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>
87 lines
2.7 KiB
Python
87 lines
2.7 KiB
Python
import json
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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.agents.falsification import FalsificationAgent, FalsificationReport
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from multi_swarm.protocol.parser import parse_strategy
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@pytest.fixture
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def trending_ohlcv() -> pd.DataFrame:
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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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def test_falsification_returns_report(trending_ohlcv: pd.DataFrame) -> None:
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src = 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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ast = parse_strategy(src)
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agent = FalsificationAgent(fees_bp=5.0, n_trials_dsr=20)
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report = agent.evaluate(ast, trending_ohlcv)
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assert isinstance(report, FalsificationReport)
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assert isinstance(report.sharpe, float)
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assert isinstance(report.dsr, float)
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assert 0.0 <= report.dsr <= 1.0
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assert isinstance(report.max_drawdown, float)
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assert isinstance(report.n_trades, int)
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def test_falsification_zero_trades_returns_zero_metrics(trending_ohlcv: pd.DataFrame) -> None:
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src = 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": "feature", "name": "close"},
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{"kind": "literal", "value": 1e9},
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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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ast = parse_strategy(src)
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agent = FalsificationAgent(fees_bp=5.0, n_trials_dsr=20)
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report = agent.evaluate(ast, trending_ohlcv)
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assert report.n_trades == 0
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assert report.sharpe == 0.0
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