2569718930@qq.com
928 lines
32 KiB
Python
928 lines
32 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_extract_market_bucket_range_supports_fahrenheit_ranges():
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layer = PolymarketReadOnlyLayer()
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market = {
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"question": "Will the highest temperature in Miami be between 80-81°F on April 21?",
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"slug": "highest-temperature-in-miami-on-april-21-2026-80-81f",
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}
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assert layer._extract_market_bucket_range(market) == (80.0, 81.0, "F")
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assert layer._extract_market_bucket_temp(market) == 80.5
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assert layer._extract_market_bucket_label(market, 80.5) == "80-81F"
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def test_fetch_token_market_data_uses_rest_orderbook_executable_prices():
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layer = PolymarketReadOnlyLayer()
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layer.fast_price_only = False
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payloads = {
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("/price", "BUY"): {"price": "0.27"},
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("/price", "SELL"): {"price": "0.23"},
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("/midpoint", None): {"midpoint": "0.50"},
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("/last-trade-price", None): {"price": "0.49"},
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("/book", None): {
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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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}
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def _fake_clob_get(path, params):
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if path == "/price":
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return payloads[(path, params.get("side"))]
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return payloads[(path, None)]
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layer._clob_get = _fake_clob_get
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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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assert data["quote_source"] == "polymarket_clob_rest"
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def test_fetch_token_market_data_fast_price_only_skips_heavy_endpoints():
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layer = PolymarketReadOnlyLayer()
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layer.fast_price_only = True
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calls = []
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payloads = {
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("/price", "BUY"): {"price": "0.23"},
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("/price", "SELL"): {"price": "0.27"},
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}
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def _fake_clob_get(path, params):
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calls.append((path, params.get("side")))
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if path == "/price":
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return payloads[(path, params.get("side"))]
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return None
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layer._clob_get = _fake_clob_get
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data = layer._fetch_token_market_data("token-1")
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assert calls == [("/price", "BUY"), ("/price", "SELL")]
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assert data["buy"] == 0.27
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assert data["sell"] == 0.23
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assert data["midpoint"] == 0.25
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assert round(data["spread"], 6) == 0.04
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assert data["last_trade_price"] is None
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assert data["book"] is None
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assert data["quote_source"] == "polymarket_clob_fast_price"
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def test_fetch_token_market_data_keeps_buy_sell_semantics_without_orderbook():
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layer = PolymarketReadOnlyLayer()
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layer.fast_price_only = False
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payloads = {
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("/price", "BUY"): {"price": "0.23"},
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("/price", "SELL"): {"price": "0.27"},
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("/midpoint", None): {"midpoint": "0.25"},
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("/last-trade-price", None): {"price": "0.24"},
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("/book", None): None,
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}
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def _fake_clob_get(path, params):
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if path == "/price":
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return payloads[(path, params.get("side"))]
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return payloads[(path, None)]
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layer._clob_get = _fake_clob_get
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data = layer._fetch_token_market_data("token-1")
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assert data["buy"] == 0.27
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assert data["sell"] == 0.23
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assert data["midpoint"] == 0.25
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def test_weather_event_slug_uses_polymarket_city_aliases():
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layer = PolymarketReadOnlyLayer()
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assert (
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layer._build_weather_event_slug("new york", "2026-04-30")
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== "highest-temperature-in-nyc-on-april-30-2026"
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)
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assert (
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layer._build_weather_event_slug("aurora", "2026-04-30")
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== "highest-temperature-in-denver-on-april-30-2026"
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)
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def test_market_quote_fallback_uses_gamma_best_bid_ask_when_clob_missing():
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layer = PolymarketReadOnlyLayer()
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market = {
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"bestBid": "0.53",
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"bestAsk": "0.54",
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"lastTradePrice": "0.54",
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"spread": "0.01",
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"outcomePrices": '["0.535", "0.465"]',
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"liquidityClob": "57040.5",
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}
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yes = layer._merge_market_quote_fallback({}, market, "yes")
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no = layer._merge_market_quote_fallback({}, market, "no")
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assert yes["buy"] == 0.54
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assert yes["sell"] == 0.53
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assert yes["midpoint"] == 0.535
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assert yes["quote_source"] == "polymarket_gamma_market_fallback"
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assert no["buy"] == 0.47
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assert round(no["sell"], 6) == 0.46
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assert round(no["midpoint"], 6) == 0.465
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assert no["book_liquidity"] == 57040.5
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def test_market_quote_fallback_preserves_clob_prices_when_available():
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layer = PolymarketReadOnlyLayer()
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market = {
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"bestBid": "0.53",
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"bestAsk": "0.54",
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"outcomePrices": '["0.535", "0.465"]',
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}
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merged = layer._merge_market_quote_fallback(
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{"buy": 0.55, "sell": 0.52, "midpoint": 0.535, "quote_source": "polymarket_clob_rest"},
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market,
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"yes",
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)
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assert merged["buy"] == 0.55
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assert merged["sell"] == 0.52
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assert merged["quote_source"] == "polymarket_clob_rest"
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def test_get_token_market_data_uses_price_cache_within_ttl():
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layer = PolymarketReadOnlyLayer()
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calls = []
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def _fake_fetch(_token_id):
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calls.append(_token_id)
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return {"buy": 0.33, "sell": 0.31, "midpoint": 0.32}
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layer._fetch_token_market_data = _fake_fetch
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first = layer._get_token_market_data("token-1")
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second = layer._get_token_market_data("token-1")
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assert first["buy"] == 0.33
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assert second["midpoint"] == 0.32
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assert calls == ["token-1"]
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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_trade_state_keeps_open_markets_tradable_after_gamma_end_date():
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layer = PolymarketReadOnlyLayer()
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state = layer._market_trade_state(
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{
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"active": True,
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"closed": False,
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"acceptingOrders": True,
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"endDate": "2020-01-01T00:00:00Z",
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}
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)
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assert state["tradable"] is True
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assert state["reason"] is None
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assert state["ended_at_utc"] == "2020-01-01T00:00:00+00:00"
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def test_lau_fau_shan_uses_shenzhen_market_city():
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layer = PolymarketReadOnlyLayer()
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captured = {}
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def _fake_find_primary_market(city_key, target_date, **_kwargs):
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captured["primary_city_key"] = city_key
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captured["target_date"] = target_date
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return (
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{
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"id": "market-1",
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"question": "Will the highest temperature in Shenzhen be 30C or higher on April 23?",
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"slug": "highest-temperature-in-shenzhen-on-april-23-2026-30c-or-higher",
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"conditionId": "condition-1",
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"active": True,
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"closed": False,
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"acceptingOrders": True,
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"volumeNum": 1000,
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"liquidityNum": 500,
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},
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None,
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)
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layer._find_primary_market = _fake_find_primary_market
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layer._extract_market_tokens = lambda _market: [
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{"outcome": "Yes", "token_id": "yes-token"},
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{"outcome": "No", "token_id": "no-token"},
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]
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layer._get_token_market_data = lambda token_id: (
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{"buy": 0.42, "sell": 0.40, "midpoint": 0.41}
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if token_id == "yes-token"
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else {"buy": 0.61, "sell": 0.59, "midpoint": 0.60}
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)
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def _fake_build_top_temperature_buckets(city_key, **_kwargs):
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captured["bucket_city_key"] = city_key
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return []
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layer._build_top_temperature_buckets = _fake_build_top_temperature_buckets
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scan = layer.build_market_scan(
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city="Lau Fau Shan",
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target_date="2026-04-23",
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temperature_bucket={"temp": 30, "probability": 0.58},
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model_probability=0.58,
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)
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assert captured["primary_city_key"] == "shenzhen"
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assert captured["bucket_city_key"] == "shenzhen"
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assert scan["city_key"] == "lau fau shan"
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assert scan["market_city_key"] == "shenzhen"
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assert scan["selected_slug"] == "highest-temperature-in-shenzhen-on-april-23-2026-30c-or-higher"
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def test_build_market_scan_lite_skips_related_buckets():
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layer = PolymarketReadOnlyLayer()
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layer._find_primary_market = lambda *_args, **_kwargs: (
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{
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"id": "market-1",
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"question": "Will the highest temperature in Shenzhen be 30C or higher on April 23?",
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"slug": "highest-temperature-in-shenzhen-on-april-23-2026-30c-or-higher",
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"conditionId": "condition-1",
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"active": True,
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"closed": False,
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"acceptingOrders": True,
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},
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None,
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)
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layer._extract_market_tokens = lambda _market: [
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{"outcome": "Yes", "token_id": "yes-token"},
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{"outcome": "No", "token_id": "no-token"},
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]
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layer._get_token_market_data = lambda token_id: (
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{"buy": 0.42, "sell": 0.40, "midpoint": 0.41}
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if token_id == "yes-token"
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else {"buy": 0.61, "sell": 0.59, "midpoint": 0.60}
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)
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called = {"bucket": 0}
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def _fake_build_top_temperature_buckets(**_kwargs):
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called["bucket"] += 1
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return [{"value": 30.0, "market_price": 0.41}]
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layer._build_top_temperature_buckets = _fake_build_top_temperature_buckets
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scan = layer.build_market_scan(
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city="Shenzhen",
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target_date="2026-04-23",
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temperature_bucket={"temp": 30, "probability": 0.58},
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model_probability=0.58,
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include_related_buckets=False,
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)
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assert scan["scan_scope"] == "lite"
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assert scan["midpoint"] == 0.41
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assert round(scan["spread"], 6) == 0.02
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assert scan["top_buckets"] == []
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assert scan["all_buckets"] == []
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assert called["bucket"] == 0
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def test_build_market_scan_aggregates_emos_probability_for_threshold_market():
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layer = PolymarketReadOnlyLayer()
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layer._find_primary_market = lambda *_args, **_kwargs: (
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{
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"id": "market-1",
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"question": "Will the highest temperature in Shenzhen be 30C or higher on April 23?",
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"slug": "highest-temperature-in-shenzhen-on-april-23-2026-30c-or-higher",
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"conditionId": "condition-1",
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"active": True,
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"closed": False,
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"acceptingOrders": True,
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},
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None,
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)
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layer._extract_market_tokens = lambda _market: [
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{"outcome": "Yes", "token_id": "yes-token"},
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{"outcome": "No", "token_id": "no-token"},
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]
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layer._get_token_market_data = lambda token_id: (
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{"buy": 0.42, "sell": 0.40, "midpoint": 0.41}
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if token_id == "yes-token"
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else {"buy": 0.61, "sell": 0.59, "midpoint": 0.60}
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)
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layer._build_top_temperature_buckets = lambda **_kwargs: []
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scan = layer.build_market_scan(
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city="Shenzhen",
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target_date="2026-04-23",
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temperature_bucket={"temp": 30, "probability": 0.30},
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model_probability=0.30,
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probability_distribution=[
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{"value": 29, "probability": 0.20},
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{"value": 30, "probability": 0.30},
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{"value": 31, "probability": 0.50},
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],
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temp_symbol="°C",
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)
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assert round(scan["model_probability"], 6) == 0.8
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assert round(scan["edge_percent"], 6) == 39.0
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def test_build_top_temperature_buckets_use_aggregated_emos_probability():
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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-12-2026-15c-or-higher",
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"question": "Will the highest temperature in Ankara be 15C or higher on March 12?",
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"volumeNum": 900,
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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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layer._extract_market_tokens = lambda market: [
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{"outcome": "Yes", "token_id": f"{market['slug']}|yes"},
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{"outcome": "No", "token_id": f"{market['slug']}|no"},
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]
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layer._get_token_market_data = lambda token_id: (
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{"midpoint": 0.41, "buy": 0.42, "sell": 0.40}
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if token_id.endswith("|yes")
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else {"midpoint": 0.59, "buy": 0.60, "sell": 0.58}
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)
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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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probability_distribution=[
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{"value": 13, "probability": 0.10},
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{"value": 14, "probability": 0.25},
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{"value": 15, "probability": 0.35},
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{"value": 16, "probability": 0.30},
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],
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temp_symbol="°C",
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limit=4,
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)
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assert round(rows[0]["probability"], 6) == 0.9
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assert round(rows[0]["edge_percent"], 6) == 49.0
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assert round(rows[1]["probability"], 6) == 0.65
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def test_hydrate_bucket_prices_uses_executable_quotes_without_midpoint():
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layer = PolymarketReadOnlyLayer()
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buckets = [
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{
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"temp": 14.0,
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"yes_token_id": "yes-token",
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"no_token_id": "no-token",
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}
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]
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def _fake_get_token_market_data(token_id):
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if token_id == "yes-token":
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return {
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"buy": 0.66,
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"sell": 0.70,
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"quote_source": "polymarket_clob_rest",
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"quote_age_ms": 0,
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}
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return {"buy": 0.30, "sell": 0.36}
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layer._get_token_market_data = _fake_get_token_market_data
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layer._hydrate_bucket_prices(buckets)
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assert buckets[0]["yes_buy"] == 0.66
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assert buckets[0]["yes_sell"] == 0.70
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assert buckets[0]["no_buy"] == 0.30
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assert buckets[0]["no_sell"] == 0.36
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assert round(buckets[0]["market_price"], 6) == 0.68
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assert round(buckets[0]["probability"], 6) == 0.68
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assert buckets[0]["quote_source"] == "polymarket_clob_rest"
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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?",
|
|
"volumeNum": 1000,
|
|
}
|
|
markets = [
|
|
primary_market,
|
|
{
|
|
"slug": "highest-temperature-in-ankara-on-march-12-2026-14c-or-higher-v2",
|
|
"question": "Will the highest temperature in Ankara be 14C or higher on March 12? (v2)",
|
|
"volumeNum": 900,
|
|
},
|
|
{
|
|
"slug": "highest-temperature-in-ankara-on-march-12-2026-13c-or-higher",
|
|
"question": "Will the highest temperature in Ankara be 13C or higher on March 12?",
|
|
"volumeNum": 1100,
|
|
},
|
|
{
|
|
"slug": "highest-temperature-in-ankara-on-march-12-2026-12c-or-higher",
|
|
"question": "Will the highest temperature in Ankara be 12C or higher on March 12?",
|
|
"volumeNum": 1200,
|
|
},
|
|
{
|
|
"slug": "highest-temperature-in-ankara-on-march-12-2026-14c-or-lower",
|
|
"question": "Will the highest temperature in Ankara be 14C or lower on March 12?",
|
|
"volumeNum": 1300,
|
|
},
|
|
]
|
|
layer._collect_related_temperature_markets = (
|
|
lambda city_key, target_date, primary_market: markets
|
|
)
|
|
|
|
def _fake_extract_market_tokens(market):
|
|
slug = str(market.get("slug") or "")
|
|
return [
|
|
{"outcome": "Yes", "token_id": f"{slug}|yes"},
|
|
{"outcome": "No", "token_id": f"{slug}|no"},
|
|
]
|
|
|
|
layer._extract_market_tokens = _fake_extract_market_tokens
|
|
|
|
midpoint_map = {
|
|
"highest-temperature-in-ankara-on-march-12-2026-14c-or-higher": 0.79,
|
|
"highest-temperature-in-ankara-on-march-12-2026-14c-or-higher-v2": 0.16,
|
|
"highest-temperature-in-ankara-on-march-12-2026-13c-or-higher": 0.06,
|
|
"highest-temperature-in-ankara-on-march-12-2026-12c-or-higher": 0.01,
|
|
"highest-temperature-in-ankara-on-march-12-2026-14c-or-lower": 0.92,
|
|
}
|
|
|
|
def _fake_get_token_market_data(token_id):
|
|
slug, side = str(token_id).split("|", 1)
|
|
if side == "yes":
|
|
midpoint = midpoint_map.get(slug, 0.5)
|
|
return {
|
|
"midpoint": midpoint,
|
|
"buy": max(0.0, min(1.0, midpoint + 0.01)),
|
|
"sell": max(0.0, min(1.0, midpoint - 0.01)),
|
|
}
|
|
midpoint = 1.0 - midpoint_map.get(slug, 0.5)
|
|
return {
|
|
"midpoint": midpoint,
|
|
"buy": max(0.0, min(1.0, midpoint + 0.01)),
|
|
"sell": max(0.0, min(1.0, midpoint - 0.01)),
|
|
}
|
|
|
|
layer._get_token_market_data = _fake_get_token_market_data
|
|
|
|
rows = layer._build_top_temperature_buckets(
|
|
city_key="ankara",
|
|
target_date="2026-03-12",
|
|
primary_market=primary_market,
|
|
limit=4,
|
|
)
|
|
|
|
values = [row.get("value") for row in rows]
|
|
token_ids = [row.get("yes_token_id") for row in rows]
|
|
assert len(values) == len(set(values))
|
|
assert len(token_ids) == len(set(token_ids))
|
|
assert rows[0]["value"] == 14.0
|
|
assert rows[0]["yes_token_id"] == (
|
|
"highest-temperature-in-ankara-on-march-12-2026-14c-or-higher|yes"
|
|
)
|
|
assert all(not str(row.get("label") or "").startswith("<=") for row in rows)
|
|
|
|
|
|
def test_find_primary_market_prefers_preferred_temperature_and_cache_key():
|
|
layer = PolymarketReadOnlyLayer()
|
|
markets = [
|
|
{
|
|
"slug": "highest-temperature-in-madrid-on-april-23-2026-22corbelow",
|
|
"question": "Will the highest temperature in Madrid be 22C or below on April 23?",
|
|
"volumeNum": 900000,
|
|
"active": True,
|
|
"closed": False,
|
|
"acceptingOrders": True,
|
|
"enableOrderBook": True,
|
|
},
|
|
{
|
|
"slug": "highest-temperature-in-madrid-on-april-23-2026-27c",
|
|
"question": "Will the highest temperature in Madrid be 27C on April 23?",
|
|
"volumeNum": 1000,
|
|
"active": True,
|
|
"closed": False,
|
|
"acceptingOrders": True,
|
|
"enableOrderBook": True,
|
|
},
|
|
]
|
|
|
|
layer._load_markets = lambda active_only=True: markets
|
|
|
|
selected_27, reason_27 = layer._find_primary_market(
|
|
"madrid",
|
|
"2026-04-23",
|
|
preferred_temp=27.0,
|
|
)
|
|
selected_22, reason_22 = layer._find_primary_market(
|
|
"madrid",
|
|
"2026-04-23",
|
|
preferred_temp=22.0,
|
|
)
|
|
|
|
assert reason_27 is None
|
|
assert reason_22 is None
|
|
assert selected_27["slug"] == "highest-temperature-in-madrid-on-april-23-2026-27c"
|
|
assert selected_22["slug"] == "highest-temperature-in-madrid-on-april-23-2026-22corbelow"
|
|
|
|
|
|
def _build_scan_test_layer():
|
|
layer = PolymarketReadOnlyLayer()
|
|
markets = [
|
|
{
|
|
"id": "m-above-14",
|
|
"slug": "highest-temperature-in-wellington-on-april-24-2026-14c-or-higher",
|
|
"question": "Will the highest temperature in Wellington be 14C or higher on April 24?",
|
|
"active": True,
|
|
"closed": False,
|
|
"acceptingOrders": True,
|
|
"enableOrderBook": True,
|
|
"liquidityNum": 12000,
|
|
"volumeNum": 4000,
|
|
"_model_prob": 0.60,
|
|
},
|
|
{
|
|
"id": "m-below-16",
|
|
"slug": "highest-temperature-in-wellington-on-april-24-2026-16c-or-lower",
|
|
"question": "Will the highest temperature in Wellington be 16C or lower on April 24?",
|
|
"active": True,
|
|
"closed": False,
|
|
"acceptingOrders": True,
|
|
"enableOrderBook": True,
|
|
"liquidityNum": 9000,
|
|
"volumeNum": 3500,
|
|
"_model_prob": 0.30,
|
|
},
|
|
{
|
|
"id": "m-above-17",
|
|
"slug": "highest-temperature-in-wellington-on-april-24-2026-17c-or-higher",
|
|
"question": "Will the highest temperature in Wellington be 17C or higher on April 24?",
|
|
"active": True,
|
|
"closed": False,
|
|
"acceptingOrders": True,
|
|
"enableOrderBook": True,
|
|
"liquidityNum": 7000,
|
|
"volumeNum": 2800,
|
|
"_model_prob": 0.20,
|
|
},
|
|
]
|
|
token_map = {
|
|
"m-above-14": {"yes": "yes-14", "no": "no-14"},
|
|
"m-below-16": {"yes": "yes-16", "no": "no-16"},
|
|
"m-above-17": {"yes": "yes-17", "no": "no-17"},
|
|
}
|
|
quote_map = {
|
|
"yes-14": {"buy": 0.48, "sell": 0.46, "midpoint": 0.40, "spread": 0.02, "book_liquidity": 14000},
|
|
"no-14": {"buy": 0.54, "sell": 0.52, "midpoint": 0.60, "spread": 0.02, "book_liquidity": 14000},
|
|
"yes-16": {"buy": 0.42, "sell": 0.40, "midpoint": 0.50, "spread": 0.02, "book_liquidity": 9000},
|
|
"no-16": {"buy": 0.56, "sell": 0.54, "midpoint": 0.50, "spread": 0.02, "book_liquidity": 9000},
|
|
"yes-17": {"buy": 0.08, "sell": 0.07, "midpoint": 0.10, "spread": 0.01, "book_liquidity": 7500},
|
|
"no-17": {"buy": 0.92, "sell": 0.91, "midpoint": 0.90, "spread": 0.01, "book_liquidity": 7500},
|
|
}
|
|
|
|
layer._collect_related_temperature_markets = lambda **_kwargs: markets
|
|
layer._aggregate_distribution_probability_for_market = (
|
|
lambda market, **_kwargs: market.get("_model_prob")
|
|
)
|
|
layer._extract_market_tokens = lambda market: [
|
|
{"outcome": "Yes", "token_id": token_map[market["id"]]["yes"]},
|
|
{"outcome": "No", "token_id": token_map[market["id"]]["no"]},
|
|
]
|
|
layer._batch_get_token_market_data = (
|
|
lambda token_ids, include_books=False: {
|
|
token_id: dict(quote_map[token_id])
|
|
for token_id in token_ids
|
|
if token_id in quote_map
|
|
}
|
|
)
|
|
return layer, markets
|
|
|
|
|
|
def test_distribution_scan_bias_flips_below_markets_into_hotter_signal():
|
|
layer, markets = _build_scan_test_layer()
|
|
|
|
scan = layer._build_distribution_scan_pack(
|
|
city_key="wellington",
|
|
target_date="2026-04-24",
|
|
primary_market=markets[0],
|
|
probability_distribution=[],
|
|
temp_symbol="°C",
|
|
scan_context={
|
|
"local_date": "2026-04-24",
|
|
"local_time": "13:10",
|
|
"peak": {"first_h": 14, "last_h": 16},
|
|
"current_max_so_far": 13.4,
|
|
"current_temp": 13.0,
|
|
"trend": {"recent": []},
|
|
"network_lead_signal": {},
|
|
},
|
|
scan_filters={"limit": 10},
|
|
)
|
|
|
|
bias = scan["distribution_bias"]
|
|
assert bias["available"] is True
|
|
assert bias["direction"] == "hotter"
|
|
assert bias["score"] > 0
|
|
|
|
|
|
def test_distribution_scan_returns_single_primary_signal_from_yes_no_mix():
|
|
layer, markets = _build_scan_test_layer()
|
|
|
|
scan = layer._build_distribution_scan_pack(
|
|
city_key="wellington",
|
|
target_date="2026-04-24",
|
|
primary_market=markets[0],
|
|
probability_distribution=[],
|
|
temp_symbol="°C",
|
|
scan_context={
|
|
"local_date": "2026-04-24",
|
|
"local_time": "13:10",
|
|
"peak": {"first_h": 14, "last_h": 16},
|
|
"current_max_so_far": 13.6,
|
|
"current_temp": 13.2,
|
|
"trend": {"recent": []},
|
|
"network_lead_signal": {},
|
|
},
|
|
scan_filters={"limit": 10, "min_edge_pct": 2},
|
|
)
|
|
|
|
assert scan["candidate_count"] >= 2
|
|
assert isinstance(scan["primary_signal"], dict)
|
|
assert scan["primary_signal"]["side"] == "yes"
|
|
assert scan["primary_signal"]["id"] == scan["rows"][0]["id"]
|
|
assert scan["signal_status"] == "ready"
|
|
|
|
|
|
def test_distribution_scan_hard_filters_block_unusable_extreme_quotes():
|
|
layer, markets = _build_scan_test_layer()
|
|
layer._batch_get_token_market_data = (
|
|
lambda token_ids, include_books=False: {
|
|
token_id: {
|
|
"buy": 0.99 if token_id.startswith("yes") else 0.01,
|
|
"sell": 0.95 if token_id.startswith("yes") else 0.0,
|
|
"midpoint": 0.97 if token_id.startswith("yes") else 0.03,
|
|
"spread": 0.04,
|
|
"book_liquidity": 100,
|
|
}
|
|
for token_id in token_ids
|
|
}
|
|
)
|
|
|
|
scan = layer._build_distribution_scan_pack(
|
|
city_key="wellington",
|
|
target_date="2026-04-24",
|
|
primary_market=markets[0],
|
|
probability_distribution=[],
|
|
temp_symbol="°C",
|
|
scan_context={
|
|
"local_date": "2026-04-24",
|
|
"local_time": "13:10",
|
|
"peak": {"first_h": 14, "last_h": 16},
|
|
"current_max_so_far": 13.6,
|
|
"current_temp": 13.2,
|
|
"trend": {"recent": []},
|
|
"network_lead_signal": {},
|
|
},
|
|
scan_filters={"limit": 10},
|
|
)
|
|
|
|
assert scan["signal_status"] == "no_signal"
|
|
assert scan["candidate_count"] == 0
|
|
assert scan["rows"] == []
|
|
|
|
|
|
def test_distribution_scan_tradable_prefers_peak_bucket_and_adjacent_only():
|
|
layer, markets = _build_scan_test_layer()
|
|
|
|
scan = layer._build_distribution_scan_pack(
|
|
city_key="wellington",
|
|
target_date="2026-04-24",
|
|
primary_market=markets[0],
|
|
probability_distribution=[
|
|
{"value": 14, "probability": 20},
|
|
{"value": 15, "probability": 48},
|
|
{"value": 16, "probability": 24},
|
|
{"value": 17, "probability": 8},
|
|
],
|
|
temp_symbol="°C",
|
|
scan_context={
|
|
"local_date": "2026-04-24",
|
|
"local_time": "13:10",
|
|
"peak": {"first_h": 14, "last_h": 16},
|
|
"current_max_so_far": 13.6,
|
|
"current_temp": 13.2,
|
|
"trend": {"recent": []},
|
|
"network_lead_signal": {},
|
|
},
|
|
scan_filters={"limit": 10, "scan_mode": "tradable", "min_edge_pct": 2},
|
|
)
|
|
|
|
assert scan["signal_status"] == "ready"
|
|
assert scan["primary_signal"]["is_peak_candidate"] is True
|
|
assert scan["primary_signal"]["peak_distance"] in {0, 1}
|
|
assert all(bool(row.get("is_peak_candidate")) for row in scan["rows"])
|
|
assert all((row.get("peak_distance") or 0) <= 1 for row in scan["rows"])
|
|
|
|
|
|
def test_distribution_scan_uses_model_cluster_to_prefer_tail_no_over_yes():
|
|
layer = PolymarketReadOnlyLayer()
|
|
markets = [
|
|
{
|
|
"id": "m-21",
|
|
"slug": "highest-temperature-in-paris-on-april-24-2026-21c",
|
|
"question": "Will the highest temperature in Paris be 21C on April 24?",
|
|
"active": True,
|
|
"closed": False,
|
|
"acceptingOrders": True,
|
|
"enableOrderBook": True,
|
|
"liquidityNum": 6000,
|
|
"volumeNum": 5000,
|
|
"_model_prob": 0.205,
|
|
},
|
|
{
|
|
"id": "m-22",
|
|
"slug": "highest-temperature-in-paris-on-april-24-2026-22c",
|
|
"question": "Will the highest temperature in Paris be 22C on April 24?",
|
|
"active": True,
|
|
"closed": False,
|
|
"acceptingOrders": True,
|
|
"enableOrderBook": True,
|
|
"liquidityNum": 6000,
|
|
"volumeNum": 5000,
|
|
"_model_prob": 0.34,
|
|
},
|
|
{
|
|
"id": "m-24",
|
|
"slug": "highest-temperature-in-paris-on-april-24-2026-24c",
|
|
"question": "Will the highest temperature in Paris be 24C on April 24?",
|
|
"active": True,
|
|
"closed": False,
|
|
"acceptingOrders": True,
|
|
"enableOrderBook": True,
|
|
"liquidityNum": 6000,
|
|
"volumeNum": 5000,
|
|
"_model_prob": 0.06,
|
|
},
|
|
]
|
|
token_map = {
|
|
"m-21": {"yes": "yes-21", "no": "no-21"},
|
|
"m-22": {"yes": "yes-22", "no": "no-22"},
|
|
"m-24": {"yes": "yes-24", "no": "no-24"},
|
|
}
|
|
quote_map = {
|
|
"yes-21": {"buy": 0.16, "sell": 0.14, "midpoint": 0.15, "spread": 0.02, "book_liquidity": 6000},
|
|
"no-21": {"buy": 0.85, "sell": 0.83, "midpoint": 0.84, "spread": 0.02, "book_liquidity": 6000},
|
|
"yes-22": {"buy": 0.34, "sell": 0.32, "midpoint": 0.33, "spread": 0.02, "book_liquidity": 6000},
|
|
"no-22": {"buy": 0.67, "sell": 0.65, "midpoint": 0.66, "spread": 0.02, "book_liquidity": 6000},
|
|
"yes-24": {"buy": 0.06, "sell": 0.05, "midpoint": 0.055, "spread": 0.01, "book_liquidity": 6000},
|
|
"no-24": {"buy": 0.948, "sell": 0.93, "midpoint": 0.94, "spread": 0.018, "book_liquidity": 6000},
|
|
}
|
|
|
|
layer._collect_related_temperature_markets = lambda **_kwargs: markets
|
|
layer._aggregate_distribution_probability_for_market = (
|
|
lambda market, **_kwargs: market.get("_model_prob")
|
|
)
|
|
layer._extract_market_tokens = lambda market: [
|
|
{"outcome": "Yes", "token_id": token_map[market["id"]]["yes"]},
|
|
{"outcome": "No", "token_id": token_map[market["id"]]["no"]},
|
|
]
|
|
layer._batch_get_token_market_data = (
|
|
lambda token_ids, include_books=False: {
|
|
token_id: dict(quote_map[token_id])
|
|
for token_id in token_ids
|
|
if token_id in quote_map
|
|
}
|
|
)
|
|
|
|
scan = layer._build_distribution_scan_pack(
|
|
city_key="paris",
|
|
target_date="2026-04-24",
|
|
primary_market=markets[1],
|
|
probability_distribution=[],
|
|
temp_symbol="°C",
|
|
scan_context={
|
|
"local_date": "2026-04-24",
|
|
"local_time": "08:54",
|
|
"peak": {"first_h": 14, "last_h": 16},
|
|
"current_max_so_far": 20.0,
|
|
"current_temp": 20.0,
|
|
"trend": {"recent": []},
|
|
"network_lead_signal": {},
|
|
"deb_prediction": 22.0,
|
|
"models": {
|
|
"Open-Meteo": 22.4,
|
|
"ICON": 22.4,
|
|
"GEM": 22.2,
|
|
"GDPS": 22.2,
|
|
"ECMWF": 21.2,
|
|
"JMA": 20.9,
|
|
"GFS": 20.6,
|
|
"AIFS": 22.9,
|
|
},
|
|
},
|
|
scan_filters={"limit": 10, "scan_mode": "tradable", "min_edge_pct": 2},
|
|
)
|
|
|
|
recommendations = {(row["target_value"], row["side"]) for row in scan["rows"]}
|
|
assert (21.0, "no") in recommendations
|
|
assert (24.0, "no") in recommendations
|
|
assert (21.0, "yes") not in recommendations
|
|
assert all(row.get("is_directional_candidate") for row in scan["rows"])
|
|
assert all(row.get("cluster_adjusted") for row in scan["rows"])
|
|
|
|
|
|
def test_batch_token_market_data_falls_back_to_single_fetch_when_batch_fails():
|
|
layer = PolymarketReadOnlyLayer()
|
|
layer._clob_post = lambda *_args, **_kwargs: None
|
|
layer._fetch_token_market_data = lambda token_id: {
|
|
"buy": 0.33,
|
|
"sell": 0.31,
|
|
"midpoint": 0.32,
|
|
"quote_source": f"fallback:{token_id}",
|
|
}
|
|
|
|
result = layer._batch_get_token_market_data(["token-a", "token-b"])
|
|
|
|
assert result["token-a"]["midpoint"] == 0.32
|
|
assert result["token-b"]["quote_source"] == "fallback:token-b"
|
|
|
|
|
|
def test_normalize_scan_filters_raises_liquidity_floor_when_high_liquidity_only():
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layer = PolymarketReadOnlyLayer()
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filters = layer._normalize_scan_filters({"high_liquidity_only": True})
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assert filters["high_liquidity_only"] is True
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assert filters["min_liquidity"] >= 5000
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