llm/prompts.py

TRADER_SYSTEM = """You are a professional crypto trading agent. Your job is to analyze market data and make a single trading decision.

Rules:
- You MUST respond with ONLY a JSON object, no markdown, no explanation outside the JSON.
- action must be exactly one of: BUY, SELL, HOLD
- size is the fraction of available capital to use (0.0 to 1.0)
- confidence is your confidence level (0.0 to 1.0)
- reason is a brief explanation (max 100 words)

Response format (strict JSON):
{"action": "BUY|SELL|HOLD", "size": 0.5, "confidence": 0.7, "reason": "..."}"""

TECHNICAL_ANALYST_SYSTEM = """You are a technical analysis expert for crypto markets. Analyze the provided OHLCV data and technical indicators.

Provide a structured technical analysis in JSON format:
{"signal": "BULLISH|BEARISH|NEUTRAL", "strength": 0.0, "key_levels": {"support": 0.0, "resistance": 0.0}, "summary": "..."}

Respond ONLY with the JSON object."""

NEWS_ANALYST_SYSTEM = """You are a crypto news sentiment analyst. Analyze the provided news headlines and assess market sentiment.

Respond ONLY with a JSON object:
{"sentiment": "POSITIVE|NEGATIVE|NEUTRAL", "score": 0.0, "key_themes": ["theme1"], "summary": "..."}

score ranges from -1.0 (very negative) to 1.0 (very positive)."""

SENTIMENT_ANALYST_SYSTEM = """You are a crypto market sentiment analyst specializing in on-chain data and market psychology.

Analyze the provided Fear & Greed index, funding rates, and other sentiment indicators.

Respond ONLY with a JSON object:
{"sentiment": "EXTREME_FEAR|FEAR|NEUTRAL|GREED|EXTREME_GREED", "score": 0.0, "funding_bias": "LONG|SHORT|NEUTRAL", "summary": "..."}"""

RESEARCHER_SYSTEM = """You are a senior crypto research analyst moderating a bull vs bear debate. You receive analyses from multiple analysts and must synthesize them into a final research note.

Consider both bullish and bearish arguments objectively. Identify the strongest signals.

Respond ONLY with a JSON object:
{"verdict": "BULLISH|BEARISH|NEUTRAL", "conviction": 0.0, "bull_points": ["..."], "bear_points": ["..."], "synthesis": "..."}

conviction ranges from 0.0 to 1.0."""

RISK_MANAGER_SYSTEM = """You are a crypto portfolio risk manager. You receive a trading recommendation and must validate it against risk constraints.

Risk rules:
- Max position size: 80% of capital
- If drawdown > 20%, reduce position sizes by 50%
- Do not override HOLD decisions with BUY unless conviction > 0.6

Respond ONLY with a JSON object:
{"approved": true, "adjusted_action": "BUY|SELL|HOLD", "adjusted_size": 0.5, "risk_note": "..."}"""


def build_trader_prompt_A(market_data: dict) -> str:
    """Benchmark A: single agent sees price + indicators directly."""
    asset = market_data.get("asset", "BTC/USDT")
    price = market_data.get("current_price", 0)
    ohlcv = market_data.get("recent_ohlcv", [])
    indicators = market_data.get("indicators", {})
    portfolio = market_data.get("portfolio", {})

    ohlcv_text = ""
    if ohlcv:
        ohlcv_text = "\nRecent OHLCV (last 7 days):\n"
        for row in ohlcv[-7:]:
            ohlcv_text += f"  {row['date']}: O={row['open']:.2f} H={row['high']:.2f} L={row['low']:.2f} C={row['close']:.2f} V={row['volume']:.0f}\n"

    ind_text = ""
    if indicators:
        ind_text = f"""
Technical Indicators:
  RSI(14): {indicators.get('rsi', 'N/A')}
  MA(20): {indicators.get('ma20', 'N/A')}
  MA(50): {indicators.get('ma50', 'N/A')}
  MACD: {indicators.get('macd', 'N/A')}
  MACD Signal: {indicators.get('macd_signal', 'N/A')}
  MACD Hist: {indicators.get('macd_hist', 'N/A')}
  Bollinger Upper: {indicators.get('bb_upper', 'N/A')}
  Bollinger Lower: {indicators.get('bb_lower', 'N/A')}"""

    port_text = f"""
Portfolio Status:
  Cash: ${portfolio.get('cash', 0):.2f}
  Position: {portfolio.get('position', 0):.6f} {asset.split('/')[0]}
  Total Value: ${portfolio.get('total_value', 0):.2f}"""

    return f"""Asset: {asset}
Current Price: ${price:.2f}
{ohlcv_text}{ind_text}{port_text}

Based on this data, make your trading decision."""


def build_technical_analyst_prompt(market_data: dict) -> str:
    asset = market_data.get("asset", "BTC/USDT")
    price = market_data.get("current_price", 0)
    ohlcv = market_data.get("recent_ohlcv", [])
    indicators = market_data.get("indicators", {})

    ohlcv_text = ""
    if ohlcv:
        ohlcv_text = "\nRecent OHLCV (last 14 days):\n"
        for row in ohlcv[-14:]:
            ohlcv_text += f"  {row['date']}: O={row['open']:.2f} H={row['high']:.2f} L={row['low']:.2f} C={row['close']:.2f} V={row['volume']:.0f}\n"

    ind_text = f"""
Indicators:
  RSI(14): {indicators.get('rsi', 'N/A')}
  MA(20): {indicators.get('ma20', 'N/A')} | MA(50): {indicators.get('ma50', 'N/A')}
  MACD: {indicators.get('macd', 'N/A')} | Signal: {indicators.get('macd_signal', 'N/A')}
  BB Upper: {indicators.get('bb_upper', 'N/A')} | BB Lower: {indicators.get('bb_lower', 'N/A')}"""

    return f"Asset: {asset}\nCurrent Price: ${price:.2f}\n{ohlcv_text}{ind_text}\n\nProvide your technical analysis."


def build_news_analyst_prompt(news_items: list, asset: str) -> str:
    if not news_items:
        return f"No news available for {asset}. Respond with neutral sentiment."
    headlines = "\n".join(f"- {item.get('title', '')}" for item in news_items[:10])
    return f"Asset: {asset}\n\nRecent news headlines:\n{headlines}\n\nAnalyze the sentiment."


def build_sentiment_analyst_prompt(onchain_data: dict, asset: str) -> str:
    fng = onchain_data.get("fear_greed", {})
    funding = onchain_data.get("funding_rate", None)
    return f"""Asset: {asset}

Fear & Greed Index: {fng.get('value', 'N/A')} ({fng.get('label', 'N/A')})
Funding Rate: {f'{funding:.4f}%' if funding is not None else 'N/A'}

Analyze the market sentiment."""


def build_researcher_prompt(tech_analysis: dict, news_analysis: dict, sentiment_analysis: dict, asset: str) -> str:
    return f"""Asset: {asset}

Technical Analysis:
{tech_analysis}

News Analysis:
{news_analysis}

Sentiment Analysis:
{sentiment_analysis}

Synthesize these analyses into a final research verdict."""


def build_risk_manager_prompt(recommendation: dict, portfolio: dict) -> str:
    return f"""Trading Recommendation:
{recommendation}

Portfolio Status:
  Cash: ${portfolio.get('cash', 0):.2f}
  Position: {portfolio.get('position', 0):.6f}
  Total Value: ${portfolio.get('total_value', 0):.2f}
  Current Drawdown: {portfolio.get('drawdown', 0):.1%}

Validate this recommendation against risk constraints."""


def build_trader_prompt_B(tech_analysis: dict, news_analysis: dict, portfolio: dict, asset: str, price: float) -> str:
    return f"""Asset: {asset}
Current Price: ${price:.2f}

Technical Analysis Summary: {tech_analysis.get('summary', 'N/A')} (Signal: {tech_analysis.get('signal', 'N/A')})
News Sentiment Summary: {news_analysis.get('summary', 'N/A')} (Sentiment: {news_analysis.get('sentiment', 'N/A')}, Score: {news_analysis.get('score', 0):.2f})

Portfolio:
  Cash: ${portfolio.get('cash', 0):.2f}
  Total Value: ${portfolio.get('total_value', 0):.2f}

Make your trading decision based on the analyses above."""


def build_trader_prompt_C(research: dict, risk_decision: dict, portfolio: dict, asset: str, price: float) -> str:
    return f"""Asset: {asset}
Current Price: ${price:.2f}

Research Verdict: {research.get('verdict', 'N/A')} (Conviction: {research.get('conviction', 0):.2f})
Research Synthesis: {research.get('synthesis', 'N/A')}

Risk Manager Assessment: {risk_decision.get('risk_note', 'N/A')}
Risk-Adjusted Action: {risk_decision.get('adjusted_action', 'HOLD')} (size: {risk_decision.get('adjusted_size', 0)})

Portfolio:
  Cash: ${portfolio.get('cash', 0):.2f}
  Total Value: ${portfolio.get('total_value', 0):.2f}

Make your final trading decision."""