chore(reset): v2.0.0 — storico certificato Deribit mainnet, ripartenza pulita
Reset del progetto su fondamenta verificate dopo la scoperta che l'intera libreria "validata OOS" era artefatto di feed contaminato (print fantasma del feed Cerbero TESTNET + storico Binance/USDT). - Storico ricostruito da Deribit MAINNET (ccxt pubblico, tokenless) e CERTIFICATO (certify_feed.py): BTC/ETH puliti su TUTTA la storia (mediana 2-6 bps vs Coinbase USD), integrita' OHLC + coerenza resample (maxΔ 0.00) + cross-venue OK. Alt esclusi (illiquidi/divergenti: LTC/DOGE 50-82% barre flat; XRP/BNB non certificabili). - Verdetto sul feed pulito: FADE / PAIRS / XS01 / TSM01 morti (ogni portafoglio Sharpe -2.3..-3.0, DD ~40%); solo SH01 e frammenti HONEST con segnale residuo, da ri-validare in isolamento. - Cleanup "restart pulito": strategie, stack live (src/live, src/portfolio, runner/executor, yml, docker), ~100 script ricerca/gate, waste/games/ portfolios, dati non certificati + cache e 60+ diari -> archiviati in Old/ (preservati, non cancellati). Diario consolidato in un unico documento. - Skeleton ricerca tenuto: Strategy ABC + indicatori + src/fractal + src/backtest/engine + load_data; tool dati certificati (rebuild_history, certify_feed, audit_feed, multi_source_check). - Universo dati ATTIVO: solo BTC/ETH (5m/15m/1h); guardrail fisico (load_data su alt -> FileNotFoundError). Esecuzione DISABILITATA, conto flat. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This commit is contained in:
Adriano Dal Pastro
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"""GATE PORT06 — fade MR01/MR02/MR07 a 15m (origine: probe ACCEL50 2026-06-12).
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Domanda onesta: i 6 sleeve fade girati a 15m (parametri live 1h NON ri-tunati,
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trasferimento pre-registrato anti-overfit) MIGLIORANO il PORT06, o sono solo una
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variante piu' veloce e correlata degli STESSI fade 1h?
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Metodo (engine CANONICO build_trades/fade_daily_equity, NON le classi Strategy):
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[1] PARITA': il builder locale a tf='1h' == sleeve canonico di build_everything.
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[2] STANDALONE daily 1h vs 15m per twin (Sharpe/DD FULL e OOS su IDX comune)
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+ stress fee 2x (0.20% RT) sul 15m (4x trade -> fee di prim'ordine).
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[3] CORRELAZIONE daily 15m vs twin 1h: se ~1 e' ridondante (il pairs 15m
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passo' a 0.37).
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[4] GATE PORT06: baseline vs ADD (19+6 sleeve) vs SWAP (15m al posto del 1h)
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vs BLEND (sleeve fade = 0.5*1h + 0.5*15m). Promosso se vs baseline l'OOS
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Sharpe non peggiora E il DD scende (criterio standard dei gate).
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uv run python scripts/analysis/fade15m_port06_gate.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 src.portfolio import weighting as W
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from scripts.analysis.combine_portfolio import (
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IDX, SPLIT, OOS_DATE, _norm, metrics, port_returns,
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)
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from scripts.analysis.risk_management import strats_for, build_trades, INIT, POS
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from scripts.analysis.report_families import build_everything
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from scripts.portfolios._defs import PORTFOLIOS
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FADE_IDS = [f"{nm}_{a}" for a in ("BTC", "ETH") for nm in ("MR01", "MR02", "MR07")]
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def fade_daily_tf(asset: str, fn, params, tf: str, fee_rt: float = 0.001) -> pd.Series:
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"""fade_daily_equity canonico, parametrizzato sul timeframe (stesso engine)."""
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df = load_data(asset, tf)
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ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
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trades = build_trades(fn(df, **params), df, fee_rt=fee_rt, trend_max=3.0)
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n = len(df)
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eq = np.full(n, INIT, dtype=float)
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cap = INIT
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for i, j, ret in sorted(trades, key=lambda t: t[1]):
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cap = max(cap + cap * POS * ret, 10.0)
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eq[j:] = cap
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s = pd.Series(eq, index=ts).resample("1D").last().reindex(IDX).ffill().bfill()
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return _norm(s)
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def build_fade15(fee_rt: float = 0.001) -> dict[str, pd.Series]:
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out = {}
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for asset in ("BTC", "ETH"):
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for nm, (fn, params) in strats_for(asset).items():
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out[f"{nm}_{asset}_15M"] = fade_daily_tf(asset, fn, params, "15m", fee_rt)
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return out
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def std(label: str, eq: pd.Series) -> str:
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r = eq.pct_change().fillna(0.0)
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f, o = metrics(r), metrics(r, lo=SPLIT)
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return (f" {label:<16s} FULL ret{f['ret']:>+8.0f}% DD{f['dd']:>6.1f}% Sh{f['sharpe']:>6.2f}"
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f" | OOS ret{o['ret']:>+7.0f}% DD{o['dd']:>5.1f}% Sh{o['sharpe']:>6.2f}")
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def port_metrics(members: dict[str, pd.Series], p) -> tuple[dict, dict]:
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ids = list(members)
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dr = pd.DataFrame({i: members[i].pct_change().fillna(0.0) for i in ids})
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clusters = {i: i for i in ids}
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w = W.weight_vector(p.weighting, ids, dr, weights=p.weights,
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caps=p.caps, clusters=clusters, lookback=p.vol_lookback)
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drp = port_returns(members, w)
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return metrics(drp), metrics(drp, lo=SPLIT)
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def prow(label: str, fo: tuple[dict, dict]) -> str:
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f, o = fo
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return (f" {label:<22s} FULL CAGR{f['cagr']:>5.0f}% DD{f['dd']:>6.2f}% Sh{f['sharpe']:>6.2f}"
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f" | OOS CAGR{o['cagr']:>5.0f}% DD{o['dd']:>6.2f}% Sh{o['sharpe']:>6.2f}")
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def main() -> None:
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print("=" * 100)
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print(" GATE PORT06 — fade 15m (MR01/02/07 x BTC/ETH) vs 1h deployato")
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print(f" Finestra comune {IDX[0].date()} -> {IDX[-1].date()}, OOS da {OOS_DATE}")
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print("=" * 100)
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print("\nCostruzione sleeve canonici (2-3 min)...")
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S, pairs, tsm, shape = build_everything()
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canon = {**S, **pairs, **tsm, **shape}
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# [1] PARITA' del builder locale a 1h
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print("\n[1] PARITA' builder locale 1h == canonico")
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for sid in FADE_IDS:
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nm, asset = sid.split("_")
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fn, params = strats_for(asset)[nm]
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mine = fade_daily_tf(asset, fn, params, "1h")
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diff = float((mine - canon[sid]).abs().max())
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print(f" {sid:<10s} max|diff| = {diff:.2e} {'OK' if diff < 1e-9 else 'VIOLAZIONE!'}")
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# [2] STANDALONE 1h vs 15m + stress fee 2x sul 15m
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print("\n[2] STANDALONE daily (engine canonico, pos 0.15 lev 3, fee 0.10% RT)")
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fade15 = build_fade15()
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fade15_fee2 = build_fade15(fee_rt=0.002)
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for sid in FADE_IDS:
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print(std(sid + " 1h", canon[sid]))
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print(std(sid + " 15m", fade15[sid + "_15M"]))
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print(std(sid + " 15m f2x", fade15_fee2[sid + "_15M"]))
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# [3] CORRELAZIONE twin 15m vs 1h
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print("\n[3] CORRELAZIONE daily 15m vs twin 1h (pairs 15m promosso a 0.37)")
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cors = []
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for sid in FADE_IDS:
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c = canon[sid].pct_change().corr(fade15[sid + "_15M"].pct_change())
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cors.append(c)
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print(f" {sid:<10s} corr = {c:.2f}")
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print(f" media = {np.mean(cors):.2f}")
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# [4] GATE PORT06
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print("\n[4] GATE PORT06 (weighting cap PAIRS 0.33 / SHAPE 0.0588)")
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p = PORTFOLIOS["PORT06"]
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base = {sid: canon[sid] for sid in p.sleeve_ids}
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add = {**base, **fade15}
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swap = dict(base)
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blend = dict(base)
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for sid in FADE_IDS:
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e15 = fade15[sid + "_15M"]
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swap[sid] = e15
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rb = 0.5 * base[sid].pct_change().fillna(0.0) + 0.5 * e15.pct_change().fillna(0.0)
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eq = (1 + rb).cumprod()
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blend[sid] = eq / eq.iloc[0]
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rows = {"BASELINE (1h)": port_metrics(base, p), "ADD (+6 sleeve 15m)": port_metrics(add, p),
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"SWAP (15m al posto 1h)": port_metrics(swap, p), "BLEND 50/50": port_metrics(blend, p)}
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for label, fo in rows.items():
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print(prow(label, fo))
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fb, ob = rows["BASELINE (1h)"]
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print("\nVERDETTO (criterio: OOS Sharpe non peggiora E DD scende vs baseline):")
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for label in ("ADD (+6 sleeve 15m)", "SWAP (15m al posto 1h)", "BLEND 50/50"):
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f, o = rows[label]
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ok = o["sharpe"] >= ob["sharpe"] - 1e-9 and (o["dd"] < ob["dd"] or f["dd"] < fb["dd"])
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print(f" {label:<22s} -> {'PROMOSSO' if ok else 'bocciato'}")
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if __name__ == "__main__":
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main()
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