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.

2026-01-12 21:11:39 +08:00
parent c4aa965a98
commit 44fac1ed25
37 changed files with 5253 additions and 393 deletions
--- a/engine/portfolio.py
+++ b/engine/portfolio.py
@@ -0,0 +1,148 @@
+"""
+Portfolio simulation utilities for backtesting.
+
+Handles long-only and long/short portfolio creation using VectorBT.
+"""
+import pandas as pd
+import vectorbt as vbt
+
+from engine.logging_config import get_logger
+
+logger = get_logger(__name__)
+
+
+def run_long_only_portfolio(
+    close: pd.Series,
+    entries: pd.DataFrame,
+    exits: pd.DataFrame,
+    init_cash: float,
+    fees: float,
+    slippage: float,
+    freq: str,
+    sl_stop: float | None,
+    tp_stop: float | None,
+    sl_trail: bool,
+    leverage: int
+) -> vbt.Portfolio:
+    """
+    Run a long-only portfolio simulation.
+    
+    Args:
+        close: Close price series
+        entries: Entry signals
+        exits: Exit signals
+        init_cash: Initial capital
+        fees: Transaction fee percentage
+        slippage: Slippage percentage
+        freq: Data frequency string
+        sl_stop: Stop loss percentage
+        tp_stop: Take profit percentage
+        sl_trail: Enable trailing stop loss
+        leverage: Leverage multiplier
+        
+    Returns:
+        VectorBT Portfolio object
+    """
+    effective_cash = init_cash * leverage
+    
+    return vbt.Portfolio.from_signals(
+        close=close,
+        entries=entries,
+        exits=exits,
+        init_cash=effective_cash,
+        fees=fees,
+        slippage=slippage,
+        freq=freq,
+        sl_stop=sl_stop,
+        tp_stop=tp_stop,
+        sl_trail=sl_trail,
+        size=1.0,
+        size_type='percent',
+    )
+
+
+def run_long_short_portfolio(
+    close: pd.Series,
+    long_entries: pd.DataFrame,
+    long_exits: pd.DataFrame,
+    short_entries: pd.DataFrame,
+    short_exits: pd.DataFrame,
+    init_cash: float,
+    fees: float,
+    slippage: float,
+    freq: str,
+    sl_stop: float | None,
+    tp_stop: float | None,
+    sl_trail: bool,
+    leverage: int
+) -> vbt.Portfolio:
+    """
+    Run a portfolio supporting both long and short positions.
+    
+    Runs two separate portfolios (long and short) and combines results.
+    Each gets half the capital.
+    
+    Args:
+        close: Close price series
+        long_entries: Long entry signals
+        long_exits: Long exit signals
+        short_entries: Short entry signals
+        short_exits: Short exit signals
+        init_cash: Initial capital
+        fees: Transaction fee percentage
+        slippage: Slippage percentage
+        freq: Data frequency string
+        sl_stop: Stop loss percentage
+        tp_stop: Take profit percentage
+        sl_trail: Enable trailing stop loss
+        leverage: Leverage multiplier
+        
+    Returns:
+        VectorBT Portfolio object (long portfolio, short stats logged)
+    """
+    effective_cash = init_cash * leverage
+    half_cash = effective_cash / 2
+    
+    # Run long-only portfolio
+    long_pf = vbt.Portfolio.from_signals(
+        close=close,
+        entries=long_entries,
+        exits=long_exits,
+        direction='longonly',
+        init_cash=half_cash,
+        fees=fees,
+        slippage=slippage,
+        freq=freq,
+        sl_stop=sl_stop,
+        tp_stop=tp_stop,
+        sl_trail=sl_trail,
+        size=1.0,
+        size_type='percent',
+    )
+    
+    # Run short-only portfolio
+    short_pf = vbt.Portfolio.from_signals(
+        close=close,
+        entries=short_entries,
+        exits=short_exits,
+        direction='shortonly',
+        init_cash=half_cash,
+        fees=fees,
+        slippage=slippage,
+        freq=freq,
+        sl_stop=sl_stop,
+        tp_stop=tp_stop,
+        sl_trail=sl_trail,
+        size=1.0,
+        size_type='percent',
+    )
+    
+    # Log both portfolio stats
+    # TODO: Implement proper portfolio combination
+    logger.info(
+        "Long portfolio: %.2f%% return, Short portfolio: %.2f%% return",
+        long_pf.total_return().mean() * 100,
+        short_pf.total_return().mean() * 100
+    )
+    
+    return long_pf