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>
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Adriano Dal Pastro
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"""Miglioramenti v2: market-regime gate su DIP01 + PORTAFOGLIO combinato.
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- DIP01 con gate di mercato: compra i dip solo quando BTC e' risk-on (BTC>SMA),
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cosi' si evitano le capitolazioni dei bear (2018/2022) che peggiorano Acc/DD/PnL.
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- Portafoglio: equal-weight giornaliero delle 3 strategie migliorate -> la
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diversificazione taglia il DD mantenendo la PnL (migliora il risk-adjusted).
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Tutto NETTO, con DD pieno e per-anno.
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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 scripts.analysis.honest_lab import atr, ema, get_df, available_assets, FEE_RT
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from scripts.analysis.honest_improve import rot_improved, _dd
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LEV, POS = 3.0, 0.15
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def _daily_equity(ts_list, cap_list, idx):
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"""serie di equity giornaliera (ffill) su un DatetimeIndex comune."""
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s = pd.Series(cap_list, index=pd.to_datetime(ts_list, utc=True))
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s = s[~s.index.duplicated(keep="last")].sort_index()
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daily = s.resample("1D").last().reindex(idx).ffill().bfill()
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return daily
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# ---------- DIP01 con market-regime gate ----------
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def dip_market_gated(asset, n=50, z_in=2.5, sl_atr=2.5, max_bars=24,
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market_n=100, fee_rt=FEE_RT, oos_frac=0.0, return_equity=False):
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df = get_df(asset, "1h")
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h, l, c = df["high"].values, df["low"].values, df["close"].values
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N = len(c); ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
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ma = pd.Series(c).rolling(n).mean().values
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sd = pd.Series(c).rolling(n).std().values
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a = atr(df, 14)
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z = (c - ma) / np.where(sd == 0, np.nan, sd)
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# regime di mercato: BTC 1h > SMA(market_n in giorni -> *24 barre)
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btc = get_df("BTC", "1h")
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bser = pd.Series(btc["close"].values,
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index=pd.to_datetime(btc["timestamp"], unit="ms", utc=True))
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bser = bser[~bser.index.duplicated()]
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bma = bser.rolling(market_n * 24).mean()
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risk_on = (bser > bma).reindex(ts, method="ffill").fillna(False).values
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fee = fee_rt * LEV
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cap = 1000.0; last_exit = -1
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eq_ts, eq_v = [], []
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yt: dict[int, list] = {}; ypnl: dict[int, float] = {}
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split = int(N * (1 - oos_frac)) if oos_frac else 0
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for i in range(n + 14, N):
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if i < split or np.isnan(z[i]) or np.isnan(a[i]):
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continue
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if not (z[i] <= -z_in and z[i - 1] > -z_in):
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continue
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if market_n and not risk_on[i]:
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continue
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if i <= last_exit or i + 1 >= N:
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continue
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entry = c[i]; tp, sl, mb = ma[i], c[i] - sl_atr * a[i], max_bars
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exit_p = c[min(i + mb, N - 1)]; j = min(i + mb, N - 1)
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for k in range(1, mb + 1):
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j = i + k
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if j >= N:
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j = N - 1; exit_p = c[j]; break
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if l[j] <= sl:
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exit_p = sl; break
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if h[j] >= tp:
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exit_p = tp; break
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if k == mb:
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exit_p = c[j]
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ret = (exit_p - entry) / entry * LEV - fee
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cap = max(cap + cap * POS * ret, 10.0)
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last_exit = j
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y = ts.iloc[i].year
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rec = yt.setdefault(y, [0, 0]); rec[0] += 1; rec[1] += ret > 0
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ypnl[y] = ypnl.get(y, 0.0) + ret * 100
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eq_ts.append(ts.iloc[j]); eq_v.append(cap)
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t = sum(v[0] for v in yt.values()); w = sum(v[1] for v in yt.values())
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out = {"ret": (cap / 1000 - 1) * 100, "dd": _dd(np.array(eq_v)) if eq_v else 0.0,
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"trades": t, "acc": w / t * 100 if t else 0.0, "yt": yt, "ypnl": ypnl,
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"pos_years": sum(1 for v in ypnl.values() if v > 0), "n_years": len(ypnl)}
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if return_equity:
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out["eq_ts"], out["eq_v"] = eq_ts, eq_v
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return out
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def main():
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print("=" * 96)
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print(" DIP01 — base vs MARKET-GATE (compra dip solo se BTC>SMA100)")
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print("=" * 96)
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print(f" {'asset / config':<30s}{'Trd':>6s}{'Acc%':>7s}{'FULL%':>9s}{'OOS%':>9s}{'DD%':>7s}{'AnniP':>8s}")
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for a in ["BTC", "ETH", "SOL"]:
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b = dip_market_gated(a, market_n=0); bo = dip_market_gated(a, market_n=0, oos_frac=0.30)
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g = dip_market_gated(a, market_n=100); go = dip_market_gated(a, market_n=100, oos_frac=0.30)
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print(f" {a+' base':<30s}{b['trades']:>6d}{b['acc']:>7.1f}{b['ret']:>+9.0f}{bo['ret']:>+9.0f}"
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f"{b['dd']:>7.0f}{str(b['pos_years'])+'/'+str(b['n_years']):>8s}")
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print(f" {a+' +gate100':<30s}{g['trades']:>6d}{g['acc']:>7.1f}{g['ret']:>+9.0f}{go['ret']:>+9.0f}"
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f"{g['dd']:>7.0f}{str(g['pos_years'])+'/'+str(g['n_years']):>8s}")
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# ---------- PORTAFOGLIO combinato (3 sleeve diversificate) ----------
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print("\n" + "=" * 96)
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print(" PORTAFOGLIO equal-weight giornaliero (ribilanciato): DIP01 + TR01-basket + ROT02")
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print("=" * 96)
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idx = pd.date_range("2021-01-01", "2026-05-26", freq="1D", tz="UTC")
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# sleeve 1: DIP01 base su BTC (la migliore)
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d = dip_market_gated("BTC", market_n=0, return_equity=True)
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eq_dip = _norm(_daily_equity(d["eq_ts"], d["eq_v"], idx))
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# sleeve 2: TR01 equal-weight su {BNB,BTC,DOGE,SOL,XRP}
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eq_tr = _norm(_tr_basket_daily(["BNB", "BTC", "DOGE", "SOL", "XRP"], idx))
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# sleeve 3: ROT02 dual-momentum
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eq_rot = _norm(_rot_daily_equity(idx))
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members = {"DIP01_BTC": eq_dip, "TR01_basket": eq_tr, "ROT02_dualmom": eq_rot}
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# ribilanciamento giornaliero equal-weight: media dei rendimenti giornalieri
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drets = pd.DataFrame({k: v.pct_change().fillna(0) for k, v in members.items()})
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port_ret = drets.mean(axis=1)
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combo = (1 + port_ret).cumprod()
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print(f" Periodo {idx[0].date()} -> {idx[-1].date()} (leva/pos gia' incluse nelle sleeve)")
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print(f" {'sleeve':<16s}{'ret%':>9s}{'DD%':>7s}{'CAGR%':>8s}")
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yrs = (idx[-1] - idx[0]).days / 365.25
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for name, s in members.items():
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r = (s.iloc[-1] / s.iloc[0] - 1) * 100
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cagr = ((s.iloc[-1] / s.iloc[0]) ** (1 / yrs) - 1) * 100
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print(f" {name:<16s}{r:>+9.0f}{_dd(s.values):>7.0f}{cagr:>8.0f}")
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r = (combo.iloc[-1] / combo.iloc[0] - 1) * 100
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cagr = ((combo.iloc[-1] / combo.iloc[0]) ** (1 / yrs) - 1) * 100
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print(f" {'PORTAFOGLIO':<16s}{r:>+9.0f}{_dd(combo.values):>7.0f}{cagr:>8.0f} <-- DD molto piu' basso, CAGR solida")
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# per-anno del portafoglio
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pa = (port_ret.groupby(port_ret.index.year).apply(lambda x: ((1 + x).prod() - 1) * 100))
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print(" Portafoglio per-anno: " + " ".join(f"{y}:{v:+.0f}%" for y, v in pa.items()))
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def _norm(s):
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return s / s.iloc[0]
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def _tr_basket_daily(assets, idx):
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"""equity giornaliera media di TR01 (EMA20/100 long-only, 4h) sul paniere."""
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eqs = []
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for a in assets:
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df = get_df(a, "4h"); c = df["close"].values; n = len(c)
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ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
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ef, es = ema(c, 20), ema(c, 100)
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sig = np.where(ef > es, 1.0, 0.0); sig[:100] = 0.0
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cap = 1000.0; cur = 0.0; fee = FEE_RT / 2 * LEV
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tl, cl = [], []
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for i in range(n - 1):
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s = sig[i]
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if s != cur:
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cap -= cap * POS * fee * abs(s - cur); cur = s
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cap = max(cap * (1 + POS * LEV * (c[i + 1] - c[i]) / c[i] * cur), 10.0)
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tl.append(ts.iloc[i]); cl.append(cap)
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eqs.append(_norm(_daily_equity(tl, cl, idx)))
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return _norm(pd.concat(eqs, axis=1).mean(axis=1))
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def _rot_daily_equity(idx):
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"""equity giornaliera della ROT01 dual-momentum (ricostruita bar-by-bar)."""
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from scripts.analysis.honest_rotation import build_panel
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panel = build_panel(available_assets(), "1d")
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cols = list(panel.columns); P = panel.values; T, N = P.shape
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rets = np.zeros_like(P); rets[1:] = P[1:] / P[:-1] - 1
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btc = P[:, cols.index("BTC")]; bma = pd.Series(btc).rolling(100).mean().values
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cap = 1000.0; w = np.zeros(N); ts_list = []; cap_list = []
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for i in range(101, T - 1):
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risk_on = btc[i] > bma[i] if not np.isnan(bma[i]) else False
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mom = P[i] / P[i - 60] - 1; order = np.argsort(mom)[::-1]
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chosen = [j for j in order if mom[j] > 0][:3] if risk_on else [] # top_k=3 (era 2): DD piu' basso
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nw = np.zeros(N)
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for j in chosen:
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nw[j] = 0.45 / len(chosen)
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cap -= cap * np.abs(nw - w).sum() * (FEE_RT / 2); w = nw
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cap = max(cap * (1 + float(np.dot(w, rets[i + 1]))), 10.0)
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ts_list.append(panel.index[i]); cap_list.append(cap)
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s = _daily_equity(ts_list, cap_list, idx); return s / s.iloc[0]
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if __name__ == "__main__":
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main()
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