Content pipeline · Batch 1
Topic report cards.
Sixteen topics across the four categories, each specified before a line of code is written: the exact libraries, the exact assets and timeframe, and the one-line answer to why a practitioner cares. Live pieces link straight to the article and its runnable notebook.
16 live0 in the writing queue4 categories × 4 formats
I
Quant Finance Foundations
4/4 live01 / 16Tutorial● live
GBM: Simulating Price Paths
- Libraries
- NumPy · Pandas · Matplotlib · yfinance · SciPy
- Assets
- SPY (S&P 500 ETF, State Street)
- Timeframe
- Jan 2018 – Dec 2024
- Use
- Scenario cones for wealth projections; the engine inside Monte-Carlo pricing & risk systems.
02 / 16Tutorial● live
Black–Scholes & the Greeks
- Libraries
- NumPy · SciPy · Matplotlib
- Assets
- QQQ options (Nasdaq 100 ETF, Invesco)
- Timeframe
- Jan 2018 – Dec 2024 (trailing 1y vol)
- Use
- Pricing & hedging options; implied vol is how the entire options market quotes prices.
03 / 16Tutorial● live
Bond Pricing, Duration & Convexity
- Libraries
- NumPy · Pandas · Matplotlib · yfinance
- Assets
- US Treasuries (2y–30y) + SHY / IEF / TLT (iShares)
- Timeframe
- Static pricing + calendar year 2022
- Use
- Measuring rate risk; duration targeting and the ±100bp stress tests in every risk report.
04 / 16Quant Insights● live
Heston vs Black–Scholes: Fitting the Vol Smile
- Libraries
- QuantLib · SciPy · NumPy
- Assets
- SPX / SPY options
- Timeframe
- 2019 – 2024 + recent option chain
- Use
- Pricing options consistently across strikes — the vol desk's answer to the smile BS can't fit.
II
Portfolio Optimization
4/4 live05 / 16Tutorial● live
MVO & the Efficient Frontier
- Libraries
- PyPortfolioOpt · Pandas · Matplotlib
- Assets
- SPY · TLT · GLD · VNQ · VEA · VWO
- Timeframe
- Jan 2015 – Dec 2024
- Use
- Core asset allocation: finding the best return per unit of risk across a portfolio.
06 / 16Tutorial● live
Black–Litterman: Equilibrium + Views
- Libraries
- PyPortfolioOpt (BL) · NumPy
- Assets
- EWJ · EWG · EWU · EWA · EWC (iShares country ETFs)
- Timeframe
- Jan 2015 – Dec 2024
- Use
- Blending market-implied returns with your own views — stable weights without extreme bets.
07 / 16Tutorial● live
Risk Parity from Scratch
- Libraries
- SciPy (build) · Riskfolio-Lib (validate)
- Assets
- SPY · TLT · GLD · DBC
- Timeframe
- Jan 2010 – Dec 2024
- Use
- Allocating by risk contribution instead of capital — the all-weather portfolio blueprint.
08 / 16Case Study● live
60/40 in 2022: When Correlation Flipped
- Libraries
- Pandas · statsmodels · seaborn
- Assets
- SPY + AGG (+ TLT for the long-duration pain)
- Timeframe
- Jan 2000 – Dec 2023, focus 2022
- Use
- Stress-testing the diversification assumption every balanced portfolio silently relies on.
III
Risk Management
4/4 live09 / 16Tutorial● live
VaR Three Ways
- Libraries
- NumPy · SciPy · Pandas · Polars · DuckDB
- Assets
- DAX (^GDAXI)
- Timeframe
- Jan 2010 – Dec 2024
- Use
- Daily loss limits and regulatory capital — the industry's standard downside risk number.
10 / 16Tutorial● live
CVaR / Expected Shortfall
- Libraries
- SciPy · Riskfolio-Lib
- Assets
- HYG + VWO (fat-tailed assets)
- Timeframe
- Jan 2007 – Dec 2024 (incl. GFC)
- Use
- Sizing tail risk when the losses beyond VaR are what actually hurt — Basel's chosen measure.
11 / 16Tutorial● live
Copulas & Tail Dependence
- Libraries
- SciPy · statsmodels · seaborn
- Assets
- S&P 500 vs FTSE 100 vs Nikkei 225
- Timeframe
- Jan 2000 – Dec 2024
- Use
- Modelling joint crashes across markets for multi-asset stress scenarios.
12 / 16Quant Insights● live
Gaussian vs t-Copula: Joint Crashes
- Libraries
- SciPy · NumPy · seaborn
- Assets
- XLF vs XLK (SPDR sectors)
- Timeframe
- Jan 2007 – Dec 2024
- Use
- Choosing a dependence model that doesn't underestimate crash correlation (2008's lesson).
IV
Algorithmic Trading
4/4 live13 / 16Tutorial● live
SMA Crossover Backtest
- Libraries
- vectorbt · Pandas · Pyfolio
- Assets
- QQQ + BTC-USD (trend behaves differently per asset)
- Timeframe
- Jan 2015 – Dec 2024
- Use
- The systematic trend-following workhorse — and backtesting discipline 101.
14 / 16Tutorial● live
Kalman Filters: Dynamic Hedge Ratios
- Libraries
- NumPy (build the filter) · statsmodels · Pandas
- Assets
- EWA / EWC pair (Australia / Canada)
- Timeframe
- Jan 2010 – Dec 2024
- Use
- Adaptive hedge ratios for pairs trading — reacting to relationships that drift over time.
15 / 16Case Study● live
GameStop: When Momentum Models Broke
- Libraries
- Pandas · Matplotlib · yfinance
- Assets
- GME + XRT + ^VIX
- Timeframe
- Oct 2020 – Mar 2021 (event window)
- Use
- Understanding crowding and short-squeeze risk before your systematic strategy meets one.
16 / 16Research Note● live
Alpha Decay: Momentum's Half-Life
- Libraries
- Alphalens · Pandas · statsmodels
- Assets
- 11 SPDR sector ETFs
- Timeframe
- Jan 2005 – Dec 2024
- Use
- Deciding how fast to trade a signal before its edge evaporates — sets turnover & capacity.