From dacb3316fa5bdc03ca3fc14d39171b9d190a5607 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E9=99=88=E5=B0=91=E6=9D=B0?= <csjxxp@gmail.com>
Date: Thu, 9 Apr 2026 21:39:23 +0800
Subject: [PATCH] fix(orchestrator): code quality fixes in config and signals

- config: remove hardcoded absolute path for quant_backtest_path (now empty string)
- config: add llm_solo_penalty (0.7) and quant_solo_penalty (0.8) fields
- signals: SignalMerger now accepts OrchestratorConfig in __init__
- signals: use config.llm_solo_penalty / quant_solo_penalty instead of magic numbers
- signals: apply quant_weight_cap / llm_weight_cap as confidence upper bounds
- signals: both-None branch raises ValueError instead of returning ticker=""
- signals: replace assert with explicit ValueError for llm-None-when-quant-None
- signals: replace datetime.utcnow() with datetime.now(timezone.utc)
---
 orchestrator/config.py  |  5 ++++-
 orchestrator/signals.py | 32 +++++++++++++++++---------------
 2 files changed, 21 insertions(+), 16 deletions(-)

diff --git a/orchestrator/config.py b/orchestrator/config.py
index 0beb6c5e..9d3eaea5 100644
--- a/orchestrator/config.py
+++ b/orchestrator/config.py
@@ -3,9 +3,12 @@ from dataclasses import dataclass, field
 
 @dataclass
 class OrchestratorConfig:
-    quant_backtest_path: str = "/Users/chenshaojie/Downloads/quant_backtest"
+    # Must be set to the local quant backtest output directory before use
+    quant_backtest_path: str = ""
     trading_agents_config: dict = field(default_factory=dict)
     quant_weight_cap: float = 0.8   # quant 置信度上限
     llm_weight_cap: float = 0.9     # llm 置信度上限
     llm_batch_days: int = 7         # LLM 每隔几天运行一次（节省 API）
     cache_dir: str = "orchestrator/cache"  # LLM 信号缓存目录
+    llm_solo_penalty: float = 0.7   # LLM 单轨时的置信度折扣
+    quant_solo_penalty: float = 0.8  # Quant 单轨时的置信度折扣
diff --git a/orchestrator/signals.py b/orchestrator/signals.py
index f9549dcc..1ccecaa3 100644
--- a/orchestrator/signals.py
+++ b/orchestrator/signals.py
@@ -1,8 +1,10 @@
 import logging
 from dataclasses import dataclass, field
-from datetime import datetime
+from datetime import datetime, timezone
 from typing import Optional
 
+from orchestrator.config import OrchestratorConfig
+
 logger = logging.getLogger(__name__)
 
 
@@ -36,30 +38,27 @@ def _sign(x: float) -> int:
 
 
 class SignalMerger:
+    def __init__(self, config: OrchestratorConfig) -> None:
+        self._config = config
+
     def merge(self, quant: Optional[Signal], llm: Optional[Signal]) -> FinalSignal:
-        now = datetime.utcnow()
+        now = datetime.now(timezone.utc)
 
         # 两者均失败
         if quant is None and llm is None:
-            ticker = ""
-            return FinalSignal(
-                ticker=ticker,
-                direction=0,
-                confidence=0.0,
-                quant_signal=None,
-                llm_signal=None,
-                timestamp=now,
-            )
+            raise ValueError("both quant and llm signals are None")
 
         ticker = (quant or llm).ticker  # type: ignore[union-attr]
 
         # 只有 LLM（quant 失败）
         if quant is None:
-            assert llm is not None
+            if llm is None:
+                raise ValueError("llm signal is None when quant is None")
             return FinalSignal(
                 ticker=ticker,
                 direction=llm.direction,
-                confidence=llm.confidence * 0.7,
+                confidence=min(llm.confidence * self._config.llm_solo_penalty,
+                               self._config.llm_weight_cap),
                 quant_signal=None,
                 llm_signal=llm,
                 timestamp=now,
@@ -70,7 +69,8 @@ class SignalMerger:
             return FinalSignal(
                 ticker=ticker,
                 direction=quant.direction,
-                confidence=quant.confidence * 0.8,
+                confidence=min(quant.confidence * self._config.quant_solo_penalty,
+                               self._config.quant_weight_cap),
                 quant_signal=quant,
                 llm_signal=None,
                 timestamp=now,
@@ -88,7 +88,9 @@ class SignalMerger:
                 ticker,
             )
         total_conf = quant.confidence + llm.confidence
-        final_confidence = abs(weighted_sum) / total_conf if total_conf > 0 else 0.0
+        raw_confidence = abs(weighted_sum) / total_conf if total_conf > 0 else 0.0
+        final_confidence = min(raw_confidence, self._config.quant_weight_cap,
+                               self._config.llm_weight_cap)
 
         return FinalSignal(
             ticker=ticker,