Multi_Swarm_Coevolutive/src/multi_swarm/orchestrator/run.py

"""End-to-end orchestrator per un run di Phase 1.

Pipeline per ogni generazione:

    population -> hypothesis_agent.propose -> falsification + adversarial
              -> compute_fitness -> persistenza -> next_generation

Tutto e' loggato sulla repository SQLite (runs, generations, genomes,
evaluations, cost_records, adversarial_findings) cosi' che la GUI Streamlit
possa leggere lo stato a run terminato (o in corso).
"""

from __future__ import annotations

import random
from dataclasses import dataclass, field
from pathlib import Path

import pandas as pd  # type: ignore[import-untyped]

from ..agents.adversarial import AdversarialAgent
from ..agents.falsification import FalsificationAgent
from ..agents.hypothesis import HypothesisAgent
from ..agents.market_summary import build_market_summary
from ..ga.fitness import compute_fitness
from ..ga.initial import build_initial_population
from ..ga.loop import GAConfig, next_generation
from ..ga.summary import generation_summary
from ..genome.hypothesis import ModelTier
from ..llm.client import LLMClient
from ..llm.cost_tracker import CostTracker
from ..persistence.repository import Repository


@dataclass
class RunConfig:
    """Parametri di un run end-to-end della Phase 1."""

    run_name: str
    population_size: int = 20
    n_generations: int = 10
    elite_k: int = 2
    tournament_k: int = 3
    p_crossover: float = 0.5
    seed: int = 42
    model_tier: ModelTier = ModelTier.C
    symbol: str = "BTC/USDT"
    timeframe: str = "1h"
    fees_bp: float = 5.0
    n_trials_dsr: int = 50
    db_path: Path = field(default_factory=lambda: Path("./runs.db"))
    prompt_mutation_weight: float = 0.0  # Phase 2.5: opt-in LLM mutator


def run_phase1(
    cfg: RunConfig,
    ohlcv: pd.DataFrame,
    llm: LLMClient,
) -> str:
    """Esegue il loop GA end-to-end e ritorna l'``id`` del run.

    Su qualunque eccezione marca il run come ``failed`` e rilancia.
    """
    rng = random.Random(cfg.seed)

    repo = Repository(cfg.db_path)
    repo.init_schema()
    config_dict = {
        **cfg.__dict__,
        "db_path": str(cfg.db_path),
        "model_tier": cfg.model_tier.value,
    }
    run_id = repo.create_run(name=cfg.run_name, config=config_dict)

    market = build_market_summary(ohlcv, symbol=cfg.symbol, timeframe=cfg.timeframe)

    hypothesis_agent = HypothesisAgent(llm=llm)
    falsification_agent = FalsificationAgent(
        fees_bp=cfg.fees_bp, n_trials_dsr=cfg.n_trials_dsr
    )
    adversarial_agent = AdversarialAgent(fees_bp=cfg.fees_bp)
    cost_tracker = CostTracker()

    population = build_initial_population(
        k=cfg.population_size, model_tier=cfg.model_tier, rng=rng
    )
    fitnesses: dict[str, float] = {}

    ga_cfg = GAConfig(
        population_size=cfg.population_size,
        elite_k=cfg.elite_k,
        tournament_k=cfg.tournament_k,
        p_crossover=cfg.p_crossover,
        prompt_mutation_weight=cfg.prompt_mutation_weight,
    )

    try:
        for gen in range(cfg.n_generations):
            for genome in population:
                if genome.id in fitnesses:
                    continue  # elite gia' valutata in generazione precedente
                repo.save_genome(run_id=run_id, generation_idx=gen, genome=genome)
                proposal = hypothesis_agent.propose(genome, market)
                # Registra costo per OGNI completion (incluse retry).
                for completion in proposal.completions:
                    cost_record = cost_tracker.record(
                        input_tokens=completion.input_tokens,
                        output_tokens=completion.output_tokens,
                        tier=completion.tier,
                        run_id=run_id,
                        agent_id=genome.id,
                    )
                    repo.save_cost_record(
                        run_id=run_id,
                        agent_id=genome.id,
                        tier=cost_record.tier.value,
                        input_tokens=cost_record.input_tokens,
                        output_tokens=cost_record.output_tokens,
                        cost_usd=cost_record.cost_usd,
                    )

                if proposal.strategy is None:
                    repo.save_evaluation(
                        run_id=run_id,
                        genome_id=genome.id,
                        fitness=0.0,
                        dsr=0.0,
                        dsr_pvalue=1.0,
                        sharpe=0.0,
                        max_dd=0.0,
                        total_return=0.0,
                        n_trades=0,
                        parse_error=proposal.parse_error,
                        raw_text=proposal.raw_text,
                    )
                    fitnesses[genome.id] = 0.0
                    continue

                fals = falsification_agent.evaluate(proposal.strategy, ohlcv)
                adv = adversarial_agent.review(proposal.strategy, ohlcv)
                for finding in adv.findings:
                    repo.save_adversarial_finding(
                        run_id=run_id,
                        genome_id=genome.id,
                        name=finding.name,
                        severity=finding.severity.value,
                        detail=finding.detail,
                    )
                fit = compute_fitness(fals, adv)
                repo.save_evaluation(
                    run_id=run_id,
                    genome_id=genome.id,
                    fitness=fit,
                    dsr=fals.dsr,
                    dsr_pvalue=fals.dsr_pvalue,
                    sharpe=fals.sharpe,
                    max_dd=fals.max_drawdown,
                    total_return=fals.total_return,
                    n_trades=fals.n_trades,
                    parse_error=None,
                    raw_text=proposal.raw_text,
                )
                fitnesses[genome.id] = fit

            gen_fitnesses = [fitnesses[g.id] for g in population]
            summary = generation_summary(gen_fitnesses, n_bins=10)
            repo.save_generation_summary(
                run_id=run_id,
                generation_idx=gen,
                n_genomes=len(population),
                fitness_median=summary["median"],
                fitness_max=summary["max"],
                fitness_p90=summary["p90"],
                entropy=summary["entropy"],
            )

            if gen < cfg.n_generations - 1:
                population = next_generation(
                    population, fitnesses, ga_cfg, rng,
                    llm=llm if cfg.prompt_mutation_weight > 0 else None,
                )

        repo.complete_run(
            run_id, total_cost=repo.total_cost(run_id), status="completed"
        )
        return run_id
    except Exception:
        repo.complete_run(
            run_id, total_cost=repo.total_cost(run_id), status="failed"
        )
        raise