Matt Dancho – Backtesting Algorithmic Trading Strategies with Python

Master Algorithmic Trading and Backtesting with Python

If you want to go beyond surface-level trading and develop real, professional-grade algorithmic strategies, Matt Dancho’s course—Backtesting Algorithmic Trading Strategies with Python—is the complete toolkit you need.

Whether you’re a beginner or an aspiring quant, this course will teach you how to design, backtest, and refine portfolio-based trading strategies using powerful Python tools. The best part? You don’t need prior experience in algorithmic trading. You’ll learn step-by-step with hands-on projects, proven strategies, and ready-to-use code templates.

 What You’ll Learn

✅ Set Up Your Quant Trading Lab from Scratch

You’ll build a complete Python-based quant trading environment—even if you’ve never coded a strategy before.

• Set up your Python Quant Lab
• Install the Quant Stack Software
• Build and manage an algorithmic trading project
• Prepare your local environment for professional testing and automation

This first step alone saves you weeks of guesswork and Googling.

✅ Build 4 Algorithmic Portfolio Trading Strategies

Using tested and risk-managed techniques, you’ll learn to build a portfolio strategy that minimises drawdowns and maximises returns.

You’ll discover:

• Volatility targeting with auto-rebalancing (Ray Dalio-style)
• Portfolio optimization using the Riskfolio-Lib Python library
• How to apply minimum return thresholds and diversify with precision

These aren’t just “theoretical” models—you’ll get code templates and strategy files you can use immediately.

✅ Professionally Backtest Your Strategies

Learn to backtest your trading strategies using event-driven frameworks like Zipline Reloaded. No more surface-level testing or random indicators.

You’ll learn:

• Professional event-based backtesting workflows
• How to simulate real trading conditions (rebalancing, slippage, commissions)
• Debugging and optimising backtest results for performance

You’ll walk away with a complete backtest engine and reporting pipeline for your custom strategies.

???? Step-by-Step Modules

???? Step 1: Quant Lab Setup ($500 Value)

• Set up the Quant Stack Python environment
• Build your first algorithmic trading project
• Use Matt’s templates to structure your workflow
• Get everything ready for real testing and data integration

???? Step 2: Building a Profitable Strategy ($2,500 Value)

• Access Matt’s top-performing volatility targeting strategy
• Build a risk-managed portfolio using Riskfolio-Lib
• Apply the “Bridgewater Cheat Code” (inspired by Ray Dalio’s fund)
• Use asset class allocation methods used by pro quant funds

???? Step 3: Professional Backtesting with Zipline Reloaded ($2,500 Value)

• Learn event-based backtesting
• Implement automated rebalancing and trading constraints
• Include slippage, commissions, and transaction costs
• Avoid the top 5 mistakes most backtesters make

???? Bonus Modules (Over $7,500 in Value)

???? Bonus #1: Backtest 21,000+ US Equities with Premium Data ($1,500 Value)

Want professional-level data? Matt shows you how to:

• Ingest data for over 21,000 US equities
• Convert that data into Zipline-compatible bundles
• Build your own local backtest database
• Use with a $50/month data subscription (optional)

???? Bonus #2: Use Free Market Data for Backtesting ($1,500 Value)

No budget for data yet? No problem.

• Learn how to ingest and convert free data sources
• Backtest using publicly available financial data
• Build strategies without paying for data until you scale

???? Bonus #3: Top 3 Variations of Volatility Targeting ($3,000 Value)

You’ll get 3 professional variants of the volatility-based strategy, designed to handle different market conditions:

• Hierarchical Risk Parity (HRP) – $1,000 Value
• CVaR Risk Measure – $1,000 Value
• Risk Factor w/ Principal Component Regression (PCR) – $1,000 Value

These are advanced models used by hedge funds and quantitative firms—and you’ll have access to the full code.

???? Why This Course Is Different

Most trading courses focus on surface-level indicators or outdated technical analysis.

Matt Dancho’s course teaches you:

• Portfolio-level trading
• Modern risk management
• Python-based quant tools
• Institutional-level backtesting

You’ll gain real, repeatable skills you can use to build your own trading system—not just copy someone else’s.

????‍???? About the Instructor

Matt Dancho is the founder of Business Science, a data science education platform that teaches professionals how to apply Python and R to finance, analytics, and trading. With a background in quantitative finance and algorithmic modelling, Matt specialises in helping individuals go from beginner to pro with a no-fluff, real-world approach.

???? Who This Course Is For

✅ Beginners with no prior trading experience

✅ Data analysts or coders entering finance

✅ Traders who want to go beyond indicators

✅ Quant enthusiasts building their own fund

✅ Investors looking to protect and grow their portfolio

✅ Anyone interested in risk-managed, rules-based portfolio strategies

❓ Frequently Asked Questions (FAQ)

Q1: Do I need to know Python before starting?

A: No. While some familiarity helps, Matt walks you through every step, including how to set up your environment and use the code templates. Beginners are welcome.

Q2: Do I need to pay for data?

A: No, but you have options. You’ll get code to use free market data for backtesting. If you want premium-level coverage (21,000+ equities), a $50/month subscription is recommended—but not required.

Q3: Will I get the full code?

A: Yes. All strategies, templates, backtesting systems, and data ingestion code are included. You’ll be able to plug-and-play or modify for your own strategies.

Q4: Can I use this for live trading?

A: This course focuses on strategy development and backtesting, not execution. However, the systems and code can be adapted for live deployment with brokers that support Python APIs.

Q5: What makes this different from other trading courses?

A: You’re learning portfolio construction, risk management, and quantitative modeling—not random trading tips. This is professional-level education designed for traders who want to build robust, long-term systems.

???? Final Thoughts: Build a Trading System You Can Trust

Don’t just gamble in the markets—engineer your strategy.

With Matt Dancho – Backtesting Algorithmic Trading Strategies with Python, you’ll learn to create professional-grade trading systems that are tested, optimized, and risk-managed.

Whether you’re starting small or aiming to build your own quant fund, this course gives you the tools to succeed with clarity, confidence, and code.