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@app.post("/api/trading/decision")
async def trading_decision(request: Dict[str, Any]):
"""
FIXED: Get trading decision based on sentiment classification.
Uses ElKulako/cryptobert (classification model) instead of generation.
Logic:
- BULLISH/POSITIVE label -> BUY
- BEARISH/NEGATIVE label -> SELL
- NEUTRAL or error -> HOLD
Always returns valid JSON (never crashes with 500).
"""
try:
symbol = request.get("symbol", "").strip().upper()
context = request.get("context", "").strip()
if not symbol:
raise HTTPException(status_code=400, detail="Symbol is required")
# Build analysis text
if context:
analysis_text = f"{symbol} {context}"
else:
analysis_text = f"{symbol} market analysis"
# Default safe response
default_response = {
"success": True,
"available": False,
"decision": "HOLD",
"confidence": 0.5,
"rationale": "Unable to analyze sentiment - defaulting to HOLD for safety",
"symbol": symbol,
"model": "fallback",
"context_provided": bool(context),
"timestamp": datetime.now().isoformat()
}
try:
from ai_models import _registry, MODEL_SPECS, ModelNotAvailable
# Try to use the trading model (crypto_trading_lm -> ElKulako/cryptobert)
trading_key = "crypto_trading_lm"
if trading_key not in MODEL_SPECS:
logger.warning("Trading model key not found in MODEL_SPECS")
return default_response
try:
# Get the classification pipeline (lazy loaded)
pipe = _registry.get_pipeline(trading_key)
spec = MODEL_SPECS[trading_key]
# Run classification
result = pipe(analysis_text[:512])
if isinstance(result, list) and result:
result = result[0]
label = result.get("label", "NEUTRAL").upper()
score = result.get("score", 0.5)
# FIXED LOGIC: Map label to trading decision
decision = "HOLD" # Default
if "BULLISH" in label or "POSITIVE" in label or "LABEL_2" in label:
decision = "BUY"
elif "BEARISH" in label or "NEGATIVE" in label or "LABEL_0" in label:
decision = "SELL"
else:
decision = "HOLD"
# Build rationale
sentiment_word = "bullish" if decision == "BUY" else ("bearish" if decision == "SELL" else "neutral")
rationale = f"Model detected {sentiment_word} sentiment (label: {label}, confidence: {score:.2f})"
if context:
rationale += f" based on: {context[:200]}"
return {
"success": True,
"available": True,
"decision": decision,
"confidence": float(score),
"rationale": rationale,
"symbol": symbol,
"model": spec.model_id,
"sentiment": sentiment_word,
"raw_label": label,
"context_provided": bool(context),
"timestamp": datetime.now().isoformat()
}
except ModelNotAvailable as e:
logger.warning(f"Trading model not available: {e}")
default_response["error"] = f"Model unavailable: {str(e)[:100]}"
default_response["note"] = "Model in cooldown or failed to load"
return default_response
except ImportError:
logger.error("ai_models module not available")
default_response["error"] = "AI models module not available"
return default_response
except Exception as e:
logger.warning(f"Sentiment analysis failed: {e}")
default_response["error"] = f"Analysis failed: {str(e)[:100]}"
default_response["note"] = "Using default HOLD signal due to analysis failure"
return default_response
except HTTPException:
raise
except Exception as e:
logger.error(f"Trading decision endpoint error: {e}")
# Never crash - always return valid JSON
return {
"success": True,
"available": False,
"error": f"Endpoint error: {str(e)[:100]}",
"decision": "HOLD",
"confidence": 0.5,
"rationale": "Error occurred during analysis - defaulting to HOLD for safety",
"symbol": request.get("symbol", "UNKNOWN"),
"timestamp": datetime.now().isoformat()
}
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