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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"""Combina i NUOVI edge (pairs + TSM01) col MASTER esistente: migliora il portafoglio?
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Aggiunge al MASTER a 9 sleeve (6 fade + 3 honest) due nuove fonti scoperte
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nell'esplorazione, poco correlate:
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- PAIRS market-neutral (ETH/BTC, LTC/ETH, ADA/ETH) -> corr ~0 col mercato
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- TSM01 (TSMOM multi-orizzonte + risk-off) -> corr ~0.53 con ROT02
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Misura correlazione delle nuove sleeve vs esistenti e confronta MASTER-9 vs
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MASTER-esteso su Ret/CAGR/DD/Sharpe, FULL e OOS (finestra comune 2021-2026).
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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 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.combine_portfolio import (
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build_all_sleeves, port_returns, metrics, yearly_returns, SPLIT, OOS_DATE, IDX,
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)
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from scripts.analysis.honest_improve2 import _daily_equity, _norm
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from scripts.analysis.pairs_research import pairs_sim
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from scripts.analysis.tsmom_research import tsmom_sim
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def daily_from(eq_ts, eq_v):
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return _norm(_daily_equity(eq_ts, eq_v, IDX))
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def main():
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print("Costruzione equity (puo' richiedere ~1-2 min)...\n")
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S = build_all_sleeves() # 9 sleeve esistenti
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# nuove sleeve
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new = {}
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for a, b in [("ETH", "BTC"), ("LTC", "ETH"), ("ADA", "ETH")]:
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r = pairs_sim(a, b, n=50, z_in=2.0, z_exit=0.5, max_bars=72)
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new[f"PR_{a}{b}"] = daily_from(r["eq_ts"], r["eq_v"])
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t = tsmom_sim()
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new["TSM01"] = daily_from(t["eq_ts"], t["eq_v"])
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allS = {**S, **new}
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# --- correlazione nuove vs esistenti ---
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dr = pd.DataFrame({k: v.pct_change().fillna(0.0) for k, v in allS.items()})
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corr = dr.corr()
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old_k = list(S); new_k = list(new)
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print("=" * 88)
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print(" CORRELAZIONE rendimenti giornalieri — NUOVE (righe) vs media esistenti")
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print("=" * 88)
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for nk in new_k:
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avg = corr.loc[nk, old_k].mean()
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mx = corr.loc[nk, old_k].abs().max()
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print(f" {nk:<12s} corr media col MASTER-9 = {avg:+.2f} |max| = {mx:.2f}")
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# --- confronto portafogli ---
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def line(label, members):
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pr = port_returns(members)
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f, o = metrics(pr), metrics(pr, lo=SPLIT)
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print(f" {label:<26s}{f['ret']:>+9.0f}{f['cagr']:>7.0f}{f['dd']:>7.1f}{f['sharpe']:>7.2f}"
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f" | {o['ret']:>+9.0f}{o['dd']:>7.1f}{o['sharpe']:>7.2f}")
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return pr
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print("\n" + "=" * 96)
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print(f" MASTER-9 vs MASTER-ESTESO (con pairs+TSM01) | OOS da {OOS_DATE} | equal-weight daily")
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print("=" * 96)
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print(f" {'portafoglio':<26s}{'Ret%':>9s}{'CAGR':>7s}{'DD%':>7s}{'Shrp':>7s}"
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f" | {'oRet%':>9s}{'oDD%':>7s}{'oShrp':>7s}")
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print(" " + "-" * 92)
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line("MASTER-9 (base)", S)
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line("MASTER +pairs (12)", {**S, **{k: v for k, v in new.items() if k.startswith('PR_')}})
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line("MASTER +TSM01 (10)", {**S, "TSM01": new["TSM01"]})
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pr_all = line("MASTER-esteso (13)", allS)
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print(" " + "-" * 92)
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pa = yearly_returns(pr_all)
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print(" MASTER-esteso per-anno: " + " ".join(f"{y}:{v:+.0f}%" for y, v in pa.items()))
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print("\n Se il MASTER-esteso ha DD piu' basso e/o Sharpe piu' alto del MASTER-9, le nuove")
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print(" famiglie aggiungono valore (diversificazione da fonti scorrelate).")
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if __name__ == "__main__":
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main()
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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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"""Verifica indipendente + ricerca PAIRS / SPREAD MEAN-REVERSION fra cripto.
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Famiglia nuova market-neutral (distinta da tutto l'esistente, single-asset).
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Idea: il log-ratio di due cripto oscilla attorno alla media; z-score estremo -> rientra.
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Engine ONESTO (no look-ahead, verificato):
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- r[i] = log(closeA[i]/closeB[i]); ma/sd = rolling(n) su r -> usano solo r[<=i].
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- z[i] = (r[i]-ma[i])/sd[i]. ENTRY a close[i] (eseguibile):
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z<=-z_in -> LONG ratio (long A / short B); z>=+z_in -> SHORT ratio.
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- EXIT quando |z[j]| <= z_exit (rientro) o time-limit max_bars, a close[j].
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- pairs = 2 GAMBE -> fee = 2*fee_rt*lev (0.20% RT/coppia a fee_rt=0.001), il doppio
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del single-asset. Rendimento neutral = retA*d - retB*d (notional uguale per gamba).
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- non-overlap, capitale composto. Filtro candele sporche: salta salti |dr|>jump_max.
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- Ritorno riportato come CAGR e Sharpe ANNUALIZZATO sul tempo reale (no sqrt(n_trade)).
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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, LEV, POS, OOS_FRAC = 0.001, 3.0, 0.15, 0.30
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BARS_YEAR = 8760 # 1h
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def aligned(a: str, b: str, tf: str = "1h"):
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da = load_data(a, tf)[["timestamp", "open", "high", "low", "close"]].rename(columns=lambda x: x + "_a" if x != "timestamp" else x)
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db = load_data(b, tf)[["timestamp", "close"]].rename(columns={"close": "close_b"})
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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 pairs_sim(a, b, tf="1h", n=50, z_in=2.0, z_exit=0.5, max_bars=72,
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jump_max=0.08, fee_rt=FEE_RT, lev=LEV, pos=POS, split_frac=0.0):
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m = aligned(a, b, tf)
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ca, cb = m["close_a"].values, m["close_b"].values
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r = np.log(ca / cb)
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dr = np.abs(np.diff(r, prepend=r[0])) # salto 1-bar del log-ratio
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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) # causale: usa r[<=i]
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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 # 2 gambe
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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: list = []; eq_v: list = []
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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:
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continue
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if 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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# exit: |z|<=z_exit o max_bars
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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 abs(z[jj]) <= z_exit:
|
||||
j = jj; break
|
||||
j = jj
|
||||
retA = (ca[j] - ca[i]) / ca[i]
|
||||
retB = (cb[j] - cb[i]) / cb[i]
|
||||
ret = (retA - retB) * d * lev - fee # long A / short B (o viceversa)
|
||||
cap = max(cap + cap * pos * ret, 10.0)
|
||||
peak = max(peak, cap); dd = max(dd, (peak - cap) / peak)
|
||||
trades += 1; wins += ret > 0; rets.append(ret * pos); last = j
|
||||
eq_ts.append(ts.iloc[j]); eq_v.append(cap)
|
||||
yearly[ts.iloc[i].year] = yearly.get(ts.iloc[i].year, 0.0) + ret * 100
|
||||
yrs_span = (ts.iloc[-1] - ts.iloc[max(split, 0)]).days / 365.25 or 1
|
||||
sharpe = float(np.mean(rets) / np.std(rets) * np.sqrt(BARS_YEAR / np.mean([max_bars])) ) if len(rets) > 1 and np.std(rets) > 0 else 0.0
|
||||
# Sharpe annualizzato sul tempo reale: usa rendimenti per-trade scalati alla frequenza media
|
||||
if len(rets) > 1 and np.std(rets) > 0:
|
||||
trades_per_year = trades / yrs_span
|
||||
sharpe = float(np.mean(rets) / np.std(rets) * np.sqrt(trades_per_year))
|
||||
ret_tot = (cap / 1000 - 1) * 100
|
||||
cagr = ((cap / 1000) ** (1 / yrs_span) - 1) * 100 if cap > 0 else -100
|
||||
return dict(trades=trades, win=wins / trades * 100 if trades else 0, ret=ret_tot, cagr=cagr,
|
||||
dd=dd * 100, sharpe=sharpe, yearly=yearly, eq_ts=eq_ts, eq_v=eq_v)
|
||||
|
||||
|
||||
def check_no_lookahead():
|
||||
"""Perturba il FUTURO del ratio e verifica che z[i] non cambi (causalita')."""
|
||||
m = aligned("ETH", "BTC")
|
||||
r = np.log(m["close_a"].values / m["close_b"].values)
|
||||
n = 50; i = 1000
|
||||
z_i = (r[i] - pd.Series(r).rolling(n).mean().values[i]) / pd.Series(r).rolling(n).std().values[i]
|
||||
r2 = r.copy(); r2[i + 1:] += 0.5 # stravolge il futuro
|
||||
z_i2 = (r2[i] - pd.Series(r2).rolling(n).mean().values[i]) / pd.Series(r2).rolling(n).std().values[i]
|
||||
print(f" no-look-ahead: z[i]={z_i:.6f} vs z[i] con futuro perturbato={z_i2:.6f} -> "
|
||||
f"{'OK (identico)' if abs(z_i - z_i2) < 1e-9 else 'VIOLAZIONE!'}")
|
||||
|
||||
|
||||
def main():
|
||||
print("=" * 104)
|
||||
print(f" PAIRS spread reversion — NETTO fee 0.20% RT/coppia (2 gambe), leva {LEV:.0f}x, OOS ultimo {int(OOS_FRAC*100)}%")
|
||||
print("=" * 104)
|
||||
check_no_lookahead()
|
||||
pairs = [("ETH", "BTC"), ("LTC", "ETH"), ("ADA", "ETH"), ("SOL", "ETH"),
|
||||
("BNB", "BTC"), ("XRP", "BTC"), ("SOL", "BTC"), ("DOGE", "BTC")]
|
||||
print(f"\n {'coppia':<10s}{'trd':>5s}{'win%':>6s}{'FULL%':>8s}{'OOS%':>8s}{'CAGR%':>7s}"
|
||||
f"{'DD%':>6s}{'oDD%':>6s}{'Shrp':>6s}{'anni+':>7s}{'fee0.4%RT':>11s}")
|
||||
print(" " + "-" * 96)
|
||||
for a, b in pairs:
|
||||
f = pairs_sim(a, b, n=50, z_in=2.0, z_exit=0.5, max_bars=72)
|
||||
o = pairs_sim(a, b, n=50, z_in=2.0, z_exit=0.5, max_bars=72, split_frac=1 - OOS_FRAC)
|
||||
hi = pairs_sim(a, b, n=50, z_in=2.0, z_exit=0.5, max_bars=72, fee_rt=0.002) # 0.4% RT/coppia
|
||||
yrs = f["yearly"]; pos_y = sum(1 for v in yrs.values() if v > 0)
|
||||
print(f" {a+'/'+b:<10s}{f['trades']:>5d}{f['win']:>6.1f}{f['ret']:>+8.0f}{o['ret']:>+8.0f}"
|
||||
f"{f['cagr']:>7.0f}{f['dd']:>6.0f}{o['dd']:>6.0f}{f['sharpe']:>6.2f}{f'{pos_y}/{len(yrs)}':>7s}"
|
||||
f"{hi['ret']:>+11.0f}")
|
||||
# correlazione con BTC daily (market-neutrality) sulla coppia migliore
|
||||
print("\n Verifica market-neutrality ETH/BTC: per-anno")
|
||||
f = pairs_sim("ETH", "BTC", n=50, z_in=2.0, z_exit=0.5, max_bars=72)
|
||||
print(" " + " ".join(f"{y}:{v:+.0f}%" for y, v in sorted(f["yearly"].items())))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,87 @@
|
||||
"""Verifica indipendente + ricerca TSM01 — Time-Series Momentum multi-orizzonte.
|
||||
|
||||
Long-only, multi-crypto, bassa frequenza. Per ogni asset il segnale è il CONSENSO
|
||||
dei segni del momentum su più orizzonti lunghi (3/6/12 mesi); si tengono equal-weight
|
||||
gli asset con consenso pieno positivo. Overlay risk-off: cash se BTC < SMA100.
|
||||
|
||||
Distinta da ROT02 (cross-sectional ranking): qui conta la PERSISTENZA assoluta lenta
|
||||
di ogni asset, non la classifica relativa. Correlazione con ROT02 ~0.53 -> fattore
|
||||
parzialmente indipendente, utile come diversificatore. DD basso (22% full / 12% OOS).
|
||||
|
||||
Engine onesto: pesi a close[i] da soli rendimenti passati, realizzo i->i+1, fee
|
||||
one-way fee_rt/2 sul turnover. NETTO, leva implicita gross. OOS = ultimo 30%.
|
||||
"""
|
||||
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 available_assets, FEE_RT
|
||||
from scripts.analysis.honest_rotation import build_panel
|
||||
|
||||
GROSS, OOS_FRAC = 0.45, 0.30
|
||||
|
||||
|
||||
def tsmom_sim(horizons=(63, 126, 252), thr=1.0, regime_n=100, gross=GROSS,
|
||||
fee_rt=FEE_RT, oos_frac=0.0, cheat=False):
|
||||
"""horizons in giorni. thr=1.0 -> consenso pieno (tutti i segni positivi)."""
|
||||
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
|
||||
years = panel.index.year.values
|
||||
btc = P[:, cols.index("BTC")]
|
||||
bma = pd.Series(btc).rolling(regime_n).mean().values
|
||||
start = max(max(horizons) + 1, regime_n + 1, int(T * (1 - oos_frac)) if oos_frac else 0)
|
||||
cap = 1000.0; w = np.zeros(N); eq = [cap]; yearly = {}
|
||||
eq_ts: list = []; eq_v: list = []
|
||||
for i in range(start, T - 1):
|
||||
risk_on = btc[i] > bma[i] if not np.isnan(bma[i]) else False
|
||||
wi = i + 1 if cheat else i # cheat: usa il futuro (test no-look-ahead)
|
||||
score = np.zeros(N)
|
||||
for h in horizons:
|
||||
score += np.sign(P[wi] / P[wi - h] - 1)
|
||||
score /= len(horizons)
|
||||
chosen = [j for j in range(N) if score[j] >= thr] if risk_on else []
|
||||
nw = np.zeros(N)
|
||||
for j in chosen:
|
||||
nw[j] = gross / 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)
|
||||
eq.append(cap)
|
||||
eq_ts.append(panel.index[i + 1]); eq_v.append(cap)
|
||||
y = int(years[i]); yearly[y] = yearly.get(y, 0.0) + float(np.dot(w, rets[i + 1])) * 100
|
||||
eq = np.array(eq); peak = np.maximum.accumulate(eq)
|
||||
dd = float(np.max((peak - eq) / peak) * 100)
|
||||
yrs = (panel.index[-1] - panel.index[start]).days / 365.25 or 1
|
||||
rets_d = np.diff(eq) / eq[:-1]
|
||||
sharpe = float(np.mean(rets_d) / np.std(rets_d) * np.sqrt(365)) if np.std(rets_d) > 0 else 0.0
|
||||
return dict(ret=(cap / 1000 - 1) * 100, cagr=((cap / 1000) ** (1 / yrs) - 1) * 100,
|
||||
dd=dd, sharpe=sharpe, yearly=yearly, eq_ts=eq_ts, eq_v=eq_v,
|
||||
pos_years=sum(1 for v in yearly.values() if v > 0), n_years=len(yearly))
|
||||
|
||||
|
||||
def main():
|
||||
print("=" * 90)
|
||||
print(" TSM01 — TSMOM multi-orizzonte (3/6/12m consenso pieno) + risk-off SMA100")
|
||||
print("=" * 90)
|
||||
# no-look-ahead: cheat deve esplodere
|
||||
base = tsmom_sim()
|
||||
ch = tsmom_sim(cheat=True)
|
||||
print(f" no-look-ahead: onesto FULL={base['ret']:+.0f}% vs cheat(futuro)={ch['ret']:+.0f}% -> "
|
||||
f"{'OK (il cheat esplode -> niente leak)' if ch['ret'] > base['ret'] * 2 else 'CONTROLLARE'}")
|
||||
o = tsmom_sim(oos_frac=1 - OOS_FRAC)
|
||||
hi = tsmom_sim(fee_rt=0.002)
|
||||
print(f"\n FULL {base['ret']:+.0f}% CAGR {base['cagr']:.0f}% DD {base['dd']:.0f}% "
|
||||
f"Sharpe {base['sharpe']:.2f} anni+ {base['pos_years']}/{base['n_years']}")
|
||||
print(f" OOS {o['ret']:+.0f}% DD {o['dd']:.0f}% | fee 0.40% RT: FULL {hi['ret']:+.0f}%")
|
||||
print(" Per-anno: " + " ".join(f"{y}:{v:+.0f}%" for y, v in sorted(base["yearly"].items())))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user