dekun/crypto_monitor
0
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Fetch native exchange OHLCV per timeframe instead of local aggregation.

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

Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
dekun
2026-06-08 07:59:49 +08:00
parent 3ac854d74c
commit 63472719ec
3 changed files with 102 additions and 72 deletions
Download Patch File Download Diff File Expand all files Collapse all files
hub_kline_store.py
+18 -51
View File
@@ -1,4 +1,4 @@
"""中控 K 线 SQLite：分周期保留、本地聚合、分页读取。"""
"""中控 K 线 SQLite：分周期保留、交易所直拉、分页读取。"""
from __future__ import annotations
@@ -12,9 +12,7 @@ from hub_ohlcv_lib import (
HUB_KLINE_1M_MAX_BARS,
HUB_KLINE_5M_1H_RETENTION_DAYS,
TIMEFRAME_MS,
aggregate_ohlcv_bars,
aggregate_ratio,
aggregation_source_for_display,
YEAR_ROLLING_STORED,
chart_chunk_limit,
chart_initial_limit,
chart_memory_cap,
@@ -26,7 +24,6 @@ from hub_ohlcv_lib import (
retention_policy_meta,
round_ohlcv_bars_to_tick,
seed_bar_target,
sync_timeframe_for_display,
)
_DEFAULT_RETENTION_DAYS = 15
@@ -200,10 +197,10 @@ def purge_1m_bar_cap(db_path: Path | None = None, *, max_bars: int | None = None
def purge_retention(db_path: Path | None = None) -> int:
"""按周期策略清理：5m/1h 一年；1m 保留最近 N 根；1d/1w 不删。"""
"""按周期策略清理：5m/15m/1h/2h/4h 一年；1m 保留最近 N 根；1d/1w 不删。"""
n = 0
n += purge_timeframe_by_days("5m", HUB_KLINE_5M_1H_RETENTION_DAYS, db_path)
n += purge_timeframe_by_days("1h", HUB_KLINE_5M_1H_RETENTION_DAYS, db_path)
for tf in sorted(YEAR_ROLLING_STORED):
n += purge_timeframe_by_days(tf, HUB_KLINE_5M_1H_RETENTION_DAYS, db_path)
n += purge_1m_bar_cap(db_path)
return n
@@ -400,19 +397,6 @@ def _trim_display_bars(
return bars
def _aggregate_display_bars(
src_bars: list[dict[str, Any]],
display_tf: str,
*,
need: int,
before_ms: int | None,
) -> list[dict[str, Any]]:
if not src_bars:
return []
agg = aggregate_ohlcv_bars(src_bars, display_tf)
return _trim_display_bars(agg, need=need, before_ms=before_ms)
def resolve_chart_bars(
exchange_key: str,
symbol: str,
@@ -427,7 +411,7 @@ def resolve_chart_bars(
) -> dict[str, Any]:
"""
分页读库：首屏 / 左拖 before_ms / 尾部 tail_refresh。
15m←5m，2h/4h←1h 现场聚合；其余直读入库周期。
各展示周期均直读交易所同步入库的同名 K 线。
"""
init_db(db_path)
purged = purge_retention(db_path)
@@ -438,8 +422,7 @@ def resolve_chart_bars(
if not sym or not ex_k:
return {"ok": False, "msg": "缺少 exchange 或 symbol"}
agg_src = aggregation_source_for_display(display_tf)
storage_tf = agg_src or sync_timeframe_for_display(display_tf)
storage_tf = display_tf
is_history = before_ms is not None and int(before_ms) > 0
need = int(
limit
@@ -450,24 +433,14 @@ def resolve_chart_bars(
now_ms = int(time.time() * 1000)
period_display = TIMEFRAME_MS[display_tf]
period_storage = TIMEFRAME_MS[storage_tf]
ratio = aggregate_ratio(display_tf, storage_tf) if agg_src else 1
if tail_refresh and not is_history:
need = min(need, max(30, ratio * 6 if agg_src else 20))
src_need = need * ratio + ratio * 4
need = min(need, 30)
cutoff = history_cutoff_ms_for_storage(storage_tf, now_ms)
source_kind = "aggregate" if agg_src else "db"
def load_display_rows() -> list[dict[str, Any]]:
if agg_src:
if is_history:
src = load_bars_before(ex_k, sym, storage_tf, int(before_ms), src_need, db_path)
else:
src = load_bars_latest(ex_k, sym, storage_tf, src_need, db_path)
return _aggregate_display_bars(
src, display_tf, need=need, before_ms=before_ms if is_history else None
)
if is_history:
return load_bars_before(ex_k, sym, storage_tf, int(before_ms), need, db_path)
rows = load_bars_before(ex_k, sym, storage_tf, int(before_ms), need, db_path)
return _trim_display_bars(rows, need=need, before_ms=int(before_ms))
return load_bars_latest(ex_k, sym, storage_tf, need, db_path)
db_rows: list[dict[str, Any]] = []
@@ -498,20 +471,16 @@ def resolve_chart_bars(
if is_history:
bms = int(before_ms)
anchor = bms - period_display
since = max(cutoff, anchor - period_storage * src_need)
fetch_limit = min(src_need + 20, 1500)
since = max(cutoff, anchor - period_storage * need)
fetch_limit = min(need + 20, 1500)
elif tail_only:
if agg_src:
src_tail = load_bars_latest(ex_k, sym, storage_tf, 5, db_path)
anchor_ms = int(src_tail[-1]["open_time_ms"]) if src_tail else now_ms
else:
anchor_ms = int(newest_db) if newest_db is not None else now_ms
since = max(cutoff, anchor_ms - period_storage * max(5, ratio * 3))
fetch_limit = min(max(20, ratio * 8), 300)
anchor_ms = int(newest_db) if newest_db is not None else now_ms
since = max(cutoff, anchor_ms - period_storage * 5)
fetch_limit = min(need + 20, 300)
else:
since = max(cutoff, now_ms - period_storage * min(src_need, seed_bar_target(storage_tf)))
since = max(cutoff, now_ms - period_storage * min(need, seed_bar_target(storage_tf)))
fetch_limit = min(
seed_bar_target(storage_tf) if force_refresh else src_need + 20,
seed_bar_target(storage_tf) if force_refresh else need + 20,
1500,
)
@@ -527,8 +496,6 @@ def resolve_chart_bars(
if price_tick is not None:
save_symbol_price_tick(ex_k, sym, price_tick, db_path)
db_rows = load_display_rows()
if fetched:
source_kind = "remote" if source_kind == "db" else source_kind
else:
remote_err = remote.get("msg") or remote.get("error") or "实例拉取 K 线失败"
if not db_rows:
@@ -589,7 +556,7 @@ def resolve_chart_bars(
"oldest_ms": oldest_ms,
"newest_ms": newest_ms,
"exhausted": exhausted,
"source": "remote" if fetched else source_kind,
"source": "remote" if fetched else "db",
"retention_policy": retention_policy_meta(),
"candles": candles,
"from_cache": from_cache,