Course · Quant Finance Basics
Quant finance, from the ground up.
A structured path from the time value of money to a working backtest — the same rigour as the research library, taught step by step. Every lesson with a case study links to runnable code, so you read it, then run it.
Foundations
FreeThe arithmetic and intuition every model inherits.
- 01.1
The time value of money
12 minDiscounting, compounding, and the yield curve — priced in pandas.
- 01.2
Risk, return, and the trade-off
10 minWhy you can't talk about one without the other.
- 01.3
Probability & distributions for finance
14 minNormal, Student-t, and why the tails matter.
Case study + notebook - 01.4
Returns, log-returns, and the pitfalls
11 minThe data hygiene that decides whether anything later is true.
Case study + notebook
Markets & Instruments
What actually trades, and how.
- 02.1
Asset classes and how they trade
12 minEquities, rates, FX, commodities, crypto — the lay of the land.
- 02.2
Bonds and the yield curve
15 minDiscount factors, duration, and what the curve is telling you.
Case study + notebook - 02.3
Options: payoffs and the basics
13 minCalls, puts, and the shape of optionality.
- 02.4
Futures, forwards, and the cost of carry
12 minLinear derivatives and the no-arbitrage link to spot.
Pricing & the Greeks
From replication to a hedged book.
- 03.1
No-arbitrage and replication
12 minThe single idea that prices everything.
- 03.2
Black–Scholes from first principles
18 minDerive the formula from a hedged portfolio, then code it.
Case study + notebook - 03.3
- 03.4
Where the model breaks: the volatility smile
12 minWhy constant vol is a fiction — and what to do about it.
Portfolio Construction
Turning noisy estimates into allocations that hold up.
- 04.1
Mean–variance and the efficient frontier
16 minMarkowitz, and the error-maximiser hiding inside it.
Case study + notebook - 04.2
The CAPM and factor models
15 minBeta, then the factor zoo — size, value, momentum.
Case study + notebook - 04.3
Covariance estimation & Ledoit–Wolf shrinkage
14 minWhy the sample matrix fails, and how shrinkage repairs it.
- 04.4
Risk parity & Hierarchical Risk Parity
13 minEqual risk contributions, then allocation without inverting a covariance matrix.
Case study + notebook - 04.5
HRP: allocation by hierarchy
12 minLópez de Prado's clustering alternative, end to end.
Risk Management
Measuring, and respecting, the tails.
- 05.1
Volatility clustering and GARCH
15 minModelling the fact that volatility comes in waves.
- 05.2
Value at Risk & CVaR, three ways
13 minHistorical, parametric, Monte Carlo — and a backtest.
Case study + notebook - 05.3
Tail risk with Extreme Value Theory
16 minGARCH margins, Pareto tails, and a t-copula.
- 05.4
Stress testing and drawdown
12 minJoint crashes, copulas, and what breaks the book.
Case study + notebook
Signals & Backtesting
Edge, honestly measured.
- 06.1
How to backtest without fooling yourself
15 minLook-ahead, survivorship, and the costs people skip.
Case study + notebook - 06.2
Cross-sectional momentum
14 minThe most documented anomaly — backtested honestly.
- 06.3
Pairs trading and cointegration
13 minEngle–Granger, the spread z-score, and realistic costs.
- 06.4
Execution, costs, and turnover
11 minWhy a great backtest can still lose money — measured on a live pair.
Case study + notebook
The Python Toolkit
From idea to a runnable, reproducible backtest.
- 07.1
NumPy and pandas for finance
14 minVectorised returns, rolling windows, and tidy data.
- 07.2
SciPy, statsmodels, and the stack
13 minOptimisation, distributions, and econometrics in practice.
- 07.3
Reproducible research with notebooks
10 minCode that anyone can re-run and trust.
- 07.4
From idea to backtest: the full loop
18 minPutting the whole course together, end to end.
Start with Module 1 — it's free.
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