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.

AGENTS.md

@@ -36,10 +36,10 @@ uvicorn src.api.server:app --reload --host 0.0.0.0 --port 8000
 ### Testing
 ```bash
 # Test database connection
-python test_db.py
+python -c "from src.data_collector.database import get_db; print('Database connection test successful')"
 
-# Run single test (no existing test framework found but for any future tests)
-python -m pytest <test_file>.py::test_<function_name> -v
+# Run single test (using pytest framework)
+python -m pytest tests/ -v -k "test_function_name"
 ```
 
 ### Environment 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.

config/ping_pong_config.yaml

@@ -1,7 +1,7 @@
 # Ping-Pong Strategy Configuration
 
 # Trading Pair & Timeframe
-symbol: BTCUSDT
+symbol: BTCUSD
 interval: "1"  # Minutes (1, 3, 5, 15, 30, 60, 120, 240, 360, 720, D, W, M)
 
 # Indicator Settings
@@ -9,25 +9,36 @@ rsi:
   period: 14
   overbought: 70
   oversold: 30
+  TF: 1 # same as symbol's interval
   enabled_for_open: true
   enabled_for_close: true
   
+
 hurst:
   period: 30
   multiplier: 1.8
+  TF: 1 # same as symbol's interval
   enabled_for_open: true
   enabled_for_close: true
 
 # Strategy Settings
 direction: "long"  # "long" or "short"
 capital: 1000.0    # Initial capital for calculations (informational)
-exchange_leverage: 3.0  # Multiplier for each 'ping' size
-max_effective_leverage: 1.0  # Cap on total position size relative to equity
+leverage_long: 10.0    # Leverage for LONG mode
+leverage_short: 5.0   # Leverage for SHORT mode
+max_effective_leverage: 2.5  # Cap on total position size relative to equity
 pos_size_margin: 20.0  # Margin per 'ping' (USD)
-take_profit_pct: 1.5   # Target profit percentage per exit (1.5 = 1.5%)
+#take_profit_pct: 1.5   # Target profit percentage per exit (1.5 = 1.5%)
 partial_exit_pct: 0.15 # 15% of position closed on each TP hit
 min_position_value_usd: 15.0 # Minimum remaining value to keep position open
 
 # Execution Settings
-loop_interval_seconds: 10 # How often to check for new data
+loop_interval_seconds: 5 # How often to check for new data
 debug_mode: false
+
+# Robustness Settings
+robustness:
+  enabled: true
+  max_retries: 3
+  retry_window_seconds: 300 # 5 minutes
+  autostart_on_reboot: true

src/strategies/backtest_engine.py

@@ -0,0 +1,142 @@
+import pandas as pd
+import numpy as np
+import yaml
+import os
+import asyncio
+import asyncpg
+from datetime import datetime
+from ping_pong_bot import PingPongStrategy
+
+class BacktestEngine:
+    def __init__(self, config_path="config/ping_pong_config.yaml"):
+        with open(config_path, 'r') as f:
+            self.config = yaml.safe_load(f)
+        
+        self.strategy = PingPongStrategy(self.config)
+        self.direction = self.config.get('direction', 'long')
+        self.strategy.direction = self.direction
+        
+        # Virtual Exchange State
+        self.balance = 1000.0  # Starting USD
+        self.equity = 1000.0
+        self.position_size = 0.0 # BTC
+        self.position_value = 0.0 # USD
+        self.entry_price = 0.0
+        
+        # Settings
+        self.fee_rate = 0.0005 # 0.05% Taker
+        self.leverage = float(self.config.get('leverage_long' if self.direction == 'long' else 'leverage_short', 5.0))
+        self.pos_size_margin = float(self.config.get('pos_size_margin', 20.0))
+        self.partial_exit_pct = float(self.config.get('partial_exit_pct', 0.15))
+        
+        self.trades = []
+        self.equity_curve = []
+
+    async def load_data(self, symbol, interval, limit=5000):
+        conn = await asyncpg.connect(
+            host=os.getenv('DB_HOST', '20.20.20.20'),
+            port=int(os.getenv('DB_PORT', 5433)),
+            user=os.getenv('DB_USER', 'btc_bot'),
+            password=os.getenv('DB_PASSWORD', ''),
+            database=os.getenv('DB_NAME', 'btc_data')
+        )
+        rows = await conn.fetch('''
+            SELECT time, open, high, low, close, volume 
+            FROM candles 
+            WHERE symbol = $1 AND interval = $2 
+            ORDER BY time ASC LIMIT $3
+        ''', symbol, interval, limit)
+        await conn.close()
+        
+        df = pd.DataFrame([dict(r) for r in rows])
+        df[['open', 'high', 'low', 'close', 'volume']] = df[['open', 'high', 'low', 'close', 'volume']].astype(float)
+        return df
+
+    def run(self, df):
+        print(f"Starting backtest on {len(df)} candles...")
+        df = self.strategy.calculate_indicators(df)
+        
+        # Start after enough candles for indicators
+        start_idx = max(self.config['rsi']['period'], self.config['hurst']['period']) + 5
+        
+        for i in range(start_idx, len(df)):
+            current_df = df.iloc[:i+1]
+            price = df.iloc[i]['close']
+            time = df.iloc[i]['time']
+            
+            signal = self.strategy.check_signals(current_df)
+            
+            if signal == "open":
+                # Entry Logic
+                qty_usd = self.pos_size_margin * self.leverage
+                qty_btc = qty_usd / price
+                fee = qty_usd * self.fee_rate
+                
+                self.balance -= fee
+                
+                if self.direction == "long":
+                    self.position_size += qty_btc
+                else: # Short
+                    self.position_size -= qty_btc
+                
+                self.entry_price = price # Simplified avg entry
+                self.trades.append({"time": time, "type": "Enter", "price": price, "fee": fee})
+                
+            elif signal == "close" and abs(self.position_size) > 0:
+                # Exit Logic
+                qty_btc_exit = abs(self.position_size) * self.partial_exit_pct
+                qty_usd_exit = qty_btc_exit * price
+                fee = qty_usd_exit * self.fee_rate
+                
+                # Realized PnL
+                if self.direction == "long":
+                    pnl = qty_btc_exit * (price - self.entry_price)
+                    self.position_size -= qty_btc_exit
+                else: # Short
+                    pnl = qty_btc_exit * (self.entry_price - price)
+                    self.position_size += qty_btc_exit
+                
+                self.balance += (pnl - fee)
+                self.trades.append({"time": time, "type": "Exit", "price": price, "pnl": pnl, "fee": fee})
+
+            # Mark to Market Equity
+            unrealized = 0
+            if self.direction == "long":
+                unrealized = self.position_size * (price - self.entry_price) if self.position_size > 0 else 0
+            else:
+                unrealized = abs(self.position_size) * (self.entry_price - price) if self.position_size < 0 else 0
+            
+            self.equity = self.balance + unrealized
+            self.equity_curve.append({"time": time, "equity": self.equity})
+
+        self.print_results()
+
+    def print_results(self):
+        total_pnl = self.equity - 1000.0
+        roi = (total_pnl / 1000.0) * 100
+        fees = sum(t['fee'] for t in self.trades)
+        
+        print("\n" + "="*30)
+        print(" BACKTEST RESULTS ")
+        print("="*30)
+        print(f"Total Trades:  {len(self.trades)}")
+        print(f"Final Equity:  ${self.equity:.2f}")
+        print(f"Total PnL:     ${total_pnl:.2f}")
+        print(f"ROI:           {roi:.2f}%")
+        print(f"Total Fees:    ${fees:.2f}")
+        print("="*30)
+
+async def main():
+    engine = BacktestEngine()
+    # Assume BTC/1m for now
+    symbol = engine.config['symbol'].replace("USDT", "").replace("USD", "")
+    interval = engine.config['interval'] + "m"
+    
+    df = await engine.load_data(symbol, interval, limit=10000)
+    if not df.empty:
+        engine.run(df)
+    else:
+        print("No data found in DB.")
+
+if __name__ == "__main__":
+    asyncio.run(main())

src/strategies/ping_pong_bot.py

@@ -84,12 +84,93 @@ class DatabaseManager:
             logger.error(f"DB Query Error for {symbol} {interval}: {e}")
             return []
 
+class PingPongStrategy:
+    """Core Strategy Logic for Ping-Pong Scalping"""
+    def __init__(self, config):
+        self.config = config
+        self.direction = config.get('direction', 'long')
+
+    def rma(self, series, length):
+        alpha = 1 / length
+        return series.ewm(alpha=alpha, adjust=False).mean()
+
+    def calculate_indicators(self, df):
+        # RSI
+        rsi_cfg = self.config['rsi']
+        delta = df['close'].diff()
+        gain = delta.where(delta > 0, 0)
+        loss = -delta.where(delta < 0, 0)
+        df['rsi'] = 100 - (100 / (1 + (self.rma(gain, rsi_cfg['period']) / self.rma(loss, rsi_cfg['period']))))
+        
+        # Hurst
+        hurst_cfg = self.config['hurst']
+        mcl = hurst_cfg['period'] / 2
+        mcl_2 = int(round(mcl / 2))
+        df['tr'] = np.maximum(df['high'] - df['low'], np.maximum(abs(df['high'] - df['close'].shift(1)), abs(df['low'] - df['close'].shift(1))))
+        df['ma_mcl'] = self.rma(df['close'], mcl)
+        df['atr_mcl'] = self.rma(df['tr'], mcl)
+        df['center'] = df['ma_mcl'].shift(mcl_2).fillna(df['ma_mcl'])
+        mcm_off = hurst_cfg['multiplier'] * df['atr_mcl']
+        df['hurst_upper'] = df['center'] + mcm_off
+        df['hurst_lower'] = df['center'] - mcm_off
+        return df
+
+    def check_signals(self, df):
+        if len(df) < 3: return None
+        # finished = candle that just closed (e.g. 10:30)
+        # prev = candle before that (e.g. 10:29)
+        finished = df.iloc[-2]
+        prev = df.iloc[-3]
+        
+        rsi_cfg, hurst_cfg = self.config['rsi'] or {}, self.config['hurst'] or {}
+        
+        def is_crossing_up(p_val, p_band, c_open, c_close, c_band):
+            # 1. Crossed up BETWEEN candles
+            between = p_val < p_band and c_close >= c_band
+            # 2. Crossed up WITHIN this candle
+            within = c_open is not None and c_open < c_band and c_close >= c_band
+            return between or within
+
+        def is_crossing_down(p_val, p_band, c_open, c_close, c_band):
+            # 1. Crossed down BETWEEN candles
+            between = p_val > p_band and c_close <= c_band
+            # 2. Crossed down WITHIN this candle
+            within = c_open is not None and c_open > c_band and c_close <= c_band
+            return between or within
+
+        # Hurst Signals
+        h_upper_cross_down = is_crossing_down(prev['close'], prev['hurst_upper'], finished['open'], finished['close'], finished['hurst_upper'])
+        h_lower_cross_down = is_crossing_down(prev['close'], prev['hurst_lower'], finished['open'], finished['close'], finished['hurst_lower'])
+
+        # RSI Signals
+        rsi_cross_up = is_crossing_up(prev['rsi'], rsi_cfg.get('oversold', 30), None, finished['rsi'], rsi_cfg.get('oversold', 30))
+        rsi_cross_down = is_crossing_down(prev['rsi'], rsi_cfg.get('overbought', 70), None, finished['rsi'], rsi_cfg.get('overbought', 70))
+        
+        l_open = (rsi_cfg.get('enabled_for_open') and rsi_cross_up) or \
+                 (hurst_cfg.get('enabled_for_open') and h_lower_cross_down)
+        
+        l_close = (rsi_cfg.get('enabled_for_close') and rsi_cross_down) or \
+                  (hurst_cfg.get('enabled_for_close') and h_upper_cross_down)
+        
+        s_open = (rsi_cfg.get('enabled_for_open') and rsi_cross_down) or \
+                 (hurst_cfg.get('enabled_for_open') and h_upper_cross_down)
+        
+        s_close = (rsi_cfg.get('enabled_for_close') and rsi_cross_up) or \
+                  (hurst_cfg.get('enabled_for_close') and h_lower_cross_down)
+
+        if self.direction == 'long':
+            return "open" if l_open else ("close" if l_close else None)
+        else:
+            return "open" if s_open else ("close" if s_close else None)
+
 class PingPongBot:
     def __init__(self, config_path="config/ping_pong_config.yaml"):
-        self.version = "1.5.7"
+        self.version = "1.7.5"
         with open(config_path, 'r') as f:
             self.config = yaml.safe_load(f)
         
+        self.strategy = PingPongStrategy(self.config)
+        
         # Explicitly load from ENV to ensure they are available
         self.api_key = os.getenv("BYBIT_API_KEY") or os.getenv("API_KEY")
         self.api_secret = os.getenv("BYBIT_API_SECRET") or os.getenv("API_SECRET")
@@ -132,51 +213,77 @@ class PingPongBot:
         
         # Bot State
         self.last_candle_time = None
+        self.last_candle_open = 0.0
+        self.last_candle_close = 0.0
         self.last_candle_price = 0.0
         self.current_indicators = {
             "rsi": {"value": 0.0, "timestamp": "N/A"},
             "hurst_lower": {"value": 0.0, "timestamp": "N/A"},
             "hurst_upper": {"value": 0.0, "timestamp": "N/A"}
         }
+        self.failure_history = []
         self.position = None
         self.wallet_balance = 0
+        self.available_balance = 0
+        self.start_equity = 0.0
+        self.start_equity_btc = 0.0
+        self.session_pnl = 0.0
+        self.session_pnl_btc = 0.0
+        self.total_fees = 0.0
+        self.total_realized_pnl = 0.0
         self.market_price = 0.0
         self.status_msg = "Initializing..."
         self.last_signal = None
         self.start_time = datetime.now()
         self.console = Console()
         
+        # Transaction Logging
+        self.tx_log_path = "logs/ping_pong_transactions.csv"
+        self._init_tx_log()
+        
         # Fixed Parameters from Config
         self.partial_exit_pct = float(self.config.get('partial_exit_pct', 0.15))
         self.min_val_usd = float(self.config.get('min_position_value_usd', 15.0))
         self.pos_size_margin = float(self.config.get('pos_size_margin', 20.0))
-        self.leverage = float(self.config.get('exchange_leverage', 3.0))
+        self.leverage_long = float(self.config.get('leverage_long', 10.0))
+        self.leverage_short = float(self.config.get('leverage_short', 3.0))
+        self.leverage = 1.0 # Current leverage
         self.max_eff_lev = float(self.config.get('max_effective_leverage', 1.0))
 
-    def rma(self, series, length):
-        alpha = 1 / length
-        return series.ewm(alpha=alpha, adjust=False).mean()
+    def _init_tx_log(self):
+        """Ensures CSV header exists and is up to date"""
+        header = "time,version,direction,symbol,trade,qty,price,leverage,pnl,fee,status\n"
+        if not os.path.exists(self.tx_log_path):
+            os.makedirs(os.path.dirname(self.tx_log_path), exist_ok=True)
+            with open(self.tx_log_path, 'w') as f:
+                f.write(header)
+        else:
+            # Check if we need to update the header from 'side' to 'trade'
+            try:
+                with open(self.tx_log_path, 'r') as f:
+                    first_line = f.readline()
+                if "side" in first_line:
+                    with open(self.tx_log_path, 'r') as f:
+                        lines = f.readlines()
+                    if lines:
+                        lines[0] = header
+                        with open(self.tx_log_path, 'w') as f:
+                            f.writelines(lines)
+                        logger.info("Updated CSV log header: 'side' -> 'trade'")
+            except Exception as e:
+                logger.error(f"Failed to update CSV header: {e}")
+
+    async def log_transaction(self, trade, qty, price, pnl=0, fee=0, status="Success"):
+        """Appends a trade record to CSV"""
+        try:
+            with open(self.tx_log_path, 'a') as f:
+                t_str = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+                f.write(f"{t_str},{self.version},{self.direction},{self.symbol},{trade},{qty},{price},{self.leverage},{pnl},{fee},{status}\n")
+        except Exception as e:
+            logger.error(f"Failed to write to CSV log: {e}")
 
     def calculate_indicators(self, df):
-        # RSI
-        rsi_cfg = self.config['rsi']
-        delta = df['close'].diff()
-        gain = delta.where(delta > 0, 0)
-        loss = -delta.where(delta < 0, 0)
-        df['rsi'] = 100 - (100 / (1 + (self.rma(gain, rsi_cfg['period']) / self.rma(loss, rsi_cfg['period']))))
-        
-        # Hurst
-        hurst_cfg = self.config['hurst']
-        mcl = hurst_cfg['period'] / 2
-        mcl_2 = int(round(mcl / 2))
-        df['tr'] = np.maximum(df['high'] - df['low'], np.maximum(abs(df['high'] - df['close'].shift(1)), abs(df['low'] - df['close'].shift(1))))
-        df['ma_mcl'] = self.rma(df['close'], mcl)
-        df['atr_mcl'] = self.rma(df['tr'], mcl)
-        df['center'] = df['ma_mcl'].shift(mcl_2).fillna(df['ma_mcl'])
-        mcm_off = hurst_cfg['multiplier'] * df['atr_mcl']
-        df['hurst_upper'] = df['center'] + mcm_off
-        df['hurst_lower'] = df['center'] - mcm_off
-        
+        df = self.strategy.calculate_indicators(df)
         last_row = df.iloc[-1]
         now_str = datetime.now().strftime("%H:%M:%S")
         self.current_indicators["rsi"] = {"value": float(last_row['rsi']), "timestamp": now_str}
@@ -214,19 +321,25 @@ class PingPongBot:
                     await self.close_all_positions()
                 
                 self.direction = new_direction
+                self.strategy.direction = new_direction
                 if self.direction == "long":
                     self.category = "inverse"
                     self.symbol = f"{self.base_coin}USD"
                     self.settle_coin = self.base_coin
+                    self.leverage = self.leverage_long
                 else:
                     self.category = "linear"
                     self.symbol = "BTCPERP" if self.base_coin == "BTC" else f"{self.base_coin}USDC"
                     self.settle_coin = "USDC"
+                    self.leverage = self.leverage_short
                 
                 # Perform swap
                 await self.swap_assets(new_direction)
                 
-                logger.info(f"Bot configured for {self.direction.upper()} | Symbol: {self.symbol} | Category: {self.category}")
+                # Sync Leverage with Bybit
+                await self.set_exchange_leverage()
+                
+                logger.info(f"Bot configured for {self.direction.upper()} | Symbol: {self.symbol} | Category: {self.category} | Leverage: {self.leverage}")
                 self.last_candle_time = None 
                 return True
             
@@ -236,6 +349,26 @@ class PingPongBot:
             self.status_msg = f"Dir Error: {str(e)[:20]}"
             return False
 
+    async def set_exchange_leverage(self):
+        """Points Bybit API to set account leverage for current category/symbol"""
+        try:
+            if not self.category or not self.symbol: return
+            logger.info(f"Setting exchange leverage to {self.leverage}x for {self.symbol}...")
+            res = await asyncio.to_thread(self.session.set_leverage,
+                category=self.category,
+                symbol=self.symbol,
+                buyLeverage=str(self.leverage),
+                sellLeverage=str(self.leverage)
+            )
+            if res['retCode'] == 0:
+                logger.info(f"Leverage successfully set to {self.leverage}x")
+            elif res['retCode'] == 110043: # Leverage not modified
+                logger.info(f"Leverage is already {self.leverage}x")
+            else:
+                logger.warning(f"Bybit Leverage Warning: {res['retMsg']} (Code: {res['retCode']})")
+        except Exception as e:
+            logger.error(f"Failed to set leverage on Bybit: {e}")
+
     async def close_all_positions(self):
         """Closes any active position in the current category/symbol"""
         try:
@@ -305,30 +438,25 @@ class PingPongBot:
             if wallet['retCode'] == 0:
                 res_list = wallet['result']['list']
                 if res_list:
-                    self.wallet_balance = float(res_list[0].get('totalWalletBalance', 0))
+                    # Use totalEquity for NAV (Net Asset Value) tracking
+                    current_equity = float(res_list[0].get('totalEquity', 0))
+                    self.wallet_balance = current_equity
+                    self.available_balance = float(res_list[0].get('totalAvailableBalance', 0))
+                    
+                    # Calculate BTC-equivalent equity
+                    current_equity_btc = current_equity / max(self.market_price, 1)
+                    
+                    if self.start_equity == 0.0:
+                        self.start_equity = current_equity
+                        self.start_equity_btc = current_equity_btc
+                    
+                    self.session_pnl = current_equity - self.start_equity
+                    self.session_pnl_btc = current_equity_btc - self.start_equity_btc
         except Exception as e:
             logger.error(f"Exchange Sync Error: {e}")
 
     def check_signals(self, df):
-        if len(df) < 2: return None
-        last, prev = df.iloc[-1], df.iloc[-2]
-        rsi_cfg, hurst_cfg = self.config['rsi'] or {}, self.config['hurst'] or {}
-        
-        # Signals defined by crossover
-        l_open = (rsi_cfg.get('enabled_for_open') and prev['rsi'] < rsi_cfg.get('oversold', 30) and last['rsi'] >= rsi_cfg.get('oversold', 30)) or \
-                 (hurst_cfg.get('enabled_for_open') and prev['close'] > prev['hurst_lower'] and last['close'] <= last['hurst_lower'])
-        l_close = (rsi_cfg.get('enabled_for_close') and prev['rsi'] > rsi_cfg.get('overbought', 70) and last['rsi'] <= rsi_cfg.get('overbought', 70)) or \
-                  (hurst_cfg.get('enabled_for_close') and prev['close'] < prev['hurst_upper'] and last['close'] >= last['hurst_upper'])
-        
-        s_open = (rsi_cfg.get('enabled_for_open') and prev['rsi'] > rsi_cfg.get('overbought', 70) and last['rsi'] <= rsi_cfg.get('overbought', 70)) or \
-                 (hurst_cfg.get('enabled_for_open') and prev['close'] < prev['hurst_upper'] and last['close'] >= last['hurst_upper'])
-        s_close = (rsi_cfg.get('enabled_for_close') and prev['rsi'] < rsi_cfg.get('oversold', 30) and last['rsi'] >= rsi_cfg.get('oversold', 30)) or \
-                  (hurst_cfg.get('enabled_for_close') and prev['close'] > prev['hurst_lower'] and last['close'] <= last['hurst_lower'])
-
-        if self.direction == 'long':
-            return "open" if l_open else ("close" if l_close else None)
-        else:
-            return "open" if s_open else ("close" if s_close else None)
+        return self.strategy.check_signals(df)
 
     async def execute_trade(self, signal):
         if not signal or not self.market_price: return
@@ -359,7 +487,10 @@ class PingPongBot:
     async def place_order(self, qty, is_close=False):
         if not self.category or not self.symbol: return
         side = "Sell" if (self.direction == "long" and is_close) or (self.direction == "short" and not is_close) else "Buy"
-        pos_idx = 1 if self.direction == "long" else 2
+        trade = "Exit" if is_close else "Enter"
+        
+        # Using positionIdx=0 for One-Way Mode to avoid Error 10001
+        pos_idx = 0
         try:
             qty_str = str(int(qty)) if self.category == "inverse" else str(round(qty, 3))
             
@@ -368,10 +499,46 @@ class PingPongBot:
                 qty=qty_str, reduceOnly=is_close, positionIdx=pos_idx
             )
             if res['retCode'] == 0:
-                self.last_signal = f"{side} {qty_str}"
-                self.status_msg = f"Order Success: {side}"
+                order_id = res['result']['orderId']
+                self.last_signal = f"{trade} {qty_str}"
+                self.status_msg = f"Order Success: {trade}"
+                
+                # Fetch execution details for fees and PnL
+                await asyncio.sleep(1.5) # Wait for fill and indexing
+                exec_info = await asyncio.to_thread(self.session.get_executions, 
+                                                    category=self.category, 
+                                                    symbol=self.symbol, 
+                                                    orderId=order_id)
+                
+                exec_fee = 0.0
+                exec_pnl = 0.0
+                exec_price = self.market_price
+                
+                if exec_info['retCode'] == 0 and exec_info['result']['list']:
+                    fills = exec_info['result']['list']
+                    # Fees and closedPnl are in settleCoin (BTC for inverse, USDC for linear)
+                    exec_fee = sum(float(f.get('execFee', 0)) for f in fills)
+                    exec_pnl = sum(float(f.get('closedPnl', 0)) for f in fills)
+                    exec_price = float(fills[0].get('execPrice', self.market_price))
+                    
+                    # Convert to USD if in BTC for consistent tracking
+                    if self.category == "inverse":
+                        usd_fee = exec_fee * exec_price
+                        usd_pnl = exec_pnl * exec_price
+                    else:
+                        usd_fee = exec_fee
+                        usd_pnl = exec_pnl
+                        
+                    self.total_fees += usd_fee
+                    self.total_realized_pnl += usd_pnl
+                    
+                    await self.log_transaction(trade, qty_str, exec_price, pnl=usd_pnl, fee=usd_fee, status="Filled")
+                else:
+                    await self.log_transaction(trade, qty_str, self.market_price, status="Filled (No Exec Info)")
             else:
                 self.status_msg = f"Order Error: {res['retMsg']}"
+                logger.error(f"Bybit Order Error: {res['retMsg']} (Code: {res['retCode']})")
+                await self.log_transaction(trade, qty_str, self.market_price, status=f"Error: {res['retMsg']}")
         except Exception as e:
             logger.error(f"Trade Error: {e}")
 
@@ -382,7 +549,24 @@ class PingPongBot:
         cfg_table.add_column("Property"); cfg_table.add_column("Value")
         cfg_table.add_row("Symbol", self.symbol or "N/A"); cfg_table.add_row("Category", self.category or "N/A")
         cfg_table.add_row("Market Price", f"${self.market_price:.2f}"); cfg_table.add_row("SMA(44, 1D)", f"${self.ma_44_val:.2f}")
-        cfg_table.add_row("Last Candle", f"{self.last_candle_time} (@${self.last_candle_price:.2f})")
+        cfg_table.add_row("Last Candle", f"{self.last_candle_time}")
+        cfg_table.add_row("Candle O / C", f"${self.last_candle_open:.2f} / ${self.last_candle_close:.2f}")
+        cfg_table.add_row("Leverage", f"{self.leverage}x")
+        
+        # Running Stats
+        runtime = datetime.now() - self.start_time
+        runtime_str = str(runtime).split('.')[0] # Remove microseconds
+        pnl_color = "green" if self.session_pnl >= 0 else "red"
+        pnl_btc_color = "green" if self.session_pnl_btc >= 0 else "red"
+        
+        net_realized_pnl = self.total_realized_pnl - self.total_fees
+        
+        cfg_table.add_row("Running Time", runtime_str)
+        cfg_table.add_row("Session PnL (USD)", f"[bold {pnl_color}]{'$' if self.session_pnl >= 0 else '-$'}{abs(self.session_pnl):.2f}[/]")
+        cfg_table.add_row("Session PnL (BTC)", f"[bold {pnl_btc_color}]{'{:+.6f}'.format(self.session_pnl_btc)} BTC[/]")
+        cfg_table.add_row("Total Fees", f"[bold red]-${self.total_fees:.2f}[/]")
+        cfg_table.add_row("Gross Realized PnL", f"[bold {'green' if self.total_realized_pnl >= 0 else 'red'}]${self.total_realized_pnl:.2f}[/]")
+        cfg_table.add_row("Net Realized PnL", f"[bold {'green' if net_realized_pnl >= 0 else 'red'}]${net_realized_pnl:.2f}[/]")
         
         ind_table = Table(title="INDICATORS", box=box.ROUNDED, expand=True)
         ind_table.add_column("Indicator"); ind_table.add_column("Value"); ind_table.add_column("Updated")
@@ -391,12 +575,31 @@ class PingPongBot:
             ind_table.add_row(k.upper().replace("_", " "), f"{v['value']:.2f}", v['timestamp'])
             
         pos_table = Table(title="POSITION", box=box.ROUNDED, expand=True)
-        pos_table.add_column("Account Equity"); pos_table.add_column("Size"); pos_table.add_column("Entry"); pos_table.add_column("PnL")
+        pos_table.add_column("Account Equity"); pos_table.add_column("Available"); pos_table.add_column("Size (BTC/USD)"); pos_table.add_column("Used Lev"); pos_table.add_column("PnL")
         if self.position:
+            p_size = float(self.position['size'])
             pnl = float(self.position['unrealisedPnl'])
-            pos_table.add_row(f"${self.wallet_balance:.2f}", self.position['size'], self.position['avgPrice'], f"[bold {'green' if pnl>=0 else 'red'}]${pnl:.2f}")
+            
+            # Categorize by Inverse (BTCUSD) vs Linear (BTCPERP)
+            if self.category == "inverse":
+                size_usd = p_size
+                size_btc = size_usd / max(self.market_price, 1)
+            else:
+                size_btc = p_size
+                size_usd = size_btc * self.market_price
+            
+            used_lev = size_usd / max(self.wallet_balance, 1)
+            pnl_str = f"[bold {'green' if pnl>=0 else 'red'}]${pnl:.2f}[/]"
+            
+            pos_table.add_row(
+                f"${self.wallet_balance:.2f}", 
+                f"${self.available_balance:.2f}",
+                f"{size_btc:.3f} / ${size_usd:.1f}", 
+                f"{used_lev:.2f}x ({self.max_eff_lev}x)", 
+                pnl_str
+            )
         else:
-            pos_table.add_row(f"${self.wallet_balance:.2f}", "0", "-", "-")
+            pos_table.add_row(f"${self.wallet_balance:.2f}", f"${self.available_balance:.2f}", "0 / $0", f"0.00x ({self.max_eff_lev}x)", "-")
         
         self.console.print(cfg_table); self.console.print(ind_table); self.console.print(pos_table)
         self.console.print(f"[dim]Status: {self.status_msg} | Last Signal: {self.last_signal}[/]")
@@ -433,7 +636,9 @@ class PingPongBot:
                         signal = self.check_signals(df)
                         if signal: await self.execute_trade(signal)
                         self.last_candle_time = latest['time']
-                        self.last_candle_price = latest['close']
+                        self.last_candle_open = float(latest['open'])
+                        self.last_candle_close = float(latest['close'])
+                        self.last_candle_price = self.last_candle_close
                         self.status_msg = f"New Candle: {latest['time'].strftime('%H:%M:%S')}"
                 
                 self.render_dashboard()
@@ -444,6 +649,49 @@ class PingPongBot:
             await asyncio.sleep(5)
 
 from math import floor
+import sys
+
+async def run_with_retries():
+    config_path = "config/ping_pong_config.yaml"
+    
+    # Load config to see robustness settings
+    try:
+        with open(config_path, 'r') as f:
+            config = yaml.safe_load(f)
+    except Exception as e:
+        print(f"CRITICAL: Failed to load config: {e}")
+        sys.exit(1)
+
+    robust_cfg = config.get('robustness', {})
+    if not robust_cfg.get('enabled', True):
+        bot = PingPongBot(config_path)
+        await bot.run()
+        return
+
+    max_retries = robust_cfg.get('max_retries', 3)
+    window = robust_cfg.get('retry_window_seconds', 300)
+    failure_history = []
+
+    while True:
+        try:
+            bot = PingPongBot(config_path)
+            await bot.run()
+            # If run() returns normally, it means the bot stopped gracefully
+            break
+        except Exception as e:
+            now = time.time()
+            failure_history.append(now)
+            
+            # Keep only failures within the window
+            failure_history = [t for t in failure_history if now - t <= window]
+            
+            if len(failure_history) > max_retries:
+                logger.error(f"FATAL: Too many failures ({len(failure_history)}) within {window}s. Stopping bot.")
+                sys.exit(1)
+            
+            wait_time = min(30, 5 * len(failure_history))
+            logger.warning(f"Bot crashed! Retry {len(failure_history)}/{max_retries} in {wait_time}s... Error: {e}")
+            await asyncio.sleep(wait_time)
+
 if __name__ == "__main__":
-    bot = PingPongBot()
-    asyncio.run(bot.run())
+    asyncio.run(run_with_retries())