Python code you can use for quant finance. Every week.

Get a step-by-step Python tutorial for algorithmic trading, market data analysis, and derivatives pricing — delivered free every Saturday. 5 minutes to read. Copy-paste ready.

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Every issue follows the same format:

Real quant code

Each tutorial walks through a complete implementation: GARCH volatility models, momentum factor backtests, options Greeks with QuantLib, data pipelines with yfinance and pandas. Code you can copy, paste, and run.

Practitioner-focused

Written by a quant with 20+ years of trading experience. No CS theory. No toy examples. Every tutorial solves a problem working quants actually face.

5-minute format

Problem → setup → implementation → results → next steps. Structured so you can read it over coffee and have working code by the time you finish.

Recent issues covered:

— Conditional volatility for options pricing with ARCH/GARCH

— Walk-forward optimization with VectorBT

— Backtesting momentum strategies with Zipline Reloaded

— Connecting and sending orders to Interactive Brokers

— Building alpha factors with machine learning

— Clean market data pipelines with the OpenBB Platform

— Portfolio risk analysis with Monte Carlo simulation

— Options strategy payoff diagrams with Python

The most important newsletter in the Python quant space — subscribers get a master’s degree level tutorial every week.

— John Fawcett, Founder & CEO of Quantopian

Written by Jason Strimpel

20+ years trading stocks and options. 15+ years coding Python. Former quant at J.P. Morgan. Author of Python for Algorithmic Trading Cookbook (Packt Publishing). Featured in Yahoo Finance and MSN.

Jason built PyQuant News to give finance practitioners the practical Python skills he spent a decade learning the hard way — no PhD required.

Get Python code for quant finance. Every Saturday. Free.

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