Scenario Generation using Generative Adversarial Networks
Bank of America Securities — WGAN-GP trained on cross-asset log returns to learn the empirical joint distribution; benchmarked against historical simulation and parametric bootstrapping across tail diagnostics, correlation structure, GARCH persistence, PCA alignment, and portfolio VaR coverage.

Gradient Boosting Alpha
Machine Learning — XGBoost and LightGBM ensemble trained on 18 fundamental, momentum, and liquidity factors to predict cross-sectional stock returns, with SHAP attribution, walk-forward validation, decile portfolio construction, and signal decay analysis.

Cross-Sectional Signals Engine
Factor Research — SEC EDGAR fundamentals combined with price data to engineer equity factor signals (value, quality, momentum, accruals), rank stocks into deciles, and evaluate IC and long-short spread performance with a Streamlit dashboard.

Vanilla Options Pricing — Black-Scholes, Binomial Tree, and Greeks
Pricing & Hedging — Black-Scholes and CRR binomial tree from scratch, full Greeks set (delta, gamma, vega, theta, rho), pricing and Greek surfaces across moneyness and expiry, scenario P&L attribution, and an interactive Streamlit dashboard.

Conditional Volatility Forecasting
Time Series — Modelling and forecasting conditional volatility with EWMA and GARCH(1,1) benchmarked against rolling historical vol across multiple horizons, with a three-state regime detector and a Streamlit dashboard.

Yield Curve Explorer
Fixed Income — Bootstrap zero-coupon term structures from US Treasury CMT data, compare interpolation methods, fit Nelson-Siegel models, price fixed-rate bonds, and stress-test with rate shock scenarios using a Streamlit dashboard.

Mean-Variance Efficient Portfolios
Portfolio Optimization — Constructing and visualizing the efficient frontier using mean-variance optimization, with analysis of minimum variance and maximum Sharpe ratio portfolios.

Portfolio Risk Engine
Risk Analytics — A Python CLI computing rolling volatility, Value at Risk, CVaR, stress tests, Kupiec backtesting, and factor decomposition for multi-asset portfolios.

Regime Detection using Hidden Markov Models and Support Vector Machines
Machine Learning — Identifying bull and bear market regimes in equity time series using unsupervised HMM and One-Class SVM with a Radial Basis kernel.

Genetic Neural Networks
Machine Learning — Training neural networks with genetic algorithms to automate buy/sell/hold signals for portfolio management of the Dow Jones Industrial Index.

Long/Short Global Macro Strategies
Portfolio Optimization — Backtesting systematic long/short portfolios across global macro factors including equities, rates, commodities, and currencies.

Predicting Interest Rates from Federal Reserve Documents
Machine Learning — Using NLP, topic modeling, and sentiment analysis on FOMC communications to forecast U.S. interest rate direction.

Asian Options Monte Carlo Pricing
Pricing & Hedging — Monte Carlo simulation methods for pricing arithmetic and geometric Asian options, including variance reduction techniques.