2569718930@qq.com
201 lines
6.5 KiB
Python
201 lines
6.5 KiB
Python
from src.data_collection.polymarket_readonly import PolymarketReadOnlyLayer
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def test_normalize_orderbook_uses_sorted_best_prices():
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layer = PolymarketReadOnlyLayer()
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raw = {
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"bids": [
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{"price": "0.24", "size": "10"},
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{"price": "0.31", "size": "5"},
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{"price": "0.27", "size": "8"},
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],
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"asks": [
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{"price": "0.44", "size": "9"},
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{"price": "0.39", "size": "6"},
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{"price": "0.42", "size": "4"},
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],
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}
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book, _liquidity = layer._normalize_orderbook(raw)
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assert book is not None
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assert book["best_bid"] == 0.31
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assert book["best_ask"] == 0.39
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assert book["bid_levels"][0][0] == 0.31
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assert book["ask_levels"][0][0] == 0.39
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def test_fetch_token_market_data_prefers_orderbook_executable_prices():
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class FakeClob:
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@staticmethod
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def get_price(_token_id: str, side: str):
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if side == "BUY":
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return {"price": "0.11"}
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return {"price": "0.88"}
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@staticmethod
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def get_midpoint(_token_id: str):
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return {"midpoint": "0.50"}
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@staticmethod
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def get_last_trade_price(_token_id: str):
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return {"price": "0.49"}
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@staticmethod
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def get_order_book(_token_id: str):
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return {
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"bids": [{"price": "0.24", "size": "10"}],
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"asks": [{"price": "0.26", "size": "12"}],
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}
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layer = PolymarketReadOnlyLayer()
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layer._get_clob_client = lambda: FakeClob()
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data = layer._fetch_token_market_data("token-1")
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# Executable BUY should match best ask from the book.
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assert data["buy"] == 0.26
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# Executable SELL should match best bid from the book.
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assert data["sell"] == 0.24
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assert data["midpoint"] == 0.5
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assert data["last_trade_price"] == 0.49
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def test_get_token_market_data_prefers_fresh_ws_cache():
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layer = PolymarketReadOnlyLayer()
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class FakeWsCache:
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enabled = True
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def subscribe(self, asset_ids):
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self.asset_ids = list(asset_ids)
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@staticmethod
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def get_market_data(_token_id):
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return {
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"buy": 0.33,
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"sell": 0.31,
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"midpoint": 0.32,
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"quote_source": "polymarket_ws",
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"quote_age_ms": 80,
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}
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layer._ws_quote_cache = FakeWsCache()
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layer._fetch_token_market_data = lambda _token_id: {"buy": 0.99}
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data = layer._get_token_market_data("token-1")
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assert data["buy"] == 0.33
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assert data["sell"] == 0.31
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assert data["quote_source"] == "polymarket_ws"
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def test_price_analysis_computes_edge_kelly_and_lock():
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layer = PolymarketReadOnlyLayer()
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analysis = layer._build_price_analysis(
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model_probability=0.62,
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yes_buy=0.52,
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yes_sell=0.50,
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no_buy=0.45,
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no_sell=0.43,
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)
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assert analysis["available"] is True
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assert abs(analysis["yes"]["edge"] - 0.10) < 0.000001
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assert round(analysis["yes"]["kelly_fraction"], 6) == round(
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(0.62 - 0.52) / (1.0 - 0.52),
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6,
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)
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assert round(analysis["yes"]["quarter_kelly"], 6) == round(
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((0.62 - 0.52) / (1.0 - 0.52)) / 4.0,
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6,
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)
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assert abs(analysis["no"]["edge"] - -0.07) < 0.000001
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assert analysis["lock"]["available"] is True
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assert round(analysis["lock"]["edge"], 6) == 0.03
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assert analysis["best_side"] == "yes"
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def test_build_top_temperature_buckets_dedupes_same_temperature():
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layer = PolymarketReadOnlyLayer()
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primary_market = {
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"slug": "highest-temperature-in-ankara-on-march-12-2026-14c-or-higher",
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"question": "Will the highest temperature in Ankara be 14C or higher on March 12?",
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"volumeNum": 1000,
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}
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markets = [
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primary_market,
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{
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"slug": "highest-temperature-in-ankara-on-march-12-2026-14c-or-higher-v2",
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"question": "Will the highest temperature in Ankara be 14C or higher on March 12? (v2)",
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"volumeNum": 900,
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},
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{
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"slug": "highest-temperature-in-ankara-on-march-12-2026-13c-or-higher",
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"question": "Will the highest temperature in Ankara be 13C or higher on March 12?",
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"volumeNum": 1100,
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},
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{
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"slug": "highest-temperature-in-ankara-on-march-12-2026-12c-or-higher",
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"question": "Will the highest temperature in Ankara be 12C or higher on March 12?",
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"volumeNum": 1200,
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},
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{
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"slug": "highest-temperature-in-ankara-on-march-12-2026-14c-or-lower",
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"question": "Will the highest temperature in Ankara be 14C or lower on March 12?",
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"volumeNum": 1300,
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},
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]
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layer._collect_related_temperature_markets = (
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lambda city_key, target_date, primary_market: markets
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)
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def _fake_extract_market_tokens(market):
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slug = str(market.get("slug") or "")
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return [
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{"outcome": "Yes", "token_id": f"{slug}|yes"},
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{"outcome": "No", "token_id": f"{slug}|no"},
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]
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layer._extract_market_tokens = _fake_extract_market_tokens
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midpoint_map = {
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"highest-temperature-in-ankara-on-march-12-2026-14c-or-higher": 0.79,
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"highest-temperature-in-ankara-on-march-12-2026-14c-or-higher-v2": 0.16,
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"highest-temperature-in-ankara-on-march-12-2026-13c-or-higher": 0.06,
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"highest-temperature-in-ankara-on-march-12-2026-12c-or-higher": 0.01,
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"highest-temperature-in-ankara-on-march-12-2026-14c-or-lower": 0.92,
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}
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def _fake_get_token_market_data(token_id):
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slug, side = str(token_id).split("|", 1)
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if side == "yes":
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midpoint = midpoint_map.get(slug, 0.5)
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return {
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"midpoint": midpoint,
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"buy": max(0.0, min(1.0, midpoint + 0.01)),
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"sell": max(0.0, min(1.0, midpoint - 0.01)),
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}
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midpoint = 1.0 - midpoint_map.get(slug, 0.5)
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return {
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"midpoint": midpoint,
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"buy": max(0.0, min(1.0, midpoint + 0.01)),
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"sell": max(0.0, min(1.0, midpoint - 0.01)),
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}
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layer._get_token_market_data = _fake_get_token_market_data
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rows = layer._build_top_temperature_buckets(
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city_key="ankara",
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target_date="2026-03-12",
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primary_market=primary_market,
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limit=4,
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)
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values = [row.get("value") for row in rows]
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assert len(values) == len(set(values))
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assert rows[0]["value"] == 14.0
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assert all(not str(row.get("label") or "").startswith("<=") for row in rows)
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