# TradingAgents/graph/signal_processing.py


from langchain_anthropic import ChatAnthropic
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_openai import ChatOpenAI


class SignalProcessor:
    """Processes trading signals to extract actionable decisions."""

    def __init__(
        self,
        quick_thinking_llm: ChatOpenAI | ChatAnthropic | ChatGoogleGenerativeAI,
    ):
        """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 decision (BUY, SELL, or HOLD)
        """
        messages = [
            (
                "system",
                "You are an efficient assistant designed to analyze paragraphs or financial reports provided by a group of analysts. Your task is to extract the investment decision: SELL, BUY, or HOLD. Provide only the extracted decision (SELL, BUY, or HOLD) as your output, without adding any additional text or information.",
            ),
            ("human", full_signal),
        ]

        result = self.quick_thinking_llm.invoke(messages).content
        # Ensure we return a string
        if isinstance(result, str):
            return result
        elif isinstance(result, list):
            return str(result)
        else:
            return str(result)