Fixed Income Trading Lectures

homework for fixed income quantitative trading course

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An institutional-grade fixed income quantitative model utilizing Principal Component Analysis (PCA) to isolate and trade the level, slope, and curvature of the US Treasury yield curve via duration-neutral butterfly spreads.

Multi-asset ML forecasting framework using ensemble methods (XGBoost, LSTM, Random Forest) to predict returns and generate alpha signals across equities, fixed income, and digital assets. Features time-series analysis, regime detection, backtesting with transaction costs, and risk-adjusted performance metrics for quantitative trading.

Capstone project, Fixed Income Quantitative Trading

Quantitative Finance Solutions for Nomura Global Quant Challenge - Fixed Income Derivatives Pricing Engine (C++) and ML-Powered Algorithmic Trading System (Python)

Quantitative analysis of BR-US fixed income carry trade using Nelson-Siegel-Svensson models, bond options valuation, and portfolio optimization. Includes live yield curve fitting, spread analysis, and risk metrics.