research: 影子交易器落在 Hummingbot 上,并修掉 Bitget 连接器的换根延迟

1m 腿的滑点余量只有几个 bp,所以要测的必须是生产路径的滑点——换个运行时
测出来的数就不作数。框架因此从「滑点已知后再定」提前到测量阶段就定为
Hummingbot(Spot/Perp 连接器均 v2.0,Bitget 是 Foundation Partner)。

新增 research/live/。前置测量:

- bench_compute.py 本机算力,1m 单币 0.318s、三币串行 1.38s
- venue_parity.py Binance 与 Bitget 同根信号重合仅 14.6~42.6%
- signal_sensitivity.py 0.25bp 扰动就换掉一半信号
- aggregate_robustness.py 但总体期望不降——脆的是信号身份,不是 alpha
- bitget_baseline.py 因此改用 Bitget 原生基线定预算:余量 BTC -0.13bp、
  ETH +4.02bp、SOL +2.92bp。BTC 本就为负,只作延迟测量的参照物

运行时选型:

- parity_env.py 容器与本机信号逐一相同(下标、中枢数、checksum 全等),
  容器内 0.26s/币反而更快。故 chanlun 直接挂载进容器,不必另起信号服务。
  装进现有 .venv 那条路走不通:Hummingbot 要 numba>=0.61.2 与
  aiohttp<3.14,与本机 Python 3.14 冲突
- latency_ccxt.py / latency_hummingbot.py / latency_compare.py 初测显示
  Hummingbot 比 ccxt.pro 慢约 1030ms,90 根逐根配对里 80~97% 更慢
- probe_ws_action.py 否掉「丢弃 snapshot」的猜测:换根首条就是 update
- probe_hb_vs_raw.py 与 latency_attribute.py 四路归因——容器网络 2~18ms、
  Hummingbot 处理 -10~-30ms,1350~1480ms 全落在解析方式上
- probe_ws_payload.py 定位根因:Bitget 换根会推一条带两根的消息
  [上一根, 新一根],而上游取 data["data"][0] 拿到的是上一根,新一根要等
  下一条单元素消息

修复:

- patched_candles.py 处理消息里的全部元素。不能简单改成 [-1]——那样上一根
  的收盘价会永远停在换根前约 1 秒的那次推送上,而信号对 0.25bp 都敏感
- verify_patch.py 60 根配对验证:拿回 1060~1090ms,与原始 WS 只差 5~14ms
  已贴理论下限,19 根已收盘 K 线 OHLCV 逐根未变。折算 ETH 省 0.54bp、
  SOL 省 0.42bp。此 bug 值得向上游反馈

影子交易器:

- shadow_hb.py 不下单,读连接器真实盘口按仓位吃单深度算成交价,与次根开盘价
  (回测 entry_delay=1 的口径)相减,分解成延迟漂移、盘口价差、深度冲击。
  盘口 10Hz 滚动缓冲 30 秒,把延迟变成自变量:每个信号记 0.5/1/2/5s 与实际
  算完时刻各一个滑点值,本机算得慢也不影响能读出的曲线
- shadow_signal.py 信号计算隔离到子进程。0.26s 是纯 CPU 且 chanlun 受 GIL
  限制,放进 asyncio 循环会把行情处理一起卡住
- shadow_report.py 首日延迟门槛与滑点曲线报表

不用 paper trade 测滑点:它的成交由 Hummingbot 自己的撮合模型模拟,
测出来是模型行为而非市场行为。

Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
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2026-08-27 23:51:57 +08:00
co-authored by Cursor
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"""最关键的一步:逐笔清单不可复现,总体期望还在不在。
signal_sensitivity 证明 0.25bp 的数据扰动就能换掉一半信号。这本身不判死刑——
趋势跟随策略允许「成交的具体是哪几笔」随机,只要总体期望稳定就仍可交易。
但如果扰动后 PF 与毛均收益也跟着塌,那回测测的就是噪声。
两种结果对应完全不同的下一步:
总体稳定 -> 改成「Bitget 原生信号测滑点 + Bitget 原生回测基线」,主线继续
总体也塌 -> 滑点根本不是瓶颈,1m 腿的问题在信号定义本身,影子交易器白写
口径与 step23 一致:SL/TP/MAX_BARS = 1.5/3.0/48ATR 取信号根,
入场为信号次根开盘价(entry_delay=1),成本 4bp 手续费 + 1bp 滑点。
3.91bp 的滑点预算就是从「毛均收益 +0.0991%」推出来的,所以毛均收益是主看指标。
输出 out/aggregate_robustness.csv。
"""
from __future__ import annotations
import argparse
import os
import sys
import time
import warnings
from pathlib import Path
import numpy as np
import pandas as pd
warnings.filterwarnings("ignore")
for v in ("OMP_NUM_THREADS", "OPENBLAS_NUM_THREADS", "MKL_NUM_THREADS"):
os.environ.setdefault(v, "1")
HERE = Path(__file__).resolve().parent
RESEARCH = HERE.parent
sys.path.insert(0, str(RESEARCH))
sys.path.insert(0, str(RESEARCH.parent))
sys.path.insert(0, str(HERE))
pd.set_option("display.width", 260)
from signal_sensitivity import TICK, perturb # noqa: E402
SYMS = ("BTC", "ETH", "SOL")
LEVELS = (0.0, 0.5, 1.0, 2.0)
SL, TP, MAX_BARS = 1.5, 3.0, 48
FEE, SLIP = 0.0004, 0.0001
def run_once(df_l: pd.DataFrame, df_h: pd.DataFrame) -> pd.DataFrame:
"""跑完整管线并逐笔模拟,返回交易表。"""
from chanlun import TF_DF
from lib.breakout import run_trades
from lib.fast_bsp3 import find_fast_bsp3
from lib.fx_signal import extract_fx_signals, signals_to_frame
from lib.nested_bsp import attach_htf_context, htf_fx_timeline
from lib.nested_level import build_htf_zones
chan_l = TF_DF(df_l, 1, "1m")
cdf = chan_l.dataframe
zones = build_htf_zones(cdf, "1m", chan=chan_l)
if zones.empty:
return pd.DataFrame()
sig = find_fast_bsp3(cdf, zones.reset_index(drop=True))
if sig.empty:
return pd.DataFrame()
chan_h = TF_DF(df_h, 1, "5m")
hdf = chan_h.dataframe
tl = htf_fx_timeline(signals_to_frame(extract_fx_signals(chan_h, hdf)), hdf)
full = attach_htf_context(sig, cdf, tl, "h1")
fin = full[full["h1_agree"] == 1]
if fin.empty:
return pd.DataFrame()
entries = list(zip(fin["entry_idx"].astype(int), fin["direction"].astype(int)))
return run_trades(cdf, entries, SL, TP, MAX_BARS,
fee=FEE + SLIP, entry_delay=1)
def stats(tr: pd.DataFrame) -> dict:
if tr.empty:
return {"笔数": 0}
g = tr["gross"].to_numpy(dtype=float)
n = tr["ret"].to_numpy(dtype=float)
win, loss = n[n > 0].sum(), -n[n < 0].sum()
return {
"笔数": len(tr),
"胜率": f"{(n > 0).mean() * 100:.1f}%",
"毛均收益": f"{g.mean() * 100:+.4f}%",
"净均收益": f"{n.mean() * 100:+.4f}%",
"PF": round(win / loss, 2) if loss > 0 else np.inf,
"t值": round(n.mean() / n.std(ddof=1) * np.sqrt(len(n)), 2) if len(n) > 1 else np.nan,
"滑点余量bp": round(g.mean() * 1e4 - 6.0, 2),
}
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument("--symbols", default="BTC,ETH,SOL")
ap.add_argument("--bars", type=int, default=200_000, help="每币用多少根 1m")
ap.add_argument("--seeds", type=int, default=2)
ap.add_argument("--levels", default=None,
help="逗号分隔的噪声档(bp);只给 0 就是纯基线复现")
ap.add_argument("--tag", default="", help="产物文件名后缀")
args = ap.parse_args()
levels = (tuple(float(x) for x in args.levels.split(","))
if args.levels else LEVELS)
from lib.data import load_local
syms = [s.strip() for s in args.symbols.split(",")]
print(f"[总体稳健性] {syms} · 每币 {args.bars} 根 1m "
f"({args.bars / 1440:.0f} 天) · 噪声档 {levels} bp\n", flush=True)
rows = []
for sym in syms:
df_l = load_local(f"{sym}/USDT:USDT", "1m")
df_h = load_local(f"{sym}/USDT:USDT", "5m")
if df_l is None or df_h is None:
print(f"{sym}: 本地无数据,跳过")
continue
df_l = df_l.tail(args.bars).reset_index(drop=True)
lo = int(df_l["timestamp"].iloc[0])
df_h = df_h[df_h.timestamp >= lo].reset_index(drop=True)
print(f"── {sym} {len(df_l)} 根 1m / {len(df_h)} 根 5m "
f"{df_l['date'].iloc[0]:%Y-%m-%d} ~ {df_l['date'].iloc[-1]:%Y-%m-%d}",
flush=True)
for bp in levels:
for k in range(1 if bp == 0 else args.seeds):
t0 = time.perf_counter()
tr = run_once(perturb(df_l, bp, TICK[sym], 2000 + k), df_h)
s = stats(tr)
rows.append({"品种": sym, "噪声bp": bp, "种子": k, **s})
print(f" 噪声 {bp:>4.2f}bp 种子{k}: " +
" · ".join(f"{k2} {v}" for k2, v in s.items()) +
f" [{time.perf_counter() - t0:.0f}s]", flush=True)
if not rows:
print("无结果")
return
tb = pd.DataFrame(rows)
print("\n" + "=" * 130)
print("########## 1. 逐币 × 噪声档 ##########")
print(tb.to_string(index=False))
print("\n########## 2. 三币合并(同噪声档取均值)##########")
num = tb.copy()
num["毛均bp"] = num["毛均收益"].str.rstrip("%").astype(float) * 100
num["净均bp"] = num["净均收益"].str.rstrip("%").astype(float) * 100
num["胜率_"] = num["胜率"].str.rstrip("%").astype(float)
agg = num.groupby("噪声bp").agg(
笔数=("笔数", "mean"), 胜率=("胜率_", "mean"),
毛均bp=("毛均bp", "mean"), 净均bp=("净均bp", "mean"),
PF=("PF", "mean"), t值=("t值", "mean")).round(2)
agg["滑点余量bp"] = (agg["毛均bp"] - 6.0).round(2)
print(agg.to_string())
print("\n########## 结论 ##########")
print(" step23 的 1m 基线(3 币 3578 笔 / 2.28 年):毛均 9.91bp · PF 2.31 · "
"t 19.92 · 余量 3.91bp")
if 0.0 not in agg.index:
print(" 本次未跑无噪声档,无法给出相对基线的比例")
return
base = agg.loc[0.0]
print(f" 无噪声基线:毛均 {base['毛均bp']:.2f}bp · PF {base['PF']:.2f} · "
f"t {base['t值']:.2f} · 滑点余量 {base['滑点余量bp']:.2f}bp")
for bp in levels[1:]:
if bp not in agg.index:
continue
r = agg.loc[bp]
print(f" 噪声 {bp}bp:毛均 {r['毛均bp']:.2f}bp "
f"({r['毛均bp'] / base['毛均bp'] * 100:.0f}% of 基线) · "
f"PF {r['PF']:.2f} · t {r['t值']:.2f} · 余量 {r['滑点余量bp']:.2f}bp")
print("\n 毛均与 PF 若基本持平 → 逐笔身份随机但总体期望稳定,主线继续,")
print(" 但必须换成 Bitget 原生回测基线,Binance 的逐笔清单不可用于对照。")
print(" 若毛均随噪声单调下滑 → 回测吃的是数据噪声,滑点不是瓶颈。")
out = RESEARCH / "out" / f"aggregate_robustness{args.tag}.csv"
tb.to_csv(out, index=False)
print(f"\n产物写入 {out}")
if __name__ == "__main__":
main()
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"""前置测量一:本机算力——影子交易器的计算延迟下限。
step39 在 Mac 上量到 2000 根窗口单次 0.20s。这台机器只有 2 vCPU 且单核更慢,
而计算延迟直接吃 1m 腿仅 3.9bp 的滑点预算,所以必须在写影子交易器之前实测。
量四件事:
1m 侧 TF_DF(2000根) + build_htf_zones + find_fast_bsp3
5m 侧 TF_DF(800根) + 分型 + 时间线(只在 5m 收盘那根变,可摊薄到 1/5)
串行 3 币 最后一个币要等多久 —— 这就是它的下单延迟
并行 3 币 2 核跑 3 进程会互相抢核,未必比串行快
输出 out/bench_compute.csv,判据是与 3.9bp 预算对应的漂移:本机 1m 波动
BTC 6.5bp/分钟,按 √t 折算,1 秒延迟约 0.8bp、5 秒约 1.9bp。
"""
from __future__ import annotations
import argparse
import os
import sys
import time
import warnings
from concurrent.futures import ProcessPoolExecutor
from pathlib import Path
import numpy as np
import pandas as pd
warnings.filterwarnings("ignore")
for v in ("OMP_NUM_THREADS", "OPENBLAS_NUM_THREADS", "MKL_NUM_THREADS"):
os.environ.setdefault(v, "1")
HERE = Path(__file__).resolve().parent
RESEARCH = HERE.parent
sys.path.insert(0, str(RESEARCH))
sys.path.insert(0, str(RESEARCH.parent))
pd.set_option("display.width", 240)
LTF, HTF = "1m", "5m"
WIN_LTF = 2000
WIN_HTF = max(WIN_LTF // 5, 800)
SYMS = ("BTC", "ETH", "SOL")
def _signal_once(sl_ltf: pd.DataFrame) -> tuple[int, float]:
"""1m 侧:切片重建结构与信号。与 step39 的 _pit_once 同一套调用。"""
from chanlun import TF_DF
from lib.fast_bsp3 import find_fast_bsp3
from lib.nested_level import build_htf_zones
t0 = time.perf_counter()
chan = TF_DF(sl_ltf, 1, LTF)
cdf = chan.dataframe
zones = build_htf_zones(cdf, LTF, chan=chan)
n_sig = 0
if not zones.empty:
sig = find_fast_bsp3(cdf, zones.reset_index(drop=True))
n_sig = len(sig)
return n_sig, time.perf_counter() - t0
def _agree_once(sl_htf: pd.DataFrame) -> tuple[int, float]:
"""5m 侧:切片重建分型时间线。与 step39 的 _pit_agree 同一套调用。"""
from chanlun import TF_DF
from lib.fx_signal import extract_fx_signals, signals_to_frame
from lib.nested_bsp import htf_fx_timeline
t0 = time.perf_counter()
chan = TF_DF(sl_htf, 1, HTF)
tl = htf_fx_timeline(signals_to_frame(extract_fx_signals(chan, chan.dataframe)),
chan.dataframe)
return len(tl), time.perf_counter() - t0
def _load(sym: str) -> tuple[pd.DataFrame, pd.DataFrame]:
from lib.data import load_local
pair = f"{sym}/USDT:USDT"
df_l = load_local(pair, LTF)
df_h = load_local(pair, HTF)
if df_l is None or df_h is None:
raise SystemExit(f"{sym} 本地数据缺失,本机只有 BTC/ETH/SOL")
return df_l, df_h
def _slices(sym: str, reps: int, seed: int) -> list[tuple[pd.DataFrame, pd.DataFrame]]:
"""预先切好窗口。读 feather 是本机 IO,实盘数据来自 WS 缓冲,不计入延迟。"""
df_l, df_h = _load(sym)
rng = np.random.default_rng(seed)
# 从末段随机取窗口,避开数据头部(指标预热)与尾部(不足一窗)
lo, hi = max(WIN_LTF, len(df_l) - 200_000), len(df_l) - 1
ltf_ts = df_l["timestamp"].to_numpy()
htf_ts = df_h["timestamp"].to_numpy()
out = []
for i in rng.integers(lo, hi, size=reps):
i = int(i)
sl_l = df_l.iloc[i - WIN_LTF + 1: i + 1].reset_index(drop=True)
# 5m 只喂到不晚于该 1m 根的部分,与实盘一致
h_end = int(np.searchsorted(htf_ts, ltf_ts[i], side="right"))
sl_h = (df_h.iloc[h_end - WIN_HTF: h_end].reset_index(drop=True)
if h_end >= WIN_HTF else None)
out.append((sl_l, sl_h))
return out
def bench_pair(task: tuple) -> dict:
"""算一根:1m 信号 + 5m 时间线。在子进程里跑,import 都在函数内。"""
import warnings as _w
_w.filterwarnings("ignore")
sys.path.insert(0, str(RESEARCH))
sys.path.insert(0, str(RESEARCH.parent))
sym, sl_l, sl_h = task
_, dt_l = _signal_once(sl_l)
dt_h = np.nan if sl_h is None else _agree_once(sl_h)[1]
return {"sym": sym, "ltf_s": dt_l, "htf_s": dt_h}
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument("--reps", type=int, default=15, help="每币采样窗口数")
ap.add_argument("--rounds", type=int, default=5, help="串行/并行各跑几轮")
args = ap.parse_args()
print(f"[本机算力] {os.cpu_count()} 逻辑核 · 1m 窗口 {WIN_LTF} 根 · "
f"5m 窗口 {WIN_HTF} 根 · 每币 {args.reps}\n", flush=True)
print("########## 1. 单币单次耗时 ##########", flush=True)
prepared = {s: _slices(s, args.reps, 7) for s in SYMS}
per = []
for s in SYMS:
d = pd.DataFrame([bench_pair((s, l, h)) for l, h in prepared[s]])
per.append(d)
print(f" {s}: 1m {d['ltf_s'].median():.3f}s (P95 {d['ltf_s'].quantile(.95):.3f}s)"
f" · 5m {d['htf_s'].median():.3f}s", flush=True)
allp = pd.concat(per, ignore_index=True)
m_ltf = allp["ltf_s"].median()
m_htf = allp["htf_s"].median()
# 5m 侧只在每 5 根 1m 里变一次,其余 4 根可复用缓存
amort = m_ltf + m_htf / 5
print(f"\n 三币合并中位:1m {m_ltf:.3f}s · 5m {m_htf:.3f}s")
print(f" 单币每根摊薄成本 {amort:.3f}s5m 每 5 根才重算一次)")
print(f" 对比 Mac 的 0.20s:本机慢 {m_ltf / 0.20:.1f}")
print("\n########## 2. 三币串行 vs 并行(最后一个币的下单延迟)##########",
flush=True)
print(" 只计算子耗时:读数据不计入,进程池常驻不计启动开销", flush=True)
ser, par2, par3 = [], [], []
pools = {w: ProcessPoolExecutor(max_workers=w) for w in (2, 3)}
try:
# 先各跑一次把子进程的 import 预热掉,否则首轮全是模块加载时间
for w, ex in pools.items():
list(ex.map(bench_pair, [(s, *prepared[s][0]) for s in SYMS]))
for k in range(args.rounds):
batch = [(s, *prepared[s][k % args.reps]) for s in SYMS]
t0 = time.perf_counter()
for t in batch:
bench_pair(t)
ser.append(time.perf_counter() - t0)
for w, bag in ((2, par2), (3, par3)):
t0 = time.perf_counter()
list(pools[w].map(bench_pair, batch))
bag.append(time.perf_counter() - t0)
print(f"{k + 1}: 串行 {ser[-1]:.2f}s · 并行2 {par2[-1]:.2f}s · "
f"并行3 {par3[-1]:.2f}s", flush=True)
finally:
for ex in pools.values():
ex.shutdown()
print("\n########## 3. 延迟折算成 bp3.9bp 预算的参照)##########")
# 各币 1m 收益标准差,用 √t 把延迟折成价格漂移的一个标准差
vol = {}
for s in SYMS:
df_l, _ = _load(s)
r = np.diff(np.log(df_l["close"].to_numpy(dtype=float)[-400_000:]))
vol[s] = float(np.std(r) * 1e4)
rows = []
for name, xs in (("串行", ser), ("并行2", par2), ("并行3", par3)):
d = float(np.median(xs))
rows.append({"方案": name, "三币总耗时": f"{d:.2f}s",
**{f"{s} 漂移1σ": f"{vol[s] * np.sqrt(d / 60):.2f}bp"
for s in SYMS}})
tb = pd.DataFrame(rows)
print(tb.to_string(index=False))
print(f"\n 1m 波动实测:" + " · ".join(f"{s} {vol[s]:.1f}bp/分钟" for s in SYMS))
print(" 漂移 1σ 是随机部分;入场时价格正朝信号方向跑,系统性追价另计。")
out = RESEARCH / "out" / "bench_compute.csv"
pd.DataFrame({
"指标": ["cpu核数", "1m窗口", "5m窗口", "1m中位s", "1m_P95s", "5m中位s",
"单币摊薄s", "串行3币s", "并行2_3币s", "并行3_3币s"],
"": [os.cpu_count(), WIN_LTF, WIN_HTF, round(m_ltf, 3),
round(allp["ltf_s"].quantile(.95), 3), round(m_htf, 3),
round(amort, 3), round(float(np.median(ser)), 2),
round(float(np.median(par2)), 2), round(float(np.median(par3)), 2)],
}).to_csv(out, index=False)
print(f"\n产物写入 {out}")
if __name__ == "__main__":
main()
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"""Bitget 原生回测基线:影子交易器要对照的那个数。
aggregate_robustness 证明总体期望在数据扰动下稳定,但逐笔清单不可复现。
所以滑点不能逐笔对照 Binance 回测,只能对照「同一 venue 上的总体毛均收益」。
这一步就是把那个数算出来。
跑法与 Binance 侧完全对齐:同一时间窗、同样 300k 根 1m、同一套 lib/ 代码、
同样 SL/TP/MAX_BARS = 1.5/3.0/48 与 entry_delay=1,只换数据源。
特别关注 SOLBitget 的 tick 是 0.001、Binance 是 0.01,粗 10 倍会让
`close > high[j-1]` 大量平局不触发。Bitget 上信号多出 74%,多出来的是否
同样赚钱,只有跑一遍才知道——这不是随机扰动,是系统性差异。
输出 out/bitget_baseline.csv。
"""
from __future__ import annotations
import argparse
import os
import sys
import time
import warnings
from pathlib import Path
import numpy as np
import pandas as pd
warnings.filterwarnings("ignore")
for v in ("OMP_NUM_THREADS", "OPENBLAS_NUM_THREADS", "MKL_NUM_THREADS"):
os.environ.setdefault(v, "1")
HERE = Path(__file__).resolve().parent
RESEARCH = HERE.parent
sys.path.insert(0, str(RESEARCH))
sys.path.insert(0, str(RESEARCH.parent))
sys.path.insert(0, str(HERE))
pd.set_option("display.width", 260)
from aggregate_robustness import run_once, stats # noqa: E402
from venue_parity import fetch_bitget # noqa: E402
# Binance 侧在同一窗口的实测值,来自 out/aggregate_robustness_*.csv 的 0bp 档
BINANCE_REF = {"BTC": 6.11, "ETH": 12.31, "SOL": 10.01}
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument("--symbols", default="BTC,ETH,SOL")
ap.add_argument("--days", type=int, default=210,
help="与 Binance 侧的 300k 根(208 天)对齐")
ap.add_argument("--refresh", action="store_true")
ap.add_argument("--tag", default="", help="产物文件名后缀,避免多次调用互相覆盖")
args = ap.parse_args()
from lib.data import load_local
syms = [s.strip() for s in args.symbols.split(",")]
print(f"[Bitget 原生基线] {syms} · 近 {args.days} 天 1m\n", flush=True)
rows = []
for sym in syms:
t0 = time.perf_counter()
bg_l = fetch_bitget(sym, "1m", args.days, args.refresh)
bg_h = fetch_bitget(sym, "5m", args.days, args.refresh)
print(f"── {sym} {len(bg_l)} 根 1m / {len(bg_h)} 根 5m "
f"{bg_l['date'].iloc[0]:%Y-%m-%d} ~ {bg_l['date'].iloc[-1]:%Y-%m-%d}"
f" [拉取 {time.perf_counter() - t0:.0f}s]", flush=True)
t1 = time.perf_counter()
tr = run_once(bg_l, bg_h)
s = stats(tr)
rows.append({"品种": sym, "venue": "Bitget", **s})
print(f" Bitget : " + " · ".join(f"{k} {v}" for k, v in s.items())
+ f" [{time.perf_counter() - t1:.0f}s]", flush=True)
# 同窗口的 Binance 对照,直接重算一遍,避免口径漂移
lo, hi = int(bg_l.timestamp.min()), int(bg_l.timestamp.max())
bn_l = load_local(f"{sym}/USDT:USDT", "1m")
bn_h = load_local(f"{sym}/USDT:USDT", "5m")
if bn_l is None:
continue
bn_l = bn_l[(bn_l.timestamp >= lo) & (bn_l.timestamp <= hi)].reset_index(drop=True)
bn_h = bn_h[(bn_h.timestamp >= lo) & (bn_h.timestamp <= hi)].reset_index(drop=True)
t1 = time.perf_counter()
s2 = stats(run_once(bn_l, bn_h))
rows.append({"品种": sym, "venue": "Binance", **s2})
print(f" Binance: " + " · ".join(f"{k} {v}" for k, v in s2.items())
+ f" [{time.perf_counter() - t1:.0f}s]", flush=True)
if not rows:
print("无结果")
return
tb = pd.DataFrame(rows)
tb["毛均bp"] = tb["毛均收益"].str.rstrip("%").astype(float) * 100
print("\n" + "=" * 120)
print("########## 同窗口 · 同代码 · 只换数据源 ##########")
print(tb.to_string(index=False))
print("\n########## 毛均收益对照(bp##########")
piv = tb.pivot_table(index="品种", columns="venue", values="毛均bp")
piv["差额"] = (piv.get("Bitget", np.nan) - piv.get("Binance", np.nan)).round(2)
cnt = tb.pivot_table(index="品种", columns="venue", values="笔数")
piv["Bitget笔数"] = cnt.get("Bitget")
piv["Binance笔数"] = cnt.get("Binance")
piv["Bitget余量bp"] = (piv.get("Bitget", np.nan) - 6.0).round(2)
print(piv.round(2).to_string())
print("\n########## 结论 ##########")
print(" Bitget余量 = 该 venue 自己的毛均收益 6bp 双边费率(VIP1 taker + API 返50%)。")
print(" 这就是影子交易器测出的滑点要去比的那条线,逐笔清单不参与比较。")
print(" 两家毛均若接近 → 总体口径可迁移;若差很多 → 以 Bitget 的为准。")
out = RESEARCH / "out" / f"bitget_baseline{args.tag}.csv"
tb.to_csv(out, index=False)
print(f"\n产物写入 {out}")
if __name__ == "__main__":
main()
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"""把 1030ms 归因到具体环节。
前面的测量已经排除两个可能:
· Bitget 换根首条推送就是 updateHummingbot 没有因丢弃 snapshot 而等待
· 容器内同进程里,Hummingbot 的 feed 只比原始 WS 慢 2~6ms,处理开销可忽略
剩下的变量是「位置」和「客户端」。本脚本对同一批 K 线三方对齐:
host_ccxt 宿主机 ccxt.pro watch_ohlcv latency_ccxt.csv
host_raw 宿主机 原始 aiohttp WS probe_raw_host.csv
cont_raw 容器内 原始 aiohttp WS probe_hb_vs_raw.csv
cont_hb 容器内 Hummingbot candles feed probe_hb_vs_raw.csv
据此可分离两件事:
cont_raw host_raw 容器网络的代价(同一份代码,只换位置)
host_ccxt host_raw ccxt.pro 客户端的差异(同一位置,只换客户端)
.venv/bin/python research/live/latency_attribute.py
"""
from __future__ import annotations
from pathlib import Path
import numpy as np
import pandas as pd
OUT = Path(__file__).resolve().parents[1] / "out"
SYMS = ("BTC", "ETH", "SOL")
def load() -> pd.DataFrame | None:
parts = []
f = OUT / "latency_ccxt.csv"
if f.exists():
d = pd.read_csv(f)
if not d.empty:
parts.append(d[["kline_ts", "sym", "t_data_ms"]]
.rename(columns={"t_data_ms": "host_ccxt"}))
f = OUT / "probe_raw_host.csv"
if f.exists():
d = pd.read_csv(f)
if not d.empty:
parts.append(d[["kline_ts", "sym", "raw_ms"]]
.rename(columns={"raw_ms": "host_raw"}))
f = OUT / "probe_hb_vs_raw.csv"
if f.exists():
d = pd.read_csv(f)
if not d.empty:
parts.append(d[["kline_ts", "sym", "raw_ms", "hb_ms"]]
.rename(columns={"raw_ms": "cont_raw",
"hb_ms": "cont_hb"}))
if not parts:
return None
m = parts[0]
for p in parts[1:]:
m = m.merge(p, on=["kline_ts", "sym"], how="outer")
return m
def main() -> None:
m = load()
if m is None or m.empty:
print("四路数据均缺失")
return
cols = [c for c in ("host_ccxt", "host_raw", "cont_raw", "cont_hb")
if c in m.columns]
print(f"各路样本数(重叠前):")
for c in cols:
print(f" {c}: {int(m[c].notna().sum())}")
print("\n########## 各路 t_data t_closems,中位)##########")
print(f"{'':<5}" + "".join(f"{c:>11}" for c in cols))
for s in SYMS:
g = m[m["sym"] == s]
line = f"{s:<5}"
for c in cols:
v = (g[c] - g["kline_ts"]).dropna()
line += f"{np.median(v):>11.0f}" if len(v) else f"{'':>11}"
print(line)
# 只在四路都有的 K 线上做差,避免不同子集的中位数互相错位
full = m.dropna(subset=cols)
print(f"\n########## 归因(仅四路齐全的 {len(full)} 根)##########")
if full.empty:
print(" 无四路齐全的 K 线;检查三个采集窗口是否重叠")
return
pairs = []
if "cont_raw" in cols and "host_raw" in cols:
pairs.append(("容器网络代价", "cont_raw", "host_raw"))
if "host_ccxt" in cols and "host_raw" in cols:
pairs.append(("ccxt.pro 客户端差异", "host_ccxt", "host_raw"))
if "cont_hb" in cols and "cont_raw" in cols:
pairs.append(("Hummingbot 处理开销", "cont_hb", "cont_raw"))
if "cont_hb" in cols and "host_ccxt" in cols:
pairs.append(("合计:容器 HB vs 宿主 ccxt", "cont_hb", "host_ccxt"))
print(f"{'环节':<26}" + "".join(f"{s:>9}" for s in SYMS))
for name, a, b in pairs:
line = f"{name:<26}"
for s in SYMS:
g = full[full["sym"] == s]
if g.empty:
line += f"{'':>9}"
continue
line += f"{np.median(g[a] - g[b]):>9.0f}"
print(line)
print("\n(单位 ms,正数表示前者更慢)")
if __name__ == "__main__":
main()
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"""延迟基准 A:ccxt.pro 侧的数据到手时刻。
要测的事件只有一个:**我们在什么时候得知第 N 根 1m 已经收盘**。
它等价于 t_data − t_close,是下单延迟里最先发生、也往往最大的一段。
与 latency_hummingbot.py 测的是同一个事件,两边跑同一段时间才可比,
所以两个脚本都按整分钟对齐输出,事后按 K 线时间戳 join。
判据:ETH 的 Bitget 原生滑点余量只有 4.02bp、SOL 2.92bp,而本机 1m 波动
ETH 8.6bp/分钟、SOL 9.6bp/分钟。按 √t 折算,1 秒延迟就是 1.1~1.2bp 的
随机漂移,且入场方向上还有系统性追价。所以这个数值本身就可能决定生死。
输出 out/latency_ccxt.csv,每根一行。
"""
from __future__ import annotations
import argparse
import asyncio
import csv
import signal
import sys
import time
from pathlib import Path
HERE = Path(__file__).resolve().parent
RESEARCH = HERE.parent
sys.path.insert(0, str(RESEARCH))
sys.path.insert(0, str(RESEARCH.parent))
SYMS = ("BTC", "ETH", "SOL")
OUT = RESEARCH / "out" / "latency_ccxt.csv"
PERIOD_MS = 60_000
_stop = False
def _on_signal(*_):
global _stop
_stop = True
async def watch(ex, sym: str, writer, fh, stats: dict) -> None:
"""watch_ohlcv 每次推送都带整段最近 K 线,靠时间戳前进判断上一根已收盘。"""
pair = f"{sym}/USDT:USDT"
last_ts = None
while not _stop:
try:
o = await ex.watch_ohlcv(pair, "1m")
except Exception as e:
print(f" {sym} watch 异常 {type(e).__name__}: {e}", flush=True)
await asyncio.sleep(1)
continue
if not o:
continue
now_ms = int(time.time() * 1000)
newest = int(o[-1][0])
if last_ts is None:
last_ts = newest
continue
if newest > last_ts:
# newest 是刚开始的那根,故 last_ts 那根在 newest 时刻收盘
closed_ts = newest
lag_ms = now_ms - closed_ts
writer.writerow({"kline_ts": closed_ts, "sym": sym,
"t_data_ms": now_ms, "lag_ms": lag_ms})
fh.flush()
stats.setdefault(sym, []).append(lag_ms)
n = len(stats[sym])
if n % 5 == 1:
med = sorted(stats[sym])[n // 2]
print(f" {sym}: 第 {n} 根,本次 lag {lag_ms}ms,中位 {med}ms",
flush=True)
last_ts = newest
async def main_async(minutes: int) -> None:
import ccxt.pro as ccxtpro
ex = ccxtpro.bitget({"options": {"defaultType": "swap"},
"enableRateLimit": True})
OUT.parent.mkdir(parents=True, exist_ok=True)
fh = OUT.open("w", newline="")
writer = csv.DictWriter(fh, fieldnames=["kline_ts", "sym", "t_data_ms", "lag_ms"])
writer.writeheader()
stats: dict = {}
print(f"[ccxt.pro 延迟] {SYMS} · 计划跑 {minutes} 分钟 · 输出 {OUT.name}",
flush=True)
tasks = [asyncio.create_task(watch(ex, s, writer, fh, stats)) for s in SYMS]
deadline = time.time() + minutes * 60
while time.time() < deadline and not _stop:
await asyncio.sleep(1)
for t in tasks:
t.cancel()
await asyncio.gather(*tasks, return_exceptions=True)
await ex.close()
fh.close()
print("\n########## t_data t_closems##########")
for s in SYMS:
v = sorted(stats.get(s, []))
if not v:
print(f" {s}: 无样本")
continue
print(f" {s}: n={len(v)} 中位 {v[len(v) // 2]}ms "
f"P90 {v[int(len(v) * .9)]}ms 最大 {v[-1]}ms")
print(f"\n产物写入 {OUT}")
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument("--minutes", type=int, default=20)
args = ap.parse_args()
signal.signal(signal.SIGINT, _on_signal)
signal.signal(signal.SIGTERM, _on_signal)
asyncio.run(main_async(args.minutes))
if __name__ == "__main__":
main()
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"""把两侧 t_data t_close 对齐,并折算成 bp,与滑点余量对照。
为什么要折算成 bp 才有意义:延迟本身不花钱,花钱的是延迟期间价格的漂移。
按随机游走,t 秒的价格标准差是 σ_1m · √(t/60),其中 σ_1m 是本币 1m 收益
的标准差。入场方向上还有系统性追价(信号触发往往伴随同向动量),所以随机
漂移只是下限,真实成本更高——这也是为什么最终仍要用真实盘口测滑点。
余量(bitget_baseline.py 得出,Bitget 原生基线减去手续费后剩下的空间):
BTC -0.13bp ETH +4.02bp SOL +2.92bp
BTC 本就为负,留着只作延迟测量的参照物,不作交易标的。
.venv/bin/python research/live/latency_compare.py
"""
from __future__ import annotations
import sys
from pathlib import Path
import numpy as np
import pandas as pd
HERE = Path(__file__).resolve().parent
RESEARCH = HERE.parent
sys.path.insert(0, str(RESEARCH))
OUT = RESEARCH / "out"
BUDGET_BP = {"BTC": -0.13, "ETH": 4.02, "SOL": 2.92}
SYMS = ("BTC", "ETH", "SOL")
def vol_bp_per_min() -> dict[str, float]:
"""从 Bitget 缓存算 1m 收益标准差,单位 bp。"""
out = {}
for s in SYMS:
f = HERE / "cache" / f"bitget_{s}_1m_30d.feather"
if not f.exists():
f = HERE / "cache" / f"bitget_{s}_1m_210d.feather"
if not f.exists():
continue
df = pd.read_feather(f)
r = np.log(df["close"].to_numpy(dtype=float))
out[s] = float(np.nanstd(np.diff(r)) * 1e4)
return out
def drift_bp(lag_ms: float, vol: float) -> float:
"""随机游走下,lag 毫秒对应的价格漂移标准差(bp)。"""
return vol * np.sqrt(max(lag_ms, 0) / 60_000.0)
def load(tag: str) -> pd.DataFrame | None:
f = OUT / f"latency_{tag}.csv"
if not f.exists():
return None
df = pd.read_csv(f)
return df if not df.empty else None
def describe(df: pd.DataFrame, name: str, vols: dict) -> None:
print(f"\n########## {name}t_data t_close ##########")
print(f"{'':<5}{'n':>5}{'中位ms':>9}{'P90ms':>9}{'最大ms':>9}"
f"{'中位漂移bp':>12}{'余量bp':>9}{'占余量':>9}")
for s in SYMS:
v = df[df["sym"] == s]["lag_ms"].to_numpy(dtype=float)
if not len(v):
continue
med, p90 = float(np.median(v)), float(np.percentile(v, 90))
vol = vols.get(s)
d = drift_bp(med, vol) if vol else float("nan")
b = BUDGET_BP[s]
share = f"{d / b * 100:.0f}%" if b > 0 else "—(负)"
print(f"{s:<5}{len(v):>5}{med:>9.0f}{p90:>9.0f}{v.max():>9.0f}"
f"{d:>12.2f}{b:>9.2f}{share:>9}")
def main() -> None:
vols = vol_bp_per_min()
print("1m 收益标准差(bp/分钟,Bitget 缓存实测):")
for s, v in vols.items():
print(f" {s}: {v:.2f}")
a, b = load("ccxt"), load("hummingbot")
if a is None or b is None:
print(f"\n数据未就绪:ccxt={'' if a is not None else ''} "
f"hummingbot={'' if b is not None else ''}")
if a is not None:
describe(a, "ccxt.pro", vols)
if b is not None:
describe(b, "Hummingbot", vols)
return
describe(a, "ccxt.pro", vols)
describe(b, "Hummingbot", vols)
# 逐根配对才能消掉「不同分钟市场活跃度不同」的干扰
m = a.merge(b, on=["kline_ts", "sym"], suffixes=("_ccxt", "_hb"))
print(f"\n########## 逐根配对(重叠 {len(m)} 根)##########")
if m.empty:
print(" 两侧无重叠 K 线,无法配对;检查采集时间窗是否错开")
return
print(f"{'':<5}{'n':>5}{'ccxt中位':>10}{'HB中位':>10}"
f"{'差值中位':>10}{'HB更慢占比':>12}{'差值→bp':>10}")
for s in SYMS:
g = m[m["sym"] == s]
if g.empty:
continue
d = (g["lag_ms_hb"] - g["lag_ms_ccxt"]).to_numpy(dtype=float)
vol = vols.get(s)
# 差值转 bp:比较两条路径各自漂移的差,而非直接对差值开方
extra = (drift_bp(float(np.median(g["lag_ms_hb"])), vol)
- drift_bp(float(np.median(g["lag_ms_ccxt"])), vol)) if vol else float("nan")
print(f"{s:<5}{len(g):>5}{np.median(g['lag_ms_ccxt']):>10.0f}"
f"{np.median(g['lag_ms_hb']):>10.0f}{np.median(d):>10.0f}"
f"{(d > 0).mean() * 100:>11.0f}%{extra:>10.2f}")
print("\n判读:差值 → bp 若显著小于余量,说明选哪个运行时不影响结论,"
"可直接用 Hummingbot(生产路径一致);若接近或超过余量,"
"则运行时本身就是成本项,需要单独优化或放弃 1m。")
if __name__ == "__main__":
main()
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"""延迟基准 B:Hummingbot 侧的数据到手时刻。
在 Hummingbot 容器内运行,测的事件与 latency_ccxt.py 完全相同:
**我们在什么时候得知第 N 根 1m 已经收盘**。
为什么必须在 Hummingbot 里测而不是复用 ccxt 的数字:如果最终执行走
Hummingbot,那决定成交价的是它的 WS 处理与事件循环延迟。用别的运行时测出
的滑点,换到 Hummingbot 上就不成立了。
用 max_records=20 让历史回填快速完成——这里只关心增量推送的时刻,不需要
2000 根窗口。
输出 out/latency_hummingbot.csv,字段与 A 侧一致,事后按 kline_ts join。
"""
from __future__ import annotations
import argparse
import asyncio
import csv
import time
from pathlib import Path
SYMS = ("BTC", "ETH", "SOL")
CONNECTOR = "bitget_perpetual"
OUT = Path("/out/latency_hummingbot.csv")
async def main_async(minutes: int) -> None:
from hummingbot.data_feed.candles_feed.candles_factory import CandlesFactory
from hummingbot.data_feed.candles_feed.data_types import CandlesConfig
feeds = {}
for s in SYMS:
cfg = CandlesConfig(connector=CONNECTOR, trading_pair=f"{s}-USDT",
interval="1m", max_records=20)
feeds[s] = CandlesFactory.get_candle(cfg)
print(f"[Hummingbot 延迟] {SYMS} · connector={CONNECTOR} · "
f"计划跑 {minutes} 分钟", flush=True)
for s, f in feeds.items():
if hasattr(f, "start"):
f.start()
else:
await f.start_network()
print(f" {s} 已启动订阅", flush=True)
# 等历史回填完成,否则 deque 尾部时间戳还在跳变
t0 = time.time()
while time.time() - t0 < 120:
if all(f.ready for f in feeds.values()):
break
await asyncio.sleep(0.5)
ready = {s: f.ready for s, f in feeds.items()}
print(f" 回填状态 {ready}{time.time() - t0:.1f}s", flush=True)
OUT.parent.mkdir(parents=True, exist_ok=True)
fh = OUT.open("w", newline="")
writer = csv.DictWriter(fh, fieldnames=["kline_ts", "sym", "t_data_ms", "lag_ms"])
writer.writeheader()
last = {}
for s, f in feeds.items():
c = f._candles
last[s] = int(c[-1][0]) if len(c) else None
stats: dict = {}
deadline = time.time() + minutes * 60
while time.time() < deadline:
for s, f in feeds.items():
c = f._candles
if not len(c):
continue
newest = int(c[-1][0])
if last[s] is None:
last[s] = newest
continue
if newest > last[s]:
now_ms = int(time.time() * 1000)
# Hummingbot 的时间戳是秒,统一成毫秒后再与本地钟相减
closed_ms = newest * 1000 if newest < 1e12 else newest
lag_ms = now_ms - closed_ms
writer.writerow({"kline_ts": closed_ms, "sym": s,
"t_data_ms": now_ms, "lag_ms": lag_ms})
fh.flush()
stats.setdefault(s, []).append(lag_ms)
n = len(stats[s])
if n % 5 == 1:
med = sorted(stats[s])[n // 2]
print(f" {s}: 第 {n} 根,本次 lag {lag_ms}ms,中位 {med}ms",
flush=True)
last[s] = newest
await asyncio.sleep(0.01)
for f in feeds.values():
f.stop()
fh.close()
print("\n########## t_data t_closems##########")
for s in SYMS:
v = sorted(stats.get(s, []))
if not v:
print(f" {s}: 无样本")
continue
print(f" {s}: n={len(v)} 中位 {v[len(v) // 2]}ms "
f"P90 {v[int(len(v) * .9)]}ms 最大 {v[-1]}ms")
print(f"\n产物写入 {OUT}")
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument("--minutes", type=int, default=20)
args = ap.parse_args()
asyncio.run(main_async(args.minutes))
if __name__ == "__main__":
main()
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"""环境等价性验证:同一份切片,在 .venv 与 Hummingbot 容器里必须算出同一组信号。
为什么要单独验这件事:容器里是 Python 3.13.14 + pandas 3.0.5 + numpy 2.4.6
本机 .venv 是 Python 3.14.4 + pandas 3.0.5 + numpy 2.5.2。pandas 同版本,
numpy 差一个小版本。信号已经被证明对 0.25bp 的数据扰动极度敏感(扰动会换掉
一半信号),所以浮点或 groupby 顺序上的任何细微差异都可能改变信号集合——
必须实测,不能推断。
用 --dump 先从 .venv 导出切片成 CSV,两个环境再读同一个 CSV,
这样数据来源差异为零,比出来的就是纯计算差异。
.venv/bin/python research/live/parity_env.py --dump # 导出切片
.venv/bin/python research/live/parity_env.py --run # 本机计算
docker run ... /app/parity_env.py --run --tag container # 容器计算
"""
from __future__ import annotations
import argparse
import json
import os
import sys
import time
import warnings
from pathlib import Path
import numpy as np
import pandas as pd
warnings.filterwarnings("ignore")
for v in ("OMP_NUM_THREADS", "OPENBLAS_NUM_THREADS", "MKL_NUM_THREADS"):
os.environ.setdefault(v, "1")
HERE = Path(__file__).resolve().parent
RESEARCH = HERE.parent
sys.path.insert(0, str(RESEARCH))
sys.path.insert(0, str(RESEARCH.parent))
LTF, HTF = "1m", "5m"
WINDOW = 2000 # step39 定下的窗口:命中率在此饱和
HTF_WINDOW = 800
SYMS = ("BTC", "ETH", "SOL")
def slice_dir() -> Path:
"""容器里挂在 /out,本机是 research/out。"""
p = Path("/out")
return p if p.is_dir() else RESEARCH / "out"
def dump() -> None:
"""从 Bitget 缓存导出末尾切片,供两个环境共用。
用 Bitget 而非 Binance 的数据:目标场地就是 Bitget,且这批缓存正是
bitget_baseline.py 算余量时用的同一份,口径可直接对上。
"""
d = slice_dir() / "parity_slices"
d.mkdir(parents=True, exist_ok=True)
cache = HERE / "cache"
for s in SYMS:
for tf, n in ((LTF, WINDOW), (HTF, HTF_WINDOW)):
src = cache / f"bitget_{s}_{tf}_30d.feather"
if not src.exists():
src = cache / f"bitget_{s}_{tf}_210d.feather"
if not src.exists():
print(f" {s} {tf}: 无缓存 {src.name},跳过")
continue
df = pd.read_feather(src)
if df is None or df.empty:
print(f" {s} {tf}: 缓存为空,跳过")
continue
out = df.tail(n).reset_index(drop=True)
f = d / f"{s}_{tf}.csv"
# 用 float 全精度写出,避免导出环节就引入舍入差异
out.to_csv(f, index=False, float_format="%.10f")
print(f" {s} {tf}: {len(out)} 根 -> {f.name}")
print(f"\n切片目录 {d}")
def signals(df_ltf: pd.DataFrame, df_htf: pd.DataFrame) -> dict:
"""复用 step39 的时点重建口径,返回信号下标与耗时。"""
from chanlun import TF_DF
from lib.fast_bsp3 import find_fast_bsp3
from lib.nested_level import build_htf_zones
t0 = time.perf_counter()
chan = TF_DF(df_ltf, 1, LTF)
cdf = chan.dataframe
zones = build_htf_zones(cdf, LTF, chan=chan)
raw: list[int] = []
if not zones.empty:
sig = find_fast_bsp3(cdf, zones.reset_index(drop=True))
if sig is not None and not sig.empty:
col = "idx" if "idx" in sig.columns else sig.columns[0]
raw = sorted(int(x) for x in sig[col].to_numpy())
dt = time.perf_counter() - t0
# 中枢边界是信号定义的核心中间量,一并指纹化:
# 若信号相同但中枢不同,说明差异只是被过滤掉了,仍是隐患
zsig = None
if not zones.empty:
num = zones.select_dtypes(include=[np.number])
zsig = float(np.nansum(num.to_numpy(dtype=float)))
return {"n_bars": int(len(df_ltf)), "n_signals": len(raw),
"signal_idx": raw, "zone_checksum": zsig,
"n_zones": int(len(zones)), "compute_s": round(dt, 4)}
def run(tag: str) -> None:
d = slice_dir() / "parity_slices"
res = {"tag": tag,
"python": sys.version.split()[0],
"pandas": pd.__version__,
"numpy": np.__version__}
per = {}
for s in SYMS:
f_ltf, f_htf = d / f"{s}_{LTF}.csv", d / f"{s}_{HTF}.csv"
if not f_ltf.exists():
print(f" {s}: 缺切片 {f_ltf}")
continue
df_ltf = pd.read_csv(f_ltf)
df_htf = pd.read_csv(f_htf) if f_htf.exists() else pd.DataFrame()
# date 存的是时间戳字符串,chanlun 的 kline builder 要真 datetime
# timestamp 是毫秒整数,保持数值不动
for df in (df_ltf, df_htf):
if not df.empty and "date" in df.columns:
df["date"] = pd.to_datetime(df["date"], utc=True)
r = signals(df_ltf, df_htf)
per[s] = r
print(f" {s}: {r['n_signals']} 信号 · {r['n_zones']} 中枢 · "
f"{r['compute_s']}s · zone_checksum={r['zone_checksum']}")
res["per_symbol"] = per
out = slice_dir() / f"parity_env_{tag}.json"
out.write_text(json.dumps(res, indent=2, ensure_ascii=False))
print(f"\n[{tag}] python {res['python']} pandas {res['pandas']} "
f"numpy {res['numpy']}")
print(f"产物写入 {out}")
def compare(a: str, b: str) -> None:
d = slice_dir()
ra = json.loads((d / f"parity_env_{a}.json").read_text())
rb = json.loads((d / f"parity_env_{b}.json").read_text())
print(f"{a}: python {ra['python']} pandas {ra['pandas']} numpy {ra['numpy']}")
print(f"{b}: python {rb['python']} pandas {rb['pandas']} numpy {rb['numpy']}")
print("\n########## 信号集合是否逐一相同 ##########")
ok = True
for s in SYMS:
pa, pb = ra["per_symbol"].get(s), rb["per_symbol"].get(s)
if not pa or not pb:
print(f" {s}: 缺结果,跳过")
continue
same_sig = pa["signal_idx"] == pb["signal_idx"]
same_zone = pa["n_zones"] == pb["n_zones"]
# checksum 是浮点求和,允许相对 1e-9 的差;超出即为真实分歧
za, zb = pa["zone_checksum"], pb["zone_checksum"]
same_cs = (za is None and zb is None) or (
za is not None and zb is not None
and abs(za - zb) <= 1e-9 * max(1.0, abs(za)))
ok = ok and same_sig and same_zone and same_cs
print(f" {s}: 信号 {'一致' if same_sig else '不一致'}"
f"{pa['n_signals']} vs {pb['n_signals']})· "
f"中枢 {'一致' if same_zone else '不一致'}"
f"{pa['n_zones']} vs {pb['n_zones']})· "
f"checksum {'一致' if same_cs else '不一致'}")
if not same_sig:
sa, sb = set(pa["signal_idx"]), set(pb["signal_idx"])
print(f"{a} 有: {sorted(sa - sb)[:10]}")
print(f"{b} 有: {sorted(sb - sa)[:10]}")
print(f" 耗时 {pa['compute_s']}s vs {pb['compute_s']}s")
print(f"\n结论:{'两环境等价,可直接在容器内算信号' if ok else '存在分歧,需把信号计算固定在单一环境'}")
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument("--dump", action="store_true")
ap.add_argument("--run", action="store_true")
ap.add_argument("--tag", default="venv")
ap.add_argument("--compare", nargs=2, metavar=("A", "B"))
args = ap.parse_args()
if args.dump:
dump()
if args.run:
run(args.tag)
if args.compare:
compare(*args.compare)
if __name__ == "__main__":
main()
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"""修正 Hummingbot bitget_perpetual candles feed 的换根延迟。
上游 _parse_websocket_message 里是:
candle = data["data"][0]
而 Bitget 在换根时会推一条带两根的消息 [上一根, 新一根]。取 [0] 拿到的是
上一根,其时间戳与 deque 尾部相同,于是只做了原地更新;新一根要等下一条
单元素消息才进入 deque——实测晚约 1.1 秒。
不能简单改成 [-1]:那样上一根的收盘价就永远停在换根前约 1 秒的那次推送上。
1m 信号对 0.25bp 的扰动都会换掉一半(见 signal_sensitivity.py),收盘价
偏一个 tick 是不能接受的。所以这里把**除最后一根外的元素就地写回 deque**,
再把最后一根交给基类走正常的 append 流程。
已向上游反馈前,本地用子类覆盖,不改动镜像。
"""
from __future__ import annotations
from typing import Any, Dict, Optional
import numpy as np
from hummingbot.data_feed.candles_feed.bitget_perpetual_candles import (
BitgetPerpetualCandles,
)
def _row_to_dict(row: list, ensure_s) -> Dict[str, Any]:
return {"timestamp": ensure_s(int(row[0])),
"open": float(row[1]), "high": float(row[2]),
"low": float(row[3]), "close": float(row[4]),
"volume": float(row[5]), "quote_asset_volume": float(row[6]),
"n_trades": 0., "taker_buy_base_volume": 0.,
"taker_buy_quote_volume": 0.}
class PatchedBitgetPerpetualCandles(BitgetPerpetualCandles):
"""与上游唯一的差别:一条消息里的多根 K 线全部处理,而非只取第一根。"""
def _parse_websocket_message(self, data: dict) -> Optional[Dict[str, Any]]:
if data == "pong":
return None
if not (data and data.get("data") and data.get("action") == "update"):
return None
rows = data["data"]
# 前面的元素都是已收盘 K 线的最终值:就地覆盖,保住真实收盘价
for row in rows[:-1]:
d = _row_to_dict(row, self.ensure_timestamp_in_seconds)
self._overwrite_existing(d)
# 最后一根交给基类:时间戳更大就 append,相同就原地更新
return _row_to_dict(rows[-1], self.ensure_timestamp_in_seconds)
def _overwrite_existing(self, d: Dict[str, Any]) -> None:
if not len(self._candles):
return
ts = int(d["timestamp"])
if int(self._candles[-1][0]) != ts:
return
self._candles[-1] = np.array(
[d["timestamp"], d["open"], d["high"], d["low"], d["close"],
d["volume"], d["quote_asset_volume"], d["n_trades"],
d["taker_buy_base_volume"], d["taker_buy_quote_volume"]]
).astype(float)
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"""定位 Hummingbot 那 1030ms 究竟出在处理链路还是容器网络。
前一轮对照有两个变量同时在变:运行时(Hummingbot vs ccxt.pro)和位置
(容器内 vs 宿主机)。所以 1030ms 无法归因。原始 WS 探针已否掉「丢弃
snapshot」这个猜测——换根首条推送就是 update,Hummingbot 确实收到了。
本脚本在**容器内同一个进程**里并行跑两条路径,共用一个时钟、一条网络:
A. Hummingbot 的 BitgetPerpetualCandles,轮询 _candles 尾部时间戳
B. 一条原始 aiohttp WS,直接订阅 candle1m
两条路径对同一根 K 线各记一个到达时刻,差值即 Hummingbot 处理链路的净开销。
若 B 也慢,则是容器网络,与框架无关。
在容器内运行:
docker run --rm -v $PWD:/repo:ro -v $PWD/research/out:/out \
-w /home/hummingbot -e PYTHONPATH=/home/hummingbot \
--entrypoint /opt/conda/envs/hummingbot/bin/python \
hummingbot/hummingbot:latest /repo/research/live/probe_hb_vs_raw.py --minutes 20
"""
from __future__ import annotations
import argparse
import asyncio
import csv
import json
import time
from pathlib import Path
SYMS = ("BTC", "ETH", "SOL")
WSS = "wss://ws.bitget.com/v2/ws/public"
def out_dir() -> Path:
p = Path("/out")
return p if p.is_dir() else Path(__file__).resolve().parents[1] / "out"
async def raw_ws(rec: dict, stop: asyncio.Event) -> None:
"""B 路径:原始 WS,收到第一条带新时间戳的推送就记时刻。"""
import aiohttp
payload = {"op": "subscribe",
"args": [{"instType": "USDT-FUTURES", "channel": "candle1m",
"instId": f"{s}USDT"} for s in SYMS]}
while not stop.is_set():
try:
async with aiohttp.ClientSession() as sess, \
sess.ws_connect(WSS, heartbeat=20) as ws:
await ws.send_str(json.dumps(payload))
print(" [raw] 已订阅", flush=True)
last: dict[str, int] = {}
while not stop.is_set():
msg = await ws.receive()
if msg.type is not aiohttp.WSMsgType.TEXT:
# 关闭类消息必须跳出重连,否则 receive() 会立刻返回造成空转
print(f" [raw] 非文本消息 {msg.type},重连", flush=True)
break
if msg.data == "pong":
continue
d = json.loads(msg.data)
if "data" not in d or "arg" not in d:
continue
sym = d["arg"]["instId"].replace("USDT", "")
kts = int(d["data"][0][0])
if last.get(sym) is not None and kts > last[sym]:
rec.setdefault((sym, kts), {})["raw_ms"] = \
int(time.time() * 1000)
last[sym] = max(kts, last.get(sym, 0))
except asyncio.CancelledError:
raise
except Exception as e:
print(f" [raw] 异常 {type(e).__name__}: {e}", flush=True)
# 无论是正常跳出还是异常,重连前都歇一下,避免服务端持续拒绝时打成风暴
if not stop.is_set():
await asyncio.sleep(1)
async def hb_feed(rec: dict, stop: asyncio.Event) -> None:
"""A 路径:Hummingbot candles feed10ms 轮询 deque 尾部。"""
from hummingbot.data_feed.candles_feed.candles_factory import CandlesFactory
from hummingbot.data_feed.candles_feed.data_types import CandlesConfig
feeds = {}
for s in SYMS:
cfg = CandlesConfig(connector="bitget_perpetual",
trading_pair=f"{s}-USDT", interval="1m",
max_records=20)
f = CandlesFactory.get_candle(cfg)
f.start()
feeds[s] = f
print(" [hb] 已订阅", flush=True)
t0 = time.time()
while time.time() - t0 < 120 and not all(f.ready for f in feeds.values()):
await asyncio.sleep(0.5)
print(f" [hb] 回填完成 {time.time() - t0:.1f}s", flush=True)
last = {s: (int(f._candles[-1][0]) if len(f._candles) else None)
for s, f in feeds.items()}
while not stop.is_set():
for s, f in feeds.items():
if not len(f._candles):
continue
newest = int(f._candles[-1][0])
if last[s] is not None and newest > last[s]:
# HB 的时间戳是秒,统一成毫秒好与原始 WS 的 kline_ts 对齐
kts = newest * 1000 if newest < 1e12 else newest
rec.setdefault((s, kts), {})["hb_ms"] = int(time.time() * 1000)
last[s] = newest
await asyncio.sleep(0.01)
for f in feeds.values():
f.stop()
async def main_async(minutes: int, raw_only: bool = False) -> None:
rec: dict = {}
stop = asyncio.Event()
tasks = [asyncio.create_task(raw_ws(rec, stop))]
if not raw_only:
tasks.append(asyncio.create_task(hb_feed(rec, stop)))
where = "宿主机 raw 单跑" if raw_only else "容器内同进程对照"
print(f"[{where}] {SYMS} · 跑 {minutes} 分钟", flush=True)
deadline = time.time() + minutes * 60
reported = set()
while time.time() < deadline:
await asyncio.sleep(2)
for k, v in rec.items():
if k in reported or "raw_ms" not in v or "hb_ms" not in v:
continue
reported.add(k)
print(f" {k[0]} @{k[1]}: raw->hb 延后 {v['hb_ms'] - v['raw_ms']}ms",
flush=True)
stop.set()
for t in tasks:
t.cancel()
await asyncio.gather(*tasks, return_exceptions=True)
if raw_only:
# 只落盘 raw 到达时刻,供与容器侧按 kline_ts 对齐
f = out_dir() / "probe_raw_host.csv"
with f.open("w", newline="") as fh:
w = csv.writer(fh)
w.writerow(["sym", "kline_ts", "raw_ms"])
for (s, k), v in sorted(rec.items(), key=lambda x: x[0][1]):
if "raw_ms" in v:
w.writerow([s, k, v["raw_ms"]])
print(f"\n宿主机 raw 样本 {sum('raw_ms' in v for v in rec.values())}")
print(f"产物写入 {f}")
return
f = out_dir() / "probe_hb_vs_raw.csv"
both = [(s, k, v) for (s, k), v in rec.items()
if "raw_ms" in v and "hb_ms" in v]
with f.open("w", newline="") as fh:
w = csv.writer(fh)
w.writerow(["sym", "kline_ts", "raw_ms", "hb_ms", "delta_ms"])
for s, k, v in sorted(both, key=lambda x: x[1]):
w.writerow([s, k, v["raw_ms"], v["hb_ms"],
v["hb_ms"] - v["raw_ms"]])
print(f"\n########## 同进程内 raw WS 与 Hummingbot 的到达差 ##########")
print(f"(正数 = Hummingbot 更慢;配对 {len(both)} 根)")
for s in SYMS:
d = sorted(v["hb_ms"] - v["raw_ms"] for ss, _, v in both if ss == s)
if not d:
print(f" {s}: 无配对样本")
continue
print(f" {s}: n={len(d)} 中位 {d[len(d) // 2]}ms "
f"P90 {d[int(len(d) * .9)]}ms 最小 {d[0]}ms 最大 {d[-1]}ms")
print(f"\n判读:中位接近 0 说明 1030ms 来自容器网络或宿主机对照本身;"
f"中位接近 1000ms 说明是 Hummingbot 处理链路的净开销。")
print(f"产物写入 {f}")
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument("--minutes", type=int, default=20)
ap.add_argument("--raw-only", action="store_true",
help="不加载 hummingbot,只跑原始 WS(供宿主机对照)")
a = ap.parse_args()
asyncio.run(main_async(a.minutes, a.raw_only))
if __name__ == "__main__":
main()
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"""验证 Hummingbot 那 1030ms 的来源:Bitget candle1m 的 snapshot / update 序列。
Hummingbot 的 bitget_perpetual candles feed 里有这么一句:
if data and data.get("data") and data["action"] == "update":
action == "snapshot" 的消息被整条丢弃。若新 K 线的首条推送恰是 snapshot,
Hummingbot 就必须等到下一条 update 才知道换根了,代价约等于一个推送间隔。
本脚本直接连原始 WS,对每条消息记录 action、K 线时间戳、到达时刻,
然后针对每次换根回答两件事:
1. 首条带新时间戳的消息,action 是什么
2. 若是 snapshot,到首条 update 之间隔了多久(= Hummingbot 白等的时间)
.venv/bin/python research/live/probe_ws_action.py --minutes 6
"""
from __future__ import annotations
import argparse
import asyncio
import json
import time
from collections import defaultdict
from pathlib import Path
WSS = "wss://ws.bitget.com/v2/ws/public"
SYMS = ("BTCUSDT", "ETHUSDT", "SOLUSDT")
OUT = Path(__file__).resolve().parents[1] / "out" / "probe_ws_action.csv"
async def run(minutes: int) -> None:
import csv
import aiohttp
payload = {"op": "subscribe",
"args": [{"instType": "USDT-FUTURES", "channel": "candle1m",
"instId": s} for s in SYMS]}
n_rows = 0
# 每个币记录:当前时间戳、该时间戳下已见过的 action 序列
cur: dict[str, int] = {}
seen: dict[str, list] = defaultdict(list)
rollovers: list[dict] = []
cnt: dict = defaultdict(lambda: defaultdict(int))
# 边收边写:进程若被杀,已采到的样本仍在盘上
OUT.parent.mkdir(parents=True, exist_ok=True)
fh = OUT.open("w", newline="")
w = csv.DictWriter(fh, fieldnames=["t_ms", "sym", "action", "kline_ts"])
w.writeheader()
print(f"[原始 WS 探针] {SYMS} · candle1m · 跑 {minutes} 分钟", flush=True)
deadline = time.time() + minutes * 60
try:
while time.time() < deadline:
try:
async with aiohttp.ClientSession() as sess, \
sess.ws_connect(WSS, heartbeat=20) as ws:
await ws.send_str(json.dumps(payload))
while time.time() < deadline:
msg = await ws.receive(timeout=30)
# 断开后 receive() 会立刻返回 CLOSED;这里若只 continue
# 就成了无等待空转,必须跳出去重连
if msg.type is not aiohttp.WSMsgType.TEXT:
print(f" 非文本消息 {msg.type},重连", flush=True)
break
raw = msg.data
if raw == "pong":
continue
try:
d = json.loads(raw)
except Exception:
continue
if "data" not in d or "arg" not in d:
if d.get("event"):
print(f" 事件: {d}", flush=True)
continue
now_ms = int(time.time() * 1000)
sym = d["arg"]["instId"]
action = d.get("action")
kts = int(d["data"][0][0])
w.writerow({"t_ms": now_ms, "sym": sym,
"action": action, "kline_ts": kts})
n_rows += 1
cnt[sym][action] += 1
if n_rows % 50 == 0:
fh.flush()
prev = cur.get(sym)
if prev is None:
cur[sym] = kts
seen[sym] = [(action, now_ms)]
continue
if kts > prev:
cur[sym] = kts
seen[sym] = [(action, now_ms)]
rollovers.append({"sym": sym, "kline_ts": kts,
"first_action": action,
"first_ms": now_ms})
print(f" {sym} 换根 -> 首条 action={action}",
flush=True)
else:
seen[sym].append((action, now_ms))
# 若首条是 snapshot,找该时间戳下首条 update 的延后量
for r in rollovers:
if (r["sym"] == sym and r["kline_ts"] == kts
and r["first_action"] == "snapshot"
and "gap_to_update_ms" not in r
and action == "update"):
r["gap_to_update_ms"] = now_ms - r["first_ms"]
print(f" {sym} snapshot->update 间隔 "
f"{r['gap_to_update_ms']}ms", flush=True)
except asyncio.CancelledError:
raise
except Exception as e:
print(f" 连接异常 {type(e).__name__}: {e}1s 后重连", flush=True)
# 重连前固定歇一下,避免服务端持续拒绝时打成重连风暴
if time.time() < deadline:
await asyncio.sleep(1)
finally:
fh.close()
print(f"\n########## 消息构成(共 {n_rows} 条)##########")
for s in SYMS:
print(f" {s}: {dict(cnt[s])}")
print(f"\n########## 换根时首条消息的 action{len(rollovers)} 次)##########")
by = defaultdict(lambda: defaultdict(int))
gaps = defaultdict(list)
for r in rollovers:
by[r["sym"]][r["first_action"]] += 1
if "gap_to_update_ms" in r:
gaps[r["sym"]].append(r["gap_to_update_ms"])
for s in SYMS:
g = sorted(gaps[s])
med = g[len(g) // 2] if g else None
print(f" {s}: 首条 action 分布 {dict(by[s])}"
+ (f" · snapshot->update 中位 {med}msn={len(g)}" if g else ""))
all_g = sorted(x for v in gaps.values() for x in v)
if all_g:
print(f"\n合计 snapshot->update 中位 {all_g[len(all_g) // 2]}ms "
f"n={len(all_g)})——这就是 Hummingbot 因丢弃 snapshot 白等的时间")
print(f"\n产物写入 {OUT}")
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument("--minutes", type=int, default=6)
asyncio.run(run(ap.parse_args().minutes))
if __name__ == "__main__":
main()
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"""查 Bitget candle1m 每条推送里到底带几根 K 线、最新的在哪一端。
归因结果显示 ccxt.pro 比原始 WS 快约 1.4 秒,而两者订阅的是同一条频道。
Hummingbot 的解析取 data["data"][0],我的探针也取 [0],两者一致地慢——
若 Bitget 一条消息里带多根、最新在末尾,取 [0] 就会系统性落后一个滑动窗口。
对每条消息记录:元素个数、首末元素的时间戳、以及首末之差。
若普遍 len>1 且末元素更新,则 [0] 就是那 1.4 秒的来源,且可用一行覆盖修好。
.venv/bin/python research/live/probe_ws_payload.py --minutes 4
"""
from __future__ import annotations
import argparse
import asyncio
import json
import time
from collections import Counter, defaultdict
WSS = "wss://ws.bitget.com/v2/ws/public"
SYMS = ("BTCUSDT", "ETHUSDT", "SOLUSDT")
async def run(minutes: int) -> None:
import aiohttp
payload = {"op": "subscribe",
"args": [{"instType": "USDT-FUTURES", "channel": "candle1m",
"instId": s} for s in SYMS]}
n_elems = Counter()
action_elems = Counter()
# 取 [0] 相对取 [-1] 的落后量:同一根 K 线,两种读法各自首次见到的时刻
first_seen_head: dict = {}
first_seen_tail: dict = {}
n_msg = 0
print(f"[载荷探针] {SYMS} · 跑 {minutes} 分钟", flush=True)
deadline = time.time() + minutes * 60
while time.time() < deadline:
try:
async with aiohttp.ClientSession() as sess, \
sess.ws_connect(WSS, heartbeat=20) as ws:
await ws.send_str(json.dumps(payload))
while time.time() < deadline:
msg = await ws.receive(timeout=30)
# 断开后 receive() 立刻返回 CLOSED,只 continue 会变成空转
if msg.type is not aiohttp.WSMsgType.TEXT:
print(f" 非文本消息 {msg.type},重连", flush=True)
break
if msg.data == "pong":
continue
d = json.loads(msg.data)
if "data" not in d or "arg" not in d:
continue
arr = d["data"]
if not arr:
continue
n_msg += 1
sym = d["arg"]["instId"].replace("USDT", "")
act = d.get("action")
n_elems[len(arr)] += 1
action_elems[(act, len(arr))] += 1
now_ms = int(time.time() * 1000)
head_ts, tail_ts = int(arr[0][0]), int(arr[-1][0])
first_seen_head.setdefault((sym, head_ts), now_ms)
first_seen_tail.setdefault((sym, tail_ts), now_ms)
if n_msg <= 6 or len(arr) > 1:
print(f" {sym} action={act} 元素数={len(arr)} "
f"首ts={head_ts} 末ts={tail_ts} "
f"跨度={(tail_ts - head_ts) // 1000}s", flush=True)
except asyncio.CancelledError:
raise
except Exception as e:
print(f" 连接异常 {type(e).__name__}: {e}1s 后重连", flush=True)
if time.time() < deadline:
await asyncio.sleep(1)
print(f"\n########## 每条消息的元素个数分布(共 {n_msg} 条)##########")
for k in sorted(n_elems):
print(f" {k} 根: {n_elems[k]}")
print("\n按 action 拆分:")
for k in sorted(action_elems, key=lambda x: (str(x[0]), x[1])):
print(f" action={k[0]} 元素数={k[1]}: {action_elems[k]}")
# 同一根 K 线,用 [-1] 读比用 [0] 读早多少
lead = defaultdict(list)
for (sym, ts), t_tail in first_seen_tail.items():
t_head = first_seen_head.get((sym, ts))
if t_head is not None:
lead[sym].append(t_head - t_tail)
print("\n########## 取 [-1] 比取 [0] 提前多少(ms##########")
for s in SYMS:
v = sorted(lead[s.replace("USDT", "")])
if not v:
print(f" {s}: 无配对")
continue
print(f" {s}: n={len(v)} 中位 {v[len(v) // 2]}ms 最大 {v[-1]}ms")
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument("--minutes", type=int, default=4)
asyncio.run(run(ap.parse_args().minutes))
if __name__ == "__main__":
main()
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"""影子交易器:在 Hummingbot 运行时上测 1m 腿的真实入场滑点。
不下单。用 Hummingbot 的 Bitget 连接器取真实盘口,按信号方向和仓位吃单深度
算出「若此刻市价单进场会成交在哪」,再与回测假设的成交价相减。
为什么必须跑在 Hummingbot 上而不是自写脚本:要测的是**生产路径**的滑点。
决定成交价的是实际执行链路的延迟,换个运行时测出来的数就不作数了。
(连接器的换根解析 bug 见 patched_candles.py,已修,拿回约 1.06 秒。)
为什么不能用 paper trade 的成交:那是 Hummingbot 自己的撮合模型模拟的,
测出来是模型行为不是市场行为。
口径对齐 aggregate_robustness.py:回测假设成交在**信号次根的开盘价**
(entry_delay=1),所以基准价就是换根后新一根的 open。滑点为正表示比回测差。
### 盘口滚动缓冲把延迟变成自变量
每 100ms 存一份盘口。信号触发后,不只记「我们实际算完时」的滑点,而是回查
t_close+0.5s / 1s / 2s / 5s 各一个。这样即使本机算得慢,也能读出「若延迟为
X 秒,滑点是多少」,决策不被自身实现拖累。
### 滑点分解
延迟漂移 中间价相对次根开盘价的偏移——主项,且入场方向上系统性追价
盘口价差 最优价相对中间价
深度冲击 吃单加权价相对最优价
Bitget 永续实测价差仅约 0.01bp、100 档深度,故预期延迟漂移占绝大部分。
docker run -d --name shadow -w /home/hummingbot \\
-e PYTHONPATH=/home/hummingbot:/repo/research:/repo/research/live:/repo \\
-v $PWD:/repo:ro -v $PWD/research/out:/out \\
--entrypoint /opt/conda/envs/hummingbot/bin/python \\
hummingbot/hummingbot:latest /repo/research/live/shadow_hb.py --hours 24
"""
from __future__ import annotations
import argparse
import asyncio
import csv
import time
from collections import deque
from concurrent.futures import ProcessPoolExecutor
from pathlib import Path
import numpy as np
import pandas as pd
SYMS = ("BTC", "ETH", "SOL")
# 多存一根:deque 尾部是尚未收盘的当前根,剔除后正好剩 step39 定下的窗口
LTF_BARS, HTF_BARS = 2001, 801 # 有效窗口 2000 / 800,命中率在此饱和
BOOK_HZ = 10 # 盘口采样 10Hz
BOOK_KEEP_S = 30 # 缓冲保留 30 秒,够回查到 +5s
BOOK_DEPTH = 25
DELAYS_S = (0.5, 1.0, 2.0, 5.0) # 回查点
NOTIONALS = (1_000.0, 5_000.0, 20_000.0)
NUM_COLS = ["timestamp", "open", "high", "low", "close", "volume"]
def out_dir() -> Path:
p = Path("/out")
return p if p.is_dir() else Path(__file__).resolve().parents[1] / "out"
def hb_to_research(cdf: pd.DataFrame) -> pd.DataFrame:
"""Hummingbot 的 candles_df 转成 research/lib/data.py 的列结构。
HB 的 timestamp 是秒且无 date 列;chanlun 的 kline builder 需要真 datetime,
时区跟 lib/data.py 取 Asia/Shanghai,保证与回测同一口径。
"""
ts_ms = (cdf["timestamp"].astype("int64") * 1000)
date = pd.to_datetime(ts_ms, unit="ms", utc=True).dt.tz_convert("Asia/Shanghai")
out = pd.DataFrame({"timestamp": ts_ms.astype("int64"), "date": date})
for c in ("open", "high", "low", "close", "volume"):
out[c] = pd.to_numeric(cdf[c], errors="coerce")
return out.dropna().drop_duplicates(subset=["timestamp"]) \
.sort_values("timestamp").reset_index(drop=True)
def walk_book(levels: list[tuple[float, float]], notional: float
) -> tuple[float, float]:
"""吃单到 notional(计价币)为止,返回 (加权成交价, 实际吃到的额度)。
深度不足时返回吃到的部分,由调用方按 filled < notional 判断是否可信。
"""
if not levels:
return float("nan"), 0.0
got = 0.0
cost = 0.0
qty = 0.0
for px, sz in levels:
avail = px * sz
take = min(avail, notional - got)
if take <= 0:
break
q = take / px
cost += q * px
qty += q
got += take
if got >= notional - 1e-9:
break
if qty <= 0:
return float("nan"), 0.0
return cost / qty, got
class BookBuffer:
"""每币一份滚动盘口。按时间戳回查,取第一个不早于目标时刻的快照。"""
def __init__(self) -> None:
self.buf: dict[str, deque] = {s: deque() for s in SYMS}
def push(self, sym: str, t_ms: int, bids: list, asks: list) -> None:
d = self.buf[sym]
d.append((t_ms, bids, asks))
cutoff = t_ms - BOOK_KEEP_S * 1000
while d and d[0][0] < cutoff:
d.popleft()
def at(self, sym: str, t_ms: int) -> tuple | None:
best = None
for snap in self.buf[sym]:
if snap[0] >= t_ms:
best = snap
break
return best
class Shadow:
def __init__(self, workers: int, hours: float) -> None:
self.workers = workers
self.deadline = time.time() + hours * 3600
self.books = BookBuffer()
self.pool: ProcessPoolExecutor | None = None
self.feeds_l: dict = {}
self.feeds_h: dict = {}
self.connector = None
self.stop = asyncio.Event()
self.n_signal = 0
self.n_bars = 0
d = out_dir()
# 追加模式:长跑期间若重启,已收集的样本不该被清掉
p_sig = d / "shadow_signals.csv"
new_sig = not p_sig.exists() or p_sig.stat().st_size == 0
self.f_sig = p_sig.open("a", newline="")
self.w_sig = csv.DictWriter(self.f_sig, fieldnames=[
"sym", "kline_ts", "direction", "h1_agree",
"t_close_ms", "t_data_ms", "t_signal_ms",
"lag_data_ms", "lag_signal_ms",
"delay_label", "delay_ms", "notional",
"baseline_px", "mid", "best_px", "fill_px", "filled",
"slip_bp", "drift_bp", "spread_bp", "impact_bp"])
if new_sig:
self.w_sig.writeheader()
p_lat = d / "shadow_latency.csv"
new_lat = not p_lat.exists() or p_lat.stat().st_size == 0
self.f_lat = p_lat.open("a", newline="")
self.w_lat = csv.DictWriter(self.f_lat, fieldnames=[
"sym", "kline_ts", "t_close_ms", "t_data_ms", "t_signal_ms",
"lag_data_ms", "lag_signal_ms", "compute_ms", "n_bars", "n_hits"])
if new_lat:
self.w_lat.writeheader()
# ---------- 启动 ----------
async def start(self) -> None:
from hummingbot.connector.derivative.bitget_perpetual.bitget_perpetual_derivative import (
BitgetPerpetualDerivative,
)
from patched_candles import PatchedBitgetPerpetualCandles
for s in SYMS:
self.feeds_l[s] = PatchedBitgetPerpetualCandles(
f"{s}-USDT", "1m", LTF_BARS)
self.feeds_h[s] = PatchedBitgetPerpetualCandles(
f"{s}-USDT", "5m", HTF_BARS)
self.feeds_l[s].start()
self.feeds_h[s].start()
print(f"[影子] {SYMS} · 1m×{LTF_BARS} + 5m×{HTF_BARS} · "
f"{self.workers} 个计算进程", flush=True)
# 只取公开数据:无密钥 + trading_required=False
self.connector = BitgetPerpetualDerivative(
bitget_perpetual_api_key="", bitget_perpetual_secret_key="",
bitget_perpetual_passphrase="",
trading_pairs=[f"{s}-USDT" for s in SYMS],
trading_required=False)
await self.connector.start_network()
print(" 连接器已启动,等盘口与历史回填", flush=True)
t0 = time.time()
while time.time() - t0 < 600:
ready = all(f.ready for f in
list(self.feeds_l.values()) + list(self.feeds_h.values()))
books = all(self._snapshot(s) is not None for s in SYMS)
if ready and books:
break
await asyncio.sleep(1)
print(f" 就绪 {time.time() - t0:.1f}s · "
f"1m {[len(self.feeds_l[s]._candles) for s in SYMS]} 根 · "
f"5m {[len(self.feeds_h[s]._candles) for s in SYMS]}", flush=True)
def _snapshot(self, sym: str):
try:
ob = self.connector.get_order_book(f"{sym}-USDT")
except Exception:
return None
if ob is None:
return None
bids = [(float(r.price), float(r.amount))
for r, _ in zip(ob.bid_entries(), range(BOOK_DEPTH))]
asks = [(float(r.price), float(r.amount))
for r, _ in zip(ob.ask_entries(), range(BOOK_DEPTH))]
if not bids or not asks:
return None
return bids, asks
# ---------- 三个循环 ----------
async def sample_books(self) -> None:
period = 1.0 / BOOK_HZ
while not self.stop.is_set():
t = int(time.time() * 1000)
for s in SYMS:
snap = self._snapshot(s)
if snap:
self.books.push(s, t, snap[0], snap[1])
await asyncio.sleep(period)
async def watch_bars(self) -> None:
last = {s: (int(self.feeds_l[s]._candles[-1][0])
if len(self.feeds_l[s]._candles) else None) for s in SYMS}
while not self.stop.is_set():
for s in SYMS:
c = self.feeds_l[s]._candles
if not len(c):
continue
newest = int(c[-1][0])
if last[s] is not None and newest > last[s]:
t_data = int(time.time() * 1000)
kts = newest * 1000 if newest < 1e12 else newest
asyncio.create_task(self.on_bar(s, kts, t_data))
last[s] = newest
await asyncio.sleep(0.01)
async def on_bar(self, sym: str, kline_ts: int, t_data: int) -> None:
"""kline_ts 是新一根的开盘时刻,也就是上一根的收盘时刻 t_close。"""
df_l = hb_to_research(self.feeds_l[sym].candles_df)
df_h = hb_to_research(self.feeds_h[sym].candles_df)
# 末行是刚开始的那根,未收盘,必须剔除,否则等于用未来数据
df_l = df_l[df_l["timestamp"] < kline_ts]
df_h = df_h[df_h["timestamp"] < kline_ts]
baseline = self._new_bar_open(sym, kline_ts)
t0 = time.perf_counter()
payload = (df_l[NUM_COLS].values.tolist(),
df_h[NUM_COLS].values.tolist())
loop = asyncio.get_running_loop()
from shadow_signal import compute_packed
res = await loop.run_in_executor(self.pool, compute_packed, payload)
compute_ms = int((time.perf_counter() - t0) * 1000)
t_signal = int(time.time() * 1000)
self.n_bars += 1
self.w_lat.writerow({
"sym": sym, "kline_ts": kline_ts, "t_close_ms": kline_ts,
"t_data_ms": t_data, "t_signal_ms": t_signal,
"lag_data_ms": t_data - kline_ts,
"lag_signal_ms": t_signal - kline_ts,
"compute_ms": compute_ms, "n_bars": res.get("n_bars", 0),
"n_hits": len(res.get("hits", []))})
self.f_lat.flush()
if res.get("error"):
print(f" [{sym}] 信号计算出错 {res['error']}", flush=True)
return
hits = res.get("hits", [])
if not hits:
return
if baseline is None or not np.isfinite(baseline):
print(f" [{sym}] 有信号但拿不到次根开盘价,跳过", flush=True)
return
for h in hits:
self.n_signal += 1
print(f" ★ [{sym}] {kline_ts} 方向 {h['direction']:+d} "
f"h1_agree={h['h1_agree']} · 数据 {t_data - kline_ts}ms "
f"信号 {t_signal - kline_ts}ms", flush=True)
# 最远的回查点在 t_close+5s,此刻尚未发生;等它过去再一次性落盘
asyncio.create_task(
self._record_later(sym, kline_ts, h, t_data, t_signal, baseline))
async def _record_later(self, sym: str, kline_ts: int, hit: dict,
t_data: int, t_signal: int, baseline: float) -> None:
target = kline_ts + int(max(DELAYS_S) * 1000) + 500
wait = target / 1000.0 - time.time()
if wait > 0:
await asyncio.sleep(wait)
self._record(sym, kline_ts, hit, t_data, t_signal, baseline)
def _new_bar_open(self, sym: str, kline_ts: int) -> float | None:
"""次根开盘价 = 回测假设的成交价。"""
c = self.feeds_l[sym]._candles
if not len(c):
return None
row = c[-1]
ts = int(row[0])
ts = ts * 1000 if ts < 1e12 else ts
return float(row[1]) if ts == kline_ts else None
def _record(self, sym: str, kline_ts: int, hit: dict,
t_data: int, t_signal: int, baseline: float) -> None:
points = [("actual", t_signal - kline_ts)]
points += [(f"{d}s", int(d * 1000)) for d in DELAYS_S]
d_sign = hit["direction"]
for label, delay_ms in points:
snap = self.books.at(sym, kline_ts + delay_ms)
if snap is None:
continue
_, bids, asks = snap
best_bid, best_ask = bids[0][0], asks[0][0]
mid = (best_bid + best_ask) / 2.0
# 多头吃卖盘,空头吃买盘
side = asks if d_sign > 0 else bids
best_px = best_ask if d_sign > 0 else best_bid
for notional in NOTIONALS:
fill, filled = walk_book(side, notional)
if not np.isfinite(fill):
continue
slip = d_sign * (fill - baseline) / baseline * 1e4
drift = d_sign * (mid - baseline) / baseline * 1e4
spread = d_sign * (best_px - mid) / mid * 1e4
impact = d_sign * (fill - best_px) / best_px * 1e4
self.w_sig.writerow({
"sym": sym, "kline_ts": kline_ts,
"direction": d_sign, "h1_agree": hit["h1_agree"],
"t_close_ms": kline_ts, "t_data_ms": t_data,
"t_signal_ms": t_signal,
"lag_data_ms": t_data - kline_ts,
"lag_signal_ms": t_signal - kline_ts,
"delay_label": label, "delay_ms": delay_ms,
"notional": notional, "baseline_px": baseline,
"mid": mid, "best_px": best_px, "fill_px": fill,
"filled": round(filled, 2),
"slip_bp": round(slip, 4), "drift_bp": round(drift, 4),
"spread_bp": round(spread, 4),
"impact_bp": round(impact, 4)})
self.f_sig.flush()
async def heartbeat(self) -> None:
while not self.stop.is_set():
await asyncio.sleep(300)
depth = {s: len(self.books.buf[s]) for s in SYMS}
print(f" [心跳] 已处理 {self.n_bars} 根 · 命中 {self.n_signal}"
f"· 盘口缓冲 {depth}", flush=True)
async def run(self) -> None:
await self.start()
tasks = [asyncio.create_task(self.sample_books()),
asyncio.create_task(self.watch_bars()),
asyncio.create_task(self.heartbeat())]
while time.time() < self.deadline:
await asyncio.sleep(5)
self.stop.set()
for t in tasks:
t.cancel()
await asyncio.gather(*tasks, return_exceptions=True)
for f in list(self.feeds_l.values()) + list(self.feeds_h.values()):
f.stop()
await self.connector.stop_network()
self.f_sig.close()
self.f_lat.close()
print(f"\n收工:{self.n_bars} 根 · {self.n_signal} 个信号", flush=True)
async def main_async(workers: int, hours: float, pool) -> None:
sh = Shadow(workers, hours)
sh.pool = pool
await sh.run()
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument("--hours", type=float, default=24.0)
ap.add_argument("--workers", type=int, default=2)
a = ap.parse_args()
# 进程池必须在事件循环和任何 WS 连接之前建好:fork 一个已带活跃 socket
# 的进程会把连接状态一起复制过去,后果不可预测
with ProcessPoolExecutor(max_workers=a.workers) as pool:
asyncio.run(main_async(a.workers, a.hours, pool))
if __name__ == "__main__":
main()
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"""影子交易器的报表:首日延迟门槛 + 滑点对延迟曲线。
两份产物对应计划里的两件事。
### 延迟门槛(提前止损用)
跑满 24 小时先看这个。若**总延迟已令预期漂移超过余量**,说明方案在这台机器
上就不成立,不必等两周样本再停。余量取 bitget_baseline.py 的实测值:
BTC 0.13bp(本就为负,只作参照)、ETH +4.02bp、SOL +2.92bp。
漂移按随机游走折算:σ_1m · √(t/60)。这是下限——入场条件是「收盘突破转强」,
那一刻价格正朝我们方向跑,延迟造成的是系统性追价,不会正负抵消。所以实测
滑点理应比这个折算值更差,两者对照本身就是个校验。
### 滑点对延迟曲线
把延迟当自变量:0.5s / 1s / 2s / 5s 各一个滑点值,外加「actual」= 本机实际
算完的时刻。这样即便本机算得慢,也能读出「若延迟压到 X 秒,滑点是多少」,
决策不被当前实现拖累。
.venv/bin/python research/live/shadow_report.py
"""
from __future__ import annotations
from pathlib import Path
import numpy as np
import pandas as pd
HERE = Path(__file__).resolve().parent
OUT = HERE.parent / "out"
SYMS = ("BTC", "ETH", "SOL")
BUDGET_BP = {"BTC": -0.13, "ETH": 4.02, "SOL": 2.92}
def vol_bp() -> dict[str, float]:
v = {}
for s in SYMS:
f = HERE / "cache" / f"bitget_{s}_1m_30d.feather"
if not f.exists():
f = HERE / "cache" / f"bitget_{s}_1m_210d.feather"
if not f.exists():
continue
c = np.log(pd.read_feather(f)["close"].to_numpy(float))
v[s] = float(np.nanstd(np.diff(c)) * 1e4)
return v
def latency_gate(lat: pd.DataFrame, vols: dict) -> None:
print("########## 一、延迟门槛 ##########")
span_h = (lat["t_close_ms"].max() - lat["t_close_ms"].min()) / 3.6e6
print(f"样本跨度 {span_h:.1f} 小时 · 共 {len(lat)}\n")
print(f"{'':<5}{'根数':>6}{'数据ms':>9}{'计算ms':>9}{'总延迟ms':>10}"
f"{'P90ms':>8}{'折算漂移bp':>12}{'余量bp':>9}{'占余量':>9}")
verdicts = {}
for s in SYMS:
g = lat[lat["sym"] == s]
if g.empty:
continue
d = float(np.median(g["lag_data_ms"]))
c = float(np.median(g["compute_ms"]))
t = float(np.median(g["lag_signal_ms"]))
p90 = float(np.percentile(g["lag_signal_ms"], 90))
vol = vols.get(s)
drift = vol * np.sqrt(t / 60_000) if vol else float("nan")
b = BUDGET_BP[s]
share = drift / b if b > 0 else float("nan")
verdicts[s] = (drift, b)
txt = f"{share * 100:.0f}%" if b > 0 else "—(负)"
print(f"{s:<5}{len(g):>6}{d:>9.0f}{c:>9.0f}{t:>10.0f}{p90:>8.0f}"
f"{drift:>12.2f}{b:>9.2f}{txt:>9}")
print("\n判读:")
for s, (drift, b) in verdicts.items():
if b <= 0:
print(f" {s}: 余量本就为负,不作交易标的,仅作延迟参照")
elif drift > b:
print(f" {s}: 折算漂移 {drift:.2f}bp 已超余量 {b:.2f}bp —— 停下改方案")
elif drift > b * 0.6:
print(f" {s}: 折算漂移 {drift:.2f}bp 吃掉余量 {b:.2f}bp 的六成以上,"
f"需要压延迟或放弃")
else:
print(f" {s}: 折算漂移 {drift:.2f}bp 对余量 {b:.2f}bp 尚有空间,继续收集")
def slippage_curve(sig: pd.DataFrame) -> None:
print("\n\n########## 二、滑点对延迟曲线 ##########")
if sig.empty:
print(" 尚无信号样本")
return
n_sig = sig.groupby(["sym", "kline_ts", "direction"]).ngroups
n_agree = sig[sig["h1_agree"] == 1].groupby(
["sym", "kline_ts", "direction"]).ngroups
print(f"信号总数 {n_sig}(其中 h1_agree=1 的 {n_agree} 个)\n")
order = ["0.5s", "1.0s", "2.0s", "5.0s", "actual"]
for scope, sub in (("全部信号", sig),
("仅 h1_agree=1(主口径)", sig[sig["h1_agree"] == 1])):
if sub.empty:
continue
print(f"--- {scope} ---")
print(f"{'延迟':<8}{'仓位':>9}{'n':>5}{'滑点均值bp':>12}"
f"{'中位bp':>9}{'漂移bp':>9}{'价差bp':>9}{'冲击bp':>9}")
for lb in order:
g0 = sub[sub["delay_label"] == lb]
if g0.empty:
continue
for nt in sorted(sub["notional"].unique()):
g = g0[g0["notional"] == nt]
if g.empty:
continue
print(f"{lb:<8}{int(nt):>9}{len(g):>5}"
f"{g['slip_bp'].mean():>12.2f}"
f"{g['slip_bp'].median():>9.2f}"
f"{g['drift_bp'].mean():>9.2f}"
f"{g['spread_bp'].mean():>9.2f}"
f"{g['impact_bp'].mean():>9.2f}")
print()
print("--- 分币种(仅 h1_agree=1,仓位 5000---")
m = sig[(sig["h1_agree"] == 1) & (sig["notional"] == 5000.0)]
if m.empty:
print(" 尚无样本")
return
print(f"{'':<5}{'延迟':<8}{'n':>5}{'滑点均值bp':>12}{'余量bp':>9}")
for s in SYMS:
for lb in order:
g = m[(m["sym"] == s) & (m["delay_label"] == lb)]
if g.empty:
continue
print(f"{s:<5}{lb:<8}{len(g):>5}{g['slip_bp'].mean():>12.2f}"
f"{BUDGET_BP[s]:>9.2f}")
def main() -> None:
vols = vol_bp()
print("1m 收益标准差(bp/分钟,Bitget 实测):"
+ " ".join(f"{s} {v:.2f}" for s, v in vols.items()) + "\n")
f_lat = OUT / "shadow_latency.csv"
if f_lat.exists() and f_lat.stat().st_size > 0:
lat = pd.read_csv(f_lat)
if not lat.empty:
latency_gate(lat, vols)
else:
print("尚无延迟数据")
f_sig = OUT / "shadow_signals.csv"
if f_sig.exists() and f_sig.stat().st_size > 0:
sig = pd.read_csv(f_sig)
slippage_curve(sig)
else:
print("\n尚无信号数据")
if __name__ == "__main__":
main()
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"""影子交易器的信号函数——在子进程里跑,不碰事件循环。
单次调用约 0.26s 的纯 CPU,且 chanlun 是纯 Python 受 GIL 限制,放进
Hummingbot 的 asyncio 循环里会把行情处理一起卡住,所以必须隔离到独立进程。
口径与 [aggregate_robustness.run_once] 逐行对齐:同样的 build_htf_zones →
find_fast_bsp3 → attach_htf_context(h1) 链路,同样的 h1_agree 过滤。两边
必须一致,否则影子测出来的滑点没法和 3.9bp 预算对照。
与回测的唯一差别是这里只关心**最后一根已收盘 K 线**上有没有信号——
实盘只能在当下下单,历史信号无意义。
未过滤信号也一并返回:过滤后样本太稀,先用未过滤的当提前读数,
两者都记,靠 h1_agree 字段区分。
"""
from __future__ import annotations
import os
import warnings
warnings.filterwarnings("ignore")
for _v in ("OMP_NUM_THREADS", "OPENBLAS_NUM_THREADS", "MKL_NUM_THREADS"):
os.environ.setdefault(_v, "1")
def compute(df_l, df_h) -> dict:
"""在 df_l 的最后一根上找信号。df_l/df_h 都只含已收盘 K 线。
返回 dict
last_idx 最后一根在 chanlun 处理后 dataframe 里的下标
n_bars 实际参与计算的根数
hits 命中列表,每项 {direction, h1_agree}
error 出错时的说明,正常为 None
"""
import pandas as pd
try:
from chanlun import TF_DF
from lib.fast_bsp3 import find_fast_bsp3
from lib.fx_signal import extract_fx_signals, signals_to_frame
from lib.nested_bsp import attach_htf_context, htf_fx_timeline
from lib.nested_level import build_htf_zones
chan_l = TF_DF(df_l, 1, "1m")
cdf = chan_l.dataframe
last = len(cdf) - 1
base = {"last_idx": last, "n_bars": int(len(df_l)), "hits": [],
"error": None}
zones = build_htf_zones(cdf, "1m", chan=chan_l)
if zones.empty:
return base
sig = find_fast_bsp3(cdf, zones.reset_index(drop=True))
if sig is None or sig.empty:
return base
# 只留落在最后一根上的信号,其余是历史,实盘下不了
cur = sig[sig["entry_idx"].astype(int) == last]
if cur.empty:
return base
# 5m 同向过滤:算得出就标 h1_agree,算不出就当未过滤照记
agree_map: dict[int, int] = {}
if df_h is not None and len(df_h) > 0:
chan_h = TF_DF(df_h, 1, "5m")
hdf = chan_h.dataframe
tl = htf_fx_timeline(
signals_to_frame(extract_fx_signals(chan_h, hdf)), hdf)
full = attach_htf_context(sig, cdf, tl, "h1")
f_cur = full[full["entry_idx"].astype(int) == last]
for _, r in f_cur.iterrows():
agree_map[int(r["direction"])] = int(r.get("h1_agree", 0))
base["hits"] = [{"direction": int(r["direction"]),
"h1_agree": agree_map.get(int(r["direction"]), 0)}
for _, r in cur.iterrows()]
return base
except Exception as e: # 子进程里异常必须带回主进程,否则只见超时不见原因
import traceback
return {"last_idx": -1, "n_bars": int(len(df_l)) if df_l is not None else 0,
"hits": [], "error": f"{type(e).__name__}: {e}",
"traceback": traceback.format_exc()}
NUM_COLS = ("timestamp", "open", "high", "low", "close", "volume")
def _rebuild(rows) -> "object":
"""只传数值列,date 在这里按 lib/data.py 的同一规则重建。
跨进程传 tz-aware 的 datetime 既慢又容易在字符串往返中丢时区,
而时区若与回测不一致,chanlun 的 K 线标签就会错位。
"""
import pandas as pd
df = pd.DataFrame(rows, columns=list(NUM_COLS))
df["timestamp"] = df["timestamp"].astype("int64")
date = pd.to_datetime(df["timestamp"], unit="ms", utc=True) \
.dt.tz_convert("Asia/Shanghai")
df.insert(1, "date", date)
return df
def compute_packed(payload: tuple) -> dict:
"""ProcessPoolExecutor 的入口:收 (l_rows, h_rows) 两组数值行。"""
l_rows, h_rows = payload
df_l = _rebuild(l_rows)
df_h = _rebuild(h_rows) if h_rows else None
return compute(df_l, df_h)
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"""对照实验:信号集对微小数据差异有多敏感。
venue_parity 量到 Bitget 与 Binance 的 1m 信号同根重合率只有 16%~41%
而两家的 close 中位差仅 0.3bp、P95 约 2.5bp。在断言「换交易所会换掉一批信号」
之前,必须先排除另一种解释:**信号定义本身就对任何 2bp 级别的扰动极度敏感**。
这两种解释的后果完全不同:
venue 差异 -> 换成 Bitget 自己的回测基线即可归因
内在脆弱 -> Binance 回测的那份逐笔清单根本不可复现,滑点无从对照
做法:拿 Binance 原始数据当基线,注入不同幅度的 iid 噪声后重跑同一管线,
看重合率随噪声幅度的衰减曲线。噪声 0 必须给出 100%,否则说明管线不确定。
顺带单独测一档「tick 粗化」:把 Bitget SOL 的 0.001 精度四舍五入到 Binance
的 0.01,看重合率是否回升——若回升,SOL 的低重合就主要是精度差异造成的。
输出 out/signal_sensitivity.csv。
"""
from __future__ import annotations
import argparse
import os
import sys
import warnings
from pathlib import Path
import numpy as np
import pandas as pd
warnings.filterwarnings("ignore")
for v in ("OMP_NUM_THREADS", "OPENBLAS_NUM_THREADS", "MKL_NUM_THREADS"):
os.environ.setdefault(v, "1")
HERE = Path(__file__).resolve().parent
RESEARCH = HERE.parent
sys.path.insert(0, str(RESEARCH))
sys.path.insert(0, str(RESEARCH.parent))
pd.set_option("display.width", 240)
sys.path.insert(0, str(HERE))
from venue_parity import overlap, pipeline # noqa: E402
SYMS = ("BTC", "ETH", "SOL")
LEVELS = (0.0, 0.25, 0.5, 1.0, 2.0, 4.0) # bp
TICK = {"BTC": 0.1, "ETH": 0.01, "SOL": 0.01}
def perturb(df: pd.DataFrame, bp: float, tick: float, seed: int) -> pd.DataFrame:
"""给 OHLC 各自注入 iid 噪声,再修复 high/low 的包含关系并按 tick 归整。"""
if bp <= 0:
return df
out = df.copy()
rng = np.random.default_rng(seed)
sd = bp / 1e4
for c in ("open", "high", "low", "close"):
v = out[c].to_numpy(dtype=float)
out[c] = v * (1.0 + rng.normal(0.0, sd, size=len(v)))
o, h, l, c = (out[x].to_numpy(dtype=float) for x in ("open", "high", "low", "close"))
out["high"] = np.maximum.reduce([h, o, c])
out["low"] = np.minimum.reduce([l, o, c])
for x in ("open", "high", "low", "close"):
out[x] = np.round(out[x] / tick) * tick
return out
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument("--symbols", default="BTC,ETH,SOL")
ap.add_argument("--seeds", type=int, default=2)
ap.add_argument("--warmup", type=int, default=2000)
args = ap.parse_args()
from lib.data import load_local
syms = [s.strip() for s in args.symbols.split(",")]
period_ms = 60_000
print(f"[信号敏感性] {syms} · 噪声档 {LEVELS} bp · 每档 {args.seeds} 个种子\n",
flush=True)
rows = []
for sym in syms:
# 与 venue_parity 用同一段窗口,便于两组数字直接对照
cache = HERE / "cache" / f"bitget_{sym}_1m_30d.feather"
if not cache.exists():
print(f"{sym}: 缺 {cache.name},先跑 venue_parity.py")
continue
bg = pd.read_feather(cache)
lo, hi = int(bg.timestamp.min()), int(bg.timestamp.max())
bn_l = load_local(f"{sym}/USDT:USDT", "1m")
bn_h = load_local(f"{sym}/USDT:USDT", "5m")
bn_l = bn_l[(bn_l.timestamp >= lo) & (bn_l.timestamp <= hi)].reset_index(drop=True)
bn_h = bn_h[(bn_h.timestamp >= lo) & (bn_h.timestamp <= hi)].reset_index(drop=True)
base = pipeline(bn_l, bn_h)
cut = bn_l["timestamp"].to_numpy()[min(args.warmup, len(bn_l) - 1)]
base = base[base.entry_ts >= cut]
base_f = base[base["h1_agree"] == 1]
print(f"── {sym} 基线 原始 {len(base)} 笔 / 过滤后 {len(base_f)}",
flush=True)
for bp in LEVELS:
n_seeds = 1 if bp == 0 else args.seeds
acc = {"原始": [], "5m同向后": []}
cnt = {"原始": [], "5m同向后": []}
for k in range(n_seeds):
pert = pipeline(perturb(bn_l, bp, TICK[sym], 1000 + k), bn_h)
if pert.empty:
continue
pert = pert[pert.entry_ts >= cut]
pert_f = pert[pert["h1_agree"] == 1]
for tag, a, b in (("原始", base, pert), ("5m同向后", base_f, pert_f)):
o = overlap(a, b, period_ms)
acc[tag].append(o["同根"])
cnt[tag].append(o["b"])
for tag in ("原始", "5m同向后"):
if not acc[tag]:
continue
rows.append({"品种": sym, "口径": tag, "噪声bp": bp,
"基线笔数": len(base if tag == "原始" else base_f),
"扰动后笔数": round(float(np.mean(cnt[tag])), 1),
"同根重合": float(np.mean(acc[tag]))})
print(f" 噪声 {bp:>4.2f}bp: 原始 {np.mean(acc['原始']) * 100:5.1f}% · "
f"过滤后 {np.mean(acc['5m同向后']) * 100:5.1f}%", flush=True)
if not rows:
print("无结果")
return
tb = pd.DataFrame(rows)
print("\n" + "=" * 100)
print("########## 噪声幅度 → 同根重合率 ##########")
piv = tb[tb["口径"] == "5m同向后"].pivot_table(
index="噪声bp", columns="品种", values="同根重合")
print((piv * 100).round(1).to_string())
print(" 行是注入的 iid 噪声幅度(bp),值是与无噪声基线的同根重合率。")
print("\n########## 与 venue_parity 的实测对照 ##########")
print(" Bitget↔Binance 实测:close 中位差 0.05~0.33bp、P95 2.0~3.0bp")
print(" 过滤后同根重合 BTC 40.9% / ETH 25.0% / SOL 15.9%")
print(" 若上表在 1~2bp 档就掉到同一水平,说明主因是信号定义的内在脆弱,")
print(" 而不是 Bitget 这家交易所特殊。")
out = RESEARCH / "out" / "signal_sensitivity.csv"
tb.to_csv(out, index=False)
print(f"\n产物写入 {out}")
if __name__ == "__main__":
main()
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"""前置测量二:venue 对齐——Bitget 与 Binance 的 1m 是不是同一批信号。
研究数据全部来自 Binance,影子交易器却跑在 Bitget。若两家的 1m K 线有差异,
信号集就会不同,而这个差异会被误记到滑点账上——那样收集一两周也不可归因。
所以先把两家同期的 1m 拉齐,跑同一套管线,比三件事:
K 线层 时间戳缺口、close 价差(bp)、high/low 差异
信号层 原始 fast_bsp3 的重合率
过滤后 加 5m 同向过滤后的重合率(这才是实际要交易的那批)
重合率高 → 后面测到的滑点可以直接对照 Binance 回测的 3.9bp 预算。
重合率低 → 必须先补 Bitget 自己的回测基线,否则实验不可归因。
输出 out/venue_parity.csv。Bitget 数据缓存在 live/cache/,重跑不必再拉。
"""
from __future__ import annotations
import argparse
import os
import sys
import time
import warnings
from pathlib import Path
import numpy as np
import pandas as pd
warnings.filterwarnings("ignore")
for v in ("OMP_NUM_THREADS", "OPENBLAS_NUM_THREADS", "MKL_NUM_THREADS"):
os.environ.setdefault(v, "1")
HERE = Path(__file__).resolve().parent
RESEARCH = HERE.parent
sys.path.insert(0, str(RESEARCH))
sys.path.insert(0, str(RESEARCH.parent))
pd.set_option("display.width", 240)
CACHE = HERE / "cache"
LTF, HTF = "1m", "5m"
HTF_RATIO = 5
SYMS = ("BTC", "ETH", "SOL")
NUMERIC = ("open", "high", "low", "close", "volume")
def _exchange():
import ccxt
# 这台机器在新加坡,直连 Bitget 0.30s。绝不要照抄交接文档里 Mac 的代理配置,
# 代理会把延迟放大到秒级,测出来的滑点就是代理的账。
# rateLimit 默认 50ms,连拉上千页 history-candles 会被 429,放宽到 120ms
return ccxt.bitget({"options": {"defaultType": "swap"},
"enableRateLimit": True, "rateLimit": 120})
def fetch_bitget(sym: str, tf: str, days: int, refresh: bool = False) -> pd.DataFrame:
"""分页拉 Bitget 永续 K 线,落盘缓存,列结构对齐 lib/data.py。"""
CACHE.mkdir(parents=True, exist_ok=True)
path = CACHE / f"bitget_{sym}_{tf}_{days}d.feather"
if path.exists() and not refresh:
return pd.read_feather(path)
ex = _exchange()
pair = f"{sym}/USDT:USDT"
period_ms = ex.parse_timeframe(tf) * 1000
t0 = time.perf_counter()
calls = 0
# 远端 history-candles 每页硬上限 200 根,而 ccxt 会按 limit 推算 endTime
# 只把窗口末尾的 200 根还给你。若照 limit=1000 步进,每页就白丢 800 根——
# 210 天曾因此只拿到应有量的 31%。故分页一律按 200 走。
PAGE = 200
def one(since: int) -> list:
"""单次取数并退避重试。history-candles 连拉上千次会触发 429。"""
for attempt in range(6):
try:
return ex.fetch_ohlcv(pair, tf, since=since, limit=PAGE)
except Exception as e:
if attempt == 5:
raise
wait = 2 ** attempt
print(f" {sym} {tf}: {type(e).__name__}{wait}s 后重试",
flush=True)
time.sleep(wait)
return []
def page(start: int, stop: int) -> list:
"""向前分页。Bitget 把 since 当开区间,故每次从上一批最后一根重取,
边界少的那一根靠去重消化。"""
nonlocal calls
got, since = [], start
while since < stop:
batch = one(since)
calls += 1
if len(batch) < 2:
break
got.extend(batch)
if batch[-1][0] <= since:
break
since = batch[-1][0]
if calls % 200 == 0:
print(f" {sym} {tf}: {len(got)} 根 / {calls} 次请求", flush=True)
return got
now = ex.milliseconds()
rows = page(now - days * 86_400_000, now)
# 补缺口:远端接口一次只给 200 根,个别区段仍可能漏,逐个补到补不动为止
for _ in range(5):
ts = np.unique(np.array([r[0] for r in rows], dtype="int64"))
if len(ts) < 2:
break
holes = np.where(np.diff(ts) > period_ms)[0]
if not len(holes):
break
before = len(ts)
for i in holes:
rows.extend(page(int(ts[i]), int(ts[i + 1])))
if len(np.unique([r[0] for r in rows])) <= before:
break
df = pd.DataFrame(rows, columns=["timestamp", *NUMERIC])
df["timestamp"] = df["timestamp"].astype("int64")
for c in NUMERIC:
df[c] = pd.to_numeric(df[c], errors="coerce")
df = (df.dropna(subset=list(NUMERIC))
.drop_duplicates(subset=["timestamp"])
.sort_values("timestamp")
.reset_index(drop=True))
df["date"] = (pd.to_datetime(df["timestamp"], unit="ms", utc=True)
.dt.tz_convert("Asia/Shanghai"))
df = df[["timestamp", "date", *NUMERIC]]
gap = int(((np.diff(df["timestamp"].to_numpy()) // period_ms) - 1).clip(0).sum())
print(f" {sym} {tf}: {len(df)} 根,{calls} 次请求,"
f"{time.perf_counter() - t0:.1f}s,残余缺口 {gap}", flush=True)
df.to_feather(path)
return df
def pipeline(df_l: pd.DataFrame, df_h: pd.DataFrame) -> pd.DataFrame:
"""全量口径跑一遍:原始信号 + 5m 同向过滤,返回带时间戳的信号表。"""
from chanlun import TF_DF
from lib.fast_bsp3 import find_fast_bsp3
from lib.fx_signal import extract_fx_signals, signals_to_frame
from lib.nested_bsp import attach_htf_context, htf_fx_timeline
from lib.nested_level import build_htf_zones
chan_l = TF_DF(df_l, 1, LTF)
cdf = chan_l.dataframe
zones = build_htf_zones(cdf, LTF, chan=chan_l)
if zones.empty:
return pd.DataFrame()
sig = find_fast_bsp3(cdf, zones.reset_index(drop=True))
if sig.empty:
return pd.DataFrame()
chan_h = TF_DF(df_h, 1, HTF)
hdf = chan_h.dataframe
tl = htf_fx_timeline(signals_to_frame(extract_fx_signals(chan_h, hdf)), hdf)
full = attach_htf_context(sig, cdf, tl, "h1")
full["entry_ts"] = cdf["timestamp"].to_numpy()[full["entry_idx"].astype(int)]
return full
def overlap(a: pd.DataFrame, b: pd.DataFrame, period_ms: int,
tol_bars: int = 1) -> dict:
"""按时间戳比对两个信号集。方向也必须一致才算命中。"""
if a.empty or b.empty:
return {"a": len(a), "b": len(b), "同根": np.nan, f"±{tol_bars}": np.nan}
bt = b["entry_ts"].to_numpy()
bd = b["direction"].to_numpy()
exact = near = 0
for ts, d in zip(a["entry_ts"].to_numpy(), a["direction"].to_numpy()):
hit = np.where((bt == ts) & (bd == d))[0]
if len(hit):
exact += 1
near += 1
continue
if np.any((np.abs(bt - ts) <= tol_bars * period_ms) & (bd == d)):
near += 1
return {"a": len(a), "b": len(b),
"同根": exact / len(a), f"±{tol_bars}": near / len(a)}
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument("--days", type=int, default=30)
ap.add_argument("--symbols", default="BTC,ETH,SOL")
ap.add_argument("--warmup", type=int, default=2000,
help="丢弃前若干根的信号,避开中枢左边界效应")
ap.add_argument("--refresh", action="store_true")
args = ap.parse_args()
from lib.data import load_local
syms = [s.strip() for s in args.symbols.split(",")]
period_ms = 60_000
print(f"[venue 对齐] {syms} · 近 {args.days} 天 1m · "
f"预热丢弃 {args.warmup}\n", flush=True)
bar_rows, sig_rows = [], []
for sym in syms:
print(f"── {sym}", flush=True)
bg_l = fetch_bitget(sym, LTF, args.days, args.refresh)
bg_h = fetch_bitget(sym, HTF, args.days, args.refresh)
pair = f"{sym}/USDT:USDT"
bn_l_all = load_local(pair, LTF)
bn_h_all = load_local(pair, HTF)
if bn_l_all is None or bn_h_all is None:
print(f" 跳过:本地无 Binance 数据")
continue
# 只比两家都有的那段时间
lo = max(bg_l["timestamp"].min(), bn_l_all["timestamp"].min())
hi = min(bg_l["timestamp"].max(), bn_l_all["timestamp"].max())
bg_l = bg_l[(bg_l.timestamp >= lo) & (bg_l.timestamp <= hi)].reset_index(drop=True)
bn_l = bn_l_all[(bn_l_all.timestamp >= lo) & (bn_l_all.timestamp <= hi)].reset_index(drop=True)
bg_h = bg_h[bg_h.timestamp <= hi].reset_index(drop=True)
bn_h = bn_h_all[(bn_h_all.timestamp >= bg_h["timestamp"].min())
& (bn_h_all.timestamp <= hi)].reset_index(drop=True)
span_d = (hi - lo) / 86_400_000
expect = int((hi - lo) / period_ms) + 1
# K 线层比对
m = bg_l.merge(bn_l, on="timestamp", suffixes=("_bg", "_bn"))
dc = (m["close_bg"] - m["close_bn"]) / m["close_bn"] * 1e4
dh = (m["high_bg"] - m["high_bn"]) / m["high_bn"] * 1e4
dl = (m["low_bg"] - m["low_bn"]) / m["low_bn"] * 1e4
bar_rows.append({
"品种": sym, "重叠天数": round(span_d, 1),
"Bitget根数": len(bg_l), "Binance根数": len(bn_l),
"应有根数": expect,
"Bitget缺口": expect - len(bg_l), "Binance缺口": expect - len(bn_l),
"共有根数": len(m),
"close中位差": f"{dc.median():+.2f}bp",
"close绝对差P95": f"{dc.abs().quantile(.95):.2f}bp",
"high绝对差P95": f"{dh.abs().quantile(.95):.2f}bp",
"low绝对差P95": f"{dl.abs().quantile(.95):.2f}bp",
})
print(f" K线:重叠 {span_d:.1f} 天,共有 {len(m)} 根,"
f"close 中位差 {dc.median():+.2f}bpP95 {dc.abs().quantile(.95):.2f}bp",
flush=True)
# 信号层比对
t0 = time.perf_counter()
s_bg = pipeline(bg_l, bg_h)
s_bn = pipeline(bn_l, bn_h)
print(f" 管线跑完 {time.perf_counter() - t0:.1f}s", flush=True)
if s_bg.empty or s_bn.empty:
print(" 信号为空,跳过信号层")
continue
cut_bg = bg_l["timestamp"].to_numpy()[min(args.warmup, len(bg_l) - 1)]
cut_bn = bn_l["timestamp"].to_numpy()[min(args.warmup, len(bn_l) - 1)]
cut = max(cut_bg, cut_bn)
s_bg = s_bg[s_bg.entry_ts >= cut]
s_bn = s_bn[s_bn.entry_ts >= cut]
f_bg = s_bg[s_bg["h1_agree"] == 1]
f_bn = s_bn[s_bn["h1_agree"] == 1]
for tag, x, y in (("原始", s_bg, s_bn), ("5m同向后", f_bg, f_bn)):
o1 = overlap(x, y, period_ms) # Bitget 的信号有多少在 Binance 也有
o2 = overlap(y, x, period_ms) # 反向
sig_rows.append({
"品种": sym, "口径": tag,
"Bitget信号": o1["a"], "Binance信号": o1["b"],
"BG→BN同根": f"{o1['同根'] * 100:.1f}%",
"BG→BN±1根": f"{o1['±1根'] * 100:.1f}%",
"BN→BG同根": f"{o2['同根'] * 100:.1f}%",
"BN→BG±1根": f"{o2['±1根'] * 100:.1f}%",
})
print(f" {tag}Bitget {o1['a']} 笔 / Binance {o1['b']} 笔,"
f"同根重合 {o1['同根'] * 100:.1f}%,±1根 {o1['±1根'] * 100:.1f}%",
flush=True)
if not bar_rows:
print("无结果")
return
print("\n" + "=" * 120)
print("########## 1. K 线层 ##########")
tb_bar = pd.DataFrame(bar_rows)
print(tb_bar.to_string(index=False))
print(" 缺口是「应有根数 − 实际根数」,永续在极端行情或维护时会漏推。")
print("\n########## 2. 信号层 ##########")
tb_sig = pd.DataFrame(sig_rows)
print(tb_sig.to_string(index=False))
print("\n########## 结论 ##########")
fin = tb_sig[tb_sig["口径"] == "5m同向后"]
if not fin.empty:
v = fin["BG→BN同根"].str.rstrip("%").astype(float)
print(f" 过滤后口径的同根重合率:{v.min():.1f}% ~ {v.max():.1f}%"
f"均值 {v.mean():.1f}%")
print(" 高 → 滑点可直接对照 Binance 回测的 3.9bp 预算;")
print(" 低 → 必须先补 Bitget 自己的 1m 回测基线,否则实验不可归因。")
out_dir = RESEARCH / "out"
tb_bar.to_csv(out_dir / "venue_parity_bars.csv", index=False)
tb_sig.to_csv(out_dir / "venue_parity_signals.csv", index=False)
print(f"\n产物写入 {out_dir}/venue_parity_bars.csv 与 venue_parity_signals.csv")
if __name__ == "__main__":
main()
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"""验证补丁是否真把那 1.1 秒拿回来了。
容器内同一进程并行跑三条路径,共用时钟与网络:
stock 上游 BitgetPerpetualCandles(取 data[0]
patched PatchedBitgetPerpetualCandles(处理全部元素)
raw 原始 aiohttp WS,按 data[-1] 判断换根(理论最快)
预期:patched ≈ raw,且比 stock 早约 1.1 秒。
同时校验补丁没有破坏数据:两条 feed 的历史 K 线应逐根相等(补丁只影响
最新一根的到达时刻与收盘价更新时机,不该改动已收盘的历史)。
docker run --rm -v $PWD:/repo:ro -v $PWD/research/out:/out \
-w /home/hummingbot -e PYTHONPATH=/home/hummingbot:/repo/research/live \
--entrypoint /opt/conda/envs/hummingbot/bin/python \
hummingbot/hummingbot:latest /repo/research/live/verify_patch.py --minutes 20
"""
from __future__ import annotations
import argparse
import asyncio
import csv
import json
import time
from pathlib import Path
SYMS = ("BTC", "ETH", "SOL")
WSS = "wss://ws.bitget.com/v2/ws/public"
def out_dir() -> Path:
p = Path("/out")
return p if p.is_dir() else Path(__file__).resolve().parents[1] / "out"
async def raw_ws(rec: dict, stop: asyncio.Event) -> None:
import aiohttp
payload = {"op": "subscribe",
"args": [{"instType": "USDT-FUTURES", "channel": "candle1m",
"instId": f"{s}USDT"} for s in SYMS]}
while not stop.is_set():
try:
async with aiohttp.ClientSession() as sess, \
sess.ws_connect(WSS, heartbeat=20) as ws:
await ws.send_str(json.dumps(payload))
last: dict[str, int] = {}
while not stop.is_set():
msg = await ws.receive()
if msg.type is not aiohttp.WSMsgType.TEXT:
break
if msg.data == "pong":
continue
d = json.loads(msg.data)
if "data" not in d or "arg" not in d or not d["data"]:
continue
sym = d["arg"]["instId"].replace("USDT", "")
kts = int(d["data"][-1][0]) # 关键:取末元素
if last.get(sym) is not None and kts > last[sym]:
rec.setdefault((sym, kts), {})["raw"] = \
int(time.time() * 1000)
last[sym] = max(kts, last.get(sym, 0))
except asyncio.CancelledError:
raise
except Exception as e:
print(f" [raw] {type(e).__name__}: {e}", flush=True)
# 正常跳出与异常都要歇一下再重连,避免服务端持续拒绝时打成风暴
if not stop.is_set():
await asyncio.sleep(1)
async def poll_feeds(feeds: dict, key: str, rec: dict,
stop: asyncio.Event) -> None:
last = {s: (int(f._candles[-1][0]) if len(f._candles) else None)
for s, f in feeds.items()}
while not stop.is_set():
for s, f in feeds.items():
if not len(f._candles):
continue
newest = int(f._candles[-1][0])
if last[s] is not None and newest > last[s]:
kts = newest * 1000 if newest < 1e12 else newest
rec.setdefault((s, kts), {})[key] = int(time.time() * 1000)
last[s] = newest
await asyncio.sleep(0.01)
async def main_async(minutes: int) -> None:
from hummingbot.data_feed.candles_feed.bitget_perpetual_candles import (
BitgetPerpetualCandles,
)
from patched_candles import PatchedBitgetPerpetualCandles
stock, patched = {}, {}
for s in SYMS:
stock[s] = BitgetPerpetualCandles(f"{s}-USDT", "1m", 20)
patched[s] = PatchedBitgetPerpetualCandles(f"{s}-USDT", "1m", 20)
for d in (stock, patched):
for f in d.values():
f.start()
print(f"[补丁验证] {SYMS} · 跑 {minutes} 分钟", flush=True)
t0 = time.time()
while time.time() - t0 < 120:
if all(f.ready for d in (stock, patched) for f in d.values()):
break
await asyncio.sleep(0.5)
print(f" 回填完成 {time.time() - t0:.1f}s", flush=True)
rec: dict = {}
stop = asyncio.Event()
tasks = [asyncio.create_task(raw_ws(rec, stop)),
asyncio.create_task(poll_feeds(stock, "stock", rec, stop)),
asyncio.create_task(poll_feeds(patched, "patched", rec, stop))]
deadline = time.time() + minutes * 60
seen = set()
while time.time() < deadline:
await asyncio.sleep(2)
for k, v in rec.items():
if k in seen or not {"stock", "patched", "raw"} <= v.keys():
continue
seen.add(k)
print(f" {k[0]}: patched 比 stock 早 "
f"{v['stock'] - v['patched']}ms · 距 raw "
f"{v['patched'] - v['raw']}ms", flush=True)
stop.set()
for t in tasks:
t.cancel()
await asyncio.gather(*tasks, return_exceptions=True)
# 数据一致性:两条 feed 的已收盘历史必须逐根相同
print("\n########## 补丁是否改动了已收盘 K 线 ##########")
for s in SYMS:
a = [list(map(float, r)) for r in list(stock[s]._candles)[:-1]]
b = [list(map(float, r)) for r in list(patched[s]._candles)[:-1]]
n = min(len(a), len(b))
ta = {int(r[0]): r for r in a[-n:]}
tb = {int(r[0]): r for r in b[-n:]}
common = sorted(set(ta) & set(tb))
diff = [t for t in common if ta[t][1:6] != tb[t][1:6]]
print(f" {s}: 共有 {len(common)} 根,OHLCV 不同 {len(diff)}"
+ (f"(示例 ts={diff[:3]}" if diff else ""))
for d in (stock, patched):
for f in d.values():
f.stop()
full = [(s, k, v) for (s, k), v in rec.items()
if {"stock", "patched", "raw"} <= v.keys()]
f = out_dir() / "verify_patch.csv"
with f.open("w", newline="") as fh:
w = csv.writer(fh)
w.writerow(["sym", "kline_ts", "raw", "patched", "stock",
"gain_ms", "patched_minus_raw_ms"])
for s, k, v in sorted(full, key=lambda x: x[1]):
w.writerow([s, k, v["raw"], v["patched"], v["stock"],
v["stock"] - v["patched"], v["patched"] - v["raw"]])
print(f"\n########## 补丁收益(配对 {len(full)} 根)##########")
print(f"{'':<5}{'n':>5}{'早于stock中位':>14}{'距raw中位':>12}")
for s in SYMS:
g = [v for ss, _, v in full if ss == s]
if not g:
print(f" {s}: 无样本")
continue
gain = sorted(v["stock"] - v["patched"] for v in g)
dr = sorted(v["patched"] - v["raw"] for v in g)
print(f"{s:<5}{len(g):>5}{gain[len(gain) // 2]:>14}"
f"{dr[len(dr) // 2]:>12}")
print(f"\n产物写入 {f}")
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument("--minutes", type=int, default=20)
asyncio.run(main_async(ap.parse_args().minutes))
if __name__ == "__main__":
main()
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品种,噪声bp,种子,笔数,胜率,毛均收益,净均收益,PF,t值,滑点余量bp,毛均bp
BTC,0.0,0,294,49.3%,+0.0611%,+0.0111%,1.13,0.88,0.11,6.11
BTC,0.5,0,322,53.4%,+0.0614%,+0.0114%,1.14,0.95,0.14,6.140000000000001
BTC,0.5,1,304,51.3%,+0.0672%,+0.0172%,1.21,1.38,0.72,6.72
BTC,1.0,0,262,53.1%,+0.0648%,+0.0148%,1.17,1.08,0.48,6.4799999999999995
BTC,1.0,1,255,48.6%,+0.0595%,+0.0095%,1.1,0.65,-0.05,5.949999999999999
BTC,2.0,0,196,53.6%,+0.0987%,+0.0487%,1.5,2.46,3.87,9.87
BTC,2.0,1,191,50.3%,+0.0799%,+0.0299%,1.28,1.51,1.99,7.99
ETH,0.0,0,288,55.6%,+0.1231%,+0.0731%,1.9,4.15,6.31,12.31
ETH,0.5,0,293,52.9%,+0.0949%,+0.0449%,1.46,2.52,3.49,9.49
ETH,0.5,1,291,60.1%,+0.1236%,+0.0736%,1.87,4.2,6.36,12.36
ETH,1.0,0,280,51.1%,+0.1004%,+0.0504%,1.48,2.65,4.04,10.040000000000001
ETH,1.0,1,270,54.8%,+0.1142%,+0.0642%,1.64,3.24,5.42,11.42
ETH,2.0,0,240,55.8%,+0.1182%,+0.0682%,1.61,3.14,5.82,11.82
ETH,2.0,1,222,58.1%,+0.1474%,+0.0974%,1.93,4.07,8.74,14.74
SOL,0.0,0,245,52.2%,+0.1001%,+0.0501%,1.44,2.41,4.01,10.01
SOL,0.5,0,232,53.4%,+0.1000%,+0.0500%,1.44,2.47,4.0,10.0
SOL,0.5,1,234,57.7%,+0.1332%,+0.0832%,1.75,3.68,7.32,13.320000000000002
SOL,1.0,0,246,57.7%,+0.1362%,+0.0862%,1.82,4.09,7.62,13.62
SOL,1.0,1,230,54.8%,+0.1191%,+0.0691%,1.58,3.01,5.91,11.91
SOL,2.0,0,195,58.5%,+0.1486%,+0.0986%,1.83,3.84,8.86,14.860000000000001
SOL,2.0,1,193,55.4%,+0.1327%,+0.0827%,1.67,3.19,7.27,13.270000000000001
1 品种 噪声bp 种子 笔数 胜率 毛均收益 净均收益 PF t值 滑点余量bp 毛均bp
2 BTC 0.0 0 294 49.3% +0.0611% +0.0111% 1.13 0.88 0.11 6.11
3 BTC 0.5 0 322 53.4% +0.0614% +0.0114% 1.14 0.95 0.14 6.140000000000001
4 BTC 0.5 1 304 51.3% +0.0672% +0.0172% 1.21 1.38 0.72 6.72
5 BTC 1.0 0 262 53.1% +0.0648% +0.0148% 1.17 1.08 0.48 6.4799999999999995
6 BTC 1.0 1 255 48.6% +0.0595% +0.0095% 1.1 0.65 -0.05 5.949999999999999
7 BTC 2.0 0 196 53.6% +0.0987% +0.0487% 1.5 2.46 3.87 9.87
8 BTC 2.0 1 191 50.3% +0.0799% +0.0299% 1.28 1.51 1.99 7.99
9 ETH 0.0 0 288 55.6% +0.1231% +0.0731% 1.9 4.15 6.31 12.31
10 ETH 0.5 0 293 52.9% +0.0949% +0.0449% 1.46 2.52 3.49 9.49
11 ETH 0.5 1 291 60.1% +0.1236% +0.0736% 1.87 4.2 6.36 12.36
12 ETH 1.0 0 280 51.1% +0.1004% +0.0504% 1.48 2.65 4.04 10.040000000000001
13 ETH 1.0 1 270 54.8% +0.1142% +0.0642% 1.64 3.24 5.42 11.42
14 ETH 2.0 0 240 55.8% +0.1182% +0.0682% 1.61 3.14 5.82 11.82
15 ETH 2.0 1 222 58.1% +0.1474% +0.0974% 1.93 4.07 8.74 14.74
16 SOL 0.0 0 245 52.2% +0.1001% +0.0501% 1.44 2.41 4.01 10.01
17 SOL 0.5 0 232 53.4% +0.1000% +0.0500% 1.44 2.47 4.0 10.0
18 SOL 0.5 1 234 57.7% +0.1332% +0.0832% 1.75 3.68 7.32 13.320000000000002
19 SOL 1.0 0 246 57.7% +0.1362% +0.0862% 1.82 4.09 7.62 13.62
20 SOL 1.0 1 230 54.8% +0.1191% +0.0691% 1.58 3.01 5.91 11.91
21 SOL 2.0 0 195 58.5% +0.1486% +0.0986% 1.83 3.84 8.86 14.860000000000001
22 SOL 2.0 1 193 55.4% +0.1327% +0.0827% 1.67 3.19 7.27 13.270000000000001
@@ -0,0 +1,8 @@
品种,噪声bp,种子,笔数,胜率,毛均收益,净均收益,PF,t值,滑点余量bp
BTC,0.0,0,294,49.3%,+0.0611%,+0.0111%,1.13,0.88,0.11
BTC,0.5,0,322,53.4%,+0.0614%,+0.0114%,1.14,0.95,0.14
BTC,0.5,1,304,51.3%,+0.0672%,+0.0172%,1.21,1.38,0.72
BTC,1.0,0,262,53.1%,+0.0648%,+0.0148%,1.17,1.08,0.48
BTC,1.0,1,255,48.6%,+0.0595%,+0.0095%,1.1,0.65,-0.05
BTC,2.0,0,196,53.6%,+0.0987%,+0.0487%,1.5,2.46,3.87
BTC,2.0,1,191,50.3%,+0.0799%,+0.0299%,1.28,1.51,1.99
1 品种 噪声bp 种子 笔数 胜率 毛均收益 净均收益 PF t值 滑点余量bp
2 BTC 0.0 0 294 49.3% +0.0611% +0.0111% 1.13 0.88 0.11
3 BTC 0.5 0 322 53.4% +0.0614% +0.0114% 1.14 0.95 0.14
4 BTC 0.5 1 304 51.3% +0.0672% +0.0172% 1.21 1.38 0.72
5 BTC 1.0 0 262 53.1% +0.0648% +0.0148% 1.17 1.08 0.48
6 BTC 1.0 1 255 48.6% +0.0595% +0.0095% 1.1 0.65 -0.05
7 BTC 2.0 0 196 53.6% +0.0987% +0.0487% 1.5 2.46 3.87
8 BTC 2.0 1 191 50.3% +0.0799% +0.0299% 1.28 1.51 1.99
@@ -0,0 +1,8 @@
品种,噪声bp,种子,笔数,胜率,毛均收益,净均收益,PF,t值,滑点余量bp
ETH,0.0,0,288,55.6%,+0.1231%,+0.0731%,1.9,4.15,6.31
ETH,0.5,0,293,52.9%,+0.0949%,+0.0449%,1.46,2.52,3.49
ETH,0.5,1,291,60.1%,+0.1236%,+0.0736%,1.87,4.2,6.36
ETH,1.0,0,280,51.1%,+0.1004%,+0.0504%,1.48,2.65,4.04
ETH,1.0,1,270,54.8%,+0.1142%,+0.0642%,1.64,3.24,5.42
ETH,2.0,0,240,55.8%,+0.1182%,+0.0682%,1.61,3.14,5.82
ETH,2.0,1,222,58.1%,+0.1474%,+0.0974%,1.93,4.07,8.74
1 品种 噪声bp 种子 笔数 胜率 毛均收益 净均收益 PF t值 滑点余量bp
2 ETH 0.0 0 288 55.6% +0.1231% +0.0731% 1.9 4.15 6.31
3 ETH 0.5 0 293 52.9% +0.0949% +0.0449% 1.46 2.52 3.49
4 ETH 0.5 1 291 60.1% +0.1236% +0.0736% 1.87 4.2 6.36
5 ETH 1.0 0 280 51.1% +0.1004% +0.0504% 1.48 2.65 4.04
6 ETH 1.0 1 270 54.8% +0.1142% +0.0642% 1.64 3.24 5.42
7 ETH 2.0 0 240 55.8% +0.1182% +0.0682% 1.61 3.14 5.82
8 ETH 2.0 1 222 58.1% +0.1474% +0.0974% 1.93 4.07 8.74
@@ -0,0 +1,8 @@
品种,噪声bp,种子,笔数,胜率,毛均收益,净均收益,PF,t值,滑点余量bp
SOL,0.0,0,245,52.2%,+0.1001%,+0.0501%,1.44,2.41,4.01
SOL,0.5,0,232,53.4%,+0.1000%,+0.0500%,1.44,2.47,4.0
SOL,0.5,1,234,57.7%,+0.1332%,+0.0832%,1.75,3.68,7.32
SOL,1.0,0,246,57.7%,+0.1362%,+0.0862%,1.82,4.09,7.62
SOL,1.0,1,230,54.8%,+0.1191%,+0.0691%,1.58,3.01,5.91
SOL,2.0,0,195,58.5%,+0.1486%,+0.0986%,1.83,3.84,8.86
SOL,2.0,1,193,55.4%,+0.1327%,+0.0827%,1.67,3.19,7.27
1 品种 噪声bp 种子 笔数 胜率 毛均收益 净均收益 PF t值 滑点余量bp
2 SOL 0.0 0 245 52.2% +0.1001% +0.0501% 1.44 2.41 4.01
3 SOL 0.5 0 232 53.4% +0.1000% +0.0500% 1.44 2.47 4.0
4 SOL 0.5 1 234 57.7% +0.1332% +0.0832% 1.75 3.68 7.32
5 SOL 1.0 0 246 57.7% +0.1362% +0.0862% 1.82 4.09 7.62
6 SOL 1.0 1 230 54.8% +0.1191% +0.0691% 1.58 3.01 5.91
7 SOL 2.0 0 195 58.5% +0.1486% +0.0986% 1.83 3.84 8.86
8 SOL 2.0 1 193 55.4% +0.1327% +0.0827% 1.67 3.19 7.27
+11
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@@ -0,0 +1,11 @@
指标,
cpu核数,2.0
1m窗口,2000.0
5m窗口,800.0
1m中位s,0.318
1m_P95s,0.345
5m中位s,0.136
单币摊薄s,0.345
串行3币s,1.38
并行2_3币s,1.27
并行3_3币s,1.28
1 指标
2 cpu核数 2.0
3 1m窗口 2000.0
4 5m窗口 800.0
5 1m中位s 0.318
6 1m_P95s 0.345
7 5m中位s 0.136
8 单币摊薄s 0.345
9 串行3币s 1.38
10 并行2_3币s 1.27
11 并行3_3币s 1.28
+7
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@@ -0,0 +1,7 @@
品种,venue,笔数,胜率,毛均收益,净均收益,PF,t值,滑点余量bp,毛均bp
BTC,Bitget,313,51.4%,+0.0587%,+0.0087%,1.11,0.71,-0.13,5.87
BTC,Binance,296,49.3%,+0.0609%,+0.0109%,1.13,0.86,0.09,6.09
ETH,Bitget,299,54.8%,+0.1002%,+0.0502%,1.56,3.07,4.02,10.02
ETH,Binance,290,55.2%,+0.1206%,+0.0706%,1.85,4.01,6.06,12.06
SOL,Bitget,329,52.3%,+0.0892%,+0.0392%,1.38,2.38,2.92,8.92
SOL,Binance,244,52.0%,+0.0987%,+0.0487%,1.42,2.34,3.87,9.87
1 品种 venue 笔数 胜率 毛均收益 净均收益 PF t值 滑点余量bp 毛均bp
2 BTC Bitget 313 51.4% +0.0587% +0.0087% 1.11 0.71 -0.13 5.87
3 BTC Binance 296 49.3% +0.0609% +0.0109% 1.13 0.86 0.09 6.09
4 ETH Bitget 299 54.8% +0.1002% +0.0502% 1.56 3.07 4.02 10.02
5 ETH Binance 290 55.2% +0.1206% +0.0706% 1.85 4.01 6.06 12.06
6 SOL Bitget 329 52.3% +0.0892% +0.0392% 1.38 2.38 2.92 8.92
7 SOL Binance 244 52.0% +0.0987% +0.0487% 1.42 2.34 3.87 9.87
+3
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@@ -0,0 +1,3 @@
品种,venue,笔数,胜率,毛均收益,净均收益,PF,t值,滑点余量bp,毛均bp
BTC,Bitget,313,51.4%,+0.0587%,+0.0087%,1.11,0.71,-0.13,5.87
BTC,Binance,296,49.3%,+0.0609%,+0.0109%,1.13,0.86,0.09,6.09
1 品种 venue 笔数 胜率 毛均收益 净均收益 PF t值 滑点余量bp 毛均bp
2 BTC Bitget 313 51.4% +0.0587% +0.0087% 1.11 0.71 -0.13 5.87
3 BTC Binance 296 49.3% +0.0609% +0.0109% 1.13 0.86 0.09 6.09
+3
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@@ -0,0 +1,3 @@
品种,venue,笔数,胜率,毛均收益,净均收益,PF,t值,滑点余量bp,毛均bp
ETH,Bitget,299,54.8%,+0.1002%,+0.0502%,1.56,3.07,4.02,10.02
ETH,Binance,290,55.2%,+0.1206%,+0.0706%,1.85,4.01,6.06,12.06
1 品种 venue 笔数 胜率 毛均收益 净均收益 PF t值 滑点余量bp 毛均bp
2 ETH Bitget 299 54.8% +0.1002% +0.0502% 1.56 3.07 4.02 10.02
3 ETH Binance 290 55.2% +0.1206% +0.0706% 1.85 4.01 6.06 12.06
+3
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@@ -0,0 +1,3 @@
品种,venue,笔数,胜率,毛均收益,净均收益,PF,t值,滑点余量bp,毛均bp
SOL,Bitget,329,52.3%,+0.0892%,+0.0392%,1.38,2.38,2.92,8.92
SOL,Binance,244,52.0%,+0.0987%,+0.0487%,1.42,2.34,3.87,9.87
1 品种 venue 笔数 胜率 毛均收益 净均收益 PF t值 滑点余量bp 毛均bp
2 SOL Bitget 329 52.3% +0.0892% +0.0392% 1.38 2.38 2.92 8.92
3 SOL Binance 244 52.0% +0.0987% +0.0487% 1.42 2.34 3.87 9.87
+55
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@@ -0,0 +1,55 @@
kline_ts,sym,t_data_ms,lag_ms
1787840400000,SOL,1787840401075,1075
1787840400000,BTC,1787840401577,1577
1787840400000,ETH,1787840401863,1863
1787840460000,ETH,1787840460075,75
1787840460000,BTC,1787840460549,549
1787840460000,SOL,1787840460822,822
1787840520000,SOL,1787840520463,463
1787840520000,ETH,1787840520756,756
1787840520000,BTC,1787840521026,1026
1787840580000,ETH,1787840580384,384
1787840580000,BTC,1787840580687,687
1787840580000,SOL,1787840581292,1292
1787840640000,BTC,1787840640204,204
1787840640000,SOL,1787840640525,525
1787840640000,ETH,1787840641155,1155
1787840700000,BTC,1787840700192,192
1787840700000,ETH,1787840701170,1170
1787840700000,SOL,1787840701185,1185
1787840760000,BTC,1787840760795,795
1787840760000,ETH,1787840761231,1231
1787840760000,SOL,1787840761346,1346
1787840820000,BTC,1787840820308,308
1787840820000,ETH,1787840820327,327
1787840820000,SOL,1787840821241,1241
1787840880000,BTC,1787840880441,441
1787840880000,ETH,1787840880933,933
1787840880000,SOL,1787840880936,936
1787840940000,SOL,1787840940330,330
1787840940000,ETH,1787840940551,551
1787840940000,BTC,1787840940920,920
1787841000000,SOL,1787841001488,1488
1787841000000,BTC,1787841001683,1683
1787841000000,ETH,1787841001862,1862
1787841060000,BTC,1787841060406,406
1787841060000,SOL,1787841060621,621
1787841060000,ETH,1787841061151,1151
1787841120000,ETH,1787841120329,329
1787841120000,BTC,1787841120436,436
1787841120000,SOL,1787841120928,928
1787841180000,SOL,1787841180360,360
1787841180000,BTC,1787841180591,591
1787841180000,ETH,1787841180910,910
1787841240000,ETH,1787841240385,385
1787841240000,BTC,1787841240416,416
1787841240000,SOL,1787841240627,627
1787841300000,ETH,1787841300833,833
1787841300000,SOL,1787841301169,1169
1787841300000,BTC,1787841301299,1299
1787841360000,ETH,1787841360166,166
1787841360000,BTC,1787841360565,565
1787841360000,SOL,1787841360991,991
1787841420000,SOL,1787841420375,375
1787841420000,ETH,1787841422267,2267
1787841420000,BTC,1787841422314,2314
1 kline_ts sym t_data_ms lag_ms
2 1787840400000 SOL 1787840401075 1075
3 1787840400000 BTC 1787840401577 1577
4 1787840400000 ETH 1787840401863 1863
5 1787840460000 ETH 1787840460075 75
6 1787840460000 BTC 1787840460549 549
7 1787840460000 SOL 1787840460822 822
8 1787840520000 SOL 1787840520463 463
9 1787840520000 ETH 1787840520756 756
10 1787840520000 BTC 1787840521026 1026
11 1787840580000 ETH 1787840580384 384
12 1787840580000 BTC 1787840580687 687
13 1787840580000 SOL 1787840581292 1292
14 1787840640000 BTC 1787840640204 204
15 1787840640000 SOL 1787840640525 525
16 1787840640000 ETH 1787840641155 1155
17 1787840700000 BTC 1787840700192 192
18 1787840700000 ETH 1787840701170 1170
19 1787840700000 SOL 1787840701185 1185
20 1787840760000 BTC 1787840760795 795
21 1787840760000 ETH 1787840761231 1231
22 1787840760000 SOL 1787840761346 1346
23 1787840820000 BTC 1787840820308 308
24 1787840820000 ETH 1787840820327 327
25 1787840820000 SOL 1787840821241 1241
26 1787840880000 BTC 1787840880441 441
27 1787840880000 ETH 1787840880933 933
28 1787840880000 SOL 1787840880936 936
29 1787840940000 SOL 1787840940330 330
30 1787840940000 ETH 1787840940551 551
31 1787840940000 BTC 1787840940920 920
32 1787841000000 SOL 1787841001488 1488
33 1787841000000 BTC 1787841001683 1683
34 1787841000000 ETH 1787841001862 1862
35 1787841060000 BTC 1787841060406 406
36 1787841060000 SOL 1787841060621 621
37 1787841060000 ETH 1787841061151 1151
38 1787841120000 ETH 1787841120329 329
39 1787841120000 BTC 1787841120436 436
40 1787841120000 SOL 1787841120928 928
41 1787841180000 SOL 1787841180360 360
42 1787841180000 BTC 1787841180591 591
43 1787841180000 ETH 1787841180910 910
44 1787841240000 ETH 1787841240385 385
45 1787841240000 BTC 1787841240416 416
46 1787841240000 SOL 1787841240627 627
47 1787841300000 ETH 1787841300833 833
48 1787841300000 SOL 1787841301169 1169
49 1787841300000 BTC 1787841301299 1299
50 1787841360000 ETH 1787841360166 166
51 1787841360000 BTC 1787841360565 565
52 1787841360000 SOL 1787841360991 991
53 1787841420000 SOL 1787841420375 375
54 1787841420000 ETH 1787841422267 2267
55 1787841420000 BTC 1787841422314 2314
+91
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@@ -0,0 +1,91 @@
kline_ts,sym,t_data_ms,lag_ms
1787836320000,SOL,1787836320241,241
1787836320000,ETH,1787836320302,302
1787836320000,BTC,1787836321824,1824
1787836380000,SOL,1787836381417,1417
1787836380000,ETH,1787836382286,2286
1787836380000,BTC,1787836382710,2710
1787836440000,SOL,1787836440534,534
1787836440000,BTC,1787836441250,1250
1787836440000,ETH,1787836441361,1361
1787836500000,ETH,1787836500123,123
1787836500000,SOL,1787836500134,134
1787836500000,BTC,1787836502484,2484
1787836560000,SOL,1787836560660,660
1787836560000,BTC,1787836561486,1486
1787836560000,ETH,1787836561748,1748
1787836620000,SOL,1787836620499,499
1787836620000,BTC,1787836621187,1187
1787836620000,ETH,1787836621913,1913
1787836680000,ETH,1787836680478,478
1787836680000,BTC,1787836681568,1568
1787836680000,SOL,1787836682143,2143
1787836740000,BTC,1787836740072,72
1787836740000,SOL,1787836740214,214
1787836740000,ETH,1787836741453,1453
1787836800000,SOL,1787836801678,1678
1787836800000,BTC,1787836803383,3383
1787836800000,ETH,1787836803485,3485
1787836860000,BTC,1787836861120,1120
1787836860000,ETH,1787836861523,1523
1787836860000,SOL,1787836861634,1634
1787836920000,BTC,1787836920212,212
1787836920000,ETH,1787836920969,969
1787836920000,SOL,1787836921292,1292
1787836980000,SOL,1787836980715,715
1787836980000,ETH,1787836981108,1108
1787836980000,BTC,1787836982025,2025
1787837040000,ETH,1787837041234,1234
1787837040000,BTC,1787837041305,1305
1787837040000,SOL,1787837042293,2293
1787837100000,BTC,1787837100436,436
1787837100000,SOL,1787837100436,436
1787837100000,ETH,1787837101273,1273
1787837160000,ETH,1787837161167,1167
1787837160000,BTC,1787837161228,1228
1787837160000,SOL,1787837161420,1420
1787837220000,SOL,1787837221236,1236
1787837220000,BTC,1787837221620,1620
1787837220000,ETH,1787837222286,2286
1787837280000,SOL,1787837280246,246
1787837280000,BTC,1787837280750,750
1787837280000,ETH,1787837281205,1205
1787837340000,ETH,1787837340155,155
1787837340000,BTC,1787837341234,1234
1787837340000,SOL,1787837342232,2232
1787837400000,SOL,1787837402232,2232
1787837400000,ETH,1787837402374,2374
1787837400000,BTC,1787837403010,3010
1787837460000,SOL,1787837461262,1262
1787837460000,BTC,1787837461403,1403
1787837460000,ETH,1787837461988,1988
1787837520000,ETH,1787837521167,1167
1787837520000,BTC,1787837521298,1298
1787837520000,SOL,1787837521298,1298
1787837580000,SOL,1787837581311,1311
1787837580000,BTC,1787837581634,1634
1787837580000,ETH,1787837581725,1725
1787837640000,SOL,1787837641179,1179
1787837640000,ETH,1787837642047,2047
1787837640000,BTC,1787837642057,2057
1787837700000,ETH,1787837700834,834
1787837700000,SOL,1787837701499,1499
1787837700000,BTC,1787837701963,1963
1787837760000,ETH,1787837761627,1627
1787837760000,SOL,1787837761828,1828
1787837760000,BTC,1787837761951,1951
1787837820000,SOL,1787837821188,1188
1787837820000,ETH,1787837821258,1258
1787837820000,BTC,1787837821561,1561
1787837880000,SOL,1787837881174,1174
1787837880000,BTC,1787837881235,1235
1787837880000,ETH,1787837881467,1467
1787837940000,SOL,1787837941579,1579
1787837940000,BTC,1787837941741,1741
1787837940000,ETH,1787837941883,1883
1787838000000,SOL,1787838002447,2447
1787838000000,ETH,1787838002649,2649
1787838000000,BTC,1787838003586,3586
1787838060000,BTC,1787838060835,835
1787838060000,ETH,1787838061561,1561
1787838060000,SOL,1787838061774,1774
1 kline_ts sym t_data_ms lag_ms
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3 1787836320000 ETH 1787836320302 302
4 1787836320000 BTC 1787836321824 1824
5 1787836380000 SOL 1787836381417 1417
6 1787836380000 ETH 1787836382286 2286
7 1787836380000 BTC 1787836382710 2710
8 1787836440000 SOL 1787836440534 534
9 1787836440000 BTC 1787836441250 1250
10 1787836440000 ETH 1787836441361 1361
11 1787836500000 ETH 1787836500123 123
12 1787836500000 SOL 1787836500134 134
13 1787836500000 BTC 1787836502484 2484
14 1787836560000 SOL 1787836560660 660
15 1787836560000 BTC 1787836561486 1486
16 1787836560000 ETH 1787836561748 1748
17 1787836620000 SOL 1787836620499 499
18 1787836620000 BTC 1787836621187 1187
19 1787836620000 ETH 1787836621913 1913
20 1787836680000 ETH 1787836680478 478
21 1787836680000 BTC 1787836681568 1568
22 1787836680000 SOL 1787836682143 2143
23 1787836740000 BTC 1787836740072 72
24 1787836740000 SOL 1787836740214 214
25 1787836740000 ETH 1787836741453 1453
26 1787836800000 SOL 1787836801678 1678
27 1787836800000 BTC 1787836803383 3383
28 1787836800000 ETH 1787836803485 3485
29 1787836860000 BTC 1787836861120 1120
30 1787836860000 ETH 1787836861523 1523
31 1787836860000 SOL 1787836861634 1634
32 1787836920000 BTC 1787836920212 212
33 1787836920000 ETH 1787836920969 969
34 1787836920000 SOL 1787836921292 1292
35 1787836980000 SOL 1787836980715 715
36 1787836980000 ETH 1787836981108 1108
37 1787836980000 BTC 1787836982025 2025
38 1787837040000 ETH 1787837041234 1234
39 1787837040000 BTC 1787837041305 1305
40 1787837040000 SOL 1787837042293 2293
41 1787837100000 BTC 1787837100436 436
42 1787837100000 SOL 1787837100436 436
43 1787837100000 ETH 1787837101273 1273
44 1787837160000 ETH 1787837161167 1167
45 1787837160000 BTC 1787837161228 1228
46 1787837160000 SOL 1787837161420 1420
47 1787837220000 SOL 1787837221236 1236
48 1787837220000 BTC 1787837221620 1620
49 1787837220000 ETH 1787837222286 2286
50 1787837280000 SOL 1787837280246 246
51 1787837280000 BTC 1787837280750 750
52 1787837280000 ETH 1787837281205 1205
53 1787837340000 ETH 1787837340155 155
54 1787837340000 BTC 1787837341234 1234
55 1787837340000 SOL 1787837342232 2232
56 1787837400000 SOL 1787837402232 2232
57 1787837400000 ETH 1787837402374 2374
58 1787837400000 BTC 1787837403010 3010
59 1787837460000 SOL 1787837461262 1262
60 1787837460000 BTC 1787837461403 1403
61 1787837460000 ETH 1787837461988 1988
62 1787837520000 ETH 1787837521167 1167
63 1787837520000 BTC 1787837521298 1298
64 1787837520000 SOL 1787837521298 1298
65 1787837580000 SOL 1787837581311 1311
66 1787837580000 BTC 1787837581634 1634
67 1787837580000 ETH 1787837581725 1725
68 1787837640000 SOL 1787837641179 1179
69 1787837640000 ETH 1787837642047 2047
70 1787837640000 BTC 1787837642057 2057
71 1787837700000 ETH 1787837700834 834
72 1787837700000 SOL 1787837701499 1499
73 1787837700000 BTC 1787837701963 1963
74 1787837760000 ETH 1787837761627 1627
75 1787837760000 SOL 1787837761828 1828
76 1787837760000 BTC 1787837761951 1951
77 1787837820000 SOL 1787837821188 1188
78 1787837820000 ETH 1787837821258 1258
79 1787837820000 BTC 1787837821561 1561
80 1787837880000 SOL 1787837881174 1174
81 1787837880000 BTC 1787837881235 1235
82 1787837880000 ETH 1787837881467 1467
83 1787837940000 SOL 1787837941579 1579
84 1787837940000 BTC 1787837941741 1741
85 1787837940000 ETH 1787837941883 1883
86 1787838000000 SOL 1787838002447 2447
87 1787838000000 ETH 1787838002649 2649
88 1787838000000 BTC 1787838003586 3586
89 1787838060000 BTC 1787838060835 835
90 1787838060000 ETH 1787838061561 1561
91 1787838060000 SOL 1787838061774 1774
+45
View File
@@ -0,0 +1,45 @@
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+45
View File
@@ -0,0 +1,45 @@
{
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File diff suppressed because it is too large Load Diff
+801
View File
@@ -0,0 +1,801 @@
timestamp,date,open,high,low,close,volume
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1787594100000,2026-08-25 01:55:00+08:00,78649.3000000000,78732.9000000000,78547.5000000000,78721.2000000000,77.9756000000
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1787595300000,2026-08-25 02:15:00+08:00,78962.2000000000,79013.3000000000,78894.1000000000,78940.9000000000,149.1010000000
1787595600000,2026-08-25 02:20:00+08:00,78940.9000000000,79024.4000000000,78878.1000000000,78926.4000000000,63.1952000000
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1787596200000,2026-08-25 02:30:00+08:00,78968.7000000000,79039.9000000000,78888.0000000000,78902.6000000000,78.8562000000
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1787668200000,2026-08-25 22:30:00+08:00,79171.9000000000,79342.3000000000,78983.5000000000,79189.5000000000,433.9567000000
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1787672100000,2026-08-25 23:35:00+08:00,79259.2000000000,79445.0000000000,79251.4000000000,79335.3000000000,286.3014000000
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1787674200000,2026-08-26 00:10:00+08:00,79292.7000000000,79312.0000000000,79120.0000000000,79130.0000000000,240.3066000000
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1787677200000,2026-08-26 01:00:00+08:00,79113.1000000000,79113.1000000000,79010.2000000000,79026.0000000000,163.6935000000
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1787707200000,2026-08-26 09:20:00+08:00,78821.9000000000,78836.1000000000,78764.8000000000,78777.5000000000,36.0565000000
1787707500000,2026-08-26 09:25:00+08:00,78777.5000000000,78777.5000000000,78641.5000000000,78666.5000000000,54.6954000000
1787707800000,2026-08-26 09:30:00+08:00,78666.5000000000,78878.2000000000,78666.5000000000,78878.1000000000,66.0593000000
1787708100000,2026-08-26 09:35:00+08:00,78878.1000000000,78920.8000000000,78844.3000000000,78867.1000000000,96.8219000000
1787708400000,2026-08-26 09:40:00+08:00,78867.1000000000,78873.1000000000,78793.4000000000,78828.2000000000,37.9826000000
1787708700000,2026-08-26 09:45:00+08:00,78828.2000000000,78829.5000000000,78753.3000000000,78797.3000000000,33.3275000000
1787709000000,2026-08-26 09:50:00+08:00,78797.3000000000,78797.3000000000,78736.7000000000,78756.0000000000,35.9636000000
1787709300000,2026-08-26 09:55:00+08:00,78756.0000000000,78779.2000000000,78723.1000000000,78779.2000000000,16.4917000000
1787709600000,2026-08-26 10:00:00+08:00,78779.2000000000,78839.9000000000,78746.6000000000,78806.7000000000,29.2030000000
1787709900000,2026-08-26 10:05:00+08:00,78806.7000000000,78813.6000000000,78703.2000000000,78708.7000000000,31.8130000000
1787710200000,2026-08-26 10:10:00+08:00,78708.7000000000,78779.2000000000,78704.6000000000,78738.1000000000,21.0887000000
1787710500000,2026-08-26 10:15:00+08:00,78738.1000000000,78900.0000000000,78708.3000000000,78858.1000000000,43.6124000000
1787710800000,2026-08-26 10:20:00+08:00,78858.1000000000,78947.0000000000,78857.4000000000,78947.0000000000,69.5206000000
1787711100000,2026-08-26 10:25:00+08:00,78947.0000000000,79210.0000000000,78947.0000000000,79190.1000000000,359.4001000000
1787711400000,2026-08-26 10:30:00+08:00,79190.1000000000,79200.6000000000,79080.0000000000,79145.5000000000,95.9808000000
1787711700000,2026-08-26 10:35:00+08:00,79145.5000000000,79180.1000000000,79113.5000000000,79146.4000000000,51.5480000000
1787712000000,2026-08-26 10:40:00+08:00,79146.4000000000,79168.7000000000,79097.0000000000,79111.5000000000,49.8051000000
1787712300000,2026-08-26 10:45:00+08:00,79111.5000000000,79144.7000000000,79056.1000000000,79081.5000000000,58.3919000000
1787712600000,2026-08-26 10:50:00+08:00,79081.5000000000,79089.4000000000,79004.0000000000,79029.0000000000,40.3900000000
1787712900000,2026-08-26 10:55:00+08:00,79029.0000000000,79099.8000000000,79029.0000000000,79073.9000000000,32.9494000000
1787713200000,2026-08-26 11:00:00+08:00,79073.9000000000,79152.1000000000,79027.4000000000,79029.7000000000,78.7434000000
1787713500000,2026-08-26 11:05:00+08:00,79029.7000000000,79077.3000000000,79002.1000000000,79015.6000000000,49.4501000000
1787713800000,2026-08-26 11:10:00+08:00,79015.6000000000,79047.8000000000,79005.6000000000,79007.5000000000,27.8534000000
1787714100000,2026-08-26 11:15:00+08:00,79007.5000000000,79034.6000000000,78947.2000000000,78964.9000000000,73.2097000000
1787714400000,2026-08-26 11:20:00+08:00,78964.9000000000,79084.5000000000,78959.1000000000,79061.9000000000,50.8514000000
1787714700000,2026-08-26 11:25:00+08:00,79061.9000000000,79076.5000000000,78988.8000000000,79008.8000000000,27.5830000000
1787715000000,2026-08-26 11:30:00+08:00,79008.8000000000,79008.8000000000,78855.0000000000,78943.6000000000,72.9228000000
1787715300000,2026-08-26 11:35:00+08:00,78943.6000000000,79089.6000000000,78943.6000000000,79008.1000000000,48.2278000000
1787715600000,2026-08-26 11:40:00+08:00,79008.1000000000,79046.1000000000,78948.7000000000,79026.0000000000,40.9954000000
1787715900000,2026-08-26 11:45:00+08:00,79026.0000000000,79039.8000000000,78907.2000000000,78948.4000000000,47.7514000000
1787716200000,2026-08-26 11:50:00+08:00,78948.4000000000,78997.3000000000,78917.8000000000,78997.3000000000,29.5309000000
1787716500000,2026-08-26 11:55:00+08:00,78997.3000000000,79084.6000000000,78997.3000000000,79052.3000000000,78.9818000000
1787716800000,2026-08-26 12:00:00+08:00,79052.3000000000,79053.2000000000,78929.2000000000,78981.9000000000,58.0942000000
1787717100000,2026-08-26 12:05:00+08:00,78981.9000000000,79118.6000000000,78956.7000000000,79118.6000000000,67.2501000000
1787717400000,2026-08-26 12:10:00+08:00,79118.6000000000,79161.9000000000,78939.0000000000,79009.1000000000,157.5232000000
1787717700000,2026-08-26 12:15:00+08:00,79009.1000000000,79104.7000000000,78942.4000000000,79097.1000000000,51.1681000000
1787718000000,2026-08-26 12:20:00+08:00,79097.1000000000,79104.6000000000,78968.6000000000,79072.7000000000,79.9744000000
1787718300000,2026-08-26 12:25:00+08:00,79072.7000000000,79092.6000000000,78968.8000000000,78981.2000000000,33.8522000000
1787718600000,2026-08-26 12:30:00+08:00,78981.2000000000,79019.7000000000,78865.1000000000,78897.7000000000,87.9345000000
1787718900000,2026-08-26 12:35:00+08:00,78897.7000000000,78976.9000000000,78850.0000000000,78903.9000000000,90.4260000000
1787719200000,2026-08-26 12:40:00+08:00,78903.9000000000,78956.4000000000,78897.4000000000,78934.2000000000,17.6264000000
1787719500000,2026-08-26 12:45:00+08:00,78934.2000000000,78934.2000000000,78740.0000000000,78824.0000000000,127.2294000000
1787719800000,2026-08-26 12:50:00+08:00,78824.0000000000,78874.2000000000,78807.0000000000,78844.0000000000,70.9775000000
1787720100000,2026-08-26 12:55:00+08:00,78844.0000000000,78849.4000000000,78747.4000000000,78794.9000000000,44.3885000000
1787720400000,2026-08-26 13:00:00+08:00,78794.9000000000,78819.5000000000,78725.3000000000,78728.5000000000,71.0507000000
1787720700000,2026-08-26 13:05:00+08:00,78728.5000000000,78772.2000000000,78592.7000000000,78772.0000000000,189.3250000000
1787721000000,2026-08-26 13:10:00+08:00,78772.0000000000,78917.7000000000,78765.0000000000,78859.6000000000,91.9919000000
1787721300000,2026-08-26 13:15:00+08:00,78859.6000000000,78859.6000000000,78711.5000000000,78713.9000000000,60.9451000000
1787721600000,2026-08-26 13:20:00+08:00,78713.9000000000,78986.0000000000,78712.2000000000,78983.3000000000,91.1822000000
1787721900000,2026-08-26 13:25:00+08:00,78983.3000000000,79030.0000000000,78904.4000000000,78983.3000000000,80.5585000000
1787722200000,2026-08-26 13:30:00+08:00,78983.3000000000,79102.0000000000,78935.1000000000,79026.7000000000,128.4107000000
1787722500000,2026-08-26 13:35:00+08:00,79026.7000000000,79045.8000000000,78947.0000000000,79035.9000000000,38.9547000000
1787722800000,2026-08-26 13:40:00+08:00,79035.9000000000,79096.0000000000,79018.0000000000,79076.4000000000,30.8892000000
1787723100000,2026-08-26 13:45:00+08:00,79076.4000000000,79076.4000000000,78994.1000000000,79027.2000000000,86.4666000000
1787723400000,2026-08-26 13:50:00+08:00,79027.2000000000,79032.3000000000,78897.8000000000,78916.3000000000,67.1955000000
1787723700000,2026-08-26 13:55:00+08:00,78916.3000000000,79049.9000000000,78916.2000000000,79046.4000000000,30.3230000000
1787724000000,2026-08-26 14:00:00+08:00,79046.4000000000,79121.8000000000,78973.3000000000,79065.9000000000,83.5112000000
1787724300000,2026-08-26 14:05:00+08:00,79065.9000000000,79171.9000000000,79065.9000000000,79132.5000000000,157.0794000000
1787724600000,2026-08-26 14:10:00+08:00,79132.5000000000,79132.5000000000,78921.7000000000,78965.9000000000,94.3401000000
1787724900000,2026-08-26 14:15:00+08:00,78965.9000000000,79019.1000000000,78839.9000000000,78839.9000000000,54.8586000000
1787725200000,2026-08-26 14:20:00+08:00,78839.9000000000,78970.0000000000,78809.5000000000,78819.9000000000,98.9609000000
1787725500000,2026-08-26 14:25:00+08:00,78819.9000000000,78918.6000000000,78780.8000000000,78861.8000000000,71.3968000000
1787725800000,2026-08-26 14:30:00+08:00,78861.8000000000,78926.3000000000,78816.3000000000,78868.0000000000,64.1381000000
1787726100000,2026-08-26 14:35:00+08:00,78868.0000000000,78905.2000000000,78765.5000000000,78833.5000000000,52.5910000000
1787726400000,2026-08-26 14:40:00+08:00,78833.5000000000,78925.2000000000,78820.9000000000,78897.5000000000,53.5718000000
1787726700000,2026-08-26 14:45:00+08:00,78897.5000000000,78930.7000000000,78817.9000000000,78850.2000000000,50.9823000000
1787727000000,2026-08-26 14:50:00+08:00,78850.2000000000,78893.1000000000,78831.4000000000,78890.6000000000,26.6060000000
1787727300000,2026-08-26 14:55:00+08:00,78890.6000000000,78909.9000000000,78853.5000000000,78902.3000000000,52.3476000000
1787727600000,2026-08-26 15:00:00+08:00,78902.3000000000,78985.7000000000,78818.3000000000,78978.5000000000,85.6009000000
1787727900000,2026-08-26 15:05:00+08:00,78978.5000000000,79054.1000000000,78884.6000000000,78884.7000000000,78.8387000000
1787728200000,2026-08-26 15:10:00+08:00,78884.7000000000,78993.6000000000,78884.7000000000,78950.0000000000,36.8243000000
1787728500000,2026-08-26 15:15:00+08:00,78950.0000000000,78979.0000000000,78914.0000000000,78927.1000000000,21.0304000000
1787728800000,2026-08-26 15:20:00+08:00,78927.1000000000,78973.0000000000,78860.1000000000,78914.0000000000,39.5767000000
1787729100000,2026-08-26 15:25:00+08:00,78914.0000000000,78998.0000000000,78895.7000000000,78900.0000000000,60.4868000000
1787729400000,2026-08-26 15:30:00+08:00,78900.0000000000,78945.2000000000,78843.7000000000,78942.1000000000,56.6650000000
1787729700000,2026-08-26 15:35:00+08:00,78942.1000000000,79014.4000000000,78926.3000000000,78959.5000000000,93.7033000000
1787730000000,2026-08-26 15:40:00+08:00,78959.5000000000,79009.0000000000,78925.0000000000,78952.4000000000,57.1537000000
1787730300000,2026-08-26 15:45:00+08:00,78952.4000000000,78970.0000000000,78915.5000000000,78956.7000000000,11.8312000000
1787730600000,2026-08-26 15:50:00+08:00,78956.7000000000,79014.4000000000,78917.0000000000,78917.0000000000,55.9245000000
1787730900000,2026-08-26 15:55:00+08:00,78917.0000000000,78961.2000000000,78873.5000000000,78881.2000000000,50.1557000000
1787731200000,2026-08-26 16:00:00+08:00,78881.2000000000,78907.1000000000,78803.0000000000,78846.2000000000,45.9262000000
1787731500000,2026-08-26 16:05:00+08:00,78846.2000000000,79049.9000000000,78783.2000000000,79012.9000000000,101.0117000000
1787731800000,2026-08-26 16:10:00+08:00,79012.9000000000,79082.0000000000,78997.0000000000,78999.9000000000,228.3931000000
1787732100000,2026-08-26 16:15:00+08:00,78999.9000000000,79077.8000000000,78944.9000000000,78959.9000000000,46.4662000000
1787732400000,2026-08-26 16:20:00+08:00,78959.9000000000,78969.3000000000,78857.7000000000,78937.4000000000,51.5003000000
1787732700000,2026-08-26 16:25:00+08:00,78937.4000000000,78970.0000000000,78936.5000000000,78944.5000000000,31.2432000000
1787733000000,2026-08-26 16:30:00+08:00,78944.5000000000,78991.3000000000,78874.6000000000,78900.5000000000,25.8756000000
1787733300000,2026-08-26 16:35:00+08:00,78900.5000000000,78913.4000000000,78862.7000000000,78885.8000000000,31.4089000000
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1787734200000,2026-08-26 16:50:00+08:00,78889.7000000000,78912.7000000000,78660.0000000000,78660.0000000000,155.2024000000
1787734500000,2026-08-26 16:55:00+08:00,78660.0000000000,78683.5000000000,78563.8000000000,78676.8000000000,212.0989000000
1787734800000,2026-08-26 17:00:00+08:00,78676.8000000000,78824.4000000000,78629.4000000000,78771.6000000000,223.7944000000
1787735100000,2026-08-26 17:05:00+08:00,78771.6000000000,78840.0000000000,78755.0000000000,78770.5000000000,48.2612000000
1787735400000,2026-08-26 17:10:00+08:00,78770.5000000000,78800.0000000000,78709.3000000000,78733.4000000000,25.3918000000
1787735700000,2026-08-26 17:15:00+08:00,78733.4000000000,78734.8000000000,78384.1000000000,78440.7000000000,360.6579000000
1787736000000,2026-08-26 17:20:00+08:00,78440.7000000000,78499.4000000000,78354.9000000000,78361.6000000000,166.5058000000
1787736300000,2026-08-26 17:25:00+08:00,78361.6000000000,78505.9000000000,78361.6000000000,78494.6000000000,93.7493000000
1787736600000,2026-08-26 17:30:00+08:00,78494.6000000000,78556.1000000000,78430.0000000000,78522.4000000000,78.3202000000
1787736900000,2026-08-26 17:35:00+08:00,78522.4000000000,78539.5000000000,78446.4000000000,78446.4000000000,67.1884000000
1787737200000,2026-08-26 17:40:00+08:00,78446.4000000000,78446.4000000000,78281.0000000000,78281.1000000000,182.1442000000
1787737500000,2026-08-26 17:45:00+08:00,78281.1000000000,78397.3000000000,78202.4000000000,78389.5000000000,181.5272000000
1787737800000,2026-08-26 17:50:00+08:00,78389.5000000000,78467.1000000000,78360.0000000000,78368.4000000000,112.6062000000
1787738100000,2026-08-26 17:55:00+08:00,78368.4000000000,78426.3000000000,78361.8000000000,78411.4000000000,39.2364000000
1787738400000,2026-08-26 18:00:00+08:00,78411.4000000000,78411.4000000000,78250.0000000000,78284.1000000000,65.6537000000
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1787739900000,2026-08-26 18:25:00+08:00,78547.1000000000,78609.9000000000,78541.0000000000,78589.2000000000,70.0312000000
1787740200000,2026-08-26 18:30:00+08:00,78589.2000000000,78625.9000000000,78529.9000000000,78624.8000000000,79.7706000000
1787740500000,2026-08-26 18:35:00+08:00,78624.8000000000,78780.0000000000,78580.0000000000,78703.8000000000,228.3401000000
1787740800000,2026-08-26 18:40:00+08:00,78703.8000000000,78733.5000000000,78625.0000000000,78694.0000000000,88.9477000000
1787741100000,2026-08-26 18:45:00+08:00,78694.0000000000,78824.5000000000,78661.2000000000,78810.0000000000,154.3668000000
1787741400000,2026-08-26 18:50:00+08:00,78810.0000000000,78852.5000000000,78725.6000000000,78740.3000000000,179.4226000000
1787741700000,2026-08-26 18:55:00+08:00,78740.3000000000,78751.0000000000,78678.4000000000,78685.8000000000,52.5525000000
1787742000000,2026-08-26 19:00:00+08:00,78685.8000000000,78750.0000000000,78643.4000000000,78707.0000000000,79.8646000000
1787742300000,2026-08-26 19:05:00+08:00,78707.0000000000,78730.4000000000,78621.1000000000,78648.3000000000,36.3542000000
1787742600000,2026-08-26 19:10:00+08:00,78648.3000000000,78670.0000000000,78625.0000000000,78669.9000000000,22.1096000000
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1787743500000,2026-08-26 19:25:00+08:00,78663.8000000000,78672.6000000000,78572.8000000000,78585.9000000000,25.7705000000
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1787744100000,2026-08-26 19:35:00+08:00,78522.2000000000,78582.0000000000,78266.0000000000,78355.4000000000,187.1617000000
1787744400000,2026-08-26 19:40:00+08:00,78355.4000000000,78465.0000000000,78312.1000000000,78440.0000000000,243.7570000000
1787744700000,2026-08-26 19:45:00+08:00,78440.0000000000,78440.1000000000,78336.2000000000,78352.5000000000,78.2868000000
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1787745300000,2026-08-26 19:55:00+08:00,78493.4000000000,78544.5000000000,78443.3000000000,78448.0000000000,55.3370000000
1787745600000,2026-08-26 20:00:00+08:00,78448.0000000000,78531.4000000000,78425.4000000000,78483.5000000000,62.2794000000
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1787746500000,2026-08-26 20:15:00+08:00,78360.3000000000,78400.0000000000,78252.1000000000,78355.1000000000,76.5651000000
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1787747100000,2026-08-26 20:25:00+08:00,78543.7000000000,78678.6000000000,78537.5000000000,78669.0000000000,153.3992000000
1787747400000,2026-08-26 20:30:00+08:00,78669.0000000000,78669.0000000000,77890.0000000000,78358.6000000000,1230.3390000000
1787747700000,2026-08-26 20:35:00+08:00,78358.6000000000,78456.0000000000,78130.0000000000,78200.1000000000,286.9540000000
1787748000000,2026-08-26 20:40:00+08:00,78200.1000000000,78346.9000000000,78184.3000000000,78230.1000000000,142.8624000000
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1787748600000,2026-08-26 20:50:00+08:00,78284.3000000000,78413.6000000000,78242.8000000000,78272.5000000000,105.4312000000
1787748900000,2026-08-26 20:55:00+08:00,78272.5000000000,78398.5000000000,78248.3000000000,78280.1000000000,80.8229000000
1787749200000,2026-08-26 21:00:00+08:00,78280.1000000000,78413.6000000000,78200.2000000000,78200.2000000000,92.6552000000
1787749500000,2026-08-26 21:05:00+08:00,78200.2000000000,78243.7000000000,78032.7000000000,78082.9000000000,193.1806000000
1787749800000,2026-08-26 21:10:00+08:00,78082.9000000000,78130.0000000000,77937.0000000000,78089.0000000000,326.2386000000
1787750100000,2026-08-26 21:15:00+08:00,78089.0000000000,78148.2000000000,78034.1000000000,78105.7000000000,100.9150000000
1787750400000,2026-08-26 21:20:00+08:00,78105.7000000000,78121.5000000000,77954.9000000000,77988.8000000000,142.0080000000
1787750700000,2026-08-26 21:25:00+08:00,77988.8000000000,78037.7000000000,77870.8000000000,78017.0000000000,216.1598000000
1787751000000,2026-08-26 21:30:00+08:00,78017.0000000000,78339.3000000000,78013.7000000000,78294.0000000000,310.0920000000
1787751300000,2026-08-26 21:35:00+08:00,78294.0000000000,78459.1000000000,78230.2000000000,78316.5000000000,347.3683000000
1787751600000,2026-08-26 21:40:00+08:00,78316.5000000000,78456.6000000000,78284.2000000000,78451.5000000000,184.9161000000
1787751900000,2026-08-26 21:45:00+08:00,78451.5000000000,78519.0000000000,78337.9000000000,78338.0000000000,249.2877000000
1787752200000,2026-08-26 21:50:00+08:00,78338.0000000000,78338.0000000000,78066.7000000000,78180.5000000000,269.6564000000
1787752500000,2026-08-26 21:55:00+08:00,78180.5000000000,78419.0000000000,78161.3000000000,78404.7000000000,139.6407000000
1787752800000,2026-08-26 22:00:00+08:00,78404.7000000000,78555.0000000000,78246.7000000000,78491.1000000000,265.4979000000
1787753100000,2026-08-26 22:05:00+08:00,78491.1000000000,78567.0000000000,78435.2000000000,78503.7000000000,128.8055000000
1787753400000,2026-08-26 22:10:00+08:00,78503.7000000000,78560.0000000000,78400.1000000000,78416.6000000000,131.0497000000
1787753700000,2026-08-26 22:15:00+08:00,78416.6000000000,78530.0000000000,78395.8000000000,78478.3000000000,83.0235000000
1787754000000,2026-08-26 22:20:00+08:00,78478.3000000000,78500.0000000000,78350.1000000000,78350.1000000000,90.7253000000
1787754300000,2026-08-26 22:25:00+08:00,78350.1000000000,78361.2000000000,78160.0000000000,78198.3000000000,161.0054000000
1787754600000,2026-08-26 22:30:00+08:00,78198.3000000000,78247.6000000000,78113.1000000000,78135.9000000000,131.3560000000
1787754900000,2026-08-26 22:35:00+08:00,78135.9000000000,78224.0000000000,78050.0000000000,78108.9000000000,167.4946000000
1787755200000,2026-08-26 22:40:00+08:00,78108.9000000000,78161.3000000000,77900.1000000000,77934.5000000000,369.3246000000
1787755500000,2026-08-26 22:45:00+08:00,77934.5000000000,77981.6000000000,77820.0000000000,77944.8000000000,370.3109000000
1787755800000,2026-08-26 22:50:00+08:00,77944.8000000000,78008.2000000000,77714.3000000000,77758.6000000000,601.2863000000
1787756100000,2026-08-26 22:55:00+08:00,77758.6000000000,77826.9000000000,77730.1000000000,77757.4000000000,193.2021000000
1787756400000,2026-08-26 23:00:00+08:00,77757.4000000000,77845.2000000000,77579.0000000000,77615.9000000000,412.4288000000
1787756700000,2026-08-26 23:05:00+08:00,77615.9000000000,77926.1000000000,77600.1000000000,77890.0000000000,324.6440000000
1787757000000,2026-08-26 23:10:00+08:00,77890.0000000000,77909.4000000000,77794.3000000000,77810.0000000000,79.3431000000
1787757300000,2026-08-26 23:15:00+08:00,77810.0000000000,77835.0000000000,77688.3000000000,77755.0000000000,112.5113000000
1787757600000,2026-08-26 23:20:00+08:00,77755.0000000000,77929.5000000000,77755.0000000000,77899.4000000000,117.8268000000
1787757900000,2026-08-26 23:25:00+08:00,77899.4000000000,78125.5000000000,77899.4000000000,78118.1000000000,258.4550000000
1787758200000,2026-08-26 23:30:00+08:00,78118.1000000000,78138.7000000000,77900.8000000000,77914.0000000000,205.1301000000
1787758500000,2026-08-26 23:35:00+08:00,77914.0000000000,77973.3000000000,77880.2000000000,77961.7000000000,175.3640000000
1787758800000,2026-08-26 23:40:00+08:00,77961.7000000000,78093.2000000000,77961.6000000000,78003.1000000000,123.4341000000
1787759100000,2026-08-26 23:45:00+08:00,78003.1000000000,78111.0000000000,78003.1000000000,78102.5000000000,97.0001000000
1787759400000,2026-08-26 23:50:00+08:00,78102.5000000000,78111.0000000000,77985.7000000000,78006.4000000000,66.7545000000
1787759700000,2026-08-26 23:55:00+08:00,78006.4000000000,78079.9000000000,77928.8000000000,77973.9000000000,35.3332000000
1787760000000,2026-08-27 00:00:00+08:00,77973.9000000000,78054.9000000000,77933.7000000000,78051.4000000000,107.8949000000
1787760300000,2026-08-27 00:05:00+08:00,78051.4000000000,78129.4000000000,78016.1000000000,78087.7000000000,91.1215000000
1787760600000,2026-08-27 00:10:00+08:00,78087.7000000000,78106.7000000000,78001.5000000000,78037.5000000000,38.5803000000
1787760900000,2026-08-27 00:15:00+08:00,78037.5000000000,78079.2000000000,77937.2000000000,78003.9000000000,35.6133000000
1787761200000,2026-08-27 00:20:00+08:00,78003.9000000000,78099.9000000000,77975.2000000000,77988.3000000000,100.5070000000
1787761500000,2026-08-27 00:25:00+08:00,77988.3000000000,78034.0000000000,77936.5000000000,78017.8000000000,86.6946000000
1787761800000,2026-08-27 00:30:00+08:00,78017.8000000000,78070.0000000000,77964.5000000000,78064.6000000000,34.4515000000
1787762100000,2026-08-27 00:35:00+08:00,78064.6000000000,78111.0000000000,77910.6000000000,77920.3000000000,103.8477000000
1787762400000,2026-08-27 00:40:00+08:00,77920.3000000000,77988.1000000000,77810.0000000000,77832.9000000000,97.1551000000
1787762700000,2026-08-27 00:45:00+08:00,77832.9000000000,78128.0000000000,77832.9000000000,78045.2000000000,118.9647000000
1787763000000,2026-08-27 00:50:00+08:00,78045.2000000000,78089.9000000000,77946.9000000000,77991.8000000000,37.5278000000
1787763300000,2026-08-27 00:55:00+08:00,77991.8000000000,78036.8000000000,77927.4000000000,77950.7000000000,27.0656000000
1787763600000,2026-08-27 01:00:00+08:00,77950.7000000000,78035.0000000000,77950.7000000000,78004.0000000000,30.4488000000
1787763900000,2026-08-27 01:05:00+08:00,78004.0000000000,78089.8000000000,77959.1000000000,78044.8000000000,32.8480000000
1787764200000,2026-08-27 01:10:00+08:00,78044.8000000000,78099.9000000000,78010.8000000000,78092.3000000000,22.2263000000
1787764500000,2026-08-27 01:15:00+08:00,78092.3000000000,78248.0000000000,78092.3000000000,78204.9000000000,172.4044000000
1787764800000,2026-08-27 01:20:00+08:00,78204.9000000000,78315.1000000000,78198.3000000000,78305.2000000000,200.7221000000
1787765100000,2026-08-27 01:25:00+08:00,78305.2000000000,78487.7000000000,78292.4000000000,78477.4000000000,331.1233000000
1787765400000,2026-08-27 01:30:00+08:00,78477.4000000000,78540.1000000000,78448.3000000000,78540.1000000000,202.2856000000
1787765700000,2026-08-27 01:35:00+08:00,78540.1000000000,78543.0000000000,78451.8000000000,78494.6000000000,132.0927000000
1787766000000,2026-08-27 01:40:00+08:00,78494.6000000000,78573.2000000000,78451.9000000000,78471.0000000000,185.6542000000
1787766300000,2026-08-27 01:45:00+08:00,78471.0000000000,78476.0000000000,78319.4000000000,78354.1000000000,94.5572000000
1787766600000,2026-08-27 01:50:00+08:00,78354.1000000000,78428.1000000000,78347.4000000000,78417.7000000000,35.6340000000
1787766900000,2026-08-27 01:55:00+08:00,78417.7000000000,78438.8000000000,78363.6000000000,78400.8000000000,23.3324000000
1787767200000,2026-08-27 02:00:00+08:00,78400.8000000000,78475.0000000000,78348.5000000000,78429.7000000000,53.1710000000
1787767500000,2026-08-27 02:05:00+08:00,78429.7000000000,78493.5000000000,78400.1000000000,78480.4000000000,37.1847000000
1787767800000,2026-08-27 02:10:00+08:00,78480.4000000000,78620.0000000000,78455.3000000000,78529.0000000000,271.8729000000
1787768100000,2026-08-27 02:15:00+08:00,78529.0000000000,78538.1000000000,78450.3000000000,78505.1000000000,44.9452000000
1787768400000,2026-08-27 02:20:00+08:00,78505.1000000000,78564.4000000000,78440.0000000000,78472.0000000000,59.7325000000
1787768700000,2026-08-27 02:25:00+08:00,78472.0000000000,78485.5000000000,78394.0000000000,78428.7000000000,52.6677000000
1787769000000,2026-08-27 02:30:00+08:00,78428.7000000000,78450.0000000000,78356.3000000000,78383.0000000000,50.0426000000
1787769300000,2026-08-27 02:35:00+08:00,78383.0000000000,78395.7000000000,78310.9000000000,78349.1000000000,89.9988000000
1787769600000,2026-08-27 02:40:00+08:00,78349.1000000000,78349.1000000000,78230.1000000000,78259.9000000000,120.0230000000
1787769900000,2026-08-27 02:45:00+08:00,78259.9000000000,78377.2000000000,78246.5000000000,78363.9000000000,45.4547000000
1787770200000,2026-08-27 02:50:00+08:00,78363.9000000000,78427.0000000000,78351.0000000000,78426.9000000000,26.9203000000
1787770500000,2026-08-27 02:55:00+08:00,78426.9000000000,78464.9000000000,78392.7000000000,78434.2000000000,38.3220000000
1787770800000,2026-08-27 03:00:00+08:00,78434.2000000000,78477.5000000000,78398.9000000000,78411.1000000000,20.6576000000
1787771100000,2026-08-27 03:05:00+08:00,78411.1000000000,78414.3000000000,78349.8000000000,78349.9000000000,15.6368000000
1787771400000,2026-08-27 03:10:00+08:00,78349.9000000000,78423.3000000000,78332.8000000000,78342.2000000000,36.5585000000
1787771700000,2026-08-27 03:15:00+08:00,78342.2000000000,78411.9000000000,78334.4000000000,78370.9000000000,27.0862000000
1787772000000,2026-08-27 03:20:00+08:00,78370.9000000000,78370.9000000000,78240.0000000000,78296.1000000000,70.3834000000
1787772300000,2026-08-27 03:25:00+08:00,78296.1000000000,78531.5000000000,78262.4000000000,78488.0000000000,150.7906000000
1787772600000,2026-08-27 03:30:00+08:00,78488.0000000000,78514.6000000000,78440.1000000000,78448.3000000000,46.0663000000
1787772900000,2026-08-27 03:35:00+08:00,78448.3000000000,78461.5000000000,78363.3000000000,78369.2000000000,53.1466000000
1787773200000,2026-08-27 03:40:00+08:00,78369.2000000000,78390.0000000000,78332.1000000000,78390.0000000000,46.7232000000
1787773500000,2026-08-27 03:45:00+08:00,78390.0000000000,78448.6000000000,78389.9000000000,78435.0000000000,22.8231000000
1787773800000,2026-08-27 03:50:00+08:00,78435.0000000000,78494.7000000000,78404.0000000000,78412.6000000000,50.3773000000
1787774100000,2026-08-27 03:55:00+08:00,78412.6000000000,78455.2000000000,78400.0000000000,78430.0000000000,46.5158000000
1787774400000,2026-08-27 04:00:00+08:00,78430.0000000000,78469.8000000000,78350.4000000000,78460.3000000000,25.2168000000
1787774700000,2026-08-27 04:05:00+08:00,78460.3000000000,78488.3000000000,78430.9000000000,78430.9000000000,18.4059000000
1787775000000,2026-08-27 04:10:00+08:00,78430.9000000000,78439.8000000000,78374.0000000000,78374.0000000000,11.4899000000
1787775300000,2026-08-27 04:15:00+08:00,78374.0000000000,78420.2000000000,78346.5000000000,78365.5000000000,29.7150000000
1787775600000,2026-08-27 04:20:00+08:00,78365.5000000000,78365.5000000000,78145.2000000000,78286.2000000000,236.1479000000
1787775900000,2026-08-27 04:25:00+08:00,78286.2000000000,78360.7000000000,78286.2000000000,78360.6000000000,25.6331000000
1787776200000,2026-08-27 04:30:00+08:00,78360.6000000000,78374.3000000000,78280.6000000000,78299.8000000000,28.7206000000
1787776500000,2026-08-27 04:35:00+08:00,78299.8000000000,78334.9000000000,78240.1000000000,78327.9000000000,18.9170000000
1787776800000,2026-08-27 04:40:00+08:00,78327.9000000000,78390.2000000000,78327.9000000000,78357.6000000000,23.3087000000
1787777100000,2026-08-27 04:45:00+08:00,78357.6000000000,78381.1000000000,78337.6000000000,78350.0000000000,11.9665000000
1787777400000,2026-08-27 04:50:00+08:00,78350.0000000000,78391.7000000000,78336.5000000000,78391.7000000000,15.3139000000
1787777700000,2026-08-27 04:55:00+08:00,78391.7000000000,78419.0000000000,78391.7000000000,78411.7000000000,27.2374000000
1787778000000,2026-08-27 05:00:00+08:00,78411.7000000000,78421.5000000000,78338.9000000000,78365.8000000000,25.8404000000
1787778300000,2026-08-27 05:05:00+08:00,78365.8000000000,78580.0000000000,78365.8000000000,78580.0000000000,147.0312000000
1787778600000,2026-08-27 05:10:00+08:00,78580.0000000000,78769.0000000000,78517.1000000000,78748.9000000000,351.8268000000
1787778900000,2026-08-27 05:15:00+08:00,78748.9000000000,78799.9000000000,78691.8000000000,78730.6000000000,165.2733000000
1787779200000,2026-08-27 05:20:00+08:00,78730.6000000000,78793.4000000000,78688.1000000000,78754.7000000000,63.8306000000
1787779500000,2026-08-27 05:25:00+08:00,78754.7000000000,78806.7000000000,78666.2000000000,78707.3000000000,97.7312000000
1787779800000,2026-08-27 05:30:00+08:00,78707.3000000000,78713.3000000000,78610.7000000000,78706.6000000000,36.9166000000
1787780100000,2026-08-27 05:35:00+08:00,78706.6000000000,78800.0000000000,78706.5000000000,78740.0000000000,38.3746000000
1787780400000,2026-08-27 05:40:00+08:00,78740.0000000000,78740.0000000000,78663.7000000000,78682.7000000000,28.9051000000
1787780700000,2026-08-27 05:45:00+08:00,78682.7000000000,78694.5000000000,78629.3000000000,78664.7000000000,28.6729000000
1787781000000,2026-08-27 05:50:00+08:00,78664.7000000000,78770.5000000000,78664.6000000000,78733.4000000000,26.9815000000
1787781300000,2026-08-27 05:55:00+08:00,78733.4000000000,78738.9000000000,78681.1000000000,78681.2000000000,24.5606000000
1787781600000,2026-08-27 06:00:00+08:00,78681.2000000000,78830.1000000000,78636.8000000000,78827.3000000000,75.5296000000
1787781900000,2026-08-27 06:05:00+08:00,78827.3000000000,78846.9000000000,78758.8000000000,78758.9000000000,92.8270000000
1787782200000,2026-08-27 06:10:00+08:00,78758.9000000000,78784.9000000000,78683.3000000000,78744.9000000000,85.0635000000
1787782500000,2026-08-27 06:15:00+08:00,78744.9000000000,78888.0000000000,78724.9000000000,78849.5000000000,155.3518000000
1787782800000,2026-08-27 06:20:00+08:00,78849.5000000000,78947.0000000000,78778.5000000000,78789.7000000000,202.3614000000
1787783100000,2026-08-27 06:25:00+08:00,78789.7000000000,78849.2000000000,78760.0000000000,78791.5000000000,49.9752000000
1787783400000,2026-08-27 06:30:00+08:00,78791.5000000000,78791.5000000000,78658.7000000000,78730.0000000000,50.8693000000
1787783700000,2026-08-27 06:35:00+08:00,78730.0000000000,78745.0000000000,78560.0000000000,78560.0000000000,95.4830000000
1787784000000,2026-08-27 06:40:00+08:00,78560.0000000000,78669.8000000000,78557.7000000000,78663.0000000000,43.5061000000
1787784300000,2026-08-27 06:45:00+08:00,78663.0000000000,78663.0000000000,78566.6000000000,78569.0000000000,20.3416000000
1787784600000,2026-08-27 06:50:00+08:00,78569.0000000000,78577.2000000000,78518.0000000000,78563.6000000000,46.6326000000
1787784900000,2026-08-27 06:55:00+08:00,78563.6000000000,78702.6000000000,78531.9000000000,78659.1000000000,106.1824000000
1787785200000,2026-08-27 07:00:00+08:00,78659.1000000000,78722.0000000000,78616.5000000000,78716.7000000000,41.0947000000
1787785500000,2026-08-27 07:05:00+08:00,78716.7000000000,78716.7000000000,78625.2000000000,78647.8000000000,39.5273000000
1787785800000,2026-08-27 07:10:00+08:00,78647.8000000000,78682.8000000000,78631.6000000000,78669.8000000000,15.1085000000
1787786100000,2026-08-27 07:15:00+08:00,78669.8000000000,78706.8000000000,78632.4000000000,78686.5000000000,22.2958000000
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1787787300000,2026-08-27 07:35:00+08:00,78852.6000000000,78967.0000000000,78852.6000000000,78912.5000000000,169.7103000000
1787787600000,2026-08-27 07:40:00+08:00,78912.5000000000,79023.3000000000,78870.1000000000,79010.2000000000,134.1431000000
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1787788200000,2026-08-27 07:50:00+08:00,79031.8000000000,79073.0000000000,78965.1000000000,79029.6000000000,85.0604000000
1787788500000,2026-08-27 07:55:00+08:00,79029.6000000000,79084.5000000000,78960.1000000000,78985.2000000000,87.2159000000
1787788800000,2026-08-27 08:00:00+08:00,78985.2000000000,78996.6000000000,78801.5000000000,78812.0000000000,108.4463000000
1787789100000,2026-08-27 08:05:00+08:00,78812.0000000000,78955.7000000000,78800.1000000000,78934.2000000000,51.7268000000
1787789400000,2026-08-27 08:10:00+08:00,78934.2000000000,78940.4000000000,78772.6000000000,78799.9000000000,63.9564000000
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1787790300000,2026-08-27 08:25:00+08:00,78813.0000000000,78877.7000000000,78784.4000000000,78868.3000000000,73.4661000000
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1 timestamp date open high low close volume
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File diff suppressed because it is too large Load Diff
+801
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@@ -0,0 +1,801 @@
timestamp,date,open,high,low,close,volume
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1787626200000,2026-08-25 10:50:00+08:00,2508.1700000000,2509.8000000000,2500.2400000000,2506.4300000000,6909.5500000000
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1787626800000,2026-08-25 11:00:00+08:00,2506.6600000000,2509.8900000000,2504.2600000000,2508.2200000000,2391.0100000000
1787627100000,2026-08-25 11:05:00+08:00,2508.2200000000,2513.6100000000,2508.2100000000,2510.5500000000,2181.2600000000
1787627400000,2026-08-25 11:10:00+08:00,2510.5500000000,2512.7500000000,2509.0000000000,2509.9900000000,1463.1300000000
1787627700000,2026-08-25 11:15:00+08:00,2509.9900000000,2511.0000000000,2505.8000000000,2509.3100000000,1522.2000000000
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1787628300000,2026-08-25 11:25:00+08:00,2506.2200000000,2507.7100000000,2503.0000000000,2504.8300000000,1516.9800000000
1787628600000,2026-08-25 11:30:00+08:00,2504.8300000000,2508.4900000000,2502.6600000000,2505.3100000000,1450.3400000000
1787628900000,2026-08-25 11:35:00+08:00,2505.3100000000,2507.1400000000,2503.0000000000,2506.0800000000,1461.3500000000
1787629200000,2026-08-25 11:40:00+08:00,2506.0800000000,2506.2800000000,2495.0800000000,2497.6300000000,6410.5100000000
1787629500000,2026-08-25 11:45:00+08:00,2497.6300000000,2500.3800000000,2487.3300000000,2493.1500000000,10715.0700000000
1787629800000,2026-08-25 11:50:00+08:00,2493.1500000000,2498.2600000000,2492.4900000000,2497.1000000000,6114.7300000000
1787630100000,2026-08-25 11:55:00+08:00,2497.1000000000,2498.6700000000,2492.2900000000,2498.2100000000,5688.2400000000
1787630400000,2026-08-25 12:00:00+08:00,2498.2100000000,2503.1900000000,2496.2700000000,2496.5200000000,2332.3700000000
1787630700000,2026-08-25 12:05:00+08:00,2496.5200000000,2498.1100000000,2491.4400000000,2498.1000000000,1622.1000000000
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1787631300000,2026-08-25 12:15:00+08:00,2498.8300000000,2499.0000000000,2490.0000000000,2492.7900000000,7493.2200000000
1787631600000,2026-08-25 12:20:00+08:00,2492.7900000000,2497.0500000000,2492.0300000000,2496.7300000000,9072.4000000000
1787631900000,2026-08-25 12:25:00+08:00,2496.7300000000,2497.0000000000,2492.3100000000,2493.3800000000,1469.4500000000
1787632200000,2026-08-25 12:30:00+08:00,2493.3800000000,2495.9400000000,2488.0000000000,2493.1100000000,1656.2100000000
1787632500000,2026-08-25 12:35:00+08:00,2493.1100000000,2496.8500000000,2490.0000000000,2491.2800000000,3074.6000000000
1787632800000,2026-08-25 12:40:00+08:00,2491.2800000000,2493.4200000000,2488.8100000000,2490.6700000000,1865.9700000000
1787633100000,2026-08-25 12:45:00+08:00,2490.6700000000,2493.6500000000,2489.3800000000,2493.2100000000,1410.3100000000
1787633400000,2026-08-25 12:50:00+08:00,2493.2100000000,2494.7100000000,2491.3500000000,2491.5200000000,1547.6100000000
1787633700000,2026-08-25 12:55:00+08:00,2491.5200000000,2494.0000000000,2489.3700000000,2493.9200000000,797.2200000000
1787634000000,2026-08-25 13:00:00+08:00,2493.9200000000,2498.6600000000,2492.6100000000,2498.1900000000,3057.5000000000
1787634300000,2026-08-25 13:05:00+08:00,2498.1900000000,2501.0100000000,2497.8900000000,2500.0600000000,1795.1400000000
1787634600000,2026-08-25 13:10:00+08:00,2500.0600000000,2500.6000000000,2498.7700000000,2499.6900000000,2255.3700000000
1787634900000,2026-08-25 13:15:00+08:00,2499.6900000000,2503.7300000000,2499.2500000000,2502.7100000000,1409.8900000000
1787635200000,2026-08-25 13:20:00+08:00,2502.7100000000,2503.9400000000,2502.0300000000,2503.9300000000,2416.9500000000
1787635500000,2026-08-25 13:25:00+08:00,2503.9300000000,2511.3400000000,2503.8100000000,2507.7400000000,9365.2700000000
1787635800000,2026-08-25 13:30:00+08:00,2507.7400000000,2510.5800000000,2505.7800000000,2509.9600000000,3085.3300000000
1787636100000,2026-08-25 13:35:00+08:00,2509.9600000000,2510.8800000000,2507.7700000000,2509.0600000000,1466.5100000000
1787636400000,2026-08-25 13:40:00+08:00,2509.0600000000,2510.1900000000,2505.1000000000,2505.4800000000,3100.7700000000
1787636700000,2026-08-25 13:45:00+08:00,2505.4800000000,2507.7900000000,2504.0000000000,2507.6700000000,500.0300000000
1787637000000,2026-08-25 13:50:00+08:00,2507.6700000000,2509.8500000000,2506.1300000000,2506.1700000000,682.4600000000
1787637300000,2026-08-25 13:55:00+08:00,2506.1700000000,2509.1800000000,2506.1100000000,2507.5700000000,1859.8800000000
1787637600000,2026-08-25 14:00:00+08:00,2507.5700000000,2509.2900000000,2506.6600000000,2507.5400000000,589.1800000000
1787637900000,2026-08-25 14:05:00+08:00,2507.5400000000,2509.0900000000,2506.0100000000,2507.7300000000,561.5400000000
1787638200000,2026-08-25 14:10:00+08:00,2507.7300000000,2509.3700000000,2506.0000000000,2508.8500000000,1273.0400000000
1787638500000,2026-08-25 14:15:00+08:00,2508.8500000000,2513.0100000000,2507.9000000000,2509.9700000000,1785.9600000000
1787638800000,2026-08-25 14:20:00+08:00,2509.9700000000,2514.7000000000,2507.4800000000,2512.1900000000,1934.5400000000
1787639100000,2026-08-25 14:25:00+08:00,2512.1900000000,2512.8100000000,2509.3000000000,2509.3100000000,1055.2100000000
1787639400000,2026-08-25 14:30:00+08:00,2509.3100000000,2509.3100000000,2500.3200000000,2503.8200000000,6070.8300000000
1787639700000,2026-08-25 14:35:00+08:00,2503.8200000000,2503.8200000000,2498.0000000000,2502.4200000000,3087.2600000000
1787640000000,2026-08-25 14:40:00+08:00,2502.4200000000,2503.2800000000,2500.3000000000,2502.9400000000,1617.8400000000
1787640300000,2026-08-25 14:45:00+08:00,2502.9400000000,2503.5800000000,2498.5300000000,2499.3900000000,1162.8500000000
1787640600000,2026-08-25 14:50:00+08:00,2499.3900000000,2500.0500000000,2497.6000000000,2498.5000000000,2907.4000000000
1787640900000,2026-08-25 14:55:00+08:00,2498.5000000000,2499.9200000000,2495.0100000000,2499.9200000000,1378.7800000000
1787641200000,2026-08-25 15:00:00+08:00,2499.9200000000,2510.6800000000,2499.1500000000,2509.6100000000,3790.6700000000
1787641500000,2026-08-25 15:05:00+08:00,2509.6100000000,2511.9900000000,2506.0000000000,2507.1000000000,4706.1400000000
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1787642100000,2026-08-25 15:15:00+08:00,2506.0900000000,2507.0400000000,2501.4000000000,2502.9900000000,1072.4800000000
1787642400000,2026-08-25 15:20:00+08:00,2502.9900000000,2505.9900000000,2502.5900000000,2503.1200000000,732.1400000000
1787642700000,2026-08-25 15:25:00+08:00,2503.1200000000,2504.9900000000,2502.2700000000,2503.7500000000,690.5400000000
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1787643300000,2026-08-25 15:35:00+08:00,2501.0700000000,2501.7300000000,2495.1000000000,2496.6000000000,1735.7100000000
1787643600000,2026-08-25 15:40:00+08:00,2496.6000000000,2499.1300000000,2496.6000000000,2498.3700000000,651.9100000000
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1787644200000,2026-08-25 15:50:00+08:00,2480.2500000000,2483.0400000000,2462.0300000000,2482.6500000000,29554.9100000000
1787644500000,2026-08-25 15:55:00+08:00,2482.6500000000,2484.6600000000,2477.6500000000,2478.1000000000,3808.2000000000
1787644800000,2026-08-25 16:00:00+08:00,2478.1000000000,2481.7000000000,2473.5400000000,2481.0300000000,6489.3200000000
1787645100000,2026-08-25 16:05:00+08:00,2481.0300000000,2481.9900000000,2479.0000000000,2480.8900000000,1157.4500000000
1787645400000,2026-08-25 16:10:00+08:00,2480.8900000000,2485.6300000000,2480.5200000000,2484.6000000000,1736.1400000000
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1787646900000,2026-08-25 16:35:00+08:00,2491.6800000000,2492.0000000000,2485.0000000000,2485.5900000000,3528.5200000000
1787647200000,2026-08-25 16:40:00+08:00,2485.5900000000,2490.2200000000,2484.5700000000,2489.4500000000,3526.5500000000
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1787648100000,2026-08-25 16:55:00+08:00,2481.3200000000,2484.6900000000,2479.8800000000,2484.1100000000,764.9200000000
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1787650200000,2026-08-25 17:30:00+08:00,2486.3200000000,2490.6000000000,2486.3200000000,2488.7800000000,424.6300000000
1787650500000,2026-08-25 17:35:00+08:00,2488.7800000000,2489.1100000000,2486.1000000000,2486.5800000000,303.0400000000
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1787656200000,2026-08-25 19:10:00+08:00,2478.8600000000,2479.2100000000,2473.2000000000,2474.4400000000,3888.4100000000
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1787671800000,2026-08-25 23:30:00+08:00,2473.4400000000,2474.4400000000,2468.8400000000,2472.0300000000,2943.7700000000
1787672100000,2026-08-25 23:35:00+08:00,2472.0300000000,2479.0000000000,2472.0300000000,2474.7300000000,2432.4000000000
1787672400000,2026-08-25 23:40:00+08:00,2474.7300000000,2476.3500000000,2471.6000000000,2476.3500000000,1132.1900000000
1787672700000,2026-08-25 23:45:00+08:00,2476.3500000000,2477.2500000000,2471.2400000000,2472.3700000000,972.1600000000
1787673000000,2026-08-25 23:50:00+08:00,2472.3700000000,2477.5300000000,2470.9600000000,2476.5200000000,932.1400000000
1787673300000,2026-08-25 23:55:00+08:00,2476.5200000000,2482.5400000000,2476.5200000000,2481.5200000000,3282.9200000000
1787673600000,2026-08-26 00:00:00+08:00,2481.5200000000,2484.3100000000,2477.9300000000,2480.1400000000,5240.8800000000
1787673900000,2026-08-26 00:05:00+08:00,2480.1400000000,2480.6500000000,2475.0000000000,2475.0000000000,1527.2700000000
1787674200000,2026-08-26 00:10:00+08:00,2475.0000000000,2476.2900000000,2471.0200000000,2471.9800000000,2272.3100000000
1787674500000,2026-08-26 00:15:00+08:00,2471.9800000000,2475.8400000000,2471.2500000000,2474.8000000000,1515.9100000000
1787674800000,2026-08-26 00:20:00+08:00,2474.8000000000,2475.2600000000,2465.1600000000,2469.3800000000,5407.1700000000
1787675100000,2026-08-26 00:25:00+08:00,2469.3800000000,2471.4700000000,2467.1000000000,2470.7500000000,1127.0000000000
1787675400000,2026-08-26 00:30:00+08:00,2470.7500000000,2476.8700000000,2470.4400000000,2473.9100000000,1155.5300000000
1787675700000,2026-08-26 00:35:00+08:00,2473.9100000000,2475.2200000000,2471.9500000000,2474.1800000000,821.8500000000
1787676000000,2026-08-26 00:40:00+08:00,2474.1800000000,2477.4900000000,2473.0800000000,2474.3000000000,670.1600000000
1787676300000,2026-08-26 00:45:00+08:00,2474.3000000000,2478.6000000000,2474.0000000000,2475.5600000000,1931.1100000000
1787676600000,2026-08-26 00:50:00+08:00,2475.5600000000,2475.7200000000,2470.4500000000,2470.9000000000,1244.8300000000
1787676900000,2026-08-26 00:55:00+08:00,2470.9000000000,2471.3300000000,2467.5600000000,2469.5000000000,2319.3600000000
1787677200000,2026-08-26 01:00:00+08:00,2469.5000000000,2470.2300000000,2462.5500000000,2465.0800000000,7804.0700000000
1787677500000,2026-08-26 01:05:00+08:00,2465.0800000000,2467.9400000000,2456.6400000000,2459.3900000000,7996.0800000000
1787677800000,2026-08-26 01:10:00+08:00,2459.3900000000,2461.8800000000,2454.2300000000,2461.6900000000,7231.5200000000
1787678100000,2026-08-26 01:15:00+08:00,2461.6900000000,2464.8400000000,2460.9000000000,2464.0200000000,2077.7800000000
1787678400000,2026-08-26 01:20:00+08:00,2464.0200000000,2465.4500000000,2457.2100000000,2458.5100000000,2099.6400000000
1787678700000,2026-08-26 01:25:00+08:00,2458.5100000000,2459.4800000000,2456.1700000000,2457.2900000000,2213.2100000000
1787679000000,2026-08-26 01:30:00+08:00,2457.2900000000,2467.2700000000,2457.2900000000,2464.5100000000,2680.1900000000
1787679300000,2026-08-26 01:35:00+08:00,2464.5100000000,2465.4300000000,2461.9300000000,2463.3100000000,855.0800000000
1787679600000,2026-08-26 01:40:00+08:00,2463.3100000000,2468.1200000000,2463.0100000000,2466.6800000000,2019.4700000000
1787679900000,2026-08-26 01:45:00+08:00,2466.6800000000,2468.8900000000,2465.9000000000,2467.7900000000,5574.6700000000
1787680200000,2026-08-26 01:50:00+08:00,2467.7900000000,2468.8900000000,2466.5800000000,2467.7600000000,718.8000000000
1787680500000,2026-08-26 01:55:00+08:00,2467.7600000000,2470.0000000000,2467.2300000000,2469.7500000000,1302.4500000000
1787680800000,2026-08-26 02:00:00+08:00,2469.7500000000,2473.7700000000,2468.8700000000,2469.7500000000,3898.1800000000
1787681100000,2026-08-26 02:05:00+08:00,2469.7500000000,2472.0900000000,2469.6600000000,2471.7500000000,871.1100000000
1787681400000,2026-08-26 02:10:00+08:00,2471.7500000000,2474.2400000000,2470.3300000000,2472.6000000000,1542.4400000000
1787681700000,2026-08-26 02:15:00+08:00,2472.6000000000,2473.7500000000,2470.4400000000,2471.1000000000,544.8200000000
1787682000000,2026-08-26 02:20:00+08:00,2471.1000000000,2473.6100000000,2470.0800000000,2471.9200000000,490.3100000000
1787682300000,2026-08-26 02:25:00+08:00,2471.9200000000,2472.1700000000,2470.6700000000,2471.5900000000,495.8800000000
1787682600000,2026-08-26 02:30:00+08:00,2471.5900000000,2471.5900000000,2466.4800000000,2466.7600000000,664.8300000000
1787682900000,2026-08-26 02:35:00+08:00,2466.7600000000,2468.2800000000,2465.0300000000,2467.9300000000,804.5100000000
1787683200000,2026-08-26 02:40:00+08:00,2467.9300000000,2468.0500000000,2465.7400000000,2466.8600000000,203.7200000000
1787683500000,2026-08-26 02:45:00+08:00,2466.8600000000,2468.9900000000,2466.7500000000,2467.5700000000,3618.2400000000
1787683800000,2026-08-26 02:50:00+08:00,2467.5700000000,2467.9500000000,2462.7800000000,2463.7400000000,2135.2000000000
1787684100000,2026-08-26 02:55:00+08:00,2463.7400000000,2466.6900000000,2463.4200000000,2466.0100000000,1421.8400000000
1787684400000,2026-08-26 03:00:00+08:00,2466.0100000000,2468.4300000000,2461.7400000000,2461.7400000000,573.2500000000
1787684700000,2026-08-26 03:05:00+08:00,2461.7400000000,2465.9600000000,2461.6700000000,2465.7900000000,861.4700000000
1787685000000,2026-08-26 03:10:00+08:00,2465.7900000000,2467.3600000000,2464.4700000000,2466.8700000000,495.4300000000
1787685300000,2026-08-26 03:15:00+08:00,2466.8700000000,2469.1800000000,2465.8100000000,2468.8100000000,1083.4600000000
1787685600000,2026-08-26 03:20:00+08:00,2468.8100000000,2468.9800000000,2465.7400000000,2468.1400000000,710.6000000000
1787685900000,2026-08-26 03:25:00+08:00,2468.1400000000,2468.1400000000,2461.2000000000,2461.3900000000,673.7200000000
1787686200000,2026-08-26 03:30:00+08:00,2461.3900000000,2461.3900000000,2455.7200000000,2459.3100000000,1875.4300000000
1787686500000,2026-08-26 03:35:00+08:00,2459.3100000000,2461.2400000000,2458.3200000000,2458.3200000000,2150.5400000000
1787686800000,2026-08-26 03:40:00+08:00,2458.3200000000,2458.7300000000,2454.0000000000,2455.1900000000,3418.0600000000
1787687100000,2026-08-26 03:45:00+08:00,2455.1900000000,2458.7500000000,2455.1900000000,2457.0000000000,1263.5600000000
1787687400000,2026-08-26 03:50:00+08:00,2457.0000000000,2461.6700000000,2456.5200000000,2460.1900000000,1672.0300000000
1787687700000,2026-08-26 03:55:00+08:00,2460.1900000000,2462.8200000000,2458.8900000000,2462.1500000000,1122.0200000000
1787688000000,2026-08-26 04:00:00+08:00,2462.1500000000,2463.2200000000,2447.4100000000,2454.6300000000,8300.2200000000
1787688300000,2026-08-26 04:05:00+08:00,2454.6300000000,2458.0000000000,2451.6300000000,2457.0000000000,2321.0300000000
1787688600000,2026-08-26 04:10:00+08:00,2457.0000000000,2457.5000000000,2454.5100000000,2456.7600000000,1885.1700000000
1787688900000,2026-08-26 04:15:00+08:00,2456.7600000000,2460.7100000000,2455.5100000000,2460.4400000000,827.9800000000
1787689200000,2026-08-26 04:20:00+08:00,2460.4400000000,2460.5100000000,2457.0000000000,2459.5900000000,441.8300000000
1787689500000,2026-08-26 04:25:00+08:00,2459.5900000000,2459.9900000000,2454.9500000000,2455.1500000000,1511.1700000000
1787689800000,2026-08-26 04:30:00+08:00,2455.1500000000,2455.9000000000,2450.6400000000,2452.7800000000,1649.0600000000
1787690100000,2026-08-26 04:35:00+08:00,2452.7800000000,2454.9200000000,2443.7900000000,2446.0900000000,3573.6600000000
1787690400000,2026-08-26 04:40:00+08:00,2446.0900000000,2447.7100000000,2441.1600000000,2445.2000000000,4943.3900000000
1787690700000,2026-08-26 04:45:00+08:00,2445.2000000000,2447.2600000000,2432.3000000000,2434.6000000000,13510.7500000000
1787691000000,2026-08-26 04:50:00+08:00,2434.6000000000,2442.1400000000,2433.6600000000,2438.0000000000,4432.5700000000
1787691300000,2026-08-26 04:55:00+08:00,2438.0000000000,2440.8000000000,2434.6100000000,2435.3900000000,2727.5600000000
1787691600000,2026-08-26 05:00:00+08:00,2435.3900000000,2440.1900000000,2433.7500000000,2436.2700000000,1741.9500000000
1787691900000,2026-08-26 05:05:00+08:00,2436.2700000000,2439.0900000000,2413.3000000000,2417.1200000000,20927.2600000000
1787692200000,2026-08-26 05:10:00+08:00,2417.1200000000,2429.3400000000,2414.3300000000,2429.2100000000,6143.8800000000
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1787692800000,2026-08-26 05:20:00+08:00,2432.8500000000,2432.8500000000,2422.4400000000,2427.6100000000,5211.5000000000
1787693100000,2026-08-26 05:25:00+08:00,2427.6100000000,2433.3900000000,2425.2200000000,2432.9000000000,2780.7100000000
1787693400000,2026-08-26 05:30:00+08:00,2432.9000000000,2437.1200000000,2431.1100000000,2435.3800000000,1410.5700000000
1787693700000,2026-08-26 05:35:00+08:00,2435.3800000000,2441.7500000000,2435.3800000000,2441.2500000000,2065.2900000000
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1787694300000,2026-08-26 05:45:00+08:00,2439.9600000000,2442.0900000000,2439.3000000000,2441.4800000000,628.9900000000
1787694600000,2026-08-26 05:50:00+08:00,2441.4800000000,2441.4900000000,2438.1900000000,2439.9900000000,749.8600000000
1787694900000,2026-08-26 05:55:00+08:00,2439.9900000000,2443.1600000000,2438.0500000000,2440.7700000000,2414.6300000000
1787695200000,2026-08-26 06:00:00+08:00,2440.7700000000,2447.6600000000,2440.7700000000,2447.4800000000,3136.5200000000
1787695500000,2026-08-26 06:05:00+08:00,2447.4800000000,2450.9300000000,2446.5500000000,2447.1600000000,2064.8300000000
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1787696100000,2026-08-26 06:15:00+08:00,2450.6800000000,2451.9900000000,2448.0000000000,2449.5600000000,1017.7500000000
1787696400000,2026-08-26 06:20:00+08:00,2449.5600000000,2450.1400000000,2445.2000000000,2447.1800000000,848.5800000000
1787696700000,2026-08-26 06:25:00+08:00,2447.1800000000,2447.3700000000,2440.7200000000,2445.3900000000,3559.9800000000
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1787697600000,2026-08-26 06:40:00+08:00,2447.3600000000,2449.5000000000,2446.6300000000,2449.1100000000,286.1900000000
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1787698200000,2026-08-26 06:50:00+08:00,2451.8800000000,2453.7800000000,2451.8600000000,2452.7100000000,1014.2600000000
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1787698800000,2026-08-26 07:00:00+08:00,2452.7600000000,2454.7300000000,2451.5000000000,2454.3900000000,458.3900000000
1787699100000,2026-08-26 07:05:00+08:00,2454.3900000000,2456.1700000000,2451.9000000000,2452.1500000000,1357.5700000000
1787699400000,2026-08-26 07:10:00+08:00,2452.1500000000,2454.2300000000,2450.5200000000,2453.9100000000,1013.0200000000
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1787700000000,2026-08-26 07:20:00+08:00,2451.1300000000,2451.1600000000,2448.2000000000,2448.3200000000,1911.3100000000
1787700300000,2026-08-26 07:25:00+08:00,2448.3200000000,2451.6800000000,2447.2600000000,2451.3800000000,1010.6800000000
1787700600000,2026-08-26 07:30:00+08:00,2451.3800000000,2451.6000000000,2446.9900000000,2449.8600000000,1528.8300000000
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1787701200000,2026-08-26 07:40:00+08:00,2446.6800000000,2446.9800000000,2441.8000000000,2443.1600000000,1397.2700000000
1787701500000,2026-08-26 07:45:00+08:00,2443.1600000000,2443.1600000000,2438.2000000000,2439.2200000000,1566.5900000000
1787701800000,2026-08-26 07:50:00+08:00,2439.2200000000,2440.1900000000,2435.6100000000,2438.3200000000,2298.5900000000
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1787703600000,2026-08-26 08:20:00+08:00,2440.5100000000,2442.5000000000,2437.3200000000,2442.4900000000,706.6100000000
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1787704200000,2026-08-26 08:30:00+08:00,2442.7100000000,2445.3300000000,2442.2500000000,2442.7500000000,793.4200000000
1787704500000,2026-08-26 08:35:00+08:00,2442.7500000000,2443.3600000000,2436.6100000000,2436.8400000000,1349.2400000000
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1787705100000,2026-08-26 08:45:00+08:00,2439.3300000000,2444.3300000000,2437.7600000000,2443.7000000000,1392.7900000000
1787705400000,2026-08-26 08:50:00+08:00,2443.7000000000,2449.3400000000,2443.3900000000,2448.7800000000,2249.9600000000
1787705700000,2026-08-26 08:55:00+08:00,2448.7800000000,2448.7800000000,2445.1700000000,2445.1700000000,643.1600000000
1787706000000,2026-08-26 09:00:00+08:00,2445.1700000000,2452.1000000000,2443.7600000000,2452.1000000000,2546.6000000000
1787706300000,2026-08-26 09:05:00+08:00,2452.1000000000,2460.0000000000,2452.1000000000,2453.6500000000,3834.2700000000
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1787706900000,2026-08-26 09:15:00+08:00,2452.6400000000,2456.5500000000,2451.4500000000,2455.2100000000,1878.4700000000
1787707200000,2026-08-26 09:20:00+08:00,2455.2100000000,2455.8700000000,2453.3300000000,2454.0000000000,730.4700000000
1787707500000,2026-08-26 09:25:00+08:00,2454.0000000000,2454.0000000000,2451.1600000000,2452.0000000000,573.1400000000
1787707800000,2026-08-26 09:30:00+08:00,2452.0000000000,2458.6600000000,2451.8600000000,2458.3000000000,1424.6800000000
1787708100000,2026-08-26 09:35:00+08:00,2458.3000000000,2461.0000000000,2457.7800000000,2457.9300000000,2354.8900000000
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1787709000000,2026-08-26 09:50:00+08:00,2454.5000000000,2455.0000000000,2452.9200000000,2453.8500000000,407.2700000000
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1787710200000,2026-08-26 10:10:00+08:00,2453.2700000000,2455.9800000000,2453.0000000000,2454.4900000000,174.8900000000
1787710500000,2026-08-26 10:15:00+08:00,2454.4900000000,2462.3000000000,2454.2500000000,2459.6600000000,1742.7300000000
1787710800000,2026-08-26 10:20:00+08:00,2459.6600000000,2461.5600000000,2459.0200000000,2460.7200000000,3478.9400000000
1787711100000,2026-08-26 10:25:00+08:00,2460.7200000000,2467.2400000000,2460.7200000000,2466.8400000000,3481.1700000000
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1787712000000,2026-08-26 10:40:00+08:00,2466.5900000000,2467.0000000000,2465.5000000000,2466.0000000000,762.9500000000
1787712300000,2026-08-26 10:45:00+08:00,2466.0000000000,2467.6800000000,2463.5700000000,2464.6000000000,1068.5100000000
1787712600000,2026-08-26 10:50:00+08:00,2464.6000000000,2465.1300000000,2463.0000000000,2463.5400000000,826.4600000000
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1787713200000,2026-08-26 11:00:00+08:00,2463.8900000000,2467.2000000000,2463.1500000000,2463.3100000000,5068.1300000000
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1787713800000,2026-08-26 11:10:00+08:00,2461.3600000000,2462.2800000000,2461.0000000000,2461.5100000000,474.3400000000
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1787716200000,2026-08-26 11:50:00+08:00,2460.3600000000,2464.0000000000,2460.0000000000,2463.9900000000,894.6300000000
1787716500000,2026-08-26 11:55:00+08:00,2463.9900000000,2466.3400000000,2463.9900000000,2464.3000000000,1494.0200000000
1787716800000,2026-08-26 12:00:00+08:00,2464.3000000000,2464.6100000000,2461.0000000000,2463.5900000000,1889.8500000000
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1787717400000,2026-08-26 12:10:00+08:00,2471.7100000000,2471.7500000000,2463.3900000000,2465.4000000000,1790.6200000000
1787717700000,2026-08-26 12:15:00+08:00,2465.4000000000,2469.0100000000,2463.8900000000,2469.0100000000,658.6800000000
1787718000000,2026-08-26 12:20:00+08:00,2469.0100000000,2469.0200000000,2465.4700000000,2468.2500000000,1001.2200000000
1787718300000,2026-08-26 12:25:00+08:00,2468.2500000000,2469.0000000000,2464.6900000000,2465.1700000000,1392.9600000000
1787718600000,2026-08-26 12:30:00+08:00,2465.1700000000,2466.0700000000,2461.4100000000,2463.2100000000,1709.6200000000
1787718900000,2026-08-26 12:35:00+08:00,2463.2100000000,2465.5300000000,2462.2600000000,2462.5900000000,1947.1100000000
1787719200000,2026-08-26 12:40:00+08:00,2462.5900000000,2464.5900000000,2462.2600000000,2463.0900000000,143.6300000000
1787719500000,2026-08-26 12:45:00+08:00,2463.0900000000,2463.0900000000,2456.0000000000,2459.4800000000,4440.4400000000
1787719800000,2026-08-26 12:50:00+08:00,2459.4800000000,2460.9400000000,2457.5600000000,2459.3000000000,1188.8900000000
1787720100000,2026-08-26 12:55:00+08:00,2459.3000000000,2459.5000000000,2454.2600000000,2455.6400000000,2486.6900000000
1787720400000,2026-08-26 13:00:00+08:00,2455.6400000000,2458.1300000000,2454.2000000000,2455.4200000000,2172.9100000000
1787720700000,2026-08-26 13:05:00+08:00,2455.4200000000,2455.9100000000,2450.0000000000,2454.6000000000,5134.8700000000
1787721000000,2026-08-26 13:10:00+08:00,2454.6000000000,2458.9700000000,2454.3700000000,2456.5300000000,2622.3900000000
1787721300000,2026-08-26 13:15:00+08:00,2456.5300000000,2458.1600000000,2452.3400000000,2453.0100000000,2001.6400000000
1787721600000,2026-08-26 13:20:00+08:00,2453.0100000000,2461.4800000000,2452.6300000000,2461.0100000000,2235.6400000000
1787721900000,2026-08-26 13:25:00+08:00,2461.0100000000,2462.9700000000,2459.1900000000,2461.8900000000,2262.7200000000
1787722200000,2026-08-26 13:30:00+08:00,2461.8900000000,2467.1200000000,2461.0700000000,2464.4700000000,3084.7600000000
1787722500000,2026-08-26 13:35:00+08:00,2464.4700000000,2465.0000000000,2461.0700000000,2462.8600000000,1038.2800000000
1787722800000,2026-08-26 13:40:00+08:00,2462.8600000000,2464.8800000000,2462.0400000000,2464.1800000000,5151.7000000000
1787723100000,2026-08-26 13:45:00+08:00,2464.1800000000,2464.1800000000,2461.7700000000,2463.2700000000,622.7700000000
1787723400000,2026-08-26 13:50:00+08:00,2463.2700000000,2463.5300000000,2459.9300000000,2459.9300000000,588.4900000000
1787723700000,2026-08-26 13:55:00+08:00,2459.9300000000,2463.7500000000,2459.9300000000,2463.2500000000,545.7900000000
1787724000000,2026-08-26 14:00:00+08:00,2463.2500000000,2465.7000000000,2461.5000000000,2463.6400000000,2575.9400000000
1787724300000,2026-08-26 14:05:00+08:00,2463.6400000000,2466.0000000000,2463.3000000000,2465.5400000000,1630.6300000000
1787724600000,2026-08-26 14:10:00+08:00,2465.5400000000,2465.6400000000,2460.0000000000,2460.5100000000,897.2000000000
1787724900000,2026-08-26 14:15:00+08:00,2460.5100000000,2461.3300000000,2455.5300000000,2455.6200000000,1446.9900000000
1787725200000,2026-08-26 14:20:00+08:00,2455.6200000000,2458.4800000000,2454.4500000000,2454.8800000000,817.5500000000
1787725500000,2026-08-26 14:25:00+08:00,2454.8800000000,2457.1700000000,2452.9500000000,2455.8600000000,1323.0700000000
1787725800000,2026-08-26 14:30:00+08:00,2455.8600000000,2458.9000000000,2455.0000000000,2455.5000000000,1438.9400000000
1787726100000,2026-08-26 14:35:00+08:00,2455.5000000000,2456.3500000000,2452.1300000000,2455.6000000000,2151.8900000000
1787726400000,2026-08-26 14:40:00+08:00,2455.6000000000,2459.6200000000,2455.1100000000,2459.0500000000,2173.9400000000
1787726700000,2026-08-26 14:45:00+08:00,2459.0500000000,2460.0000000000,2456.3500000000,2457.4200000000,1954.9500000000
1787727000000,2026-08-26 14:50:00+08:00,2457.4200000000,2459.2900000000,2457.1700000000,2459.2900000000,231.7200000000
1787727300000,2026-08-26 14:55:00+08:00,2459.2900000000,2459.2900000000,2457.3600000000,2458.7500000000,1248.3200000000
1787727600000,2026-08-26 15:00:00+08:00,2458.7500000000,2464.7500000000,2456.0000000000,2463.7900000000,3356.6300000000
1787727900000,2026-08-26 15:05:00+08:00,2463.7900000000,2467.2100000000,2460.9000000000,2461.1500000000,2587.8800000000
1787728200000,2026-08-26 15:10:00+08:00,2461.1500000000,2464.5500000000,2461.1500000000,2463.6500000000,794.1200000000
1787728500000,2026-08-26 15:15:00+08:00,2463.6500000000,2464.7200000000,2462.2000000000,2462.5200000000,674.5900000000
1787728800000,2026-08-26 15:20:00+08:00,2462.5200000000,2464.6600000000,2460.2000000000,2462.7300000000,769.5500000000
1787729100000,2026-08-26 15:25:00+08:00,2462.7300000000,2465.4700000000,2461.3200000000,2462.0300000000,493.3500000000
1787729400000,2026-08-26 15:30:00+08:00,2462.0300000000,2463.5900000000,2460.3500000000,2463.5900000000,1281.2400000000
1787729700000,2026-08-26 15:35:00+08:00,2463.5900000000,2465.6400000000,2462.6100000000,2464.4000000000,778.8000000000
1787730000000,2026-08-26 15:40:00+08:00,2464.4000000000,2465.0000000000,2462.7500000000,2463.5500000000,1049.4400000000
1787730300000,2026-08-26 15:45:00+08:00,2463.5500000000,2464.5000000000,2461.7100000000,2464.2200000000,405.1600000000
1787730600000,2026-08-26 15:50:00+08:00,2464.2200000000,2465.5400000000,2461.0000000000,2461.0000000000,1324.2100000000
1787730900000,2026-08-26 15:55:00+08:00,2461.0000000000,2462.6700000000,2460.0200000000,2460.3400000000,935.5600000000
1787731200000,2026-08-26 16:00:00+08:00,2460.3400000000,2461.8400000000,2457.7200000000,2459.8000000000,1413.1900000000
1787731500000,2026-08-26 16:05:00+08:00,2459.8000000000,2466.0000000000,2458.1900000000,2465.0100000000,1466.1100000000
1787731800000,2026-08-26 16:10:00+08:00,2465.0100000000,2468.2800000000,2463.6000000000,2463.6000000000,3883.9700000000
1787732100000,2026-08-26 16:15:00+08:00,2463.6000000000,2464.8900000000,2460.5000000000,2461.2600000000,5398.3200000000
1787732400000,2026-08-26 16:20:00+08:00,2461.2600000000,2461.5600000000,2456.3900000000,2459.6800000000,1294.7900000000
1787732700000,2026-08-26 16:25:00+08:00,2459.6800000000,2462.4300000000,2459.6800000000,2461.1500000000,662.6900000000
1787733000000,2026-08-26 16:30:00+08:00,2461.1500000000,2462.2600000000,2458.5500000000,2460.5600000000,613.8200000000
1787733300000,2026-08-26 16:35:00+08:00,2460.5600000000,2461.4900000000,2459.5900000000,2460.5000000000,785.0200000000
1787733600000,2026-08-26 16:40:00+08:00,2460.5000000000,2463.2800000000,2459.4100000000,2462.5100000000,585.0100000000
1787733900000,2026-08-26 16:45:00+08:00,2462.5100000000,2462.7500000000,2459.0600000000,2459.6900000000,223.0000000000
1787734200000,2026-08-26 16:50:00+08:00,2459.6900000000,2460.5900000000,2453.9400000000,2453.9500000000,1560.9900000000
1787734500000,2026-08-26 16:55:00+08:00,2453.9500000000,2455.7800000000,2452.0100000000,2454.9900000000,2688.4700000000
1787734800000,2026-08-26 17:00:00+08:00,2454.9900000000,2459.0600000000,2452.7300000000,2457.3500000000,2990.8600000000
1787735100000,2026-08-26 17:05:00+08:00,2457.3500000000,2459.6800000000,2456.2500000000,2457.0000000000,959.3800000000
1787735400000,2026-08-26 17:10:00+08:00,2457.0000000000,2458.0000000000,2454.4400000000,2454.5100000000,888.6800000000
1787735700000,2026-08-26 17:15:00+08:00,2454.5100000000,2455.2000000000,2444.0900000000,2446.3000000000,10243.9300000000
1787736000000,2026-08-26 17:20:00+08:00,2446.3000000000,2449.3200000000,2444.0100000000,2444.9600000000,2951.0800000000
1787736300000,2026-08-26 17:25:00+08:00,2444.9600000000,2450.0000000000,2444.9600000000,2449.4000000000,1979.7700000000
1787736600000,2026-08-26 17:30:00+08:00,2449.4000000000,2452.2700000000,2446.6200000000,2451.0000000000,1856.5200000000
1787736900000,2026-08-26 17:35:00+08:00,2451.0000000000,2451.4600000000,2446.7500000000,2447.3300000000,1101.0600000000
1787737200000,2026-08-26 17:40:00+08:00,2447.3300000000,2447.8100000000,2443.0000000000,2443.4700000000,3290.3700000000
1787737500000,2026-08-26 17:45:00+08:00,2443.4700000000,2447.5000000000,2440.6100000000,2447.4900000000,4580.7800000000
1787737800000,2026-08-26 17:50:00+08:00,2447.4900000000,2448.2600000000,2444.4200000000,2444.5100000000,966.2500000000
1787738100000,2026-08-26 17:55:00+08:00,2444.5100000000,2446.9700000000,2444.4200000000,2446.4700000000,712.4500000000
1787738400000,2026-08-26 18:00:00+08:00,2446.4700000000,2446.7400000000,2443.5300000000,2444.6600000000,1189.3600000000
1787738700000,2026-08-26 18:05:00+08:00,2444.6600000000,2445.9500000000,2443.0500000000,2444.3100000000,945.2600000000
1787739000000,2026-08-26 18:10:00+08:00,2444.3100000000,2449.0100000000,2443.3300000000,2448.1200000000,3078.3200000000
1787739300000,2026-08-26 18:15:00+08:00,2448.1200000000,2450.6900000000,2447.7000000000,2450.4900000000,1614.1000000000
1787739600000,2026-08-26 18:20:00+08:00,2450.4900000000,2451.9700000000,2448.6400000000,2451.9700000000,1573.5700000000
1787739900000,2026-08-26 18:25:00+08:00,2451.9700000000,2453.9800000000,2450.7100000000,2453.3700000000,1225.9500000000
1787740200000,2026-08-26 18:30:00+08:00,2453.3700000000,2456.7300000000,2452.5000000000,2456.7000000000,1778.1000000000
1787740500000,2026-08-26 18:35:00+08:00,2456.7000000000,2469.8300000000,2456.7000000000,2464.9700000000,10405.4300000000
1787740800000,2026-08-26 18:40:00+08:00,2464.9700000000,2466.5000000000,2461.2900000000,2463.5900000000,5587.9600000000
1787741100000,2026-08-26 18:45:00+08:00,2463.5900000000,2474.2300000000,2462.0800000000,2474.0400000000,7883.3000000000
1787741400000,2026-08-26 18:50:00+08:00,2474.0400000000,2474.9900000000,2468.1100000000,2469.4700000000,5460.2900000000
1787741700000,2026-08-26 18:55:00+08:00,2469.4700000000,2470.2500000000,2467.1900000000,2467.6300000000,1141.0600000000
1787742000000,2026-08-26 19:00:00+08:00,2467.6300000000,2472.4600000000,2467.6300000000,2471.2200000000,1516.0700000000
1787742300000,2026-08-26 19:05:00+08:00,2471.2200000000,2471.6500000000,2466.5300000000,2467.7000000000,936.5300000000
1787742600000,2026-08-26 19:10:00+08:00,2467.7000000000,2469.0000000000,2465.3000000000,2467.2000000000,741.5700000000
1787742900000,2026-08-26 19:15:00+08:00,2467.2000000000,2467.9000000000,2464.5800000000,2465.3600000000,707.8500000000
1787743200000,2026-08-26 19:20:00+08:00,2465.3600000000,2467.3100000000,2464.3200000000,2465.4800000000,1040.4000000000
1787743500000,2026-08-26 19:25:00+08:00,2465.4800000000,2466.0000000000,2463.6500000000,2464.6400000000,1019.5000000000
1787743800000,2026-08-26 19:30:00+08:00,2464.6400000000,2464.8500000000,2463.3000000000,2463.5600000000,544.4100000000
1787744100000,2026-08-26 19:35:00+08:00,2463.5600000000,2466.7200000000,2454.8900000000,2458.7500000000,3696.0200000000
1787744400000,2026-08-26 19:40:00+08:00,2458.7500000000,2462.1600000000,2456.6500000000,2460.7200000000,1747.1500000000
1787744700000,2026-08-26 19:45:00+08:00,2460.7200000000,2461.0700000000,2458.4000000000,2458.5400000000,871.7600000000
1787745000000,2026-08-26 19:50:00+08:00,2458.5400000000,2464.8200000000,2458.5200000000,2464.4900000000,1538.0000000000
1787745300000,2026-08-26 19:55:00+08:00,2464.4900000000,2467.8200000000,2461.2000000000,2461.6400000000,1801.2400000000
1787745600000,2026-08-26 20:00:00+08:00,2461.6400000000,2464.7000000000,2459.2400000000,2462.0500000000,1794.4600000000
1787745900000,2026-08-26 20:05:00+08:00,2462.0500000000,2462.1800000000,2457.5000000000,2460.2200000000,1269.2400000000
1787746200000,2026-08-26 20:10:00+08:00,2460.2200000000,2462.6200000000,2459.0000000000,2459.3200000000,947.9100000000
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1787747100000,2026-08-26 20:25:00+08:00,2466.8100000000,2470.6300000000,2466.2600000000,2469.6000000000,3110.4600000000
1787747400000,2026-08-26 20:30:00+08:00,2469.6000000000,2469.7400000000,2442.0300000000,2459.0000000000,18165.8500000000
1787747700000,2026-08-26 20:35:00+08:00,2459.0000000000,2462.4100000000,2450.5600000000,2454.4700000000,8335.8300000000
1787748000000,2026-08-26 20:40:00+08:00,2454.4700000000,2459.1600000000,2453.4500000000,2453.4500000000,2436.5800000000
1787748300000,2026-08-26 20:45:00+08:00,2453.4500000000,2458.2700000000,2451.3100000000,2456.0000000000,2289.5000000000
1787748600000,2026-08-26 20:50:00+08:00,2456.0000000000,2459.1000000000,2451.0100000000,2453.2500000000,1667.3900000000
1787748900000,2026-08-26 20:55:00+08:00,2453.2500000000,2457.1200000000,2452.3400000000,2453.5600000000,2155.7200000000
1787749200000,2026-08-26 21:00:00+08:00,2453.5600000000,2457.0400000000,2450.7500000000,2450.7800000000,7522.4300000000
1787749500000,2026-08-26 21:05:00+08:00,2450.7800000000,2453.2800000000,2446.3800000000,2448.5000000000,3432.7900000000
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1787750100000,2026-08-26 21:15:00+08:00,2444.7700000000,2447.1600000000,2440.5300000000,2443.8300000000,3725.3300000000
1787750400000,2026-08-26 21:20:00+08:00,2443.8300000000,2446.4900000000,2437.3700000000,2439.5000000000,7201.9000000000
1787750700000,2026-08-26 21:25:00+08:00,2439.5000000000,2445.1100000000,2435.1900000000,2443.4500000000,10179.6500000000
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1787752500000,2026-08-26 21:55:00+08:00,2451.2800000000,2457.0200000000,2450.0600000000,2456.3000000000,1801.8800000000
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1787754300000,2026-08-26 22:25:00+08:00,2452.0100000000,2453.1000000000,2447.3300000000,2449.3100000000,2577.5400000000
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1787755200000,2026-08-26 22:40:00+08:00,2447.7600000000,2449.4600000000,2443.2000000000,2443.8000000000,3140.1300000000
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1787760000000,2026-08-27 00:00:00+08:00,2448.8200000000,2451.0800000000,2446.1300000000,2451.0800000000,693.6600000000
1787760300000,2026-08-27 00:05:00+08:00,2451.0800000000,2454.5300000000,2449.9600000000,2452.2800000000,5152.8300000000
1787760600000,2026-08-27 00:10:00+08:00,2452.2800000000,2452.4800000000,2448.4000000000,2449.0900000000,927.8700000000
1787760900000,2026-08-27 00:15:00+08:00,2449.0900000000,2450.1000000000,2446.4500000000,2448.9800000000,1127.7800000000
1787761200000,2026-08-27 00:20:00+08:00,2448.9800000000,2452.1800000000,2447.5900000000,2447.5900000000,1236.9900000000
1787761500000,2026-08-27 00:25:00+08:00,2447.5900000000,2449.4300000000,2446.2000000000,2448.0200000000,5098.1600000000
1787761800000,2026-08-27 00:30:00+08:00,2448.0200000000,2450.8700000000,2446.6700000000,2450.8500000000,456.0200000000
1787762100000,2026-08-27 00:35:00+08:00,2450.8500000000,2452.0000000000,2439.7000000000,2440.3800000000,8883.1600000000
1787762400000,2026-08-27 00:40:00+08:00,2440.3800000000,2442.5200000000,2436.2100000000,2437.1300000000,3670.4100000000
1787762700000,2026-08-27 00:45:00+08:00,2437.1300000000,2450.2600000000,2436.8800000000,2447.1500000000,5883.5700000000
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1787763300000,2026-08-27 00:55:00+08:00,2445.1100000000,2446.1300000000,2442.9400000000,2443.3700000000,824.0900000000
1787763600000,2026-08-27 01:00:00+08:00,2443.3700000000,2446.2400000000,2442.7900000000,2444.4900000000,539.3300000000
1787763900000,2026-08-27 01:05:00+08:00,2444.4900000000,2447.2500000000,2442.4400000000,2445.1000000000,610.9700000000
1787764200000,2026-08-27 01:10:00+08:00,2445.1000000000,2446.1700000000,2442.2800000000,2446.1400000000,398.3200000000
1787764500000,2026-08-27 01:15:00+08:00,2446.1400000000,2451.5700000000,2446.0000000000,2450.8600000000,1976.8900000000
1787764800000,2026-08-27 01:20:00+08:00,2450.8600000000,2455.9200000000,2450.3700000000,2455.4800000000,2912.4000000000
1787765100000,2026-08-27 01:25:00+08:00,2455.4800000000,2462.6300000000,2455.3000000000,2461.6600000000,8770.3300000000
1787765400000,2026-08-27 01:30:00+08:00,2461.6600000000,2466.4600000000,2460.6100000000,2463.9000000000,4940.9200000000
1787765700000,2026-08-27 01:35:00+08:00,2463.9000000000,2464.2500000000,2460.0700000000,2462.6700000000,2835.0200000000
1787766000000,2026-08-27 01:40:00+08:00,2462.6700000000,2464.9800000000,2460.0000000000,2460.1300000000,1577.2000000000
1787766300000,2026-08-27 01:45:00+08:00,2460.1300000000,2460.5100000000,2454.2900000000,2456.3100000000,1938.4300000000
1787766600000,2026-08-27 01:50:00+08:00,2456.3100000000,2461.0000000000,2456.2500000000,2460.2200000000,1340.5700000000
1787766900000,2026-08-27 01:55:00+08:00,2460.2200000000,2461.7500000000,2458.4100000000,2459.5700000000,540.3300000000
1787767200000,2026-08-27 02:00:00+08:00,2459.5700000000,2462.4300000000,2457.4400000000,2460.8400000000,413.4600000000
1787767500000,2026-08-27 02:05:00+08:00,2460.8400000000,2465.4300000000,2460.1800000000,2464.3200000000,644.9500000000
1787767800000,2026-08-27 02:10:00+08:00,2464.3200000000,2471.2000000000,2463.7100000000,2466.2500000000,7631.5500000000
1787768100000,2026-08-27 02:15:00+08:00,2466.2500000000,2467.6100000000,2464.0500000000,2465.7000000000,771.3200000000
1787768400000,2026-08-27 02:20:00+08:00,2465.7000000000,2467.8000000000,2463.3600000000,2464.3500000000,1625.6800000000
1787768700000,2026-08-27 02:25:00+08:00,2464.3500000000,2464.7600000000,2459.8600000000,2461.3800000000,3671.2600000000
1787769000000,2026-08-27 02:30:00+08:00,2461.3800000000,2465.5000000000,2460.8300000000,2463.1100000000,1038.6200000000
1787769300000,2026-08-27 02:35:00+08:00,2463.1100000000,2465.8000000000,2462.4900000000,2463.2800000000,2239.3800000000
1787769600000,2026-08-27 02:40:00+08:00,2463.2800000000,2463.2800000000,2460.6200000000,2463.1800000000,2103.6600000000
1787769900000,2026-08-27 02:45:00+08:00,2463.1800000000,2468.4700000000,2463.0100000000,2468.2900000000,759.2700000000
1787770200000,2026-08-27 02:50:00+08:00,2468.2900000000,2471.5100000000,2466.4700000000,2471.3900000000,1687.3300000000
1787770500000,2026-08-27 02:55:00+08:00,2471.3900000000,2473.5200000000,2470.1500000000,2471.4500000000,1577.8400000000
1787770800000,2026-08-27 03:00:00+08:00,2471.4500000000,2472.0000000000,2469.8600000000,2470.7500000000,573.9100000000
1787771100000,2026-08-27 03:05:00+08:00,2470.7500000000,2471.6300000000,2467.7900000000,2468.0600000000,1188.5100000000
1787771400000,2026-08-27 03:10:00+08:00,2468.0600000000,2471.8700000000,2467.5400000000,2469.1700000000,1507.9600000000
1787771700000,2026-08-27 03:15:00+08:00,2469.1700000000,2472.2800000000,2468.5200000000,2471.5100000000,2697.3400000000
1787772000000,2026-08-27 03:20:00+08:00,2471.5100000000,2471.6500000000,2469.7000000000,2470.1600000000,569.1900000000
1787772300000,2026-08-27 03:25:00+08:00,2470.1600000000,2483.2200000000,2469.1900000000,2479.0000000000,18950.0500000000
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1787773200000,2026-08-27 03:40:00+08:00,2468.8500000000,2470.2000000000,2466.6800000000,2469.8700000000,864.1500000000
1787773500000,2026-08-27 03:45:00+08:00,2469.8700000000,2472.5900000000,2469.3200000000,2471.7500000000,851.2200000000
1787773800000,2026-08-27 03:50:00+08:00,2471.7500000000,2472.7500000000,2468.6600000000,2470.1400000000,1523.6100000000
1787774100000,2026-08-27 03:55:00+08:00,2470.1400000000,2471.7200000000,2468.0100000000,2469.3100000000,843.8000000000
1787774400000,2026-08-27 04:00:00+08:00,2469.3100000000,2471.8900000000,2467.4000000000,2471.7900000000,638.6800000000
1787774700000,2026-08-27 04:05:00+08:00,2471.7900000000,2473.5200000000,2470.3700000000,2470.3800000000,539.3500000000
1787775000000,2026-08-27 04:10:00+08:00,2470.3800000000,2471.9500000000,2467.6700000000,2467.8100000000,680.7100000000
1787775300000,2026-08-27 04:15:00+08:00,2467.8100000000,2469.7500000000,2466.2800000000,2468.2800000000,423.1700000000
1787775600000,2026-08-27 04:20:00+08:00,2468.2800000000,2468.2800000000,2458.5600000000,2464.5700000000,2670.1600000000
1787775900000,2026-08-27 04:25:00+08:00,2464.5700000000,2467.9600000000,2464.5700000000,2467.9600000000,1155.6900000000
1787776200000,2026-08-27 04:30:00+08:00,2467.9600000000,2468.8000000000,2464.5600000000,2464.9200000000,513.5900000000
1787776500000,2026-08-27 04:35:00+08:00,2464.9200000000,2466.4600000000,2462.7000000000,2465.2700000000,785.3400000000
1787776800000,2026-08-27 04:40:00+08:00,2465.2700000000,2468.8600000000,2465.2600000000,2467.5900000000,492.1700000000
1787777100000,2026-08-27 04:45:00+08:00,2467.5900000000,2469.2800000000,2467.1000000000,2468.2500000000,590.2700000000
1787777400000,2026-08-27 04:50:00+08:00,2468.2500000000,2469.6800000000,2467.1900000000,2469.5000000000,283.3500000000
1787777700000,2026-08-27 04:55:00+08:00,2469.5000000000,2471.9600000000,2469.5000000000,2471.3800000000,723.8900000000
1787778000000,2026-08-27 05:00:00+08:00,2471.3800000000,2472.0000000000,2469.2100000000,2469.5400000000,376.8700000000
1787778300000,2026-08-27 05:05:00+08:00,2469.5400000000,2485.3400000000,2469.5300000000,2483.8100000000,7527.4500000000
1787778600000,2026-08-27 05:10:00+08:00,2483.8100000000,2494.0500000000,2479.0900000000,2491.7100000000,21347.8700000000
1787778900000,2026-08-27 05:15:00+08:00,2491.7100000000,2495.3000000000,2486.7700000000,2490.7700000000,6129.8200000000
1787779200000,2026-08-27 05:20:00+08:00,2490.7700000000,2494.5100000000,2488.3200000000,2493.4800000000,3087.9600000000
1787779500000,2026-08-27 05:25:00+08:00,2493.4800000000,2504.3600000000,2490.9400000000,2499.2600000000,10530.1200000000
1787779800000,2026-08-27 05:30:00+08:00,2499.2600000000,2500.4900000000,2493.7500000000,2495.1400000000,2447.7500000000
1787780100000,2026-08-27 05:35:00+08:00,2495.1400000000,2500.1300000000,2494.4900000000,2495.3400000000,1173.2100000000
1787780400000,2026-08-27 05:40:00+08:00,2495.3400000000,2495.3700000000,2492.5900000000,2492.9600000000,2179.0200000000
1787780700000,2026-08-27 05:45:00+08:00,2492.9600000000,2493.9900000000,2490.2500000000,2491.6700000000,2427.3100000000
1787781000000,2026-08-27 05:50:00+08:00,2491.6700000000,2497.0800000000,2491.6700000000,2496.0000000000,802.6800000000
1787781300000,2026-08-27 05:55:00+08:00,2496.0000000000,2497.6600000000,2494.1200000000,2495.7400000000,510.0400000000
1787781600000,2026-08-27 06:00:00+08:00,2495.7400000000,2502.9100000000,2493.2500000000,2502.6300000000,1779.7400000000
1787781900000,2026-08-27 06:05:00+08:00,2502.6300000000,2506.5900000000,2500.6000000000,2500.6000000000,8461.8800000000
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1787782500000,2026-08-27 06:15:00+08:00,2502.1900000000,2514.9800000000,2501.5500000000,2509.6300000000,10606.7600000000
1787782800000,2026-08-27 06:20:00+08:00,2509.6300000000,2514.9300000000,2505.1200000000,2505.7200000000,2900.6400000000
1787783100000,2026-08-27 06:25:00+08:00,2505.7200000000,2507.8900000000,2504.1400000000,2505.4300000000,1125.1700000000
1787783400000,2026-08-27 06:30:00+08:00,2505.4300000000,2506.0000000000,2498.2400000000,2499.3700000000,3633.0900000000
1787783700000,2026-08-27 06:35:00+08:00,2499.3700000000,2499.9900000000,2493.9700000000,2494.2400000000,2425.7300000000
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1787784300000,2026-08-27 06:45:00+08:00,2496.9200000000,2496.9600000000,2492.0400000000,2492.8200000000,1327.0000000000
1787784600000,2026-08-27 06:50:00+08:00,2492.8200000000,2492.8200000000,2489.0300000000,2490.0000000000,1975.3900000000
1787784900000,2026-08-27 06:55:00+08:00,2490.0000000000,2495.3500000000,2488.2900000000,2492.5500000000,1713.8700000000
1787785200000,2026-08-27 07:00:00+08:00,2492.5500000000,2494.8500000000,2489.5200000000,2494.0000000000,1288.8700000000
1787785500000,2026-08-27 07:05:00+08:00,2494.0000000000,2494.0000000000,2487.1200000000,2488.5700000000,1390.0200000000
1787785800000,2026-08-27 07:10:00+08:00,2488.5700000000,2490.5500000000,2487.8500000000,2489.4200000000,532.4800000000
1787786100000,2026-08-27 07:15:00+08:00,2489.4200000000,2493.6800000000,2488.8800000000,2493.5200000000,619.9100000000
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1787786700000,2026-08-27 07:25:00+08:00,2495.8700000000,2499.9800000000,2494.0800000000,2498.0000000000,1575.1100000000
1787787000000,2026-08-27 07:30:00+08:00,2498.0000000000,2501.4600000000,2496.6400000000,2497.8000000000,2054.3200000000
1787787300000,2026-08-27 07:35:00+08:00,2497.8000000000,2505.0000000000,2497.2600000000,2503.5900000000,2197.3400000000
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1787788200000,2026-08-27 07:50:00+08:00,2504.5500000000,2506.0000000000,2501.6600000000,2504.2100000000,1514.9400000000
1787788500000,2026-08-27 07:55:00+08:00,2504.2100000000,2506.9900000000,2503.3800000000,2505.7200000000,2391.0900000000
1787788800000,2026-08-27 08:00:00+08:00,2505.7200000000,2506.0300000000,2498.6100000000,2498.7900000000,4048.8000000000
1787789100000,2026-08-27 08:05:00+08:00,2498.7900000000,2509.2300000000,2498.4500000000,2509.1500000000,1548.1200000000
1787789400000,2026-08-27 08:10:00+08:00,2509.1500000000,2509.4700000000,2500.6500000000,2501.4300000000,2276.0300000000
1787789700000,2026-08-27 08:15:00+08:00,2501.4300000000,2505.3800000000,2499.2000000000,2505.1900000000,1734.7200000000
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1787790300000,2026-08-27 08:25:00+08:00,2499.7700000000,2499.9700000000,2497.5400000000,2499.7500000000,1174.7900000000
1787790600000,2026-08-27 08:30:00+08:00,2499.7500000000,2500.5800000000,2497.1700000000,2497.4900000000,1664.7500000000
1787790900000,2026-08-27 08:35:00+08:00,2497.4900000000,2498.6000000000,2494.2800000000,2495.6000000000,1843.8100000000
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1787792400000,2026-08-27 09:00:00+08:00,2490.5700000000,2492.8800000000,2487.0100000000,2490.8600000000,2933.6200000000
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1787793000000,2026-08-27 09:10:00+08:00,2489.9900000000,2491.0100000000,2485.5000000000,2488.1500000000,2193.5200000000
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1787793900000,2026-08-27 09:25:00+08:00,2495.6500000000,2504.7800000000,2495.3000000000,2501.9100000000,2323.7100000000
1787794200000,2026-08-27 09:30:00+08:00,2501.9100000000,2504.8900000000,2496.7400000000,2499.8100000000,2483.7300000000
1787794500000,2026-08-27 09:35:00+08:00,2499.8100000000,2501.8500000000,2497.0100000000,2497.9700000000,1933.0600000000
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1787795400000,2026-08-27 09:50:00+08:00,2501.0700000000,2504.6400000000,2499.6200000000,2499.9400000000,4074.8800000000
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1787799600000,2026-08-27 11:00:00+08:00,2493.6000000000,2493.9000000000,2492.1200000000,2493.3700000000,178.1800000000
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763 1787821500000 2026-08-27 17:05:00+08:00 2527.3500000000 2532.5000000000 2525.5000000000 2531.3000000000 2489.6500000000
764 1787821800000 2026-08-27 17:10:00+08:00 2531.3000000000 2535.9900000000 2531.0500000000 2534.3300000000 1601.8300000000
765 1787822100000 2026-08-27 17:15:00+08:00 2534.3300000000 2536.5400000000 2529.7500000000 2536.3900000000 5464.3200000000
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767 1787822700000 2026-08-27 17:25:00+08:00 2539.2900000000 2566.5700000000 2539.2800000000 2553.3400000000 46237.0800000000
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783 1787827500000 2026-08-27 18:45:00+08:00 2505.0600000000 2505.9100000000 2499.8800000000 2500.7600000000 3052.3700000000
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785 1787828100000 2026-08-27 18:55:00+08:00 2504.2000000000 2504.9500000000 2500.3800000000 2502.8100000000 3925.4400000000
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790 1787829600000 2026-08-27 19:20:00+08:00 2506.0000000000 2508.2000000000 2504.3300000000 2507.0000000000 1957.1400000000
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793 1787830500000 2026-08-27 19:35:00+08:00 2504.2200000000 2504.7700000000 2498.1500000000 2498.2500000000 1621.6500000000
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800 1787832600000 2026-08-27 20:10:00+08:00 2500.3400000000 2505.8200000000 2500.3400000000 2503.9600000000 1748.7200000000
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File diff suppressed because it is too large Load Diff
+801
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@@ -0,0 +1,801 @@
timestamp,date,open,high,low,close,volume
1787593500000,2026-08-25 01:45:00+08:00,94.8020000000,95.1660000000,94.7650000000,95.1650000000,5145.8000000000
1787593800000,2026-08-25 01:50:00+08:00,95.1650000000,95.2850000000,95.0670000000,95.2220000000,4671.0000000000
1787594100000,2026-08-25 01:55:00+08:00,95.2220000000,95.3640000000,95.0540000000,95.3520000000,6549.1000000000
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1787595300000,2026-08-25 02:15:00+08:00,95.9500000000,96.3650000000,95.9030000000,96.1890000000,7868.8000000000
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1787596800000,2026-08-25 02:40:00+08:00,95.8960000000,96.1710000000,95.8960000000,96.1660000000,5269.9000000000
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682 1787797500000 2026-08-27 10:25:00+08:00 101.2050000000 101.2690000000 101.0600000000 101.0910000000 6342.0000000000
683 1787797800000 2026-08-27 10:30:00+08:00 101.0910000000 101.2880000000 101.0180000000 101.2620000000 5731.1000000000
684 1787798100000 2026-08-27 10:35:00+08:00 101.2620000000 101.2620000000 100.9750000000 101.0070000000 6478.3000000000
685 1787798400000 2026-08-27 10:40:00+08:00 101.0070000000 101.0210000000 100.8140000000 100.8400000000 9682.0000000000
686 1787798700000 2026-08-27 10:45:00+08:00 100.8400000000 101.3000000000 100.8280000000 101.1490000000 16549.9000000000
687 1787799000000 2026-08-27 10:50:00+08:00 101.1490000000 101.3920000000 101.1160000000 101.2400000000 6704.2000000000
688 1787799300000 2026-08-27 10:55:00+08:00 101.2400000000 101.2930000000 101.0660000000 101.1240000000 1620.7000000000
689 1787799600000 2026-08-27 11:00:00+08:00 101.1240000000 101.1670000000 101.0510000000 101.1350000000 4273.2000000000
690 1787799900000 2026-08-27 11:05:00+08:00 101.1350000000 101.2160000000 101.0600000000 101.1350000000 4633.2000000000
691 1787800200000 2026-08-27 11:10:00+08:00 101.1350000000 101.1900000000 100.9400000000 100.9840000000 4961.9000000000
692 1787800500000 2026-08-27 11:15:00+08:00 100.9840000000 101.2870000000 100.9030000000 101.2870000000 6688.0000000000
693 1787800800000 2026-08-27 11:20:00+08:00 101.2870000000 101.7270000000 101.1480000000 101.6380000000 15248.1000000000
694 1787801100000 2026-08-27 11:25:00+08:00 101.6380000000 101.7750000000 101.4450000000 101.6080000000 6777.6000000000
695 1787801400000 2026-08-27 11:30:00+08:00 101.6080000000 101.6290000000 101.3200000000 101.3540000000 4534.5000000000
696 1787801700000 2026-08-27 11:35:00+08:00 101.3540000000 101.4160000000 101.2730000000 101.2790000000 7377.8000000000
697 1787802000000 2026-08-27 11:40:00+08:00 101.2790000000 101.3900000000 101.2630000000 101.2660000000 4115.1000000000
698 1787802300000 2026-08-27 11:45:00+08:00 101.2660000000 101.5160000000 101.0320000000 101.0430000000 16359.0000000000
699 1787802600000 2026-08-27 11:50:00+08:00 101.0430000000 101.0610000000 100.8420000000 101.0050000000 10186.0000000000
700 1787802900000 2026-08-27 11:55:00+08:00 101.0050000000 101.0330000000 100.8780000000 101.0330000000 5267.5000000000
701 1787803200000 2026-08-27 12:00:00+08:00 101.0330000000 101.1850000000 101.0120000000 101.0860000000 8278.6000000000
702 1787803500000 2026-08-27 12:05:00+08:00 101.0860000000 101.3300000000 101.0470000000 101.2590000000 6189.3000000000
703 1787803800000 2026-08-27 12:10:00+08:00 101.2590000000 101.3770000000 101.1980000000 101.3490000000 9492.4000000000
704 1787804100000 2026-08-27 12:15:00+08:00 101.3490000000 101.4900000000 101.1940000000 101.2050000000 29151.6000000000
705 1787804400000 2026-08-27 12:20:00+08:00 101.2050000000 101.2570000000 101.0500000000 101.2510000000 4050.8000000000
706 1787804700000 2026-08-27 12:25:00+08:00 101.2510000000 101.3570000000 101.1530000000 101.1530000000 4810.0000000000
707 1787805000000 2026-08-27 12:30:00+08:00 101.1530000000 101.1530000000 100.7640000000 100.7950000000 8989.3000000000
708 1787805300000 2026-08-27 12:35:00+08:00 100.7950000000 101.1890000000 100.6430000000 101.1690000000 32015.4000000000
709 1787805600000 2026-08-27 12:40:00+08:00 101.1690000000 101.4780000000 101.1360000000 101.2760000000 7849.9000000000
710 1787805900000 2026-08-27 12:45:00+08:00 101.2760000000 101.2940000000 100.8670000000 100.9660000000 7341.4000000000
711 1787806200000 2026-08-27 12:50:00+08:00 100.9660000000 100.9930000000 100.8110000000 100.9340000000 10138.0000000000
712 1787806500000 2026-08-27 12:55:00+08:00 100.9340000000 101.0680000000 100.9280000000 101.0510000000 7805.9000000000
713 1787806800000 2026-08-27 13:00:00+08:00 101.0510000000 101.3520000000 100.9650000000 100.9660000000 15289.4000000000
714 1787807100000 2026-08-27 13:05:00+08:00 100.9660000000 101.1960000000 100.7900000000 101.1310000000 12463.4000000000
715 1787807400000 2026-08-27 13:10:00+08:00 101.1310000000 101.1860000000 100.9270000000 100.9450000000 4914.5000000000
716 1787807700000 2026-08-27 13:15:00+08:00 100.9450000000 100.9450000000 100.7790000000 100.8650000000 9796.0000000000
717 1787808000000 2026-08-27 13:20:00+08:00 100.8650000000 101.1420000000 100.8650000000 101.1310000000 3227.0000000000
718 1787808300000 2026-08-27 13:25:00+08:00 101.1310000000 101.2900000000 100.8910000000 100.9420000000 16547.1000000000
719 1787808600000 2026-08-27 13:30:00+08:00 100.9420000000 101.0170000000 100.7100000000 100.7880000000 7735.8000000000
720 1787808900000 2026-08-27 13:35:00+08:00 100.7880000000 101.3600000000 100.7470000000 101.2750000000 21901.9000000000
721 1787809200000 2026-08-27 13:40:00+08:00 101.2750000000 101.4790000000 101.1210000000 101.1970000000 12908.5000000000
722 1787809500000 2026-08-27 13:45:00+08:00 101.1970000000 101.6740000000 101.1490000000 101.6450000000 8145.7000000000
723 1787809800000 2026-08-27 13:50:00+08:00 101.6450000000 102.0610000000 101.6310000000 101.7980000000 38790.8000000000
724 1787810100000 2026-08-27 13:55:00+08:00 101.7980000000 102.2440000000 101.5490000000 102.0000000000 31259.9000000000
725 1787810400000 2026-08-27 14:00:00+08:00 102.0000000000 102.0140000000 101.6260000000 101.6260000000 6816.3000000000
726 1787810700000 2026-08-27 14:05:00+08:00 101.6260000000 101.6960000000 101.4700000000 101.4790000000 3595.4000000000
727 1787811000000 2026-08-27 14:10:00+08:00 101.4790000000 101.4860000000 101.0850000000 101.3870000000 19689.7000000000
728 1787811300000 2026-08-27 14:15:00+08:00 101.3870000000 101.3890000000 100.8310000000 100.9610000000 36756.4000000000
729 1787811600000 2026-08-27 14:20:00+08:00 100.9610000000 101.2040000000 100.9250000000 100.9730000000 6911.4000000000
730 1787811900000 2026-08-27 14:25:00+08:00 100.9730000000 101.0530000000 100.8850000000 100.9520000000 6721.0000000000
731 1787812200000 2026-08-27 14:30:00+08:00 100.9520000000 101.1020000000 100.9430000000 101.1010000000 22363.7000000000
732 1787812500000 2026-08-27 14:35:00+08:00 101.1010000000 101.2210000000 101.0130000000 101.0340000000 9732.0000000000
733 1787812800000 2026-08-27 14:40:00+08:00 101.0340000000 101.1280000000 100.9920000000 101.0900000000 5490.0000000000
734 1787813100000 2026-08-27 14:45:00+08:00 101.0900000000 101.3070000000 101.0840000000 101.1860000000 25332.1000000000
735 1787813400000 2026-08-27 14:50:00+08:00 101.1860000000 101.3030000000 101.1860000000 101.1910000000 5483.1000000000
736 1787813700000 2026-08-27 14:55:00+08:00 101.1910000000 101.2950000000 101.1830000000 101.2450000000 596.5000000000
737 1787814000000 2026-08-27 15:00:00+08:00 101.2450000000 101.2900000000 101.1690000000 101.2170000000 5074.6000000000
738 1787814300000 2026-08-27 15:05:00+08:00 101.2170000000 101.2300000000 101.1040000000 101.1340000000 1302.3000000000
739 1787814600000 2026-08-27 15:10:00+08:00 101.1340000000 101.3350000000 101.1210000000 101.2310000000 1821.8000000000
740 1787814900000 2026-08-27 15:15:00+08:00 101.2310000000 101.2520000000 101.0800000000 101.1880000000 5608.2000000000
741 1787815200000 2026-08-27 15:20:00+08:00 101.1880000000 101.3130000000 101.0150000000 101.0180000000 3942.1000000000
742 1787815500000 2026-08-27 15:25:00+08:00 101.0180000000 101.1510000000 101.0000000000 101.1140000000 8977.2000000000
743 1787815800000 2026-08-27 15:30:00+08:00 101.1140000000 101.3620000000 101.0160000000 101.3200000000 14462.0000000000
744 1787816100000 2026-08-27 15:35:00+08:00 101.3200000000 101.4980000000 101.2300000000 101.4660000000 14405.8000000000
745 1787816400000 2026-08-27 15:40:00+08:00 101.4660000000 101.4770000000 101.2660000000 101.3890000000 21681.2000000000
746 1787816700000 2026-08-27 15:45:00+08:00 101.3890000000 101.5230000000 101.3420000000 101.5140000000 2812.0000000000
747 1787817000000 2026-08-27 15:50:00+08:00 101.5140000000 101.8700000000 101.4790000000 101.7990000000 18681.7000000000
748 1787817300000 2026-08-27 15:55:00+08:00 101.7990000000 102.0600000000 101.6470000000 101.7390000000 36492.9000000000
749 1787817600000 2026-08-27 16:00:00+08:00 101.7390000000 102.8150000000 101.6690000000 102.5650000000 52402.0000000000
750 1787817900000 2026-08-27 16:05:00+08:00 102.5650000000 104.1770000000 102.1070000000 104.0170000000 190003.2000000000
751 1787818200000 2026-08-27 16:10:00+08:00 104.0170000000 104.2170000000 102.6960000000 102.9790000000 127519.4000000000
752 1787818500000 2026-08-27 16:15:00+08:00 102.9790000000 103.0000000000 102.6390000000 102.9680000000 24036.2000000000
753 1787818800000 2026-08-27 16:20:00+08:00 102.9680000000 103.7000000000 102.9070000000 103.4560000000 69744.0000000000
754 1787819100000 2026-08-27 16:25:00+08:00 103.4560000000 104.2070000000 103.4050000000 104.1340000000 43746.3000000000
755 1787819400000 2026-08-27 16:30:00+08:00 104.1340000000 104.8260000000 104.1100000000 104.2600000000 109860.0000000000
756 1787819700000 2026-08-27 16:35:00+08:00 104.2600000000 104.2640000000 103.7540000000 103.8570000000 33877.6000000000
757 1787820000000 2026-08-27 16:40:00+08:00 103.8570000000 104.2370000000 103.7540000000 104.2370000000 27589.2000000000
758 1787820300000 2026-08-27 16:45:00+08:00 104.2370000000 105.0420000000 104.2370000000 104.4640000000 165174.1000000000
759 1787820600000 2026-08-27 16:50:00+08:00 104.4640000000 104.6970000000 104.2560000000 104.4700000000 24833.7000000000
760 1787820900000 2026-08-27 16:55:00+08:00 104.4700000000 104.7240000000 104.3790000000 104.6300000000 17355.2000000000
761 1787821200000 2026-08-27 17:00:00+08:00 104.6300000000 104.6870000000 104.0600000000 104.1770000000 39466.9000000000
762 1787821500000 2026-08-27 17:05:00+08:00 104.1770000000 104.6220000000 104.1010000000 104.5650000000 10944.1000000000
763 1787821800000 2026-08-27 17:10:00+08:00 104.5650000000 104.9400000000 104.5010000000 104.9330000000 33295.4000000000
764 1787822100000 2026-08-27 17:15:00+08:00 104.9330000000 105.0000000000 104.6230000000 104.9340000000 35608.4000000000
765 1787822400000 2026-08-27 17:20:00+08:00 104.9340000000 105.6060000000 104.8090000000 105.2740000000 119915.4000000000
766 1787822700000 2026-08-27 17:25:00+08:00 105.2740000000 105.7900000000 105.1150000000 105.3600000000 73049.6000000000
767 1787823000000 2026-08-27 17:30:00+08:00 105.3600000000 105.3990000000 104.7740000000 104.8330000000 23286.0000000000
768 1787823300000 2026-08-27 17:35:00+08:00 104.8330000000 105.0460000000 104.7070000000 104.8260000000 19894.1000000000
769 1787823600000 2026-08-27 17:40:00+08:00 104.8260000000 104.9800000000 104.5710000000 104.8280000000 19901.0000000000
770 1787823900000 2026-08-27 17:45:00+08:00 104.8280000000 104.9890000000 104.6840000000 104.7230000000 8522.4000000000
771 1787824200000 2026-08-27 17:50:00+08:00 104.7230000000 104.7610000000 104.4410000000 104.5570000000 20251.9000000000
772 1787824500000 2026-08-27 17:55:00+08:00 104.5570000000 104.5580000000 104.1820000000 104.3620000000 14522.3000000000
773 1787824800000 2026-08-27 18:00:00+08:00 104.3620000000 104.5150000000 104.2230000000 104.5150000000 13181.9000000000
774 1787825100000 2026-08-27 18:05:00+08:00 104.5150000000 104.9610000000 104.4830000000 104.9610000000 25516.5000000000
775 1787825400000 2026-08-27 18:10:00+08:00 104.9610000000 104.9830000000 104.2570000000 104.4720000000 25331.1000000000
776 1787825700000 2026-08-27 18:15:00+08:00 104.4720000000 104.6560000000 104.2920000000 104.3110000000 16401.3000000000
777 1787826000000 2026-08-27 18:20:00+08:00 104.3110000000 104.4550000000 104.1000000000 104.2040000000 9075.2000000000
778 1787826300000 2026-08-27 18:25:00+08:00 104.2040000000 104.2040000000 103.0380000000 103.9320000000 117891.0000000000
779 1787826600000 2026-08-27 18:30:00+08:00 103.9320000000 104.0270000000 103.6470000000 103.8390000000 20496.6000000000
780 1787826900000 2026-08-27 18:35:00+08:00 103.8390000000 103.8850000000 103.2630000000 103.3490000000 23580.3000000000
781 1787827200000 2026-08-27 18:40:00+08:00 103.3490000000 104.1460000000 103.3490000000 104.0630000000 13359.2000000000
782 1787827500000 2026-08-27 18:45:00+08:00 104.0630000000 104.2000000000 103.8310000000 103.8920000000 8382.5000000000
783 1787827800000 2026-08-27 18:50:00+08:00 103.8920000000 104.0000000000 103.8310000000 103.9860000000 5801.4000000000
784 1787828100000 2026-08-27 18:55:00+08:00 103.9860000000 104.0810000000 103.9360000000 104.0580000000 11496.9000000000
785 1787828400000 2026-08-27 19:00:00+08:00 104.0580000000 104.5860000000 103.9250000000 104.3950000000 37203.8000000000
786 1787828700000 2026-08-27 19:05:00+08:00 104.3950000000 104.6300000000 104.2940000000 104.3660000000 11859.3000000000
787 1787829000000 2026-08-27 19:10:00+08:00 104.3660000000 104.4140000000 104.1630000000 104.3400000000 5628.1000000000
788 1787829300000 2026-08-27 19:15:00+08:00 104.3400000000 104.5970000000 104.3300000000 104.5880000000 9925.7000000000
789 1787829600000 2026-08-27 19:20:00+08:00 104.5880000000 104.8880000000 104.5350000000 104.6730000000 18517.2000000000
790 1787829900000 2026-08-27 19:25:00+08:00 104.6730000000 104.6970000000 103.9370000000 104.0070000000 15351.9000000000
791 1787830200000 2026-08-27 19:30:00+08:00 104.0070000000 104.1600000000 103.7100000000 104.0120000000 13272.3000000000
792 1787830500000 2026-08-27 19:35:00+08:00 104.0120000000 104.0160000000 103.5620000000 103.6670000000 14459.2000000000
793 1787830800000 2026-08-27 19:40:00+08:00 103.6670000000 103.9500000000 103.5650000000 103.8850000000 19409.3000000000
794 1787831100000 2026-08-27 19:45:00+08:00 103.8850000000 103.9130000000 103.6710000000 103.6820000000 7604.9000000000
795 1787831400000 2026-08-27 19:50:00+08:00 103.6820000000 103.9240000000 103.3280000000 103.9220000000 14174.0000000000
796 1787831700000 2026-08-27 19:55:00+08:00 103.9220000000 103.9470000000 103.8370000000 103.8890000000 2831.5000000000
797 1787832000000 2026-08-27 20:00:00+08:00 103.8890000000 104.2500000000 103.8790000000 104.1200000000 7127.8000000000
798 1787832300000 2026-08-27 20:05:00+08:00 104.1200000000 104.1960000000 103.9190000000 104.1960000000 3958.0000000000
799 1787832600000 2026-08-27 20:10:00+08:00 104.1960000000 104.3960000000 104.0800000000 104.1900000000 13990.2000000000
800 1787832900000 2026-08-27 20:15:00+08:00 104.1900000000 104.2970000000 104.0530000000 104.0560000000 15560.8000000000
801 1787833200000 2026-08-27 20:20:00+08:00 104.0560000000 104.2190000000 104.0560000000 104.2180000000 175.4000000000
+67
View File
@@ -0,0 +1,67 @@
sym,kline_ts,raw_ms,hb_ms,delta_ms
BTC,1787840340000,1787840341754,1787840341756,2
ETH,1787840340000,1787840342288,1787840342291,3
SOL,1787840340000,1787840342325,1787840342331,6
ETH,1787840400000,1787840431414,1787840401868,-29546
SOL,1787840400000,1787840430598,1787840402172,-28426
BTC,1787840400000,1787840431414,1787840402868,-28546
SOL,1787840460000,1787840460814,1787840460825,11
ETH,1787840460000,1787840461120,1787840461127,7
BTC,1787840460000,1787840461459,1787840461460,1
SOL,1787840520000,1787840565101,1787840520468,-44633
ETH,1787840520000,1787840564798,1787840521932,-42866
BTC,1787840520000,1787840563680,1787840522276,-41404
BTC,1787840580000,1787840581715,1787840581715,0
ETH,1787840580000,1787840581827,1787840581838,11
SOL,1787840580000,1787840582309,1787840582312,3
SOL,1787840640000,1787840691434,1787840641589,-49845
BTC,1787840640000,1787840691899,1787840641610,-50289
ETH,1787840640000,1787840691434,1787840642306,-49128
BTC,1787840700000,1787840701780,1787840701750,-30
SOL,1787840700000,1787840702226,1787840702195,-31
ETH,1787840700000,1787840702714,1787840702679,-35
BTC,1787840760000,1787840762085,1787840762065,-20
ETH,1787840760000,1787840762265,1787840762237,-28
SOL,1787840760000,1787840762447,1787840762419,-28
SOL,1787840820000,1787840834553,1787840821260,-13293
ETH,1787840820000,1787840834163,1787840821362,-12801
BTC,1787840820000,1787840834004,1787840822443,-11561
SOL,1787840880000,1787840880937,1787840880935,-2
BTC,1787840880000,1787840881754,1787840881753,-1
ETH,1787840880000,1787840882233,1787840882240,7
SOL,1787840940000,1787840962907,1787840942014,-20893
ETH,1787840940000,1787840962907,1787840942176,-20731
BTC,1787840940000,1787840962936,1787840942328,-20608
ETH,1787841000000,1787841001864,1787841001867,3
SOL,1787841000000,1787841002763,1787841002764,1
BTC,1787841000000,1787841002884,1787841002885,1
BTC,1787841060000,1787841107709,1787841061698,-46011
SOL,1787841060000,1787841107709,1787841061959,-45750
ETH,1787841060000,1787841107269,1787841062325,-44944
SOL,1787841120000,1787841120928,1787841120935,7
ETH,1787841120000,1787841121335,1787841121339,4
BTC,1787841120000,1787841121683,1787841121682,-1
BTC,1787841180000,1787841181770,1787841181774,4
SOL,1787841180000,1787841240652,1787841181814,-58838
ETH,1787841180000,1787841182245,1787841182229,-16
ETH,1787841240000,1787841241494,1787841241502,8
BTC,1787841240000,1787841241507,1787841241513,6
SOL,1787841240000,1787841241662,1787841241665,3
ETH,1787841300000,1787841300834,1787841300836,2
SOL,1787841300000,1787841301166,1787841301170,4
BTC,1787841300000,1787841301298,1787841301301,3
ETH,1787841360000,1787841381586,1787841361274,-20312
BTC,1787841360000,1787841381753,1787841361800,-19953
SOL,1787841360000,1787841381419,1787841362314,-19105
SOL,1787841420000,1787841421471,1787841421473,2
BTC,1787841420000,1787841422318,1787841422319,1
ETH,1787841420000,1787841423273,1787841423278,5
BTC,1787841480000,1787841526296,1787841481733,-44563
SOL,1787841480000,1787841526311,1787841481794,-44517
ETH,1787841480000,1787841526095,1787841482320,-43775
ETH,1787841540000,1787841541006,1787841541016,10
BTC,1787841540000,1787841541224,1787841541228,4
SOL,1787841540000,1787841541664,1787841541674,10
ETH,1787841600000,1787841600978,1787841600988,10
BTC,1787841600000,1787841602626,1787841602625,-1
SOL,1787841600000,1787841602730,1787841602737,7
1 sym kline_ts raw_ms hb_ms delta_ms
2 BTC 1787840340000 1787840341754 1787840341756 2
3 ETH 1787840340000 1787840342288 1787840342291 3
4 SOL 1787840340000 1787840342325 1787840342331 6
5 ETH 1787840400000 1787840431414 1787840401868 -29546
6 SOL 1787840400000 1787840430598 1787840402172 -28426
7 BTC 1787840400000 1787840431414 1787840402868 -28546
8 SOL 1787840460000 1787840460814 1787840460825 11
9 ETH 1787840460000 1787840461120 1787840461127 7
10 BTC 1787840460000 1787840461459 1787840461460 1
11 SOL 1787840520000 1787840565101 1787840520468 -44633
12 ETH 1787840520000 1787840564798 1787840521932 -42866
13 BTC 1787840520000 1787840563680 1787840522276 -41404
14 BTC 1787840580000 1787840581715 1787840581715 0
15 ETH 1787840580000 1787840581827 1787840581838 11
16 SOL 1787840580000 1787840582309 1787840582312 3
17 SOL 1787840640000 1787840691434 1787840641589 -49845
18 BTC 1787840640000 1787840691899 1787840641610 -50289
19 ETH 1787840640000 1787840691434 1787840642306 -49128
20 BTC 1787840700000 1787840701780 1787840701750 -30
21 SOL 1787840700000 1787840702226 1787840702195 -31
22 ETH 1787840700000 1787840702714 1787840702679 -35
23 BTC 1787840760000 1787840762085 1787840762065 -20
24 ETH 1787840760000 1787840762265 1787840762237 -28
25 SOL 1787840760000 1787840762447 1787840762419 -28
26 SOL 1787840820000 1787840834553 1787840821260 -13293
27 ETH 1787840820000 1787840834163 1787840821362 -12801
28 BTC 1787840820000 1787840834004 1787840822443 -11561
29 SOL 1787840880000 1787840880937 1787840880935 -2
30 BTC 1787840880000 1787840881754 1787840881753 -1
31 ETH 1787840880000 1787840882233 1787840882240 7
32 SOL 1787840940000 1787840962907 1787840942014 -20893
33 ETH 1787840940000 1787840962907 1787840942176 -20731
34 BTC 1787840940000 1787840962936 1787840942328 -20608
35 ETH 1787841000000 1787841001864 1787841001867 3
36 SOL 1787841000000 1787841002763 1787841002764 1
37 BTC 1787841000000 1787841002884 1787841002885 1
38 BTC 1787841060000 1787841107709 1787841061698 -46011
39 SOL 1787841060000 1787841107709 1787841061959 -45750
40 ETH 1787841060000 1787841107269 1787841062325 -44944
41 SOL 1787841120000 1787841120928 1787841120935 7
42 ETH 1787841120000 1787841121335 1787841121339 4
43 BTC 1787841120000 1787841121683 1787841121682 -1
44 BTC 1787841180000 1787841181770 1787841181774 4
45 SOL 1787841180000 1787841240652 1787841181814 -58838
46 ETH 1787841180000 1787841182245 1787841182229 -16
47 ETH 1787841240000 1787841241494 1787841241502 8
48 BTC 1787841240000 1787841241507 1787841241513 6
49 SOL 1787841240000 1787841241662 1787841241665 3
50 ETH 1787841300000 1787841300834 1787841300836 2
51 SOL 1787841300000 1787841301166 1787841301170 4
52 BTC 1787841300000 1787841301298 1787841301301 3
53 ETH 1787841360000 1787841381586 1787841361274 -20312
54 BTC 1787841360000 1787841381753 1787841361800 -19953
55 SOL 1787841360000 1787841381419 1787841362314 -19105
56 SOL 1787841420000 1787841421471 1787841421473 2
57 BTC 1787841420000 1787841422318 1787841422319 1
58 ETH 1787841420000 1787841423273 1787841423278 5
59 BTC 1787841480000 1787841526296 1787841481733 -44563
60 SOL 1787841480000 1787841526311 1787841481794 -44517
61 ETH 1787841480000 1787841526095 1787841482320 -43775
62 ETH 1787841540000 1787841541006 1787841541016 10
63 BTC 1787841540000 1787841541224 1787841541228 4
64 SOL 1787841540000 1787841541664 1787841541674 10
65 ETH 1787841600000 1787841600978 1787841600988 10
66 BTC 1787841600000 1787841602626 1787841602625 -1
67 SOL 1787841600000 1787841602730 1787841602737 7
+58
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@@ -0,0 +1,58 @@
sym,kline_ts,raw_ms
BTC,1787840340000,1787840384033
SOL,1787840340000,1787840384079
ETH,1787840340000,1787840384123
ETH,1787840400000,1787840401868
SOL,1787840400000,1787840402160
BTC,1787840400000,1787840402871
SOL,1787840460000,1787840460819
ETH,1787840460000,1787840461121
BTC,1787840460000,1787840461470
SOL,1787840520000,1787840533834
ETH,1787840520000,1787840533710
BTC,1787840520000,1787840533834
BTC,1787840580000,1787840581716
ETH,1787840580000,1787840581825
SOL,1787840580000,1787840582312
SOL,1787840640000,1787840661218
BTC,1787840640000,1787840661749
ETH,1787840640000,1787840661702
BTC,1787840700000,1787840701749
SOL,1787840700000,1787840702191
ETH,1787840700000,1787840702676
BTC,1787840760000,1787840787762
ETH,1787840760000,1787840786876
SOL,1787840760000,1787840788002
SOL,1787840820000,1787840821248
ETH,1787840820000,1787840821354
BTC,1787840820000,1787840822285
SOL,1787840880000,1787840934032
BTC,1787840880000,1787840933040
ETH,1787840880000,1787840933292
SOL,1787840940000,1787840942011
ETH,1787840940000,1787840942175
BTC,1787840940000,1787840942324
ETH,1787841000000,1787841001862
SOL,1787841000000,1787841002766
BTC,1787841000000,1787841002883
BTC,1787841060000,1787841082937
SOL,1787841060000,1787841083195
ETH,1787841060000,1787841083586
SOL,1787841120000,1787841120930
ETH,1787841120000,1787841121350
BTC,1787841120000,1787841121681
BTC,1787841180000,1787841206517
SOL,1787841180000,1787841205070
ETH,1787841180000,1787841205494
ETH,1787841240000,1787841241493
BTC,1787841240000,1787841241509
SOL,1787841240000,1787841241664
ETH,1787841300000,1787841331429
SOL,1787841300000,1787841330590
BTC,1787841300000,1787841330681
ETH,1787841360000,1787841361268
BTC,1787841360000,1787841361795
SOL,1787841360000,1787841362414
SOL,1787841420000,1787841421469
BTC,1787841420000,1787841422314
ETH,1787841420000,1787841423274
1 sym kline_ts raw_ms
2 BTC 1787840340000 1787840384033
3 SOL 1787840340000 1787840384079
4 ETH 1787840340000 1787840384123
5 ETH 1787840400000 1787840401868
6 SOL 1787840400000 1787840402160
7 BTC 1787840400000 1787840402871
8 SOL 1787840460000 1787840460819
9 ETH 1787840460000 1787840461121
10 BTC 1787840460000 1787840461470
11 SOL 1787840520000 1787840533834
12 ETH 1787840520000 1787840533710
13 BTC 1787840520000 1787840533834
14 BTC 1787840580000 1787840581716
15 ETH 1787840580000 1787840581825
16 SOL 1787840580000 1787840582312
17 SOL 1787840640000 1787840661218
18 BTC 1787840640000 1787840661749
19 ETH 1787840640000 1787840661702
20 BTC 1787840700000 1787840701749
21 SOL 1787840700000 1787840702191
22 ETH 1787840700000 1787840702676
23 BTC 1787840760000 1787840787762
24 ETH 1787840760000 1787840786876
25 SOL 1787840760000 1787840788002
26 SOL 1787840820000 1787840821248
27 ETH 1787840820000 1787840821354
28 BTC 1787840820000 1787840822285
29 SOL 1787840880000 1787840934032
30 BTC 1787840880000 1787840933040
31 ETH 1787840880000 1787840933292
32 SOL 1787840940000 1787840942011
33 ETH 1787840940000 1787840942175
34 BTC 1787840940000 1787840942324
35 ETH 1787841000000 1787841001862
36 SOL 1787841000000 1787841002766
37 BTC 1787841000000 1787841002883
38 BTC 1787841060000 1787841082937
39 SOL 1787841060000 1787841083195
40 ETH 1787841060000 1787841083586
41 SOL 1787841120000 1787841120930
42 ETH 1787841120000 1787841121350
43 BTC 1787841120000 1787841121681
44 BTC 1787841180000 1787841206517
45 SOL 1787841180000 1787841205070
46 ETH 1787841180000 1787841205494
47 ETH 1787841240000 1787841241493
48 BTC 1787841240000 1787841241509
49 SOL 1787841240000 1787841241664
50 ETH 1787841300000 1787841331429
51 SOL 1787841300000 1787841330590
52 BTC 1787841300000 1787841330681
53 ETH 1787841360000 1787841361268
54 BTC 1787841360000 1787841361795
55 SOL 1787841360000 1787841362414
56 SOL 1787841420000 1787841421469
57 BTC 1787841420000 1787841422314
58 ETH 1787841420000 1787841423274
+37
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@@ -0,0 +1,37 @@
品种,口径,噪声bp,基线笔数,扰动后笔数,同根重合
BTC,原始,0.0,73,73.0,1.0
BTC,5m同向后,0.0,38,38.0,1.0
BTC,原始,0.25,73,84.5,0.5410958904109588
BTC,5m同向后,0.25,38,44.0,0.5131578947368421
BTC,原始,0.5,73,74.5,0.363013698630137
BTC,5m同向后,0.5,38,36.5,0.3026315789473684
BTC,原始,1.0,73,63.0,0.22602739726027396
BTC,5m同向后,1.0,38,28.0,0.15789473684210525
BTC,原始,2.0,73,47.0,0.10273972602739725
BTC,5m同向后,2.0,38,22.0,0.09210526315789473
BTC,原始,4.0,73,25.5,0.03424657534246575
BTC,5m同向后,4.0,38,9.0,0.013157894736842105
ETH,原始,0.0,78,78.0,1.0
ETH,5m同向后,0.0,33,33.0,1.0
ETH,原始,0.25,78,74.5,0.5384615384615384
ETH,5m同向后,0.25,33,37.0,0.5303030303030303
ETH,原始,0.5,78,72.5,0.41666666666666663
ETH,5m同向后,0.5,33,35.5,0.4393939393939394
ETH,原始,1.0,78,66.0,0.28205128205128205
ETH,5m同向后,1.0,33,31.0,0.28787878787878785
ETH,原始,2.0,78,54.5,0.14743589743589744
ETH,5m同向后,2.0,33,24.5,0.10606060606060606
ETH,原始,4.0,78,31.0,0.038461538461538464
ETH,5m同向后,4.0,33,12.5,0.015151515151515152
SOL,原始,0.0,46,46.0,1.0
SOL,5m同向后,0.0,28,28.0,1.0
SOL,原始,0.25,46,47.0,0.9673913043478262
SOL,5m同向后,0.25,28,29.0,1.0
SOL,原始,0.5,46,47.0,0.5108695652173914
SOL,5m同向后,0.5,28,28.0,0.5178571428571428
SOL,原始,1.0,46,43.5,0.2826086956521739
SOL,5m同向后,1.0,28,24.0,0.35714285714285715
SOL,原始,2.0,46,38.5,0.16304347826086957
SOL,5m同向后,2.0,28,20.0,0.2142857142857143
SOL,原始,4.0,46,26.5,0.06521739130434782
SOL,5m同向后,4.0,28,13.5,0.08928571428571427
1 品种 口径 噪声bp 基线笔数 扰动后笔数 同根重合
2 BTC 原始 0.0 73 73.0 1.0
3 BTC 5m同向后 0.0 38 38.0 1.0
4 BTC 原始 0.25 73 84.5 0.5410958904109588
5 BTC 5m同向后 0.25 38 44.0 0.5131578947368421
6 BTC 原始 0.5 73 74.5 0.363013698630137
7 BTC 5m同向后 0.5 38 36.5 0.3026315789473684
8 BTC 原始 1.0 73 63.0 0.22602739726027396
9 BTC 5m同向后 1.0 38 28.0 0.15789473684210525
10 BTC 原始 2.0 73 47.0 0.10273972602739725
11 BTC 5m同向后 2.0 38 22.0 0.09210526315789473
12 BTC 原始 4.0 73 25.5 0.03424657534246575
13 BTC 5m同向后 4.0 38 9.0 0.013157894736842105
14 ETH 原始 0.0 78 78.0 1.0
15 ETH 5m同向后 0.0 33 33.0 1.0
16 ETH 原始 0.25 78 74.5 0.5384615384615384
17 ETH 5m同向后 0.25 33 37.0 0.5303030303030303
18 ETH 原始 0.5 78 72.5 0.41666666666666663
19 ETH 5m同向后 0.5 33 35.5 0.4393939393939394
20 ETH 原始 1.0 78 66.0 0.28205128205128205
21 ETH 5m同向后 1.0 33 31.0 0.28787878787878785
22 ETH 原始 2.0 78 54.5 0.14743589743589744
23 ETH 5m同向后 2.0 33 24.5 0.10606060606060606
24 ETH 原始 4.0 78 31.0 0.038461538461538464
25 ETH 5m同向后 4.0 33 12.5 0.015151515151515152
26 SOL 原始 0.0 46 46.0 1.0
27 SOL 5m同向后 0.0 28 28.0 1.0
28 SOL 原始 0.25 46 47.0 0.9673913043478262
29 SOL 5m同向后 0.25 28 29.0 1.0
30 SOL 原始 0.5 46 47.0 0.5108695652173914
31 SOL 5m同向后 0.5 28 28.0 0.5178571428571428
32 SOL 原始 1.0 46 43.5 0.2826086956521739
33 SOL 5m同向后 1.0 28 24.0 0.35714285714285715
34 SOL 原始 2.0 46 38.5 0.16304347826086957
35 SOL 5m同向后 2.0 28 20.0 0.2142857142857143
36 SOL 原始 4.0 46 26.5 0.06521739130434782
37 SOL 5m同向后 4.0 28 13.5 0.08928571428571427
+4
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@@ -0,0 +1,4 @@
品种,重叠天数,Bitget根数,Binance根数,应有根数,Bitget缺口,Binance缺口,共有根数,close中位差,close绝对差P95,high绝对差P95,low绝对差P95
BTC,28.8,41522,41522,41522,0,0,41522,-0.34bp,2.44bp,2.56bp,2.54bp
ETH,28.8,41524,41524,41524,0,0,41524,-0.05bp,2.03bp,2.18bp,2.19bp
SOL,28.8,41525,41525,41525,0,0,41525,-0.27bp,2.98bp,3.04bp,2.89bp
1 品种 重叠天数 Bitget根数 Binance根数 应有根数 Bitget缺口 Binance缺口 共有根数 close中位差 close绝对差P95 high绝对差P95 low绝对差P95
2 BTC 28.8 41522 41522 41522 0 0 41522 -0.34bp 2.44bp 2.56bp 2.54bp
3 ETH 28.8 41524 41524 41524 0 0 41524 -0.05bp 2.03bp 2.18bp 2.19bp
4 SOL 28.8 41525 41525 41525 0 0 41525 -0.27bp 2.98bp 3.04bp 2.89bp
+7
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@@ -0,0 +1,7 @@
品种,口径,Bitget信号,Binance信号,BG→BN同根,BG→BN±1根,BN→BG同根,BN→BG±1根
BTC,原始,92,76,41.3%,42.4%,50.0%,51.3%
BTC,5m同向后,47,38,42.6%,44.7%,52.6%,55.3%
ETH,原始,87,80,32.2%,36.8%,35.0%,40.0%
ETH,5m同向后,39,35,28.2%,33.3%,31.4%,37.1%
SOL,原始,84,47,15.5%,17.9%,27.7%,31.9%
SOL,5m同向后,48,29,14.6%,16.7%,24.1%,27.6%
1 品种 口径 Bitget信号 Binance信号 BG→BN同根 BG→BN±1根 BN→BG同根 BN→BG±1根
2 BTC 原始 92 76 41.3% 42.4% 50.0% 51.3%
3 BTC 5m同向后 47 38 42.6% 44.7% 52.6% 55.3%
4 ETH 原始 87 80 32.2% 36.8% 35.0% 40.0%
5 ETH 5m同向后 39 35 28.2% 33.3% 31.4% 37.1%
6 SOL 原始 84 47 15.5% 17.9% 27.7% 31.9%
7 SOL 5m同向后 48 29 14.6% 16.7% 24.1% 27.6%
+61
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@@ -0,0 +1,61 @@
sym,kline_ts,raw,patched,stock,gain_ms,patched_minus_raw_ms
BTC,1787842080000,1787842080301,1787842080159,1787842081169,1010,-142
ETH,1787842080000,1787842080301,1787842080311,1787842081371,1060,10
SOL,1787842080000,1787842080375,1787842080382,1787842081382,1000,7
BTC,1787842140000,1787842140130,1787842140139,1787842141199,1060,9
ETH,1787842140000,1787842140289,1787842140291,1787842141703,1412,2
SOL,1787842140000,1787842141133,1787842141137,1787842141137,0,4
SOL,1787842200000,1787842200923,1787842201179,1787842202321,1142,256
BTC,1787842200000,1787842202047,1787842202078,1787842203110,1032,31
ETH,1787842200000,1787842202154,1787842202170,1787842202170,0,16
BTC,1787842260000,1787842260404,1787842260418,1787842261601,1183,14
ETH,1787842260000,1787842260434,1787842260439,1787842261663,1224,5
SOL,1787842260000,1787842260714,1787842260721,1787842261725,1004,7
ETH,1787842320000,1787842320240,1787842320244,1787842321374,1130,4
SOL,1787842320000,1787842320300,1787842320305,1787842321395,1090,5
BTC,1787842320000,1787842320500,1787842320517,1787842321809,1292,17
ETH,1787842380000,1787842380241,1787842380243,1787842380243,0,2
BTC,1787842380000,1787842380560,1787842380577,1787842381627,1050,17
SOL,1787842380000,1787842381189,1787842381192,1787842381192,0,3
SOL,1787842440000,1787842440068,1787842440070,1787842441201,1131,2
ETH,1787842440000,1787842440222,1787842440232,1787842441272,1040,10
BTC,1787842440000,1787842440315,1787842440324,1787842441344,1020,9
ETH,1787842500000,1787842500226,1787842500241,1787842501593,1352,15
SOL,1787842500000,1787842500681,1787842500695,1787842501970,1275,14
BTC,1787842500000,1787842500909,1787842500948,1787842500917,-31,39
ETH,1787842560000,1787842560439,1787842560445,1787842561488,1043,6
SOL,1787842560000,1787842560946,1787842560949,1787842561963,1014,3
BTC,1787842560000,1787842561234,1787842561232,1787842561244,12,-2
BTC,1787842620000,1787842620114,1787842620130,1787842621432,1302,16
ETH,1787842620000,1787842620471,1787842620473,1787842621494,1021,2
SOL,1787842620000,1787842620838,1787842620846,1787842622028,1182,8
SOL,1787842680000,1787842680288,1787842680290,1787842681420,1130,2
ETH,1787842680000,1787842681393,1787842681399,1787842681399,0,6
BTC,1787842680000,1787842681461,1787842681471,1787842682633,1162,10
BTC,1787842740000,1787842740269,1787842740267,1787842741470,1203,-2
SOL,1787842740000,1787842740344,1787842740338,1787842741448,1110,-6
ETH,1787842740000,1787842740530,1787842740531,1787842741783,1252,1
SOL,1787842800000,1787842800565,1787842800567,1787842800567,0,2
BTC,1787842800000,1787842802054,1787842802256,1787842804097,1841,202
ETH,1787842800000,1787842802138,1787842802144,1787842802165,21,6
SOL,1787842860000,1787842860317,1787842860322,1787842861497,1175,5
BTC,1787842860000,1787842860585,1787842860598,1787842860587,-11,13
ETH,1787842860000,1787842861083,1787842861092,1787842862477,1385,9
BTC,1787842920000,1787842920148,1787842920156,1787842921437,1281,8
SOL,1787842920000,1787842920425,1787842920429,1787842922367,1938,4
ETH,1787842920000,1787842920717,1787842920732,1787842920721,-11,15
ETH,1787842980000,1787842980474,1787842980480,1787842982269,1789,6
SOL,1787842980000,1787842980717,1787842980723,1787842980712,-11,6
BTC,1787842980000,1787842980754,1787842980774,1787842980764,-10,20
BTC,1787843040000,1787843040222,1787843040252,1787843041283,1031,30
SOL,1787843040000,1787843040818,1787843040827,1787843040817,-10,9
ETH,1787843040000,1787843040902,1787843040888,1787843041910,1022,-14
BTC,1787843100000,1787843100596,1787843100604,1787843102150,1546,8
ETH,1787843100000,1787843100950,1787843100766,1787843102039,1273,-184
SOL,1787843100000,1787843101319,1787843101321,1787843101332,11,2
SOL,1787843160000,1787843160576,1787843160588,1787843161749,1161,12
BTC,1787843160000,1787843160787,1787843160801,1787843161800,999,14
ETH,1787843160000,1787843160889,1787843160892,1787843162154,1262,3
SOL,1787843220000,1787843220113,1787843220125,1787843221113,988,12
BTC,1787843220000,1787843220377,1787843220377,1787843221802,1425,0
ETH,1787843220000,1787843221220,1787843221246,1787843222268,1022,26
1 sym kline_ts raw patched stock gain_ms patched_minus_raw_ms
2 BTC 1787842080000 1787842080301 1787842080159 1787842081169 1010 -142
3 ETH 1787842080000 1787842080301 1787842080311 1787842081371 1060 10
4 SOL 1787842080000 1787842080375 1787842080382 1787842081382 1000 7
5 BTC 1787842140000 1787842140130 1787842140139 1787842141199 1060 9
6 ETH 1787842140000 1787842140289 1787842140291 1787842141703 1412 2
7 SOL 1787842140000 1787842141133 1787842141137 1787842141137 0 4
8 SOL 1787842200000 1787842200923 1787842201179 1787842202321 1142 256
9 BTC 1787842200000 1787842202047 1787842202078 1787842203110 1032 31
10 ETH 1787842200000 1787842202154 1787842202170 1787842202170 0 16
11 BTC 1787842260000 1787842260404 1787842260418 1787842261601 1183 14
12 ETH 1787842260000 1787842260434 1787842260439 1787842261663 1224 5
13 SOL 1787842260000 1787842260714 1787842260721 1787842261725 1004 7
14 ETH 1787842320000 1787842320240 1787842320244 1787842321374 1130 4
15 SOL 1787842320000 1787842320300 1787842320305 1787842321395 1090 5
16 BTC 1787842320000 1787842320500 1787842320517 1787842321809 1292 17
17 ETH 1787842380000 1787842380241 1787842380243 1787842380243 0 2
18 BTC 1787842380000 1787842380560 1787842380577 1787842381627 1050 17
19 SOL 1787842380000 1787842381189 1787842381192 1787842381192 0 3
20 SOL 1787842440000 1787842440068 1787842440070 1787842441201 1131 2
21 ETH 1787842440000 1787842440222 1787842440232 1787842441272 1040 10
22 BTC 1787842440000 1787842440315 1787842440324 1787842441344 1020 9
23 ETH 1787842500000 1787842500226 1787842500241 1787842501593 1352 15
24 SOL 1787842500000 1787842500681 1787842500695 1787842501970 1275 14
25 BTC 1787842500000 1787842500909 1787842500948 1787842500917 -31 39
26 ETH 1787842560000 1787842560439 1787842560445 1787842561488 1043 6
27 SOL 1787842560000 1787842560946 1787842560949 1787842561963 1014 3
28 BTC 1787842560000 1787842561234 1787842561232 1787842561244 12 -2
29 BTC 1787842620000 1787842620114 1787842620130 1787842621432 1302 16
30 ETH 1787842620000 1787842620471 1787842620473 1787842621494 1021 2
31 SOL 1787842620000 1787842620838 1787842620846 1787842622028 1182 8
32 SOL 1787842680000 1787842680288 1787842680290 1787842681420 1130 2
33 ETH 1787842680000 1787842681393 1787842681399 1787842681399 0 6
34 BTC 1787842680000 1787842681461 1787842681471 1787842682633 1162 10
35 BTC 1787842740000 1787842740269 1787842740267 1787842741470 1203 -2
36 SOL 1787842740000 1787842740344 1787842740338 1787842741448 1110 -6
37 ETH 1787842740000 1787842740530 1787842740531 1787842741783 1252 1
38 SOL 1787842800000 1787842800565 1787842800567 1787842800567 0 2
39 BTC 1787842800000 1787842802054 1787842802256 1787842804097 1841 202
40 ETH 1787842800000 1787842802138 1787842802144 1787842802165 21 6
41 SOL 1787842860000 1787842860317 1787842860322 1787842861497 1175 5
42 BTC 1787842860000 1787842860585 1787842860598 1787842860587 -11 13
43 ETH 1787842860000 1787842861083 1787842861092 1787842862477 1385 9
44 BTC 1787842920000 1787842920148 1787842920156 1787842921437 1281 8
45 SOL 1787842920000 1787842920425 1787842920429 1787842922367 1938 4
46 ETH 1787842920000 1787842920717 1787842920732 1787842920721 -11 15
47 ETH 1787842980000 1787842980474 1787842980480 1787842982269 1789 6
48 SOL 1787842980000 1787842980717 1787842980723 1787842980712 -11 6
49 BTC 1787842980000 1787842980754 1787842980774 1787842980764 -10 20
50 BTC 1787843040000 1787843040222 1787843040252 1787843041283 1031 30
51 SOL 1787843040000 1787843040818 1787843040827 1787843040817 -10 9
52 ETH 1787843040000 1787843040902 1787843040888 1787843041910 1022 -14
53 BTC 1787843100000 1787843100596 1787843100604 1787843102150 1546 8
54 ETH 1787843100000 1787843100950 1787843100766 1787843102039 1273 -184
55 SOL 1787843100000 1787843101319 1787843101321 1787843101332 11 2
56 SOL 1787843160000 1787843160576 1787843160588 1787843161749 1161 12
57 BTC 1787843160000 1787843160787 1787843160801 1787843161800 999 14
58 ETH 1787843160000 1787843160889 1787843160892 1787843162154 1262 3
59 SOL 1787843220000 1787843220113 1787843220125 1787843221113 988 12
60 BTC 1787843220000 1787843220377 1787843220377 1787843221802 1425 0
61 ETH 1787843220000 1787843221220 1787843221246 1787843222268 1022 26