feat: add local PC support and setup instructions for backtesting (v1.7.8)

BACKTESTING_GUIDE.md

@@ -10,7 +10,23 @@ To run the backtester, use the following command:
 docker exec -it btc_ping_pong_bot python src/strategies/backtest_engine.py
 ```
 
-## 2. Backtest Engine (`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:
+1.  **Clone/Sync your repo** to your local machine.
+2.  **Install dependencies**:
+    ```bash
+    pip install -r requirements.txt
+    ```
+3.  **Configure `.env`**: Ensure your local `.env` file has the correct `DB_HOST` (e.g., `20.20.20.20`).
+4.  **Run the engine**:
+    ```bash
+    # 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:
@@ -25,23 +41,35 @@ The backtest engine reuses the core logic from `ping_pong_bot.py` via the `PingP
 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**. 
+## 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.
 
-### Installation:
-Inside the Docker container:
-```bash
-docker exec -it btc_ping_pong_bot pip install optuna
-```
+### Examples:
+*   **Test 15m SMA 200:**
+    ```bash
+    docker exec -it btc_ping_pong_bot python src/strategies/backtest_engine.py --ma_period 200 --ma_interval 15m
+    ```
+*   **Test 1h SMA 50:**
+    ```bash
+    docker exec -it btc_ping_pong_bot python src/strategies/backtest_engine.py --ma_period 50 --ma_interval 1h
+    ```
+*   **Test 4h SMA 100:**
+    ```bash
+    docker exec -it btc_ping_pong_bot python src/strategies/backtest_engine.py --ma_period 100 --ma_interval 4h --limit 20000
+    ```
 
-### 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
+### How it works:
+When `--ma_period` is provided, the engine:
+1.  Loads the MA timeframe data from the DB.
+2.  Merges it with the 1m price data.
+3.  Switches modes (`long` <=> `short`) whenever the 1m price crosses the MA.
+4.  **Automatically closes** the existing position on a mode switch, just like the live bot.
 
-## 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`.
+## 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.