pyr0ball
bccedb1fe5
eBay omits the 12-month positive percentage for returning sellers and buyer-only accounts with no recent sales. Previously ratio=0.0 with count>0 triggered established_bad_actor; now it returns None from the scorer (score_is_partial=True) and emits a soft no_recent_seller_data flag instead. ratio=0.0 with count=0 is still treated as no-history.
85 lines
3.5 KiB
Python
85 lines
3.5 KiB
Python
"""Five metadata trust signals, each scored 0–20."""
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from __future__ import annotations
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import json
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from typing import Optional
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from app.db.models import Seller
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ELECTRONICS_CATEGORIES = {"ELECTRONICS", "COMPUTERS_TABLETS", "VIDEO_GAMES", "CELL_PHONES"}
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# Coefficient of variation (stddev/mean) above which the price distribution is
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# considered too heterogeneous to trust the market median for scam detection.
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# e.g. "Lenovo RTX intel" mixes $200 old ThinkPads with $2000 Legions → CV ~1.0+
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_HETEROGENEOUS_CV_THRESHOLD = 0.6
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class MetadataScorer:
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def score(
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self,
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seller: Seller,
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market_median: Optional[float],
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listing_price: float,
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price_cv: Optional[float] = None,
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) -> dict[str, Optional[int]]:
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return {
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"account_age": self._account_age(seller.account_age_days) if seller.account_age_days is not None else None,
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"feedback_count": self._feedback_count(seller.feedback_count),
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"feedback_ratio": self._feedback_ratio(seller.feedback_ratio, seller.feedback_count),
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"price_vs_market": self._price_vs_market(listing_price, market_median, price_cv),
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"category_history": self._category_history(seller.category_history_json),
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}
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def _account_age(self, days: int) -> int:
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if days < 7: return 0
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if days < 30: return 5
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if days < 90: return 10
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if days < 365: return 15
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return 20
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def _feedback_count(self, count: int) -> int:
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if count < 3: return 0
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if count < 10: return 5
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if count < 50: return 10
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if count < 200: return 15
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return 20
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def _feedback_ratio(self, ratio: float, count: int) -> Optional[int]:
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# ratio=0.0 with count>0 means the 12-month percentage wasn't on the page —
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# eBay omits the ratio for returning/buyer-only sellers with no recent sales.
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# Treat as missing rather than "literally 0% positive" (which eBay doesn't allow
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# on active accounts — those get suspended long before reaching 0%).
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if ratio == 0.0 and count > 0:
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return None
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if ratio < 0.80 and count > 20: return 0
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if ratio < 0.90: return 5
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if ratio < 0.95: return 10
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if ratio < 0.98: return 15
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return 20
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def _price_vs_market(self, price: float, median: Optional[float], price_cv: Optional[float] = None) -> Optional[int]:
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if median is None: return None # data unavailable → aggregator sets score_is_partial
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if median <= 0: return None
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if price_cv is not None and price_cv > _HETEROGENEOUS_CV_THRESHOLD:
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return None # mixed model/generation search — median is unreliable
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ratio = price / median
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if ratio < 0.50: return 0 # >50% below = scam
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if ratio < 0.70: return 5 # >30% below = suspicious
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if ratio < 0.85: return 10
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if ratio <= 1.20: return 20
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return 15 # above market = still ok, just expensive
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def _category_history(self, category_history_json: str) -> Optional[int]:
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try:
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history = json.loads(category_history_json)
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except (ValueError, TypeError):
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return None # unparseable → data unavailable
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if not history:
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return None # empty dict → no category data from this source
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electronics_sales = sum(
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v for k, v in history.items() if k in ELECTRONICS_CATEGORIES
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)
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if electronics_sales == 0: return 0
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if electronics_sales < 5: return 8
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if electronics_sales < 20: return 14
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return 20
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