Matt Dancho – Backtesting Algorithmic Trading Strategies with Python

Master Algorithmic Trading: Backtesting Strategies with Python

Dive into the world of algorithmic trading with Matt Dancho’s expert-led course, designed to teach you the ins and outs of backtesting trading strategies using Python. This course is perfect for both beginners and seasoned traders looking to refine their skills and create profitable trading strategies.

What You’ll Learn

This comprehensive course covers everything you need to know to set up, backtest, and implement successful trading strategies. Here’s a breakdown of the key skills you’ll acquire:

Setting Up Your Trading Portfolio

• Learn how to set up a trading portfolio project and Quant Lab software from scratch, even if you have no prior experience in algorithmic trading.

Algorithmic Portfolio Trading Strategies

• Gain access to four powerful algorithmic portfolio trading strategies designed to minimize risk and protect your investments. These strategies include volatility targeting, portfolio construction optimization, and setting minimum return thresholds.

Professional Backtesting Techniques

• Master the art of backtesting your portfolio trading strategy to see how it would have performed under various market conditions. This step is crucial for ensuring the robustness and profitability of your strategies.

Making Trading a Reality

• Turn your trading aspirations into reality without the stress and sleepless nights. Learn how to grow your investment portfolio effectively and efficiently.

Course Breakdown: Your Path to Mastery

Step 1: Trading Project and Python Quant Lab Setup

• Get the Quant Stack Python Software installed and ready for action.

• Set up your algorithmic trading project from scratch.

• Create your Python environment tailored for trading.

• Everything you need to begin building and backtesting portfolio trading strategies.

Step 2: Creating a Profitable Algorithmic Portfolio Trading Strategy

• Learn our top portfolio-based trading strategy: Volatility targeting with auto-rebalancing.

• Access our code template for constructing a risk-managed portfolio using the Riskfolio-Lib Python library.

• Discover how to increase returns using the “Ray Dalio Bridgewater Cheat Code”.

Step 3: Backtesting the Right Way

• Detailed walkthrough of event-based backtesting to ensure accuracy.

• Backtested portfolio strategies with Zipline Reloaded for robust performance analysis.

• Learn how to avoid common mistakes in backtesting portfolios.

• Include rebalancing, slippage, and trading commissions in your backtests for realistic results.

Exclusive Bonuses

Bonus #1: Code to Backtest 21,000+ US Equities Using Premium Data

• Solves the “I need professional market data for high-quality backtesting” problem.

• Get code templates for ingesting professional data for 21,000+ US Equities.

• Convert data to Zipline Bundles for seamless backtesting.

• Requires a $50/month Premium Market Data Subscription (only needed for this section of the course).

Bonus #2: Code to Use Free Market Data for Backtesting

• Solves the “I need free market data for when I am first beginning to backtest non-professionally” problem.

• Get code templates for ingesting free market data.

• Convert the data to Zipline Bundles for easy backtesting.

• Does NOT require a data subscription—it’s free data!

Bonus #3: Top 3 Variations of Volatility Targeting Strategy

• Solves the “I need more portfolio trading strategies for different market conditions” problem.

• Get 3 different variants of the portfolio-based algorithmic trading strategy.

• Variant #1: Hierarchical Risk Parity.

• Variant #2: CVaR Risk Measure.

• Variant #3: Risk Factor with Principal Component Regression.

Downloadable Course Content

• Step 1: Trading Project and Python Quant Lab Setup

  • Get the Quant Stack Python Software installed and ready for action.
  • Set up your algorithmic trading project from scratch.
  • Create your Python environment tailored for trading.
  • Everything you need to begin building and backtesting portfolio trading strategies.

• Step 2: How to Create a Profitable Algorithmic Portfolio Trading Strategy

  • Get our top portfolio-based trading strategy: Volatility targeting with auto-rebalancing.
  • Access our code template for constructing a risk-managed portfolio using 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

  • Detailed walkthrough of event-based backtesting to ensure accuracy.
  • Backtested portfolio strategies with Zipline Reloaded for robust performance analysis.
  • Learn how to avoid common mistakes in backtesting portfolios.
  • Include rebalancing, slippage, and trading commissions in your backtests for realistic results.