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)

PLUS Pounce Direct Listings (user-created marketplace):
- DNS-verified owner listings
- Instant buy option
- 0% commission

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 itertools import groupby
from fastapi import APIRouter, Depends, Query, HTTPException
from pydantic import BaseModel
from sqlalchemy import select, func, and_, or_
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.models.listing import DomainListing, ListingStatus, VerificationStatus
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]


class MarketFeedItem(BaseModel):
    """Unified market feed item - combines auctions and Pounce Direct listings."""
    id: str
    domain: str
    tld: str
    price: float
    currency: str = "USD"
    price_type: str  # "bid" or "fixed"
    status: str  # "auction" or "instant"
    
    # Source info
    source: str  # "Pounce", "GoDaddy", "Sedo", etc.
    is_pounce: bool = False
    verified: bool = False
    
    # Auction-specific
    time_remaining: Optional[str] = None
    end_time: Optional[datetime] = None
    num_bids: Optional[int] = None
    
    # Pounce Direct specific
    slug: Optional[str] = None
    seller_verified: bool = False
    
    # URLs
    url: str  # Internal for Pounce, external for auctions
    is_external: bool = True
    
    # Scoring
    pounce_score: int = 50
    
    # Valuation (optional)
    valuation: Optional[AuctionValuation] = None
    
    class Config:
        from_attributes = True


class MarketFeedResponse(BaseModel):
    """Response for unified market feed."""
    items: List[MarketFeedItem]
    total: int
    pounce_direct_count: int
    auction_count: int
    sources: List[str]
    last_updated: datetime
    filters_applied: dict = {}


# ============== 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 - links directly to the auction page."""
    # Use the scraped auction URL directly if available
    if auction_url and auction_url.startswith("http"):
        return auction_url
    
    # Fallback to platform-specific search/listing pages
    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}",
        "DropCatch": f"https://www.dropcatch.com/domain/{domain}",
        "ExpiredDomains": f"https://www.expireddomains.net/domain-name-search/?q={domain}",
        "Afternic": f"https://www.afternic.com/search?k={domain}",
        "Dynadot": f"https://www.dynadot.com/market/auction/{domain}",
        "Porkbun": f"https://porkbun.com/checkout/search?q={domain}",
    }
    return platform_urls.get(platform, f"https://www.google.com/search?q={domain}+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)
    
    # VANITY FILTER: For public (non-logged-in) users, only show premium-looking domains
    # This ensures the first impression is high-quality, not spam domains
    if current_user is None:
        # Premium TLDs only (no .cc, .website, .info spam clusters)
        premium_tlds = ['com', 'io', 'ai', 'co', 'de', 'ch', 'net', 'org', 'app', 'dev', 'xyz']
        query = query.where(DomainAuction.tld.in_(premium_tlds))
        # No domains with more than 15 characters (excluding TLD)
        # Note: We filter further in Python for complex rules
    
    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())
    
    # VANITY FILTER PART 2: Apply Python-side filtering for public users
    # This ensures only premium-looking domains are shown to non-logged-in users
    if current_user is None:
        def is_premium_domain(domain_name: str) -> bool:
            """Check if a domain looks premium/professional"""
            # Extract just the domain part (without TLD)
            parts = domain_name.rsplit('.', 1)
            name = parts[0] if parts else domain_name
            
            # Rule 1: No more than 15 characters
            if len(name) > 15:
                return False
            
            # Rule 2: No more than 1 hyphen
            if name.count('-') > 1:
                return False
            
            # Rule 3: No more than 2 digits total
            digit_count = sum(1 for c in name if c.isdigit())
            if digit_count > 2:
                return False
            
            # Rule 4: Must be at least 3 characters
            if len(name) < 3:
                return False
            
            # Rule 5: No random-looking strings (too many consonants in a row)
            consonants = 'bcdfghjklmnpqrstvwxyz'
            consonant_streak = 0
            max_streak = 0
            for c in name.lower():
                if c in consonants:
                    consonant_streak += 1
                    max_streak = max(max_streak, consonant_streak)
                else:
                    consonant_streak = 0
            if max_streak > 4:
                return False
            
            return True
        
        auctions = [a for a in auctions if is_premium_domain(a.domain)]
    
    # 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.post("/seed")
async def seed_auctions(
    current_user: User = Depends(get_current_user),
    db: AsyncSession = Depends(get_db),
):
    """
    Seed the database with realistic sample auction data.
    Useful for development and demo purposes.
    """
    try:
        result = await auction_scraper.seed_sample_auctions(db)
        return {
            "status": "success",
            "message": "Sample auctions seeded",
            "result": result,
        }
    except Exception as e:
        logger.error(f"Seeding failed: {e}")
        raise HTTPException(status_code=500, detail=f"Seeding 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 auction data to find sweet spots:
    - Auctions ending soon (snipe potential)
    - Low bid counts (overlooked gems)
    - Good price points
    
    Opportunity Score = time_urgency × competition_factor × price_factor
    """
    # Get active auctions
    query = (
        select(DomainAuction)
        .where(DomainAuction.is_active == True)
        .order_by(DomainAuction.end_time.asc())
        .limit(100)
    )
    
    result = await db.execute(query)
    auctions = list(result.scalars().all())
    
    if not auctions:
        return {
            "opportunities": [],
            "message": "No active auctions found.",
            "strategy_tips": [
                "🔄 Check back soon for new auctions",
                "⏰ Best opportunities often appear as auctions near their end",
            ],
            "generated_at": datetime.utcnow().isoformat(),
        }
    
    opportunities = []
    
    for auction in auctions:
        hours_left = (auction.end_time - datetime.utcnow()).total_seconds() / 3600
        
        # Skip auctions that have ended or are too far out
        if hours_left <= 0 or hours_left > 72:
            continue
        
        # Time urgency: Higher score for auctions ending soon
        if hours_left < 1:
            time_score = 5.0
            urgency = "Ending in minutes!"
        elif hours_left < 4:
            time_score = 3.0
            urgency = "Ending very soon"
        elif hours_left < 12:
            time_score = 2.0
            urgency = "Ending today"
        elif hours_left < 24:
            time_score = 1.5
            urgency = "Ending tomorrow"
        else:
            time_score = 1.0
            urgency = "Active"
        
        # Competition factor: Lower bids = better opportunity
        if auction.num_bids < 3:
            competition_score = 3.0
            competition = "Almost no competition"
        elif auction.num_bids < 10:
            competition_score = 2.0
            competition = "Low competition"
        elif auction.num_bids < 20:
            competition_score = 1.2
            competition = "Moderate competition"
        else:
            competition_score = 0.8
            competition = "High competition"
        
        # Price factor: Reasonable price points are opportunities
        if auction.current_bid < 100:
            price_score = 2.0
            price_range = "Budget-friendly"
        elif auction.current_bid < 500:
            price_score = 1.5
            price_range = "Mid-range"
        elif auction.current_bid < 2000:
            price_score = 1.2
            price_range = "Premium"
        else:
            price_score = 1.0
            price_range = "High-value"
        
        # Calculate overall opportunity score
        opportunity_score = round(time_score * competition_score * price_score, 1)
        
        # Only include if score is interesting (> 3)
        if opportunity_score < 3:
            continue
        
        listing = await _convert_to_listing(auction, db, include_valuation=False)
        
        recommendation = (
            "🔥 Hot" if opportunity_score >= 10 else
            "⚡ Great" if opportunity_score >= 6 else
            "👀 Watch"
        )
        
        opportunities.append({
            "auction": listing.model_dump(),
            "analysis": {
                "opportunity_score": opportunity_score,
                "time_score": time_score,
                "competition_score": competition_score,
                "price_score": price_score,
                "urgency": urgency,
                "competition": competition,
                "price_range": price_range,
                "recommendation": recommendation,
                "reasoning": f"{urgency} • {competition} • {price_range}",
            }
        })
    
    # Sort by opportunity score
    opportunities.sort(key=lambda x: x["analysis"]["opportunity_score"], reverse=True)
    
    return {
        "opportunities": opportunities[:15],
        "strategy_tips": [
            "⏰ Auctions ending soon have snipe potential",
            "📉 Low bid count = overlooked opportunities",
            "💡 Set a max budget and stick to it",
        ],
        "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)


def _calculate_pounce_score_v2(domain: str, tld: str, num_bids: int = 0, age_years: int = 0, is_pounce: bool = False) -> int:
    """
    Pounce Score v2.0 - Enhanced scoring algorithm.
    
    Factors:
    - Length (shorter = more valuable)
    - TLD premium
    - Market activity (bids)
    - Age bonus
    - Pounce Direct bonus (verified listings)
    - Penalties (hyphens, numbers, etc.)
    """
    score = 50  # Baseline
    name = domain.rsplit('.', 1)[0] if '.' in domain else domain
    
    # A) LENGTH BONUS (exponential for short domains)
    length_scores = {1: 50, 2: 45, 3: 40, 4: 30, 5: 20, 6: 15, 7: 10}
    score += length_scores.get(len(name), max(0, 15 - len(name)))
    
    # B) TLD PREMIUM
    tld_scores = {
        'com': 20, 'ai': 25, 'io': 18, 'co': 12, 
        'ch': 15, 'de': 10, 'net': 8, 'org': 8,
        'app': 10, 'dev': 10, 'xyz': 5
    }
    score += tld_scores.get(tld.lower(), 0)
    
    # C) MARKET ACTIVITY (bids = demand signal)
    if num_bids >= 20:
        score += 15
    elif num_bids >= 10:
        score += 10
    elif num_bids >= 5:
        score += 5
    elif num_bids >= 2:
        score += 2
    
    # D) AGE BONUS (established domains)
    if age_years and age_years > 15:
        score += 10
    elif age_years and age_years > 10:
        score += 7
    elif age_years and age_years > 5:
        score += 3
    
    # E) POUNCE DIRECT BONUS (verified = trustworthy)
    if is_pounce:
        score += 10
    
    # F) PENALTIES
    if '-' in name:
        score -= 25
    if any(c.isdigit() for c in name) and len(name) > 3:
        score -= 20
    if len(name) > 15:
        score -= 15
    
    # G) CONSONANT CHECK (no gibberish like "xkqzfgh")
    consonants = 'bcdfghjklmnpqrstvwxyz'
    max_streak = 0
    current_streak = 0
    for c in name.lower():
        if c in consonants:
            current_streak += 1
            max_streak = max(max_streak, current_streak)
        else:
            current_streak = 0
    if max_streak > 4:
        score -= 15
    
    return max(0, min(100, score))


def _is_premium_domain(domain_name: str) -> bool:
    """Check if a domain looks premium/professional (Vanity Filter)."""
    parts = domain_name.rsplit('.', 1)
    name = parts[0] if parts else domain_name
    tld = parts[1].lower() if len(parts) > 1 else ""
    
    # Premium TLDs only
    premium_tlds = ['com', 'io', 'ai', 'co', 'de', 'ch', 'net', 'org', 'app', 'dev', 'xyz']
    if tld and tld not in premium_tlds:
        return False
    
    # Length check
    if len(name) > 15:
        return False
    if len(name) < 3:
        return False
    
    # Hyphen check
    if name.count('-') > 1:
        return False
    
    # Digit check
    if sum(1 for c in name if c.isdigit()) > 2:
        return False
    
    # Consonant cluster check
    consonants = 'bcdfghjklmnpqrstvwxyz'
    max_streak = 0
    current_streak = 0
    for c in name.lower():
        if c in consonants:
            current_streak += 1
            max_streak = max(max_streak, current_streak)
        else:
            current_streak = 0
    if max_streak > 4:
        return False
    
    return True


# ============== UNIFIED MARKET FEED ==============

@router.get("/feed", response_model=MarketFeedResponse)
async def get_market_feed(
    # Source filter
    source: str = Query("all", enum=["all", "pounce", "external"]),
    
    # Search & filters
    keyword: Optional[str] = Query(None, description="Search in domain names"),
    tld: Optional[str] = Query(None, description="Filter by TLD"),
    min_price: Optional[float] = Query(None, ge=0),
    max_price: Optional[float] = Query(None, ge=0),
    min_score: int = Query(0, ge=0, le=100),
    ending_within: Optional[int] = Query(None, description="Auctions ending within X hours"),
    verified_only: bool = Query(False, description="Only show verified Pounce listings"),
    
    # Sort
    sort_by: str = Query("score", enum=["score", "price_asc", "price_desc", "time", "newest"]),
    
    # Pagination
    limit: int = Query(50, le=200),
    offset: int = Query(0, ge=0),
    
    # Auth
    current_user: Optional[User] = Depends(get_current_user_optional),
    db: AsyncSession = Depends(get_db),
):
    """
    🚀 UNIFIED MARKET FEED — The heart of Pounce
    
    Combines:
    - 💎 Pounce Direct: DNS-verified user listings (instant buy)
    - 🏢 External Auctions: Scraped from GoDaddy, Sedo, NameJet, etc.
    
    For non-authenticated users:
    - Vanity filter applied (premium domains only)
    - Pounce Score visible but limited details
    
    For authenticated users (Trader/Tycoon):
    - Full access to all domains
    - Advanced filtering
    - Valuation data
    
    POUNCE EXCLUSIVE domains are highlighted and appear first.
    """
    items: List[MarketFeedItem] = []
    pounce_count = 0
    auction_count = 0
    
    # ═══════════════════════════════════════════════════════════════
    # 1. POUNCE DIRECT LISTINGS (Our USP!)
    # ═══════════════════════════════════════════════════════════════
    if source in ["all", "pounce"]:
        listing_query = select(DomainListing).where(
            DomainListing.status == ListingStatus.ACTIVE.value
        )
        
        if keyword:
            listing_query = listing_query.where(
                DomainListing.domain.ilike(f"%{keyword}%")
            )
        
        if verified_only:
            listing_query = listing_query.where(
                DomainListing.verification_status == VerificationStatus.VERIFIED.value
            )
        
        if min_price is not None:
            listing_query = listing_query.where(DomainListing.asking_price >= min_price)
        if max_price is not None:
            listing_query = listing_query.where(DomainListing.asking_price <= max_price)
        
        result = await db.execute(listing_query)
        listings = result.scalars().all()
        
        for listing in listings:
            domain_tld = listing.domain.rsplit('.', 1)[1] if '.' in listing.domain else ""
            
            # Apply TLD filter
            if tld and domain_tld.lower() != tld.lower().lstrip('.'):
                continue
            
            pounce_score = listing.pounce_score or _calculate_pounce_score_v2(
                listing.domain, domain_tld, is_pounce=True
            )
            
            # Apply score filter
            if pounce_score < min_score:
                continue
            
            items.append(MarketFeedItem(
                id=f"pounce-{listing.id}",
                domain=listing.domain,
                tld=domain_tld,
                price=listing.asking_price or 0,
                currency=listing.currency or "USD",
                price_type="fixed" if listing.price_type == "fixed" else "negotiable",
                status="instant",
                source="Pounce",
                is_pounce=True,
                verified=listing.is_verified,
                seller_verified=listing.is_verified,
                slug=listing.slug,
                url=f"/buy/{listing.slug}",
                is_external=False,
                pounce_score=pounce_score,
            ))
            pounce_count += 1
    
    # ═══════════════════════════════════════════════════════════════
    # 2. EXTERNAL AUCTIONS (Scraped from platforms)
    # ═══════════════════════════════════════════════════════════════
    if source in ["all", "external"]:
        auction_query = select(DomainAuction).where(DomainAuction.is_active == True)
        
        if keyword:
            auction_query = auction_query.where(
                DomainAuction.domain.ilike(f"%{keyword}%")
            )
        
        if tld:
            auction_query = auction_query.where(
                DomainAuction.tld == tld.lower().lstrip('.')
            )
        
        if min_price is not None:
            auction_query = auction_query.where(DomainAuction.current_bid >= min_price)
        if max_price is not None:
            auction_query = auction_query.where(DomainAuction.current_bid <= max_price)
        
        if ending_within:
            cutoff = datetime.utcnow() + timedelta(hours=ending_within)
            auction_query = auction_query.where(DomainAuction.end_time <= cutoff)
        
        result = await db.execute(auction_query)
        auctions = result.scalars().all()
        
        for auction in auctions:
            # Apply vanity filter for non-authenticated users
            if current_user is None and not _is_premium_domain(auction.domain):
                continue
            
            pounce_score = _calculate_pounce_score_v2(
                auction.domain,
                auction.tld,
                num_bids=auction.num_bids,
                age_years=auction.age_years or 0,
                is_pounce=False
            )
            
            # Apply score filter
            if pounce_score < min_score:
                continue
            
            items.append(MarketFeedItem(
                id=f"auction-{auction.id}",
                domain=auction.domain,
                tld=auction.tld,
                price=auction.current_bid,
                currency=auction.currency,
                price_type="bid",
                status="auction",
                source=auction.platform,
                is_pounce=False,
                verified=False,
                time_remaining=_format_time_remaining(auction.end_time),
                end_time=auction.end_time,
                num_bids=auction.num_bids,
                url=_get_affiliate_url(auction.platform, auction.domain, auction.auction_url),
                is_external=True,
                pounce_score=pounce_score,
            ))
            auction_count += 1
    
    # ═══════════════════════════════════════════════════════════════
    # 3. SORT (Pounce Direct always appears first within same score)
    # ═══════════════════════════════════════════════════════════════
    if sort_by == "score":
        items.sort(key=lambda x: (-x.pounce_score, -int(x.is_pounce), x.domain))
    elif sort_by == "price_asc":
        items.sort(key=lambda x: (x.price, -int(x.is_pounce), x.domain))
    elif sort_by == "price_desc":
        items.sort(key=lambda x: (-x.price, -int(x.is_pounce), x.domain))
    elif sort_by == "time":
        # Pounce Direct first (no time limit), then by end time
        def time_sort_key(x):
            if x.is_pounce:
                return (0, datetime.max)
            return (1, x.end_time or datetime.max)
        items.sort(key=time_sort_key)
    elif sort_by == "newest":
        items.sort(key=lambda x: (-int(x.is_pounce), x.domain))
    
    total = len(items)
    
    # Pagination
    items = items[offset:offset + limit]
    
    # Get unique sources
    sources = list(set(item.source for item in items))
    
    # 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 MarketFeedResponse(
        items=items,
        total=total,
        pounce_direct_count=pounce_count,
        auction_count=auction_count,
        sources=sources,
        last_updated=last_updated,
        filters_applied={
            "source": source,
            "keyword": keyword,
            "tld": tld,
            "min_price": min_price,
            "max_price": max_price,
            "min_score": min_score,
            "ending_within": ending_within,
            "verified_only": verified_only,
            "sort_by": sort_by,
        }
    )