"""
BrainSteel Fin — Decio Agent (v2)
Chief Strategist & Risk Officer
5-Pillar decision engine: Confiança < 70% → HOLD. BUY/SELL → SL 2% / TP 5%.
"""
import os, json, requests
from datetime import datetime

class DecioAgent:
    def __init__(self, brief_data):
        self.name = "Decio"
        self.brief_data = brief_data
        self.openrouter_key = os.getenv("DECIO_API_KEY", os.getenv("OPENROUTER_API_KEY", ""))
        self.openrouter_url = "https://openrouter.ai/api/v1/chat/completions"
        self.model = "deepseek/deepseek-v4-flash:free"

    # ── CORE RULE: confidence < 70% → always HOLD ─────────────────────────
    def _risk_filter(self, confidence, signal):
        """Décio is a mathematician: no confidence ≥70%, no operation."""
        return confidence < 70 or signal not in ("ALTA", "BAIXA")

    # ── Stop Loss / Take Profit ────────────────────────────────────────────────
    def _calc_levels(self, price, action):
        if action == "BUY":
            return round(price * 0.98, 2), round(price * 1.05, 2)
        elif action == "SELL":
            return round(price * 1.02, 2), round(price * 0.95, 2)
        return 0, 0

    # ── LLM Decision (with 5-pillar context) ─────────────────────────────────
    def _decide_llm(self):
        if not self.openrouter_key:
            return None

        briefing = self.brief_data.get("briefing_for_decio", self.brief_data.get("summary", ""))
        pillars = self.brief_data.get("_all_pillars", {})
        signal = self.brief_data.get("signal", "NEUTRO")
        confidence = self.brief_data.get("confidence", 50)
        price = self.brief_data.get("price", 0)

        # Build rich context for LLM
        tech = pillars.get("Technical", {})
        deriv = pillars.get("Derivatives", {})
        liq = pillars.get("Liquidations", {})
        fgi = pillars.get("FearGreed", {})
        sent = pillars.get("Sentiment", {})
        dom = pillars.get("Dominance", {})

        context = f"""Você é o Décio, estrategista soberano da BrainSteel Fin.
Analítico, frio, matemático. Preservação de capital > lucro rápido.

 BRIEFING DO BRIEF:
{briefing}

 PILARES DE ANÁLISE DO BRIEF:
  Technical: RSI={tech.get('rsi','?')} | MACD hist={tech.get('macd_histogram','?')} | Var 24h={tech.get('change_24h','?')}%
  Derivatives: Funding={deriv.get('funding_rate_pct','?')}%. Mark={deriv.get('mark_price','?')}
  Liquidations: Long Ratio={liq.get('long_ratio_pct','?')}%. Sentiment={liq.get('sentiment','?')}
  Fear&Greed: Index={fgi.get('fgi_value','?')} ({fgi.get('fgi_class','?')})
  Sentiment: {sent.get('sentiment_label','?')}
  BTC Dominance: {dom.get('btc_dominance_pct','?')}%
  Bullish signals: {self.brief_data.get('bullish_signals',0)} | Bearish: {self.brief_data.get('bearish_signals',0)}

 REGRAS OPERACIONAIS:
  Confiança < 70% → decisão SEMPRE HOLD (nunca opera)
Confiança ≥ 70% + sinal=ALTA → BUY
Confiança ≥ 70% + sinal=BAIXA → SELL
  BUY/SELL → Stop Loss = preço × 0.98 (-2%) | Take Profit = preço × 1.05 (+5%)
  HOLD → stop_loss=0, take_profit=0

 Responda EXCLUSIVAMENTE com JSON válido (sem texto fora do JSON):
{{"decision": "BUY|SELL|HOLD", "confidence": N, "justification": "frase curta", "stop_loss": N, "take_profit": N}}"""

        try:
            headers = {
                "Authorization": f"Bearer {self.openrouter_key}",
                "Content-Type": "application/json",
                "HTTP-Referer": "https://brainsteel.fin",
                "X-Title": "BrainSteel Fin"
            }
            payload = {
                "model": self.model,
                "messages": [{"role": "user", "content": context}],
                "max_tokens": 400
            }
            r = requests.post(self.openrouter_url, json=payload, headers=headers, timeout=30)
            if r.status_code == 200:
                resp_data = r.json()
                content = resp_data["choices"][0]["message"]["content"].strip()
                content = content.strip("` \n")
                if content.startswith("json"):
                    content = content[4:]
                data = json.loads(content)
                # Capture token usage
                usage = resp_data.get("usage", {})
                if usage:
                    try:
                        from agents.token_tracker import log_tokens
                        log_tokens("Decio", self.model, usage)
                    except:
                        pass
                # Apply risk filter
                if self._risk_filter(data.get("confidence", 0), signal):
                    data["decision"] = "HOLD"
                    data["justification"] = "Confiança < 70% — preservando capital"
                    data["stop_loss"] = 0
                    data["take_profit"] = 0
                return data
        except:
            pass
        return None

    # ── Local decision (fallback) ────────────────────────────────────────────
    def _local_decision(self):
        confidence = self.brief_data.get("confidence", 50)
        signal = self.brief_data.get("signal", "NEUTRO")
        price = self.brief_data.get("price", 0)
        net = self.brief_data.get("net_signal", 0)
        rsi = self.brief_data.get("rsi", 50)

        # Overbought/oversold confirmation
        if signal == "ALTA" and rsi > 75:
            # Extremely overbought — skip BUY even if signal says ALTA
            decision = "HOLD"
            sl, tp = 0, 0
            justification = "ALTA sinal cancelada — RSI overbought (75+)"
        elif signal == "BAIXA" and rsi < 25:
            decision = "HOLD"
            sl, tp = 0, 0
            justification = "BAIXA sinal cancelada — RSI oversold (25-)"
        elif self._risk_filter(confidence, signal):
            decision = "HOLD"
            sl, tp = 0, 0
            justification = f"Confiança {confidence}% < 70% — preservando capital"
        elif signal == "ALTA":
            decision = "BUY"
            sl, tp = self._calc_levels(price, "BUY")
            justification = f"Sinal ALTA conf {confidence}%, RSI {rsi}, net {net:+d}"
        elif signal == "BAIXA":
            decision = "SELL"
            sl, tp = self._calc_levels(price, "SELL")
            justification = f"Sinal BAIXA conf {confidence}%, RSI {rsi}, net {net:+d}"
        else:
            decision = "HOLD"
            sl, tp = 0, 0
            justification = "Sinal neutro — aguardando clareza"

        return {
            "decision": decision,
            "confidence": confidence,
            "justification": justification,
            "stop_loss": sl,
            "take_profit": tp,
            "signal": signal
        }

    # ── Dynamic Allocation (Kelly Scale) ─────────────────────────────────────
    def _calc_allocation(self, confidence, decision):
        """Calcula a alocação de banca dinâmica baseada na confiança do sinal."""
        if decision not in ("BUY", "SELL") or confidence < 70:
            return 0
        if confidence >= 90:
            return 35  # Mão forte (alta convicção)
        elif confidence >= 80:
            return 25  # Mão padrão
        else:
            return 15  # Mão leve (cautela, 70-79%)

    # ── Main run ───────────────────────────────────────────────────────────
    def run(self):
        llm_result = self._decide_llm()
        result = llm_result if llm_result else self._local_decision()

        price = self.brief_data.get("price", 0)
        conf = result.get("confidence", 50)
        action = result["decision"]
        amount_pct = self._calc_allocation(conf, action)

        output = {
            "action": action,
            "amount_pct": amount_pct,
            "stop_loss": result.get("stop_loss", 0),
            "take_profit": result.get("take_profit", 0),
            "confidence": conf,
            "justification": result.get("justification", "")[:200],
            "signal": result.get("signal", self.brief_data.get("signal", "NEUTRO")),
            "price": price,
            "timestamp": datetime.now().isoformat()
        }

        decision_str = f"{output['action']} | Conf: {output['confidence']}%"
        if output["action"] in ("BUY", "SELL"):
            decision_str += f" | SL: ${output['stop_loss']:,.0f} | TP: ${output['take_profit']:,.0f}"
        decision_str += f" | {output['justification'][:80]}"

        output["decision"] = decision_str
        output["agent"] = "Decio"
        return output