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