229 lines
7.9 KiB
Python
229 lines
7.9 KiB
Python
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"""
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Reporting module for backtest results.
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Handles summary printing, CSV exports, and plotting.
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"""
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from pathlib import Path
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import pandas as pd
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import plotly.graph_objects as go
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import vectorbt as vbt
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from plotly.subplots import make_subplots
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from engine.logging_config import get_logger
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logger = get_logger(__name__)
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class Reporter:
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"""Reporter for backtest results with market-specific metrics."""
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def __init__(self, output_dir: str = "backtest_logs"):
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self.output_dir = Path(output_dir)
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self.output_dir.mkdir(exist_ok=True)
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def print_summary(self, result) -> None:
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"""
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Print backtest summary to console via logger.
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Args:
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result: BacktestResult or vbt.Portfolio object
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"""
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(portfolio, market_type, leverage, funding_paid,
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liq_count, liq_loss, adjusted_return) = self._extract_result_data(result)
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# Extract period info
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idx = portfolio.wrapper.index
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start_date = idx[0].strftime("%Y-%m-%d")
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end_date = idx[-1].strftime("%Y-%m-%d")
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# Extract price info
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close = portfolio.close
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start_price = close.iloc[0].mean() if hasattr(close.iloc[0], 'mean') else close.iloc[0]
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end_price = close.iloc[-1].mean() if hasattr(close.iloc[-1], 'mean') else close.iloc[-1]
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price_change = ((end_price - start_price) / start_price) * 100
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# Extract fees
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stats = portfolio.stats()
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total_fees = stats.get('Total Fees Paid', 0)
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raw_return = portfolio.total_return().mean() * 100
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# Build summary
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summary_lines = [
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"",
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"=" * 50,
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"BACKTEST RESULTS",
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"=" * 50,
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f"Market Type: [{market_type.upper()}]",
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f"Leverage: [{leverage}x]",
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f"Period: [{start_date} to {end_date}]",
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f"Price: [{start_price:,.2f} -> {end_price:,.2f} ({price_change:+.2f}%)]",
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]
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# Show adjusted return if liquidations occurred
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if liq_count > 0 and adjusted_return is not None:
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summary_lines.append(f"Raw Return: [%{raw_return:.2f}] (before liq adjustment)")
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summary_lines.append(f"Adj Return: [%{adjusted_return:.2f}] (after liq losses)")
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else:
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summary_lines.append(f"Total Return: [%{raw_return:.2f}]")
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summary_lines.extend([
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f"Sharpe Ratio: [{portfolio.sharpe_ratio().mean():.2f}]",
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f"Max Drawdown: [%{portfolio.max_drawdown().mean() * 100:.2f}]",
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f"Total Trades: [{portfolio.trades.count().mean():.0f}]",
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f"Win Rate: [%{portfolio.trades.win_rate().mean() * 100:.2f}]",
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f"Total Fees: [{total_fees:,.2f}]",
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])
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if funding_paid != 0:
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summary_lines.append(f"Funding Paid: [{funding_paid:,.2f}]")
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if liq_count > 0:
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summary_lines.append(f"Liquidations: [{liq_count}] (${liq_loss:,.2f} margin lost)")
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summary_lines.append("=" * 50)
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logger.info("\n".join(summary_lines))
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def save_reports(self, result, filename_prefix: str) -> None:
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"""
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Save trade log, stats, and liquidation events to CSV files.
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Args:
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result: BacktestResult or vbt.Portfolio object
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filename_prefix: Prefix for output filenames
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"""
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(portfolio, market_type, leverage, funding_paid,
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liq_count, liq_loss, adjusted_return) = self._extract_result_data(result)
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# Save trades
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self._save_csv(
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data=portfolio.trades.records_readable,
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path=self.output_dir / f"{filename_prefix}_trades.csv",
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description="trade log"
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)
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# Save stats with market-specific additions
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stats = portfolio.stats()
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stats['Market Type'] = market_type
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stats['Leverage'] = leverage
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stats['Total Funding Paid'] = funding_paid
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stats['Liquidations'] = liq_count
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stats['Liquidation Loss'] = liq_loss
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if adjusted_return is not None:
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stats['Adjusted Return'] = adjusted_return
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self._save_csv(
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data=stats,
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path=self.output_dir / f"{filename_prefix}_stats.csv",
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description="stats"
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)
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# Save liquidation events if any
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if hasattr(result, 'liquidation_events') and result.liquidation_events:
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liq_df = pd.DataFrame([
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{
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'entry_time': e.entry_time,
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'entry_price': e.entry_price,
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'liquidation_time': e.liquidation_time,
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'liquidation_price': e.liquidation_price,
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'actual_price': e.actual_price,
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'direction': e.direction,
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'margin_lost_pct': e.margin_lost_pct
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}
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for e in result.liquidation_events
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])
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self._save_csv(
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data=liq_df,
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path=self.output_dir / f"{filename_prefix}_liquidations.csv",
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description="liquidation events"
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)
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def plot(self, portfolio: vbt.Portfolio, show: bool = True) -> None:
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"""Display portfolio plot."""
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if show:
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portfolio.plot().show()
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def plot_wfa(
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self,
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wfa_results: pd.DataFrame,
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stitched_curve: pd.Series | None = None,
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show: bool = True
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) -> None:
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"""
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Plot Walk-Forward Analysis results.
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Args:
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wfa_results: DataFrame with WFA window results
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stitched_curve: Stitched out-of-sample equity curve
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show: Whether to display the plot
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"""
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fig = make_subplots(
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rows=2, cols=1,
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shared_xaxes=False,
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vertical_spacing=0.1,
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subplot_titles=(
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"Walk-Forward Test Scores (Sharpe)",
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"Stitched Out-of-Sample Equity"
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)
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)
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fig.add_trace(
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go.Bar(
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x=wfa_results['window'],
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y=wfa_results['test_score'],
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name="Test Sharpe"
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),
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row=1, col=1
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)
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if stitched_curve is not None:
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fig.add_trace(
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go.Scatter(
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x=stitched_curve.index,
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y=stitched_curve.values,
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name="Equity",
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mode='lines'
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),
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row=2, col=1
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)
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fig.update_layout(height=800, title_text="Walk-Forward Analysis Report")
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if show:
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fig.show()
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def _extract_result_data(self, result) -> tuple:
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"""
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Extract data from BacktestResult or raw Portfolio.
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Returns:
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Tuple of (portfolio, market_type, leverage, funding_paid, liq_count,
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liq_loss, adjusted_return)
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"""
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if hasattr(result, 'portfolio'):
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return (
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result.portfolio,
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result.market_type.value,
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result.leverage,
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result.total_funding_paid,
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result.liquidation_count,
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getattr(result, 'total_liquidation_loss', 0.0),
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getattr(result, 'adjusted_return', None)
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)
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return (result, "unknown", 1, 0.0, 0, 0.0, None)
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def _save_csv(self, data, path: Path, description: str) -> None:
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"""
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Save data to CSV with consistent error handling.
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Args:
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data: DataFrame or Series to save
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path: Output file path
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description: Human-readable description for logging
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"""
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try:
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data.to_csv(path)
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logger.info("Saved %s to %s", description, path)
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except Exception as e:
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logger.error("Could not save %s: %s", description, e)
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