Backtesting & Optimization Refresher

Fundamentals of Backtesting a Trading Agent

A reinforcement learning trading agent must be evaluated under various market conditions to assess its profitability. Here’s a concise guide:

Market Context Matters: Financial markets are unstable, requiring tests in multiple contexts.
Purpose of Backtesting: Simulates the strategy on historical data to gauge past performance, offering insights before involving real capital.
Diverse Time Periods: Include periods like the COVID-19 crash, present-day data, and both bullish and bearish markets to ensure varied testing scenarios.
Backtesting Steps:
1. Load a Trained Agent: Begin with a previously saved model.
2. Select Historical Data: Choose a specific period for testing.
3. Track Performance Metrics: Follow metrics such as profit, drawdown, and sharpe ratio. Record market indicators for each trade.
4. Run the Backtest: Let the agent trade over the chosen period.
5. Evaluate Results: Analyze performance metrics and compare with training data.
6. Adjust Strategy: Refine based on insights and filter out conditions, like high volatility, where the agent underperforms.

The upcoming lessons will delve deeper into encoding backtests in Python.

The following Investopedia Article gives a nice refresher of Backtesting.

SOLUTION:

To test the strategy's performance under a range of market conditions and assess its robustness

SOLUTION:

Include at least one period of abnormal market behavior, such as COVID-19

SOLUTION:

Adjust the strategy based on insights from the backtest and refine it iteratively