Projects
A collection of quantitative trading strategies and financial models. Each project includes comprehensive backtesting, risk metrics, and methodology documentation.
Proprietary Custom Models
A collection of quantitative models, backtesting frameworks, and trading strategies. Includes mean reversion systems, factor models, risk analytics, and portfolio construction tools built in Python.
Hybrid-Adaptive Quant Trading System
Combines GAF pattern recognition with ConvNeXt-Tiny for market regime and direction prediction. LLM confidence calibration filters low-confidence signals. Vector memory stores historical patterns for adaptive strategy refinement.
AI-Native High-Frequency Trading Engine
Multi-modal artificial intelligence trading system integrating Gramian Angular Field transformation with EfficientNet-B3 Convolutional Neural Network backbone, Hidden Markov Model memory cache system for market regime detection, transformer-based multi-asset pattern recognition with cross-asset attention mechanisms, and Proximal Policy Optimization reinforcement learning agent for adaptive strategy selection and position sizing. Demonstrates production-grade operational performance with microsecond-level order book operations (5.3M ops/sec), sub-millisecond total pipeline latency (<1ms), and exceptional memory efficiency (<150MB footprint) through CPU-optimized 4-bit and 8-bit integer quantization techniques.