Matt Dancho – Backtesting Algorithmic Trading Strategies with Python

Jan 14, 2026

Course Overview

Matt Dancho – Backtesting Algorithmic Trading Strategies with Python

Build and Backtest Algorithmic Trading Portfolios with Python

…So You Can Grow Your Investments responsibly WITHOUT Risky Single-Stock Strategies Or Algorithmic Trading Experience

Why beginners fail at growing their investments…

  • Don’t Know How to Get Started
    Are you overwhelmed with the number of strategies out there for algorithmic trading?
  • Losing Money With Risky Single-Stock Strategies
    Are you losing money on your first trades, do you feel uneasy with your single-stock risk, or are your trades underwater?
  • Lack of Financial Domain Experience
    Do you find yourself struggling to gain confidence, understand financial jargon, and make sense of it all?

If that’s you…

Then keep reading, my friend, because what happens in the next few minutes could decide if you stay stuck for another year or gain the knowledge that will help you grow your investment portfolio responsibly.

Trust us, we know what it’s like…

  • Trying single-stock moving average crossover strategies (and “bleeding red”)
  • Spending money on courses that do NOT work
  • To feel like I could be growing my investments more (but not knowing how)
  • To feel like I’m taking on too much risk
  • To try every YouTube trading strategy there is, and still lose money

What You’ll Learn In Backtesting Algorithmic Trading Strategies with Python

Step 1: Trading Project and Python Quant Lab Setup ($500 Value)

  • Get the Quant Stack Python Software installed
  • Set up your algorithmic trading project
  • Create your Python environment
  • Everything you need to begin building and backtesting portfolio trading strategies

Step 2: How to Create a Profitable Algorithmic Portfolio Trading Strategy ($2,500 Value)

  • Get our top portfolio-based trading strategy: Volatility targeting with auto-rebalancing ($2,500 Value)
  • Get our code template for how to construct a risk-managed portfolio with the Riskfolio-Lib Python library
  • Discover how to increase returns using the “Ray Dalio Bridgewater Cheat Code”

Step 3: Learn how to Backtest the right way ($2,500 Value)

  • Detailed walkthrough of event-based backtesting ($2,500 Value)
  • Backtested portfolio strategies with Zipline Reloaded
  • How to avoid mistakes in backtesting portfolios
  • How to include rebalancing, slippage, and trading commissions
More courses from the same author: Matt Dancho

»Instant Delivery

Skill Level: All level

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Regular Price:497$
Sale Price:35$
Course Content

01-Welcome to Backtesting Algorithmic Trading Strategies with Python

  • 01-Welcome Course Overview and Curriculum
    00:00
  • 02-Course Goal Consistently Make Money the Right Way
    00:00
  • 03-How to Get Help
    00:00

02-How the Course Works The Roadmap

03-The Quant Scientist Algorithmic Trading Framework

04-Step 1 Trading Project Quant Lab Software Setup

05-11 Course Code

06-12 Python Software Installation Quant Lab Setup

07-13 Unannounced Bonus Matts 5000000 Portfolio Strategy

08-Step 2 Algorithmic Portfolio Trading Strategy

09-21 Making the Algorithmic Trading Strategy Volatility Targeting Risk Parity

10-Step 3 Backtesting the Trading Strategy

11-21 Backtesting the Trading Strategy Volatility Targeting Risk Parity

12-What did you think of the course If you liked it please help us with a testimonial

13-Bonus 1 Code to Backtest 21000 US Equities

14-Bonus 2 Code to Use Free Data for Backtesting

15-Bonus 21 – Mac Users

16-Bonus 22 – Windows Users

17-Bonus 3 Three Variations of the Volatility Targeting Strategy

18-Bonus 3 Strategy 1 Hierarchical Risk Parity

19-Bonus 3 Strategy 2 Using CVaR Risk Measure

20-Bonus 3 Strategy 3 Risk Factors with Principal Component Regression PCR

21-Testimonial If You Enjoyed the Course Please Leave Us a Testimonial

22-Next Steps Where To Go From Here

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