Files
Multi_Swarm_Coevolutive/scripts/run_phase1.py
T
Adriano bf70acc322 feat(adversarial): flat_too_long_threshold parametrico (CLI ablation)
Estende AdversarialAgent con flat_too_long_threshold (default 0.95)
configurabile, simmetrico a fees_eat_alpha_threshold. Propagato a
RunConfig.flat_too_long_threshold e flag CLI --flat-too-long-threshold.

Motivazione: pop30-combo ha registrato 75 finding flat_too_long HIGH
(secondo killer dopo fees_eat_alpha 87). Rilassare la soglia 0.95→0.98
ammette strategie più passive ma marginalmente attive — analogo
all'ablation fees già verificata (+23% stabile).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-12 13:45:38 +02:00

116 lines
4.0 KiB
Python

from __future__ import annotations
import argparse
from datetime import datetime
from multi_swarm.cerbero.client import CerberoClient
from multi_swarm.config import load_settings
from multi_swarm.data.cerbero_ohlcv import CerberoOHLCVLoader, OHLCVRequest
from multi_swarm.genome.hypothesis import ModelTier
from multi_swarm.llm.client import LLMClient
from multi_swarm.orchestrator.run import RunConfig, run_phase1
def parse_args() -> argparse.Namespace:
p = argparse.ArgumentParser(description="Multi-Swarm Phase 1 runner")
p.add_argument("--name", default="phase1-spike-001")
p.add_argument("--population-size", type=int, default=20)
p.add_argument("--n-generations", type=int, default=10)
p.add_argument("--elite-k", type=int, default=2)
p.add_argument("--tournament-k", type=int, default=3)
p.add_argument("--p-crossover", type=float, default=0.5)
p.add_argument("--seed", type=int, default=42)
p.add_argument(
"--exchange",
default="deribit",
choices=["deribit", "bybit", "hyperliquid"],
)
p.add_argument("--symbol", default="BTC-PERPETUAL")
p.add_argument("--timeframe", default="1h")
p.add_argument("--start", default="2024-01-01T00:00:00+00:00")
p.add_argument("--end", default="2026-01-01T00:00:00+00:00")
p.add_argument("--fees-bp", type=float, default=5.0)
p.add_argument("--n-trials-dsr", type=int, default=50)
p.add_argument(
"--prompt-mutation-weight",
type=float,
default=0.0,
help="Phase 2.5: probabilità (0-1) che la mutazione invochi LLM mutator tier B",
)
p.add_argument(
"--fees-eat-alpha-threshold",
type=float,
default=0.5,
help="Adversarial gate: kill se fees/gross_pnl > soglia (default 0.5, ablation 0.7)",
)
p.add_argument(
"--flat-too-long-threshold",
type=float,
default=0.95,
help="Adversarial gate: kill se signal flat > soglia delle bar (default 0.95, ablation 0.98)",
)
return p.parse_args()
def main() -> None:
args = parse_args()
settings = load_settings()
token = (
settings.cerbero_mainnet_token.get_secret_value()
if settings.cerbero_mainnet_token
else settings.cerbero_testnet_token.get_secret_value()
)
cerbero = CerberoClient(
base_url=settings.cerbero_base_url,
token=token,
bot_tag=settings.cerbero_bot_tag,
)
loader = CerberoOHLCVLoader(client=cerbero, cache_dir=settings.series_dir)
req = OHLCVRequest(
symbol=args.symbol,
timeframe=args.timeframe,
start=datetime.fromisoformat(args.start),
end=datetime.fromisoformat(args.end),
exchange=args.exchange,
)
ohlcv = loader.load(req)
print(f"OHLCV loaded: {len(ohlcv)} bars from {ohlcv.index[0]} to {ohlcv.index[-1]}")
llm = LLMClient(
openrouter_api_key=settings.openrouter_api_key.get_secret_value(),
model_tier_s=settings.llm_model_tier_s,
model_tier_a=settings.llm_model_tier_a,
model_tier_b=settings.llm_model_tier_b,
model_tier_c=settings.llm_model_tier_c,
model_tier_d=settings.llm_model_tier_d,
openrouter_base_url=settings.openrouter_base_url,
)
cfg = RunConfig(
run_name=args.name,
population_size=args.population_size,
n_generations=args.n_generations,
elite_k=args.elite_k,
tournament_k=args.tournament_k,
p_crossover=args.p_crossover,
seed=args.seed,
model_tier=ModelTier.C,
symbol=args.symbol,
timeframe=args.timeframe,
fees_bp=args.fees_bp,
n_trials_dsr=args.n_trials_dsr,
db_path=settings.db_path,
prompt_mutation_weight=args.prompt_mutation_weight,
fees_eat_alpha_threshold=args.fees_eat_alpha_threshold,
flat_too_long_threshold=args.flat_too_long_threshold,
)
run_id = run_phase1(cfg, ohlcv=ohlcv, llm=llm)
print(f"Run completed: {run_id}")
if __name__ == "__main__":
main()