Update pyfolio/mcp_output/mcp_plugin/mcp_service.py

pyfolio/mcp_output/mcp_plugin/mcp_service.py

@@ -9,7 +9,8 @@ source_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.pa
 sys.path.insert(0, source_path)
 
 from fastmcp import FastMCP
-from pyfolio import timeseries
+import empyrical as ep
+import scipy.stats as stats
 from pyfolio.tears import (
     create_full_tear_sheet,
     create_simple_tear_sheet,
@@ -50,22 +51,22 @@ def _convert_to_dataframe(data: Dict[str, Any]) -> pd.DataFrame:
 def _calculate_performance_stats(returns_series: pd.Series) -> Dict[str, float]:
     """Calculate key performance statistics."""
     try:
-        stats = {}
-        stats['total_return'] = timeseries.cum_returns_final(returns_series)
-        stats['annual_return'] = timeseries.annual_return(returns_series)
-        stats['annual_volatility'] = timeseries.annual_volatility(returns_series)
-        stats['sharpe_ratio'] = timeseries.sharpe_ratio(returns_series)
-        stats['max_drawdown'] = timeseries.max_drawdown(returns_series)
-        stats['calmar_ratio'] = timeseries.calmar_ratio(returns_series)
-        stats['stability'] = timeseries.stability_of_timeseries(returns_series)
-        stats['omega_ratio'] = timeseries.omega_ratio(returns_series)
-        stats['sortino_ratio'] = timeseries.sortino_ratio(returns_series)
-        stats['skew'] = timeseries.stats.skew(returns_series)
-        stats['kurtosis'] = timeseries.stats.kurtosis(returns_series)
-        stats['tail_ratio'] = timeseries.tail_ratio(returns_series)
+        perf_stats = {}
+        perf_stats['total_return'] = ep.cum_returns_final(returns_series)
+        perf_stats['annual_return'] = ep.annual_return(returns_series)
+        perf_stats['annual_volatility'] = ep.annual_volatility(returns_series)
+        perf_stats['sharpe_ratio'] = ep.sharpe_ratio(returns_series)
+        perf_stats['max_drawdown'] = ep.max_drawdown(returns_series)
+        perf_stats['calmar_ratio'] = ep.calmar_ratio(returns_series)
+        perf_stats['stability'] = ep.stability_of_timeseries(returns_series)
+        perf_stats['omega_ratio'] = ep.omega_ratio(returns_series)
+        perf_stats['sortino_ratio'] = ep.sortino_ratio(returns_series)
+        perf_stats['skew'] = stats.skew(returns_series)
+        perf_stats['kurtosis'] = stats.kurtosis(returns_series)
+        perf_stats['tail_ratio'] = ep.tail_ratio(returns_series)
         
         # Convert numpy types to Python types for JSON serialization
-        return {k: float(v) if pd.notna(v) else None for k, v in stats.items()}
+        return {k: float(v) if pd.notna(v) else None for k, v in perf_stats.items()}
     except Exception as e:
         return {"error": str(e)}