mirror of
https://github.com/NicolasBohn/NexQuant.git
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TPTBusiness
cbe1c52e00
Rename all source files, scripts, tests, documentation, and configuration from Predix/predix to NexQuant/nexquant across the entire codebase.
88 lines
2.9 KiB
Python
88 lines
2.9 KiB
Python
"""
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NexQuant Risk Management - Korrelation, Portfolio-Optimierung
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"""
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import numpy as np
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import pandas as pd
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class CorrelationAnalyzer:
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def __init__(self, lookback: int = 60):
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self.lookback = lookback
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def calculate_matrix(self, returns: pd.DataFrame) -> pd.DataFrame:
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return returns.dropna().corr()
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def find_uncorrelated(self, corr: pd.DataFrame, threshold: float = 0.3) -> list[str]:
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result = []
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for f in corr.columns:
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others = [x for x in corr.columns if x != f]
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if corr.loc[f, others].abs().mean() < threshold:
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result.append(f)
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return result
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class PortfolioOptimizer:
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def mean_variance(self, exp_ret: pd.Series, cov: pd.DataFrame) -> np.ndarray:
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try:
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w = np.linalg.inv(cov.values) @ exp_ret.values
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return w / np.sum(w)
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except (np.linalg.LinAlgError, ValueError):
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return np.ones(len(exp_ret)) / len(exp_ret)
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def risk_parity(self, cov: pd.DataFrame, max_iter: int = 100) -> np.ndarray:
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n = cov.shape[0]
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w = np.ones(n) / n
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for _ in range(max_iter):
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marginal = cov.values @ w
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vol = np.sqrt(w @ cov.values @ w)
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if vol == 0: break
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risk_contrib = w * marginal / vol
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scale = np.sum(risk_contrib) / (n * risk_contrib + 1e-10)
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new_w = w * scale
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new_w = new_w / np.sum(new_w)
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if np.max(np.abs(new_w - w)) < 1e-6: break
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w = new_w
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return w
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class AdvancedRiskManager:
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def __init__(self, max_pos: float = 0.2, max_lev: float = 5.0, max_dd: float = 0.20):
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self.max_pos = max_pos
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self.max_lev = max_lev
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self.max_dd = max_dd
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self.corr_analyzer = CorrelationAnalyzer()
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self.optimizer = PortfolioOptimizer()
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def check_limits(self, weights: np.ndarray, vol: float, dd: float) -> dict[str, bool]:
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return {
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"position_limit": np.max(np.abs(weights)) <= self.max_pos,
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"leverage_limit": np.sum(np.abs(weights)) <= self.max_lev,
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"drawdown_limit": abs(dd) <= self.max_dd,
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}
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if __name__ == "__main__":
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print("=== Risk Test ===")
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np.random.seed(42)
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n, names = 252, ["Mom", "MeanRev", "Vol", "Volu", "ML"]
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ret = pd.DataFrame(np.random.randn(n, 5), columns=names)
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corr = CorrelationAnalyzer().calculate_matrix(ret)
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print("Korrelationsmatrix:")
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print(corr.round(2))
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opt = PortfolioOptimizer()
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exp_ret = pd.Series([0.1, 0.08, 0.06, 0.07, 0.12], index=names)
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cov = ret.cov() * 252
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mv = opt.mean_variance(exp_ret, cov)
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print("\nMean-Variance:")
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for n, w in zip(names, mv): print(f" {n}: {w:.2%}")
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rp = opt.risk_parity(cov)
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print("\nRisk Parity:")
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for n, w in zip(names, rp): print(f" {n}: {w:.2%}")
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rm = AdvancedRiskManager()
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checks = rm.check_limits(mv, 0.15, -0.08)
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print(f"\nLimits OK: {all(checks.values())}")
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print("✅ Test bestanden!")
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