Fetch native exchange OHLCV per timeframe instead of local aggregation.

Store and serve 15m/2h/4h directly from the exchange so market charts match venue candles.

Co-authored-by: Cursor <cursoragent@cursor.com>

hub_ohlcv_lib.py

@@ -31,17 +31,13 @@ CHART_TIMEFRAME_ORDER = (
 )
 DAILY_PLUS_TIMEFRAMES = frozenset({"1d", "1w"})
 
-# 入库 / 同步真源（交易所拉取）
-STORED_TIMEFRAMES = frozenset({"1m", "5m", "1h", "1d", "1w"})
+# 入库 / 同步真源（各周期直拉交易所，不做本地聚合）
+STORED_TIMEFRAMES = frozenset(CHART_TIMEFRAMES)
 PERMANENT_STORED_TIMEFRAMES = frozenset({"1d", "1w"})
-YEAR_ROLLING_STORED = frozenset({"5m", "1h"})
+YEAR_ROLLING_STORED = frozenset({"5m", "15m", "1h", "2h", "4h"})
 
-# 展示周期 → 本地聚合源（不落库）
-CHART_DISPLAY_AGGREGATE_FROM: dict[str, str] = {
-    "15m": "5m",
-    "2h": "1h",
-    "4h": "1h",
-}
+# 行情区不做展示周期聚合；保留空映射供兼容读取
+CHART_DISPLAY_AGGREGATE_FROM: dict[str, str] = {}
 
 SMALL_DISPLAY_TFS = frozenset({"1m", "5m", "15m"})
 MID_DISPLAY_TFS = frozenset({"1h", "2h", "4h"})
@@ -151,13 +147,17 @@ def seed_bar_target(storage_tf: str) -> int:
 
 
 def retention_policy_meta() -> dict[str, Any]:
+    year = {"mode": "days", "days": HUB_KLINE_5M_1H_RETENTION_DAYS}
     return {
         "1m": {"mode": "bars", "max_bars": HUB_KLINE_1M_MAX_BARS},
-        "5m": {"mode": "days", "days": HUB_KLINE_5M_1H_RETENTION_DAYS},
-        "1h": {"mode": "days", "days": HUB_KLINE_5M_1H_RETENTION_DAYS},
+        "5m": dict(year),
+        "15m": dict(year),
+        "1h": dict(year),
+        "2h": dict(year),
+        "4h": dict(year),
         "1d": {"mode": "permanent"},
         "1w": {"mode": "permanent"},
-        "aggregate_from": dict(CHART_DISPLAY_AGGREGATE_FROM),
+        "aggregate_from": {},
     }
 
 
@@ -399,6 +399,68 @@ def align_bar_open_ms(open_time_ms: int, period_ms: int) -> int:
     return (int(open_time_ms) // period_ms) * period_ms
 
 
+def snap_to_bar_grid(ts_ms: int, origin_ms: int, step_ms: int) -> int:
+    step = max(1, int(step_ms))
+    origin = int(origin_ms)
+    if ts_ms <= origin:
+        return origin
+    idx = (int(ts_ms) - origin + step - 1) // step
+    return origin + idx * step
+
+
+def fill_missing_ohlcv_bars(
+    bars: list[dict[str, Any]],
+    period_ms: int,
+    start_ms: int | None = None,
+    end_ms: int | None = None,
+) -> list[dict[str, Any]]:
+    """细周期缺口用上一根收盘价填平，保证聚合后 K 线时间轴连续。"""
+    by_ts: dict[int, dict[str, Any]] = {}
+    for b in bars or []:
+        try:
+            by_ts[int(b["open_time_ms"])] = b
+        except (KeyError, TypeError, ValueError):
+            continue
+    if not by_ts:
+        return []
+    keys = sorted(by_ts.keys())
+    step_ms = max(1, int(period_ms))
+    origin = keys[0]
+    aligned_start = snap_to_bar_grid(
+        int(start_ms if start_ms is not None else keys[0]), origin, step_ms
+    )
+    aligned_end = max(
+        int(end_ms if end_ms is not None else keys[-1]),
+        keys[-1],
+    )
+    out: list[dict[str, Any]] = []
+    last: dict[str, Any] | None = None
+    for ts_key in keys:
+        if ts_key <= aligned_start:
+            last = by_ts[ts_key]
+    ts = aligned_start
+    while ts <= aligned_end:
+        cur = by_ts.get(ts)
+        if cur is not None:
+            last = cur
+            out.append(cur)
+        elif last is not None:
+            c = float(last["close"])
+            out.append(
+                {
+                    "open_time_ms": ts,
+                    "open": c,
+                    "high": c,
+                    "low": c,
+                    "close": c,
+                    "volume": 0.0,
+                    "filled": True,
+                }
+            )
+        ts += step_ms
+    return out
+
+
 def aggregate_ohlcv_bars(
     bars: list[dict[str, Any]], target_timeframe: str
 ) -> list[dict[str, Any]]: