Backtesting Algorithmic Trading Strategies with Python by Matt Dancho

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Algorithmic trading no longer belongs only to hedge funds and banks. With the right tools and a clear process, individual investors can design, test, and refine systematic strategies to grow their portfolios responsibly—without gambling on single stocks or guessing based on social media tips.

Backtesting Algorithmic Trading Strategies with Python by Matt Dancho is built for exactly that purpose. This course gives you a professional-grade, three-step framework to build and backtest portfolio-based strategies using Python, even if you’re new to algorithmic trading.

• Course size: 912 MB

• Course price: USD 23.10

If you feel overwhelmed by trading strategies, uneasy about single-stock risk, or unsure how to move from theory to a working system, this course shows you how to design, test, and refine portfolio strategies in a structured, data-driven way.

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Overview this course

Backtesting Algorithmic Trading Strategies with Python is a practical, project-based program that takes you from “I don’t know where to start” to “I can build and test my own portfolio strategy.”

Instead of focusing on risky, single-stock trading, the course centers on portfolio-level approaches that use diversification, volatility targeting, and risk management as core principles. You learn how to:

• Set up a Python Quant Lab environment for algorithmic trading

• Build portfolio-based strategies using robust libraries like Riskfolio-Lib

• Perform event-based backtests with tools like Zipline Reloaded

• Incorporate realistic assumptions such as rebalancing, slippage, and trading costs

The course is designed as a guided path: you start by configuring your environment, then move into building strategies, and finally learn how to backtest them properly—and extend them with bonus material on data and strategy variations.

Why Should You Choose This Course?

Many beginners struggle in three key areas: they don’t know where to start, they rely on single-stock trading ideas, and they feel lost in financial jargon. This course tackles those problems directly.

You should consider this course if you want:

• A structured entry into algorithmic trading

  • No more random hopping between YouTube strategies or disconnected blog posts.

  • A clear, professional-grade 3-step process that guides you from setup to backtesting.

• Portfolio-based strategies instead of stock picking

  • The focus is on allocating across multiple assets, not trying to guess which single stock will explode.

  • You learn to think like a portfolio manager rather than a day trader.

• Realistic, professional-style backtesting

  • You don’t just test ideas in spreadsheets—you use proper backtesting frameworks that model events, rebalancing, transaction costs, and slippage.

• Code you can actually reuse and extend

  • The course gives you templates, not just screenshots.

  • You can adapt the code to your own data, instruments, and risk preferences.

• A responsible approach to growth

  • The emphasis is on risk-managed portfolio construction, volatility targeting, and minimum return thresholds—not “get-rich-quick” tactics.

What You’ll Learn

By the end of Backtesting Algorithmic Trading Strategies with Python, you’ll have a practical foundation in both Python-based quant development and portfolio strategy design.

Key learning outcomes include:

1. Setting Up Your Trading Project & Python Quant Lab

You’ll learn how to:

• Install and configure a Python quant stack suitable for algorithmic trading

• Set up a well-organized trading project structure

• Create and manage your Python environment for reproducible research and backtesting

• Prepare the tools you need to start building and testing portfolio strategies

This gives you the technical base to work like a quant, not just a casual trader.

2. Building Profitable, Portfolio-Based Algorithmic Strategies

You’ll move beyond single-stock ideas and into portfolio-oriented strategies, including:

• A core portfolio strategy based on volatility targeting with auto-rebalancing

• How to use Riskfolio-Lib to construct risk-managed portfolios

• Techniques to optimize portfolio construction, limit concentration, and manage volatility

• Insights inspired by institutional approaches (including the so-called “Ray Dalio Bridgewater cheat code” style concepts) to balance risk and return

You also receive 4 algorithmic portfolio trading strategies, giving you starting points you can test, study, and customize.

3. Backtesting the Right Way

You’ll learn how to run professional-style backtests that go far beyond simplistic historical simulations:

• How event-based backtesting works, and why it matters

• How to backtest portfolio strategies using Zipline Reloaded

• How to incorporate rebalancing logic into your strategies

• How to model slippage, trading commissions, and transaction costs to make results more realistic

• How to avoid common backtesting mistakes that can lead to misleading or overly optimistic results

This section ensures that when you say a strategy “works,” you’re basing that on careful, rigorous tests.

4. Bonus Systems and Data Workflows

The course also addresses major practical challenges—especially data and strategy diversity—through three key bonus modules:

• Bonus #1: Professional Market Data Backtesting

  • Code templates for using premium data on more than 21,000+ US equities

  • Methods for converting this data into Zipline bundles

  • Designed for when you’re ready to run higher-quality, professional-level backtests

  • (Requires a separate paid data subscription for this part)

• Bonus #2: Free Market Data Backtesting

  • Code templates for pulling and processing free market data

  • Converting free data into Zipline bundles

  • Ideal when you’re just starting and want to test ideas without ongoing data costs

• Bonus #3: Volatility Targeting Strategy Variations

  • Three advanced variants tailored for different market conditions:

    • Hierarchical Risk Parity (HRP)

    • CVaR (Conditional Value-at-Risk) risk measure

    • Risk factor models with principal component regression

  • These give you multiple frameworks for handling risk and correlation structures in your portfolio.

Core Benefits

By working through this course, you don’t just pick up isolated tricks—you build a repeatable, professional-style research and trading workflow. Core benefits include:

• A clear, end-to-end process

  • From environment setup to strategy design and backtesting, everything fits into one coherent framework.

• Greater confidence in your strategies

  • You’ll know how to test ideas properly rather than relying on intuition or unverified signals.

• Reduced reliance on risky single-stock bets

  • By focusing on portfolio construction, you distribute risk and aim for more stable performance.

• Hands-on coding experience in Python

  • You get comfortable using real quant libraries and tools used in industry-style workflows.

• Scalability for future strategies

  • Once you master the pipeline, you can plug in new data, assets, and ideas without starting from scratch.

• A stronger understanding of risk management

  • Concepts like volatility targeting, minimum return thresholds, and risk measures become practical tools you can apply immediately.

Who Should Take This Course?

This course is designed for learners who want to manage their investments more intelligently by using data and systematic methods. It’s a strong fit for:

• Beginner-to-intermediate traders

  • You have little or no experience with algorithmic trading but are motivated to learn a structured approach.

• Self-directed investors

  • You want to move beyond ad-hoc strategies, social media tips, or basic technical analysis.

• Developers and data professionals

  • You already know some Python and want to apply your skills to quant finance and portfolio management.

• Students and career-switchers interested in quant roles

  • This course gives you practical exposure to tools and workflows used in algorithmic trading.

• Anyone tired of high-risk, single-stock strategies

  • If you are looking for a more responsible, portfolio-centered way to grow your investments, this training is aligned with that goal.

Conclusion

Backtesting Algorithmic Trading Strategies with Python by Matt Dancho provides a practical, responsible path into algorithmic trading and portfolio design. Instead of chasing high-risk single-stock strategies, you learn to build and test portfolio-based systems grounded in volatility targeting, diversification, and realistic backtesting.

Within its 912 MB of resources—lesson materials, code templates, and project structures—you gain the tools to set up a Python Quant Lab, construct risk-managed portfolios, and evaluate your ideas under different market conditions. This is supported by valuable bonus modules on professional and free data workflows, as well as advanced variations of volatility targeting strategies.

At a price of USD 23.10, the course offers an accessible, concrete way to move from uncertainty and scattered strategies to a structured, professional-style research and backtesting process. While no course can remove all market risk, this program helps you approach your investment decisions with more clarity, discipline, and analytical rigor.

If you’re ready to stop guessing and start testing your strategies like a professional, Backtesting Algorithmic Trading Strategies with Python gives you the framework to build, evaluate, and refine systematic portfolios with confidence.