Adriano Dal Pastro
8d69a0cef5
- Gioco GRID TRADERS (sessione 3, regola STRATEGIA_GRIGLIA.md): grid_engine (backtest causale fee-aware della griglia geometrica), grid_brief (digest anonimo per dimensionare la griglia), grid_arena (torneo 100 agenti); diario docs/diary/2026-06-10-grid-traders-game3.md - Gioco OPZIONI: options_engine (BS + skew fittato + DVOL storica), options_arena, opt_calibrate (superficie premi REALE da cerbero-bite) - Gioco SESSION: session_engine/session_arena (pattern orari intraday) - arena: vincolo GAME_NO_LIVE=1 (vieta pairs e fade zscore/breakout/momentum gia' live, coercizione a trend/ma_cross) + normalize del candidato PRIMA della valutazione nel hill-climb - Gate: grid_game_gate (griglia ETH vincitrice vs PORT06, mark-to-market), pairs30m_gate (ETH/BTC 30m ridondante col 15m gia' deployato?) - reset_flatten: flatten one-shot del conto testnet per il reset portafoglio - .gitignore: data/portfolio_paper_stats/ (stato runtime sleeve paper-only) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
252 lines
10 KiB
Python
252 lines
10 KiB
Python
"""
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Arena del gioco GRID TRADERS (sessione 3): 100 agenti ciechi configurano una
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griglia di trading secondo STRATEGIA_GRIGLIA.md su due serie anonime
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(A=BTC, B=ETH, mai rivelate; gli agenti le vedono come X/Y). Torneo standard:
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3 finestre TRAIN/VALID/TEST, 90 epoche di hill-climb sul TRAIN, ogni 10 epoche
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l'orchestratore blocca il 10% meno profittevole in VALID, fino a 10 superstiti.
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TEST = OOS puro mai toccato dall'ottimizzazione.
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uv run python -m scripts.games.grid_arena # 100 random (smoke)
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GAME_SPECS_DIR=... GAME_OUT=... uv run python -m scripts.games.grid_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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from scripts.games.engine import load_anon, splits3
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from scripts.games import grid_engine
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from scripts.games.grid_engine import evaluate, max_levels
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OUT = Path("data/games")
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OUT.mkdir(parents=True, exist_ok=True)
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# Spazio parametri (min, max, tipo). range_*/sl_buf/tp_buf in FRAZIONE.
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PSPACE = dict(
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range_down=(0.02, 0.30, "f"),
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range_up=(0.02, 0.30, "f"),
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levels=(4, 30, "i"),
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sl_buf=(0.01, 0.15, "f"),
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tp_buf=(0.01, 0.15, "f"),
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max_bars=(48, 3000, "i"),
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)
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SERIES = ["A", "B"]
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TIMEFRAMES = ["15m", "30m", "1h", "2h", "4h", "1d"] # no 5m (costo computazionale)
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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), 4)
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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 {"series": rng.choice(SERIES), "tf": rng.choice(TIMEFRAMES), "params": p}
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def _normalize(spec):
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"""Clampa la spec nello spazio valido e APPLICA il vincolo break-even (§4):
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se il passo e' troppo fitto riduce GRID_LEVELS (come da spec: 'vanno ridotti
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i GRID_LEVELS o allargato il range')."""
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out = {"series": spec.get("series") if spec.get("series") in SERIES else "A",
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"tf": spec.get("tf") if spec.get("tf") in TIMEFRAMES else "1h",
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"params": {}}
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src = spec.get("params", spec)
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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, 4)
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p = out["params"]
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lmax = max_levels(p["range_down"], p["range_up"])
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if lmax >= 2:
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p["levels"] = min(p["levels"], lmax)
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return out
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def mutate(spec, rng, strength=0.25):
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"""Hill-climb: perturba 1-2 parametri; raramente cambia serie.
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Il timeframe e' l'identita' dell'agente -> non muta."""
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s = json.loads(json.dumps(spec))
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for k in rng.sample(list(PSPACE), 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, 4)
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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 = -1e9
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self.valid_fit = -1e9
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self.metrics = {}
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self.vmetrics = {}
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self.alive = True
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self.culled_epoch = None
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@property
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def tf(self):
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return self.spec["tf"]
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def score(self, datasets, sm):
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data = datasets[self.tf]
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tr, va, _ = sm[self.tf]
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self.metrics = evaluate(data, self.spec, tr)
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self.vmetrics = evaluate(data, self.spec, va)
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self.train_fit = self.metrics["fitness"]
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self.valid_fit = self.vmetrics["fitness"]
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def run_tournament(specs, briefs=None, seed=2026, epochs=90, cull_every=10,
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cull_n=10, out_name="grid_result.json", log=print):
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rng = random.Random(seed)
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used_tfs = sorted({_normalize(s)["tf"] for s in specs})
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datasets = {tf: load_anon(tf) for tf in used_tfs}
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sm = {tf: splits3(datasets[tf], 0.60, 0.20) for tf in used_tfs}
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briefs = briefs or [""] * len(specs)
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agents = [Agent(i, s, briefs[i] if i < len(briefs) else "")
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for i, s in enumerate(specs)]
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for a in agents:
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a.score(datasets, sm)
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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 "
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f"{max(a.valid_fit for a in agents):.1f}")
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history = []
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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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data = datasets[a.tf]
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tr, va, _ = sm[a.tf]
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m = evaluate(data, 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(data, a.spec, va)
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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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a.culled_epoch = ep
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log(f"[epoch {ep:2d}] cull {k:2d} -> {len(alive()):3d} vivi | "
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f"best VALID {max(a.valid_fit for a in alive()):.1f} | "
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f"worst-alive {min(a.valid_fit for a in alive()):.1f}")
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history.append({"epoch": ep, "alive": len(alive()),
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"best_valid": max(a.valid_fit for a in alive())})
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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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data = datasets[a.tf]
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_, _, te = sm[a.tf]
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results.append({"rank": rank, "agent": a.id, "spec": a.spec,
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"brief": a.brief, "tf": a.tf,
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"train": a.metrics, "valid": a.vmetrics,
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"test": evaluate(data, a.spec, te),
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"full": evaluate(data, a.spec, None)})
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payload = {"n_agents": len(specs), "epochs": epochs,
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"survivors": len(survivors), "results": results,
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"history": history, "game": "grid",
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"rule": "STRATEGIA_GRIGLIA.md",
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"reveal": {"A": "BTC", "B": "ETH"}}
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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 GRID TRADERS (top %d) ==========" % top)
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log("VALID = finestra del giudice | TEST = OOS puro (mai ottimizzato)")
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log(f"{'#':>2} {'ag':>4} {'tf':>4} {'ser':>3} {'rng-/+':>11} {'lvl':>3} "
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f"{'sl/tp buf':>11} {'mbars':>5} {'TEpnl%':>7} {'TEwin':>5} "
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f"{'TEtpm':>6} {'TEsh':>5} {'VApnl%':>7}")
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for r in payload["results"][:top]:
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sp = r["spec"]; p = sp["params"]; te = r["test"]; va = r["valid"]
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log(f"{r['rank']:>2} {r['agent']:>4} {sp['tf']:>4} {sp['series']:>3} "
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f"{p['range_down']*100:>4.1f}/{p['range_up']*100:>4.1f}% {p['levels']:>3} "
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f"{p['sl_buf']*100:>4.1f}/{p['tp_buf']*100:>4.1f}% {p['max_bars']:>5} "
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f"{te['pnl_pct']:>7.0f} {te['win_rate']*100:>4.0f}% {te['tpm']:>6.1f} "
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f"{te['sharpe']:>5.1f} {va['pnl_pct']:>7.0f}")
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def load_specs(specs_dir, n=100):
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"""Carica le spec proposte dagli agenti ciechi (X->A, Y->B, pct->frazione)."""
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rng = random.Random(7)
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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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src = raw.get("params", raw)
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params = {
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"range_down": float(src.get("range_down_pct", src.get("range_down", 10))) ,
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"range_up": float(src.get("range_up_pct", src.get("range_up", 10))),
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"levels": src.get("grid_levels", src.get("levels", 10)),
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"sl_buf": float(src.get("sl_buf_pct", src.get("sl_buf", 5))),
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"tp_buf": float(src.get("tp_buf_pct", src.get("tp_buf", 5))),
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"max_bars": src.get("max_bars", 500),
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}
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# gli agenti parlano in percentuale -> frazione
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for k in ("range_down", "range_up", "sl_buf", "tp_buf"):
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if params[k] > 1.0:
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params[k] = params[k] / 100.0
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spec = _normalize({
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"series": {"X": "A", "Y": "B"}.get(raw.get("series"), raw.get("series")),
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"tf": raw.get("tf", "1h"), "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)
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briefs.append("(spec mancante -> sostituto casuale)")
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specs.append(spec)
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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}/{n} spec da agenti reali, {n - n_real} sostituiti casuali")
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return specs, briefs
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def main():
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slip = float(os.environ.get("GAME_SLIP", "0.0"))
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grid_engine.set_slippage(slip)
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if slip > 0:
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print(f"SLIPPAGE attivo: {slip*100:.3f}%/lato")
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epochs = int(os.environ.get("GAME_EPOCHS", "90"))
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if "--from-specs" in sys.argv:
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sd = os.environ.get("GAME_SPECS_DIR", "data/games/specs_grid")
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on = os.environ.get("GAME_OUT", "grid_result.json")
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specs, briefs = load_specs(sd)
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payload = run_tournament(specs, briefs=briefs, epochs=epochs, 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)],
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seed=42, epochs=epochs)
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leaderboard(payload)
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rev = payload["reveal"]
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w = payload["results"][0]
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sp = w["spec"]; p = sp["params"]
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print(f"\n>>> RIVELAZIONE: Serie X = {rev['A']}, Serie Y = {rev['B']}. "
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f"Gli agenti non lo sapevano. <<<")
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print(f"\nVINCITORE: agente #{w['agent']} su {sp['tf']} serie {sp['series']} | "
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f"griglia -{p['range_down']*100:.1f}%/+{p['range_up']*100:.1f}% "
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f"x{p['levels']} livelli, SL buf {p['sl_buf']*100:.1f}%, "
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f"TP buf {p['tp_buf']*100:.1f}%, max {p['max_bars']} barre")
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print(f" ipotesi dell'agente: {w['brief']}")
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print(f" TEST(OOS): PnL {w['test']['pnl_pct']:.0f}% | win "
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f"{w['test']['win_rate']*100:.0f}% | {w['test']['tpm']:.1f} trade/mese | "
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f"Sharpe {w['test']['sharpe']:.1f}")
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if __name__ == "__main__":
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main()
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