result: hedge-fund reframe -> moat is cheap leverage, not the strategy
Multi-strat with foxhunt's own ideas. (1) Combine uncorrelated premia: ~0.72 Sharpe 2019-26 but ~=60/40, only when streams net-positive (traditional 2010-26 combine +0.51 < equity +0.68 = dilution). (2) Edge-decay-trust allocation (Page-Hinkley theta, resurrection) genuinely helps: +0.16->+0.27, correctly down-weights decayed streams. (3) Static risk layer crushed returns (one-way latch); ADAPTIVE layer (continuous self-recovering DD de-lever + Kelly-floor + z-score corr + EMA vol) beat it (+0.03->+0.14, maxDD -18.7->-14.5) -- value is drawdown control. (4) THE MOAT = cheap financing: adaptive 1x Sharpe +0.48 vs 2x +0.14; retail 6-7% margin kills leverage benefit. Funds lever ~0.7 Sharpe only via prime-brokerage SOFR+1-2%. Deployable best = ~1x adaptive-risk-managed diversified book (~0.5-0.7 Sharpe, unlevered), scales with capital. Foxhunt ideas improve execution (validated); engine value = risk-mgmt not alpha. Ceiling ~0.7 ironclad. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This commit is contained in:
jgrusewski
104
scripts/surfer/multistrat.py
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104
scripts/surfer/multistrat.py
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@@ -0,0 +1,104 @@
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#!/usr/bin/env python3
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"""Nano multi-strat: operate like a hedge fund. Combine uncorrelated premia (equity, bond, gold,
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commodity, trend, crypto) risk-parity-weighted + vol-targeted; show the correlation matrix (the
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diversification engine), combined Sharpe vs each piece alone, and how leverage scales the return.
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Not market-neutral — it's the diversified-premia + leverage (All-Weather/alt-risk-premia) model.
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The point: each stream is modest, but uncorrelated streams combine to a higher Sharpe, then leverage
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turns modest Sharpe into real return. Risk management = the engine's actual job."""
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import math
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import os
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import sys
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import numpy as np
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from signal_sweep import load_panel, build_returns # noqa: E402
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import pit_sweep # noqa: E402
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TV = 0.10
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def vt(r, tv=TV, win=63, cap=4.0):
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out = np.zeros_like(r)
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for t in range(win, len(r)):
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rv = np.std(r[t - win:t]) * math.sqrt(252)
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out[t] = r[t] * min(cap, tv / (rv + 1e-9))
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return out
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def stats(r):
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r = r[np.isfinite(r)]
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if len(r) < 50 or r.std() == 0:
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return (float("nan"),) * 4
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ann = r.mean() * 252; vol = r.std() * math.sqrt(252)
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eq = np.cumprod(1 + r); dd = float((eq / np.maximum.accumulate(eq) - 1).min())
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return ann, vol, ann / vol, dd
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def run(streams, days, year, label):
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names = list(streams)
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M = np.column_stack([vt(streams[n]) for n in names]) # each vol-normalized to 10%
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T = M.shape[0]
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print(f"\n===== {label} ({T} days) =====")
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print(" standalone Sharpe: " + " ".join(f"{n}:{stats(streams[n])[2]:+.2f}" for n in names))
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# correlation matrix of the (vol-normalized) streams
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C = np.corrcoef(np.nan_to_num(M).T)
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print(" correlation matrix:")
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print(" " + " ".join(f"{n[:5]:>6}" for n in names))
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for i, n in enumerate(names):
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print(f" {n[:5]:>5} " + " ".join(f"{C[i, j]:>+6.2f}" for j in range(len(names))))
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avg_corr = (C.sum() - len(names)) / (len(names) ** 2 - len(names))
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# combined: equal-risk-weight then vol-target the bundle
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combo = vt(np.nanmean(M, axis=1))
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a, v, sh, dd = stats(combo)
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best = max(stats(streams[n])[2] for n in names)
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print(f" avg pairwise corr: {avg_corr:+.2f} (low = diversification works)")
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print(f" COMBINED (risk-parity + vol-target 10%): Sharpe {sh:+.2f} ann {100*a:+.1f}% maxDD {100*dd:+.1f}%")
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print(f" vs best single stream Sharpe {best:+.2f} -> diversification lift {sh-best:+.2f}")
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print(f" per-year Sharpe: " + " ".join(f"{y}:{stats(combo[year == y])[2]:+.1f}" for y in sorted(set(year)) if (year == y).sum() > 100))
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print(f" LEVERAGE scaling (same Sharpe {sh:+.2f}): "
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+ " ".join(f"{lev}x->{100*a*lev:+.0f}%/yr@{int(100*v*lev)}%vol" for lev in (1, 2, 3)))
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return combo
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def main():
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roots, days, close, op, inst = load_panel()[:5]
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lc, R, ov, intr = build_returns(close, op, inst)
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T, N = close.shape
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year = (1970 + days / 365.25).astype(int)
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def C(r):
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return roots.index(r)
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# trend: diversified long/short TS-momentum across all futures, vol-targeted
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vol63 = np.full_like(lc, np.nan)
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for t in range(63, T):
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vol63[t] = np.nanstd(R[t - 63:t], axis=0)
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iv = 1.0 / np.where(vol63 > 0, vol63, np.nan)
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tsig = np.full_like(lc, np.nan); tsig[252:] = np.sign(lc[252:] - lc[:-252])
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avail = np.isfinite(close) & np.isfinite(vol63) & (vol63 > 0) & np.isfinite(tsig)
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w = np.where(avail, tsig * iv, 0.0); g = np.abs(w).sum(1, keepdims=True); g[g == 0] = 1; w = w / g
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trend = np.zeros(T); trend[1:] = np.sum(w[:-1] * R[1:], axis=1)
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fut = {"equity": R[:, C("ES")], "bond": R[:, C("ZN")], "gold": R[:, C("GC")],
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"commod": R[:, C("CL")], "trend": trend}
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run(fut, days, year, "TRADITIONAL 5-stream (2010-2026)")
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# +crypto: align BTC daily returns to futures days
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syms, cdays, cc, _, _ = pit_sweep.load()
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j = syms.index("BTCUSDT"); lcb = np.log(cc[:, j])
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btc = {int(cdays[t]): (lcb[t] - lcb[t - 1]) for t in range(1, len(cdays)) if np.isfinite(lcb[t]) and np.isfinite(lcb[t - 1])}
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mask = np.array([int(d) in btc for d in days])
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idx = np.where(mask)[0]
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if len(idx) > 300:
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sub = {k: v[idx] for k, v in fut.items()}
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sub["crypto"] = np.array([btc[int(days[i])] for i in idx])
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run(sub, days[idx], (1970 + days[idx] / 365.25).astype(int), "6-stream +CRYPTO (2019-2026)")
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print("\nVERDICT: combined Sharpe > best single (diversification real) + leverage scales modest Sharpe")
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print("to real return = the hedge-fund operating model. Honest: this is alt-risk-premia (~0.5-1.0 live),")
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print("levered; NOT market-neutral (long beta falls in everything-down); leverage adds tail + financing.")
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if __name__ == "__main__":
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main()
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123
scripts/surfer/multistrat_book.py
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123
scripts/surfer/multistrat_book.py
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@@ -0,0 +1,123 @@
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#!/usr/bin/env python3
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"""Deployable nano multi-strat book with 2x leverage + the foxhunt risk-management layer.
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Streams (risk-parity, vol-normalized): equity, bond, gold, commodity, trend, crypto.
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RISK LAYER (the foxhunt principles applied to the book):
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1. vol-target -> scale exposure to TARGET_VOL on trailing realized vol
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2. drawdown CB -> de-lever at -15% (1x) / -25% (0.5x) / -35% (0.25x) [CMDP circuit-breaker]
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3. corr de-risk -> when avg cross-stream corr spikes (diversification breaking in a crisis), cut leverage
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4. Kelly cap -> leverage <= Kelly fraction (mean/var), never over-lever a degrading book
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L[t] = min(MAXLEV, L_vol, L_kelly) * dd_mult * corr_mult, applied to yesterday's signal.
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Financing cost charged on the borrowed (>1x) portion. Backtest risk-managed-2x vs naive-2x; emit
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live target weights for a given capital. ETF mapping for deployment in the verdict.
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"""
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import math
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import os
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import sys
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import numpy as np
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from signal_sweep import load_panel, build_returns # noqa: E402
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import pit_sweep # noqa: E402
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TARGET_VOL = 0.10
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MAXLEV = 2.0
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FIN = 0.06 # 6%/yr financing on borrowed portion
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DD1, DD2, DD3 = -0.15, -0.25, -0.35
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CORR_HI = 0.45 # avg pairwise corr above which to de-risk
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def volnorm(r, tv=TARGET_VOL, win=63):
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out = np.zeros_like(r)
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for t in range(win, len(r)):
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rv = np.std(r[t - win:t]) * math.sqrt(252)
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out[t] = r[t] * min(5.0, tv / (rv + 1e-9))
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return out
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def stats(r):
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r = r[np.isfinite(r)]
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ann = r.mean() * 252; vol = r.std() * math.sqrt(252)
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eq = np.cumprod(1 + r); dd = float((eq / np.maximum.accumulate(eq) - 1).min())
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return ann, vol, (ann / vol if vol > 0 else float("nan")), dd
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def build_streams():
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roots, days, close, op, inst = load_panel()[:5]
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lc, R, ov, intr = build_returns(close, op, inst)
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T, N = close.shape
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vol63 = np.full_like(lc, np.nan)
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for t in range(63, T):
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vol63[t] = np.nanstd(R[t - 63:t], axis=0)
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iv = 1.0 / np.where(vol63 > 0, vol63, np.nan)
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tsig = np.full_like(lc, np.nan); tsig[252:] = np.sign(lc[252:] - lc[:-252])
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avail = np.isfinite(close) & np.isfinite(vol63) & (vol63 > 0) & np.isfinite(tsig)
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w = np.where(avail, tsig * iv, 0.0); g = np.abs(w).sum(1, keepdims=True); g[g == 0] = 1; w = w / g
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trend = np.zeros(T); trend[1:] = np.sum(w[:-1] * R[1:], axis=1)
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c = {r: roots.index(r) for r in ("ES", "ZN", "GC", "CL")}
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fut = {"equity": R[:, c["ES"]], "bond": R[:, c["ZN"]], "gold": R[:, c["GC"]], "commod": R[:, c["CL"]], "trend": trend}
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syms, cdays, cc, _, _ = pit_sweep.load()
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j = syms.index("BTCUSDT"); lcb = np.log(cc[:, j])
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btc = {int(cdays[t]): (lcb[t] - lcb[t - 1]) for t in range(1, len(cdays)) if np.isfinite(lcb[t]) and np.isfinite(lcb[t - 1])}
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idx = np.array([i for i, d in enumerate(days) if int(d) in btc])
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streams = {k: v[idx] for k, v in fut.items()}
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streams["crypto"] = np.array([btc[int(days[i])] for i in idx])
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return streams, days[idx], (1970 + days[idx] / 365.25).astype(int)
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def risk_managed(M, maxlev, with_risk=True):
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"""M: [T, S] vol-normalized stream returns. Returns book daily returns under the risk layer."""
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base = np.nanmean(M, axis=1) # risk-parity combination
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T = len(base)
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out = np.zeros(T); eq = 1.0; peak = 1.0
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for t in range(63, T - 1):
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rv = np.std(base[t - 63:t]) * math.sqrt(252)
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L = min(maxlev, TARGET_VOL / (rv + 1e-9)) # (1) vol-target, capped at maxlev
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if with_risk:
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mu = base[t - 63:t].mean() * 252; var = (base[t - 63:t].std() ** 2) * 252
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L = min(L, max(0.0, mu / (var + 1e-9))) # (4) Kelly cap
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dd = eq / peak - 1.0 # (2) drawdown circuit-breaker
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ddm = 1.0 if dd > DD1 else (0.5 if dd > DD2 else (0.25 if dd > DD3 else 0.0))
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cm = np.corrcoef(np.nan_to_num(M[t - 63:t]).T) # (3) correlation de-risk
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ac = (cm.sum() - cm.shape[0]) / (cm.shape[0] ** 2 - cm.shape[0])
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corrm = 1.0 if ac < CORR_HI else max(0.4, 1 - (ac - CORR_HI) * 2)
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L *= ddm * corrm
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L = max(0.0, min(L, maxlev))
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r = L * base[t + 1] - FIN * max(L - 1.0, 0.0) / 252 # financing on borrowed portion
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out[t + 1] = r
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eq *= (1 + r); peak = max(peak, eq)
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return out
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def main():
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streams, days, year = build_streams()
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names = list(streams)
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M = np.column_stack([volnorm(streams[n]) for n in names])
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print(f"DEPLOYABLE MULTI-STRAT BOOK — {len(names)} streams, {M.shape[0]} days ({names})")
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for lab, wr, lev in [("risk-managed 2x", True, 2.0), ("NAIVE 2x (no risk layer)", False, 2.0),
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("risk-managed 1x", True, 1.0)]:
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r = risk_managed(M, lev, wr)
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a, v, sh, dd = stats(r)
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print(f" {lab:>26}: Sharpe {sh:+.2f} ann {100*a:+.1f}% vol {100*v:.0f}% maxDD {100*dd:+.1f}%")
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rm = risk_managed(M, 2.0, True)
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print(f" per-year Sharpe (risk-managed 2x): " + " ".join(f"{y}:{stats(rm[year == y])[2]:+.1f}" for y in sorted(set(year)) if (year == y).sum() > 150))
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# live sizing: current leverage + target $ per stream for capital
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base = np.nanmean(M, axis=1); t = len(base) - 1
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rv = np.std(base[t - 63:t]) * math.sqrt(252)
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Lv = min(MAXLEV, TARGET_VOL / (rv + 1e-9))
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cap = 35000
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print(f"\n LIVE SIZING (today): vol-target leverage {Lv:.2f}x -> deploy ${cap*Lv:,.0f} gross on ${cap:,.0f}")
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per = cap * Lv / len(names)
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etf = {"equity": "SPY", "bond": "IEF", "gold": "GLD", "commod": "PDBC", "trend": "DBMF", "crypto": "BTC(spot)"}
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for n in names:
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print(f" {n:>7} ({etf[n]:>9}): ${per:,.0f}")
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print("\n VERDICT: risk-managed 2x should keep maxDD bounded (~-20-30%) vs naive 2x (~-40-50%) at higher")
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print(" return than 1x -> the hedge-fund model at retail. Deploy via the ETFs above (2x Reg-T margin) +")
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print(" small crypto sleeve. Same ~0.7-Sharpe book; leverage+risk-layer = the fund. Honest: still beta,")
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print(" drawdowns real, financing drags, single-window crypto sleeve -> size crypto small.")
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if __name__ == "__main__":
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main()
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85
scripts/surfer/multistrat_book_v2.py
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85
scripts/surfer/multistrat_book_v2.py
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@@ -0,0 +1,85 @@
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#!/usr/bin/env python3
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"""Multi-strat book v2 — upgraded with foxhunt risk-stack IDEAS (not crude reimplementation):
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- PER-STREAM EDGE-DECAY TRUST (Page-Hinkley-style decay detection -> continuous theta in [0,1])
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that down-weights a stream whose premium is degrading and re-weights it when it recovers
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(resurrection discipline) -> ADAPTIVE allocation vs static risk-parity.
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[pearl_edge_decay_detection_is_a_missing_abstraction_layer + dead_signal_resurrection_discipline]
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- KELLY-fraction leverage cap [pearl_position_sizing_missing_adaptation_layer]
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- CMDP drawdown circuit-breaker [pearl_cmdp_consec_loss_counter]
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- correlation-spike de-risk (diversification breaks in crises)
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Compares static-risk-parity vs edge-decay-adaptive, both risk-managed 2x. Does the foxhunt idea help?
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"""
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import math
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import os
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import sys
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import numpy as np
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from multistrat_book import build_streams, volnorm, stats, TARGET_VOL, FIN, DD1, DD2, DD3, CORR_HI # noqa: E402
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def edge_decay_trust(stream, win=126, ema=0.94):
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"""Page-Hinkley-style per-stream edge health -> theta in [0.1,1]. Detects decay (trailing
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risk-adj return falling), floors at 0.1 so a dead stream can RESURRECT when it recovers."""
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T = len(stream); theta = np.ones(T)
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th = 1.0
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for t in range(win, T):
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seg = stream[t - win:t]
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sr = seg.mean() / (seg.std() + 1e-9) * math.sqrt(252)
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target = float(np.clip((sr - (-0.5)) / (0.5 - (-0.5)), 0.1, 1.0)) # SR>0.5 ->1, <-0.5 ->0.1
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th = ema * th + (1 - ema) * target # smooth (asymmetric-ish via EMA)
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theta[t] = th
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return theta
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def risk_layer(combo, M, maxlev, with_risk=True):
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T = len(combo); out = np.zeros(T); eq = 1.0; peak = 1.0
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for t in range(63, T - 1):
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rv = np.std(combo[t - 63:t]) * math.sqrt(252)
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L = min(maxlev, TARGET_VOL / (rv + 1e-9))
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if with_risk:
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mu = combo[t - 63:t].mean() * 252; var = (combo[t - 63:t].std() ** 2) * 252
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L = min(L, max(0.0, mu / (var + 1e-9)))
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dd = eq / peak - 1.0
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ddm = 1.0 if dd > DD1 else (0.5 if dd > DD2 else (0.25 if dd > DD3 else 0.0))
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cm = np.corrcoef(np.nan_to_num(M[t - 63:t]).T)
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ac = (cm.sum() - cm.shape[0]) / (cm.shape[0] ** 2 - cm.shape[0])
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corrm = 1.0 if ac < CORR_HI else max(0.4, 1 - (ac - CORR_HI) * 2)
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L *= ddm * corrm
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L = max(0.0, min(L, maxlev))
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r = L * combo[t + 1] - FIN * max(L - 1.0, 0.0) / 252
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out[t + 1] = r; eq *= (1 + r); peak = max(peak, eq)
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return out
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def main():
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streams, days, year = build_streams()
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names = list(streams)
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M = np.column_stack([volnorm(streams[n]) for n in names])
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T = M.shape[0]
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# edge-decay trust per stream
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Theta = np.column_stack([edge_decay_trust(M[:, i]) for i in range(len(names))])
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static = np.nanmean(M, axis=1) # equal risk-parity
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tw = Theta / np.maximum(Theta.sum(1, keepdims=True), 1e-9) # trust-weighted
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adaptive = np.nansum(tw * np.nan_to_num(M), axis=1)
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print(f"MULTI-STRAT v2 (foxhunt edge-decay-adaptive) — {len(names)} streams, {T} days")
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print(f" streams: {names}")
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for lab, combo in [("static risk-parity", static), ("edge-decay ADAPTIVE", adaptive)]:
|
||||
for ln, lev, wr in [("2x risk-managed", 2.0, True), ("naive 2x", 2.0, False)]:
|
||||
r = risk_layer(combo, M, lev, wr); a, v, sh, dd = stats(r)
|
||||
print(f" {lab:>20} | {ln:>16}: Sharpe {sh:+.2f} ann {100*a:+.1f}% maxDD {100*dd:+.1f}%")
|
||||
# show what the trust layer is doing (avg theta per stream + recent)
|
||||
print(" edge-trust (avg | latest) per stream:")
|
||||
for i, n in enumerate(names):
|
||||
print(f" {n:>7}: {Theta[126:, i].mean():.2f} | {Theta[-1, i]:.2f}")
|
||||
radap = risk_layer(adaptive, M, 2.0, True)
|
||||
print(f" per-year Sharpe (adaptive 2x): " + " ".join(f"{y}:{stats(radap[year == y])[2]:+.1f}" for y in sorted(set(year)) if (year == y).sum() > 150))
|
||||
print("\n VERDICT: edge-decay-adaptive Sharpe > static AND lower maxDD = the foxhunt trust-layer idea adds")
|
||||
print(" real value (down-weights decaying streams, resurrects recovered ones). If ~equal, static suffices.")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
79
scripts/surfer/multistrat_book_v3.py
Normal file
79
scripts/surfer/multistrat_book_v3.py
Normal file
@@ -0,0 +1,79 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Multi-strat book v3 — risk layer rebuilt with foxhunt's ADAPTIVE-controller discipline.
|
||||
|
||||
Fixes the v1/v2 static risk layer (which crushed returns — a one-way latch per
|
||||
pearl_cmdp_consec_loss_counter_is_one_way_latch). Controllers (all floored / resurrection-capable):
|
||||
- EMA online vol (not fixed-window std) [Welford/EMA online stats]
|
||||
- Kelly leverage with FLOOR + bootstrap [pearl_bootstrap_must_respect_clamp_range]
|
||||
- drawdown de-lever CONTINUOUS + self-recovering [fix the one-way latch -> resurrection]
|
||||
- correlation de-risk Z-SCORED vs own distribution [adaptive, not fixed threshold]
|
||||
- leverage floor so nothing dies permanently [pearl_dead_signal_resurrection_discipline]
|
||||
Combination = edge-decay-adaptive (v2). Compare naive 2x / static-risk 2x / ADAPTIVE-risk 2x.
|
||||
"""
|
||||
import math
|
||||
import os
|
||||
import sys
|
||||
|
||||
import numpy as np
|
||||
|
||||
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
||||
from multistrat_book import build_streams, volnorm, stats, TARGET_VOL, FIN, risk_managed # noqa: E402
|
||||
from multistrat_book_v2 import edge_decay_trust # noqa: E402
|
||||
|
||||
KELLY_FLOOR, LEV_FLOOR = 0.5, 0.3
|
||||
DD_DEADBAND, DD_SENS, DD_FLOOR = 0.05, 3.0, 0.40
|
||||
|
||||
|
||||
def adaptive_risk(combo, M, maxlev):
|
||||
T = len(combo); out = np.zeros(T); eq = 1.0; peak = 1.0
|
||||
ema_mu = float(np.nanmean(combo[:63])); ema_var = float(np.nanvar(combo[:63])) + 1e-12
|
||||
chist = []
|
||||
for t in range(63, T - 1):
|
||||
x = combo[t]
|
||||
ema_mu = 0.97 * ema_mu + 0.03 * x # EMA online stats
|
||||
ema_var = 0.97 * ema_var + 0.03 * (x - ema_mu) ** 2
|
||||
rv = math.sqrt(max(ema_var, 1e-12) * 252)
|
||||
L_vol = TARGET_VOL / (rv + 1e-9)
|
||||
kelly = min(maxlev, max(KELLY_FLOOR, (ema_mu * 252) / (ema_var * 252 + 1e-9))) # floored Kelly
|
||||
dd = eq / peak - 1.0
|
||||
dd_mult = float(np.clip(1.0 - DD_SENS * max(0.0, -dd - DD_DEADBAND), DD_FLOOR, 1.0)) # continuous + recovers
|
||||
cm = np.corrcoef(np.nan_to_num(M[t - 63:t]).T)
|
||||
ac = (cm.sum() - cm.shape[0]) / (cm.shape[0] ** 2 - cm.shape[0])
|
||||
chist.append(ac)
|
||||
if len(chist) > 60:
|
||||
h = np.array(chist[-120:]); z = (ac - h.mean()) / (h.std() + 1e-9)
|
||||
corr_mult = float(np.clip(1.0 - 0.20 * max(0.0, z), 0.5, 1.0))
|
||||
else:
|
||||
corr_mult = 1.0
|
||||
L = float(np.clip(min(L_vol, kelly) * dd_mult * corr_mult, LEV_FLOOR, maxlev))
|
||||
r = L * combo[t + 1] - FIN * max(L - 1.0, 0.0) / 252
|
||||
out[t + 1] = r; eq *= (1 + r); peak = max(peak, eq)
|
||||
return out
|
||||
|
||||
|
||||
def main():
|
||||
streams, days, year = build_streams()
|
||||
names = list(streams)
|
||||
M = np.column_stack([volnorm(streams[n]) for n in names])
|
||||
Theta = np.column_stack([edge_decay_trust(M[:, i]) for i in range(len(names))])
|
||||
tw = Theta / np.maximum(Theta.sum(1, keepdims=True), 1e-9)
|
||||
combo = np.nansum(tw * np.nan_to_num(M), axis=1) # edge-decay-adaptive combination
|
||||
|
||||
print(f"MULTI-STRAT v3 (adaptive allocation + adaptive risk layer) — {len(names)} streams, {M.shape[0]} days")
|
||||
res = {
|
||||
"naive 2x (no risk layer)": np.concatenate([[0], 2.0 * combo[1:] - FIN * 1.0 / 252]),
|
||||
"static risk layer 2x": risk_managed(M, 2.0, True), # v1 static (recomputes nanmean inside)
|
||||
"ADAPTIVE risk layer 2x": adaptive_risk(combo, M, 2.0),
|
||||
"ADAPTIVE risk layer 1x": adaptive_risk(combo, M, 1.0),
|
||||
}
|
||||
for lab, r in res.items():
|
||||
a, v, sh, dd = stats(r)
|
||||
print(f" {lab:>26}: Sharpe {sh:+.2f} ann {100*a:+.1f}% vol {100*v:.0f}% maxDD {100*dd:+.1f}%")
|
||||
radap = res["ADAPTIVE risk layer 2x"]
|
||||
print(f" per-year Sharpe (ADAPTIVE 2x): " + " ".join(f"{y}:{stats(radap[year == y])[2]:+.1f}" for y in sorted(set(year)) if (year == y).sum() > 150))
|
||||
print("\n VERDICT: ADAPTIVE-risk 2x should beat naive 2x AND static-risk 2x on Sharpe AND maxDD =")
|
||||
print(" proper foxhunt-style risk management makes leverage survivable without killing return.")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user