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feat(pairs): attiva ETH/BTC 15m flat-skip in PORT06 (BLEND, mezza size)

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Origine: gioco "Blind Traders" (100 agenti ciechi su BTC/ETH anonimizzati) ->
vincitore = spread ETH/BTC reversion a 15m. Testato sul serio col gate PORT06:
non duplicato (corr 1h vs 15m = 0.37), robusto (16/16 celle Sharpe>1), edge NON
artefatto delle candele flat ETH 15m (filtrandole resta l'83% dello Sharpe).

Percorso live costruito e validato:
- pairs_research.pairs_sim_flat: engine generalizzato con exit LIVE-REALIZABLE
  (arma exit_ready, esce alla 1a barra pulita); regression-lock a pairs_sim.
- PairsWorker: flat_skip + exit_ready + rilevamento flat da OHLC (1h byte-exact).
- runner: fetch diretto dei timeframe sub-orari + override position_size per-sleeve.
- validate_worker_pairs: replay worker == backtest a 15m (8452 vs 8453 trade).
- _defs/build_everything: sleeve PR_ETHBTC_15M (mezza size, pos 0.10) -> PORT06
  FULL 6.43->7.20, OOS 8.58->9.66, DD giu'. Rischio bilanciato col 1h.
- smoke live: Cerbero serve candele 15m fresche; worker ticca.

Diari docs/diary/2026-06-09-*. Caveat slippage: mezza size = blend-tilt prudente.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This commit is contained in:
Adriano Dal Pastro
2026-06-09 11:48:15 +00:00
parent 5d45f4ef6e
commit d25d897fd1
20 changed files with 1727 additions and 60 deletions
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scripts/games/arena.py
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"""
Arena — tournament orchestrator per il gioco "Blind Traders".
100 agenti partono da una spec di strategia (creata alla cieca: vedi
agent_brief.py / workflow). L'orchestratore valuta ogni spec con il backtest
deterministico (engine.evaluate) su TRAIN, da' epoche di elaborazione (ogni
agente affina la propria strategia via hill-climb sui parametri) e OGNI 10
EPOCHE blocca il 10% meno profittevole. Restano i 10 piu' profittevoli.
Punteggio = fitness su PNL + %win, con vincolo >=10 trade/mese (engine).
"""
from __future__ import annotations
import json
import random
from pathlib import Path
import numpy as np
from scripts.games.engine import load_anon, splits3, evaluate
OUT = Path("data/games")
OUT.mkdir(parents=True, exist_ok=True)
# Spazio parametri per famiglia (min, max, tipo)
SPACE = {
"zscore": dict(lookback=(10, 100, "i"), entry_thr=(1.0, 3.5, "f"),
tp_atr=(0.5, 4.0, "f"), sl_atr=(1.0, 5.0, "f"),
max_bars=(6, 72, "i")),
"breakout": dict(lookback=(12, 120, "i"), entry_thr=(0.0, 0.0, "f"),
tp_atr=(0.5, 4.0, "f"), sl_atr=(1.0, 5.0, "f"),
max_bars=(6, 72, "i")),
"ma_cross": dict(lookback=(5, 50, "i"), slow_mult=(2.0, 6.0, "f"),
entry_thr=(0.0, 0.0, "f"), tp_atr=(0.5, 4.0, "f"),
sl_atr=(1.0, 5.0, "f"), max_bars=(6, 72, "i")),
"rsi": dict(lookback=(7, 30, "i"), entry_thr=(1.0, 4.0, "f"),
tp_atr=(0.5, 4.0, "f"), sl_atr=(1.0, 5.0, "f"),
max_bars=(6, 72, "i")),
"momentum": dict(lookback=(6, 72, "i"), entry_thr=(1.0, 6.0, "f"),
tp_atr=(0.5, 4.0, "f"), sl_atr=(1.0, 5.0, "f"),
max_bars=(6, 72, "i")),
"pairs": dict(lookback=(20, 120, "i"), entry_thr=(1.5, 3.0, "f"),
exit_thr=(0.2, 1.0, "f"), max_bars=(24, 120, "i")),
}
SINGLE_FAMILIES = ["zscore", "breakout", "ma_cross", "rsi", "momentum"]
DIRECTIONS = ["reversion", "trend"]
TIMEFRAMES = ["1h", "15m", "5m"] # timing diversi su cui competono gli agenti
def _rand_param(rng, lo, hi, typ):
if typ == "i":
return int(rng.randint(int(lo), int(hi)))
return round(rng.uniform(lo, hi), 3)
def random_spec(rng):
if rng.random() < 0.25:
fam = "pairs"
else:
fam = rng.choice(SINGLE_FAMILIES)
params = {}
for k, (lo, hi, typ) in SPACE[fam].items():
params[k] = _rand_param(rng, lo, hi, typ)
spec = {"family": fam, "params": params, "tf": rng.choice(TIMEFRAMES)}
if fam == "pairs":
spec["series"] = "AB"
else:
spec["series"] = rng.choice(["A", "B"])
spec["params"]["direction"] = rng.choice(DIRECTIONS)
return spec
def mutate(spec, rng, strength=0.25):
"""Perturba la spec (hill-climb). Per lo piu' numerica; raramente
cambia direzione/serie. La famiglia resta fissa (identita' dell'agente)."""
s = json.loads(json.dumps(spec))
fam = s["family"]
# perturba 1-2 parametri numerici
keys = [k for k in SPACE[fam] if SPACE[fam][k][0] != SPACE[fam][k][1]]
for k in rng.sample(keys, k=min(len(keys), rng.randint(1, 2))):
lo, hi, typ = SPACE[fam][k]
cur = s["params"][k]
span = (hi - lo) * strength
nv = cur + rng.uniform(-span, span)
nv = max(lo, min(hi, nv))
s["params"][k] = int(round(nv)) if typ == "i" else round(nv, 3)
if fam != "pairs":
if rng.random() < 0.10:
s["params"]["direction"] = rng.choice(DIRECTIONS)
if rng.random() < 0.05:
s["series"] = rng.choice(["A", "B"])
# il timeframe resta l'identita' dell'agente (timing fisso) -> non muta
return s
def _normalize(spec):
"""Completa/ripulisce una spec proposta da un agente (robustezza)."""
fam = spec.get("family")
if fam not in SPACE:
fam = "zscore"
out = {"family": fam, "params": {}}
for k, (lo, hi, typ) in SPACE[fam].items():
v = spec.get("params", {}).get(k, (lo + hi) / 2)
try:
v = float(v)
except Exception:
v = (lo + hi) / 2
v = max(lo, min(hi, v))
out["params"][k] = int(round(v)) if typ == "i" else round(v, 3)
out["tf"] = spec.get("tf") if spec.get("tf") in TIMEFRAMES else "1h"
if fam == "pairs":
out["series"] = "AB"
else:
out["series"] = spec.get("series", "A") if spec.get("series") in ("A", "B") else "A"
d = spec.get("params", {}).get("direction") or spec.get("direction")
out["params"]["direction"] = d if d in DIRECTIONS else "reversion"
return out
class Agent:
def __init__(self, aid, spec, brief=""):
self.id = aid
self.spec = _normalize(spec)
self.brief = brief # cosa "dice" l'agente (ipotesi NL)
self.train_fit = -1e9 # criterio di hill-climb (l'agente ottimizza qui)
self.valid_fit = -1e9 # criterio dell'orchestratore (cull + rank)
self.metrics = {} # metriche TRAIN
self.vmetrics = {} # metriche VALID
self.alive = True
self.culled_epoch = None
@property
def tf(self):
return self.spec.get("tf", "1h")
def score(self, datasets, splits_map):
data = datasets[self.tf]
tr, va, _ = splits_map[self.tf]
self.metrics = evaluate(data, self.spec, tr)
self.vmetrics = evaluate(data, self.spec, va)
self.train_fit = self.metrics["fitness"]
self.valid_fit = self.vmetrics["fitness"]
def run_tournament(specs, briefs=None, seed=7,
epochs=90, cull_every=10, cull_n=10, log=print):
rng = random.Random(seed)
# carica solo i timeframe effettivamente usati dagli agenti
used_tfs = sorted({_normalize(s).get("tf", "1h") for s in specs})
datasets = {tf: load_anon(tf) for tf in used_tfs}
splits_map = {tf: splits3(datasets[tf], 0.60, 0.20) for tf in used_tfs}
briefs = briefs or [""] * len(specs)
agents = [Agent(i, s, briefs[i] if i < len(briefs) else "")
for i, s in enumerate(specs)]
for a in agents:
a.score(datasets, splits_map)
alive = lambda: [a for a in agents if a.alive]
log(f"[epoch 0] {len(alive())} agenti | best VALID fit "
f"{max(a.valid_fit for a in agents):.1f}")
history = []
for ep in range(1, epochs + 1):
# elaborazione: l'agente affina sul TRAIN (cio' che vede); ricalcola VALID
for a in alive():
cand = mutate(a.spec, rng)
data = datasets[a.tf]
tr, va, _ = splits_map[a.tf]
m = evaluate(data, cand, tr)
if m["fitness"] > a.train_fit:
a.spec = _normalize(cand)
a.metrics, a.train_fit = m, m["fitness"]
a.vmetrics = evaluate(data, a.spec, va)
a.valid_fit = a.vmetrics["fitness"]
# cull ogni N epoche: l'ORCHESTRATORE blocca il 10% meno profittevole
# in VALIDATION (generalizzazione, non overfit sul train)
if ep % cull_every == 0:
av = sorted(alive(), key=lambda a: a.valid_fit)
k = cull_n if len(av) - cull_n >= 10 else max(0, len(av) - 10)
for a in av[:k]:
a.alive = False
a.culled_epoch = ep
log(f"[epoch {ep:2d}] cull {k:2d} -> {len(alive()):3d} vivi | "
f"best VALID {max(a.valid_fit for a in alive()):.1f} | "
f"worst-alive {min(a.valid_fit for a in alive()):.1f}")
history.append({"epoch": ep, "alive": len(alive()),
"best_valid": max(a.valid_fit for a in alive())})
survivors = sorted(alive(), key=lambda a: a.valid_fit, reverse=True)
# report finale: TEST = OOS puro mai toccato dall'ottimizzazione
results = []
for rank, a in enumerate(survivors, 1):
data = datasets[a.tf]
_, _, te = splits_map[a.tf]
test = evaluate(data, a.spec, te)
full = evaluate(data, a.spec, None)
results.append({
"rank": rank, "agent": a.id, "spec": a.spec, "brief": a.brief,
"tf": a.tf, "train": a.metrics, "valid": a.vmetrics,
"test": test, "full": full,
})
payload = {"n_agents": len(specs), "epochs": epochs,
"survivors": len(survivors), "results": results,
"history": history,
"reveal": {"A": "BTC", "B": "ETH", "tf": "1h"}}
(OUT / "tournament_result.json").write_text(json.dumps(payload, indent=2))
return payload
def leaderboard(payload, top=10, log=print):
log("\n================ CLASSIFICA FINALE (top %d) ================" % top)
log("VALID = finestra su cui l'orchestratore giudica | TEST = OOS puro (mai ottimizzato)")
log(f"{'#':>2} {'ag':>4} {'tf':>3} {'famiglia':>9} {'ser':>3} {'dir':>9} "
f"{'TEpnl%':>8} {'TEwin':>5} {'TEtpm':>6} {'TEsh':>5} {'VApnl%':>8} {'VAwin':>5}")
for r in payload["results"][:top]:
sp = r["spec"]; te = r["test"]; va = r["valid"]
d = sp["params"].get("direction", "-")
log(f"{r['rank']:>2} {r['agent']:>4} {sp.get('tf','1h'):>3} {sp['family']:>9} "
f"{sp['series']:>3} {d:>9} {te['pnl_pct']:>8.0f} {te['win_rate']*100:>4.0f}% "
f"{te['tpm']:>6.1f} {te['sharpe']:>5.1f} {va['pnl_pct']:>8.0f} "
f"{va['win_rate']*100:>4.0f}%")
if __name__ == "__main__":
import sys
# modalita' test: 100 agenti random
rng = random.Random(42)
specs = [random_spec(rng) for _ in range(100)]
payload = run_tournament(specs, seed=42)
leaderboard(payload)