Harness the power of Long Short-Term Memory (LSTM) neural networks to predict stock market trends and forecast future prices. This project demonstrates the application of deep learning in financial time series analysis, offering insights into potential market movements.
- Advanced Time Series Analysis: Utilize LSTM networks to capture complex temporal dependencies in stock price data.
- Comprehensive Data Pipeline: From data collection to preprocessing, our robust pipeline ensures high-quality input for the model.
- Customizable Model Architecture: Easily adjust the LSTM layers and hyperparameters to optimize performance.
- Performance Visualization: Generate insightful plots comparing predicted vs. actual stock prices.
- Quantitative Evaluation: Assess model accuracy using industry-standard metrics like MSE, RMSE, and MAE.
- Data Acquisition: Fetch historical stock data (default: AMZN) using
yfinance, ensuring up-to-date and reliable financial information. - Feature Engineering: Calculate technical indicators like Moving Averages (MA) to enrich the dataset.
- Data Normalization: Apply MinMax scaling to standardize input features, crucial for neural network training.
- Sequence Generation: Create time-stepped sequences of stock prices, mimicking the temporal nature of financial markets.
- LSTM Model Construction: Design a deep LSTM architecture with dropout layers to prevent overfitting.
- Training and Validation: Implement a train-test split to ensure model generalization and prevent look-ahead bias.
- Future Price Prediction: Utilize the trained model to forecast upcoming stock price movements.
- Performance Analysis: Evaluate the model's predictive power using statistical measures and visual comparisons.
# Clone the repository
git clone https://github.com/yourusername/stock_prediction_lstm.git
# Navigate to the project directory
cd stock_prediction_lstm
# Create and activate a virtual environment
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
# Install dependencies
pip install -r requirements.txtExecute the main script to run the entire pipeline:
python stock_prediction_lstm.py- numpy
- pandas
- matplotlib
- yfinance
- scikit-learn
- PyTorch
For a complete list of dependencies, refer to requirements.txt.
Our LSTM model consists of:
- Multiple stacked LSTM layers for capturing hierarchical patterns
- Dropout layers for regularization and overfitting prevention
- Dense layer for final prediction output
The project generates various plots, including:
- Historical stock prices with moving averages
- Training and validation loss curves
- Predicted vs. actual stock prices
Customize the model by adjusting these parameters in stock_prediction_lstm.py:
STOCK_SYMBOL: Change the target stock (default: 'AMZN')SEQUENCE_LENGTH: Modify the input sequence lengthEPOCHSandBATCH_SIZE: Fine-tune the training process
- Incorporate sentiment analysis from financial news
- Implement ensemble methods for improved accuracy
- Add support for multi-variate time series input
This project is for educational purposes only. Always consult with a financial advisor before making investment decisions based on any predictive model.
Eakansh - GitHub Profile
- Thanks to the open-source community for the invaluable tools and libraries
- Inspired by cutting-edge research in financial technology and deep learning
Stock Prediction, LSTM, Deep Learning, Time Series Analysis, Financial Forecasting, PyTorch, Python, Data Science, Machine Learning, Algorithmic Trading