diff --git a/docs/TODO.md b/docs/TODO.md index 11bb985..0c61078 100644 --- a/docs/TODO.md +++ b/docs/TODO.md @@ -47,13 +47,10 @@ ## Ricerca dispersion/correlation (2026-06-08, 165 agenti) — follow-up opzionale -- [ ] **Gate PORT06 di `index_comp_disp` W=168 (BTC)** — l'unico candidato della ricerca - dispersion che merita un test formale (`combine_v2`): misurare corr col MASTER e - ΔSharpe/ΔDD. Config in `scripts/analysis/dispersion_edges/index_comp_disp.py` - (rel_len=12, z_thr=1.5, disp_q=0.7, TP=1.0ATR, SL=1.5ATR, mb=24). P(migliora)~20-25%: - è fade-BTC, rischio sovrapposizione con le MR. Se non decorrela → scartare. - Diario `docs/diary/2026-06-08-dispersion-correlation-search.md`. **Bassa priorità** - (nessun nuovo motore emerso; vale la lezione FR01). +- [x] ~~Gate PORT06 di `index_comp_disp` W=168~~ — FATTO (2026-06-08): PROMOSSO MARGINALE. + Decorrela bene (corr 0.06 col MASTER) ma OOS PIATTO (Sharpe −0.01). **Documentato e + rimandato** (non deployato): gate in `dispersion_edges/gate_index_comp_disp.py`, + riprendere solo se si costruisce una famiglia DISP più ampia. Diario aggiornato. ## Monitoraggio (osservare, non agire subito) diff --git a/docs/diary/2026-06-08-dispersion-correlation-search.md b/docs/diary/2026-06-08-dispersion-correlation-search.md index 17a7fff..2cc8a41 100644 --- a/docs/diary/2026-06-08-dispersion-correlation-search.md +++ b/docs/diary/2026-06-08-dispersion-correlation-search.md @@ -56,3 +56,21 @@ corr_vol_interact, leadlag_corr, corr_trend, disp_compression_breakout, corr_dis **Conclusione onesta: nessun nuovo motore di ritorno.** Il dispersion-trading realizzato funziona solo come l'ennesima faccia della mean-reversion già sfruttata. + +## Gate PORT06 del candidato n.1 (2026-06-08) — PROMOSSO MARGINALE, NON deployato + +`scripts/analysis/dispersion_edges/gate_index_comp_disp.py` (config W=168: rel_len=12, +z_win=336, z_thr=1.5, disp_q=0.7, TP=1.0ATR, SL=1.5ATR, mb=24; equity daily innestata +come 18° sleeve, pesi cap): + +- **Sorpresa positiva**: decorrela DAVVERO. corr daily col candidato: MR01_BTC +0.01, + MR02_BTC +0.05, MR07_BTC +0.06, DIP01_BTC +0.02, MASTER(EW) +0.06. Il timore di + ridondanza con le fade BTC era infondato (gate dispersione + TP vicino = profilo trade + distinto). Standalone 311 trade, FULL +67% / OOS +30%. +- **Ma beneficio nel rumore**: PORT06 BASE→+DISP: FULL Sharpe 6.43→6.47, FULL DD + 3.96→3.73 (migliora), **OOS Sharpe 8.58→8.56 (−0.01), OOS DD 1.36→1.40 (+0.04)** — + l'OOS è PIATTO. Passa il gate tecnico ma il guadagno è solo nel FULL (regime storico). +- **Decisione (utente): documenta e rimanda.** NON deployato — wiring (nuova Strategy+worker, + famiglia DISP, peso cap) + resterebbe simulato (no executor), per un beneficio OOS nullo. + Gate script committato e pronto: riprendere SOLO se si costruisce una famiglia DISP più + ampia (più asset/sleeve) che insieme sposti l'OOS. Esito ~20% previsto, confermato. diff --git a/scripts/analysis/dispersion_edges/gate_index_comp_disp.py b/scripts/analysis/dispersion_edges/gate_index_comp_disp.py new file mode 100644 index 0000000..69e4b56 --- /dev/null +++ b/scripts/analysis/dispersion_edges/gate_index_comp_disp.py @@ -0,0 +1,156 @@ +"""GATE PORT06 del candidato index_comp_disp W=168 (ricerca dispersion 2026-06-08). + +Edge confermato avversarialmente: fade della componente idiosincratica di BTC verso +l'indice EW, gated da alta dispersione. Config: rel_len=12, z_win=336, z_thr=1.5, +disp_168 >= quantile rolling 0.7 (win 720), TP=1.0*ATR14, SL=1.5*ATR14, max_bars=24. + +Domanda del gate (lezione FR01: robusto != migliora-il-portafoglio): + 1) correlazione daily col MASTER e con le fade BTC esistenti (e' un diversificatore?) + 2) PORT06 BASE (17 sleeve) vs +DISP (18 sleeve) con pesi cap: DeltaSharpe/DeltaDD FULL e OOS. +PROMOSSO solo se decorrela E migliora (o non degrada) l'OOS. + + uv run python scripts/analysis/dispersion_edges/gate_index_comp_disp.py +""" +from __future__ import annotations + +import sys +from pathlib import Path + +import numpy as np +import pandas as pd + +PROJECT_ROOT = Path(__file__).resolve().parents[3] +sys.path.insert(0, str(PROJECT_ROOT)) + +from scripts.analysis.dispersion_lab import features, align_to +from scripts.analysis.explore_lab import get_df, atr +from scripts.analysis.combine_portfolio import _norm, IDX, port_returns, metrics, SPLIT, OOS_DATE +from scripts.analysis.honest_improve2 import _daily_equity +from scripts.portfolios._defs import PORTFOLIOS +from src.portfolio import weighting as W + +FEE_RT, LEV, POS, INIT = 0.001, 3.0, 0.15, 1000.0 +CFG = dict(rel_len=12, z_win=336, z_thr=1.5, disp_q=0.7, disp_q_win=720, + tp_atr=1.0, sl_atr=1.5, max_bars=24) + + +def _last_rank(x): + if x.shape[0] < 2: + return np.nan + return float((x[:-1] < x[-1]).mean()) + + +def build_trades(asset="BTC"): + """Entries CAUSALI + exit intrabar (TP/SL/max_bars) -> [(i, j, ret_netto)].""" + df = get_df(asset, "1h") + F = features() + fa = align_to(F, df) + c, h, l = df["close"].values, df["high"].values, df["low"].values + n = len(c) + a14 = atr(df, 14) + rel = fa[f"rel_{asset}"].values.astype(float) + disp = fa["disp_168"].values.astype(float) + # somma rolling rel su rel_len, z-score causale (mean/std rolling z_win shift 1) + rs = pd.Series(rel).rolling(CFG["rel_len"]).sum() + rmean = rs.rolling(CFG["z_win"]).mean().shift(1) + rstd = rs.rolling(CFG["z_win"]).std().shift(1) + z = ((rs - rmean) / rstd.replace(0, np.nan)).values + dpct = pd.Series(disp).rolling(CFG["disp_q_win"]).apply(_last_rank, raw=True).values + fee = FEE_RT * LEV + out = [] + last = -1 + for i in range(n - 1): + if i <= last or not np.isfinite(z[i]) or not np.isfinite(dpct[i]): + continue + if dpct[i] < CFG["disp_q"] or abs(z[i]) < CFG["z_thr"]: + continue + ai = a14[i] + if not np.isfinite(ai) or ai <= 0: + continue + d = -1 if z[i] > 0 else 1 + tp = c[i] + d * CFG["tp_atr"] * ai + sl = c[i] - d * CFG["sl_atr"] * ai + mb = CFG["max_bars"] + j = min(i + mb, n - 1) + exit_p = c[j] + for k in range(1, mb + 1): + j = i + k + if j >= n: + j = n - 1; exit_p = c[j]; break + if d == 1: + if l[j] <= sl: exit_p = sl; break + if h[j] >= tp: exit_p = tp; break + else: + if h[j] >= sl: exit_p = sl; break + if l[j] <= tp: exit_p = tp; break + if k == mb: exit_p = c[j] + out.append((i, j, (exit_p - c[i]) / c[i] * d * LEV - fee)) + last = j + return df, out + + +def daily_equity(df, trades): + ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True) + cap = INIT; eq_ts, eq_v = [], [] + for i, j, ret in sorted(trades, key=lambda t: t[1]): + cap = max(cap + cap * POS * ret, 10.0) + eq_ts.append(ts.iloc[j]); eq_v.append(cap) + return _norm(_daily_equity(eq_ts, eq_v, IDX)) + + +def pmetrics(members, p, extra=None): + ids = list(p.sleeve_ids) + ([extra] if extra else []) + dr = pd.DataFrame({i: members[i].pct_change().fillna(0.0) for i in ids}) + if extra: + caps = dict(p.caps); caps["DISP"] = caps.get("DISP", None) + w = W.weight_vector(p.weighting, ids, dr, weights=p.weights, + caps=p.caps, clusters={**{i:(p.clusters or {}).get(i,i) for i in p.sleeve_ids}, + **({extra:"disp"} if extra else {})}, + lookback=p.vol_lookback) + drp = port_returns({i: members[i] for i in ids}, w) + return metrics(drp), metrics(drp, lo=SPLIT) + + +def main(): + p = PORTFOLIOS["PORT06"] + print("=" * 100) + print(" GATE PORT06 — candidato index_comp_disp W=168 (BTC) | famiglia DISP nuova") + print(f" config {CFG} | OOS da {OOS_DATE}") + print("=" * 100) + + from src.portfolio.sleeves import all_sleeve_equities + eq_base = dict(all_sleeve_equities()) + + df, trades = build_trades("BTC") + disp_eq = daily_equity(df, trades) + fr = (disp_eq.iloc[-1] / disp_eq.iloc[0] - 1) * 100 + o = disp_eq.iloc[SPLIT:]; ofr = (o.iloc[-1] / o.iloc[0] - 1) * 100 + print(f"\n[1] candidato standalone: {len(trades)} trade | FULL {fr:+.0f}% | OOS {ofr:+.0f}%") + + # correlazione daily col MASTER e con le fade BTC + dr_cand = disp_eq.pct_change().fillna(0.0) + print("\n[2] correlazione daily col candidato (decorrela?):") + for sid in ["MR01_BTC", "MR02_BTC", "MR07_BTC", "DIP01_BTC"]: + corr = dr_cand.corr(eq_base[sid].pct_change().fillna(0.0)) + print(f" {sid:<12} corr {corr:+.3f}") + master_dr = pd.DataFrame({i: eq_base[i].pct_change().fillna(0.0) for i in p.sleeve_ids}).mean(axis=1) + print(f" {'MASTER(EW)':<12} corr {dr_cand.corr(master_dr):+.3f}") + + # PORT06 base vs +DISP + f_b, o_b = pmetrics(eq_base, p) + members = dict(eq_base); members["DISP_BTC"] = disp_eq + f_e, o_e = pmetrics(members, p, extra="DISP_BTC") + print("\n[3] PORT06 BASE (17) vs +DISP (18):") + print(f" {'':<10}{'FULL Sh':>9}{'FULL DD%':>10}{'OOS Sh':>9}{'OOS DD%':>9}") + print(f" {'BASE':<10}{f_b['sharpe']:>9.2f}{f_b['dd']:>10.2f}{o_b['sharpe']:>9.2f}{o_b['dd']:>9.2f}") + print(f" {'+DISP':<10}{f_e['sharpe']:>9.2f}{f_e['dd']:>10.2f}{o_e['sharpe']:>9.2f}{o_e['dd']:>9.2f}") + print(f" {'DELTA':<10}{f_e['sharpe']-f_b['sharpe']:>+9.2f}{f_e['dd']-f_b['dd']:>+10.2f}" + f"{o_e['sharpe']-o_b['sharpe']:>+9.2f}{o_e['dd']-o_b['dd']:>+9.2f}") + + promoted = (o_e['sharpe'] >= o_b['sharpe'] - 0.02 and o_e['dd'] <= o_b['dd'] + 0.20 + and f_e['sharpe'] >= f_b['sharpe'] - 0.02) + print("\n VERDETTO: " + (">>> PROMOSSO <<<" if promoted else ">>> BOCCIATO (diluisce, come FR01) <<<")) + + +if __name__ == "__main__": + main()