pounce/backend/app/api/auctions.py

"""
Smart Pounce - Domain Auction Aggregator

This module provides auction data from our database of scraped listings.
Data is scraped from public auction platforms - NO APIS used.

Data Sources (Web Scraping):
- ExpiredDomains.net (aggregator)
- GoDaddy Auctions (public listings)
- Sedo (public search)
- NameJet (public auctions)

IMPORTANT:
- All data comes from web scraping of public pages
- No mock data - everything is real scraped data
- Data is cached in PostgreSQL/SQLite for performance
- Scraper runs on schedule (see scheduler.py)

Legal Note (Switzerland):
- No escrow/payment handling = no GwG/FINMA requirements
- Users click through to external platforms
- We only provide market intelligence
"""
import logging
from datetime import datetime, timedelta
from typing import Optional, List
from fastapi import APIRouter, Depends, Query, HTTPException
from pydantic import BaseModel
from sqlalchemy import select, func, and_
from sqlalchemy.ext.asyncio import AsyncSession

from app.database import get_db
from app.api.deps import get_current_user, get_current_user_optional
from app.models.user import User
from app.models.auction import DomainAuction, AuctionScrapeLog
from app.services.valuation import valuation_service
from app.services.auction_scraper import auction_scraper

logger = logging.getLogger(__name__)
router = APIRouter()


# ============== Schemas ==============

class AuctionValuation(BaseModel):
    """Valuation details for an auction."""
    estimated_value: float
    value_ratio: float
    potential_profit: float
    confidence: str
    valuation_formula: str


class AuctionListing(BaseModel):
    """A domain auction listing from the database."""
    domain: str
    platform: str
    platform_url: str
    current_bid: float
    currency: str
    num_bids: int
    end_time: datetime
    time_remaining: str
    buy_now_price: Optional[float] = None
    reserve_met: Optional[bool] = None
    traffic: Optional[int] = None
    age_years: Optional[int] = None
    tld: str
    affiliate_url: str
    valuation: Optional[AuctionValuation] = None
    
    class Config:
        from_attributes = True


class AuctionSearchResponse(BaseModel):
    """Response for auction search."""
    auctions: List[AuctionListing]
    total: int
    platforms_searched: List[str]
    last_updated: datetime
    data_source: str = "scraped"
    valuation_note: str = (
        "Values are estimated using our algorithm: "
        "$50 × Length × TLD × Keyword × Brand factors. "
        "See /portfolio/valuation/{domain} for detailed breakdown."
    )


class PlatformStats(BaseModel):
    """Statistics for an auction platform."""
    platform: str
    active_auctions: int
    avg_bid: float
    ending_soon: int


class ScrapeStatus(BaseModel):
    """Status of auction scraping."""
    last_scrape: Optional[datetime]
    total_auctions: int
    platforms: List[str]
    next_scrape: Optional[datetime]


# ============== Helper Functions ==============

def _format_time_remaining(end_time: datetime) -> str:
    """Format time remaining in human-readable format."""
    delta = end_time - datetime.utcnow()
    
    if delta.total_seconds() <= 0:
        return "Ended"
    
    hours = int(delta.total_seconds() // 3600)
    minutes = int((delta.total_seconds() % 3600) // 60)
    
    if hours > 24:
        days = hours // 24
        return f"{days}d {hours % 24}h"
    elif hours > 0:
        return f"{hours}h {minutes}m"
    else:
        return f"{minutes}m"


def _get_affiliate_url(platform: str, domain: str, auction_url: str) -> str:
    """Get affiliate URL for a platform."""
    # Use the scraped auction URL directly
    if auction_url:
        return auction_url
    
    # Fallback to platform search
    platform_urls = {
        "GoDaddy": f"https://auctions.godaddy.com/trpItemListing.aspx?domain={domain}",
        "Sedo": f"https://sedo.com/search/?keyword={domain}",
        "NameJet": f"https://www.namejet.com/Pages/Auctions/BackorderSearch.aspx?q={domain}",
        "ExpiredDomains": f"https://www.expireddomains.net/domain-name-search/?q={domain}",
        "Afternic": f"https://www.afternic.com/search?k={domain}",
    }
    return platform_urls.get(platform, f"https://www.google.com/search?q={domain}+auction")


async def _convert_to_listing(
    auction: DomainAuction, 
    db: AsyncSession, 
    include_valuation: bool = True
) -> AuctionListing:
    """Convert database auction to API response."""
    valuation_data = None
    
    if include_valuation:
        try:
            result = await valuation_service.estimate_value(auction.domain, db, save_result=False)
            
            if "error" not in result:
                estimated_value = result["estimated_value"]
                value_ratio = round(estimated_value / auction.current_bid, 2) if auction.current_bid > 0 else 99
                
                valuation_data = AuctionValuation(
                    estimated_value=estimated_value,
                    value_ratio=value_ratio,
                    potential_profit=round(estimated_value - auction.current_bid, 2),
                    confidence=result.get("confidence", "medium"),
                    valuation_formula=result.get("calculation", {}).get("formula", "N/A"),
                )
        except Exception as e:
            logger.error(f"Valuation error for {auction.domain}: {e}")
    
    return AuctionListing(
        domain=auction.domain,
        platform=auction.platform,
        platform_url=auction.auction_url or "",
        current_bid=auction.current_bid,
        currency=auction.currency,
        num_bids=auction.num_bids,
        end_time=auction.end_time,
        time_remaining=_format_time_remaining(auction.end_time),
        buy_now_price=auction.buy_now_price,
        reserve_met=auction.reserve_met,
        traffic=auction.traffic,
        age_years=auction.age_years,
        tld=auction.tld,
        affiliate_url=_get_affiliate_url(auction.platform, auction.domain, auction.auction_url),
        valuation=valuation_data,
    )


# ============== Endpoints ==============

@router.get("", response_model=AuctionSearchResponse)
async def search_auctions(
    keyword: Optional[str] = Query(None, description="Search keyword in domain names"),
    tld: Optional[str] = Query(None, description="Filter by TLD (e.g., 'com', 'io')"),
    platform: Optional[str] = Query(None, description="Filter by platform"),
    min_bid: Optional[float] = Query(None, ge=0, description="Minimum current bid"),
    max_bid: Optional[float] = Query(None, ge=0, description="Maximum current bid"),
    ending_soon: bool = Query(False, description="Only show auctions ending in < 1 hour"),
    sort_by: str = Query("ending", enum=["ending", "bid_asc", "bid_desc", "bids", "value_ratio"]),
    limit: int = Query(20, le=100),
    offset: int = Query(0, ge=0),
    current_user: Optional[User] = Depends(get_current_user_optional),
    db: AsyncSession = Depends(get_db),
):
    """
    Search domain auctions from our scraped database.
    
    All data comes from web scraping of public auction pages.
    NO mock data - everything is real scraped data.
    
    Data Sources:
    - ExpiredDomains.net (aggregator)
    - GoDaddy Auctions (coming soon)
    - Sedo (coming soon)
    - NameJet (coming soon)
    
    Smart Pounce Strategy:
    - Look for value_ratio > 1.0 (estimated value exceeds current bid)
    - Focus on auctions ending soon with low bid counts
    """
    # Build query
    query = select(DomainAuction).where(DomainAuction.is_active == True)
    
    if keyword:
        query = query.where(DomainAuction.domain.ilike(f"%{keyword}%"))
    
    if tld:
        query = query.where(DomainAuction.tld == tld.lower().lstrip("."))
    
    if platform:
        query = query.where(DomainAuction.platform == platform)
    
    if min_bid is not None:
        query = query.where(DomainAuction.current_bid >= min_bid)
    
    if max_bid is not None:
        query = query.where(DomainAuction.current_bid <= max_bid)
    
    if ending_soon:
        cutoff = datetime.utcnow() + timedelta(hours=1)
        query = query.where(DomainAuction.end_time <= cutoff)
    
    # Count total
    count_query = select(func.count()).select_from(query.subquery())
    total_result = await db.execute(count_query)
    total = total_result.scalar() or 0
    
    # Sort
    if sort_by == "ending":
        query = query.order_by(DomainAuction.end_time.asc())
    elif sort_by == "bid_asc":
        query = query.order_by(DomainAuction.current_bid.asc())
    elif sort_by == "bid_desc":
        query = query.order_by(DomainAuction.current_bid.desc())
    elif sort_by == "bids":
        query = query.order_by(DomainAuction.num_bids.desc())
    else:
        query = query.order_by(DomainAuction.end_time.asc())
    
    # Pagination
    query = query.offset(offset).limit(limit)
    
    result = await db.execute(query)
    auctions = list(result.scalars().all())
    
    # Convert to response with valuations
    listings = []
    for auction in auctions:
        listing = await _convert_to_listing(auction, db, include_valuation=True)
        listings.append(listing)
    
    # Sort by value_ratio if requested (after valuation)
    if sort_by == "value_ratio":
        listings.sort(
            key=lambda x: x.valuation.value_ratio if x.valuation else 0,
            reverse=True
        )
    
    # Get platforms searched
    platforms_result = await db.execute(
        select(DomainAuction.platform).distinct()
    )
    platforms = [p for (p,) in platforms_result.all()]
    
    # Get last update time
    last_update_result = await db.execute(
        select(func.max(DomainAuction.updated_at))
    )
    last_updated = last_update_result.scalar() or datetime.utcnow()
    
    return AuctionSearchResponse(
        auctions=listings,
        total=total,
        platforms_searched=platforms or ["No data yet - scrape pending"],
        last_updated=last_updated,
        data_source="scraped from public auction sites",
    )


@router.get("/ending-soon", response_model=List[AuctionListing])
async def get_ending_soon(
    hours: int = Query(1, ge=1, le=24, description="Hours until end"),
    limit: int = Query(10, le=50),
    current_user: Optional[User] = Depends(get_current_user_optional),
    db: AsyncSession = Depends(get_db),
):
    """
    Get auctions ending soon - best opportunities for sniping.
    
    Data is scraped from public auction sites - no mock data.
    """
    cutoff = datetime.utcnow() + timedelta(hours=hours)
    
    query = (
        select(DomainAuction)
        .where(
            and_(
                DomainAuction.is_active == True,
                DomainAuction.end_time <= cutoff,
                DomainAuction.end_time > datetime.utcnow(),
            )
        )
        .order_by(DomainAuction.end_time.asc())
        .limit(limit)
    )
    
    result = await db.execute(query)
    auctions = list(result.scalars().all())
    
    listings = []
    for auction in auctions:
        listing = await _convert_to_listing(auction, db, include_valuation=True)
        listings.append(listing)
    
    return listings


@router.get("/hot", response_model=List[AuctionListing])
async def get_hot_auctions(
    limit: int = Query(10, le=50),
    current_user: Optional[User] = Depends(get_current_user_optional),
    db: AsyncSession = Depends(get_db),
):
    """
    Get hottest auctions by bidding activity.
    
    Data is scraped from public auction sites - no mock data.
    """
    query = (
        select(DomainAuction)
        .where(DomainAuction.is_active == True)
        .order_by(DomainAuction.num_bids.desc())
        .limit(limit)
    )
    
    result = await db.execute(query)
    auctions = list(result.scalars().all())
    
    listings = []
    for auction in auctions:
        listing = await _convert_to_listing(auction, db, include_valuation=True)
        listings.append(listing)
    
    return listings


@router.get("/stats", response_model=List[PlatformStats])
async def get_platform_stats(
    current_user: Optional[User] = Depends(get_current_user_optional),
    db: AsyncSession = Depends(get_db),
):
    """
    Get statistics for each auction platform.
    
    Data is scraped from public auction sites - no mock data.
    """
    # Get stats per platform
    stats_query = (
        select(
            DomainAuction.platform,
            func.count(DomainAuction.id).label("count"),
            func.avg(DomainAuction.current_bid).label("avg_bid"),
        )
        .where(DomainAuction.is_active == True)
        .group_by(DomainAuction.platform)
    )
    
    result = await db.execute(stats_query)
    platform_data = result.all()
    
    # Get ending soon counts
    cutoff = datetime.utcnow() + timedelta(hours=1)
    ending_query = (
        select(
            DomainAuction.platform,
            func.count(DomainAuction.id).label("ending_count"),
        )
        .where(
            and_(
                DomainAuction.is_active == True,
                DomainAuction.end_time <= cutoff,
            )
        )
        .group_by(DomainAuction.platform)
    )
    
    ending_result = await db.execute(ending_query)
    ending_data = {p: c for p, c in ending_result.all()}
    
    stats = []
    for platform, count, avg_bid in platform_data:
        stats.append(PlatformStats(
            platform=platform,
            active_auctions=count,
            avg_bid=round(avg_bid or 0, 2),
            ending_soon=ending_data.get(platform, 0),
        ))
    
    return sorted(stats, key=lambda x: x.active_auctions, reverse=True)


@router.get("/scrape-status", response_model=ScrapeStatus)
async def get_scrape_status(
    current_user: Optional[User] = Depends(get_current_user_optional),
    db: AsyncSession = Depends(get_db),
):
    """Get status of auction scraping."""
    # Get last successful scrape
    last_scrape_query = (
        select(AuctionScrapeLog)
        .where(AuctionScrapeLog.status == "success")
        .order_by(AuctionScrapeLog.completed_at.desc())
        .limit(1)
    )
    result = await db.execute(last_scrape_query)
    last_log = result.scalar_one_or_none()
    
    # Get total auctions
    total_query = select(func.count(DomainAuction.id)).where(DomainAuction.is_active == True)
    total_result = await db.execute(total_query)
    total = total_result.scalar() or 0
    
    # Get platforms
    platforms_result = await db.execute(
        select(DomainAuction.platform).distinct()
    )
    platforms = [p for (p,) in platforms_result.all()]
    
    return ScrapeStatus(
        last_scrape=last_log.completed_at if last_log else None,
        total_auctions=total,
        platforms=platforms or ["Pending initial scrape"],
        next_scrape=datetime.utcnow() + timedelta(hours=1),  # Approximation
    )


@router.post("/trigger-scrape")
async def trigger_scrape(
    current_user: User = Depends(get_current_user),
    db: AsyncSession = Depends(get_db),
):
    """
    Manually trigger auction scraping (admin only for now).
    
    In production, this runs automatically every hour.
    """
    try:
        result = await auction_scraper.scrape_all_platforms(db)
        return {
            "status": "success",
            "message": "Scraping completed",
            "result": result,
        }
    except Exception as e:
        logger.error(f"Manual scrape failed: {e}")
        raise HTTPException(status_code=500, detail=f"Scrape failed: {str(e)}")


@router.get("/opportunities")
async def get_smart_opportunities(
    current_user: User = Depends(get_current_user),
    db: AsyncSession = Depends(get_db),
):
    """
    Smart Pounce Algorithm - Find the best auction opportunities.
    
    Analyzes scraped auction data (NO mock data) to find:
    - Auctions ending soon with low bids
    - Domains with high estimated value vs current bid
    
    Opportunity Score = value_ratio × time_factor × bid_factor
    """
    # Get active auctions
    query = (
        select(DomainAuction)
        .where(DomainAuction.is_active == True)
        .order_by(DomainAuction.end_time.asc())
        .limit(50)
    )
    
    result = await db.execute(query)
    auctions = list(result.scalars().all())
    
    if not auctions:
        return {
            "opportunities": [],
            "message": "No active auctions. Trigger a scrape to fetch latest data.",
            "valuation_method": "Our algorithm calculates: $50 × Length × TLD × Keyword × Brand factors.",
            "strategy_tips": [
                "🔄 Click 'Trigger Scrape' to fetch latest auction data",
                "🎯 Look for value_ratio > 1.0 (undervalued domains)",
                "⏰ Auctions ending soon often have best opportunities",
            ],
            "generated_at": datetime.utcnow().isoformat(),
        }
    
    opportunities = []
    
    for auction in auctions:
        valuation = await valuation_service.estimate_value(auction.domain, db, save_result=False)
        
        if "error" in valuation:
            continue
        
        estimated_value = valuation["estimated_value"]
        current_bid = auction.current_bid
        
        value_ratio = estimated_value / current_bid if current_bid > 0 else 10
        
        hours_left = (auction.end_time - datetime.utcnow()).total_seconds() / 3600
        time_factor = 2.0 if hours_left < 1 else (1.5 if hours_left < 4 else 1.0)
        
        bid_factor = 1.5 if auction.num_bids < 10 else 1.0
        
        opportunity_score = value_ratio * time_factor * bid_factor
        
        listing = await _convert_to_listing(auction, db, include_valuation=True)
        opportunities.append({
            "auction": listing.model_dump(),
            "analysis": {
                "estimated_value": estimated_value,
                "current_bid": current_bid,
                "value_ratio": round(value_ratio, 2),
                "potential_profit": round(estimated_value - current_bid, 2),
                "opportunity_score": round(opportunity_score, 2),
                "time_factor": time_factor,
                "bid_factor": bid_factor,
                "recommendation": (
                    "Strong buy" if opportunity_score > 5 else
                    "Consider" if opportunity_score > 2 else
                    "Monitor"
                ),
                "reasoning": _get_opportunity_reasoning(
                    value_ratio, hours_left, auction.num_bids, opportunity_score
                ),
            }
        })
    
    opportunities.sort(key=lambda x: x["analysis"]["opportunity_score"], reverse=True)
    
    return {
        "opportunities": opportunities[:10],
        "data_source": "Real scraped auction data (no mock data)",
        "valuation_method": (
            "Our algorithm calculates: $50 × Length × TLD × Keyword × Brand factors. "
            "See /portfolio/valuation/{domain} for detailed breakdown of any domain."
        ),
        "strategy_tips": [
            "🎯 Focus on value_ratio > 1.0 (estimated value exceeds current bid)",
            "⏰ Auctions ending in < 1 hour often have best snipe opportunities",
            "📉 Low bid count (< 10) might indicate overlooked gems",
            "💡 Premium TLDs (.com, .ai, .io) have highest aftermarket demand",
        ],
        "generated_at": datetime.utcnow().isoformat(),
    }


def _get_opportunity_reasoning(value_ratio: float, hours_left: float, num_bids: int, score: float) -> str:
    """Generate human-readable reasoning for the opportunity."""
    reasons = []
    
    if value_ratio > 2:
        reasons.append(f"Significantly undervalued ({value_ratio:.1f}× estimated value)")
    elif value_ratio > 1:
        reasons.append(f"Undervalued ({value_ratio:.1f}× estimated value)")
    else:
        reasons.append(f"Current bid exceeds our estimate ({value_ratio:.2f}×)")
    
    if hours_left < 1:
        reasons.append("⚡ Ending very soon - final chance to bid")
    elif hours_left < 4:
        reasons.append("⏰ Ending soon - limited time remaining")
    
    if num_bids < 5:
        reasons.append("📉 Very low competition - potential overlooked opportunity")
    elif num_bids < 10:
        reasons.append("📊 Moderate competition")
    else:
        reasons.append(f"🔥 High demand ({num_bids} bids)")
    
    return " | ".join(reasons)