gahusb
c5a88fab66
aggregate_1min_to_5min: 1분봉 5개 → 5분봉 1개 (open=첫, close=마지막, high=max, low=min, volume=sum). classify_minute_momentum: 직전 5개 5분봉 양봉 개수 + 거래량 60분 multiplier → 5-level (strong_up/weak_up/neutral/weak_down/strong_down). 40 tests pass. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
"""분봉 OHLCV → 5-level 모멘텀 분류."""
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from __future__ import annotations
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from collections import deque
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# 분류 카테고리
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STRONG_UP = "strong_up"
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WEAK_UP = "weak_up"
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NEUTRAL = "neutral"
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WEAK_DOWN = "weak_down"
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STRONG_DOWN = "strong_down"
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_BARS_PER_5MIN = 5
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_LOOKBACK_5MIN_BARS = 5
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_VOLUME_AVG_WINDOW = 12 # 60분 = 5분봉 12개
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def aggregate_1min_to_5min(minute_bars: list[dict]) -> list[dict]:
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"""1분봉 N개 → 5분봉 floor(N/5) 개. 시간 오름차순.
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각 5분봉: open=첫 1분봉 open, high=max, low=min, close=마지막 close, volume=sum.
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"""
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bars_5min = []
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chunks = len(minute_bars) // _BARS_PER_5MIN
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for i in range(chunks):
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chunk = minute_bars[i * _BARS_PER_5MIN : (i + 1) * _BARS_PER_5MIN]
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bars_5min.append({
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"datetime": chunk[0]["datetime"],
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"open": chunk[0]["open"],
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"high": max(b["high"] for b in chunk),
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"low": min(b["low"] for b in chunk),
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"close": chunk[-1]["close"],
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"volume": sum(b["volume"] for b in chunk),
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})
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return bars_5min
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def classify_minute_momentum(minute_bars: deque) -> str:
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"""1분봉 deque → 5-level 모멘텀 분류.
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Returns: STRONG_UP / WEAK_UP / NEUTRAL / WEAK_DOWN / STRONG_DOWN
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"""
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minute_list = list(minute_bars)
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if len(minute_list) < _BARS_PER_5MIN * _LOOKBACK_5MIN_BARS:
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return NEUTRAL # 데이터 부족
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bars_5min = aggregate_1min_to_5min(minute_list)
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if len(bars_5min) < _LOOKBACK_5MIN_BARS:
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return NEUTRAL
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recent = bars_5min[-_LOOKBACK_5MIN_BARS:]
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up_count = sum(1 for b in recent if b["close"] > b["open"])
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# 거래량 multiplier: recent 5 avg vs 60분 avg
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recent_vol_avg = sum(b["volume"] for b in recent) / len(recent)
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long_window = bars_5min[-_VOLUME_AVG_WINDOW:]
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long_vol_avg = sum(b["volume"] for b in long_window) / len(long_window)
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vol_mult = recent_vol_avg / long_vol_avg if long_vol_avg > 0 else 1.0
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# 5-level 분류
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if up_count == 5 and vol_mult >= 1.5:
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return STRONG_UP
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elif up_count >= 3 and vol_mult >= 1.0:
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return WEAK_UP
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elif up_count == 0 and vol_mult >= 1.5:
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return STRONG_DOWN
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elif up_count <= 2 and vol_mult < 1.0:
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return WEAK_DOWN
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else:
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return NEUTRAL
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