TradingAgents/tradingagents/graph/signal_processing.py

67 lines
2.5 KiB
Python

# TradingAgents/graph/signal_processing.py
from langchain_openai import ChatOpenAI
class SignalProcessor:
"""Processes trading signals to extract actionable decisions."""
def __init__(self, quick_thinking_llm: ChatOpenAI):
"""Initialize with an LLM for processing."""
self.quick_thinking_llm = quick_thinking_llm
def process_signal(self, full_signal: str) -> str:
"""
Process a full trading signal to extract the core decision.
Args:
full_signal: Complete trading signal text
Returns:
Extracted rating (BUY, OVERWEIGHT, HOLD, UNDERWEIGHT, or SELL)
"""
normalized = self._normalize_known_rating(full_signal)
if normalized:
return normalized
messages = [
(
"system",
"You are an efficient assistant that extracts the trading decision from analyst reports. "
"The report may express the final recommendation in English or Chinese. "
"Map Chinese ratings as follows: 买入=BUY, 增持=OVERWEIGHT, 持有=HOLD, 减持=UNDERWEIGHT, 卖出=SELL. "
"Extract the rating as exactly one of: BUY, OVERWEIGHT, HOLD, UNDERWEIGHT, SELL. "
"Output only the single rating word, nothing else.",
),
("human", full_signal),
]
return self.quick_thinking_llm.invoke(messages).content
@staticmethod
def _normalize_known_rating(full_signal: str) -> str | None:
text = (full_signal or "").upper()
english_markers = {
"FINAL TRANSACTION PROPOSAL: **BUY**": "BUY",
"FINAL TRANSACTION PROPOSAL: **OVERWEIGHT**": "OVERWEIGHT",
"FINAL TRANSACTION PROPOSAL: **HOLD**": "HOLD",
"FINAL TRANSACTION PROPOSAL: **UNDERWEIGHT**": "UNDERWEIGHT",
"FINAL TRANSACTION PROPOSAL: **SELL**": "SELL",
}
for marker, rating in english_markers.items():
if marker in text:
return rating
chinese_markers = {
"最终交易建议: **买入**": "BUY",
"最终交易建议: **增持**": "OVERWEIGHT",
"最终交易建议: **持有**": "HOLD",
"最终交易建议: **减持**": "UNDERWEIGHT",
"最终交易建议: **卖出**": "SELL",
}
for marker, rating in chinese_markers.items():
if marker in full_signal:
return rating
return None