Krikorios
b5e2b02cb8
- Restructure tabs to analysis-focused workflow: * Analysis Hub: AI analysis, risk management, manual trade logger * Daily Prep: Market summary, alerts, checklist, news, trading plan * Journal & Review: Trading journal, habit tracker, advanced analytics * Live Charts: Technical analysis with streaming charts - Add ManualTradeLogger component for logging trades from MT5/TradingView/cTrader - Remove execution-focused components (TradeControls, PortfolioTracker) - Update XAU/USD price to realistic ,084.99 - Add indicator preferences and AI plan service - Add comprehensive documentation on decision coverage and implementation
120 lines
3.7 KiB
Python
120 lines
3.7 KiB
Python
from fastapi import APIRouter, HTTPException, Depends
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from sqlalchemy.orm import Session
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from typing import List, Optional
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from app.services.openrouter import openrouter_service
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from app.schemas.schemas import (
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AIAnalysisRequest,
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AIAnalysisResponse,
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AIPlanGenerationRequest,
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AIPlanGenerationResponse,
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AIPlanFeedback
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)
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from app.services.decisions import log_decision
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from app.services.ai_plan_service import ai_plan_service
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from app.db.database import get_db
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router = APIRouter(prefix="/ai", tags=["AI Analysis"])
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@router.post("/analyze", response_model=AIAnalysisResponse)
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async def analyze_scenario(request: AIAnalysisRequest):
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"""
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Analyze trading scenario using AI (Claude 3.5 Sonnet via OpenRouter)
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Provides:
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- Trading recommendation (BUY/SELL/HOLD)
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- Confidence level
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- Detailed reasoning
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- Support and resistance levels
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- Risk assessment
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"""
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try:
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analysis = await openrouter_service.analyze_scenario(request)
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# Log decision (best-effort) with minimal metadata
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try:
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log_decision(
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symbol="XAU/USD",
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timeframe="unknown",
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style="unknown",
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recommendation=analysis.recommendation.value if hasattr(analysis, 'recommendation') else str(analysis.recommendation),
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confidence=float(analysis.confidence),
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risk_level=analysis.risk_level.value if hasattr(analysis, 'risk_level') else str(analysis.risk_level),
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rationale=analysis.reasoning,
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inputs_hash=None,
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cost={},
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)
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except Exception:
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pass
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return analysis
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except Exception as e:
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raise HTTPException(
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status_code=500, detail=f"AI analysis failed: {str(e)}"
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)
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@router.post("/generate-plan", response_model=AIPlanGenerationResponse)
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async def generate_trading_plan(
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request: AIPlanGenerationRequest,
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user_id: Optional[str] = None,
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db: Session = Depends(get_db)
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):
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"""
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Generate a comprehensive daily trading plan using AI
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Uses user's indicator preferences and market data to create:
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- Market bias (BULLISH/BEARISH/NEUTRAL)
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- Entry zones and targets
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- Support and resistance levels
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- Risk management parameters
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- Trading strategy notes
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"""
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try:
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plan = await ai_plan_service.generate_plan(db, request, user_id)
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return plan
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except Exception as e:
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raise HTTPException(
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status_code=500,
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detail=f"AI plan generation failed: {str(e)}"
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)
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@router.get("/plans/history", response_model=List[AIPlanGenerationResponse])
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async def get_plan_history(
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user_id: Optional[str] = None,
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limit: int = 10,
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db: Session = Depends(get_db)
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):
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"""Get historical AI-generated trading plans"""
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try:
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plans = await ai_plan_service.get_plan_history(db, user_id, limit)
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return plans
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except Exception as e:
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raise HTTPException(
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status_code=500,
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detail=f"Failed to fetch plan history: {str(e)}"
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)
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@router.post("/plans/feedback")
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async def submit_plan_feedback(
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feedback: AIPlanFeedback,
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db: Session = Depends(get_db)
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):
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"""Submit feedback on an AI-generated plan"""
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try:
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plan = await ai_plan_service.submit_feedback(
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db,
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feedback.plan_id,
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feedback.accepted,
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feedback.modified,
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feedback.feedback
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)
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return {"success": True, "message": "Feedback submitted successfully"}
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except Exception as e:
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raise HTTPException(
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status_code=500,
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detail=f"Failed to submit feedback: {str(e)}"
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)
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