ES Futures VPOC Strategy Backtester
🟩 CompletedAlgorithmic Trading & Machine Learning
Overview
Advanced algorithmic trading strategy for E-mini S&P 500 (ES) futures that combines Volume Point of Control (VPOC) analysis with statistical validation and machine learning. The strategy identifies high-probability trading opportunities by analyzing volume distribution patterns, value area migrations, and market microstructure. ML enhancements include feature engineering, neural network models, and distributed training for AMD GPUs.
Objectives
- Develop and backtest VPOC-based trading strategies
- Integrate machine learning for signal generation and risk management
- Validate strategy performance with statistical and ML metrics
Technologies
Python (pandas, numpy, matplotlib, seaborn, scikit-learn, statsmodels, pandas-ta, torch, mpi4py), Jupyter Notebooks (strategy prototyping, analysis)
