55 lines
1.7 KiB
Python
55 lines
1.7 KiB
Python
"""K 线周期聚合：日线 -> 周/月/年。
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生产环境用 TimescaleDB Continuous Aggregates 在库里预物化（性能）；
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MVP 在应用层用 pandas resample 即可，逻辑等价、便于切换。
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OHLCV 聚合规则：开=周期内首根开、高=最高、低=最低、收=末根收、量=求和。
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"""
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from __future__ import annotations
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import pandas as pd
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from ..domain import Bar
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# pandas resample 规则（周一为周首；月/年以首日对齐）
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_RULES = {"1w": "W-MON", "1M": "MS", "1y": "YS"}
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# 各周期的"年交易日数"（用于夏普等指标的年化）
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_BARS_PER_YEAR = {"1d": 252, "1w": 52, "1M": 12, "1y": 1}
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def bars_per_year(timeframe: str) -> int:
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return _BARS_PER_YEAR.get(timeframe, 252)
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def resample_bars(bars: list[Bar], timeframe: str) -> list[Bar]:
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"""把日线 bars 聚合为目标周期；日线或未知周期原样返回。"""
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if not bars or timeframe in ("1d", "d", "day", "", None):
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return bars
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rule = _RULES.get(timeframe)
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if rule is None:
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return bars
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df = pd.DataFrame(
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[{"ts": b.ts, "open": b.open, "high": b.high, "low": b.low, "close": b.close, "volume": b.volume}
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for b in bars]
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).set_index("ts").sort_index()
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agg = (
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df.resample(rule)
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.agg({"open": "first", "high": "max", "low": "min", "close": "last", "volume": "sum"})
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.dropna()
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)
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return [
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Bar(
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ts=ts.to_pydatetime(),
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open=float(row["open"]),
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high=float(row["high"]),
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low=float(row["low"]),
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close=float(row["close"]),
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volume=float(row["volume"]),
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)
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for ts, row in agg.iterrows()
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]
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