103 lines
3.2 KiB
Python
103 lines
3.2 KiB
Python
import logging
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from dataclasses import dataclass, field
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from datetime import datetime, timezone
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from typing import Optional
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from orchestrator.config import OrchestratorConfig
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logger = logging.getLogger(__name__)
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@dataclass
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class Signal:
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ticker: str
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direction: int # +1 买入, -1 卖出, 0 持有
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confidence: float # 0.0 ~ 1.0
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source: str # "quant" | "llm"
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timestamp: datetime
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metadata: dict = field(default_factory=dict) # 原始输出，用于调试
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@dataclass
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class FinalSignal:
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ticker: str
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direction: int # sign(quant_dir×quant_conf + llm_dir×llm_conf)，sign(0)→0(HOLD)
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confidence: float # abs(weighted_sum) / total_conf
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quant_signal: Optional[Signal]
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llm_signal: Optional[Signal]
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timestamp: datetime
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def _sign(x: float) -> int:
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"""Return +1, -1, or 0."""
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if x > 0:
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return 1
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elif x < 0:
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return -1
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return 0
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class SignalMerger:
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def __init__(self, config: OrchestratorConfig) -> None:
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self._config = config
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def merge(self, quant: Optional[Signal], llm: Optional[Signal]) -> FinalSignal:
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now = datetime.now(timezone.utc)
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# 两者均失败
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if quant is None and llm is None:
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raise ValueError("both quant and llm signals are None")
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ticker = (quant or llm).ticker # type: ignore[union-attr]
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# 只有 LLM（quant 失败）
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if quant is None:
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if llm is None:
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raise ValueError("llm signal is None when quant is None")
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return FinalSignal(
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ticker=ticker,
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direction=llm.direction,
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confidence=min(llm.confidence * self._config.llm_solo_penalty,
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self._config.llm_weight_cap),
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quant_signal=None,
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llm_signal=llm,
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timestamp=now,
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)
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# 只有 Quant（llm 失败）
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if llm is None:
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return FinalSignal(
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ticker=ticker,
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direction=quant.direction,
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confidence=min(quant.confidence * self._config.quant_solo_penalty,
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self._config.quant_weight_cap),
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quant_signal=quant,
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llm_signal=None,
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timestamp=now,
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)
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# 两者都有：加权合并
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weighted_sum = (
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quant.direction * quant.confidence
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+ llm.direction * llm.confidence
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)
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final_direction = _sign(weighted_sum)
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if final_direction == 0:
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logger.info(
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"SignalMerger: weighted_sum=0 for %s — signals cancel out, HOLD",
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ticker,
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)
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total_conf = quant.confidence + llm.confidence
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raw_confidence = abs(weighted_sum) / total_conf if total_conf > 0 else 0.0
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final_confidence = min(raw_confidence, self._config.quant_weight_cap,
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self._config.llm_weight_cap)
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return FinalSignal(
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ticker=ticker,
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direction=final_direction,
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confidence=final_confidence,
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quant_signal=quant,
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llm_signal=llm,
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timestamp=now,
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)
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