Stock Price Prediction Using Yfinance, LSTM, and RNN

Business Context

Accurate stock price prediction plays a pivotal role in financial markets, influencing investment decisions, risk management, and portfolio optimization. This project leverages recurrent neural networks (RNNs) and long short-term memory (LSTM) networks for stock price prediction, showcasing the application of deep learning techniques in financial forecasting.

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Practical Application

Stock price prediction benefits a wide range of stakeholders, including:

• Investors: Make informed investment decisions and identify opportunities.
• Traders: Adapt strategies to anticipated price movements.
• Financial Analysts: Analyze market trends to provide actionable insights.

By accurately forecasting stock prices, stakeholders can manage risks and optimize portfolios more effectively.

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Challenges and Limitations

Stock price prediction is inherently challenging due to:

• Market Volatility: Unpredictable price fluctuations driven by external events.
• Data Noise: Irregularities and inconsistencies in financial data.
• External Factors: Global events, policy changes, and other variables impacting market behavior.

This project acknowledges these challenges and aims to highlight how RNNs and LSTMs address temporal dependencies in financial data.

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Objective

The project aims to achieve the following outcomes:

• Improved forecasting accuracy of stock prices.
• Effective use of RNN and LSTM networks.
• Insights into how deep learning models capture temporal dependencies in time series data.

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Data Description

The dataset consists of historical stock prices for Apple Inc. (AAPL), sourced using the Yahoo Finance API. Key features include:

• Open: Opening price of the stock.
• Close: Closing price of the stock.
• High: Highest price for the trading day.
• Low: Lowest price for the trading day.
• Volume: Number of shares traded.

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Tech Stack

• Programming Language: Python
• Libraries:
  • Keras and TensorFlow for building and training neural networks.
  • Statsmodels for statistical analysis.
  • NumPy and Pandas for data manipulation.
  • yfinance for retrieving stock data.
  • pandas-datareader for accessing financial data.
  • pandas-ta for technical analysis indicators.

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Approach

1. Neural Network Basics

• Understand the concepts of RNNs and LSTMs for handling sequential data.

2. Load Stock Data

• Retrieve historical stock price data using Yahoo Finance API.

3. Data Preprocessing

• Scale and normalize stock price data.
• Create sliding windows for time series analysis.

4. RNN Model Development

• Build and train recurrent neural networks for stock price prediction.

5. LSTM Model Development

• Extend the analysis by implementing LSTM networks to capture long-term dependencies.

6. Incorporate Technical Indicators

• Use additional features, such as technical analysis indicators, for improved predictions.

7. Model Evaluation

• Assess the performance of the RNN and LSTM models using appropriate metrics.
• Generate sequences for forecasting.

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Modular Code Structure

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├── lib/                                   # Reference folder with original IPython notebooks.
├── ml_pipeline/                           # Folder with modular Python scripts.
│   ├── data_preparation.py                # Functions for data preprocessing.
│   ├── model_training.py                  # Functions for training RNN and LSTM models.
│   ├── evaluation.py                      # Functions for evaluating model performance.
│   ├── engine.py                          # Main script to execute the pipeline.
├── output/                                # Stores saved models and evaluation results.
├── requirements.txt                       # Lists all required libraries and their versions.
└── README.md                              # Project documentation.

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Getting Started

1. Clone the Repository

git clone <repository_url>
cd <repository_folder>

2. Install Dependencies

Install the required Python libraries using:

pip install -r requirements.txt

3. Run the Project

Execute the pipeline by running the engine.py script:

python ml_pipeline/engine.py

4. Explore Results

• Review model outputs and predictions in the output/ folder.
• Analyze the evaluation metrics for model performance.

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Results

• Forecasting Accuracy:
  • RNN and LSTM models effectively captured temporal patterns in stock prices.
• Technical Indicator Integration:
  • Enhanced predictions by incorporating additional features.
• Deep Learning Insights:
  • Demonstrated how RNNs and LSTMs model sequential dependencies in financial data.

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Contributing

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a feature branch:

git checkout -b feature-name

3. Commit your changes:

git commit -m "Add feature"

4. Push your branch:

git push origin feature-name

5. Open a pull request.

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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Contact

For any questions or suggestions, please reach out to:

• Name: Abhinav Navneet
• Email: [email protected]
• GitHub: AjNavneet

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Acknowledgments

Special thanks to:

• Keras and TensorFlow for providing robust deep learning frameworks.
• Yahoo Finance API for stock data retrieval.
• Statsmodels and pandas-ta for technical analysis.
• The Python open-source community for their excellent tools and libraries.

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