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backend/app/data/aggregation.py

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