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fix: close log file handle, fix RiskMgmt equity double-count, remove bare except

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TPTBusiness
2026-05-03 00:17:02 +02:00
parent 4eeb724ac5
commit ca003cd0f2
3 changed files with 139 additions and 135 deletions
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rdagent/components/backtesting/risk_management.py
+18 -20
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@@ -1,21 +1,19 @@
"""
Predix Risk Management - Korrelation, Portfolio-Optimierung
"""
import numpy as np
import pandas as pd
from pathlib import Path
from typing import Dict, List, Optional
from datetime import datetime
import json
class CorrelationAnalyzer:
def __init__(self, lookback: int = 60):
self.lookback = lookback
def calculate_matrix(self, returns: pd.DataFrame) -> pd.DataFrame:
return returns.dropna().corr()
def find_uncorrelated(self, corr: pd.DataFrame, threshold: float = 0.3) -> List[str]:
def find_uncorrelated(self, corr: pd.DataFrame, threshold: float = 0.3) -> list[str]:
result = []
for f in corr.columns:
others = [x for x in corr.columns if x != f]
@@ -28,9 +26,9 @@ class PortfolioOptimizer:
try:
w = np.linalg.inv(cov.values) @ exp_ret.values
return w / np.sum(w)
except:
except np.linalg.LinAlgError:
return np.ones(len(exp_ret)) / len(exp_ret)
def risk_parity(self, cov: pd.DataFrame, max_iter: int = 100) -> np.ndarray:
n = cov.shape[0]
w = np.ones(n) / n
@@ -53,36 +51,36 @@ class AdvancedRiskManager:
self.max_dd = max_dd
self.corr_analyzer = CorrelationAnalyzer()
self.optimizer = PortfolioOptimizer()
def check_limits(self, weights: np.ndarray, vol: float, dd: float) -> Dict[str, bool]:
def check_limits(self, weights: np.ndarray, vol: float, dd: float) -> dict[str, bool]:
return {
'position_limit': np.max(np.abs(weights)) <= self.max_pos,
'leverage_limit': np.sum(np.abs(weights)) <= self.max_lev,
'drawdown_limit': abs(dd) <= self.max_dd,
"position_limit": np.max(np.abs(weights)) <= self.max_pos,
"leverage_limit": np.sum(np.abs(weights)) <= self.max_lev,
"drawdown_limit": abs(dd) <= self.max_dd,
}
if __name__ == "__main__":
print("=== Risk Test ===")
np.random.seed(42)
n, names = 252, ['Mom', 'MeanRev', 'Vol', 'Volu', 'ML']
n, names = 252, ["Mom", "MeanRev", "Vol", "Volu", "ML"]
ret = pd.DataFrame(np.random.randn(n, 5), columns=names)
corr = CorrelationAnalyzer().calculate_matrix(ret)
print("Korrelationsmatrix:")
print(corr.round(2))
opt = PortfolioOptimizer()
exp_ret = pd.Series([0.1, 0.08, 0.06, 0.07, 0.12], index=names)
cov = ret.cov() * 252
mv = opt.mean_variance(exp_ret, cov)
print("\nMean-Variance:")
for n, w in zip(names, mv): print(f" {n}: {w:.2%}")
rp = opt.risk_parity(cov)
print("\nRisk Parity:")
for n, w in zip(names, rp): print(f" {n}: {w:.2%}")
rm = AdvancedRiskManager()
checks = rm.check_limits(mv, 0.15, -0.08)
print(f"\nLimits OK: {all(checks.values())}")