feat(pairs): attiva ETH/BTC 15m flat-skip in PORT06 (BLEND, mezza size)
Origine: gioco "Blind Traders" (100 agenti ciechi su BTC/ETH anonimizzati) -> vincitore = spread ETH/BTC reversion a 15m. Testato sul serio col gate PORT06: non duplicato (corr 1h vs 15m = 0.37), robusto (16/16 celle Sharpe>1), edge NON artefatto delle candele flat ETH 15m (filtrandole resta l'83% dello Sharpe). Percorso live costruito e validato: - pairs_research.pairs_sim_flat: engine generalizzato con exit LIVE-REALIZABLE (arma exit_ready, esce alla 1a barra pulita); regression-lock a pairs_sim. - PairsWorker: flat_skip + exit_ready + rilevamento flat da OHLC (1h byte-exact). - runner: fetch diretto dei timeframe sub-orari + override position_size per-sleeve. - validate_worker_pairs: replay worker == backtest a 15m (8452 vs 8453 trade). - _defs/build_everything: sleeve PR_ETHBTC_15M (mezza size, pos 0.10) -> PORT06 FULL 6.43->7.20, OOS 8.58->9.66, DD giu'. Rischio bilanciato col 1h. - smoke live: Cerbero serve candele 15m fresche; worker ticca. Diari docs/diary/2026-06-09-*. Caveat slippage: mezza size = blend-tilt prudente. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Adriano Dal Pastro
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"""Check candele FLAT (O=H=L=C, liquidita' zero) sui pairs ETH/BTC a 15m.
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Rischio noto (CLAUDE.md): ETH 15m ha 14-30%/anno di candele flat per bassa liquidita'
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del perpetuo. Su un pairs, un close stale gonfia lo z-score (l'altra gamba si muove,
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questa e' ferma) -> segnale di "reversione" FINTO che rientra solo quando la gamba
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stale si sblocca: profitto NON eseguibile dal vivo. Questo gonfierebbe il backtest 15m.
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Test:
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[1] prevalenza candele flat per anno (ETH 15m, BTC 15m).
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[2] quanti trade del pairs 15m hanno ENTRY/EXIT su una candela flat (gamba stale).
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[3] re-sim flat-aware: entry/exit SOLO su barre pulite (non-flat in ENTRAMBE le gambe)
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-> quanto sopravvive l'edge? (parita': senza flat-skip == pairs_sim).
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[4] gate PORT06 col 15m flat-filtrato vs baseline 1h.
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uv run python scripts/analysis/pairs15m_flatcheck.py
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"""
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from __future__ import annotations
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import sys
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from pathlib import Path
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import numpy as np
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import pandas as pd
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PROJECT_ROOT = Path(__file__).resolve().parents[2]
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sys.path.insert(0, str(PROJECT_ROOT))
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from src.data.downloader import load_data
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from scripts.analysis.pairs_research import pairs_sim, OOS_FRAC, FEE_RT, LEV, POS, BARS_YEAR
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from scripts.analysis.report_families import daily_from
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from scripts.analysis.combine_portfolio import metrics, SPLIT, OOS_DATE
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from scripts.analysis.pairs15m_port06_gate import port_metrics, eth_btc_daily, UNIV_1H, GAME_15M
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from scripts.portfolios._defs import PORTFOLIOS
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from src.portfolio.sleeves import all_sleeve_equities
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def aligned2(a, b, tf="15m"):
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"""Merge con OHLC di ENTRAMBE le gambe (serve per rilevare i flat su entrambe)."""
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da = load_data(a, tf)[["timestamp", "open", "high", "low", "close"]].rename(
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columns=lambda x: x + "_a" if x != "timestamp" else x)
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db = load_data(b, tf)[["timestamp", "open", "high", "low", "close"]].rename(
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columns=lambda x: x + "_b" if x != "timestamp" else x)
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m = da.merge(db, on="timestamp", how="inner").reset_index(drop=True)
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m["dt"] = pd.to_datetime(m["timestamp"], unit="ms", utc=True)
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return m
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def is_flat(o, h, l, c):
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return (o == h) & (h == l) & (l == c)
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def flat_prevalence(asset, tf="15m"):
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d = load_data(asset, tf)
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d = d.copy()
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d["dt"] = pd.to_datetime(d["timestamp"], unit="ms", utc=True)
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fl = is_flat(d["open"].values, d["high"].values, d["low"].values, d["close"].values)
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d["flat"] = fl
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by = d.groupby(d["dt"].dt.year)["flat"].mean() * 100
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return by, fl.mean() * 100
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def pairs_sim_flataware(a, b, tf="15m", n=66, z_in=1.674, z_exit=1.0, max_bars=35,
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jump_max=0.08, fee_rt=FEE_RT, lev=LEV, pos=POS,
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split_frac=0.0, skip_flat=True):
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"""Come pairs_sim ma: entry/exit consentiti SOLO su barre pulite (se skip_flat).
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Ritorna anche n_entry_flat / n_exit_flat (diagnostica, calcolata sempre)."""
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m = aligned2(a, b, tf)
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ca, cb = m["close_a"].values, m["close_b"].values
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flat_a = is_flat(m["open_a"].values, m["high_a"].values, m["low_a"].values, ca)
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flat_b = is_flat(m["open_b"].values, m["high_b"].values, m["low_b"].values, cb)
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flat = flat_a | flat_b # barra "sporca" se una delle due gambe e' flat
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r = np.log(ca / cb)
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dr = np.abs(np.diff(r, prepend=r[0]))
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ma = pd.Series(r).rolling(n).mean().values
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sd = pd.Series(r).rolling(n).std().values
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z = (r - ma) / np.where(sd == 0, np.nan, sd)
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ts = m["dt"]; N = len(r)
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split = int(N * split_frac)
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fee = 2 * fee_rt * lev
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cap = peak = 1000.0; dd = 0.0; last = -1
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trades = wins = 0; rets = []; yearly = {}
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eq_ts, eq_v = [], []
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n_entry_flat = n_exit_flat = 0
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for i in range(n + 1, N - 1):
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if i < split or np.isnan(z[i]) or dr[i] > jump_max or i <= last:
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continue
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if z[i] <= -z_in:
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d = 1
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elif z[i] >= z_in:
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d = -1
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else:
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continue
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if flat[i]:
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n_entry_flat += 1
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if skip_flat:
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continue # non si entra su una gamba stale
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# exit: |z|<=z_exit o max_bars; se skip_flat, salta le barre flat come uscita
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j = min(i + max_bars, N - 1)
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for k in range(1, max_bars + 1):
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jj = i + k
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if jj >= N:
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j = N - 1; break
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if skip_flat and flat[jj]:
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j = jj # avanza, non esce su barra stale
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continue
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if abs(z[jj]) <= z_exit:
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j = jj; break
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j = jj
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if flat[j]:
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n_exit_flat += 1
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if skip_flat:
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# spingi all'ultima barra pulita entro l'orizzonte
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back = j
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while back > i and flat[back]:
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back -= 1
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j = back if back > i else j
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retA = (ca[j] - ca[i]) / ca[i]
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retB = (cb[j] - cb[i]) / cb[i]
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ret = (retA - retB) * d * lev - fee
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cap = max(cap + cap * pos * ret, 10.0)
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peak = max(peak, cap); dd = max(dd, (peak - cap) / peak)
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trades += 1; wins += ret > 0; rets.append(ret * pos); last = j
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eq_ts.append(ts.iloc[j]); eq_v.append(cap)
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yearly[ts.iloc[i].year] = yearly.get(ts.iloc[i].year, 0.0) + ret * 100
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yrs_span = (ts.iloc[-1] - ts.iloc[max(split, 0)]).days / 365.25 or 1
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sharpe = 0.0
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if len(rets) > 1 and np.std(rets) > 0:
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sharpe = float(np.mean(rets) / np.std(rets) * np.sqrt(trades / yrs_span))
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ret_tot = (cap / 1000 - 1) * 100
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return dict(trades=trades, win=wins / trades * 100 if trades else 0, ret=ret_tot,
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dd=dd * 100, sharpe=sharpe, yearly=yearly, eq_ts=eq_ts, eq_v=eq_v,
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n_entry_flat=n_entry_flat, n_exit_flat=n_exit_flat)
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def main():
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print("=" * 100)
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print(" CHECK FLAT-CANDLE — ETH/BTC pairs 15m (gate condizionato)")
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print("=" * 100)
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# [1] prevalenza
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print("\n[1] Prevalenza candele flat (O=H=L=C) per anno, 15m:")
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for asset in ("ETH", "BTC"):
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by, tot = flat_prevalence(asset, "15m")
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print(f" {asset}: media {tot:.1f}% | " +
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" ".join(f"{y}:{v:.0f}%" for y, v in by.items()))
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# [2] quanti trade toccano un flat (sim SENZA skip per diagnostica)
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diag = pairs_sim_flataware("ETH", "BTC", **GAME_15M, skip_flat=False)
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tr = diag["trades"]
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print(f"\n[2] Trade 15m totali: {tr} | entry su barra flat: {diag['n_entry_flat']} "
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f"({diag['n_entry_flat']/tr*100:.1f}%) | exit su barra flat: {diag['n_exit_flat']} "
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f"({diag['n_exit_flat']/tr*100:.1f}%)")
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# [3] parita' + edge filtrato
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print("\n[3] Edge 15m: NO-skip (== pairs_sim) vs FLAT-AWARE (entry/exit solo barre pulite):")
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# parita': flataware skip_flat=False deve ~== pairs_sim
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base_ps = pairs_sim("ETH", "BTC", **GAME_15M, pos=POS, lev=LEV)
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print(f" parita' pairs_sim : trd {base_ps['trades']:>5d} Sh {base_ps['sharpe']:.2f} "
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f"DD {base_ps['dd']:.0f}% ret {base_ps['ret']:+.0f}%")
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print(f" flataware (no-skip) : trd {diag['trades']:>5d} Sh {diag['sharpe']:.2f} "
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f"DD {diag['dd']:.0f}% ret {diag['ret']:+.0f}%")
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filt = pairs_sim_flataware("ETH", "BTC", **GAME_15M, skip_flat=True)
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filt_o = pairs_sim_flataware("ETH", "BTC", **GAME_15M, skip_flat=True, split_frac=1 - OOS_FRAC)
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print(f" FLAT-AWARE (skip) : trd {filt['trades']:>5d} Sh {filt['sharpe']:.2f} "
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f"DD {filt['dd']:.0f}% ret {filt['ret']:+.0f}% | OOS Sh {filt_o['sharpe']:.2f} DD {filt_o['dd']:.0f}%")
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drop = (1 - filt['trades'] / diag['trades']) * 100
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sh_keep = filt['sharpe'] / diag['sharpe'] * 100 if diag['sharpe'] else 0
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verdict = "EDGE NON artefatto flat" if sh_keep > 70 else "EDGE in larga parte ARTEFATTO flat"
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print(f" -> rimossi {drop:.1f}% dei trade; Sharpe trattenuto {sh_keep:.0f}% ({verdict})")
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# [4] gate PORT06 col 15m flat-filtrato
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print("\n[4] GATE PORT06 — ETH/BTC: baseline 1h vs SWAP 15m-FLATAWARE vs BLEND:")
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p = PORTFOLIOS["PORT06"]
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pair_ids = [s.sid for s in p.sleeves if s.sid.startswith("PR_")]
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eq_base = dict(all_sleeve_equities())
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e1h, _ = eth_btc_daily(UNIV_1H)
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e15f = daily_from(filt["eq_ts"], filt["eq_v"])
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# blend 1h + 15m-flataware (50/50 daily-rebalanced)
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from scripts.analysis.pairs15m_port06_gate import blend
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eblend = blend(e1h, e15f, 0.5)
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corr = e1h.pct_change().fillna(0).corr(e15f.pct_change().fillna(0))
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print(f" corr 1h vs 15m-flataware: {corr:.3f}")
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print(f" {'variante':<18s} | {'FULL Sh':>8s}{'FULL DD%':>9s}{'CAGR':>6s} | {'OOS Sh':>7s}{'OOS DD%':>8s}")
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print(" " + "-" * 70)
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res = {}
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for tag, eth in [("baseline 1h", e1h), ("SWAP 15m-flat", e15f), ("BLEND 1h+15m-flat", eblend)]:
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members = dict(eq_base); members["PR_ETHBTC"] = eth
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f, o = port_metrics(members, p)
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res[tag] = (f, o)
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print(f" {tag:<18s} | {f['sharpe']:>8.2f}{f['dd']:>9.2f}{f['cagr']:>5.0f}%"
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f" | {o['sharpe']:>7.2f}{o['dd']:>8.2f}")
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fb, ob = res["baseline 1h"]
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print("\n VERDETTO (vs baseline 1h, fee backtest): Sharpe non peggiora E DD <= baseline")
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for tag in ("SWAP 15m-flat", "BLEND 1h+15m-flat"):
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f, o = res[tag]
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ok = o["sharpe"] >= ob["sharpe"] - 0.02 and o["dd"] <= ob["dd"] + 1e-9 and f["sharpe"] >= fb["sharpe"] - 0.02 and f["dd"] <= fb["dd"] + 1e-9
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print(f" {tag:<18s}: OOS {ob['sharpe']:.2f}->{o['sharpe']:.2f} DD {ob['dd']:.2f}->{o['dd']:.2f}"
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f" | FULL {fb['sharpe']:.2f}->{f['sharpe']:.2f} DD {fb['dd']:.2f}->{f['dd']:.2f} => {'PROMOSSO' if ok else 'bocciato'}")
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if __name__ == "__main__":
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main()
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