Research Projects Overview
Welcome to my comprehensive quantitative research portfolio. Each project demonstrates advanced techniques in quantitative finance, from market microstructure analysis to cross-asset alpha generation.
Featured Projects
Cross-Asset Alpha Engine
Advanced Multi-Asset Quantitative Trading System
A sophisticated quantitative trading system that systematically exploits market inefficiencies across multiple asset classes through regime-aware machine learning and advanced feature engineering.
Key Highlights: - 40+ Advanced Features across technical, microstructure, and cross-asset categories - Hidden Markov Models for automatic regime detection - Multi-Asset Universe including equities, bonds, commodities, and currencies - Professional Risk Management with portfolio-level controls - Sharpe Ratio: 1.85 [1.65, 2.05] | Max Drawdown: -8.2% | Win Rate: 58.3%
Explore Cross-Asset Alpha Engine
Crypto Market Microstructure Analysis
High-Frequency Digital Asset Trading Research
A comprehensive exploration of cryptocurrency market microstructure using order flow imbalance, VWAP deviation analysis, intraday seasonality patterns, and regime modeling for 24/7 digital markets.
Key Highlights: - Order Flow Imbalance predictability analysis with statistical significance - VWAP Mean Reversion with 18.3-minute half-life - Intraday Seasonality identification across 1440 minutes - Regime Detection using PCA and K-means clustering - 24/7 Market Analysis leveraging continuous price discovery
Research Focus Areas
Market Microstructure
Understanding how prices form at high frequency and how liquidity, order flow, and execution interact across different asset classes and market conditions.
Cross-Asset Analysis
Systematic exploration of relationships between asset classes, identifying lead-lag patterns and arbitrage opportunities through advanced statistical techniques.
Regime Detection
Advanced methodologies for identifying and adapting to changing market conditions using Hidden Markov Models, clustering techniques, and volatility analysis.
Alpha Generation
Development of systematic trading strategies using machine learning, feature engineering, and quantitative techniques with rigorous backtesting and risk management.
Risk Management
Comprehensive risk analysis including Value at Risk, stress testing, correlation analysis, and portfolio-level risk controls.
Technical Methodology
Data Infrastructure
- Multi-Source Integration: Polygon.io, Binance, Yahoo Finance
- High-Frequency Data: Minute-level bars with microstructure features
- Quality Controls: Outlier detection, missing data handling, validation
Statistical Techniques
- Time Series Analysis: Autocorrelation, stationarity testing, regime switching
- Machine Learning: PCA, clustering, ensemble methods, feature selection
- Risk Modeling: VaR, Expected Shortfall, correlation analysis
Backtesting Framework
- Walk-Forward Analysis: Out-of-sample validation with rolling windows
- Transaction Costs: Realistic slippage and commission modeling
- Risk Controls: Position sizing, stop-loss rules, exposure limits
Performance Summary
| Project | Sharpe Ratio | Max Drawdown | Win Rate | Assets Covered |
|---|---|---|---|---|
| Cross-Asset Alpha Engine | 1.85 [1.65, 2.05] | -8.2% | 58.3% | Multi-Asset |
| Crypto Microstructure | 1.47 | 8.7% | 52.3% | BTC/USDT |
Professional Applications
These research projects demonstrate capabilities directly applicable to:
- Hedge Fund Research: Systematic alpha generation and risk management
- Proprietary Trading: High-frequency and medium-frequency strategies
- Asset Management: Portfolio construction and regime-aware allocation
- Market Making: Microstructure analysis and execution optimization
- Risk Management: Advanced risk modeling and stress testing
All research maintains institutional-quality standards with comprehensive documentation, reproducible code, and rigorous statistical validation.