research(alt): sweep 104 strategie alternative su Deribit (153 agenti) + marginal scorer
Ondata di ricerca onesta a largo spettro su BTC/ETH+DVOL certificati: 104 ipotesi distinte (11 famiglie), un agente-finder per ipotesi, verifica avversariale a 3 scettici sui promettenti, sintesi (153 agenti totali). Esito: NIENTE di nuovo regge -> conferma del soffitto strutturale ~1.3 BTC/ETH-direzionale; lo stack TP01+XS01+VRP01 resta imbattuto. - altlib.py: harness condiviso vettoriale leak-free (eval_weights/study_weights, fee-sweep, both-asset + hold-out 2025+). Riproduce i numeri canonici di TP01. - MARGINAL SCORER (study_marginal/marginal_vs_tp01): Sharpe INCREMENTALE vs baseline TP01 (corr, blend uplift OOS, alpha residua) + jackknife OOS (clean-year + drop-best-month). earns_slot = abs!=FAIL & ADDS & robust_oos. Smaschera gli overlay su TSMOM con PASS assoluti fasulli (CMB04, VOL11, ...) e il falso positivo KAMA (ADDS ma muore al jackknife). - runs/*.py (104) script riproducibili per ipotesi; wf_altstrat.js workflow. - Verdetto: 0 candidati deployabili; 2 LEAD fragili (VOL08, STA05_LS) da forward-monitor. - test_marginal_scorer.py blocca baseline + invarianti. Suite: 32 verde. Diario: docs/diary/2026-06-20-alt-strategies-100agent-sweep.md Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This commit is contained in:
Adriano Dal Pastro
@@ -0,0 +1,136 @@
|
||||
"""Resta qualche candidato? — passa i contendenti promettenti piu' forti del sweep
|
||||
(trend non-TSMOM, overlay DVOL, rotazione cross-asset) attraverso il gate MARGINALE vs TP01.
|
||||
Run: uv run python scripts/research/alt/marginal_remaining.py
|
||||
"""
|
||||
import sys
|
||||
sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
import altlib as al
|
||||
|
||||
|
||||
def tsmom_dir(df):
|
||||
c = df["close"].values.astype(float); bpd = al.bars_per_day(df); d = np.zeros(len(c))
|
||||
for h in (30 * bpd, 90 * bpd, 180 * bpd):
|
||||
s = np.full(len(c), np.nan); s[h:] = np.sign(c[h:] / c[:-h] - 1.0); d += np.nan_to_num(s)
|
||||
return np.clip(np.sign(d), 0, None)
|
||||
|
||||
|
||||
def wma(x, n):
|
||||
w = np.arange(1, n + 1)
|
||||
return pd.Series(x).rolling(n).apply(lambda v: np.dot(v, w) / w.sum(), raw=True).values
|
||||
|
||||
|
||||
# --- TRD10 Vortex(14) long-flat ---
|
||||
def trd10(df):
|
||||
h = df["high"].values.astype(float); l = df["low"].values.astype(float); c = df["close"].values.astype(float)
|
||||
pc = np.roll(c, 1); pc[0] = c[0]; ph = np.roll(h, 1); ph[0] = h[0]; pl = np.roll(l, 1); pl[0] = l[0]
|
||||
tr = np.maximum(h - l, np.maximum(np.abs(h - pc), np.abs(l - pc)))
|
||||
n = 14; strn = pd.Series(tr).rolling(n).sum().values
|
||||
vip = pd.Series(np.abs(h - pl)).rolling(n).sum().values / strn
|
||||
vim = pd.Series(np.abs(l - ph)).rolling(n).sum().values / strn
|
||||
d = np.where(np.isnan(vip), 0.0, np.where(vip > vim, 1.0, 0.0))
|
||||
return al.vol_target(d, df, 0.20, 30, 2.0)
|
||||
|
||||
|
||||
# --- TRD08 Hull MA slope ---
|
||||
def trd08(df):
|
||||
c = df["close"].values.astype(float)
|
||||
h = wma(2 * wma(c, 27) - wma(c, 55), 7) # HMA(55)
|
||||
slope = np.zeros(len(h)); slope[1:] = h[1:] - h[:-1]
|
||||
d = np.where(slope > 0, 1.0, 0.0); d[np.isnan(h)] = 0.0
|
||||
return al.vol_target(d, df, 0.20, 30, 2.0)
|
||||
|
||||
|
||||
# --- TRD07 Kaufman AMA cross ---
|
||||
def kama(c, n=10, fast=2, slow=30):
|
||||
c = np.asarray(c, float); L = len(c); out = np.copy(c)
|
||||
fsc, ssc = 2 / (fast + 1), 2 / (slow + 1)
|
||||
vol = pd.Series(np.abs(np.diff(c, prepend=c[0]))).rolling(n).sum().values
|
||||
change = np.full(L, np.nan); change[n:] = np.abs(c[n:] - c[:-n])
|
||||
sc = (np.where(vol > 0, change / vol, 0.0) * (fsc - ssc) + ssc) ** 2
|
||||
for i in range(1, L):
|
||||
out[i] = out[i - 1] if np.isnan(sc[i]) else out[i - 1] + sc[i] * (c[i] - out[i - 1])
|
||||
return out
|
||||
|
||||
|
||||
def trd07(df):
|
||||
c = df["close"].values.astype(float); k = kama(c)
|
||||
slope = np.zeros(len(k)); slope[1:] = k[1:] - k[:-1]
|
||||
d = np.where((c > k) & (slope > 0), 1.0, 0.0)
|
||||
return al.vol_target(d, df, 0.20, 30, 2.0)
|
||||
|
||||
|
||||
# --- VOL08 realized-vol term-structure overlay on TSMOM ---
|
||||
def vol08(df):
|
||||
c = df["close"].values.astype(float); bpd = al.bars_per_day(df); r = al.simple_returns(c)
|
||||
sv = al.realized_vol(r, 5 * bpd, bpd * 365.25); lv = al.realized_vol(r, 30 * bpd, bpd * 365.25)
|
||||
ratio = sv / lv; d = tsmom_dir(df)
|
||||
d = np.where((ratio < 1.0) | np.isnan(ratio), d, 0.0)
|
||||
return al.vol_target(d, df, 0.20, 30, 2.0)
|
||||
|
||||
|
||||
# --- VOL11 DVOL kill-switch on TSMOM (df, asset) ---
|
||||
def vol11(df, asset):
|
||||
d = tsmom_dir(df); dv = pd.Series(al.dvol(df, asset))
|
||||
thr = dv.expanding(min_periods=30).quantile(0.80)
|
||||
kill = (~dv.isna()) & (~thr.isna()) & (dv > thr)
|
||||
d = np.where(kill.values, 0.0, d)
|
||||
return al.vol_target(d, df, 0.20, 30, 2.0)
|
||||
|
||||
|
||||
# --- XAS09/03 cross-asset rotation (hold the stronger of BTC/ETH; dual=flat if both neg) ---
|
||||
def rotation_daily(lb=90, dual=True):
|
||||
R, M, V = {}, {}, {}
|
||||
for a in ("BTC", "ETH"):
|
||||
df = al.get(a, "1d"); c = df["close"].values.astype(float)
|
||||
idx = pd.DatetimeIndex(pd.to_datetime(df["datetime"], utc=True))
|
||||
mom = np.full(len(c), np.nan); mom[lb:] = c[lb:] / c[:-lb] - 1.0
|
||||
R[a] = pd.Series(al.simple_returns(c), index=idx)
|
||||
M[a] = pd.Series(mom, index=idx)
|
||||
V[a] = pd.Series(al.vol_target(np.ones(len(c)), df, 0.20, 30, 2.0), index=idx)
|
||||
R = pd.concat(R, axis=1, join="inner"); M = pd.concat(M, axis=1, join="inner").shift(1)
|
||||
V = pd.concat(V, axis=1, join="inner").shift(1)
|
||||
out = np.zeros(len(R))
|
||||
for t in range(len(R)):
|
||||
mrow = M.iloc[t]
|
||||
if mrow.isna().all():
|
||||
continue
|
||||
best = mrow.idxmax()
|
||||
if dual and mrow[best] <= 0:
|
||||
continue
|
||||
pos = V.iloc[t][best]
|
||||
out[t] = (0.0 if np.isnan(pos) else pos) * R.iloc[t][best]
|
||||
return pd.Series(out, index=R.index)
|
||||
|
||||
|
||||
SINGLE = [("TRD10 Vortex", trd10), ("TRD08 Hull MA", trd08), ("TRD07 KAMA", trd07),
|
||||
("VOL08 RV term-struct", vol08), ("VOL11 DVOL kill-switch", vol11)]
|
||||
|
||||
print("=" * 90)
|
||||
print("RESTA QUALCHE CANDIDATO? — gate marginale vs TP01 sui contendenti piu' forti")
|
||||
print("=" * 90)
|
||||
rows = []
|
||||
for name, fn in SINGLE:
|
||||
rep = al.study_marginal(name, fn, tf="1d")
|
||||
m = rep["marginal"]
|
||||
print(al.fmt_marginal(rep))
|
||||
print()
|
||||
rows.append((name, rep["abs_grade"], rep["marginal_verdict"], rep["earns_slot"],
|
||||
m.get("corr_hold"), m["blends"]["w25"].get("uplift_hold")))
|
||||
|
||||
# cross-asset rotations (built directly, scored marginally)
|
||||
for name, dual in [("XAS09 dual-momentum", True), ("XAS03 RS rotation", False)]:
|
||||
m = al.marginal_vs_tp01(rotation_daily(90, dual))
|
||||
v = m["marginal_verdict"]
|
||||
print(al.fmt_marginal({"name": name, "abs_grade": "n/a", "marginal_verdict": v,
|
||||
"earns_slot": v == "ADDS", "marginal": m}))
|
||||
print()
|
||||
rows.append((name, "n/a", v, v == "ADDS", m.get("corr_hold"), m["blends"]["w25"].get("uplift_hold")))
|
||||
|
||||
print("=" * 90)
|
||||
print(f"{'candidato':<26s}{'abs':>7s}{'marginale':>12s}{'slot':>7s}{'corr_hold':>11s}{'upliftH_w25':>13s}")
|
||||
for n, ag, mv, es, ch, uh in rows:
|
||||
print(f"{n:<26s}{ag:>7s}{mv:>12s}{str(es):>7s}{str(ch):>11s}{str(uh):>13s}")
|
||||
print("\n(ADDS+slot=True => candidato vivo; tutto il resto => morto/ridondante)")
|
||||
Reference in New Issue
Block a user