"""News & Sentiment Agent.

Two modes:
- **LLM mode** (Phase 2): when ANTHROPIC_API_KEY or OPENAI_API_KEY is set,
  an LLM estimates sentiment from the symbol's recent price context.
- **Mock mode** (default): deterministic sentiment derived from recent price
  action, seeded per symbol per day.

Both return the identical AgentResult shape, and any LLM failure silently
falls back to mock — the pipeline never breaks on a vendor outage.
"""
import random
from datetime import date

from .base import Agent, AgentResult
from ..services.llm import llm_available, score_sentiment


class SentimentAgent(Agent):
    name = "sentiment"

    def run(self, context: dict) -> AgentResult:
        symbol = context["symbol"]
        closes = [b["close"] for b in context["history"]]
        change_5d = (closes[-1] / closes[-6] - 1) if len(closes) > 6 else 0.0
        change_20d = (closes[-1] / closes[-21] - 1) if len(closes) > 21 else 0.0

        llm_result = None
        if llm_available():
            price_context = (
                f"last close {closes[-1]:.2f}, 5-day change {change_5d * 100:+.1f}%, "
                f"20-day change {change_20d * 100:+.1f}%"
            )
            llm_result = score_sentiment(symbol, price_context)

        if llm_result:
            raw = llm_result["sentiment"]
            label = llm_result["label"]
            source = "llm"
            note = f"LLM sentiment {label} ({raw:+.2f}): {llm_result['reason']}"
        else:
            rng = random.Random(f"{symbol}:{date.today().isoformat()}:news")
            raw = max(-1.0, min(1.0, change_5d * 8 + rng.uniform(-0.15, 0.15)))
            label = "positive" if raw > 0.15 else "negative" if raw < -0.15 else "neutral"
            source = "mock"
            note = f"Sentiment {label} ({raw:+.2f}) — mock model (set an LLM API key for live analysis)"

        score = round((raw + 1) / 2 * 100, 1)  # -1..1 → 0..100
        direction = "CALL" if raw > 0.15 else "PUT" if raw < -0.15 else None
        return AgentResult(
            agent=self.name,
            score=score,
            direction=direction,
            data={"sentiment": round(raw, 3), "label": label, "source": source},
            notes=[note],
        )