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b54319d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 | import pandas as pd
import numpy as np
from scipy import stats
from typing import Dict, Any, Tuple, Optional
def calculate_rank_ic(df: pd.DataFrame, pred_col: str, target_col: str, date_col: str = "date") -> pd.DataFrame:
"""
Computes daily Spearman Rank Information Coefficient (Rank IC):
Rank IC_t = SpearmanCorr(y_pred_t, y_true_t)
"""
ic_records = []
for d, group in df.groupby(date_col):
clean_g = group[[pred_col, target_col]].dropna()
if len(clean_g) > 3:
corr, _ = stats.spearmanr(clean_g[pred_col], clean_g[target_col])
if not np.isnan(corr):
ic_records.append({"date": d, "rank_ic": corr})
return pd.DataFrame(ic_records)
def calculate_ic_ir(ic_df: pd.DataFrame) -> Dict[str, float]:
"""
Computes Mean Rank IC and IC Information Ratio (IC IR):
IC IR = Mean(IC) / Std(IC)
"""
if ic_df.empty or "rank_ic" not in ic_df.columns:
return {"mean_ic": 0.0, "std_ic": 0.0, "ic_ir": 0.0}
mean_ic = float(ic_df["rank_ic"].mean())
std_ic = float(ic_df["rank_ic"].std())
ic_ir = mean_ic / (std_ic + 1e-8)
return {
"mean_ic": mean_ic,
"std_ic": std_ic,
"ic_ir": ic_ir,
"ann_ic_ir": ic_ir * np.sqrt(52.0), # Assuming weekly rebalancing
}
def stationary_bootstrap_ci(series: pd.Series, p: float = 0.2, n_bootstrap: int = 1000, ci_level: float = 0.95) -> Tuple[float, float, float]:
"""
Stationary Bootstrap (Politis & Romano 1994) for Time Series Confidence Intervals:
Blocks have geometric length distribution with mean 1/p.
Returns: (point_estimate, ci_lower, ci_upper)
"""
arr = series.dropna().values
n = len(arr)
if n < 10:
return (float(np.mean(arr)), float(np.mean(arr)), float(np.mean(arr)))
boot_means = []
for _ in range(n_bootstrap):
boot_idx = []
cur = np.random.randint(0, n)
while len(boot_idx) < n:
block_len = np.random.geometric(p)
for b in range(block_len):
if len(boot_idx) >= n:
break
boot_idx.append((cur + b) % n)
cur = np.random.randint(0, n)
boot_means.append(np.mean(arr[boot_idx]))
lower = float(np.percentile(boot_means, (1.0 - ci_level) / 2.0 * 100))
upper = float(np.percentile(boot_means, (1.0 + ci_level) / 2.0 * 100))
point = float(np.mean(arr))
return point, lower, upper
def probabilistic_sharpe_ratio(
observed_sr: float,
benchmark_sr: float = 0.0,
n_obs: int = 252,
skewness: float = 0.0,
kurtosis: float = 3.0,
) -> float:
"""
Probabilistic Sharpe Ratio (PSR) (Bailey & Lopez de Prado 2012):
PSR(SR*) = Phi( (SR - SR*) * sqrt(N - 1) / sqrt(1 - skew * SR + (kurt - 1) / 4 * SR^2) )
"""
sr_std = np.sqrt((1.0 - skewness * observed_sr + (kurtosis - 1.0) / 4.0 * (observed_sr ** 2)) / max(1, n_obs - 1))
z = (observed_sr - benchmark_sr) / (sr_std + 1e-8)
return float(stats.norm.cdf(z))
def deflated_sharpe_ratio(
observed_sr: float,
sharpe_var: float,
n_trials: int,
n_obs: int = 252,
skewness: float = 0.0,
kurtosis: float = 3.0,
) -> float:
"""
Deflated Sharpe Ratio (DSR) (Bailey & Lopez de Prado 2014):
Adjusts the benchmark Sharpe Ratio for multiple testing across N trial models:
SR* = sqrt(sharpe_var) * ((1 - euler_mascheroni) * Phi^-1(1 - 1/N) + euler_mascheroni * Phi^-1(1 - 1/(N * e)))
"""
if n_trials <= 1:
benchmark_sr = 0.0
else:
euler_mascheroni = 0.5772156649
z1 = stats.norm.ppf(1.0 - 1.0 / n_trials)
z2 = stats.norm.ppf(1.0 - 1.0 / (n_trials * np.e))
benchmark_sr = np.sqrt(max(1e-6, sharpe_var)) * ((1.0 - euler_mascheroni) * z1 + euler_mascheroni * z2)
return probabilistic_sharpe_ratio(observed_sr, benchmark_sr, n_obs, skewness, kurtosis)
def calculate_financial_metrics(
portfolio_returns: pd.Series,
benchmark_returns: Optional[pd.Series] = None,
ann_factor: int = 52, # Weekly frequency default
rf: float = 0.0,
) -> Dict[str, Any]:
"""
Computes comprehensive financial performance and risk metrics.
"""
rets = portfolio_returns.dropna()
if len(rets) < 5:
return {}
cum_rets = (1.0 + rets).cumprod()
total_ret = cum_rets.iloc[-1] - 1.0
n_periods = len(rets)
ann_ret = (1.0 + total_ret) ** (ann_factor / n_periods) - 1.0
ann_vol = rets.std() * np.sqrt(ann_factor)
sharpe = (ann_ret - rf) / (ann_vol + 1e-8)
# Maximum Drawdown
peak = cum_rets.cummax()
drawdown = (cum_rets - peak) / peak
max_dd = float(drawdown.min())
calmar = ann_ret / (abs(max_dd) + 1e-8)
# Win Rate & Profit Factor
pos_rets = rets[rets > 0]
neg_rets = rets[rets < 0]
win_rate = float(len(pos_rets) / len(rets))
profit_factor = float(pos_rets.sum() / (abs(neg_rets.sum()) + 1e-8))
# Skewness & Kurtosis
skew = float(stats.skew(rets))
kurt = float(stats.kurtosis(rets, fisher=False))
metrics = {
"total_return": float(total_ret),
"annualized_return": float(ann_ret),
"annualized_volatility": float(ann_vol),
"sharpe_ratio": float(sharpe),
"max_drawdown": float(max_dd),
"calmar_ratio": float(calmar),
"win_rate": float(win_rate),
"profit_factor": float(profit_factor),
"skewness": skew,
"kurtosis": kurt,
"psr": probabilistic_sharpe_ratio(sharpe, 0.0, n_periods, skew, kurt),
}
if benchmark_returns is not None and not benchmark_returns.dropna().empty:
b_rets = benchmark_returns.reindex(rets.index).fillna(0.0)
cov = np.cov(rets, b_rets)[0, 1]
b_var = b_rets.var()
beta = float(cov / (b_var + 1e-8))
alpha = float(ann_ret - beta * ((1.0 + b_rets.mean()) ** ann_factor - 1.0))
metrics["market_beta"] = beta
metrics["alpha"] = alpha
return metrics
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