eezd
23a9a5175e
The trader labels crypto news published after 2026-10-16 15:00 with the configured model (category and BTC direction) and, four hours later, backfills BTC/ETH 1m opens at publish, +5m, +1h and +4h from the klines table. A drawer on the trading page shows the cumulative returns of trading the labelled direction, after fees. Nothing is traded; this only collects an out-of-sample record.
33 lines
1.4 KiB
Python
33 lines
1.4 KiB
Python
from datetime import datetime, timedelta
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import pytest
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from alphapulse.trading.signal_stats import Signal, dedupe, summarize
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T0 = datetime(2026, 10, 16, 15, 0)
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def signal(minutes: float, category: str, direction: str, entry: str = "100", after: str = "101") -> Signal:
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return Signal(T0 + timedelta(minutes=minutes), category, direction, {"BTCUSDT": {"entry": entry, "1h": after, "4h": after}})
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def test_dedupe_keeps_first_report_of_same_story_per_category():
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kept = dedupe([signal(0, "event", "bullish"), signal(5, "event", "bearish"), signal(5, "price_report", "bullish"), signal(11, "event", "bullish")])
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assert [(s.published_at - T0, s.category) for s in kept] == [
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(timedelta(0), "event"), (timedelta(minutes=5), "price_report"), (timedelta(minutes=11), "event"),
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]
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def test_summarize_trades_label_direction_and_skips_neutral():
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rows = summarize([
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signal(0, "event", "bullish", after="101"), # 做多 +100bp
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signal(20, "event", "bearish", after="101"), # 做空 −100bp
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signal(40, "event", "bearish", after="98"), # 做空 +200bp
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signal(60, "event", "neutral", after="150"), # 中性不交易
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], ("BTCUSDT",), ("4h",))
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assert len(rows) == 1
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row = rows[0]
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assert (row["n"], row["win_rate"]) == (3, pytest.approx(2 / 3))
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assert row["mean_bp"] == pytest.approx(200 / 3)
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assert row["net_bp"] == pytest.approx(200 / 3 - 8)
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