Adriano
44eb6436c1
Sostituisce la grammatica S-expression con uno schema JSON stretto. La grammatica S-expression falliva il parsing nel 64% delle generazioni del modello Qwen3-235B sul run reale; JSON e' nativo per gli LLM moderni e si parsa con json.loads. Cambiamenti principali: - grammar.py: costanti rinominate LOGICAL_OPS / COMPARATOR_OPS / CROSSOVER_OPS / ACTION_VALUES / KIND_VALUES. - parser.py: nuovo AST a dataclass tipizzato (OpNode, IndicatorNode, FeatureNode, LiteralNode, Rule, Strategy); parse_strategy ora consuma JSON tramite json.loads. - validator.py: walk dispatchato per tipo (isinstance) invece di pattern-matching su 'kind'; arity check su operatori e indicator. - compiler.py: traversal del nuovo AST tipizzato, dispatch per isinstance; logica indicator/feature/literal invariata. - hypothesis.py: prompt SYSTEM riscritto con esempi JSON e vincoli espliciti su no-nesting; estrazione via fence ```json``` + fallback brace-balanced. - __init__.py: re-export pubblico delle entita' del protocollo. - Tutti i test (parser, validator, compiler, hypothesis_agent, falsification, adversarial, e2e, smoke_run) migrati a JSON. - Rimossa dipendenza sexpdata da pyproject.toml + uv.lock. Test: 135 passed (era 122; aggiunti casi parser/validator). ruff + mypy strict clean. Smoke run end-to-end OK. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
77 lines
2.2 KiB
Python
77 lines
2.2 KiB
Python
from __future__ import annotations
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import json
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from pathlib import Path
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import numpy as np
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import pandas as pd # type: ignore[import-untyped]
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from multi_swarm.genome.hypothesis import HypothesisAgentGenome, 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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_MOCK_STRATEGY = json.dumps(
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{
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"rules": [
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{
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"condition": {
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"op": "gt",
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"args": [
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{"kind": "indicator", "name": "rsi", "params": [14]},
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{"kind": "literal", "value": 70.0},
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],
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},
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"action": "entry-short",
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},
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{
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"condition": {
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"op": "lt",
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"args": [
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{"kind": "indicator", "name": "rsi", "params": [14]},
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{"kind": "literal", "value": 30.0},
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],
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},
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"action": "entry-long",
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},
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]
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}
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)
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class MockLLMClient:
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def complete(
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self, genome: HypothesisAgentGenome, system: str, user: str,
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max_tokens: int = 2000,
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) -> CompletionResult:
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text = "```json\n" + _MOCK_STRATEGY + "\n```"
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return CompletionResult(
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text=text, input_tokens=120, output_tokens=60,
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tier=genome.model_tier, model="mock",
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)
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def main() -> None:
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idx = pd.date_range("2024-01-01", periods=1000, freq="1h", tz="UTC")
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close = 100 + np.cumsum(np.random.RandomState(0).normal(0.01, 1.0, 1000))
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ohlcv = pd.DataFrame(
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{"open": close, "high": close + 0.5, "low": close - 0.5, "close": close, "volume": 1.0},
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index=idx,
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)
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cfg = RunConfig(
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run_name="smoke",
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population_size=3,
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n_generations=1,
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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=0,
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model_tier=ModelTier.C,
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db_path=Path("./runs.db"),
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)
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run_id = run_phase1(cfg, ohlcv=ohlcv, llm=MockLLMClient()) # type: ignore[arg-type]
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print(f"Smoke run completed: {run_id}")
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if __name__ == "__main__":
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main()
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