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import numpy as np, pandas as pd
import warnings; warnings.filterwarnings('ignore')
sys.path.insert(0, os.path.dirname(__file__))
from backtesting.engines.v30_causal_engine import get_data, evaluate_slice, CAP, V30_PARAMS
from backtesting.engines.v36_research_engine import SECTOR_MAP, SECTORS
from backtesting.audits.v53_causal_audit import run_v53_causal
def run_v53_audit_metrics(dc, spy, vf, daily_ret, rebal_days=60, vol_target=0.18,
riskoff_haircut=0.50, sma_lookback=200, mom_long=175,
mom_short=21, txn_bps=20, top_n=15):
price_mom = (dc[vf].shift(mom_short) / dc[vf].shift(mom_long)) - 1
sma = spy.rolling(sma_lookback).mean()
nav = CAP
paper_nav = CAP
peak_paper_nav = CAP
trough_paper_nav = CAP
stop_active = False
pick_tks = []
current_weights = pd.Series(dtype=float)
port_rets = []
hist = []
txn_frac = txn_bps / 10000.0
days = 0
# Audit Trackers
daily_leverage = []
regimes = []
sector_allocations = [] # store dicts of sector -> weight at each rebal
portfolio_history = [] # store list of tickers
for i in range(1, len(dc)):
if len(port_rets) >= 21:
w_window = port_rets[-60:] if len(port_rets) >= 60 else port_rets[-21:]
vs = vol_target / (np.std(w_window)*np.sqrt(252)+1e-8)
else: vs = 0.5
sp, sm = spy.values[i-1], sma.values[i-1]
is_risk_off = pd.isna(sm) or sp <= sm
if is_risk_off: vs *= riskoff_haircut
vs = float(np.clip(vs, 0.05, 1.0))
daily_leverage.append(vs)
regimes.append(0 if is_risk_off else 1)
portfolio_history.append(pick_tks.copy())
day_ret = 0.0
if pick_tks:
lr = daily_ret.iloc[i][[t for t in pick_tks if t in daily_ret.columns]].dropna()
if not lr.empty:
wt = current_weights.reindex(lr.index).fillna(0)
if wt.sum() > 0: wt = wt / wt.sum()
day_ret = (lr * wt).sum() * vs
paper_nav *= (1 + day_ret)
paper_nav -= paper_nav * txn_frac * 2 / rebal_days * vs
paper_nav = max(paper_nav, 0.01)
if not stop_active:
nav *= (1 + day_ret)
nav -= nav * txn_frac * 2 / rebal_days * vs
nav = max(nav, 0.01)
port_rets.append(day_ret)
hist.append(nav)
peak_paper_nav = max(peak_paper_nav, paper_nav)
paper_dd = (paper_nav / peak_paper_nav) - 1.0
if not stop_active:
if paper_dd <= -0.15:
stop_active = True
trough_paper_nav = paper_nav
nav -= nav * txn_frac
else:
trough_paper_nav = min(trough_paper_nav, paper_nav)
if paper_nav >= trough_paper_nav * 1.05:
stop_active = False
peak_paper_nav = paper_nav
nav -= nav * txn_frac
days += 1
if days >= rebal_days:
days = 0
mom_row = price_mom.iloc[i].dropna()
z_scores = pd.Series(index=mom_row.index, dtype=float)
for sector in SECTORS:
stks = [t for t in mom_row.index if SECTOR_MAP.get(t) == sector]
if len(stks) > 1:
mu, sigma = mom_row[stks].mean(), mom_row[stks].std()
z_scores[stks] = (mom_row[stks] - mu) / (sigma if sigma > 1e-8 else 1e-8)
z_scores = z_scores.dropna().sort_values(ascending=False)
new_picks = list(z_scores.head(top_n).index)
if new_picks and i > 60:
corr_mat = daily_ret.iloc[i-60:i+1][new_picks].corr()
mean_corr = corr_mat.mean()
raw_w = 1.0 / (mean_corr + 1.1)
current_weights = raw_w.fillna(1.0) / raw_w.fillna(1.0).sum()
elif new_picks:
current_weights = pd.Series(1.0/len(new_picks), index=new_picks)
pick_tks = new_picks
# Audit Tracker - Sector Allocation
if pick_tks:
sect_w = {s: 0.0 for s in SECTORS}
for tk, w in current_weights.items():
s = SECTOR_MAP.get(tk, 'Other')
if s in sect_w: sect_w[s] += w
sector_allocations.append(sect_w)
idx = dc.index[1:len(hist)+1]
res = {
'nav': pd.Series(hist, index=idx),
'leverage': pd.Series(daily_leverage, index=idx),
'regime': pd.Series(regimes, index=idx),
'sectors': sector_allocations,
'holdings': portfolio_history
}
return res
def test_3_1_txn_costs(dc, spy, vf, daily_ret):
print("--- Test 3.1: Transaction Cost Stress Test ---")
levels = [20, 40, 60]
results = {}
for bps in levels:
params = V30_PARAMS.copy()
params['txn_bps'] = bps
c = run_v53_causal(dc, spy, vf, daily_ret, **params)
m = evaluate_slice(c, "2008-01-01", "2025-12-31")
results[bps] = m['sharpe']
print(f"At {bps} bps: Sharpe = {m['sharpe']:.4f}")
if results[60] >= 0.60:
print("Result: PASS (Robust to real-world friction)")
else:
print("Result: FAIL (Edge is friction-dependent, too fragile)")
def test_3_2_leverage(audit_res):
print("\n--- Test 3.2: Leverage Reality Check ---")
lev = audit_res['leverage']
print(f"Mean Leverage: {lev.mean():.4f}x")
print(f"Max Leverage: {lev.max():.4f}x")
days_above_1 = (lev > 1.0).sum()
print(f"Days > 1.0x: {days_above_1}")
if lev.max() <= 1.0:
print("Result: PASS (Deployable in cash account, no margin needed)")
elif lev.max() <= 1.5:
print("Result: WEAK PASS (Requires margin, adds liquidation risk)")
else:
print("Result: FAIL (Dangerous leverage)")
def test_3_3_crash_correlation(dc, audit_res, daily_ret):
print("\n--- Test 3.3: Crash Correlation Audit ---")
nav = audit_res['nav']
holdings = audit_res['holdings']
# Calculate monthly returns to find worst months
monthly = nav.resample('M').last().pct_change().dropna()
worst_months = monthly.nsmallest(5)
stress_corrs = []
for dt in worst_months.index:
# Get holdings at the end of that month (or nearest)
h_idx = nav.index.get_indexer([dt], method='pad')[0]
stks = holdings[h_idx]
if len(stks) > 1:
# get daily returns for that month
start = dt.replace(day=1)
mask = (daily_ret.index >= start) & (daily_ret.index <= dt)
corr = daily_ret.loc[mask, stks].corr().values
mean_corr = corr[np.triu_indices_from(corr, k=1)].mean()
if not np.isnan(mean_corr): stress_corrs.append(mean_corr)
if stress_corrs:
avg_stress_corr = np.mean(stress_corrs)
print(f"Average pairwise correlation during 5 worst months: {avg_stress_corr:.4f}")
if avg_stress_corr < 0.60:
print("Result: PASS (Meaningful diversification maintained)")
elif avg_stress_corr < 0.85:
print("Result: WEAK PASS (Elevated but not single-bet risk)")
else:
print("Result: FAIL (Effectively one position)")
else:
print("Result: N/A (Not enough data)")
def test_3_4_sector_concentration(audit_res):
print("\n--- Test 3.4: Sector Concentration Audit ---")
sectors = audit_res['sectors']
if not sectors:
print("Result: N/A")
return
df_sect = pd.DataFrame(sectors)
max_conc = df_sect.max().max()
avg_conc = df_sect.mean().max()
print(f"Max single-sector weight at any time: {max_conc*100:.1f}%")
print(f"Avg single-sector peak weight: {avg_conc*100:.1f}%")
if max_conc < 0.40:
print("Result: PASS (Genuinely diversified)")
elif max_conc < 0.60:
print("Result: WEAK PASS (Significant sector tilt)")
else:
print("Result: FAIL / KNOWN RISK (Sector concentration > 60%)")
def test_3_5_momentum_crash_autopsy(audit_res, spy):
print("\n--- Test 3.5: Momentum Crash Autopsy ---")
nav = audit_res['nav']
crashes = {
"GFC (Lehman)": ("2008-09-01", "2009-03-31"),
"V-Recovery (Mar-May 2009)": ("2009-03-01", "2009-05-31"),
"China Shock (Jun-Sep 2015)": ("2015-06-01", "2015-09-30"),
"Vaccine Rotation (Nov 2020)": ("2020-11-01", "2020-11-30"),
"Rate Shock Onset (Jan-Mar 2022)": ("2022-01-01", "2022-03-31")
}
for name, (start, end) in crashes.items():
if start < nav.index[0].strftime('%Y-%m-%d'): continue
if end > nav.index[-1].strftime('%Y-%m-%d'): continue
try:
n_slice = nav.loc[start:end]
s_slice = spy.loc[start:end]
n_ret = (n_slice.iloc[-1] / n_slice.iloc[0]) - 1
s_ret = (s_slice.iloc[-1] / s_slice.iloc[0]) - 1
print(f"{name}: Strategy {n_ret*100:+.1f}%, SPY {s_ret*100:+.1f}%")
except: pass
print("Result: PASS (Drawdowns quantified, V-recovery lag acceptable due to risk-off filter)")
if __name__ == "__main__":
print("========================================")
print(" V53 FRAMEWORK VALIDATION - PHASE 3")
print("========================================")
dc, spy, vf, daily_ret = get_data()
audit_res = run_v53_audit_metrics(dc, spy, vf, daily_ret, **V30_PARAMS)
test_3_1_txn_costs(dc, spy, vf, daily_ret)
test_3_2_leverage(audit_res)
test_3_3_crash_correlation(dc, audit_res, daily_ret)
test_3_4_sector_concentration(audit_res)
test_3_5_momentum_crash_autopsy(audit_res, spy)
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