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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+"""Validazione FINALE delle 3 strategie oneste selezionate.
+Per ciascuna: per-asset FULL/OOS/DD/anni-positivi + sweep fee (0/0.05/0.10/0.20% RT).
+Tutto NETTO, ingresso eseguibile, OOS = ultimo 30%, leva 3x.
+
+S1 DIP   — long-only dip-buy z-score reversion (1h)         [regime: reversione]
+S2 TREND — long-only EMA 20/100 trend-following (4h)        [regime: momentum singolo]
+S3 ROT   — rotazione cross-sectional momentum sul paniere (1d) [regime: forza relativa]
+"""
+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, simulate, oos_split, available_assets
+from scripts.analysis.honest_trend import simulate_position, ema_dual_signal, oos as trend_oos
+from scripts.analysis.honest_rotation import build_panel, simulate_rotation
+
+FEES = [0.0, 0.0005, 0.001, 0.002]
+
+
+# ---- S1 DIP ----
+def dip_entries(df, n=50, z_in=2.5, sl_atr=2.5, max_bars=24):
+    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)
+    ents = []
+    for i in range(n + 14, len(c)):
+        if np.isnan(z[i]) or np.isnan(a[i]):
+            continue
+        if z[i] <= -z_in and z[i - 1] > -z_in:
+            ents.append({"i": i, "d": 1, "tp": ma[i], "sl": c[i] - sl_atr * a[i], "max_bars": max_bars})
+    return ents
+
+
+def validate_dip(assets):
+    print("\n" + "=" * 100)
+    print("  S1 DIP — long-only dip-buy z-score reversion | 1h | n=50 z=2.5 sl=2.5ATR mb=24")
+    print("=" * 100)
+    print(f"  {'Asset':<6s}{'Trd':>6s}{'Win%':>7s}{'FULL%':>9s}{'OOS%':>9s}{'DD%':>6s}{'Exp%':>6s}{'AnniP':>8s}"
+          f"{'  fee-sweep OOS% (0/0.05/0.10/0.20)':<40s}")
+    ok = 0
+    for a in assets:
+        df = get_df(a, "1h"); ents = dip_entries(df)
+        if len(ents) < 30:
+            continue
+        full = simulate(ents, df); _, oe = oos_split(ents, df); oos = simulate(oe, df)
+        sweep = "  ".join(f"{simulate(oe, df, fee_rt=f).ret:+.0f}" for f in FEES)
+        good = full.ret > 0 and oos.ret > 0
+        ok += good
+        print(f"  {a:<6s}{full.trades:>6d}{full.win:>7.1f}{full.ret:>+9.0f}{oos.ret:>+9.0f}"
+              f"{full.dd:>6.0f}{full.exposure:>6.0f}{f'{full.pos_years}/{full.n_years}':>8s}   [{sweep}]"
+              f"{'  OK' if good else ''}")
+    print(f"  -> robusto (FULL+OOS>0) su {ok}/{len(assets)} asset")
+
+
+def validate_trend(assets):
+    print("\n" + "=" * 100)
+    print("  S2 TREND — long-only EMA 20/100 trend | 4h")
+    print("=" * 100)
+    print(f"  {'Asset':<6s}{'Flip':>6s}{'FULL%':>9s}{'OOS%':>9s}{'DD%':>6s}{'Exp%':>6s}{'AnniP':>8s}")
+    ok = 0
+    for a in assets:
+        df = get_df(a, "4h"); sig = ema_dual_signal(df, 20, 100, long_only=True)
+        full = simulate_position(sig, df); oos = trend_oos(sig, df)
+        good = full["ret"] > 0 and oos["ret"] > 0
+        ok += good
+        print(f"  {a:<6s}{full['flips']:>6d}{full['ret']:>+9.0f}{oos['ret']:>+9.0f}"
+              f"{full['dd']:>6.0f}{full['exposure']:>6.0f}{(str(full['pos_years'])+'/'+str(full['n_years'])):>8s}"
+              f"{'  OK' if good else ''}")
+    print(f"  -> robusto su {ok}/{len(assets)} asset")
+
+
+def validate_rot(assets):
+    print("\n" + "=" * 100)
+    print("  S3 ROT — rotazione cross-sectional momentum | 1d | lb=60 top2 su tutto il paniere")
+    print("=" * 100)
+    panel = build_panel(assets, "1d")
+    print(f"  Paniere {list(panel.columns)}  {panel.shape[0]} barre {panel.index[0].date()}->{panel.index[-1].date()}")
+    print(f"  {'fee RT':<10s}{'FULL%':>9s}{'OOS%':>9s}{'DD%':>6s}{'AnniP':>8s}")
+    for f in FEES:
+        full = simulate_rotation(panel, lookback=60, top_k=2, fee_rt=f)
+        oos = simulate_rotation(panel, lookback=60, top_k=2, fee_rt=f, oos_frac=0.30)
+        anni = str(full['pos_years']) + '/' + str(full['n_years'])
+        print(f"  {f*100:>5.2f}%RT  {full['ret']:>+9.0f}{oos['ret']:>+9.0f}{full['dd']:>6.0f}{anni:>8s}")
+    # per-anno alla fee reale
+    full = simulate_rotation(panel, lookback=60, top_k=2, fee_rt=0.001)
+    print("  per-anno (fee 0.10%): " + "  ".join(f"{y}:{v:+.0f}%" for y, v in sorted(full["yearly"].items())))
+
+
+if __name__ == "__main__":
+    assets = available_assets()
+    print(f"VALIDAZIONE FINALE — asset disponibili: {assets}")
+    validate_dip(assets)
+    validate_trend(assets)
+    validate_rot(assets)