140 lines
4.9 KiB
Python
140 lines
4.9 KiB
Python
import httpx
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import json
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from typing import List
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from app.config import settings
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from app.schemas.schemas import (
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AIAnalysisRequest,
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AIAnalysisResponse,
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Recommendation,
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RiskLevel,
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SupportResistance,
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)
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class OpenRouterService:
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def __init__(self):
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self.base_url = settings.OPENROUTER_BASE_URL
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self.api_key = settings.OPENROUTER_API_KEY
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self.model = settings.OPENROUTER_MODEL
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async def analyze_scenario(self, request: AIAnalysisRequest) -> AIAnalysisResponse:
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"""
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Analyze trading scenario using Claude 3.5 Sonnet via OpenRouter
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Args:
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request: AIAnalysisRequest with price data and indicators
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Returns:
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AIAnalysisResponse with recommendation and analysis
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"""
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# Prepare recent price data for analysis
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recent_prices = request.price_data[-50:] if len(request.price_data) > 50 else request.price_data
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# Format price data for the AI
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price_summary = f"Current Price: ${request.current_price:.2f}\n"
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price_summary += f"Recent Close Prices: {[f'${p.close:.2f}' for p in recent_prices[-10:]]}\n"
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# Calculate basic statistics
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prices = [p.close for p in recent_prices]
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avg_price = sum(prices) / len(prices)
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price_range = max(prices) - min(prices)
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# Create analysis prompt
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prompt = f"""You are a senior quantitative analyst specializing in gold (XAU/USD) trading. Analyze the following market data and provide a trading recommendation.
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Market Data:
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{price_summary}
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Average Price (last 50 periods): ${avg_price:.2f}
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Price Range: ${price_range:.2f}
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Technical Indicators:
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{json.dumps(request.indicators, indent=2)}
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Based on this data, provide:
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1. A clear recommendation: BUY, SELL, or HOLD
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2. Confidence level (0-100%)
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3. Detailed reasoning (2-3 sentences)
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4. Support and resistance levels (up to 3 each)
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5. Risk level assessment: LOW, MEDIUM, or HIGH
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Respond in JSON format:
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{{
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"recommendation": "BUY|SELL|HOLD",
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"confidence": 0-100,
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"reasoning": "Your detailed analysis here",
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"support_levels": [price1, price2, price3],
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"resistance_levels": [price1, price2, price3],
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"risk_level": "LOW|MEDIUM|HIGH"
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}}
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"""
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headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json",
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"HTTP-Referer": settings.OPENROUTER_SITE_URL,
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"X-Title": settings.OPENROUTER_SITE_NAME,
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}
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payload = {
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"model": self.model,
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"messages": [
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{
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"role": "system",
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"content": "You are a professional gold trading analyst. Always respond with valid JSON.",
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},
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{"role": "user", "content": prompt},
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],
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"temperature": 0.7,
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"max_tokens": 1000,
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}
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async with httpx.AsyncClient(timeout=60.0) as client:
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response = await client.post(
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f"{self.base_url}/chat/completions",
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headers=headers,
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json=payload,
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)
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response.raise_for_status()
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data = response.json()
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# Extract AI response
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ai_content = data["choices"][0]["message"]["content"]
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# Parse JSON response
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try:
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# Try to extract JSON from markdown code blocks if present
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if "```json" in ai_content:
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json_start = ai_content.find("```json") + 7
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json_end = ai_content.find("```", json_start)
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ai_content = ai_content[json_start:json_end].strip()
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elif "```" in ai_content:
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json_start = ai_content.find("```") + 3
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json_end = ai_content.find("```", json_start)
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ai_content = ai_content[json_start:json_end].strip()
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analysis_data = json.loads(ai_content)
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except json.JSONDecodeError:
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# Fallback to default response if JSON parsing fails
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return AIAnalysisResponse(
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recommendation=Recommendation.HOLD,
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confidence=50.0,
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reasoning="Unable to parse AI response. Please try again.",
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support_resistance=SupportResistance(support=[], resistance=[]),
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risk_level=RiskLevel.MEDIUM,
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)
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# Map to response schema
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return AIAnalysisResponse(
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recommendation=Recommendation(analysis_data.get("recommendation", "HOLD")),
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confidence=float(analysis_data.get("confidence", 50)),
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reasoning=analysis_data.get("reasoning", "Analysis completed."),
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support_resistance=SupportResistance(
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support=analysis_data.get("support_levels", []),
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resistance=analysis_data.get("resistance_levels", []),
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),
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risk_level=RiskLevel(analysis_data.get("risk_level", "MEDIUM")),
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)
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openrouter_service = OpenRouterService()
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