Market Regime Detection

[alo7lika]

Is your feature request related to a problem? Please describe.
The problem relates to the difficulty in identifying and adapting to different market conditions (bull, bear, or neutral) for better trading or investment decision-making. Without an automated mechanism to detect these regimes, investors may miss opportunities or take on unnecessary risks due to market changes.

Describe the solution you'd like
I would like a model that can automatically detect different market regimes based on historical stock price movements and market indicators. This model should categorize historical data into bull, bear, and neutral markets using clustering techniques and provide a means to predict future market conditions based on recent patterns.

Describe alternatives you've considered

• Simple Threshold Approach: Defining bull and bear markets using simple thresholds based on price movement (e.g., a drop of 20% indicates a bear market). However, this approach lacks flexibility and does not account for market volatility and other influencing factors.
• Rule-Based Algorithms: Using rule-based algorithms that rely on technical indicators like RSI or moving averages to define market regimes. While useful, they may not adapt well to different market dynamics and might not capture regime shifts accurately.

Approach to be followed (optional)

• Data Collection: Gather historical stock price data and economic indicators using APIs such as Yahoo Finance.
• Feature Engineering: Calculate features like daily returns, moving averages, RSI, and volatility measures.
• Data Preprocessing: Normalize features and handle missing data to prepare for clustering analysis.
• Clustering: Implement clustering algorithms (e.g., K-means, DBSCAN) to classify historical price movements into market regimes.
• Regime Labeling: Analyze clusters to label them as bull, bear, or neutral markets based on average returns and volatility.
• Model Training: Train a supervised learning model (e.g., logistic regression) or a regime-switching model to predict future market regimes based on current features.
• Backtesting and Validation: Backtest the model using historical data to ensure its robustness and accuracy.
• Real-Time Updates: Develop a pipeline for the model to update with new data and provide real-time regime predictions continuously.

Additional context
This model would give investors a powerful tool for making data-driven decisions by detecting and predicting market regimes. Integrating visualizations for identified regimes and cumulative returns would enhance the model's interpretability and effectiveness in real-world applications.

[github-actions]

Thank you for creating this issue! 🎉 We'll look into it as soon as possible. In the meantime, please make sure to provide all the necessary details and context. Your contributions are highly appreciated! 😊

[alo7lika]

ADD LABELS GSSOC EXT 24 AND HACKTOBERFEST

[Neilblaze]

@alo7lika Go for it! And don't worry, labels will be added by the time you raise the PR.

[alo7lika]

@Neilblaze assign me this task

[alo7lika]

@Neilblaze I have created a PR . Please review it

[Neilblaze]

closing via #130

[github-actions]

Hello @alo7lika! Your issue #126 has been closed. Thank you for your contribution!