2569718930@qq.com
91 lines
2.9 KiB
Python
91 lines
2.9 KiB
Python
from src.data_collection import polymarket_client as pm
|
|
|
|
|
|
def test_extract_best_prices():
|
|
book = {
|
|
"bids": [{"price": "0.41", "size": "100"}, {"price": "0.39", "size": "80"}],
|
|
"asks": [{"price": "0.45", "size": "90"}, {"price": "0.47", "size": "70"}],
|
|
"last_trade_price": "0.44",
|
|
}
|
|
out = pm._extract_best_prices(book)
|
|
assert out["best_bid"] == 0.41
|
|
assert out["best_ask"] == 0.45
|
|
assert out["spread"] == 0.04
|
|
assert out["last_trade_price"] == 0.44
|
|
|
|
|
|
def test_build_city_market_snapshot_buy_sell_and_alerts(monkeypatch):
|
|
pm._prev_snapshots.clear()
|
|
|
|
markets = [
|
|
{
|
|
"id": "m1",
|
|
"question": "Highest temperature in Ankara on March 7?",
|
|
"city": "ankara",
|
|
"date": "2026-03-07",
|
|
"slug": "m1",
|
|
"url": "https://polymarket.com/event/m1",
|
|
"volume": 1000.0,
|
|
"liquidity": 500.0,
|
|
"outcomes": [
|
|
{"name": "6-7°C", "token_id": "t1", "last_price": 0.32},
|
|
{"name": "8-9°C", "token_id": "t2", "last_price": 0.40},
|
|
],
|
|
}
|
|
]
|
|
|
|
books = {
|
|
"t1": {
|
|
"bids": [{"price": "0.30", "size": "50"}],
|
|
"asks": [{"price": "0.36", "size": "55"}],
|
|
"last_trade_price": "0.34",
|
|
"timestamp": "2026-03-06T10:00:00Z",
|
|
},
|
|
# one-sided book + thin liquidity to trigger anomaly
|
|
"t2": {
|
|
"bids": [],
|
|
"asks": [{"price": "0.52", "size": "10"}],
|
|
"last_trade_price": "0.51",
|
|
"timestamp": "2026-03-06T10:00:00Z",
|
|
},
|
|
}
|
|
|
|
def fake_get_city_markets(**kwargs):
|
|
return markets
|
|
|
|
def fake_fetch_order_books(*args, **kwargs):
|
|
return books
|
|
|
|
monkeypatch.setattr(pm, "get_city_markets", fake_get_city_markets)
|
|
monkeypatch.setattr(pm, "fetch_order_books", fake_fetch_order_books)
|
|
|
|
# Seed previous snapshot for token t1, so we can detect a price jump alert
|
|
pm._prev_snapshots["t1"] = {
|
|
"ts": 1.0,
|
|
"best_bid": 0.20,
|
|
"best_ask": 0.24,
|
|
"spread": 0.04,
|
|
"last_trade_price": 0.22,
|
|
}
|
|
|
|
snap = pm.build_city_market_snapshot(city="ankara", target_date="2026-03-07")
|
|
|
|
assert snap["city"] == "ankara"
|
|
assert snap["target_date"] == "2026-03-07"
|
|
assert snap["summary"]["market_count"] == 1
|
|
assert snap["summary"]["outcome_count"] == 2
|
|
|
|
first_market = snap["markets"][0]
|
|
row_t1 = next(x for x in first_market["outcomes"] if x["token_id"] == "t1")
|
|
row_t2 = next(x for x in first_market["outcomes"] if x["token_id"] == "t2")
|
|
|
|
# Buy uses ask, sell uses bid
|
|
assert row_t1["buy_price"] == 0.36
|
|
assert row_t1["sell_price"] == 0.30
|
|
assert round(row_t1["spread"], 2) == 0.06
|
|
|
|
# one-sided orderbook has no sell price
|
|
assert row_t2["buy_price"] == 0.52
|
|
assert row_t2["sell_price"] is None
|
|
assert "one_sided_orderbook" in row_t2["anomaly_flags"]
|