# 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:
```bash
docker exec -it btc_ping_pong_bot python src/strategies/backtest_engine.py
```

## 2. 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:
```bash
# From the project root
docker exec -it btc_ping_pong_bot python src/strategies/backtest_engine.py
```

## 3. Strategy Optimization (Optional)
To find the absolute best parameters for RSI and Hurst, you can use **Optuna**. 

### Installation:
Inside the Docker container:
```bash
docker exec -it btc_ping_pong_bot pip install optuna
```

### Planned Optimizer (`optimize_strategy.py`):
Once installed, we can implement an optimization script that searches for:
*   `rsi_period`: 7 to 21
*   `hurst_multiplier`: 1.2 to 2.5
*   `partial_exit_pct`: 0.05 to 0.30

## 4. Local DB Data
The engine connects to your local PostgreSQL DB using the credentials in your `.env` file. It specifically queries the `candles` table for the symbol and interval defined in `config/ping_pong_config.yaml`.

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