chore(reset): v2.0.0 — storico certificato Deribit mainnet, ripartenza pulita
Reset del progetto su fondamenta verificate dopo la scoperta che l'intera libreria "validata OOS" era artefatto di feed contaminato (print fantasma del feed Cerbero TESTNET + storico Binance/USDT). - Storico ricostruito da Deribit MAINNET (ccxt pubblico, tokenless) e CERTIFICATO (certify_feed.py): BTC/ETH puliti su TUTTA la storia (mediana 2-6 bps vs Coinbase USD), integrita' OHLC + coerenza resample (maxΔ 0.00) + cross-venue OK. Alt esclusi (illiquidi/divergenti: LTC/DOGE 50-82% barre flat; XRP/BNB non certificabili). - Verdetto sul feed pulito: FADE / PAIRS / XS01 / TSM01 morti (ogni portafoglio Sharpe -2.3..-3.0, DD ~40%); solo SH01 e frammenti HONEST con segnale residuo, da ri-validare in isolamento. - Cleanup "restart pulito": strategie, stack live (src/live, src/portfolio, runner/executor, yml, docker), ~100 script ricerca/gate, waste/games/ portfolios, dati non certificati + cache e 60+ diari -> archiviati in Old/ (preservati, non cancellati). Diario consolidato in un unico documento. - Skeleton ricerca tenuto: Strategy ABC + indicatori + src/fractal + src/backtest/engine + load_data; tool dati certificati (rebuild_history, certify_feed, audit_feed, multi_source_check). - Universo dati ATTIVO: solo BTC/ETH (5m/15m/1h); guardrail fisico (load_data su alt -> FileNotFoundError). Esecuzione DISABILITATA, conto flat. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Adriano Dal Pastro
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"""
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Arena del gioco-OPZIONI: 100 agenti ciechi propongono STRUTTURE in opzioni su due
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serie anonime (A=BTC, B=ETH). Torneo identico al gioco-prezzi (3 finestre TRAIN/VALID/
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TEST, 90 epoche, cull 10% ogni 10 epoche -> 10 finalisti), ma le strategie sono opzioni
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prezzate con BS + skew + DVOL (scripts/games/options_engine.py).
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uv run python -m scripts.games.options_arena # 100 agenti random (test)
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GAME_SPECS_DIR=... GAME_OUT=... uv run python -m scripts.games.options_arena --from-specs
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"""
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from __future__ import annotations
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import json
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import os
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import random
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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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from scripts.games.options_engine import (load_opt, splits3, evaluate, STRUCTURES)
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OUT = Path("data/games"); OUT.mkdir(parents=True, exist_ok=True)
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# spazio parametri: (min, max, tipo)
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PSPACE = dict(otm=(0.02, 0.20, "f"), width=(0.02, 0.12, "f"), dte=(7, 45, "i"))
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SERIES = ["A", "B"]
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def _rand(rng, lo, hi, typ):
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return int(rng.randint(int(lo), int(hi))) if typ == "i" else round(rng.uniform(lo, hi), 3)
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def random_spec(rng):
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p = {k: _rand(rng, *v) for k, v in PSPACE.items()}
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return {"structure": rng.choice(STRUCTURES), "series": rng.choice(SERIES), "params": p}
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def _normalize(spec):
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st = spec.get("structure")
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if st not in STRUCTURES:
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st = "short_put"
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out = {"structure": st, "series": spec.get("series") if spec.get("series") in SERIES else "A",
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"params": {}}
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src = spec.get("params", {})
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for k, (lo, hi, typ) in PSPACE.items():
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v = src.get(k, (lo + hi) / 2)
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try:
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v = float(v)
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except Exception:
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v = (lo + hi) / 2
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v = max(lo, min(hi, v))
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out["params"][k] = int(round(v)) if typ == "i" else round(v, 3)
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# flatten per evaluate (structure/otm/width/dte)
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out["structure"] = st
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return out
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def _flat(spec):
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return {"structure": spec["structure"], **spec["params"]}
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def mutate(spec, rng, strength=0.25):
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s = json.loads(json.dumps(spec))
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keys = list(PSPACE)
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for k in rng.sample(keys, k=rng.randint(1, 2)):
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lo, hi, typ = PSPACE[k]
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span = (hi - lo) * strength
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nv = max(lo, min(hi, s["params"][k] + rng.uniform(-span, span)))
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s["params"][k] = int(round(nv)) if typ == "i" else round(nv, 3)
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if rng.random() < 0.12:
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s["structure"] = rng.choice(STRUCTURES)
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if rng.random() < 0.05:
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s["series"] = rng.choice(SERIES)
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return s
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class Agent:
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def __init__(self, aid, spec, brief=""):
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self.id = aid
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self.spec = _normalize(spec)
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self.brief = brief
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self.train_fit = self.valid_fit = -1e9
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self.metrics = self.vmetrics = {}
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self.alive = True
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@property
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def series(self):
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return self.spec["series"]
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def score(self, datasets, splits_map):
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d = datasets[self.series]; tr, va, _ = splits_map[self.series]
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self.metrics = evaluate(d, _flat(self.spec), tr)
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self.vmetrics = evaluate(d, _flat(self.spec), va)
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self.train_fit = self.metrics["fitness"]; self.valid_fit = self.vmetrics["fitness"]
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def run_tournament(specs, briefs=None, seed=2026, epochs=90, cull_every=10, cull_n=10,
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out_name="options_result.json", log=print):
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rng = random.Random(seed)
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datasets = {"A": load_opt("BTC"), "B": load_opt("ETH")}
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splits_map = {k: splits3(datasets[k]) for k in datasets}
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briefs = briefs or [""] * len(specs)
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agents = [Agent(i, s, briefs[i] if i < len(briefs) else "") for i, s in enumerate(specs)]
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for a in agents:
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a.score(datasets, splits_map)
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alive = lambda: [a for a in agents if a.alive]
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log(f"[epoch 0] {len(alive())} agenti | best VALID {max(a.valid_fit for a in agents):.1f}")
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for ep in range(1, epochs + 1):
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for a in alive():
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cand = _normalize(mutate(a.spec, rng))
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d = datasets[cand["series"]]; tr, va, _ = splits_map[cand["series"]]
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m = evaluate(d, _flat(cand), tr)
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if m["fitness"] > a.train_fit:
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a.spec, a.metrics, a.train_fit = cand, m, m["fitness"]
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a.vmetrics = evaluate(d, _flat(cand), va); a.valid_fit = a.vmetrics["fitness"]
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if ep % cull_every == 0:
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av = sorted(alive(), key=lambda a: a.valid_fit)
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k = cull_n if len(av) - cull_n >= 10 else max(0, len(av) - 10)
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for a in av[:k]:
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a.alive = False
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log(f"[epoch {ep:2d}] cull {k:2d} -> {len(alive()):3d} | best VALID "
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f"{max(a.valid_fit for a in alive()):.1f} | worst {min(a.valid_fit for a in alive()):.1f}")
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survivors = sorted(alive(), key=lambda a: a.valid_fit, reverse=True)
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results = []
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for rank, a in enumerate(survivors, 1):
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d = datasets[a.series]; _, _, te = splits_map[a.series]
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results.append({"rank": rank, "agent": a.id, "spec": a.spec, "brief": a.brief,
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"series": a.series, "train": a.metrics, "valid": a.vmetrics,
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"test": evaluate(d, _flat(a.spec), te), "full": evaluate(d, _flat(a.spec), None)})
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payload = {"n_agents": len(specs), "survivors": len(survivors), "results": results,
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"reveal": {"A": "BTC", "B": "ETH"}, "game": "options"}
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(OUT / out_name).write_text(json.dumps(payload, indent=2))
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return payload
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def leaderboard(payload, top=10, log=print):
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log("\n========= CLASSIFICA FINALE OPZIONI (top %d) =========" % top)
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log(f"{'#':>2} {'ag':>4} {'ser':>3} {'struttura':>14} {'otm':>5} {'dte':>4} "
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f"{'TEpnl%':>8} {'TEwin':>5} {'TEtpm':>6} {'TEsh':>6}")
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for r in payload["results"][:top]:
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sp = r["spec"]; te = r["test"]; p = sp["params"]
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log(f"{r['rank']:>2} {r['agent']:>4} {sp['series']:>3} {sp['structure']:>14} "
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f"{p['otm']:>5.2f} {p['dte']:>4} {te['pnl_pct']:>8.0f} {te['win_rate']*100:>4.0f}% "
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f"{te['tpm']:>6.0f} {te['sharpe']:>6.1f}")
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def load_specs(specs_dir, n=100):
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rng = random.Random(7); specs, briefs = [], []
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for i in range(n):
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f = Path(specs_dir) / f"agent_{i}.json"
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spec = None
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if f.exists():
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try:
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raw = json.loads(f.read_text())
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params = {k: raw.get(k, raw.get("params", {}).get(k)) for k in PSPACE}
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spec = _normalize({"structure": raw.get("structure"),
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"series": {"X": "A", "Y": "B"}.get(raw.get("series"), raw.get("series")),
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"params": params})
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briefs.append(str(raw.get("hypothesis", ""))[:300])
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except Exception:
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spec = None
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if spec is None:
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spec = random_spec(rng); briefs.append("(spec mancante -> random)")
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specs.append(spec)
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return specs, briefs
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def main():
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if "--from-specs" in sys.argv:
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sd = os.environ.get("GAME_SPECS_DIR", "data/games/specs_opt")
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on = os.environ.get("GAME_OUT", "options_result.json")
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specs, briefs = load_specs(sd)
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n_real = sum(1 for b in briefs if "mancante" not in b)
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print(f"caricati {n_real}/100 spec da agenti reali")
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payload = run_tournament(specs, briefs=briefs, out_name=on)
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else:
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rng = random.Random(42)
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payload = run_tournament([random_spec(rng) for _ in range(100)], seed=42)
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leaderboard(payload)
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rev = payload["reveal"]; w = payload["results"][0]
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print(f"\n>>> RIVELAZIONE: A={rev['A']}, B={rev['B']}. Gli agenti non lo sapevano. <<<")
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print(f"VINCITORE: #{w['agent']} {w['series']} {w['spec']['structure']} "
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f"otm{w['spec']['params']['otm']} dte{w['spec']['params']['dte']}")
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print(f" ipotesi: {w['brief']}")
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print(f" TEST: PnL {w['test']['pnl_pct']:.0f}% | win {w['test']['win_rate']*100:.0f}% | "
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f"{w['test']['tpm']:.0f} tr/mese | Sharpe {w['test']['sharpe']:.1f}")
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if __name__ == "__main__":
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main()
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