198 lines
8.8 KiB
Python
198 lines
8.8 KiB
Python
"""
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BrainSteel Fin — Decio Agent (v2)
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Chief Strategist & Risk Officer
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5-Pillar decision engine: Confiança < 70% → HOLD. BUY/SELL → SL 2% / TP 5%.
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"""
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import os, json, requests
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from datetime import datetime
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class DecioAgent:
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def __init__(self, brief_data):
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self.name = "Decio"
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self.brief_data = brief_data
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self.openrouter_key = os.getenv("DECIO_API_KEY", os.getenv("OPENROUTER_API_KEY", ""))
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self.openrouter_url = "https://openrouter.ai/api/v1/chat/completions"
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self.model = "deepseek/deepseek-v4-flash:free"
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# ── CORE RULE: confidence < 70% → always HOLD ─────────────────────────
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def _risk_filter(self, confidence, signal):
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"""Décio is a mathematician: no confidence ≥70%, no operation."""
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return confidence < 70 or signal not in ("ALTA", "BAIXA")
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# ── Stop Loss / Take Profit ────────────────────────────────────────────────
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def _calc_levels(self, price, action):
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if action == "BUY":
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return round(price * 0.98, 2), round(price * 1.05, 2)
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elif action == "SELL":
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return round(price * 1.02, 2), round(price * 0.95, 2)
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return 0, 0
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# ── LLM Decision (with 5-pillar context) ─────────────────────────────────
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def _decide_llm(self):
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if not self.openrouter_key:
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return None
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briefing = self.brief_data.get("briefing_for_decio", self.brief_data.get("summary", ""))
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pillars = self.brief_data.get("_all_pillars", {})
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signal = self.brief_data.get("signal", "NEUTRO")
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confidence = self.brief_data.get("confidence", 50)
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price = self.brief_data.get("price", 0)
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# Build rich context for LLM
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tech = pillars.get("Technical", {})
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deriv = pillars.get("Derivatives", {})
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liq = pillars.get("Liquidations", {})
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fgi = pillars.get("FearGreed", {})
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sent = pillars.get("Sentiment", {})
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dom = pillars.get("Dominance", {})
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context = f"""Você é o Décio, estrategista soberano da BrainSteel Fin.
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Analítico, frio, matemático. Preservação de capital > lucro rápido.
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BRIEFING DO BRIEF:
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{briefing}
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PILARES DE ANÁLISE DO BRIEF:
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Technical: RSI={tech.get('rsi','?')} | MACD hist={tech.get('macd_histogram','?')} | Var 24h={tech.get('change_24h','?')}%
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Derivatives: Funding={deriv.get('funding_rate_pct','?')}%. Mark={deriv.get('mark_price','?')}
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Liquidations: Long Ratio={liq.get('long_ratio_pct','?')}%. Sentiment={liq.get('sentiment','?')}
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Fear&Greed: Index={fgi.get('fgi_value','?')} ({fgi.get('fgi_class','?')})
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Sentiment: {sent.get('sentiment_label','?')}
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BTC Dominance: {dom.get('btc_dominance_pct','?')}%
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Bullish signals: {self.brief_data.get('bullish_signals',0)} | Bearish: {self.brief_data.get('bearish_signals',0)}
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REGRAS OPERACIONAIS:
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Confiança < 70% → decisão SEMPRE HOLD (nunca opera)
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Confiança ≥ 70% + sinal=ALTA → BUY
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Confiança ≥ 70% + sinal=BAIXA → SELL
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BUY/SELL → Stop Loss = preço × 0.98 (-2%) | Take Profit = preço × 1.05 (+5%)
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HOLD → stop_loss=0, take_profit=0
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Responda EXCLUSIVAMENTE com JSON válido (sem texto fora do JSON):
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{{"decision": "BUY|SELL|HOLD", "confidence": N, "justification": "frase curta", "stop_loss": N, "take_profit": N}}"""
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try:
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headers = {
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"Authorization": f"Bearer {self.openrouter_key}",
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"Content-Type": "application/json",
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"HTTP-Referer": "https://brainsteel.fin",
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"X-Title": "BrainSteel Fin"
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}
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payload = {
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"model": self.model,
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"messages": [{"role": "user", "content": context}],
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"max_tokens": 400
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}
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r = requests.post(self.openrouter_url, json=payload, headers=headers, timeout=30)
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if r.status_code == 200:
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resp_data = r.json()
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content = resp_data["choices"][0]["message"]["content"].strip()
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content = content.strip("` \n")
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if content.startswith("json"):
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content = content[4:]
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data = json.loads(content)
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# Capture token usage
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usage = resp_data.get("usage", {})
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if usage:
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try:
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from agents.token_tracker import log_tokens
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log_tokens("Decio", self.model, usage)
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except:
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pass
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# Apply risk filter
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if self._risk_filter(data.get("confidence", 0), signal):
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data["decision"] = "HOLD"
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data["justification"] = "Confiança < 70% — preservando capital"
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data["stop_loss"] = 0
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data["take_profit"] = 0
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return data
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except:
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pass
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return None
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# ── Local decision (fallback) ────────────────────────────────────────────
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def _local_decision(self):
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confidence = self.brief_data.get("confidence", 50)
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signal = self.brief_data.get("signal", "NEUTRO")
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price = self.brief_data.get("price", 0)
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net = self.brief_data.get("net_signal", 0)
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rsi = self.brief_data.get("rsi", 50)
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# Overbought/oversold confirmation
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if signal == "ALTA" and rsi > 75:
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# Extremely overbought — skip BUY even if signal says ALTA
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decision = "HOLD"
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sl, tp = 0, 0
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justification = "ALTA sinal cancelada — RSI overbought (75+)"
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elif signal == "BAIXA" and rsi < 25:
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decision = "HOLD"
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sl, tp = 0, 0
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justification = "BAIXA sinal cancelada — RSI oversold (25-)"
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elif self._risk_filter(confidence, signal):
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decision = "HOLD"
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sl, tp = 0, 0
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justification = f"Confiança {confidence}% < 70% — preservando capital"
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elif signal == "ALTA":
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decision = "BUY"
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sl, tp = self._calc_levels(price, "BUY")
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justification = f"Sinal ALTA conf {confidence}%, RSI {rsi}, net {net:+d}"
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elif signal == "BAIXA":
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decision = "SELL"
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sl, tp = self._calc_levels(price, "SELL")
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justification = f"Sinal BAIXA conf {confidence}%, RSI {rsi}, net {net:+d}"
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else:
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decision = "HOLD"
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sl, tp = 0, 0
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justification = "Sinal neutro — aguardando clareza"
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return {
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"decision": decision,
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"confidence": confidence,
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"justification": justification,
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"stop_loss": sl,
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"take_profit": tp,
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"signal": signal
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}
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# ── Dynamic Allocation (Kelly Scale) ─────────────────────────────────────
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def _calc_allocation(self, confidence, decision):
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"""Calcula a alocação de banca dinâmica baseada na confiança do sinal."""
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if decision not in ("BUY", "SELL") or confidence < 70:
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return 0
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if confidence >= 90:
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return 35 # Mão forte (alta convicção)
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elif confidence >= 80:
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return 25 # Mão padrão
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else:
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return 15 # Mão leve (cautela, 70-79%)
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# ── Main run ───────────────────────────────────────────────────────────
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def run(self):
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llm_result = self._decide_llm()
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result = llm_result if llm_result else self._local_decision()
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price = self.brief_data.get("price", 0)
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conf = result.get("confidence", 50)
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action = result["decision"]
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amount_pct = self._calc_allocation(conf, action)
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output = {
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"action": action,
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"amount_pct": amount_pct,
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"stop_loss": result.get("stop_loss", 0),
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"take_profit": result.get("take_profit", 0),
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"confidence": conf,
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"justification": result.get("justification", "")[:200],
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"signal": result.get("signal", self.brief_data.get("signal", "NEUTRO")),
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"price": price,
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"timestamp": datetime.now().isoformat()
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}
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decision_str = f"{output['action']} | Conf: {output['confidence']}%"
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if output["action"] in ("BUY", "SELL"):
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decision_str += f" | SL: ${output['stop_loss']:,.0f} | TP: ${output['take_profit']:,.0f}"
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decision_str += f" | {output['justification'][:80]}"
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output["decision"] = decision_str
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output["agent"] = "Decio"
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return output |