Introduction
This project focuses on predicting market regimes—bullish, bearish, and sideways—and using these predictions to power a backtesting model for the SPY ETF (tracking the S&P 500). By integrating regime forecasting with performance analysis, the model aims to outperform the market based on historical data.
Approach and Goals
📊 Data Collection and Cleaning
Source reliable financial data and preprocess it to eliminate inconsistencies, ensuring the accuracy of inputs for modeling.
🧮 Feature Engineering
Generate new variables and indicators to provide deeper insights into market behavior and asset characteristics.
🤖 Predictive Modeling with HMM
Utilize Hidden Markov Models to identify market regimes (bullish, bearish, sideways) and predict market trends.
🎯 Model Training and Forecasting
Train the model using historical data to forecast daily returns and refine predictions over time.
⏳ Backtesting the Strategy
Simulate the model's performance using historical data to evaluate effectiveness and optimize parameters.
📈 Benchmarking Against SPY ETF
Compare the model's returns to standard benchmarks, demonstrating its potential to enhance trading strategies.