Quant Finance

Python pathway

Programming Foundations / Practice rooms / Track curriculum and enrollment

1. Financial Foundations

   Calculate simple and log price returns, combine them into cumulative growth, and compound or discount amounts using explicit rate conventions. Practice with a synthetic price series and finish with a report of endpoint return, CAGR and mean daily return.

   • Prices and Returns: 5 lessons
   • Cumulative Growth: 5 lessons
   • Compounding and Discounting: 5 lessons
   • Rates of Return: 5 lessons
   • Capstone: Analyzing a Price Series: 5 lessons

2. The Time Value of Money

   Place signed cash flows on a timeline and value them under explicit discount and reinvestment assumptions. Build annuity and perpetuity models, NPV, bracketed IRR and whole-period payback, then combine them in a report for an illustrative investment forecast.

   • Cash Flows on a Timeline: 5 lessons
   • Annuities and Perpetuities: 5 lessons
   • Net Present Value: 5 lessons
   • Internal Rate of Return: 5 lessons
   • Capstone: Evaluating an Investment: 5 lessons

3. Fixed Income and Bonds

   Price a plain fixed-rate bond from promised coupons and redemption, infer its yield with a validated bracket, and measure local yield sensitivity with duration, DV01 and convexity. Compare shock estimates with repricing and assemble a numerical report under explicit annual coupon-date assumptions.

   • Pricing a Bond: 5 lessons
   • Yield: 5 lessons
   • Duration: 5 lessons
   • Convexity: 5 lessons
   • Capstone: A Bond Analyzer: 5 lessons

4. Risk and Statistics

   Measure return dispersion, explore normal and lognormal models, and calculate covariance and correlation between assets. Estimate historical and normal-model loss thresholds, with explicit assumptions and time horizons. Combine annualized volatility, daily tail statistics and a Sharpe ratio into a small report for synthetic daily returns.

   • Volatility: 5 lessons
   • Distributions of Returns: 5 lessons
   • Covariance and Correlation: 5 lessons
   • Value at Risk: 5 lessons
   • Capstone: A Risk Report: 5 lessons

5. Portfolio Theory

   Calculate portfolio weights, expected return and covariance-based risk. Explore two-asset allocations, sample portfolios and solve the net-budget minimum-variance problem. Compare a sampled Sharpe search with equal weight, then assemble allocation vectors for four illustrative assets with explicit assumptions and constraints.

   • Portfolios: 5 lessons
   • The Two-Asset Frontier: 5 lessons
   • The Efficient Frontier: 5 lessons
   • The Maximum-Sharpe Portfolio: 5 lessons
   • Capstone: Optimizing a Portfolio: 5 lessons

6. Stochastic Models of Prices

   Build random walks, sampled Brownian motion and constant-parameter geometric Brownian price paths. Use Monte Carlo samples to estimate means, event probabilities and a discounted European call payoff. Finish with a reproducible terminal-price report that separates outcome dispersion from uncertainty in the estimated mean.

   • The Random Walk: 5 lessons
   • Brownian Motion: 5 lessons
   • Geometric Brownian Motion: 5 lessons
   • Monte Carlo Simulation: 5 lessons
   • Capstone: A Monte Carlo Engine: 5 lessons

7. Options Pricing

   Calculate option payoffs and spot moneyness, then relate matching European call and put prices through parity. Build a binomial terminal sum and Black-Scholes call/put formulas under explicit assumptions. The capstone compares an analytic call price with Monte Carlo, estimates vega and recovers implied volatility through validated bisection.

   • Option Payoffs: 5 lessons
   • Put-Call Parity: 5 lessons
   • The Binomial Tree: 5 lessons
   • The Black-Scholes Formula: 5 lessons
   • Capstone: A Pricing Engine: 5 lessons

8. The Greeks and Hedging

   Compute local Black-Scholes sensitivities for European options on a stock without dividends. Connect their units to signed position exposure, stock hedging and price-change approximations. Assemble a call-risk summary with explicit model limits.

   • Delta: 5 lessons
   • Gamma: 5 lessons
   • Vega and Theta: 5 lessons
   • Rho and Hedging: 5 lessons
   • Capstone: A Risk Dashboard: 5 lessons

9. Time Series and Trading Signals

   Build trailing price indicators, historical volatility estimates and descriptive dependence statistics. Define signal states, exposure units and return timing, then assemble an inspectable unit-weight crossover pipeline on synthetic data.

   • Moving Averages: 5 lessons
   • Estimating Volatility: 5 lessons
   • Autocorrelation and Mean Reversion: 5 lessons
   • From Signals to Returns: 5 lessons
   • Capstone: A Signal Pipeline: 5 lessons

10. Capstone: Backtesting a Strategy

    Track compounded equity and historical drawdowns, calculate defined performance metrics, compare matched benchmarks and model proportional trading costs. Assemble an inspectable single-asset crossover backtest with explicit execution and risk limits.

    • Equity Curve and Drawdown: 5 lessons
    • Performance Metrics: 5 lessons
    • Comparing with a Benchmark: 5 lessons
    • Transaction Costs: 5 lessons
    • Capstone: A Small Backtest: 5 lessons