76a118ddbf
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Backend: - Add YieldDomain, YieldTransaction, YieldPayout, AffiliatePartner models - Create IntentDetector service for keyword-based intent classification - Implement /api/v1/yield/* endpoints (dashboard, domains, transactions, partners) - Support domain activation, DNS verification, and revenue tracking Frontend: - Add /terminal/yield page with dashboard and activate wizard - Add YIELD to sidebar navigation under 'Monetize' section - Add 4th pillar 'Yield' to landing page 'Beyond Hunting' section - Extend API client with yield endpoints and types Features: - AI-powered intent detection (medical, finance, legal, realestate, etc.) - Swiss/German geo-targeting with city recognition - Revenue estimation based on intent category and geo - DNS verification via nameservers or CNAME - 70/30 revenue split tracking
269 lines
8.6 KiB
Python
269 lines
8.6 KiB
Python
"""TLD Price change tracking and alerting service."""
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import logging
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from datetime import datetime, timedelta
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from dataclasses import dataclass
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from typing import Optional
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from sqlalchemy import select, func, and_
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.models.tld_price import TLDPrice
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from app.models.user import User
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from app.services.email_service import email_service
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logger = logging.getLogger(__name__)
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@dataclass
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class PriceChange:
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"""Represents a TLD price change."""
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tld: str
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registrar: str
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old_price: float
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new_price: float
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change_amount: float
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change_percent: float
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detected_at: datetime
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@property
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def is_significant(self) -> bool:
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"""Check if price change is significant (>5%)."""
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return abs(self.change_percent) >= 5.0
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@property
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def direction(self) -> str:
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"""Get price change direction."""
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if self.change_percent > 0:
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return "up"
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elif self.change_percent < 0:
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return "down"
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return "stable"
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class PriceTracker:
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"""
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Tracks TLD price changes and sends alerts.
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Features:
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- Detect significant price changes (>5%)
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- Send email alerts to subscribed users
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- Generate price change reports
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"""
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# Threshold for significant price change (percentage)
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SIGNIFICANT_CHANGE_THRESHOLD = 5.0
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async def detect_price_changes(
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self,
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db: AsyncSession,
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hours: int = 24,
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) -> list[PriceChange]:
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"""
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Detect price changes in the last N hours.
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Args:
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db: Database session
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hours: Look back period in hours
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Returns:
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List of significant price changes
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"""
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changes: list[PriceChange] = []
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now = datetime.utcnow()
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cutoff = now - timedelta(hours=hours)
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# PERF: Avoid N+1 queries (distinct(tld, registrar) + per-pair LIMIT 2).
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# We fetch the latest 2 rows per (tld, registrar) using a window function.
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ranked = (
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select(
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TLDPrice.tld.label("tld"),
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TLDPrice.registrar.label("registrar"),
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TLDPrice.registration_price.label("price"),
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TLDPrice.recorded_at.label("recorded_at"),
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func.row_number()
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.over(
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partition_by=(TLDPrice.tld, TLDPrice.registrar),
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order_by=TLDPrice.recorded_at.desc(),
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)
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.label("rn"),
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)
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).subquery()
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rows = (
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await db.execute(
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select(
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ranked.c.tld,
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ranked.c.registrar,
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ranked.c.price,
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ranked.c.recorded_at,
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ranked.c.rn,
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)
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.where(ranked.c.rn <= 2)
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.order_by(ranked.c.tld, ranked.c.registrar, ranked.c.rn)
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)
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).all()
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from itertools import groupby
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for (tld, registrar), grp in groupby(rows, key=lambda r: (r.tld, r.registrar)):
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pair = list(grp)
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if len(pair) < 2:
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continue
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newest = pair[0] if pair[0].rn == 1 else pair[1]
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previous = pair[1] if pair[0].rn == 1 else pair[0]
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# Only consider if the newest price is within the requested window
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if newest.recorded_at is None or newest.recorded_at < cutoff:
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continue
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if not previous.price or previous.price == 0:
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continue
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change_amount = float(newest.price) - float(previous.price)
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change_percent = (change_amount / float(previous.price)) * 100
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if abs(change_percent) >= self.SIGNIFICANT_CHANGE_THRESHOLD:
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changes.append(
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PriceChange(
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tld=tld,
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registrar=registrar,
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old_price=float(previous.price),
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new_price=float(newest.price),
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change_amount=change_amount,
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change_percent=change_percent,
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detected_at=newest.recorded_at,
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)
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)
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# Sort by absolute change percentage (most significant first)
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changes.sort(key=lambda x: abs(x.change_percent), reverse=True)
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logger.info(f"Detected {len(changes)} significant price changes in last {hours}h")
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return changes
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async def send_price_alerts(
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self,
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db: AsyncSession,
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changes: list[PriceChange],
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min_tier: str = "professional",
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) -> int:
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"""
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Send price change alerts to subscribed users.
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Args:
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db: Database session
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changes: List of price changes to alert about
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min_tier: Minimum subscription tier to receive alerts
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Returns:
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Number of alerts sent
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"""
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if not changes:
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return 0
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if not email_service.is_enabled:
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logger.warning("Email service not configured, skipping price alerts")
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return 0
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# Get users with appropriate subscription
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# For now, send to all active users (can filter by tier later)
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result = await db.execute(
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select(User).where(User.is_active == True)
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)
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users = result.scalars().all()
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alerts_sent = 0
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for change in changes[:10]: # Limit to top 10 changes
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for user in users:
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try:
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success = await email_service.send_price_change_alert(
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to_email=user.email,
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tld=change.tld,
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old_price=change.old_price,
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new_price=change.new_price,
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change_percent=change.change_percent,
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registrar=change.registrar,
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)
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if success:
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alerts_sent += 1
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except Exception as e:
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logger.error(f"Failed to send alert to {user.email}: {e}")
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logger.info(f"Sent {alerts_sent} price change alerts")
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return alerts_sent
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async def get_trending_changes(
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self,
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db: AsyncSession,
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days: int = 7,
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limit: int = 10,
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) -> list[dict]:
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"""
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Get trending price changes for dashboard display.
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Args:
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db: Database session
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days: Look back period
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limit: Max results to return
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Returns:
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List of trending price changes
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"""
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changes = await self.detect_price_changes(db, hours=days * 24)
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return [
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{
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"tld": c.tld,
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"registrar": c.registrar,
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"old_price": c.old_price,
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"new_price": c.new_price,
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"change_percent": round(c.change_percent, 2),
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"direction": c.direction,
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"detected_at": c.detected_at.isoformat(),
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}
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for c in changes[:limit]
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]
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async def generate_price_report(
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self,
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db: AsyncSession,
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days: int = 30,
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) -> dict:
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"""Generate a comprehensive price change report."""
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changes = await self.detect_price_changes(db, hours=days * 24)
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increases = [c for c in changes if c.direction == "up"]
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decreases = [c for c in changes if c.direction == "down"]
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return {
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"period_days": days,
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"total_changes": len(changes),
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"price_increases": len(increases),
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"price_decreases": len(decreases),
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"avg_increase": round(sum(c.change_percent for c in increases) / len(increases), 2) if increases else 0,
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"avg_decrease": round(sum(c.change_percent for c in decreases) / len(decreases), 2) if decreases else 0,
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"biggest_increase": {
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"tld": increases[0].tld,
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"change": round(increases[0].change_percent, 2),
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} if increases else None,
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"biggest_decrease": {
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"tld": decreases[0].tld,
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"change": round(decreases[0].change_percent, 2),
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} if decreases else None,
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"top_changes": [
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{
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"tld": c.tld,
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"change_percent": round(c.change_percent, 2),
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"new_price": c.new_price,
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}
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for c in changes[:10]
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],
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}
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# Singleton instance
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price_tracker = PriceTracker()
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