Four mini-projects build a coherent toolkit for market risk and option analytics. (1) Two equal-weight portfolios show a defensive basket (beta = 0.23) earns 7 % with half the S&P’s volatility, while an aggressive basket (beta = 2.11) returns 118 % but endures deep draw-downs. (2) Shapiro-Wilk, Anderson–Darling, Jarque–Bera and KS tests on 2010–2025 daily ETF data reject normality in > 98 % of rolling 1-yr windows, confirming fat-tail risk. (3) Vectorised Black–Scholes grids visualise steep time-decay of option value and the S-shaped delta curve that drives gamma exposure. (4) Monte-Carlo paths under Heston and GARCH reveal daily Delta-hedging leaves sizeable residual P&L (–6 % to +4 % in Heston); adding a vega-neutral sigma-hedge trims variance by ≈ 75 %. Together these studies deliver reusable Python code for data ingestion, statistical testing, option Greeks and stochastic-vol hedging, paving the way for multi-factor portfolio design and volatility trading.