diff --git a/.gitignore b/.gitignore index d24d9e8..0dc5c6c 100644 --- a/.gitignore +++ b/.gitignore @@ -24,6 +24,13 @@ __pycache__/ # Local data data/ +# Exchange history fetched by research/live/*.py. 40MB and re-fetchable from +# the venue, so it stays local; the small result CSVs it feeds are committed. +research/live/cache/ + +# Scratch outputs from short shakedown runs, superseded by the real collection. +research/out/archive/ + # Virtualenvs. venv writes its own .gitignore since 3.11, but only for the # directory it creates — declare it here so other layouts are covered too. .venv/ diff --git a/research/live/aggregate_robustness.py b/research/live/aggregate_robustness.py new file mode 100644 index 0000000..e57fed7 --- /dev/null +++ b/research/live/aggregate_robustness.py @@ -0,0 +1,186 @@ +"""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 = 1.5/3.0/48，ATR 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() diff --git a/research/live/bench_compute.py b/research/live/bench_compute.py new file mode 100644 index 0000000..a20f417 --- /dev/null +++ b/research/live/bench_compute.py @@ -0,0 +1,204 @@ +"""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} 次\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}s（5m 每 5 根才重算一次）") + print(f" 对比 Mac 的 0.20s：本机慢 {m_ltf / 0.20:.1f} 倍") + + 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" 轮 {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 bp（3.9bp 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"], + "值": [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() diff --git a/research/live/bitget_baseline.py b/research/live/bitget_baseline.py new file mode 100644 index 0000000..7a0b081 --- /dev/null +++ b/research/live/bitget_baseline.py @@ -0,0 +1,120 @@ +"""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 SOL：Bitget 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() diff --git a/research/live/latency_attribute.py b/research/live/latency_attribute.py new file mode 100644 index 0000000..8f6d25d --- /dev/null +++ b/research/live/latency_attribute.py @@ -0,0 +1,116 @@ +"""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 update，Hummingbot 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_close（ms，中位）##########") + print(f"{'币':<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() diff --git a/research/live/latency_ccxt.py b/research/live/latency_ccxt.py new file mode 100644 index 0000000..fd3d5a1 --- /dev/null +++ b/research/live/latency_ccxt.py @@ -0,0 +1,120 @@ +"""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_close（ms）##########") + 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() diff --git a/research/live/latency_compare.py b/research/live/latency_compare.py new file mode 100644 index 0000000..a0e1fe1 --- /dev/null +++ b/research/live/latency_compare.py @@ -0,0 +1,122 @@ +"""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"{'币':<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={'有' if a is not None else '无'} " + f"hummingbot={'有' if b is not None else '无'}") + 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"{'币':<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() diff --git a/research/live/latency_hummingbot.py b/research/live/latency_hummingbot.py new file mode 100644 index 0000000..b01360e --- /dev/null +++ b/research/live/latency_hummingbot.py @@ -0,0 +1,117 @@ +"""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_close（ms）##########") + 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() diff --git a/research/live/parity_env.py b/research/live/parity_env.py new file mode 100644 index 0000000..8628778 --- /dev/null +++ b/research/live/parity_env.py @@ -0,0 +1,191 @@ +"""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" 仅 {a} 有: {sorted(sa - sb)[:10]}") + print(f" 仅 {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() diff --git a/research/live/patched_candles.py b/research/live/patched_candles.py new file mode 100644 index 0000000..61fe510 --- /dev/null +++ b/research/live/patched_candles.py @@ -0,0 +1,65 @@ +"""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) diff --git a/research/live/probe_hb_vs_raw.py b/research/live/probe_hb_vs_raw.py new file mode 100644 index 0000000..c9cb9cf --- /dev/null +++ b/research/live/probe_hb_vs_raw.py @@ -0,0 +1,186 @@ +"""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 feed，10ms 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())} 根") + 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() diff --git a/research/live/probe_ws_action.py b/research/live/probe_ws_action.py new file mode 100644 index 0000000..4bd3da7 --- /dev/null +++ b/research/live/probe_ws_action.py @@ -0,0 +1,156 @@ +"""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}ms（n={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() diff --git a/research/live/probe_ws_payload.py b/research/live/probe_ws_payload.py new file mode 100644 index 0000000..5adb22a --- /dev/null +++ b/research/live/probe_ws_payload.py @@ -0,0 +1,110 @@ +"""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]} 条") + 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]} 条") + + # 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() diff --git a/research/live/shadow_hb.py b/research/live/shadow_hb.py new file mode 100644 index 0000000..7cf1b91 --- /dev/null +++ b/research/live/shadow_hb.py @@ -0,0 +1,396 @@ +"""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]} 根", 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} 个 " + 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() diff --git a/research/live/shadow_report.py b/research/live/shadow_report.py new file mode 100644 index 0000000..f89466c --- /dev/null +++ b/research/live/shadow_report.py @@ -0,0 +1,157 @@ +"""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)} 根\n") + print(f"{'币':<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"{'币':<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() diff --git a/research/live/shadow_signal.py b/research/live/shadow_signal.py new file mode 100644 index 0000000..c65ed79 --- /dev/null +++ b/research/live/shadow_signal.py @@ -0,0 +1,109 @@ +"""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) diff --git a/research/live/signal_sensitivity.py b/research/live/signal_sensitivity.py new file mode 100644 index 0000000..d0a3f21 --- /dev/null +++ b/research/live/signal_sensitivity.py @@ -0,0 +1,150 @@ +"""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)} 笔", + 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() diff --git a/research/live/venue_parity.py b/research/live/venue_parity.py new file mode 100644 index 0000000..d9741fa --- /dev/null +++ b/research/live/venue_parity.py @@ -0,0 +1,306 @@ +"""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} 根", 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}根": 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}根": 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} 根\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}bp，P95 {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() diff --git a/research/live/verify_patch.py b/research/live/verify_patch.py new file mode 100644 index 0000000..7addce4 --- /dev/null +++ b/research/live/verify_patch.py @@ -0,0 +1,179 @@ +"""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)} 根" + + (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"{'币':<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() diff --git a/research/out/aggregate_robustness.csv b/research/out/aggregate_robustness.csv new file mode 100644 index 0000000..306d78f --- /dev/null +++ b/research/out/aggregate_robustness.csv @@ -0,0 +1,22 @@ +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 diff --git a/research/out/aggregate_robustness_BTC.csv b/research/out/aggregate_robustness_BTC.csv new file mode 100644 index 0000000..c576e72 --- /dev/null +++ b/research/out/aggregate_robustness_BTC.csv @@ -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 diff --git a/research/out/aggregate_robustness_ETH.csv b/research/out/aggregate_robustness_ETH.csv new file mode 100644 index 0000000..2388339 --- /dev/null +++ b/research/out/aggregate_robustness_ETH.csv @@ -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 diff --git a/research/out/aggregate_robustness_SOL.csv b/research/out/aggregate_robustness_SOL.csv new file mode 100644 index 0000000..8cd5755 --- /dev/null +++ b/research/out/aggregate_robustness_SOL.csv @@ -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 diff --git a/research/out/bench_compute.csv b/research/out/bench_compute.csv new file mode 100644 index 0000000..01ba264 --- /dev/null +++ b/research/out/bench_compute.csv @@ -0,0 +1,11 @@ +指标,值 +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 diff --git a/research/out/bitget_baseline.csv b/research/out/bitget_baseline.csv new file mode 100644 index 0000000..0e18891 --- /dev/null +++ b/research/out/bitget_baseline.csv @@ -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 diff --git a/research/out/bitget_baseline_BTC.csv b/research/out/bitget_baseline_BTC.csv new file mode 100644 index 0000000..46bdc40 --- /dev/null +++ b/research/out/bitget_baseline_BTC.csv @@ -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 diff --git a/research/out/bitget_baseline_ETH.csv b/research/out/bitget_baseline_ETH.csv new file mode 100644 index 0000000..5810a57 --- /dev/null +++ b/research/out/bitget_baseline_ETH.csv @@ -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 diff --git a/research/out/bitget_baseline_SOL.csv b/research/out/bitget_baseline_SOL.csv new file mode 100644 index 0000000..c706d92 --- /dev/null +++ b/research/out/bitget_baseline_SOL.csv @@ -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 diff --git a/research/out/latency_ccxt.csv b/research/out/latency_ccxt.csv new file mode 100644 index 0000000..37de3af --- /dev/null +++ b/research/out/latency_ccxt.csv @@ -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 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+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 diff --git a/research/out/latency_hummingbot.csv b/research/out/latency_hummingbot.csv new file mode 100644 index 0000000..073df5f --- /dev/null +++ b/research/out/latency_hummingbot.csv @@ -0,0 +1,91 @@ +kline_ts,sym,t_data_ms,lag_ms +1787836320000,SOL,1787836320241,241 +1787836320000,ETH,1787836320302,302 +1787836320000,BTC,1787836321824,1824 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