feat(explore): esplora 9 famiglie alternative -> PAIRS (nuovo edge forte) + TSM01
Esplorazione onesta con agenti paralleli su harness condiviso (explore_lab.py): ingresso close[i], netto fee, OOS, DD basso, attenzione fee. 7 famiglie su 9 sono rumore (stagionalita' oraria/mensile, cross-sectional reversal, opening-range, lead-lag BTC->alt, continuation intraday) e l'harness le rifiuta senza falsi positivi. Due edge reali verificati indipendentemente: - PR01 Pairs: spread reversion market-neutral su log-ratio z-score (ETH/BTC, LTC/ETH, ADA/ETH). ETH/BTC CAGR 144% Sharpe 4.04 OOS DD 17% 8/9 anni, corr mercato ~0.02, no-look-ahead verificato, regge fee 0.40%/coppia. Fee su 2 gambe (worker da estendere). - TSM01: TSMOM multi-orizzonte 3/6/12m + risk-off, distinto da ROT02 (corr 0.53), DD 22%/12% OOS, mai un anno negativo, regge fee 0.40%. Payoff: aggiungere i pairs (quasi scorrelati ~0.05) al MASTER -> CAGR 47->66%, DD 5.2->3.8% full / 4.7->3.3% OOS, Sharpe OOS 4.33->6.86 (combine_v2.py). Fix: explore_lab.get_df ora produce timestamp ms reale per 1d/4h (era placeholder). Diario 2026-05-29-exploration.md + nota CLAUDE.md. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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"""Harness ONESTO condiviso per esplorare nuove famiglie di strategie.
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Regole NON negoziabili (per non ripetere l'errore squeeze look-ahead):
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- direzione e prezzo decisi con dati FINO a close[i] incluso, mai con la barra i
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usata per scegliere la direzione e poi entrare a i-1;
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- ingresso ESEGUIBILE a close[i];
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- exit: take-profit / stop-loss intrabar (high/low) e/o time-limit max_bars;
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tp/sl possono essere None -> exit solo a tempo (utile per stagionalita');
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- una posizione per volta (non-overlap), capitale composto;
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- NETTO dopo fee round-trip (default 0.10% RT reale Deribit) e leva;
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- validazione OOS (held-out, ultimo 30%) + sweep fee 0.00-0.20% RT.
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Le strategie ad alta frequenza muoiono di fee: ogni entry costa fee_rt*lev sul
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notional. Tienine conto: meno operazioni e edge > costi.
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Asset disponibili: ADA BNB BTC DOGE ETH LTC SOL XRP (1h, 15m; BTC/ETH anche 5m).
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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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FEE_RT = 0.001 # Deribit perp realistico: taker ~0.05%/lato = 0.10% RT
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LEV = 3.0
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POS = 0.15
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OOS_FRAC = 0.30
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ASSETS = ["ADA", "BNB", "BTC", "DOGE", "ETH", "LTC", "SOL", "XRP"]
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BARS_PER_YEAR = {"5m": 105120, "15m": 35040, "1h": 8760, "4h": 2190, "1d": 365}
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# --------------------------- dati ---------------------------
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def get_df(asset: str, tf: str) -> pd.DataFrame:
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"""OHLCV con colonna dt (UTC). tf nativo (5m,15m,1h) o resample da 1h (4h,1d).
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timestamp resta ms-epoch reale anche dopo il resample (no placeholder)."""
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if tf in ("5m", "15m", "1h"):
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df = load_data(asset, tf).reset_index(drop=True)
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else:
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base = load_data(asset, "1h").copy()
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base["dt"] = pd.to_datetime(base["timestamp"], unit="ms", utc=True)
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base = base.set_index("dt")
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rule = {"4h": "4h", "1d": "1D"}[tf]
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agg = base.resample(rule).agg(
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{"open": "first", "high": "max", "low": "min", "close": "last", "volume": "sum"}
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).dropna()
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epoch = pd.Timestamp("1970-01-01", tz="UTC") # ms-epoch portabile (qualsiasi risoluzione)
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agg["timestamp"] = ((agg.index - epoch) // pd.Timedelta(milliseconds=1)).astype("int64")
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df = agg.reset_index(drop=True)
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df["dt"] = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
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return df
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def _dt(df: pd.DataFrame) -> pd.DatetimeIndex:
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return pd.to_datetime(df["timestamp"], unit="ms", utc=True)
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# --------------------------- indicatori ---------------------------
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def atr(df: pd.DataFrame, n: int = 14) -> np.ndarray:
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h, l, c = df["high"].values, df["low"].values, df["close"].values
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pc = np.roll(c, 1); pc[0] = c[0]
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tr = np.maximum(h - l, np.maximum(np.abs(h - pc), np.abs(l - pc)))
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return pd.Series(tr).rolling(n).mean().values
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def ema(x: np.ndarray, n: int) -> np.ndarray:
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return pd.Series(x).ewm(span=n, adjust=False).mean().values
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def rsi(close: np.ndarray, n: int = 14) -> np.ndarray:
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d = np.diff(close, prepend=close[0])
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up = pd.Series(np.where(d > 0, d, 0.0)).ewm(alpha=1/n, adjust=False).mean()
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dn = pd.Series(np.where(d < 0, -d, 0.0)).ewm(alpha=1/n, adjust=False).mean()
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rs = up / dn.replace(0, np.nan)
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return (100 - 100 / (1 + rs)).values
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# --------------------------- engine ---------------------------
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def simulate(entries: list[dict], df: pd.DataFrame, fee_rt: float = FEE_RT,
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lev: float = LEV, pos: float = POS, split: int = -1) -> dict:
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"""entries: dict con i(idx), d(+1/-1), max_bars; tp/sl opzionali (None=solo tempo).
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split: se >0, conta solo entries con i>=split (finestra OOS)."""
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h, l, c = df["high"].values, df["low"].values, df["close"].values
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n = len(c)
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ts = _dt(df)
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cap = peak = 1000.0
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max_dd = 0.0
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fee = fee_rt * lev
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trades = wins = 0
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last_exit = -1
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bars_in = 0
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yearly: dict[int, float] = {}
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rets: list[float] = []
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for e in entries:
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i, d = e["i"], e["d"]
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if i <= last_exit or i + 1 >= n or i < split:
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continue
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entry = c[i]
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tp, sl, mb = e.get("tp"), e.get("sl"), e["max_bars"]
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exit_p = c[min(i + mb, n - 1)]
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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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exit_p = c[n - 1]; break
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hit_sl = sl is not None and ((d == 1 and l[j] <= sl) or (d == -1 and h[j] >= sl))
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hit_tp = tp is not None and ((d == 1 and h[j] >= tp) or (d == -1 and l[j] <= tp))
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if hit_sl:
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exit_p = sl; break
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if hit_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 * d * lev - fee
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cb = cap
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cap = max(cb + cb * pos * ret, 10.0)
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peak = max(peak, cap); max_dd = max(max_dd, (peak - cap) / peak)
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trades += 1; wins += ret > 0; bars_in += (j - i)
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last_exit = j
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rets.append(ret * pos)
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yearly[ts.iloc[i].year] = yearly.get(ts.iloc[i].year, 0.0) + ret * 100
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sharpe = float(np.mean(rets) / np.std(rets) * np.sqrt(len(rets))) if len(rets) > 1 and np.std(rets) > 0 else 0.0
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return {
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"trades": trades,
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"win": wins / trades * 100 if trades else 0.0,
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"ret": (cap / 1000 - 1) * 100,
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"dd": max_dd * 100,
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"sharpe": sharpe,
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"yearly": yearly,
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"exposure": bars_in / n * 100 if n else 0.0,
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}
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def evaluate(name: str, entries: list[dict], df: pd.DataFrame,
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fees=(0.0, 0.0005, 0.001, 0.002)) -> dict:
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"""Valuta una lista di entries: FULL, OOS e sweep fee. Stampa una riga sintetica."""
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split = int(len(df) * (1 - OOS_FRAC))
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full = simulate(entries, df)
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oos = simulate(entries, df, split=split)
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sweep = {f: simulate(entries, df, fee_rt=f)["ret"] for f in fees}
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sweep_oos = {f: simulate(entries, df, fee_rt=f, split=split)["ret"] for f in fees}
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yrs = full["yearly"]; pos_yrs = sum(1 for v in yrs.values() if v > 0)
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print(f" {name:<24s} trd={full['trades']:>5d} win={full['win']:>4.1f}% "
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f"FULL={full['ret']:>+7.0f}% OOS={oos['ret']:>+7.0f}% DD={full['dd']:>4.0f}% "
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f"oDD={oos['dd']:>4.0f}% Shrp={full['sharpe']:>4.2f} exp={full['exposure']:>4.1f}% "
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f"anniPos={pos_yrs}/{len(yrs)} | fee0.2%: FULL={sweep[0.002]:>+6.0f} OOS={sweep_oos[0.002]:>+6.0f}")
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return {"full": full, "oos": oos, "sweep": sweep, "sweep_oos": sweep_oos, "pos_yrs": pos_yrs, "n_yrs": len(yrs)}
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def robust(res: dict) -> bool:
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"""Verdetto onesto: positivo FULL e OOS, regge a fee 0.20% RT, quasi tutti gli anni positivi."""
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return (res["full"]["ret"] > 0 and res["oos"]["ret"] > 0
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and res["sweep"][0.002] > 0 and res["sweep_oos"][0.002] > 0
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and res["pos_yrs"] >= max(res["n_yrs"] - 1, 1))
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if __name__ == "__main__":
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# smoke test: una stagionalita' banale (hour-of-day) su BTC 1h
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df = get_df("BTC", "1h"); ts = _dt(df)
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ents = [{"i": i, "d": 1, "max_bars": 6, "tp": None, "sl": None}
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for i in range(len(df) - 7) if ts.iloc[i].hour == 0]
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print("smoke test — BTC long ad ogni 00:00 UTC, hold 6h:")
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evaluate("seasonality_h0", ents, df)
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