074787f067
Features: - FastAPI backend with stocks, news, signals, watchlist, analytics endpoints - React frontend with TailwindCSS dark mode trading dashboard - Celery workers for news fetching, sentiment analysis, pattern detection - TimescaleDB schema for time-series stock data - Docker Compose setup for all services - OpenAI integration for sentiment analysis
141 lines
4.0 KiB
Python
141 lines
4.0 KiB
Python
"""
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Analytics API Endpoints
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"""
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from typing import Optional
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from datetime import datetime, timedelta
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from fastapi import APIRouter, Depends, Query
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy import select, func
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from app.core.database import get_db
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from app.models.stock import Stock
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from app.models.news import NewsArticle
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from app.models.signal import BuySignal
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from app.models.panic import PanicEvent
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router = APIRouter()
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@router.get("/dashboard")
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async def get_dashboard_stats(db: AsyncSession = Depends(get_db)):
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"""Get overview statistics for the dashboard."""
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now = datetime.utcnow()
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last_24h = now - timedelta(hours=24)
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last_7d = now - timedelta(days=7)
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# Count stocks
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stocks_count = await db.execute(
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select(func.count(Stock.id)).where(Stock.is_active == True)
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)
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# Count news (last 24h)
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news_count = await db.execute(
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select(func.count(NewsArticle.id))
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.where(NewsArticle.published_at >= last_24h)
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)
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# Count active signals
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signals_count = await db.execute(
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select(func.count(BuySignal.id))
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.where(BuySignal.status == "active")
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)
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# Average sentiment (last 24h)
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avg_sentiment = await db.execute(
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select(func.avg(NewsArticle.sentiment_score))
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.where(NewsArticle.published_at >= last_24h)
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.where(NewsArticle.sentiment_score.isnot(None))
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)
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return {
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"stocks_tracked": stocks_count.scalar() or 0,
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"news_last_24h": news_count.scalar() or 0,
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"active_signals": signals_count.scalar() or 0,
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"avg_sentiment_24h": round(avg_sentiment.scalar() or 0, 2),
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"timestamp": now.isoformat(),
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}
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@router.get("/sentiment/trend")
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async def get_sentiment_trend(
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db: AsyncSession = Depends(get_db),
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days: int = Query(7, ge=1, le=30, description="Number of days"),
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):
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"""Get sentiment trend over time."""
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# This would aggregate sentiment by day
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# Placeholder for now
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return {
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"days": days,
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"trend": [],
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"message": "Sentiment trend data will be populated by the workers",
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}
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@router.get("/sector/panic")
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async def get_sector_panic_levels(db: AsyncSession = Depends(get_db)):
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"""Get current panic levels by sector."""
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# This would calculate average sentiment by sector
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# Placeholder for now
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return {
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"sectors": [],
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"message": "Sector panic data will be populated by the workers",
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}
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@router.get("/patterns/top")
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async def get_top_patterns(
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db: AsyncSession = Depends(get_db),
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limit: int = Query(10, ge=1, le=50),
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):
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"""Get top historical patterns with best recovery rates."""
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# This would query historical_patterns table
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# Placeholder for now
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return {
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"patterns": [],
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"message": "Pattern data will be populated by the pattern matcher",
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}
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@router.get("/panic-events/recent")
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async def get_recent_panic_events(
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db: AsyncSession = Depends(get_db),
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days: int = Query(30, ge=1, le=90, description="Number of days"),
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limit: int = Query(20, ge=1, le=50),
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):
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"""Get recent panic events."""
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since = datetime.utcnow() - timedelta(days=days)
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query = (
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select(PanicEvent)
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.where(PanicEvent.start_time >= since)
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.order_by(PanicEvent.start_time.desc())
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.limit(limit)
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)
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result = await db.execute(query)
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events = result.scalars().all()
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return {
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"days": days,
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"count": len(events),
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"events": events,
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}
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@router.get("/performance")
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async def get_signal_performance(
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db: AsyncSession = Depends(get_db),
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days: int = Query(90, ge=30, le=365, description="Number of days to analyze"),
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):
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"""Get performance metrics for past signals."""
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# This would analyze triggered signals and their outcomes
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# Placeholder for now
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return {
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"days": days,
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"total_signals": 0,
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"triggered_signals": 0,
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"avg_return": 0,
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"win_rate": 0,
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"message": "Performance data requires historical signal outcomes",
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}
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