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--- |
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language: |
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- en |
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tags: |
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- finance |
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- stock-market |
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- lstm |
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- time-series |
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- prediction |
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- nse |
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- india |
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- technical-analysis |
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license: mit |
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datasets: |
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- nse-bhavcopy |
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metrics: |
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- mae |
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- mse |
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library_name: tensorflow |
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pipeline_tag: time-series-forecasting |
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--- |
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# NSE LSTM Model - Indian Stock Market Prediction |
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## Overview |
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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. |
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## Model Details |
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- **Architecture**: LSTM with Dropout layers |
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- **Input Shape**: (batch_size, 5, 25) - 5 days × 25 features |
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- **Output**: Single prediction value for next day's price |
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- **Training Data**: 6,795,445 records across 3,622 symbols |
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- **Features**: OHLCV data + 20 technical indicators |
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- **Model Size**: 0.23 MB |
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- **Parameters**: 16,289 |
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## Features |
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- **Price Data**: OPEN, HIGH, LOW, CLOSE, VOLUME |
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- **Technical Indicators**: |
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- Moving Averages (5, 10, 20, 50 day) |
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- Bollinger Bands (20 day) |
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- RSI (14 day) |
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- MACD |
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- Volume indicators (OBV, VPT) |
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## Usage |
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### Python |
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```python |
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import tensorflow as tf |
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import pickle |
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import numpy as np |
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# Load model and scaler |
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model = tf.keras.models.load_model("nse_lstm_model.keras") |
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with open("nse_lstm_scaler.pkl", "rb") as f: |
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scaler = pickle.load(f) |
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# Prepare input data (5 days × 25 features) |
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input_data = np.random.randn(1, 5, 25) # Your normalized features here |
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# Make prediction |
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prediction = model.predict(input_data) |
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print(f"Predicted price change: {prediction[0][0]}") |
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``` |
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### Input Data Format |
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Your input should be normalized data with shape (batch_size, 5, 25): |
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- **5**: Number of days (lookback period) |
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- **25**: Number of features (OHLCV + technical indicators) |
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### Output |
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The model outputs a single value representing the predicted price change/movement for the next day. |
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## Data Sources |
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- **NSE Bhavcopy**: Daily equity data from 2004-2025 |
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- **Symbols**: 3,622 unique equity symbols |
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- **Frequency**: Daily data points |
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- **Coverage**: All major Indian stocks |
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## Performance |
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- **Training MAE**: 0.0216 |
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- **Validation MAE**: 0.0217 |
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- **Memory Efficient**: Processes large datasets with minimal memory usage |
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- **Fast Inference**: Optimized for real-time predictions |
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## License |
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MIT License - Free for commercial and research use. |
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## Citation |
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If you use this model in your research, please cite: |
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``` |
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@software{nse_lstm_model, |
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title={NSE LSTM Model - Indian Stock Market Prediction}, |
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author={Your Name}, |
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year={2025}, |
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url={https://huggingface.co/thoutam/nse-lstm-model} |
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} |
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``` |
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## Support |
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For questions or issues, please open an issue on the Hugging Face repository. |
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