feat(orchestrator): end-to-end Phase 1 runner with persistence
Loop GA completo: build_initial_population -> hypothesis.propose -> falsification + adversarial -> compute_fitness -> persistenza -> next_generation. Stato run/gen/genomes/evals/cost/findings su SQLite, elite skip-eval, run marcato failed su eccezione. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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"""End-to-end orchestrator per un run di Phase 1.
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Pipeline per ogni generazione:
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population -> hypothesis_agent.propose -> falsification + adversarial
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-> compute_fitness -> persistenza -> next_generation
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Tutto e' loggato sulla repository SQLite (runs, generations, genomes,
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evaluations, cost_records, adversarial_findings) cosi' che la GUI Streamlit
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possa leggere lo stato a run terminato (o in corso).
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"""
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from __future__ import annotations
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import random
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from dataclasses import dataclass, field
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from pathlib import Path
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import pandas as pd # type: ignore[import-untyped]
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from ..agents.adversarial import AdversarialAgent
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from ..agents.falsification import FalsificationAgent
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from ..agents.hypothesis import HypothesisAgent
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from ..agents.market_summary import build_market_summary
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from ..ga.fitness import compute_fitness
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from ..ga.initial import build_initial_population
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from ..ga.loop import GAConfig, next_generation
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from ..ga.summary import generation_summary
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from ..genome.hypothesis import ModelTier
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from ..llm.client import LLMClient
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from ..llm.cost_tracker import CostTracker
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from ..persistence.repository import Repository
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@dataclass
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class RunConfig:
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"""Parametri di un run end-to-end della Phase 1."""
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run_name: str
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population_size: int = 20
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n_generations: int = 10
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elite_k: int = 2
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tournament_k: int = 3
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p_crossover: float = 0.5
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seed: int = 42
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model_tier: ModelTier = ModelTier.C
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symbol: str = "BTC/USDT"
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timeframe: str = "1h"
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fees_bp: float = 5.0
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n_trials_dsr: int = 50
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db_path: Path = field(default_factory=lambda: Path("./runs.db"))
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def run_phase1(
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cfg: RunConfig,
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ohlcv: pd.DataFrame,
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llm: LLMClient,
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) -> str:
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"""Esegue il loop GA end-to-end e ritorna l'``id`` del run.
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Su qualunque eccezione marca il run come ``failed`` e rilancia.
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"""
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rng = random.Random(cfg.seed)
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repo = Repository(cfg.db_path)
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repo.init_schema()
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config_dict = {
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**cfg.__dict__,
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"db_path": str(cfg.db_path),
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"model_tier": cfg.model_tier.value,
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}
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run_id = repo.create_run(name=cfg.run_name, config=config_dict)
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market = build_market_summary(ohlcv, symbol=cfg.symbol, timeframe=cfg.timeframe)
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hypothesis_agent = HypothesisAgent(llm=llm)
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falsification_agent = FalsificationAgent(
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fees_bp=cfg.fees_bp, n_trials_dsr=cfg.n_trials_dsr
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)
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adversarial_agent = AdversarialAgent(fees_bp=cfg.fees_bp)
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cost_tracker = CostTracker()
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population = build_initial_population(
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k=cfg.population_size, model_tier=cfg.model_tier, rng=rng
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)
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fitnesses: dict[str, float] = {}
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ga_cfg = GAConfig(
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population_size=cfg.population_size,
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elite_k=cfg.elite_k,
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tournament_k=cfg.tournament_k,
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p_crossover=cfg.p_crossover,
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)
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try:
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for gen in range(cfg.n_generations):
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for genome in population:
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if genome.id in fitnesses:
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continue # elite gia' valutata in generazione precedente
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repo.save_genome(run_id=run_id, generation_idx=gen, genome=genome)
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proposal = hypothesis_agent.propose(genome, market)
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cost_record = cost_tracker.record(
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input_tokens=proposal.completion.input_tokens,
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output_tokens=proposal.completion.output_tokens,
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tier=proposal.completion.tier,
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run_id=run_id,
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agent_id=genome.id,
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)
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repo.save_cost_record(
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run_id=run_id,
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agent_id=genome.id,
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tier=cost_record.tier.value,
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input_tokens=cost_record.input_tokens,
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output_tokens=cost_record.output_tokens,
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cost_usd=cost_record.cost_usd,
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)
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if proposal.strategy is None:
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repo.save_evaluation(
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run_id=run_id,
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genome_id=genome.id,
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fitness=0.0,
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dsr=0.0,
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dsr_pvalue=1.0,
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sharpe=0.0,
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max_dd=0.0,
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total_return=0.0,
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n_trades=0,
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parse_error=proposal.parse_error,
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raw_text=proposal.raw_text,
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)
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fitnesses[genome.id] = 0.0
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continue
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fals = falsification_agent.evaluate(proposal.strategy, ohlcv)
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adv = adversarial_agent.review(proposal.strategy, ohlcv)
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for finding in adv.findings:
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repo.save_adversarial_finding(
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run_id=run_id,
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genome_id=genome.id,
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name=finding.name,
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severity=finding.severity.value,
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detail=finding.detail,
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)
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fit = compute_fitness(fals, adv)
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repo.save_evaluation(
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run_id=run_id,
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genome_id=genome.id,
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fitness=fit,
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dsr=fals.dsr,
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dsr_pvalue=fals.dsr_pvalue,
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sharpe=fals.sharpe,
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max_dd=fals.max_drawdown,
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total_return=fals.total_return,
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n_trades=fals.n_trades,
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parse_error=None,
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raw_text=proposal.raw_text,
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)
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fitnesses[genome.id] = fit
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gen_fitnesses = [fitnesses[g.id] for g in population]
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summary = generation_summary(gen_fitnesses, n_bins=10)
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repo.save_generation_summary(
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run_id=run_id,
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generation_idx=gen,
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n_genomes=len(population),
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fitness_median=summary["median"],
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fitness_max=summary["max"],
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fitness_p90=summary["p90"],
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entropy=summary["entropy"],
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)
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if gen < cfg.n_generations - 1:
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population = next_generation(population, fitnesses, ga_cfg, rng)
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repo.complete_run(
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run_id, total_cost=repo.total_cost(run_id), status="completed"
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)
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return run_id
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except Exception:
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repo.complete_run(
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run_id, total_cost=repo.total_cost(run_id), status="failed"
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)
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raise
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@@ -0,0 +1,77 @@
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from pathlib import Path
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import numpy as np
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import pandas as pd
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import pytest
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from multi_swarm.genome.hypothesis import ModelTier
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from multi_swarm.llm.client import CompletionResult
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from multi_swarm.orchestrator.run import RunConfig, run_phase1
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from multi_swarm.persistence.repository import Repository
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@pytest.fixture
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def synthetic_ohlcv():
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idx = pd.date_range("2024-01-01", periods=500, freq="1h", tz="UTC")
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close = 100 + np.cumsum(np.random.RandomState(0).normal(0.01, 1.0, 500))
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return pd.DataFrame(
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{
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"open": close,
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"high": close + 0.5,
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"low": close - 0.5,
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"close": close,
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"volume": 1.0,
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},
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index=idx,
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)
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@pytest.fixture
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def fake_llm(mocker):
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"""LLM mock che ritorna sempre una strategia valida."""
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fake = mocker.MagicMock()
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fake.complete.return_value = CompletionResult(
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text=(
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"```lisp\n(strategy "
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"(when (gt (indicator rsi 14) 70.0) (entry-short)) "
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"(when (lt (indicator rsi 14) 30.0) (entry-long)))\n```"
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),
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input_tokens=200,
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output_tokens=80,
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tier=ModelTier.C,
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model="qwen",
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)
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return fake
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def test_e2e_minimal_run_completes(
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tmp_path: Path,
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synthetic_ohlcv,
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fake_llm,
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mocker,
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):
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cfg = RunConfig(
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run_name="e2e-test",
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population_size=5,
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n_generations=2,
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elite_k=1,
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tournament_k=2,
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p_crossover=0.5,
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seed=42,
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model_tier=ModelTier.C,
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symbol="BTC/USDT",
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timeframe="1h",
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fees_bp=5.0,
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n_trials_dsr=10,
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db_path=tmp_path / "runs.db",
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)
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run_id = run_phase1(cfg, ohlcv=synthetic_ohlcv, llm=fake_llm)
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repo = Repository(db_path=tmp_path / "runs.db")
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run = repo.get_run(run_id)
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assert run["status"] == "completed"
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gens = repo.list_generations(run_id)
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assert len(gens) == 2
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evals = repo.list_evaluations(run_id)
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assert len(evals) >= 5 # almeno una popolazione
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