agents/analysts.py

from agents.base import Agent
from llm.prompts import (
    TECHNICAL_ANALYST_SYSTEM,
    NEWS_ANALYST_SYSTEM,
    SENTIMENT_ANALYST_SYSTEM,
    build_technical_analyst_prompt,
    build_news_analyst_prompt,
    build_sentiment_analyst_prompt,
)


class TechnicalAnalyst(Agent):
    def __init__(self, llm_client):
        super().__init__("TechnicalAnalyst", TECHNICAL_ANALYST_SYSTEM, llm_client)

    def build_prompt(self, context: dict) -> str:
        return build_technical_analyst_prompt(context)

    def parse(self, raw: str) -> dict:
        result = super().parse(raw)
        signal = result.get("signal", "NEUTRAL").upper()
        if signal not in ("BULLISH", "BEARISH", "NEUTRAL"):
            signal = "NEUTRAL"
        return {
            "signal": signal,
            "strength": float(result.get("strength", 0.5)),
            "key_levels": result.get("key_levels", {}),
            "summary": str(result.get("summary", "")),
        }


class NewsAnalyst(Agent):
    def __init__(self, llm_client):
        super().__init__("NewsAnalyst", NEWS_ANALYST_SYSTEM, llm_client)

    def build_prompt(self, context: dict) -> str:
        return build_news_analyst_prompt(
            context.get("news", []),
            context.get("asset", "BTC/USDT"),
        )

    def parse(self, raw: str) -> dict:
        result = super().parse(raw)
        sentiment = result.get("sentiment", "NEUTRAL").upper()
        if sentiment not in ("POSITIVE", "NEGATIVE", "NEUTRAL"):
            sentiment = "NEUTRAL"
        return {
            "sentiment": sentiment,
            "score": float(result.get("score", 0.0)),
            "key_themes": result.get("key_themes", []),
            "summary": str(result.get("summary", "")),
        }


class SentimentAnalyst(Agent):
    def __init__(self, llm_client):
        super().__init__("SentimentAnalyst", SENTIMENT_ANALYST_SYSTEM, llm_client)

    def build_prompt(self, context: dict) -> str:
        return build_sentiment_analyst_prompt(
            context.get("onchain", {}),
            context.get("asset", "BTC/USDT"),
        )

    def parse(self, raw: str) -> dict:
        result = super().parse(raw)
        return {
            "sentiment": str(result.get("sentiment", "NEUTRAL")),
            "score": float(result.get("score", 0.0)),
            "funding_bias": str(result.get("funding_bias", "NEUTRAL")),
            "summary": str(result.get("summary", "")),
        }