Build and Backtest Algorithmic Trading Portfolios with Python – Quant Science

• 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

Build And Backtest A Profitable Investment Portfolio Responsibly WITHOUT Risky Single-Stock Strategies Or Algorithmic Trading Experience.

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

LISTEN, WE REALIZE THAT:
1. You need professional market data for high-quality backtests
2. You need free market data for when you are first beginning to backtest non-professionally
3. You need more portfolio trading strategies for different market conditions

Bonus #1: Code to Backtest 21,000+ US Equities using Premium Data ($1,500 Value)

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

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