Remove deprecated modules and files related to the backtesting framework, including backtest.py, cli.py, config.py, data.py, intrabar.py, logging_utils.py, market_costs.py, metrics.py, trade.py, and supertrend indicators. Introduce a new structure for the backtesting engine with improved organization and functionality, including a CLI handler, data manager, and reporting capabilities. Update dependencies in pyproject.toml to support the new architecture.

strategies/examples.py

@@ -0,0 +1,97 @@
+"""
+Example trading strategies for backtesting.
+
+These are simple strategies demonstrating the framework usage.
+"""
+import pandas as pd
+import vectorbt as vbt
+
+from engine.market import MarketType
+from strategies.base import BaseStrategy
+
+
+class RsiStrategy(BaseStrategy):
+    """
+    RSI mean-reversion strategy.
+    
+    Long entry when RSI crosses below oversold level.
+    Long exit when RSI crosses above overbought level.
+    """
+    default_market_type = MarketType.SPOT
+    default_leverage = 1
+    
+    def run(
+        self, 
+        close: pd.Series, 
+        period: int = 14, 
+        rsi_lower: int = 30, 
+        rsi_upper: int = 70,
+        **kwargs
+    ) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame]:
+        """
+        Generate RSI-based trading signals.
+        
+        Args:
+            close: Price series
+            period: RSI calculation period
+            rsi_lower: Oversold threshold (buy signal)
+            rsi_upper: Overbought threshold (sell signal)
+            
+        Returns:
+            4-tuple of (long_entries, long_exits, short_entries, short_exits)
+        """
+        # Calculate RSI
+        rsi = vbt.RSI.run(close, window=period)
+        
+        # Long signals: buy oversold, sell overbought
+        long_entries = rsi.rsi_crossed_below(rsi_lower)
+        long_exits = rsi.rsi_crossed_above(rsi_upper)
+        
+        # No short signals for this strategy (spot-focused)
+        short_entries = BaseStrategy.create_empty_signals(long_entries)
+        short_exits = BaseStrategy.create_empty_signals(long_entries)
+        
+        return long_entries, long_exits, short_entries, short_exits
+
+
+class MaCrossStrategy(BaseStrategy):
+    """
+    Moving Average crossover strategy.
+    
+    Long entry when fast MA crosses above slow MA.
+    Long exit when fast MA crosses below slow MA.
+    """
+    default_market_type = MarketType.SPOT
+    default_leverage = 1
+    
+    def run(
+        self, 
+        close: pd.Series, 
+        fast_window: int = 10, 
+        slow_window: int = 20,
+        **kwargs
+    ) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame]:
+        """
+        Generate MA crossover trading signals.
+        
+        Args:
+            close: Price series
+            fast_window: Fast MA period
+            slow_window: Slow MA period
+            
+        Returns:
+            4-tuple of (long_entries, long_exits, short_entries, short_exits)
+        """
+        # Calculate Moving Averages
+        fast_ma = vbt.MA.run(close, window=fast_window)
+        slow_ma = vbt.MA.run(close, window=slow_window)
+        
+        # Long signals
+        long_entries = fast_ma.ma_crossed_above(slow_ma)
+        long_exits = fast_ma.ma_crossed_below(slow_ma)
+        
+        # No short signals for this strategy
+        short_entries = BaseStrategy.create_empty_signals(long_entries)
+        short_exits = BaseStrategy.create_empty_signals(long_entries)
+        
+        return long_entries, long_exits, short_entries, short_exits