pounce/backend/app/api/auctions.py

"""
Smart Pounce - Domain Auction Aggregator

This module aggregates domain auctions from multiple platforms:
- GoDaddy Auctions
- Sedo
- NameJet
- SnapNames
- DropCatch

IMPORTANT: This is a META-SEARCH feature.
We don't host our own auctions - we aggregate and display auctions
from other platforms, earning affiliate commissions on clicks.

Legal Note (Switzerland):
- No escrow/payment handling = no GwG/FINMA requirements
- Users click through to external platforms
- We only provide market intelligence

VALUATION:
All estimated values are calculated using our transparent algorithm:
  Value = $50 × Length_Factor × TLD_Factor × Keyword_Factor × Brand_Factor

See /api/v1/portfolio/valuation/{domain} for full calculation details.
"""
import logging
from datetime import datetime, timedelta
from typing import Optional, List
from fastapi import APIRouter, Depends, Query
from pydantic import BaseModel
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.services.valuation import valuation_service

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


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

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


class AuctionListing(BaseModel):
    """A domain auction listing from any platform."""
    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
    valuation: Optional[AuctionValuation] = None


class AuctionSearchResponse(BaseModel):
    """Response for auction search."""
    auctions: List[AuctionListing]
    total: int
    platforms_searched: List[str]
    last_updated: datetime
    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


# ============== Mock Data (for demo - replace with real scrapers) ==============

MOCK_AUCTIONS = [
    {
        "domain": "cryptopay.io",
        "platform": "GoDaddy",
        "current_bid": 2500,
        "num_bids": 23,
        "end_time": datetime.utcnow() + timedelta(hours=2, minutes=34),
        "buy_now_price": 15000,
        "reserve_met": True,
        "traffic": 1200,
        "age_years": 8,
    },
    {
        "domain": "aitools.com",
        "platform": "Sedo",
        "current_bid": 8750,
        "num_bids": 45,
        "end_time": datetime.utcnow() + timedelta(hours=5, minutes=12),
        "buy_now_price": None,
        "reserve_met": True,
        "traffic": 3400,
        "age_years": 12,
    },
    {
        "domain": "nftmarket.co",
        "platform": "NameJet",
        "current_bid": 850,
        "num_bids": 12,
        "end_time": datetime.utcnow() + timedelta(hours=1, minutes=5),
        "buy_now_price": 5000,
        "reserve_met": False,
        "traffic": 500,
        "age_years": 4,
    },
    {
        "domain": "cloudservices.net",
        "platform": "GoDaddy",
        "current_bid": 1200,
        "num_bids": 8,
        "end_time": datetime.utcnow() + timedelta(hours=12, minutes=45),
        "buy_now_price": 7500,
        "reserve_met": True,
        "traffic": 800,
        "age_years": 15,
    },
    {
        "domain": "blockchain.tech",
        "platform": "Sedo",
        "current_bid": 3200,
        "num_bids": 31,
        "end_time": datetime.utcnow() + timedelta(hours=0, minutes=45),
        "buy_now_price": None,
        "reserve_met": True,
        "traffic": 2100,
        "age_years": 6,
    },
    {
        "domain": "startupfund.io",
        "platform": "NameJet",
        "current_bid": 650,
        "num_bids": 5,
        "end_time": datetime.utcnow() + timedelta(hours=8, minutes=20),
        "buy_now_price": 3000,
        "reserve_met": False,
        "traffic": 150,
        "age_years": 3,
    },
    {
        "domain": "metaverse.ai",
        "platform": "GoDaddy",
        "current_bid": 12500,
        "num_bids": 67,
        "end_time": datetime.utcnow() + timedelta(hours=3, minutes=15),
        "buy_now_price": 50000,
        "reserve_met": True,
        "traffic": 5000,
        "age_years": 2,
    },
    {
        "domain": "defiswap.com",
        "platform": "Sedo",
        "current_bid": 4500,
        "num_bids": 28,
        "end_time": datetime.utcnow() + timedelta(hours=6, minutes=30),
        "buy_now_price": 20000,
        "reserve_met": True,
        "traffic": 1800,
        "age_years": 5,
    },
    {
        "domain": "healthtech.app",
        "platform": "DropCatch",
        "current_bid": 420,
        "num_bids": 7,
        "end_time": datetime.utcnow() + timedelta(hours=0, minutes=15),
        "buy_now_price": None,
        "reserve_met": None,
        "traffic": 300,
        "age_years": 2,
    },
    {
        "domain": "gameverse.io",
        "platform": "SnapNames",
        "current_bid": 1100,
        "num_bids": 15,
        "end_time": datetime.utcnow() + timedelta(hours=4, minutes=0),
        "buy_now_price": 5500,
        "reserve_met": True,
        "traffic": 900,
        "age_years": 4,
    },
]

# Platform affiliate URLs
PLATFORM_URLS = {
    "GoDaddy": "https://auctions.godaddy.com/trpItemListing.aspx?miession=&domain=",
    "Sedo": "https://sedo.com/search/?keyword=",
    "NameJet": "https://www.namejet.com/Pages/Auctions/BackorderSearch.aspx?q=",
    "SnapNames": "https://www.snapnames.com/results.aspx?q=",
    "DropCatch": "https://www.dropcatch.com/domain/",
}


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) -> str:
    """Get affiliate URL for a platform."""
    base_url = PLATFORM_URLS.get(platform, "")
    return f"{base_url}{domain}"


async def _convert_to_listing(auction: dict, db: AsyncSession, include_valuation: bool = True) -> AuctionListing:
    """Convert raw auction data to AuctionListing with valuation."""
    domain = auction["domain"]
    tld = domain.rsplit(".", 1)[-1] if "." in domain else ""
    current_bid = auction["current_bid"]
    
    valuation_data = None
    
    if include_valuation:
        try:
            # Get real valuation from our service
            result = await valuation_service.estimate_value(domain, db, save_result=False)
            
            if "error" not in result:
                estimated_value = result["estimated_value"]
                value_ratio = round(estimated_value / current_bid, 2) if current_bid > 0 else 99
                
                valuation_data = AuctionValuation(
                    estimated_value=estimated_value,
                    value_ratio=value_ratio,
                    potential_profit=round(estimated_value - 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 {domain}: {e}")
    
    return AuctionListing(
        domain=domain,
        platform=auction["platform"],
        platform_url=PLATFORM_URLS.get(auction["platform"], ""),
        current_bid=current_bid,
        currency="USD",
        num_bids=auction["num_bids"],
        end_time=auction["end_time"],
        time_remaining=_format_time_remaining(auction["end_time"]),
        buy_now_price=auction.get("buy_now_price"),
        reserve_met=auction.get("reserve_met"),
        traffic=auction.get("traffic"),
        age_years=auction.get("age_years"),
        tld=tld,
        affiliate_url=_get_affiliate_url(auction["platform"], domain),
        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 across multiple platforms.
    
    This is a META-SEARCH feature:
    - We aggregate listings from GoDaddy, Sedo, NameJet, etc.
    - Clicking through uses affiliate links
    - We do NOT handle payments or transfers
    
    All auctions include estimated values calculated using our algorithm:
    Value = $50 × Length_Factor × TLD_Factor × Keyword_Factor × Brand_Factor
    
    Smart Pounce Strategy:
    - Look for value_ratio > 1.0 (estimated value exceeds current bid)
    - Focus on auctions ending soon with low bid counts
    - Track keywords you're interested in
    """
    auctions = MOCK_AUCTIONS.copy()
    
    # Apply filters
    if keyword:
        keyword_lower = keyword.lower()
        auctions = [a for a in auctions if keyword_lower in a["domain"].lower()]
    
    if tld:
        tld_clean = tld.lower().lstrip(".")
        auctions = [a for a in auctions if a["domain"].endswith(f".{tld_clean}")]
    
    if platform:
        auctions = [a for a in auctions if a["platform"].lower() == platform.lower()]
    
    if min_bid is not None:
        auctions = [a for a in auctions if a["current_bid"] >= min_bid]
    
    if max_bid is not None:
        auctions = [a for a in auctions if a["current_bid"] <= max_bid]
    
    if ending_soon:
        cutoff = datetime.utcnow() + timedelta(hours=1)
        auctions = [a for a in auctions if a["end_time"] <= cutoff]
    
    # Sort (before valuation for efficiency, except value_ratio)
    if sort_by == "ending":
        auctions.sort(key=lambda x: x["end_time"])
    elif sort_by == "bid_asc":
        auctions.sort(key=lambda x: x["current_bid"])
    elif sort_by == "bid_desc":
        auctions.sort(key=lambda x: x["current_bid"], reverse=True)
    elif sort_by == "bids":
        auctions.sort(key=lambda x: x["num_bids"], reverse=True)
    
    total = len(auctions)
    auctions = auctions[offset:offset + limit]
    
    # Convert to response format with valuations
    listings = []
    for a in auctions:
        listing = await _convert_to_listing(a, 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
        )
    
    return AuctionSearchResponse(
        auctions=listings,
        total=total,
        platforms_searched=list(PLATFORM_URLS.keys()),
        last_updated=datetime.utcnow(),
    )


@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.
    
    Smart Pounce Tip:
    - Auctions ending in < 1 hour often have final bidding frenzy
    - Low-bid auctions ending soon can be bargains
    - Look for value_ratio > 1.0 (undervalued domains)
    """
    cutoff = datetime.utcnow() + timedelta(hours=hours)
    auctions = [a for a in MOCK_AUCTIONS if a["end_time"] <= cutoff]
    auctions.sort(key=lambda x: x["end_time"])
    auctions = auctions[:limit]
    
    listings = []
    for a in auctions:
        listing = await _convert_to_listing(a, 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.
    
    These auctions have the most competition - high demand indicators.
    High demand often correlates with quality domains.
    """
    auctions = sorted(MOCK_AUCTIONS, key=lambda x: x["num_bids"], reverse=True)
    auctions = auctions[:limit]
    
    listings = []
    for a in auctions:
        listing = await _convert_to_listing(a, 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),
):
    """
    Get statistics for each auction platform.
    
    Useful for understanding where the best deals are.
    """
    stats = {}
    
    for auction in MOCK_AUCTIONS:
        platform = auction["platform"]
        if platform not in stats:
            stats[platform] = {
                "platform": platform,
                "auctions": [],
                "ending_soon_count": 0,
            }
        stats[platform]["auctions"].append(auction)
        
        if auction["end_time"] <= datetime.utcnow() + timedelta(hours=1):
            stats[platform]["ending_soon_count"] += 1
    
    result = []
    for platform, data in stats.items():
        auctions = data["auctions"]
        result.append(PlatformStats(
            platform=platform,
            active_auctions=len(auctions),
            avg_bid=round(sum(a["current_bid"] for a in auctions) / len(auctions), 2),
            ending_soon=data["ending_soon_count"],
        ))
    
    return sorted(result, key=lambda x: x.active_auctions, reverse=True)


@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.
    
    Our algorithm scores each auction based on:
    1. Value Ratio: estimated_value / current_bid (higher = better deal)
    2. Time Factor: Auctions ending soon get 2× boost
    3. Bid Factor: Low bid count (< 10) gets 1.5× boost
    
    Opportunity Score = value_ratio × time_factor × bid_factor
    
    Recommendations:
    - "Strong buy": Score > 5 (significantly undervalued)
    - "Consider": Score 2-5 (potential opportunity)
    - "Monitor": Score < 2 (fairly priced)
    
    Requires authentication to personalize results.
    """
    opportunities = []
    
    for auction in MOCK_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],
        "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)