Backtesting a Trading Strategy in Python With AI Generated Code
Join Ryan O'Connell, CFA, FRM, as he guides you through backtesting a trading strategy using Python, enhanced with AI-generated code, to achieve optimal investment results. Start by setting up Datalore and creating a new Jupyter Notebook, then move on to retrieving and analyzing historical stock data to calculate daily returns. Learn how to identify the biggest losers each day and simulate a mean reversion trading strategy to evaluate its effectiveness. This tutorial also covers how to calculate key portfolio performance metrics, including Sharpe Ratio and Standard Deviation, and compares these against a benchmark. Finish with visual insights as you plot the growth of your portfolio and the benchmark over time, and download the code to apply these powerful techniques to your trading strategies.
🤖 Sign Up For Datalore:
https://jb.gg/check-out-datalore
💾 Download Free Code & AI Prompts Automatically:
https://jb.gg/datalore-report
🖺 Link to Full Article:
https://jb.gg/blog-datalore
Chapters
0:00 - The Trading Strategy We Will Backtest
1:34 - Signing Up for the Development Environment: Datalore
2:11 - Creating a New Jupyter Notebook
3:38 - Download The Free Python File & AI Prompts
4:35 - Retrieve Historical Stock Data
12:58 - Calculate Daily Stock Returns
14:57 - Identify the 10 Biggest Losers Each Day
17:06 - Simulate the Mean Reversion Trading Strategy
22:28- Calculate Portfolio Performance Metrics
25:01 - Compare Performance Metrics of Portfolio vs Benchmark
29:22 - Plot the Growth of Portfolio & Benchmark Overtime
32:10 - Check Out the Full Article
*Disclosure: This is not financial advice and should not be taken as such. The information contained in this video is an opinion. Some of the information could be wrong. This channel is owned and operated by Portfolio Constructs LLC.
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