📊 Excited to share My Latest Side Project 📈📉
A deep dive into identifying market regime shifts using Machine Learning! [Source code included]
🌟 Inspired by an ongoing machine learning project at Wealth Management Cube Ltd, I have executed data cleaning, visualization, feature selection, modeling and verification in this side project. Here's some key highlights:
🔍 Market Regime Classification:
Leveraged the power of Hidden Markov Models (HMM) for classifying market regimes within the S&P 500, aiming to capture distinct behavioral patterns.
💼 The Significance of Log Returns:
Chose log returns over simple/cumulative returns, ensuring stationary, additive measures for comparisons, and minimizing asset price level influences.
📉 Model Exploration: Experimented with various models like Gaussian Mixture Models, Greedy Gaussian Segmentation, LSTM, XGBoost, and more. This exploration amplified my insights into each model's unique attributes and potential constraints.
🔮 Future Endeavors:
🕵️♂️Flexible Feature Integration: Delve deeper the integration of features like candlestick patterns to refine model precision.
📊 Decoding Short-Term Market Trends: Addressing the complex short-term trends remains a challenge I'm eager to tackle
🔄 Exploring Alternative Models: The Markov AR Switching model and others may provide more insights into market dynamics.
📈 Merging ML & Trading Strategies: Eager to combine ML with robust trading strategies based on market regimes.
🔗 For those keen on diving deeper, check out my GitHub Repository. Feel free to fork, pull, and drop me an email at LIKAWAI@link.cuhk.edu.hk!
🚀 Your support propels my journey. If this resonates with you, give it a thumbs up or star it on GitHub!
🌟Together, let's reach for the stars!
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https://github.com/RaymondRaman/ML-project---Identify-Regime-Shift-in-Stock-Market