*How to Backtest the Awesome Oscillator Trading Strategy in Python (Step-by-Step Tutorial)*

In this video, I walk you through a complete backtest of the Awesome Oscillator strategy in Python. We'll build the backtest from scratch, explore the strategy’s logic, and compare the results against random entries to see if the signals actually outperform.

*What’s in This Video?*
Overview of the Awesome Oscillator strategy and its “saucer” pattern
Step-by-step coding of the backtest in Python
Profit factor analysis and performance comparison
Evaluation of whether the strategy outperforms random entries

By the end of this video, you’ll not only have a working backtest but also a reusable framework that you can modify to test different strategies, instruments, and timeframes.

*Backtest Details*
Market Tested: S&P 500 (SPX500)
Timeframe: 1-Hour Chart
Indicators Used: Awesome Oscillator (AO)
Entry Criteria: Saucer pattern with 2 red bars followed by 1 green bar above the zero line

*Tools Used for Backtesting*
Python
Pandas, NumPy, Matplotlib
pandas_ta for indicator calculations

*Source Code*
The full backtest code is available on GitHub: github.com/russs123/backtests

If you found this tutorial helpful, let me know in the comments which other strategy you’d like me to backtest next.

*Disclaimer*
This content is for informational and educational purposes only and does not constitute financial advice. Always do your own research before making any trading decisions.

*Chapters:*
00:00 – Intro
00:20 – Strategy Explanation
01:10 – Load Price Data
02:50 – Calculate Indicator
04:35 – Generate Signals
07:10 - Calculate Returns
11:35 - Plot Results
12:20 - Random Entries
13:25 - Compare Results

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