80 lines
2.8 KiB
Python
80 lines
2.8 KiB
Python
from datetime import datetime, timedelta
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import pandas as pd
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from app.core.signal_taxonomy import signal_code
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from app.services.altcoin_confirm import detect_box_breakout_pullback_1h, detect_box_breakout_pullback_4h
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def _bar(ts, open_, high, low, close, volume):
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return {
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"timestamp": ts,
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"open": open_,
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"high": high,
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"low": low,
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"close": close,
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"volume": volume,
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}
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def _box_breakout_rows(fail=False):
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start = datetime(2026, 5, 20)
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rows = []
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for i in range(32):
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base = 1.03 + (i % 5) * 0.006
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rows.append(_bar(start + timedelta(hours=4 * i), base, 1.095 + (i % 3) * 0.002, 0.985 - (i % 2) * 0.002, base + 0.004, 1000 + (i % 4) * 30))
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rows.append(_bar(start + timedelta(hours=4 * 32), 1.085, 1.16, 1.08, 1.135, 2600))
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if fail:
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rows.append(_bar(start + timedelta(hours=4 * 33), 1.13, 1.135, 1.00, 1.055, 1800))
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else:
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rows.append(_bar(start + timedelta(hours=4 * 33), 1.13, 1.145, 1.092, 1.118, 1600))
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for i in range(12):
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close = (1.04 + i * 0.002) if fail else (1.12 + i * 0.008)
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rows.append(_bar(start + timedelta(hours=4 * (34 + i)), close - 0.01, close + 0.018, close - 0.025, close, 1300 + i * 40))
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return rows
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def test_detect_box_breakout_pullback_4h_finds_box_top_retest():
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df = pd.DataFrame(_box_breakout_rows())
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result = detect_box_breakout_pullback_4h(df)
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assert result["detected"] is True
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assert result["score"] >= 7
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assert result["quality"] in {"良好", "优质"}
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assert result["entry_zone"] > 1.08
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assert result["stop_level"] < result["entry_zone"]
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assert "箱体" in result["signals"][0]
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assert signal_code(result["signals"][0]) == "box_breakout_pullback_4h"
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def test_detect_box_breakout_pullback_4h_rejects_failed_retest():
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df = pd.DataFrame(_box_breakout_rows(fail=True))
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result = detect_box_breakout_pullback_4h(df)
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assert result["detected"] is False
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def test_detect_box_breakout_pullback_1h_finds_early_retest():
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start = datetime(2026, 5, 20)
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rows = []
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for i in range(44):
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base = 1.0 + (i % 4) * 0.003
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rows.append(_bar(start + timedelta(hours=i), base, 1.035 + (i % 3) * 0.001, 0.985 - (i % 2) * 0.001, base + 0.002, 1000 + (i % 5) * 20))
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rows.append(_bar(start + timedelta(hours=44), 1.032, 1.085, 1.03, 1.068, 2300))
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rows.append(_bar(start + timedelta(hours=45), 1.064, 1.073, 1.034, 1.055, 1700))
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for i in range(8):
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close = 1.058 + i * 0.004
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rows.append(_bar(start + timedelta(hours=46 + i), close - 0.006, close + 0.012, close - 0.018, close, 1250 + i * 25))
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result = detect_box_breakout_pullback_1h(pd.DataFrame(rows))
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assert result["detected"] is True
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assert result["score"] >= 7
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assert result["quality"] in {"良好", "优质"}
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assert signal_code(result["signals"][0]) == "box_breakout_pullback_1h"
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