"""把 `inner_ms` 拆成和本地一致的分档,用来定位两边测不一致的那部分。 起因:本地量到的构成是 TF_DF 占 ~80%、信号链 ~20%,服务器报的是 chan 构建 22ms、信号链 86ms。按机器差(×1.36)也解释不了四倍差距,说明两边测的不是 同一件事,或者有个环节只在服务器上贵。 用同一批窗口跑,比较分档而不是总数。两边都跑一遍再对表: python research/live/probe_inner.py --syms BTC,ETH,SOL --repeat 5 分档口径(与 shadow_signal.compute 的调用顺序一致): rebuild payload → DataFrame ind_ltf/htf 两条腿各自的 add_indicators(TF_DF 内部会做,这里单独计时) chan_ltf/htf TF_DF 构建(lean) zones build_htf_zones ladder add_zone_ladder bsp find_fast_bsp3 timeline htf_fx_timeline(5m 分型时间线) attach attach_htf_agree + attach_zone_ladder 注意 `ind_*` 与 `chan_*` 在真实路径里是合一的(TF_DF 内部调 add_indicators), 这里拆开只为定位。总和会略大于实际 inner_ms。 """ 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() sys.path.insert(0, str(HERE.parents[1])) sys.path.insert(0, str(HERE.parents[2])) sys.path.insert(0, str(HERE.parent)) LTF_BARS, HTF_BARS = 2001, 801 def med(fn, n: int): ts = [] out = None for _ in range(n): t = time.perf_counter() out = fn() ts.append((time.perf_counter() - t) * 1000) return float(np.median(ts)), out def probe(sym: str, repeat: int) -> dict: from chanlun import TF_DF from chanlun.analysis.fast_bsp import ( add_zone_ladder, attach_htf_agree, attach_zone_ladder, build_htf_zones, find_fast_bsp3, htf_fx_timeline, ) from lib.data import fetch_ohlcv dl = fetch_ohlcv(f"{sym}/USDT:USDT", "1m", LTF_BARS * 3).tail(LTF_BARS).reset_index(drop=True) dh = fetch_ohlcv(f"{sym}/USDT:USDT", "5m", HTF_BARS * 3).tail(HTF_BARS).reset_index(drop=True) probe_tf = TF_DF(lean=True) r = {"sym": sym} r["ind_ltf"], _ = med(lambda: probe_tf.add_indicators(dl.copy()), repeat) r["ind_htf"], _ = med(lambda: probe_tf.add_indicators(dh.copy()), repeat) r["chan_ltf"], cl = med(lambda: TF_DF(dl, 1, "1m", lean=True), repeat) r["chan_htf"], ch = med(lambda: TF_DF(dh, 1, "5m", lean=True), repeat) cdf = cl.dataframe r["zones"], z = med(lambda: build_htf_zones(cdf, "1m", chan=cl), repeat) if z is None or z.empty: r["n_zones"] = 0 return r z0 = z.reset_index(drop=True) r["ladder"], zl = med(lambda: add_zone_ladder(z0), repeat) r["bsp"], sg = med(lambda: find_fast_bsp3(cdf, zl), repeat) r["timeline"], tl = med(lambda: htf_fx_timeline(ch, ch.dataframe), repeat) r["attach"], _ = med( lambda: attach_zone_ladder(attach_htf_agree(sg, cdf, tl), zl), repeat) r["n_zones"], r["n_sig"] = len(z0), len(sg) # 增量口径:init 一次后追加,看稳态单根成本 c = TF_DF(lean=True) c.init_stream(dl.iloc[:-60].reset_index(drop=True), 1, "1m") ts = [] for k in range(len(dl) - 60, len(dl)): t = time.perf_counter() c.append_bar(dl.iloc[k]) ts.append((time.perf_counter() - t) * 1000) r["append_bar"] = float(np.median(ts[20:])) return r def main() -> None: ap = argparse.ArgumentParser() ap.add_argument("--syms", default="BTC,ETH,SOL") ap.add_argument("--repeat", type=int, default=5) args = ap.parse_args() rows = [probe(s.strip(), args.repeat) for s in args.syms.split(",") if s.strip()] d = pd.DataFrame(rows).set_index("sym") parts = [c for c in ("ind_ltf", "ind_htf", "chan_ltf", "chan_htf", "zones", "ladder", "bsp", "timeline", "attach") if c in d] d["合计"] = d[parts].sum(axis=1) pd.set_option("display.width", 220) print("\n分档耗时(ms,中位)") print(d[parts + ["合计", "append_bar"]].round(2).to_string()) print("\n占比(%)") print((d[parts].div(d["合计"], axis=0) * 100).round(1).to_string()) print("\n规模") print(d[[c for c in ("n_zones", "n_sig") if c in d]].to_string()) print("\n注:ind_* 与 chan_* 在真实路径里合一(TF_DF 内部调 add_indicators)," "拆开只为定位,合计会略大于实际 inner_ms。") if __name__ == "__main__": main()