feat(analysis): 3 strategie oneste validate OOS multi-crypto (DIP/TR/ROT)

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>
2026-05-28 23:28:00 +02:00
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+"""Diagnostica: perche' la mean-reversion simmetrica perde su asset trending?
+Test: long-only vs short-only, e MR FILTRATA DAL TREND (buy-dip in uptrend,
+sell-rip in downtrend) per evitare di fadeare i trend forti.
+"""
+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 (  # noqa: E402
+    atr, ema, get_df, simulate, oos_split, available_assets, FEE_RT,
+)
+
+
+def zscore_entries(df, n=50, z_in=2.5, sl_atr=2.5, max_bars=24,
+                   trend_n=0, side="both"):
+    """Z-score revert con filtro trend opzionale.
+    trend_n>0: EMA di lungo periodo. Long solo se close>EMA (uptrend),
+               short solo se close<EMA (downtrend).
+    side: 'both' | 'long' | 'short'
+    """
+    c = df["close"].values
+    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)
+    et = ema(c, trend_n) if trend_n > 0 else None
+    start = max(n + 14, trend_n + 1 if trend_n else 0)
+    ents = []
+    for i in range(start, len(c)):
+        if np.isnan(z[i]) or np.isnan(a[i]):
+            continue
+        long_ok = (et is None or c[i] > et[i]) and side in ("both", "long")
+        short_ok = (et is None or c[i] < et[i]) and side in ("both", "short")
+        if z[i] <= -z_in and z[i - 1] > -z_in and long_ok:
+            ents.append({"i": i, "d": 1, "tp": ma[i], "sl": c[i] - sl_atr * a[i], "max_bars": max_bars})
+        elif z[i] >= z_in and z[i - 1] < z_in and short_ok:
+            ents.append({"i": i, "d": -1, "tp": ma[i], "sl": c[i] + sl_atr * a[i], "max_bars": max_bars})
+    return ents
+
+
+def row(label, df, ents):
+    if len(ents) < 20:
+        print(f"  {label:<34s} {'<20 trd':>50s}")
+        return None
+    full = simulate(ents, df)
+    _, oe = oos_split(ents, df)
+    oos = simulate(oe, df)
+    print(f"  {label:<34s}{full.trades:>6d}{full.win:>7.1f}{full.ret:>+9.0f}"
+          f"{oos.ret:>+9.0f}{full.dd:>6.0f}{f'{full.pos_years}/{full.n_years}':>8s}")
+    return full, oos
+
+
+if __name__ == "__main__":
+    assets = available_assets()
+    print(f"HONEST DIAG — z-score revert, fee {FEE_RT*100:.2f}% RT, leva 3x | OOS 30%")
+    for tf in ["1h"]:
+        for a in assets:
+            df = get_df(a, tf)
+            print(f"\n  === {a} {tf} ===   {'Trd':>5s}{'Win%':>7s}{'FULL%':>8s}{'OOS%':>8s}{'DD%':>6s}{'AnniP':>8s}")
+            base = dict(n=50, z_in=2.5, sl_atr=2.5, max_bars=24)
+            row("both, no filter", df, zscore_entries(df, **base, side="both"))
+            row("long-only, no filter", df, zscore_entries(df, **base, side="long"))
+            row("short-only, no filter", df, zscore_entries(df, **base, side="short"))
+            row("both + trend200 filter", df, zscore_entries(df, **base, trend_n=200, side="both"))
+            row("both + trend500 filter", df, zscore_entries(df, **base, trend_n=500, side="both"))
+            row("long + trend200 filter", df, zscore_entries(df, **base, trend_n=200, side="long"))