config(PORT06): cap SHAPE 0.0588 — SH01 resta senza SL (ricerca multi-agente: 11 famiglie di stop, 0 sopravvissute)
Crash ETH 2026-06-05: SH01 ETH −15.6% su un trade (exit solo a orizzonte, nessuna protezione). Ricerca con harness dedicato sh01_exit_lab (cache walk-forward, engine fill gap-aware worse(livello,open), parity esatta con explore_lab, train<=2023-11-01): ATR intrabar/close-confirm, %, chandelier, breakeven, giveback, loser-timestop, disaster-cap close+intrabar, swing, vol-regime — NESSUNA passa il gate (ogni stop stretto rompe BTC, ogni stop largo non tocca la coda ETH; nei crash il fill e' al gap). Mitigazione: peso famiglia SHAPE 11.8%->5.9% in PORT06 (FULL 6.47->6.43 DD 4.10->3.96, OOS 8.82->8.58 DD 1.30->1.36) — la prossima coda impatta il conto per meta'. Regression-lock test aggiornato. Diario: docs/diary/2026-06-05-sh01-sl-research.md Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Adriano Dal Pastro
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"""SH01 exit policy 03 — pct_fixed.
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SL fisso in PERCENTUALE del prezzo d'ingresso: sl = entry * (1 - d*p).
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Griglia p in {0.01, 0.015, 0.02, 0.03, 0.04, 0.05}, modalita' {intrabar, close}
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-> 12 celle. Il livello e' FISSO (deciso a open_trade su close[i]) -> nessun
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look-ahead nei bar successivi (i livelli usano solo dati <= i).
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Protocollo: grid SOLO sul train; plateau (>=3 celle adiacenti migliorative);
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poi OOS una volta per la config scelta + le 2 vicine.
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cd /opt/docker/PythagorasGoal && uv run python scripts/analysis/sh01_exit_policies/03_pct_fixed.py
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"""
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from __future__ import annotations
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import sys
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sys.path.insert(0, "/opt/docker/PythagorasGoal")
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from scripts.analysis.sh01_exit_lab import ( # noqa: E402
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ASSETS, OOS_START_MS, ExitPolicy, load_sleeves, simulate,
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)
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class PctFixed(ExitPolicy):
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"""SL fisso a una frazione p del prezzo d'ingresso."""
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def __init__(self, p: float, mode: str = "intrabar"):
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self.p = p
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self.mode = mode
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self.name = f"pct_fixed p={p:.3f} {mode}"
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def open_trade(self, ctx, i, d):
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entry = ctx["close"][i]
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sl = entry * (1.0 - d * self.p) # long: sotto; short: sopra
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return {"sl": sl}
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def levels(self, ctx, i, d, j, st):
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return st["sl"], self.mode
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# ----------------------------------------------------------------------------- grid
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P_GRID = [0.01, 0.015, 0.02, 0.03, 0.04, 0.05]
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MODES = ["intrabar", "close"]
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def _row(m):
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return (f"ret={m['ret']:>+7.0f}% dd={m['dd']:>4.0f}% shrp={m['sharpe']:>5.2f} "
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f"worst={m['worst']:>+5.1f}% stop={m['stop_rate']:>4.1f}%")
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def main():
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sleeves = load_sleeves()
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# baseline (no stop)
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print("=" * 110)
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print("BASELINE (orizzonte puro, no SL) — TRAIN:")
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base = {}
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for a in ASSETS:
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m = simulate(sleeves[a], ExitPolicy(), t_hi=OOS_START_MS)
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base[a] = m
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print(f" {a}: {_row(m)}")
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print()
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# ---------------- grid TRAIN only
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print("=" * 110)
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print("GRID — TRAIN ONLY (selezione qui):")
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train = {}
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for mode in MODES:
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print(f"\n mode={mode}")
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for p in P_GRID:
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pol = PctFixed(p, mode)
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row = {}
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for a in ASSETS:
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m = simulate(sleeves[a], pol, t_hi=OOS_START_MS)
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row[a] = m
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train[(mode, p)] = row
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print(f" p={p:.3f} | BTC {_row(row['BTC'])}")
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print(f" | ETH {_row(row['ETH'])}")
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# improvement flags vs baseline on TRAIN: ETH gate (sharpe up, dd down, worst less neg)
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# + BTC not degraded (sharpe>=0.95x, ret>=0.80x)
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print("\n" + "=" * 110)
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print("TRAIN improvement check (cell = migliorativa se ETH sharpe^ dd v worst^ AND BTC sharpe>=95% ret>=80%):")
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bE, bB = base["ETH"], base["BTC"]
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improved = {}
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for mode in MODES:
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flags = []
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for p in P_GRID:
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r = train[(mode, p)]
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eth, btc = r["ETH"], r["BTC"]
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eth_ok = (eth["sharpe"] > bE["sharpe"] and eth["dd"] < bE["dd"]
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and eth["worst"] > bE["worst"])
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btc_ok = (btc["sharpe"] >= 0.95 * bB["sharpe"]
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and btc["ret"] >= 0.80 * bB["ret"])
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cell = eth_ok and btc_ok
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improved[(mode, p)] = cell
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flags.append("Y" if cell else (".|E" if not eth_ok else ".|B"))
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print(f" mode={mode:<9s} " + " ".join(f"p={p:.3f}:{f}" for p, f in zip(P_GRID, flags)))
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# plateau detection: >=3 adjacent p's (same mode) all improved
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print("\nPLATEAU (>=3 p adiacenti migliorativi nella stessa modalita'):")
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plateau_cells = []
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for mode in MODES:
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run = []
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runs = []
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for p in P_GRID:
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if improved[(mode, p)]:
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run.append(p)
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else:
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if len(run) >= 1:
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runs.append(run)
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run = []
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if run:
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runs.append(run)
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for run in runs:
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mark = " <-- PLATEAU" if len(run) >= 3 else ""
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print(f" mode={mode}: run {run} (len {len(run)}){mark}")
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if len(run) >= 3:
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plateau_cells.extend((mode, p) for p in run)
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if not plateau_cells:
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print("\nNESSUN PLATEAU sul train -> famiglia NON passa. OOS solo informativo.")
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else:
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print(f"\nplateau cells: {plateau_cells}")
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# ---------------- pick best cell on TRAIN within plateau (or best overall if no plateau)
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def score(cell):
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r = train[cell]
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# ETH train e' il banco di prova (baseline negativo) -> max ETH sharpe,
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# tie-break ETH dd minore, poi BTC sharpe.
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return (r["ETH"]["sharpe"], -r["ETH"]["dd"], r["BTC"]["sharpe"])
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pool = plateau_cells if plateau_cells else list(train.keys())
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best = max(pool, key=score)
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print(f"\nCHOSEN (train): mode={best[0]} p={best[1]:.3f}")
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# neighbors (same mode, adjacent p)
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mode_b, p_b = best
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idx = P_GRID.index(p_b)
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neigh = [(mode_b, P_GRID[k]) for k in (idx - 1, idx, idx + 1) if 0 <= k < len(P_GRID)]
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# ---------------- OOS verdict (chosen + 2 neighbors) — looked at ONCE
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print("\n" + "=" * 110)
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print("OOS VERDICT (config scelta + 2 vicine) — guardato UNA volta:")
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print("\nBaseline OOS:")
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base_oos = {}
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for a in ASSETS:
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m = simulate(sleeves[a], ExitPolicy(), t_lo=OOS_START_MS)
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base_oos[a] = m
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print(f" {a}: {_row(m)}")
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chosen_oos = None
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for cell in neigh:
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pol = PctFixed(cell[1], cell[0])
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tag = " <== CHOSEN" if cell == best else ""
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print(f"\n mode={cell[0]} p={cell[1]:.3f}{tag}")
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res = {}
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for a in ASSETS:
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tr = simulate(sleeves[a], pol, t_hi=OOS_START_MS)
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oo = simulate(sleeves[a], pol, t_lo=OOS_START_MS)
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res[a] = {"train": tr, "oos": oo}
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print(f" {a} TRAIN {_row(tr)}")
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print(f" {a} OOS {_row(oo)}")
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if cell == best:
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chosen_oos = res
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# ---------------- gate evaluation on chosen
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print("\n" + "=" * 110)
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print("GATE (tutte e 4, train E oos):")
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r = chosen_oos
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bE_o, bB_o = base_oos["ETH"], base_oos["BTC"]
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def g(label, cond):
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print(f" [{'PASS' if cond else 'FAIL'}] {label}")
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return cond
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# a) ETH: sharpe^ dd v worst^ su train E oos
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a_tr = (r["ETH"]["train"]["sharpe"] > bE["sharpe"]
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and r["ETH"]["train"]["dd"] < bE["dd"]
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and r["ETH"]["train"]["worst"] > bE["worst"])
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a_oo = (r["ETH"]["oos"]["sharpe"] > bE_o["sharpe"]
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and r["ETH"]["oos"]["dd"] < bE_o["dd"]
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and r["ETH"]["oos"]["worst"] > bE_o["worst"])
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A = g("a) ETH sharpe^ dd v worst^ (train E oos)", a_tr and a_oo)
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# b) BTC sharpe>=95% ret>=80% baseline (train E oos)
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b_tr = (r["BTC"]["train"]["sharpe"] >= 0.95 * bB["sharpe"]
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and r["BTC"]["train"]["ret"] >= 0.80 * bB["ret"])
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b_oo = (r["BTC"]["oos"]["sharpe"] >= 0.95 * bB_o["sharpe"]
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and r["BTC"]["oos"]["ret"] >= 0.80 * bB_o["ret"])
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B = g("b) BTC sharpe>=95% ret>=80% (train E oos)", b_tr and b_oo)
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# c) ret ETH oos >= 80% baseline
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C = g("c) ret ETH oos >= 80% baseline", r["ETH"]["oos"]["ret"] >= 0.80 * bE_o["ret"])
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# d) plateau
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D = g("d) plateau confermato", bool(plateau_cells) and best in plateau_cells)
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passes = A and B and C and D
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print(f"\n ==> GATE {'PASS' if passes else 'FAIL'}")
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return passes
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if __name__ == "__main__":
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main()
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