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About Course:
You know Python can help in a lot of ways:
- Advance your career
- Earn passive income trading
- Improve your trading performance
You know to unlock these goals, you need Python for data, analysis, and trading.
It’s one thing to “learn Python.” But it’s a completely different thing to use Python for quant finance.
For many of us, getting started with Python is a mystery.
You know there is immense power at your fingertips, but you just can’t quite figure out how to go from theory to practice.
Modules, IDEs, swaps, options, Jupyter, functions, automation, back testing, loops, classes, CVaR, Sharpe, list comprehensions, walk forward analysis…
Module 1:
Getting the Python Basics Right
If you’re brand new to Python, you’ll fast-track you’re learning with exactly what you need to know—no overwhelm, no complexity.
Module 2:
The Python Quant Stack
Get familiar with the most important Python libraries for algo trading and data analysis—Pandas—so you can work with market data.
Module 3:
Algorithmic Trading, Back testing, and Strategy Formation
Yes! Retail traders can compete. Get a framework to form trading ideas, test them, and get them executed.
Module 4:
Treat Your Backtest Like an Experiment
Understand why most people get backtesting wrong—and the secret of avoiding losing money because of a backtest.
Module 5:
How to Engineer Alpha Factors with Python
Get the tools and techniques professional money managers use to manage portfolios and hedge away unwanted risk.
Module 6:
Prototyping and Optimizing Strategies with VectorBT
Get working code to run millions of simulations with the cutting-edge VectorBT backtesting library.
Module 7:
How to Backtest a Trading Strategy with Zipline Reloaded
Build factor pipelines to screen and sort a universe of 21,000+ equities to build and backtest real-life factor portfolios.
Module 8:
Risk and Performance Analysis with PyFolio and AlphaLens
Get the code to quickly asses strategy risk and performance—including factor performance—and assess alpha decay.
