Adriano
c38311e5fa
Implementazione completa Phase 2.5 (LLM prompt mutator) seguendo il piano in docs/superpowers/plans/2026-05-11-mutate-prompt-llm-phase-2-5.md. Nuovo modulo src/multi_swarm/genome/mutation_prompt_llm.py: - 6 mutation instructions (tighten_threshold, swap_comparator, add_condition, remove_condition, change_timeframe, add_temporal_gate) - mutate_prompt_llm(g, llm, rng, mutator_tier=ModelTier.B): clona genome con tier B per la call mutator, costruisce system+user prompt con istruzione scelta random, estrae prompt da tag <prompt>...</prompt>, valida - is_valid_prompt(): 3 check (lunghezza >= 50, keyword tecnica, diff > 5% Levenshtein-like via difflib.SequenceMatcher) - Fallback random_mutate su qualsiasi validation fail O LLM exception Esteso src/multi_swarm/genome/mutation.py con weighted_random_mutate dispatcher: con probabilità prompt_mutation_weight invoca mutate_prompt_llm, altrimenti random_mutate. Backward compat: llm=None oppure weight=0 → solo scalare. Integrazione GA loop + RunConfig: - GAConfig.prompt_mutation_weight: float = 0.0 (default off) - next_generation(llm=...) propagato all'invocazione mutator - RunConfig.prompt_mutation_weight con stesso default - run_phase1: passa llm a next_generation solo se weight > 0 - scripts/run_phase1.py: flag CLI --prompt-mutation-weight Tests (+18, 175 totale): - tests/unit/test_mutation_prompt_llm.py (12): extract_prompt, is_valid_prompt 3 check, operator success + fallback su 3 modi (invalid/identical/exception), tier B per LLM call, istruzione scelta dal pool - tests/unit/test_mutation_dispatcher.py (4): weight 0/1/None + distribuzione 30/70 su 1000 estrazioni con tolleranza ±5% - tests/integration/test_ga_loop_with_prompt_mutator.py (2): loop con weight=1.0 produce prompt evoluti; backward compat weight=0 invariato Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
102 lines
3.4 KiB
Python
102 lines
3.4 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",
|
|
)
|
|
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,
|
|
)
|
|
|
|
run_id = run_phase1(cfg, ohlcv=ohlcv, llm=llm)
|
|
print(f"Run completed: {run_id}")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|