Ping-Pong Bot: Backtesting & Optimization Guide

This guide explains how to use the local backtesting environment to test and optimize your BTC trading strategy using historical data from your PostgreSQL database.

1. Prerequisites

The backtesting engine requires pandas, numpy, and asyncpg. These are already installed in your btc_ping_pong_bot Docker container.

To run the backtester, use the following command:

docker exec -it btc_ping_pong_bot python src/strategies/backtest_engine.py

2. Local PC Setup (Recommended for Memory Savings)

Running the backtest on your local machine is much faster and saves server memory.

Steps:

Clone/Sync your repo to your local machine.
Install dependencies:
```
pip install -r requirements.txt
```
Configure .env: Ensure your local .env file has the correct DB_HOST (e.g., 20.20.20.20).

Run the engine:

# From the project root
python3 src/strategies/backtest_engine.py --start_date 2024-01-01 --end_date 2024-01-31

3. Backtest Engine (`backtest_engine.py`)

The backtest engine reuses the core logic from ping_pong_bot.py via the PingPongStrategy class.

Key Features:

Virtual Exchange: Simulates a $1,000 account with customizable leverage and fees.
Fee Simulation: Applies a 0.05% taker fee (configurable) to every entry and exit.
Mark-to-Market: Calculates real-time equity based on current price and position size.
Data Sourcing: Automatically pulls the last 10,000 candles from your btc_data database.

How to Run:

# From the project root
docker exec -it btc_ping_pong_bot python src/strategies/backtest_engine.py

3. Regime Testing (MA Switching)

You can test different Moving Average (MA) settings to see which regime detector works best for switching between long and short modes.

Examples:

Test 15m SMA 200:

docker exec -it btc_ping_pong_bot python src/strategies/backtest_engine.py --ma_period 200 --ma_interval 15m

Test 1h SMA 50:

docker exec -it btc_ping_pong_bot python src/strategies/backtest_engine.py --ma_period 50 --ma_interval 1h

Test 4h SMA 100:

docker exec -it btc_ping_pong_bot python src/strategies/backtest_engine.py --ma_period 100 --ma_interval 4h --limit 20000

How it works:

When --ma_period is provided, the engine:

Loads the MA timeframe data from the DB.
Merges it with the 1m price data.
Switches modes (long <=> short) whenever the 1m price crosses the MA.
Automatically closes the existing position on a mode switch, just like the live bot.

4. Parameter Overrides

You can quickly override strategy settings without editing the config file:

--direction: Force a specific mode (long or short).
--limit: Change the number of 1m candles to test (default 10,000).
--config: Use a different configuration file.

5. Interpreting Results

Final Equity: Your simulated account balance after all trades.
ROI: Return on Investment (Percentage).
Total Fees: Total cost paid to the "Virtual Exchange". High fees indicate over-trading.
Trade Count: Total number of Enter/Exit signals triggered.

6. Next Steps

Run the backtester to see baseline performance.
Adjust parameters in config/ping_pong_config.yaml.
Rerun the backtest to see the impact of your changes.
(Optional) Ask me to implement the optimize_strategy.py script once you have Optuna installed.

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