feat: implement backtesting environment and refine strategy logic (v1.7.5)

- Extract strategy logic into a reusable PingPongStrategy class for bot and backtester.
- Create src/strategies/backtest_engine.py for local historical testing using DB data.
- Add BACKTESTING_GUIDE.md with instructions on how to use the new environment.
- Update dashboard to show Net Realized PnL (including fees).
- Verified bot and backtester compatibility with existing Docker setup.

BACKTESTING_GUIDE.md

@@ -0,0 +1,56 @@
+# 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.