Adriano
212427ffa1
Ricerca onesta post-squeeze su 8 crypto (2018-2026), engine fee-aware con ingresso eseguibile a close[i], uscita TP/SL intrabar, OOS held-out, sweep fee. Lezione madre: shortare cripto perde OOS sistematicamente (campione net-bull) -> tutte le strategie robuste sono long-biased. Tre meccanismi distinti e complementari: - DIP01 dip-buy z-score reversion (long-only, 1h) robusto BTC/ETH/SOL - TR01 EMA 20/100 trend-following (long-only, 4h) robusto su 5/8 asset - ROT01 rotazione cross-sectional momentum sul paniere (1d) OOS +44%, param-insensitive Engine e validazione: scripts/analysis/honest_lab.py + honest_final.py (+ honest_candidates/diag/diag2/trend/rotation). Diario in docs/diary/. Onesto sull'obiettivo: €50/giorno su €1000 in pochi mesi non e' raggiungibile a rischio sano (~1825%/anno); edge reali 30-60% OOS pluriennale. Via realistica: portafoglio delle 3, leva moderata, crescita composta. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
97 lines
3.9 KiB
Python
97 lines
3.9 KiB
Python
"""Strategia #3 candidata: ROTAZIONE cross-sectional momentum (multi-crypto).
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Una sola strategia che usa l'INTERO paniere: ad ogni ribilanciamento alloca il
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capitale agli asset con momentum migliore (long-only). Cattura la dispersione tra
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crypto (gli alt forti corrono molto piu' di BTC nei bull) senza shortare nulla.
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Onesto: i pesi a close[i] usano solo rendimenti passati; il rendimento del bar
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i->i+1 e' realizzato con quei pesi. Fee sul turnover. Allineamento per timestamp.
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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 get_df, available_assets, FEE_RT # noqa: E402
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LEV = 3.0
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GROSS = 0.45 # esposizione lorda = LEV*POS del singolo (0.15*3) per confronto equo
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def build_panel(assets: list[str], tf: str) -> pd.DataFrame:
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"""Matrice close allineata per timestamp (inner join)."""
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closes = {}
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for a in assets:
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df = get_df(a, tf)
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s = pd.Series(df["close"].values,
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index=pd.to_datetime(df["timestamp"], unit="ms", utc=True))
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closes[a] = s[~s.index.duplicated()]
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panel = pd.DataFrame(closes).dropna()
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return panel
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def simulate_rotation(panel: pd.DataFrame, lookback=30, top_k=2,
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fee_rt=FEE_RT, gross=GROSS, abs_filter=True,
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oos_frac=0.0) -> dict:
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"""Ad ogni barra: ranking per rendimento passato `lookback`; pesi uguali sui
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top_k con momentum>0 (se abs_filter); altrimenti cash. gross = esposizione tot.
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oos_frac>0: parte a investire solo dall'ultimo frac del campione."""
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P = panel.values
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T, N = P.shape
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rets = np.zeros_like(P)
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rets[1:] = P[1:] / P[:-1] - 1
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years = panel.index.year.values
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start = max(lookback + 1, int(T * (1 - oos_frac)) if oos_frac else lookback + 1)
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cap = peak = 1000.0
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max_dd = 0.0
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w = np.zeros(N)
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yearly: dict[int, float] = {}
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turn_total = 0.0
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for i in range(start, T - 1):
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mom = P[i] / P[i - lookback] - 1
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order = np.argsort(mom)[::-1]
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new_w = np.zeros(N)
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chosen = [j for j in order if (mom[j] > 0 or not abs_filter)][:top_k]
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if chosen:
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for j in chosen:
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new_w[j] = gross / len(chosen)
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# fee sul turnover (one-way = fee_rt/2 su ogni variazione di peso)
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turnover = np.abs(new_w - w).sum()
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cap -= cap * turnover * (fee_rt / 2)
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turn_total += turnover
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w = new_w
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port_ret = float(np.dot(w, rets[i + 1])) # rendimento bar i->i+1
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cap = max(cap * (1 + port_ret), 10.0)
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peak = max(peak, cap); max_dd = max(max_dd, (peak - cap) / peak)
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yearly[years[i]] = yearly.get(years[i], 0.0) + port_ret * 100
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return {
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"ret": (cap / 1000 - 1) * 100,
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"dd": max_dd * 100,
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"turnover": turn_total,
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"yearly": yearly,
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"pos_years": sum(1 for v in yearly.values() if v > 0),
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"n_years": len(yearly),
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}
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if __name__ == "__main__":
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assets = available_assets()
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print(f"ROTATION cross-sectional momentum — fee {FEE_RT*100:.2f}% RT, gross {GROSS} | OOS 30%")
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print(f" Paniere: {assets}")
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for tf in ["1d", "4h"]:
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panel = build_panel(assets, tf)
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print(f"\n === {tf} === panel {panel.shape[0]} barre, {panel.index[0].date()} -> {panel.index[-1].date()}")
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print(f" {'config':<22s}{'FULL%':>9s}{'OOS%':>9s}{'DD%':>6s}{'Turn':>7s}{'AnniP':>8s}")
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for lb in [20, 30, 60, 90]:
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for k in [1, 2, 3]:
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full = simulate_rotation(panel, lookback=lb, top_k=k)
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oos = simulate_rotation(panel, lookback=lb, top_k=k, oos_frac=0.30)
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anni = f"{full['pos_years']}/{full['n_years']}"
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print(f" lb{lb:<3d} top{k:<14d}{full['ret']:>+9.0f}{oos['ret']:>+9.0f}"
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f"{full['dd']:>6.0f}{full['turnover']:>7.0f}{anni:>8s}")
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