Featured projects in quantitative finance, portfolio construction, and systematic investing.
Each project applies quantitative methods to a specific problem in finance — from clustering-based portfolio construction and market regime detection to adaptive portfolio strategies and RRG-based rotation analysis.
Projects include interactive visualizations, source code links, and methodology explanations. Data is updated periodically via automated pipelines.
| 1. | Diversified Stock Portfolio Using Clustering Analysis (2024) S&P 500 portfolio construction using K-means clustering on risk/return features (correlation, beta, returns, volatility, Sharpe ratio). Backtested vs index. Python, K-means, backtesting | Active |
| 2. | Relative Rotation Graph (RRG) — US Equity (2025) Dynamic RRG for US stocks vs S&P 500. JdK RS-Ratio and RS-Momentum with animation. Python, yfinance, Recharts | Active |
| 3. | Adaptive Portfolio Strategies: Sequential Allocation Methods (2025) Comprehensive analysis of 14 sequential portfolio allocation strategies on diversified ETF portfolio. Includes momentum-based, reversion-based, and pattern-learning approaches with transaction cost analysis. Python, sequential optimization, backtesting | Active |
| 4. | Optimal Execution with RL Agent (DQN) (2026) Deep Q-Learning execution agent for slicing large orders under microstructure-style market impact. Compared against TWAP, passive, aggressive, and random baselines. Python, Gymnasium, Stable-Baselines3, execution simulation | Active |
| 5. | Market Regime Detection Using Gaussian Models (2025) Comprehensive market regime identification across 21 global indices using Gaussian Mixture Models (GMM) and Greedy Gaussian Segmentation (GSS). Detects bull, bear, and transition regimes for adaptive portfolio management. Python, GMM, GSS, regime detection | Active |
| 6. | Stock Sentiment Tracker (2025) US equity sentiment tracker using VADER lexical analysis on financial news headlines. Tracks prices, sentiment scores, and correlations for S&P 500 stocks. Updated daily. Python, VADER, NLP, financial news APIs | Active |
| 7. | Yield Curve Intelligence (2026) Yield-curve research project built from yfinance market data: Nelson-Siegel fitting, slope/inversion diagnostics, and regime-aware risk context. Python, yfinance, Nelson-Siegel, macro risk signals | Active |