensorflow
pipeline_tag: time-series-forecasting
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.