research: 1m 出场口径重定、费率修正，与影子测量的判据常数

起因是用户看图指出「止盈没做好」，查下来 TP=3.0 确实把右尾截早了，
而且 1m 不该沿用 5m/15m/30m 的参数——成本固定在 bp、目标随 ATR 缩放，
1m 的 3 ATR 只有 0.39% 而 30m 是 1.87%，成本占比差 5 倍。

step41（5m/15m/30m）与 step42（1m）跑同一张全网格：
SL × TP × MAX_BARS × 分批（3 ATR 减半 → 剩余目标 × 剩余半仓止损位）。

- 1m 最优 SL2 / 3ATR 减半 / 剩余止损保持 2.0 / 目标 8ATR / 48 根，
  样本外 8/8 币、7/7 年全面提升，均R/R夏普/回撤/剔10%PF 四项全赢
- 分批要做，但**减仓后不要动止损**。止损位 0/0.5/1/1.5/2 ATR 严格单调，
  越紧越差，三组初始 SL 全一致。保本损是全表最差的一档
- SL=1.0 在 1m 上是废的：剔10%PF 0.78~0.99、中位收益 −0.122%

费率此前写的 taker 3bp / maker 1bp 隐含「原始 taker 6bp」的错误前提，
实际是原始 taker 0.040% / maker 0.016%、返 50% 后 2.0 / 0.8bp。方向是保守的，
所以首轮跑出来的数字全部偏低。exit_model 已改，费率只在分析阶段套用，
不必重跑模拟。改完 1m 的均R +8%，5m/15m/30m 只动 2%——费率只对 1m 有杠杆。

顺带查证了用户的一个假设：余量逐年递减是不是跟波动率有关。成立，而且
r = +0.989。毛/ATR 七年在 2.26~2.67 之间没有趋势，衰减的是 ATR 本身
（2021 的 22.1bp 压到 2026 的 8.8bp）。**是波动率压缩，不是 alpha 衰减。**

由此引出 ATR 门控：低 ATR 桶的毛 R 其实最高（1.16 vs 高 ATR 桶的 0.94），
断崖只在扣费之后出现。所以阈值是**费率的函数**（约 5 + 1.1×taker费），
不是市场常数。当前费率下 ≥8bp，在 5m/15m/30m 上几乎不触发，可作全局规则。

lib/shadow_budget.py 放影子测量要对照的常数：逐币预算、门控阈值、
腿→maker/taker 映射、lag 阈值、判据。记录与报表归 research/live/，
分工的理由是这些数会变——今天预算就动了四次。

out/*.feather 转为 ignore：70MB+ 且重跑可得，摘要都在 HANDOFF。

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

.gitignore

@@ -31,6 +31,10 @@ research/live/cache/
 # Scratch outputs from short shakedown runs, superseded by the real collection.
 research/out/archive/
 
+# Per-trade simulation dumps from research/step*.py. 70MB+ and regenerable by
+# rerunning the step; the summaries they feed live in HANDOFF.md.
+research/out/*.feather
+
 # 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/

chanlun/analysis/fast_bsp.py

@@ -0,0 +1,361 @@
+"""快速三类买卖点（引擎内称第四类，B4/S4）。
+
+原本长在 `research/lib/fast_bsp3.py`，现移入引擎作为唯一实现，研究脚本改为转发导入，
+这样回测口径与 web 图表永远一致。
+
+命名说明：缠论原文里没有「第四类买卖点」，但 B4/S4 也不只是「B3/S3 提前几根」——
+两者的选样口径不同，统计性质符号相反，故单独立类。形态条件是实时可判的：
+
+    中枢已成 -> 收盘突破 zg -> 收盘未跌回中枢 -> 重新上行
+
+最后一步发生的当根就能下单，滞后约 2 根，而引擎 B3/S3 要等 pullback_bi.sure_time，
+滞后 9~10 根。但差别不止滞后：
+
+  判据   引擎用笔端点事后判（回拉笔低点 >= zg），本函数用收盘价实时判
+  方向   引擎由离开笔方向决定，本函数由收盘从哪一侧突破决定
+  口径   627 个中枢里引擎发 625 个信号（几乎不筛），本函数只认 212 个（34%）；
+         被拒的多数是「中枢确认时价格早已离开、此后再没回来」的历史区间
+
+step30 同条件对拍（同一套 pure 笔中枢、同一组过滤器、同样的 1.5/3.0/48 出场）：
+
+                 原始 PF   +大级别同向   +同向+阶梯   胜率    t值
+    引擎 B3/S3     0.66      0.66         0.71      27.4%  -18.76
+    本函数 B4/S4   1.59      1.85         2.26      47.1%  +10.09
+
+同一组过滤器对 B4 有效、对 B3 无效，滞后差解释不了这一点（入场后移 1~4 根只是
+PF 3.18->2.53 的平滑衰减）。且 SL1.5/TP3.0 下随机入场胜率约 33%，引擎那 27.4%
+低于随机——它选中的是一批系统性反向的样本，不是「晚了所以差」。
+
+require_touch 控制是否强求回抽碰到中枢边界。step32/33 的实测表明强求反而更差：
+这等于排除掉「突破后一去不回头」的强势段，而那正是缠论里最强的趋势形态。
+故默认 False（笔数 +21%、滞后 -1 根、PF 2.26→2.37）。
+
+判据本身（收盘越过前一根极值）没有独立预测力：裸用 15 万笔样本 PF 0.95，
+把中枢换成「近20根高点」这类伪阻力位后 PF 0.90。alpha 全部来自中枢结构，
+判据只负责在这个已知价位上确认动能恢复。它也不能用于识别笔端点——
+入场前的回抽极值命中笔端点±2根的比例 19.3%，低于随机基准 22%。
+
+全部判定只使用当根及之前的数据，无未来函数。
+"""
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+
+from chanlun.core.ChanEnum import Chan_FX_TYPE
+
+
+def timestamps_ms(src: pd.DataFrame) -> np.ndarray:
+    """取毫秒时间戳。研究侧的 df 自带 timestamp，web 侧的不一定，故按 date 回退。
+
+    回退写法不能用 `date.astype("int64") // 10**6`：该值单位取决于列精度，
+    对毫秒精度的列会把时间戳砸平。
+    """
+    if "timestamp" in src.columns:
+        return src["timestamp"].to_numpy()
+    d = pd.to_datetime(src["date"])
+    if getattr(d.dt, "tz", None) is None:
+        d = d.dt.tz_localize("UTC")
+    return (d.dt.tz_convert("UTC").dt.tz_localize(None)
+             .astype("datetime64[ms]").astype("int64").to_numpy())
+
+
+def ensure_timestamp(df: pd.DataFrame) -> pd.DataFrame:
+    """保证 df 带 timestamp 列，缺失时补一份副本，不改动调用方的对象。"""
+    if "timestamp" in df.columns:
+        return df
+    out = df.copy()
+    out["timestamp"] = timestamps_ms(out)
+    return out
+
+
+def build_htf_zones(df_htf: pd.DataFrame, tf: str, chan=None) -> pd.DataFrame:
+    """算 pure 笔中枢，返回带生效时间的区间表。
+
+    available_ts —— 该中枢最早可被使用的时间戳（其确认时刻）。
+    传入已构建好的 chan 可避免重复跑一遍 pipeline（大数据集上省一半时间）。
+    """
+    if chan is None:
+        from chanlun import TF_DF
+
+        chan = TF_DF(df_htf, 1, tf)
+    zs_list = chan.cal_bi_zs_list_pure(chan.bi_list)
+    # 用引擎自己的 dataframe 对齐，避免调用方传入的 df 与引擎内部行数不一致
+    src = chan.dataframe if getattr(chan, "dataframe", None) is not None else df_htf
+    return zones_from_zs_list(zs_list, src)
+
+
+def zones_from_zs_list(zs_list, src: pd.DataFrame) -> pd.DataFrame:
+    """把已算好的 pure 笔中枢转成区间表。
+
+    调用方手工跑过 cal_bi_zs_list_pure 时走这里，免得再算一遍（web 的 analyze_chan
+    就是这种用法）。
+    """
+    ts_of = dict(zip(src["date"].dt.strftime("%Y-%m-%d %H:%M:%S"), timestamps_ms(src)))
+
+    rows = []
+    for zs in zs_list:
+        bis = getattr(zs, "bi_list", [])
+        if not bis:
+            continue
+        # 中枢可用时刻：构成它的最后一笔被确认之时
+        last_bi = bis[-1]
+        sure_key = str(getattr(last_bi, "sure_time", "") or "")
+        end_key = str(getattr(last_bi, "end_time", "") or "")
+        avail = ts_of.get(sure_key) or ts_of.get(end_key)
+        if avail is None:
+            continue
+        start_key = str(bis[0].start_time)
+        rows.append({
+            "zg": float(zs.zg), "zd": float(zs.zd),
+            "gg": float(getattr(zs, "gg", zs.zg)), "dd": float(getattr(zs, "dd", zs.zd)),
+            "start_ts": ts_of.get(start_key, avail),
+            "available_ts": int(avail),
+        })
+    out = pd.DataFrame(rows)
+    return out.sort_values("available_ts").reset_index(drop=True) if not out.empty else out
+
+
+def add_zone_ladder(zones: pd.DataFrame) -> pd.DataFrame:
+    """标注每个中枢相对前一个中枢是否同向推进（缠论「趋势 vs 盘整」）。
+
+    z_above / z_below 分别对应向上、向下推进。买信号要求 z_above、卖信号要求 z_below
+    时，30m/2h 的 PF 从 2.72 升到 3.41。
+    """
+    out = zones.copy()
+    if out.empty:
+        out["z_above"], out["z_below"] = pd.Series(dtype=bool), pd.Series(dtype=bool)
+        return out
+    pg, pdn = out["zg"].shift(), out["zd"].shift()
+    out["z_above"] = (out["zd"] > pg).fillna(False)
+    out["z_below"] = (out["zg"] < pdn).fillna(False)
+    return out
+
+
+def htf_fx_timeline(chan_htf, df_htf: pd.DataFrame | None = None) -> pd.DataFrame:
+    """把大级别分型压成一条按确认时间排序的时间线。
+
+    confirm_ts 是该分型最早可被使用的时刻。timestamp 是K线开盘时刻，而分型要等这根K线
+    收盘才算数，所以整体后移一个大级别周期；否则小级别会提前一整根大级别K线拿到信号。
+
+    只取方向与确认时刻——同向过滤用不到背驰强度，省掉 MACD 面积计算。
+    """
+    src = chan_htf.dataframe if getattr(chan_htf, "dataframe", None) is not None else df_htf
+    if src is None or len(src) == 0:
+        return pd.DataFrame(columns=["confirm_ts", "fx_ts", "direction", "price"])
+    ts = timestamps_ms(src)
+    idx_of = {t: i for i, t in enumerate(src["date"].dt.strftime("%Y-%m-%d %H:%M:%S"))}
+    period = int(np.median(np.diff(ts))) if len(ts) > 1 else 0
+
+    rows = []
+    for klc in getattr(chan_htf, "klc_list", []):
+        if klc.fx not in (Chan_FX_TYPE.TOP, Chan_FX_TYPE.BOTTOM):
+            continue
+        if klc.next is None or klc.next.end_klu is None:
+            continue
+        e_key, c_key = str(klc.end_time), str(klc.next.end_klu.time)
+        if e_key not in idx_of or c_key not in idx_of:
+            continue
+        fx_idx, confirm_idx = idx_of[e_key], idx_of[c_key]
+        if confirm_idx <= fx_idx:
+            continue
+        d = 1 if klc.fx == Chan_FX_TYPE.BOTTOM else -1
+        rows.append({
+            "confirm_ts": int(ts[confirm_idx]) + period,
+            "fx_ts": int(ts[fx_idx]),
+            "direction": d,
+            "price": float(klc.low if d == 1 else klc.high),
+        })
+    out = pd.DataFrame(rows)
+    if out.empty:
+        return pd.DataFrame(columns=["confirm_ts", "fx_ts", "direction", "price"])
+    return out.sort_values("confirm_ts").reset_index(drop=True)
+
+
+def attach_htf_agree(sig: pd.DataFrame, df_ltf: pd.DataFrame, tl: pd.DataFrame) -> pd.DataFrame:
+    """给每个小级别信号挂上「入场时刻之前最近的大级别分型」是否同向。
+
+    产出 htf_dir（+1 底 / -1 顶）与 htf_agree（1 同向 / 0 反向 / NaN 无可用分型）。
+    """
+    out = sig.copy()
+    if sig.empty or tl.empty:
+        out["htf_dir"] = np.nan
+        out["htf_agree"] = np.nan
+        return out
+
+    ts_ltf = timestamps_ms(df_ltf)
+    entry_ts = ts_ltf[out["entry_idx"].to_numpy().astype(int)]
+    k = np.searchsorted(tl["confirm_ts"].to_numpy(), entry_ts, side="right") - 1
+    valid = k >= 0
+    k_safe = np.clip(k, 0, len(tl) - 1)
+
+    fx_dir = tl["direction"].to_numpy()[k_safe].astype(float)
+    out["htf_dir"] = np.where(valid, fx_dir, np.nan)
+    out["htf_agree"] = np.where(
+        valid, (fx_dir == out["direction"].to_numpy()).astype(float), np.nan
+    )
+    return out
+
+
+def attach_zone_ladder(sig: pd.DataFrame, zones: pd.DataFrame) -> pd.DataFrame:
+    """按信号方向取该中枢的阶梯标记：买看 z_above、卖看 z_below。
+
+    zone_i 是 find_fast_bsp3 内 enumerate 出的位置序号，故用 iloc 定位。
+    """
+    out = sig.copy()
+    if sig.empty:
+        out["ladder_ok"] = pd.Series(dtype=bool)
+        return out
+    z = zones if "z_above" in zones.columns else add_zone_ladder(zones)
+    above = z["z_above"].to_numpy()
+    below = z["z_below"].to_numpy()
+    zi = out["zone_i"].to_numpy().astype(int)
+    ok = np.where(out["direction"].to_numpy() == 1, above[zi], below[zi])
+    out["ladder_ok"] = ok.astype(bool)
+    return out
+
+
+def find_fast_bsp3(
+    df: pd.DataFrame,
+    zones: pd.DataFrame,
+    scan: int = 200,
+    pullback_win: int = 30,
+    tol: float = -1.0,
+    max_per_zone: int = 1,
+    diag: dict | None = None,
+    require_touch: bool = False,
+) -> pd.DataFrame:
+    """扫描每个中枢，找突破后回抽不回中枢的入场点。
+
+    zones 需含 zg / zd / available_ts，且 available_ts 已是可用时刻。
+    max_per_zone > 1 时，同一中枢在首次入场后继续往后找二次、三次突破回抽，
+    用来检验「趋势里同一中枢反复给机会」是否值得做。
+
+    tol 是「算作回抽中」的边界容差。require_touch=False 时它不再是入场门槛，
+    仅决定哪些K线被视为回抽中而跳过转强判定，故 tol 越大入场越晚。
+    默认负值 = 完全禁用该跳过，突破后每根都检查转强，滞后压到 2.2 根。
+    step34 实测滞后与收益严格单调：9.9根 PF1.88 / 5.8根 2.37 / 2.2根 2.86。
+
+    返回列：
+      entry_idx   实时可下单的K线
+      direction   +1 三买 / -1 三卖
+      bo_idx      突破根
+      pb_idx      回抽极值根
+      lag         entry_idx - bo_idx
+      depth       回抽深度（相对中枢边界，负值表示曾插入中枢）
+      occ         这是该中枢的第几次入场
+    """
+    if zones.empty:
+        return pd.DataFrame()
+
+    ts = df["timestamp"].to_numpy()
+    close = df["close"].to_numpy(dtype=float)
+    high = df["high"].to_numpy(dtype=float)
+    low = df["low"].to_numpy(dtype=float)
+    n = len(df)
+    rows = []
+
+    def note(key: str) -> None:
+        if diag is not None:
+            diag[key] = diag.get(key, 0) + 1
+
+    for zone_i, (_, z) in enumerate(zones.iterrows()):
+        note("中枢总数")
+        zg, zd = float(z["zg"]), float(z["zd"])
+        if zg <= zd:
+            note("×无效中枢")
+            continue
+        start = int(np.searchsorted(ts, z["available_ts"], side="left"))
+        if start >= n - 2:
+            note("×中枢太靠后")
+            continue
+        # 允许多次入场时按比例放宽扫描窗口，否则后几次机会会被窗口截断
+        scan_end = min(start + scan * max_per_zone, n)
+        cursor = start
+
+        for occ in range(1, max_per_zone + 1):
+            if cursor >= n - 2:
+                break
+            # 第一步：找突破。要求突破前确实待在中枢内，避免把远处的价格当突破。
+            was_inside = False
+            bo_idx, d = None, 0
+            for j in range(cursor, scan_end):
+                c = close[j]
+                if zd <= c <= zg:
+                    was_inside = True
+                    continue
+                if not was_inside:
+                    continue
+                bo_idx, d = j, (1 if c > zg else -1)
+                break
+            if bo_idx is None:
+                if occ == 1:
+                    note("×窗口内未突破")
+                break
+
+            edge = zg if d == 1 else zd
+            # 第二步：突破后监控回抽，回抽不跌回中枢且重新顺势 -> 入场
+            touched = False
+            pb_idx = None
+            pb_ext = None
+            entry_idx = None
+            fell_back = False
+            for j in range(bo_idx + 1, min(bo_idx + pullback_win + 1, n)):
+                # 收盘跌回中枢 -> 突破失效
+                if zd <= close[j] <= zg:
+                    fell_back = True
+                    break
+                # 回抽触及边界附近（允许 tol 的毛刺）
+                near = (low[j] <= edge * (1 + tol)) if d == 1 else (high[j] >= edge * (1 - tol))
+                if near:
+                    touched = True
+                    ext = low[j] if d == 1 else high[j]
+                    if pb_ext is None or ((ext < pb_ext) if d == 1 else (ext > pb_ext)):
+                        pb_ext, pb_idx = ext, j
+                    continue
+                # 回抽后重新顺势：收盘创出前一根之上（三买）/ 之下（三卖）
+                # require_touch=False 时不强求回抽碰到中枢边界，
+                # 这样「突破后一去不回头」的强势段也能收进来。
+                if (touched and pb_idx is not None) or not require_touch:
+                    go = close[j] > high[j - 1] if d == 1 else close[j] < low[j - 1]
+                    if go:
+                        entry_idx = j
+                        break
+            if entry_idx is None:
+                if occ == 1:
+                    note("×突破后跌回中枢" if fell_back
+                         else "×回抽未触及边界" if not touched
+                         else "×触及边界但未转强")
+                # 这次突破没走成，从突破点之后继续找下一次
+                cursor = bo_idx + 1
+                continue
+            if pb_ext is None:
+                # 未触及边界就转强（require_touch=False）：取突破至入场间的实际极值
+                seg = slice(bo_idx + 1, entry_idx + 1)
+                pb_ext = float(low[seg].min() if d == 1 else high[seg].max())
+                pb_idx = int((low[seg].argmin() if d == 1 else high[seg].argmax())
+                             + bo_idx + 1)
+            if occ == 1:
+                note("√成交")
+
+            # 回抽深度：>0 表示未插入中枢，越大表示回抽越浅
+            depth = (pb_ext - zg) / zg if d == 1 else (zd - pb_ext) / zd
+            rows.append({
+                "entry_idx": entry_idx, "direction": d,
+                "bo_idx": bo_idx, "pb_idx": pb_idx,
+                "lag": entry_idx - bo_idx,
+                "depth": depth,
+                "zg": zg, "zd": zd,
+                "width_pct": (zg - zd) / close[bo_idx],
+                "occ": occ,
+                "zone_i": zone_i,
+            })
+            cursor = entry_idx + 1
+
+    out = pd.DataFrame(rows)
+    if out.empty:
+        return out
+    # 相邻中枢可能突破到同一根K线，同一时刻只能有一个仓位，保留最早成型的那个
+    return (out.sort_values(["entry_idx", "occ", "zone_i"])
+               .drop_duplicates("entry_idx", keep="first")
+               .reset_index(drop=True))

chanlun/core/ChanEnum.py

@@ -281,6 +281,10 @@ class Chan_BSP_TYPE(Enum):
     S1 = auto()
     S2 = auto()
     S3 = auto()
+    # 第四类：三类买卖点的低滞后变体，几何位置同 B3/S3，但不等笔确认。
+    # 见 chanlun/analysis/fast_bsp.py
+    B4 = auto()
+    S4 = auto()
     NONE = auto()
 """
 class Chan_BSP_TYPE(Enum):

chanlun/core/ChanFastBSP.py

@@ -0,0 +1,40 @@
+from chanlun.core.ChanEnum import Chan_BSP_DIR, Chan_BSP_TYPE
+
+
+class ChanFastBSP():
+	"""第四类买卖点（B4/S4）。
+
+	与 ChanBSP 的区别在于它不挂在笔上：fast_bsp 刻意不等笔确认，入场点是一根具体的
+	K线而非一笔的端点，所以时间与价格直接取自 K 线，没有 bi / klc 可依附。
+
+	htf_agree 与 ladder_ok 是两个独立的过滤标志，不在这里合成——上层（图表或策略）
+	自己决定要不要用、怎么组合。
+	"""
+
+	def __init__(self, time, price, ddir, entry_idx, bo_time=None, pb_time=None,
+	             lag=0, depth=0.0, zg=None, zd=None, occ=1,
+	             htf_dir=None, htf_agree=None, ladder_ok=None):
+		self.time = time
+		self.price = float(price)
+		self.dir = ddir
+		self.type = Chan_BSP_TYPE.B4 if ddir == Chan_BSP_DIR.BUY else Chan_BSP_TYPE.S4
+		self.entry_idx = int(entry_idx)
+		self.bo_time = bo_time
+		self.pb_time = pb_time
+		self.lag = int(lag)
+		self.depth = float(depth)
+		self.zg = float(zg) if zg is not None else None
+		self.zd = float(zd) if zd is not None else None
+		self.occ = int(occ)
+		self.htf_dir = htf_dir
+		self.htf_agree = htf_agree
+		self.ladder_ok = ladder_ok
+		# 入场即成立，没有「等待确认」这个状态；留此字段是为了与 ChanBSP 的序列化对齐
+		self.is_sure = True
+		self.start_time = time
+		self.end_time = time
+		self.sure_time = time
+
+	def __repr__(self):
+		name = str(self.type).replace('Chan_BSP_TYPE.', '')
+		return f"<ChanFastBSP {name} {self.time} {self.price} lag={self.lag}>"

chanlun/pipeline/builders/fast_bsp.py

@@ -0,0 +1,148 @@
+"""第四类买卖点（B4/S4）接入 TF_DF。
+
+判定逻辑全在 chanlun/analysis/fast_bsp.py，这里只负责把引擎的中枢/K线喂进去，
+再把结果包成 ChanFastBSP。
+
+刻意不在 init_TF_DF 里默认计算：现有构造路径的开销保持不变，由调用方按需触发。
+"""
+from __future__ import annotations
+
+import re
+
+import pandas as pd
+
+from chanlun.analysis.fast_bsp import (
+	add_zone_ladder,
+	attach_htf_agree,
+	attach_zone_ladder,
+	ensure_timestamp,
+	find_fast_bsp3,
+	htf_fx_timeline,
+	zones_from_zs_list,
+)
+from chanlun.core.ChanEnum import Chan_BSP_DIR
+from chanlun.core.ChanFastBSP import ChanFastBSP
+
+# 区间套配对：小级别出中枢与买卖点，大级别只出分型定方向。
+# 取值来自 research/HANDOFF.md §1.5，是回测里实际用过的组合。
+FAST_BSP_HTF_PAIR = {
+	'1m': '5m',
+	'5m': '30m',
+	'15m': '1h',
+	'30m': '2h',
+}
+
+# 未列入配对表的周期回落到这个倍数
+FAST_BSP_HTF_FALLBACK_RATIO = 4
+
+_TF_UNIT_MINUTES = {'m': 1, 'h': 60, 'd': 1440, 'w': 10080}
+
+
+def timeframe_minutes(tf: str) -> int | None:
+	"""'30m' -> 30，'2h' -> 120。无法解析时返回 None。"""
+	if not tf:
+		return None
+	m = re.fullmatch(r'(\d+)\s*([mhdw])', str(tf).strip().lower())
+	if not m:
+		return None
+	return int(m.group(1)) * _TF_UNIT_MINUTES[m.group(2)]
+
+
+def resolve_htf(tf: str) -> tuple[str, int] | None:
+	"""给小级别找配套的大级别，返回 (标签, 分钟数)。"""
+	minutes = timeframe_minutes(tf)
+	if minutes is None:
+		return None
+	paired = FAST_BSP_HTF_PAIR.get(str(tf).strip().lower())
+	if paired:
+		return paired, timeframe_minutes(paired)
+	return f'{minutes * FAST_BSP_HTF_FALLBACK_RATIO}m', minutes * FAST_BSP_HTF_FALLBACK_RATIO
+
+
+class FastBspBuilderMixin:
+	def build_fast_bsp_htf(self, df, timeframe=None):
+		"""对同一份 df 重采样得到大级别，不额外拉数据。
+
+		大级别只用来取分型方向，样本太少就没有过滤意义，故重采样后不足 60 根时放弃。
+		"""
+		tf = timeframe or getattr(self, 'timeframe', None)
+		htf = resolve_htf(tf)
+		ltf_minutes = timeframe_minutes(tf)
+		if htf is None or not ltf_minutes:
+			return None
+		label, minutes = htf
+		if not minutes or len(df) * ltf_minutes < minutes * 60:
+			return None
+		try:
+			from chanlun.pipeline.timeframe import TF_DF
+
+			return TF_DF(df, minutes, label)
+		except Exception:
+			return None
+
+	def cal_fast_bsp(self, df=None, bi_zs_list=None, htf_chan=None, with_htf=True,
+	                 timeframe=None, **kw):
+		"""算第四类买卖点，返回 ChanFastBSP 列表。
+
+		bi_zs_list 传入已算好的 pure 笔中枢可免去重复计算。
+		with_htf=False 时跳过大级别构建，只留 ladder_ok 这一个过滤标志。
+		kw 透传给 find_fast_bsp3（scan / pullback_win / tol / require_touch 等）。
+		"""
+		src = df if df is not None else getattr(self, 'dataframe', None)
+		if src is None or len(src) == 0:
+			self.fast_bsp_list = []
+			return self.fast_bsp_list
+
+		src = ensure_timestamp(src)
+		if bi_zs_list is None:
+			bi_zs_list = getattr(self, 'bi_zs_list', None)
+			if not bi_zs_list:
+				bi_zs_list = self.cal_bi_zs_list_pure(self.cal_bi_list(self.get_klc_list(self.cal_kl_data(src))))
+
+		zones = zones_from_zs_list(bi_zs_list, src)
+		if zones.empty:
+			self.fast_bsp_list = []
+			return self.fast_bsp_list
+
+		zones = add_zone_ladder(zones)
+		sig = find_fast_bsp3(src, zones, **kw)
+		if sig.empty:
+			self.fast_bsp_list = []
+			return self.fast_bsp_list
+
+		sig = attach_zone_ladder(sig, zones)
+
+		if with_htf:
+			if htf_chan is None:
+				htf_chan = self.build_fast_bsp_htf(src, timeframe)
+			sig = attach_htf_agree(sig, src, htf_fx_timeline(htf_chan) if htf_chan is not None else pd.DataFrame())
+		else:
+			sig['htf_dir'] = None
+			sig['htf_agree'] = None
+
+		times = src['date'].dt.strftime('%Y-%m-%d %H:%M:%S').to_numpy()
+		close = src['close'].to_numpy(dtype=float)
+
+		out = []
+		for r in sig.itertuples(index=False):
+			entry_idx = int(r.entry_idx)
+			agree = getattr(r, 'htf_agree', None)
+			htf_dir = getattr(r, 'htf_dir', None)
+			out.append(ChanFastBSP(
+				time=times[entry_idx],
+				price=close[entry_idx],
+				ddir=Chan_BSP_DIR.BUY if r.direction == 1 else Chan_BSP_DIR.SELL,
+				entry_idx=entry_idx,
+				bo_time=times[int(r.bo_idx)],
+				pb_time=times[int(r.pb_idx)] if r.pb_idx == r.pb_idx else None,
+				lag=r.lag,
+				depth=r.depth,
+				zg=r.zg,
+				zd=r.zd,
+				occ=r.occ,
+				htf_dir=None if htf_dir is None or htf_dir != htf_dir else int(htf_dir),
+				htf_agree=None if agree is None or agree != agree else bool(agree),
+				ladder_ok=bool(r.ladder_ok),
+			))
+		self.fast_bsp_list = out
+		return out

chanlun/pipeline/orchestrator.py

@@ -169,6 +169,8 @@ class ChanLun():
 		return self.tf_df.find_second_bsp(bi_list, first_bsp_list)
 	def find_all_bsp(self, bi_list, bi_zs_list):
 		return self.tf_df.find_all_bsp(bi_list, bi_zs_list)
+	def cal_fast_bsp(self, df=None, bi_zs_list=None, htf_chan=None, with_htf=True, timeframe=None, **kw):
+		return self.tf_df.cal_fast_bsp(df, bi_zs_list, htf_chan, with_htf, timeframe, **kw)
 	def get_zs_list(self, bi_list, seg_list):
 		return self.tf_df.get_zs_list(bi_list, seg_list)
 	def cal_bi_zs(self, seg_list):

chanlun/pipeline/timeframe.py

@@ -30,13 +30,14 @@ from chanlun.core.ChanZS import ChanZS, ChanZS_Big
 from chanlun.indicators.ChanMACD import ChanMACD
 from chanlun.pipeline.builders.bi import BiBuilderMixin
 from chanlun.pipeline.builders.bsp import BspBuilderMixin
+from chanlun.pipeline.builders.fast_bsp import FastBspBuilderMixin
 from chanlun.pipeline.builders.incremental import IncrementalBuilderMixin
 from chanlun.pipeline.builders.indicators import IndicatorsBuilderMixin
 from chanlun.pipeline.builders.kline import KlineBuilderMixin
 from chanlun.pipeline.builders.seg import SegBuilderMixin
 from chanlun.pipeline.builders.zs import ZsBuilderMixin
 
-class TF_DF(IndicatorsBuilderMixin, KlineBuilderMixin, BiBuilderMixin, SegBuilderMixin, ZsBuilderMixin, BspBuilderMixin, IncrementalBuilderMixin):
+class TF_DF(IndicatorsBuilderMixin, KlineBuilderMixin, BiBuilderMixin, SegBuilderMixin, ZsBuilderMixin, BspBuilderMixin, FastBspBuilderMixin, IncrementalBuilderMixin):
 	def __init__(self, df=None, interval=0, timeframe=None):
 		if df is not None:
 			self.init_TF_DF(df, interval, timeframe)
@@ -61,6 +62,7 @@ class TF_DF(IndicatorsBuilderMixin, KlineBuilderMixin, BiBuilderMixin, SegBuilde
 		self.zs_list = []
 		self.bi_zs_list = []
 		self.bsp_list = []
+		self.fast_bsp_list = []
 		self.seg_list = []
 		self.klc_fx_list = []
 		self.klu_list = self.cal_kl_data(self.dataframe)

research/HANDOFF.md

@@ -62,6 +62,45 @@ run_trades(cdf, entries, SL, TP, MAX_BARS, fee=..., entry_delay=1)
 - 研究口径成本：**4bp 手续费 + 1bp 滑点 = 5bp**（所有带「已扣 5bp」的数字都是这个）
 - ATR 取信号根的值（`atr[sig_idx]`），不是入场根
 
+> ⚠️ **TP=3.0 已被证明偏小（step41/42，见 3.5 与 5.3）**，且出场参数要按级别分开定。
+> 本文档所有历史数字仍是 TP=3.0 口径，**改口径前不要混用**。
+
+#### 成本模型：不是一刀切的 5bp（用户 2026-08-27 指出）
+
+用户指出「只有开仓才是 taker」。对了一半——**止盈挂限价确实是 maker，
+但止损是 stop-market，成交时仍是 taker**（做成 stop-limit 会有急跌不成交的
+风险，那种损失远大于省下的费）。所以按出场原因分别计费。
+
+**实际费率档位（用户 2026-08-27 给的，单边）：返佣前 taker 0.040% /
+maker 0.016%，API 返 50% → taker 2.0bp / maker 0.8bp。**
+
+| 环节 | 性质 | 单边费率 |
+|---|---|---|
+| 入场（次根开盘市价） | taker + 滑点 | **2.0bp** |
+| 止盈（限价被动成交） | **maker，无滑点** | **0.8bp** |
+| 止损（stop-market） | taker + 滑点 | 2.0bp |
+| 超时（到点市价平） | taker + 滑点 | 2.0bp |
+
+> ⚠️ **step41/42 首轮跑的时候这里写的是 taker 3.0 / maker 1.0bp**，
+> 隐含「原始 taker 6bp、返 50% 得 3bp」的错误前提。真实原始是 4bp。
+> **方向是保守的**——taker 高估了 50%，所以那两步跑出来的净收益、
+> 滑点余量、均R 全部偏低，不是偏高。`exit_model.py` 已改成实际值，
+> 但**本文档里凡是标注「旧费率 3/1bp」的数字尚未逐条重算**。
+
+`SL1.5/TP3/48根` 1m 样本外实测（止盈成交占 60%）：
+
+| 口径 | 平均成本 | 说明 |
+|---|---|---|
+| 全 taker 双边 | 8.00bp | 最早的口径，双重高估 |
+| 混合费率 @ 3/1bp | 6.20bp | step41/42 首轮 |
+| **混合费率 @ 实际 2/0.8bp** | **2.00bp** | 当前 |
+| 研究口径 固定 5bp | 5.00bp | §2 那批老数字 |
+
+**注意方向变了：研究口径的 5bp 现在是偏保守的。** 实际混合费率只有 2bp。
+实现在 `research/lib/exit_model.py`，step41/42 共用。
+**分批离场不额外增加费用**（手续费按名义额收，出场 0.5+0.5 总额不变），
+而且第一批必然是止盈成交，所以分批在成本上还占便宜。
+
 ### 1.4 阶梯过滤（顺向推进）
 
 对应缠论「趋势 vs 盘整」：当前中枢相对前一个中枢是否同向推进。
@@ -165,6 +204,38 @@ push = z_above if direction == 1 else z_below
 - **持仓重叠**（step35 §3-4）：单腿平均并发 0.01，组合 9 腿平均并发 0.1，
   有仓位时间仅 8.6% → t 值没被虚高，各笔基本独立
 
+### 3.35 跨 venue：信号身份不迁移，但总体期望迁移（live 前置测量）
+
+**回测全部跑在 Binance 数据上，实盘要在 Bitget 成交。** 服务器侧的
+`research/live/venue_parity.py` 把这件事量了：
+
+| 品种 | Bitget信号 | Binance信号 | 同根重合 |
+|---|---|---|---|
+| BTC | 47 | 38 | 42.6% |
+| ETH | 39 | 35 | 28.2% |
+| SOL | 48 | 29 | **14.6%** |
+
+（5m 同向过滤后口径。SOL 差得最多是因为 tick 粗细不同：Bitget 0.001、
+Binance 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%。**这是系统性差异，不是随机扰动。**）
+
+`signal_sensitivity.py` 进一步显示 **0.25bp 的数据扰动就能换掉一半信号**。
+
+但 `aggregate_robustness.py` 的结论是：**总体期望不塌**。
+加 0.5 / 1 / 2bp 噪声重跑，毛均收益与 PF 都在原区间内摆动，没有单调恶化。
+
+> **脆的是信号身份，不是 alpha。** 这决定了两件事：
+> 一、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；
+> 二、影子测量必须跑在生产路径上（Bitget 原生数据 + Hummingbot），
+> 换个运行时测出来的数就不作数。
+
+同窗口 Bitget vs Binance 毛均收益（210 天、TP=3.0 口径）：
+BTC 5.87 vs 6.09、ETH 10.02 vs 12.06、SOL 8.92 vs 9.87bp。
+**Bitget 一致略低 0.2~2bp，但没有量级差异，总体口径可迁移。**
+
+⚠️ 上面那批数字用的是 `毛均收益 − 6.0bp`、只有同向过滤、`TP=3.0`，
+**不能直接当预算用**，正确的逐币预算见 `lib/shadow_budget.py` 与 5.3。
+
 ### 3.4 alpha 的来源（step32 消融）
 
 逐条拆掉 `fast_bsp3` 的条件后发现：**alpha 完全来自缠论中枢的上下文定位，
@@ -173,6 +244,122 @@ push = z_above if direction == 1 else z_below
 这个结论回答了用户当时的问题（「如果有这个，那就可以去判断任何笔的端点了」）：
 **不能**。脱离中枢上下文，那个触发条件没有预测力。
 
+### 3.5 出场：TP=3.0 把右尾截早了（step41）
+
+起因是用户看图指出「止盈没做好」。查下来确实如此。
+
+先量 MFE（每笔在被止损打掉之前最远走到几个 ATR）：
+
+| 分组 | 级别 | 笔数 | 中位 MFE | ≥3ATR | ≥5ATR | ≥8ATR |
+|---|---|---|---|---|---|---|
+| 样本内 | 15m/30m/5m | 2309 | 3.66 / 4.30 / 4.27 | 55~62% | 41~45% | 23~28% |
+| 样本外 | 15m/30m/5m | 5859 | 4.08 / 3.80 / 4.30 | 57~61% | 42~45% | 25~28% |
+
+**中位数就已经高于 TP=3**，样本内外一致。抬高 TP 的样本外结果：
+
+| 出场 | 胜率 | 均收益 | 中位 | PF | t值 | 剔10%PF | 均持仓 |
+|---|---|---|---|---|---|---|---|
+| tp3（现用） | 62.6% | +0.559% | +0.588% | 2.71 | +28.31 | 1.64 | 11.2 |
+| tp4 | 58.7% | +0.692% | +0.579% | 2.95 | +29.24 | 1.71 | 15.3 |
+| **tp5** | 56.5% | **+0.783%** | +0.485% | **3.12** | **+29.36** | **1.75** | 18.8 |
+| tp6 | 55.3% | +0.860% | +0.369% | 3.28 | +29.17 | 1.77 | 21.7 |
+| tp8 | 54.1% | +0.983% | +0.284% | 3.56 | +28.27 | 1.78 | 25.7 |
+
+- **8 个样本外币逐个看，均收益与 PF 全部提升**；2020~2026 七年 PF 也全部提升
+- 剔掉最好 10% 后 PF 仍从 1.64 升到 1.75 → 不是几笔暴利撑起来的
+- t 值在 tp5 见顶（+29.36）后走平，tp6/tp8 靠的是越来越薄的右尾
+- 基线复现校验：step41 的 tp3 = 5859 笔 / PF 2.71 / t +28.31 / 剔10% 1.64，
+  对上 §2.2 的 2.73 / +28.50 / 1.66，差在千分位（同根内止损与新高并存时保守
+  假定止损先到），说明对比是在正确基线上做的
+
+**代价不是零**：胜率 62.6%→56.5%，中位 +0.588%→+0.485%（过半单子变差，
+输在「摸到 3ATR 没走到 5ATR 又反手止损」那批），**平均持仓 11.2→18.8 根**，
+§3.3 的并发数据需重算。心理上也更难拿。
+
+#### 分批离场更优（用户提的方案，改一处后成立）
+
+用户方案：到 3 ATR 平一半 + 止损移到开仓价，剩下跑到大目标。
+**前两步对，「止损移到开仓价」是错的。** 用户随后要求把剩余半仓的止损位
+也扫一遍（0 / 0.5 / 1 / 1.5 / 2 ATR），结果**严格单调，三组初始 SL 全一致**：
+
+样本外 5859 笔，初始 SL1.5、分批→8 ATR、48 根：
+
+| 剩余半仓止损位 | 净均收益 | 中位 | PF | t值 | 剔10%PF |
+|---|---|---|---|---|---|
+| 保本损（0） | +0.611% | +0.420% | 2.81 | +25.66 | 1.50 |
+| 留损 0.5 ATR | +0.688% | +0.455% | 3.03 | +27.65 | 1.65 |
+| 留损 1.0 ATR | +0.732% | +0.475% | 3.17 | +28.64 | 1.74 |
+| **留损 1.5 ATR（不动）** | +0.754% | +0.498% | 3.23 | +29.25 | 1.79 |
+| 留损 2.0 ATR（放宽） | +0.762% | +0.510% | 3.25 | +29.48 | 1.81 |
+
+**没有中间最优点，越松越好，但增量在「不动」处饱和**：
+保本→0.5 加 0.077%，0.5→1 加 0.044%，1→1.5 加 0.022%，1.5→2 只加 0.008%。
+所以**止损保持原位即可，放得比原止损更宽是拿实打实的风险换千分之一的收益**。
+
+机理与移动止损失败同源（§4）：回抽到成本价是突破后的正常结构，不是信号失效。
+信号已用「摸到 3 ATR」自证，此时收紧止损等于用最坏价格淘汰掉最好的单子。
+
+**胜率与整仓 TP3 完全相同是结构决定的**：摸到 3 ATR 的单子在分批下必然是赢家
+（半仓 +3 ATR，剩余半仓最坏 −1.5 ATR，净 +0.75 ATR）。这正是分批相对整仓
+高 TP 的价值——同一批单子在整仓 TP5 下会变成 −1.5 ATR 的亏损。
+
+#### 初始 SL 扫描：必须用 R 倍数口径，否则会把仓位差异算成策略优势
+
+用户要求扫 1 / 1.5 / 2 ATR 初始止损。**百分比收益会误导**：SL 越宽每笔风险越大，
+固定风险仓位下仓位越小，raw % 里 SL2 看着比 SL1.5 好 68%，其中大半是仓位效应。
+
+样本外，分批→8 ATR、留损=初始 SL、48 根，§2.3 的 1%风险 + 20倍上限 + 复利：
+
+| 配置 | 均R | R夏普 | 最差1%R | 平均杠杆 | 最大回撤(样本外/内) | Sharpe(外/内) |
+|---|---|---|---|---|---|---|
+| 现用 SL1.5 整仓TP3 | +0.722 | 0.494 | −1.55 | 1.97x | 12.1% / 11.9% | 14.79 / 8.33 |
+| SL1.0 分批→8 | +1.023 | 0.381 | −1.93 | 2.70x | 16.0% / 14.7% | 11.36 / 6.72 |
+| **SL1.5 分批→8** | **+1.023** | 0.530 | −1.55 | 1.97x | 13.5% / 9.4% | 15.79 / 9.30 |
+| **SL2.0 分批→8** | +0.943 | **0.638** | **−1.37** | 1.53x | 13.1% / **6.4%** | **19.00 / 11.35** |
+
+- **SL=1.0 明确最差**：均R 与 SL1.5 打平，但 R夏普 只有 0.381、回撤最大、
+  最差单笔 −1.93R。止损太紧被噪声打掉，和 §3.2「滞后是第一驱动」是一类问题
+- **SL=1.5 期望值最高**（均R 1.023），**SL=2.0 风险调整最好**
+  （R夏普 0.638、回撤 6.4%、Sharpe 11.35，样本内外一致）
+- SL2 只让出 8% 的单位风险期望，换来 20% 更高的 R夏普和更好的最差单笔。
+  **纠正一次误判：修正仓位效应后 SL=2.0 仍然占优，不是纯粹的仓位错觉**
+- 按剔10%PF 排全网格，前 14 名全是 SL=2 的分批配置，
+  第一名 `SL2 分批→8ATR 留损2 48根`：剔10%PF 2.26（现用 1.59）、
+  净 +0.920%、PF 3.84、滑点余量 61.79bp（现用 40.05bp）
+- 该配置样本外 **8/8 币、7/7 年**对现用全面提升，无例外
+
+> 上表的年化数字是 §2.3 的口径 A（复利+杠杆），且把 8 个币当一个账户串起来算，
+> 严重虚高，**故意不记录**。只有回撤、Sharpe、R 这些比值可以横向比。
+
+#### 换成实际费率（2/0.8bp）后：几乎没变
+
+step41 首轮跑的是错误的 3/1bp（见 §1.3），已用实际费率重算分析段
+（feather 存的是毛收益+出场原因，不必重跑模拟）：
+
+| 组 | 口径 | 平均费 | 净均收益 | 余量bp | 剔10%PF | 均R |
+|---|---|---|---|---|---|---|
+| 样本外 | 现用 SL1.5/TP3/48 | 3.00→**2.00bp** | 0.579→**0.589%** | 41.34→**42.05** | 2.01→2.05 | 0.790→0.811 |
+| 样本外 | 最优 SL2 分批→8 留损2/48 | 4.45→**3.01bp** | 0.941→**0.955%** | 62.17→**63.13** | 2.69→2.75 | 0.975→0.998 |
+| 样本内 | 最优 SL2 分批→8 留损2/48 | 4.43→**2.99bp** | 0.866→**0.881%** | 57.15→**58.10** | 2.91→2.98 | 0.960→0.984 |
+
+**结论一个都没变，量级也没变**（余量动了 2%）。原因还是那条：
+成本固定在 bp、目标随 ATR 缩放，所以**费率只对 1m 有杠杆**——
+同一次改动在 1m 上把均R 抬了 8%，在这里只有 2%。
+
+#### ATR 门控在这三条腿上是惰性的
+
+1m 上有效的 `ATR ≥ 8bp` 门控（见 5.3），搬到这里几乎不触发：
+
+| 级别 | ATR中位 | <8bp 占比 | 门控后保留 | 均R 变化 |
+|---|---|---|---|---|
+| 5m | 33.6bp | 2.60% | 97.4% | 1.007 → 1.006 |
+| 15m | 60.2bp | 0.16% | 99.8% | 0.995 → 0.992 |
+| 30m | 82.5bp | 0.00% | 100% | 0.947 → 0.947 |
+
+**无害也无用**，所以可以当全局规则统一写进执行层，不必按级别分叉：
+`ATR ≥ 5 + 1.1 × taker费(bp)`，当前费率下即 ≥ 8bp。
+它只在 1m 上真正起作用（那里 21.5% 的信号在阈值以下）。
+
 ---
 
 ## 4. 已验证无效 / 不要重做的方向
@@ -180,9 +367,12 @@ push = z_above if direction == 1 else z_below
 | 方向 | 结论 | 出处 |
 |---|---|---|
 | 动态止盈（小级别三买后等大级别三买再调止盈） | 无效。持仓期太短（8~13 根），大级别确认来得太罕见 | step27 |
+| 移动止损（1.5 ATR 跟踪距离） | 明显劣于固定 TP：样本外 PF 2.32、剔10%PF 0.94（TP=3 是 2.71/1.64）。突破后的正常回抽就能把它洗出去，和 §3.2「滞后是第一驱动」同源 | step41 |
+| **分批后收紧剩余半仓的止损（保本损及一切更紧的位置）** | 直觉上该做，数据上是负的。止损位 0/0.5/1/1.5/2 ATR 严格单调，越紧越差，三组初始 SL 全一致：保本损比不动少赚 0.14%/笔，剔10%PF 从 1.79 塌到 1.50。与移动止损同源——回抽到成本价是正常结构，不是信号失效。**分批要做，止损保持原位** | step41 §3.5 |
+| 初始止损收到 1.0 ATR | 5m/15m/30m 上均R 与 1.5 打平但 R夏普 只有 0.381（1.5 是 0.530）、回撤 16.0%、最差单笔 −1.93R。**1m 上更极端**：剔10%PF 0.78~0.99、中位收益 −0.122%、回撤 25.5%。1 ATR 就是噪声宽度 | step41/42 |
 | 同一中枢重复入场（二次、三次） | 质量骤降，**只做首次** | step25 |
-| 缠论引擎原生 `find_all_bsp` 的 B3/S3 | 因 `is_sure` 确认滞后，统计上呈逆势、显著亏损。几何位置没错，但实时不可用 | step30/31 |
+| 缠论引擎原生 `find_all_bsp` 的 B3/S3 | 统计上呈逆势、显著亏损：胜率 27.4%（低于 SL1.5/TP3.0 的随机基准 33%）、PF 0.66、t −18.76。**不是滞后造成的**——同一组过滤器把 B4 从 1.59 提到 2.26，对它无效（0.66→0.71），且入场后移 1~4 根只是平滑衰减。它几乎不筛（627 个中枢发 625 个信号），把「价格早已离开、再没回来」的历史区间也当信号发出来。B4/S4 因此单独立类，不是它的提前版 | step30/31 |
-| 刷交易额换 VIP 费率 | 成本收益不划算，见 5.3 | step23 |
+| 刷交易额换 VIP 费率 | 成本收益不划算，见 5.3。**但前提已变**：该结论算于「原始 taker 6bp、全 taker、TP=3.0、无门控」，四个前提现在全不成立（实际 2/0.8bp、混合费率、分批出场、有 ATR 门控）。已在返 50% 的档位上，继续压缩的空间本就有限，但**若要重估需重跑，不要直接引用旧结论** | step23 |
 | 线段（`Chan_XD`）做大级别 | 滞后太大，且中枢极少 | 早期，用户也这么说 |
 
 ---
@@ -238,6 +428,11 @@ push = z_above if direction == 1 else z_below
 
 按 Bitget 真实费率算滑点预算：
 
+> ⚠️ **下表的费率档位是错的**：它假设原始 taker 0.12%、返 50% 得 0.060% 双边。
+> 实际是原始 taker 0.040% / maker 0.016%，返 50% 后 taker 2.0bp / maker 0.8bp
+> （见 §1.3）。整张表连同下面那张「四条腿对比」都**只保留跨级别的相对关系**，
+> 绝对数字全部作废。
+
 | 档位 | 双边费率 | PF=1 滑点上限 | t>2 实用上限 |
 |---|---|---|---|
 | VIP1 taker + API 返50% | 0.060% | **3.9bp** | ~2.5bp |
@@ -256,8 +451,162 @@ push = z_above if direction == 1 else z_below
 
 **1m 和 30m 的余量差 23 倍。**
 
-> **重要判断：1m 若失败，唯一的结论是「执行成本超了 3.9bp」，
+> **重要判断：1m 若失败，绝不能拿它去否定 5m/30m。**
-> 绝不能拿它去否定 5m/30m。** 三条腿的成本敏感度不在一个量级上。
+> 三条腿的成本敏感度不在一个量级上。
+
+#### ⚠️ 上面的 3.91bp 已被 step42 推翻：那是 30m 的参数套在 1m 上量出来的
+
+用户指出：`SL1.5/TP3.0/MAXB48` 是在 5m/15m/30m 上定的，1m 不该混用。
+**成本固定 5bp，而目标随 ATR 缩放**，1m 的 3 ATR 只有 0.39%、30m 是 1.87%，
+成本占比差 5 倍，最优 TP 不可能相同。查证后确实如此，而且原因比成本更根本：
+
+| 级别 | MFE 中位 | ≥6 ATR | ≥10 ATR |
+|---|---|---|---|
+| **1m** | **5.16 ATR** | **46.1%** | **28.6%** |
+| 5m | 4.29 | 37.8% | 18.6% |
+| 15m | 3.98 | 34.7% | 17.0% |
+| 30m | 3.94 | 36.6% | 18.7% |
+
+**1m 以 ATR 计的右尾是其他级别的两倍。** 机理：1m 的 ATR 量的是分钟级噪声，
+但信号一旦成立抓到的是几小时级别的趋势，所以「行程 / ATR」比值天然更大；
+30m 的 ATR 本身已经吃进了一部分趋势。所以 TP=3 ATR 在 1m 上是个远比在 30m 上
+更紧的目标——**这不是调参问题，是量纲问题**。
+
+（MFE 本身也随初始止损变化，止损越松活得越久、右尾越长：
+1m 上 SL1.0 中位 2.37 ATR、SL1.5 是 5.16、SL2.0 是 6.73。
+所以 TP 和 SL 必须一起定，不能固定一个扫另一个。）
+
+#### 1m 全网格重测（step42，11 币 × 7 年，25476 笔）
+
+> ⚠️ 本小节的表用的是**旧费率 3/1bp**（跑的时候费率档位写错了，见 §1.3）。
+> 实际是 2/0.8bp，所以下面所有净收益、余量、均R **都偏保守**。
+> 用实际费率重算的版本在再下一小节，**决策请用那一版**。
+
+扫描空间：`SL ∈ {1.0, 1.5, 2.0}` × `TP ∈ {2,3,4,5,6,8,10,12}` ×
+`MAXB ∈ {48, 96}` × 分批（3 ATR 减半 → 剩余目标 {5,6,8,不设} × 留损 {0,0.5,1,1.5,2}）。
+
+**结论与 5m/15m/30m 同向，但幅度更大，且 1m 上没有取舍——最优解在每个指标上都赢。**
+
+样本外（8 个未调参币，17734 笔）：
+
+| 口径 | 胜率 | 毛bp | 净均收益 | 中位 | PF | 剔10%PF | **滑点余量** | 均R | R夏普 | 最大回撤 | 均持仓 |
+|---|---|---|---|---|---|---|---|---|---|---|---|
+| 现用 SL1.5 / 整仓TP3 / 48根 | 60.8% | 20.27 | +0.141% | +0.169% | 2.09 | 1.24 | **11.06bp** | +0.434 | 0.274 | 14.7% | 9.7 |
+| SL1 分批→8ATR 留损1 / 48根 | 45.9% | 20.29 | +0.133% | −0.122% | 2.01 | 0.96 | 9.07bp | +0.649 | 0.225 | 25.5% | 17.5 |
+| SL1.5 分批→8ATR 留损1.5 / 48根 | 58.9% | 28.60 | +0.219% | +0.132% | 2.70 | 1.49 | 15.12bp | +0.781 | 0.376 | 14.1% | 23.7 |
+| **SL2 分批→8ATR 留损2 / 48根** | 67.6% | 34.90 | **+0.284%** | +0.233% | **3.41** | **2.01** | **20.12bp** | **+0.792** | **0.500** | **12.6%** | 28.4 |
+
+（滑点余量口径已改：分子是毛收益减实际手续费，分母是 taker 名义额，
+即**每条 taker 腿能承受多少滑点**。旧表按双边固定 6bp 扣、不除 taker 腿，
+所以数字偏大——上一版写的 14.27bp 就是旧口径，与 11.06bp 是同一件事的两种算法。）
+
+- **滑点余量 11.06 → 20.12bp，接近翻倍**；均 R +0.434 → +0.792（+82%）
+- **8/8 样本外币、7/7 年份全部提升**，无例外。最弱的 2026 年：
+  现用余量 4.69bp / 剔10%PF 0.92（已跌破 1），最优 9.84bp / 1.45
+- 样本内同向：净 +0.100% → +0.221%，剔10%PF 1.07 → 1.72，回撤 19.7% → 10.2%
+- **`MAX_BARS=48` 不用改**：1m 上 96 根在同配置下一致略差（剔10%PF 2.01 → 1.95），
+  这点与 5m/15m/30m 不同，是 1m 唯一沿用原参数的地方
+- 纯整仓高 TP（TP8/TP10）毛收益更高（44~47bp）但胜率跌到 59~60%、
+  中位收益反而更低；分批版 PF 与剔10%PF 更好，是更稳的那一侧
+
+**1m 上 SL=1.0 是废的**（比其他级别更明确）：剔10%PF 只有 0.78~0.99，
+中位收益 −0.122%（一半以上的交易是亏的），最差 1% 到 −3.69R，回撤 25.5%。
+1 ATR 在 1m 上就是噪声宽度，止损放在噪声里必然被反复扫。
+
+**减仓后剩余半仓的止损位——三组初始 SL 下都是同一条单调曲线：**
+
+| 初始SL | 保本损(0) | 留损0.5 | 留损1 | 留损1.5 | 留损2 |
+|---|---|---|---|---|---|
+| SL1.0 剔10%PF | 0.78 | 0.91 | 0.96 | 0.98 | **0.99** |
+| SL1.5 剔10%PF | 1.23 | 1.38 | 1.45 | 1.49 | **1.50** |
+| SL2.0 剔10%PF | 1.68 | 1.86 | 1.95 | 2.00 | **2.01** |
+
+**越松越好，到「不动」（= 初始 SL）为止基本饱和**，再放宽只有 +0.01 的边际。
+保本损在 SL2 上直接砍掉 0.33 的剔10%PF，是全表最差的一档。
+结论与 5m/15m/30m 完全一致：**减仓后不要动止损**。
+
+#### 逐年余量在衰减——那是波动率压缩，不是 alpha 衰减
+
+用户看到余量逐年递减后提的假设：「是不是跟波动率有关，2026 是熊市、2025 是牛市」。
+**查证成立，而且相关性高到接近恒等式。**
+
+`余量bp` 与 `当年 ATR 中位数` 的跨年相关系数 **r = +0.989**（n=7）。
+
+| 年 | ATR中位 | 毛bp | **毛/ATR** | 余量bp(旧费率) | 均R |
+|---|---|---|---|---|---|
+| 2020 | 16.3 | 39.8 | 2.44 | 23.98 | 0.939 |
+| 2021 牛 | **22.1** | 58.7 | 2.65 | **36.49** | 1.031 |
+| 2022 | 15.4 | 36.5 | 2.37 | 21.30 | 0.876 |
+| 2023 | 9.8 | 24.9 | 2.53 | 13.88 | 0.846 |
+| 2024 | 11.7 | 31.3 | 2.67 | 18.07 | 0.877 |
+| 2025 | 11.6 | 27.8 | 2.40 | 15.76 | 0.883 |
+| 2026 熊 | **8.8** | 19.8 | 2.26 | **10.28** | 0.713 |
+
+**关键是「毛/ATR」列：2.26~2.67，七年无趋势。** 信号质量以 ATR 计几乎没衰减。
+衰减的是 ATR 本身——2021 的 22.1bp 压到 2026 的 8.8bp，掉了 60%。
+余量是 bp 口径，自然跟着掉。这就是 §5.3 的论证从跨级别搬到跨时间：
+**成本固定在 bp，目标随 ATR 缩放**。低波动年份等于「1m 变得更像 1m」。
+
+保留意见：2026 的毛/ATR 2.26 与余量/ATR 1.17 都是七年最低，
+**可能还有一点真衰减**，但 8 个月 1682 笔分不出是噪声还是趋势。
+
+#### ATR 门控：低 ATR 的信号毛质量不差，是被固定成本吃掉的
+
+按信号根 ATR 分桶看**毛** R（零成本口径），结论与直觉相反：
+
+| ATR桶 | 毛R | 净R @2/0.8bp | 净R @6/2bp |
+|---|---|---|---|
+| <6bp | **1.156** | — | 0.025 |
+| 6-8 | **1.160** | — | 0.527 |
+| 8-10 | 1.227 | — | 0.736 |
+| 12-15 | 1.203 | — | 0.870 |
+| >30 | **0.940** | — | 0.829 |
+
+**低 ATR 桶的毛 R 不但不差，还是最高的一批**（1.16 vs 高 ATR 桶的 0.94）。
+断崖只在扣费之后出现。所以「低波动时段信号变差」是错的说法，
+正确说法是**固定成本在低波动时占比过大**。
+
+**推论：门控阈值是费率的函数，不是市场常数。** 换 VIP 档 / 换返佣 /
+换交易所都要重算，不能抄：
+
+| 单边 taker | 最优 ATR 阈值 |
+|---|---|
+| 0（假想） | 5bp |
+| 1.5bp | 8bp |
+| 3bp | 9bp |
+| 6bp | 12bp |
+
+大致 `阈值 ≈ 5 + 1.1 × taker费(bp)`，亚线性。
+
+#### 实际费率（2/0.8bp）下的最终口径——**决策用这一版**
+
+| 口径 | 笔数 | 平均费 | 净bp | 余量bp | PF | 剔10%PF | 均R | R夏普 |
+|---|---|---|---|---|---|---|---|---|
+| 现用 SL1.5/TP3/48 | 17734 | 2.00bp | 18.3 | 13.06 | 2.73 | 1.96 | 0.702 | 0.482 |
+| 最优 SL2 分批→8 留损2/48 | 17734 | 2.99bp | 31.9 | 21.45 | 4.18 | 2.85 | 0.958 | 0.639 |
+| **最优 + ATR≥8bp 门控** | 13929 | 3.00bp | 36.5 | **25.39** | **4.23** | **2.98** | **0.987** | **0.662** |
+
+逐年（最优 + 8bp 门控，样本外）：
+
+| 年 | ATR中位 | 无门控余量 | 门控余量 | 保留% | 笔/周 | 剔10%PF | 均R |
+|---|---|---|---|---|---|---|---|
+| 2021 牛 | 22.1 | 37.47 | 37.80 | 98.6% | 55.5 | 3.41 | 1.064 |
+| 2023 | 9.8 | 14.84 | **19.45** | 63.5% | 28.4 | 2.84 | 0.982 |
+| 2025 | 11.6 | 16.73 | 20.79 | 71.2% | 45.5 | 3.11 | 1.007 |
+| 2026 熊 | 8.8 | 11.21 | **15.19** | 55.4% | 27.5 | 2.51 | 0.824 |
+
+- **门控是自适应的**：高波动年份几乎不触发（2021 保留 98.6%），
+  低波动年份才发力（2026 保留 55.4%、余量 +36%）。不是拟合出来的常数
+- **费率降低后门控的边际价值也降低**：旧费率下门控带来 +5.6% 均R，
+  实际费率下只有 +3.0%。阈值曲线在 5~12bp 之间很平（均R 0.972~0.987），
+  **8bp 是峰值但不敏感**，不必纠结精确值
+- **影子交易判据：14~15bp**（2026 最差年份口径）。近两年合计是 19.17bp，
+  全样本外 25.39bp。**用 2026 那个数做及格线，别用平均值**
+
+**与旧 3.91bp 的差距来源未完全对齐**：旧数字来自 step23（2.28 年数据、
+3578 笔、毛均收益 0.0991%），本步是 7 年、25476 笔、毛均收益 0.2027%，
+且滤网口径可能不同（§5.3 原文写的是「1m 同向信号」，未提阶梯），
+费率口径也已改成混合 maker/taker。**在重新对齐之前，两个数字不要混用。**
 
 风险点：入场条件恰好是「收盘突破转强」，那一刻价格正朝我们的方向跑，
 所以延迟造成的是**系统性的追高/追低，不会正负抵消**。这正是
@@ -311,15 +660,77 @@ push = z_above if direction == 1 else z_below
 --probe 60 --workers 3`，约 4 分钟。瓶颈不在窗口重建，而在 `run_one` 开头
 那次 120 万根 1m 的全量 `TF_DF`。）
 
-### 第 2 步：向用户确认交易所（当前的阻塞项）
+### ~~第 2 步：向用户确认交易所~~ ✅ 已定：Bitget + Hummingbot
-
-这个问题必须他回答，因为它决定框架：
-
-- 留 Bitget → 只有 Hummingbot 支持，或用 ccxt 自己写执行层
-- 换 Hyperliquid / Binance / Bybit / OKX → freqtrade 和 Nautilus 都能用
 
 他已选定**先只上 1m**（在我问「先从哪条腿落地」时选的 `1m_first`）。
 
+#### 延迟已实测完成（2026-08-27，服务器侧）
+
+四路对照（宿主机 ccxt.pro / 宿主机原始 WS / 容器原始 WS / 容器 Hummingbot，
+同一批 K 线对齐），把最初测到的「Hummingbot 慢 1030ms」拆开了：
+
+- 容器网络 2~18ms
+- **Hummingbot 自身处理开销 −10~−30ms（不慢反快）**
+- 剩下 1350~1480ms 全在上游连接器的一个 bug 上
+
+**Bug**：Bitget 换根时推的是带两根 K 线的消息 `[上一根, 新一根]`，
+而上游解析写的是 `data["data"][0]`，拿到的是上一根；新一根要等下一条
+单元素消息、约 1.1 秒后才进 deque。ccxt 遍历全部元素，所以没这问题。
+
+**修法**：不能简单改成 `[-1]`——那样上一根的收盘价会永远停在换根前
+约 1 秒的那次推送上，而我们的信号对 0.25bp 的扰动都会换掉一半结果，
+收盘价偏一个 tick 不可接受。正确做法是处理消息里的**全部元素**：
+前面的就地写回，最后一根交基类 append。
+
+20 分钟 / 60 根配对验证：
+
+| 币 | 上游 lag | 补丁后 lag | 上游漂移 | 补丁后漂移 |
+|---|---|---|---|---|
+| BTC | 1454ms | **547ms** | 0.81bp | **0.50bp** |
+| ETH | 1683ms | **506ms** | 1.20bp | **0.66bp** |
+| SOL | 1408ms | **642ms** | 1.29bp | **0.87bp** |
+
+补丁后与原始 WS 只差 5~14ms，贴着理论下限；19 根已收盘 K 线 OHLCV 逐根相同。
+
+**漂移 0.50~0.87bp 对 15.19bp 的预算只用掉 6%，延迟这一关过了。**
+
+⚠️ 但这是**无条件漂移**（60 根里绝大多数是普通换根），而我们只在
+「收盘突破转强」那一刻入场，那一刻价格正朝我们方向跑。§3.2 已量化
+滞后与收益严格单调且是系统性追高。**信号根上的条件漂移预计显著更高，
+影子测量必须按信号根条件化统计**，见第 3 步。
+另外测的是 BTC/ETH/SOL——既是流动性最好的，又恰好是样本内那三个；
+真正要跑的 8 个样本外币盘口更薄，至少补测 TRX 和 ADA。
+
+#### 容器内可行性（同批确认）
+
+Python 3.13.14 + pandas 3.0.5 + numpy 2.4.6，与本机 `.venv` 的 pandas 同版本。
+同一份切片喂进去，三个币的**信号下标、中枢数、中枢 checksum 全部逐一相同**，
+容器内 0.26s/币 比本机 0.29s 还快。**chanlun 直接挂载进容器即可，
+不需要另起信号服务。**（装进现有 `.venv` 走不通：Hummingbot 要
+`numba>=0.61.2` 和 `aiohttp<3.14`，与本机 Python 3.14 冲突。）
+
+2000 根 1m 够取：单次上限 1000、分两页，1m 限 30 天。
+
+#### 补丁落地方式：子类覆盖（已定）
+
+不改镜像，用子类覆盖连接器的解析方法，升级 Hummingbot 不冲突。
+
+⚠️ **这个方案有一个缺口必须堵**：如果上游把被覆盖的方法改名，
+子类覆盖会变成**死代码，静默退回 1.4s，不报任何错**。
+所以启动时要断言基类上确实存在该方法名（有条件的话连签名一起校验），
+把静默失效转成启动即失败。
+
+⚠️ **无论用哪种补丁方式，都要有运行时 lag 探针**——补丁机制只防得住
+「上游代码变了」，防不住 Bitget 再改一次消息格式：
+
+- 每根 K 线收盘记 `本地接收时间 − K线收盘时间`
+- 最近 30 根取滚动中位数
+- **中位数 > 800ms 告警并停止开新仓**（补丁后实测 506~642ms，
+  理论下限约 500ms；800ms 留足抖动余量又能对 1.4s 的退化一刀切死）
+
+要停开仓而不只是打日志，因为损失是**经济性且静默的**——
+不会崩、不会报错，只会让收益慢慢变差，几周后才从统计里看出来。
+
 ### 第 3 步：写影子交易器测滑点（核心工作）
 
 **关键判断：测滑点不需要任何交易框架。** 完整框架解决的是「如何可靠下单」，
@@ -344,24 +755,86 @@ push = z_above if direction == 1 else z_below
 
 滑点应分解成三块：**延迟漂移**（`t_book` 中间价 − 次根开盘价）、
 **盘口价差**（ask − 中间价）、**深度冲击**（加权成交价 − ask）。
-预期延迟漂移是主项，价差可忽略（0.01bp）。
+延迟漂移已单独测过（第 2 步，0.50~0.87bp 无条件），
+但**信号根上的条件漂移才是要测的量**。
 
-建议路径 `research/live/shadow.py`，复用 `research/lib/` 下同一套信号代码，
+#### 形态已定：Hummingbot V2 controller（不再写独立 shadow.py）
-这样两边数字直接可比。代码量约两三百行。
 
-实现要点：
+- 信号承载在 V2 controller 里，chanlun 挂载进容器直接用
+- **chanlun 计算走 `ProcessPoolExecutor`**：0.26s 是纯 CPU 且受 GIL 限制，
+  直接放进 async 会卡住事件循环
+- 滑点测量**读连接器真实盘口**，不用 paper trade 的模拟成交
+
+#### 必须记录的字段（否则数据回来无法对判据）
+
+时间戳四件套，拆开才知道该优化哪里：
+
+| 字段 | 含义 |
+|---|---|
+| `t_close` | K 线收盘时刻 |
+| `t_data` | 数据到手时刻 → 拉取延迟 |
+| `t_signal` | 信号算完时刻 → 计算延迟 |
+| `t_book` | 盘口快照时刻 → 下单时点 |
+
+**每笔还必须记 `atr_pct`**——判据是 bp 口径而 ATR 逐年变化（见 5.3），
+不记 ATR 就无法把实测滑点换算成 R、也无法验证门控是否正确执行。
+
+**统计口径三条硬要求：**
+
+1. **只统计通过全部滤网的信号根**（同向 + 阶梯 + `ATR ≥ 8bp` 门控）。
+   在未过滤的根上测滑点会把判据算宽——那些单子我们根本不做
+2. **条件漂移与无条件漂移分开报**。无条件值（0.5~0.87bp）已知，
+   要的是信号根上的值。两者的差就是「系统性追高」的大小
+3. **出场腿单独统计**：止盈是 maker 不吃滑点，止损/超时是 taker 要吃。
+   §1.3 的费率模型按出场原因分别计费，滑点也必须这样分，
+   否则会把 60% 不吃滑点的止盈腿混进平均值里，低估真实成本
+
+对判据时用**每条 taker 腿的滑点**（入场 + 止损/超时出场），
+不是每笔的总滑点——余量 15.19bp 就是这个口径。
+
+**记录与报表已在服务器侧实现**：`research/live/shadow_hb.py`（采集）与
+`shadow_report.py`（报表）。它的设计比研究侧曾经写过的那版好，不要重复造：
+**每个信号在 0.5s / 1s / 2s / 5s / actual 五个延迟上各测一次**——同信号内的
+受控对比，能直接读出「延迟压到 X 秒滑点是多少」，决策不被当前实现拖累；
+另外还扫了仓位，滑点也已拆成漂移 / 价差 / 冲击。
+
+**判据常数留在研究侧**：`research/lib/shadow_budget.py`
+（逐币 `BUDGET_BP`、`ATR_GATE_BP`、`LEG_IS_TAKER`、`lag_healthy()`、
+`verdict()`）。分工的理由是这些数会变——2026-08-27 一天之内预算就动了四次，
+费率也改了一次，写死在 live 侧的任何一份都会静默过期。
+
+其他实现要点：
 - 数据格式必须与 `lib/data.py` 一致：`timestamp`(int64 ms), `date`(tz
   Asia/Shanghai), `open/high/low/close/volume`
-- ccxt 拉 Bitget 需 `proxies = {'http': 'http://127.0.0.1:7897', 'https': ...}`
+- 持仓也要影子管理：出场按 1m 自己的参数（3 ATR 减半 / 留损 2 / 目标 8 ATR
-  和 `options={'defaultType':'swap'}`
+  / 48 根超时），出场同样记录真实盘口
-- 持仓也要影子管理：SL/TP/48 根超时，出场同样记录真实盘口
 - 状态要落盘，重启不能丢
 
 ### 第 4 步：根据滑点结果决策
 
-- 滑点 ≤ 2.5bp → 1m 可行，`t>2`，可以进入真实 dry run
+~~旧阈值（2.5 / 3.9bp）已作废~~：那是 TP=3.0、全 taker、原始费率记错
-- 2.5~3.9bp → 1m 勉强不亏但无统计显著性，考虑争取 60% 手动返佣或 VIP2
+三重错误叠加出来的，见 5.3。**新判据如下。**
-- > 3.9bp → 1m 放弃，**但 5m/30m 不受影响**（余量 26bp / 89bp）
+
+前提：1m 用自己的出场参数（`SL2 / 3ATR减半 / 留损2 / 目标8ATR / 48根`）
++ `ATR ≥ 8bp` 门控 + 实际费率 2/0.8bp。
+
+**判据用 2026 那个最差年份的 15.19bp，不要用全样本的 25.39bp。**
+低波动环境是当前状态，不是尾部情形。
+
+| 实测滑点（每条 taker 腿） | 判断 |
+|---|---|
+| ≤ 5bp | 1m 可行，各年份都有厚余量，进真实 dry run |
+| 5~10bp | 可行但只在中高波动期，门控阈值要上调（见 5.3 的费率-阈值关系） |
+| 10~15bp | 仅 2021 型牛市能做，常态环境不要开 |
+| > 15bp | 1m 放弃 |
+
+**5m/30m 不受影响这一句仍成立**（余量 26bp / 89bp，且那是旧费率下的保守值）。
+
+另外两点：
+- 滑点必须用 **WebSocket** 测。代理下 REST 单次往返约 2.4s，而信号计算只要
+  0.20s（§5.4），REST 测出来的全是代理的账
+- 入场条件是「收盘突破转强」，那一刻价格正朝我们方向跑，
+  **延迟造成系统性追高、不会正负抵消**（§3.2 已量化滞后与收益严格单调）
 
 ### 第 5 步（后续）：1m 与 5m 分实例
 
@@ -420,8 +893,8 @@ API 限流风险隔离三个好处。
 | 文件 | 作用 |
 |---|---|
 | `data.py` | 数据层，本地 feather 优先 |
-| `fast_bsp3.py` | **核心**：自研低滞后第三类买卖点。滞后 2.2 根 |
+| `fast_bsp3.py` | **实现已移入引擎**（`chanlun/analysis/fast_bsp.py`），这里只剩转发导入。所有 step 脚本的 `from lib.fast_bsp3 import find_fast_bsp3` 不用改 |
-| `nested_level.py` | `build_htf_zones`：构建中枢，可传入已建好的 `chan` 复用 |
+| `nested_level.py` | `build_htf_zones` 同样移入引擎并转发；`annotate_position` 仍在本地 |
 | `nested_bsp.py` | `htf_fx_timeline` / `attach_htf_context`：区间套上下文。
 `confirm_ts` 已 `+period` 修正 K 线收盘时刻 |
 | `fx_signal.py` | 分型信号提取与背驰 |
@@ -431,6 +904,23 @@ API 限流风险隔离三个好处。
 ### 8.2 缠论引擎 `chanlun/`
 
 - `TF_DF(df, 1, tf)` 是入口，`.dataframe` 拿到含缠论标注的 DataFrame
+- `analysis/fast_bsp.py`：**核心**，自研第四类买卖点 B4/S4（滞后 2.2 根），
+  2026-08-27 从 `research/lib/` 搬来，回测与 web 图表现在共用同一份代码。
+  搬运时逐条比对过 BTC/ETH/SOL × 15m/30m 六组，中枢与信号完全一致。
+  **它不是「B3/S3 提前几根」**：判据（收盘价实时 vs 笔端点事后）、方向来源
+  （突破侧 vs 离开笔）、选样口径（627 个中枢里认 212 个 vs 引擎发 625 个）都不同。
+  step30 同条件对拍见 §4——同一组过滤器把 B4 从 PF 1.59 提到 2.26，对引擎
+  B3/S3 无效（0.66→0.71）；引擎那批胜率 27.4% 低于 SL1.5/TP3.0 的随机基准 33%，
+  是系统性反向样本，不是滞后造成的衰减
+  除 `find_fast_bsp3` / `build_htf_zones` 外，还有过滤用的 `add_zone_ladder`
+  （中枢阶梯，§1.4）与 `htf_fx_timeline` / `attach_htf_agree`（区间套同向，§1.1 第三层）
+- `pipeline/builders/fast_bsp.py`：`TF_DF.cal_fast_bsp()`，把上面那套包成
+  `ChanFastBSP`（类型 `Chan_BSP_TYPE.B4/S4`）。大级别由同一份 df 重采样得到，
+  配对表 `FAST_BSP_HTF_PAIR` 就是 §1.5 那张表。
+  **不在 `init_TF_DF` 里默认计算**，要显式调用
+- web 端：`/api/analyze` 返回 `fast_bsp_list` / `element_fast_bsp_list` /
+  `sub_sub_fast_bsp_list`，每条带 `htf_agree` 与 `ladder_ok` 两个过滤标志。
+  图上深色 B4/S4 = 两个标志都满足，浅色 = 未通过过滤
 - `pipeline/builders/incremental.py`：增量更新（`init_stream` / `append_bar` /
   `replace_last_bar` / `rebuild_bi_zs`）。**注释写明「最后一笔 is_sure 允许收回」**，
   这是 step38 要验证的那个隐患（已证实无影响）。
@@ -462,7 +952,10 @@ API 限流风险隔离三个好处。
 | 36 | 偏差审计（生效时刻、多空、时间样本外） |
 | 37 | **跨品种样本外**（8 个新币） |
 | 38 | **时点重建**（无未来函数） |
-| 39 | **1m 时点重建 + 窗口扫描**（新写，待跑完） |
+| 39 | **1m 时点重建 + 窗口扫描**（2000 根窗口饱和） |
+| 40 | 第四类买卖点的过滤消融（web 端原始 vs 过滤） |
+| 41 | **出场口径全扫**（SL × TP × MAX_BARS × 分批 × 剩余半仓止损位 + 真实费率模型）。TP=3 截早了；分批要做但止损保持原位；SL=1.0 最差 |
+| 42 | **1m 单独定出场**（同一张全网格，11 币 7 年 25476 笔）。1m 右尾是其他级别 2 倍，TP 不能混用；`SL2 分批→8ATR 留损2 48根` 在均R/R夏普/回撤/剔10%PF 四项全赢，滑点余量 11.06→20.12bp；`MAX_BARS=48` 反而不用改 |
 
 输出都在 `research/out/`。**2026-08-27 清理过一轮**：step1~20 的输出（早期方法论
 已被推翻，含未来函数偏差）与旧口径备份一并删除，只留支撑当前结论的证据。
@@ -486,14 +979,49 @@ API 限流风险隔离三个好处。
 
 - [x] **step39**：1m 时点重建 + 窗口扫描 → **2000 根窗口饱和，计算 0.20s，
       假阳性 0%，1m 回测口径可信**
-- [ ] **向用户确认交易所**（阻塞后续框架选择，这是当前第一件事）
+- [ ] **对齐 step23 与 step42 的 1m 数字**（3.91bp vs 11.06bp）。差距可能来自
-- [ ] 写 `research/live/shadow.py` 影子交易器，测 1m 真实滑点。
+      数据年限（2.28 年 vs 7 年）或滤网（§5.3 原文只提「同向」，未提阶梯）。
-      **务必用 WebSocket 而非 REST 轮询**——代理下单次 REST 往返约 2.4s，
+      §6 第 4 步的三条决策阈值全部依赖这个数字，对齐前不要据它做去留判断
-      而信号计算只要 0.20s，网络是延迟主项
+- [ ] **决定是否改出场口径**（step41/42 样本外均已过，见 3.5 与 5.3）。
+      **出场参数要按级别分开定，不能一套走天下。** 5m/15m/30m 的首选是
+      「初始 SL 2.0 ATR、到 3 ATR 平一半、剩余半仓止损保持原位、跑到 8 ATR、
+      48 根超时」；若更看重单位风险期望而非回撤，初始 SL 用 1.5。
+      **1m 用同一套形状，但 SL=2.0 是无争议的最优**（均 R、R夏普、回撤、
+      剔10%PF 四项全赢，见 5.3），`MAX_BARS=48` 不变。
+      改的话 `run_trades` 要支持分批（现在只支持整仓单目标），
+      并重跑 step28/35/37 刷新本文档的年化/回撤/并发数字——**不要新旧口径混用**。
+      注意分批后平均持仓从 11 根拉到 30 根，§3.3 的并发数据必须重算
+- [x] **用实际费率（2/0.8bp）重算 step41/42 的分析段** → 1m 见 5.3 末尾
+      （均R +8%，判据 15.19bp）；5m/15m/30m 见 3.5 末尾（余量只动 2%，结论不变）
+- [x] **ATR 门控搬到 5m/15m/30m 验证** → 惰性（30m 触发 0%、15m 0.16%、5m 2.6%），
+      可作全局规则 `ATR ≥ 5 + 1.1×taker费(bp)`，当前即 ≥ 8bp
+- [x] **确认交易所与框架** → Bitget + Hummingbot，先只上 1m
+- [x] **测执行延迟** → 上游连接器 bug 已定位并修好，补丁后 lag 506~642ms、
+      无条件漂移 0.50~0.87bp，占预算 6%。见 §6 第 2 步
+- [ ] **写 V2 controller + 盘口缓冲，跑影子测量**（当前第一件事）。
+      字段与统计口径见 §6 第 3 步，**三条硬要求别漏**：只统计过滤后的信号根、
+      条件漂移与无条件漂移分开报、出场腿按 maker/taker 分开统计
+- [ ] **改掉 `shadow_report.py` 的 `BUDGET_BP`**（当前 BTC −0.13 / ETH 4.02 /
+      SOL 2.92）。那三个数由六处差异叠加而来：只有同向没有阶梯过滤、
+      费率按 6bp 双边 taker、`TP=3.0`、余量没除 taker 名义额、无 ATR 门控、
+      且 BTC/ETH/SOL 恰是 ATR 最低的三个币。正确值 8.58 / 20.64 / 16.83，
+      直接从 `lib/shadow_budget.py` import。
+      **这条优先级最高**：报表里 `drift > b` 与 `drift > b*0.6` 是自动停机开关，
+      用错的预算会让它比真实情况早约五倍触发，误杀一个可行的策略
+- [ ] **live 信号路径补上中枢阶梯过滤与 ATR 门控**。现在是
+      `fin = full[full["h1_agree"] == 1]`，只有同向。缺这两项测的就不是
+      我们要交易的那批信号，改常数解决不了
+- [ ] **子类覆盖加启动断言** + **运行时 lag 探针（>800ms 停开仓）**，见 §6 第 2 步
+- [ ] 补测 ADA / DOGE / AVAX 的漂移——真正要交易的是这批（预算 15~22bp）。
+      BTC/ETH/SOL 流动性最好，测滑点偏乐观是安全方向，但判可行性不能只看它们。
+      **BTC 大概率不该做 1m**：ATR 中位 2026 仅 6.5bp（11 币最低），
+      门控刷掉 58.5%，修正后预算 8.58bp 仍是最低。TRX 2026 门控后只剩 4.2%
 - [ ] 收集 1~2 周影子数据，与回测口径逐笔对比
-- [ ] 按 6.4 的判据决策 1m 是否可行
+- [ ] 按 §6 第 4 步的四档判据决策 1m 是否可行
-- [ ] 若可行：选框架（Bitget→Hummingbot）做真实 dry run
+- [ ] 若可行：真实 dry run
 - [ ] 补一项研究阶段的缺口：**资金费率**从未计入任何回测。
+      注：1m 新参数下平均持仓 28.4 根 = 28 分钟，很少跨 8 小时结算点，
+      这一项对 1m 影响小；30m 腿才是真问题。
       30m 腿平均持仓 6.5 小时会跨结算点，需要评估影响
 - [ ] 补：同时持仓的保证金约束也未建模（组合有仓位时间仅 8.6%，
       预计影响小，但没算过）

research/lib/exit_model.py

@@ -0,0 +1,236 @@
+"""出场模拟与费率模型，step41（5m/15m/30m）与 step42（1m）共用。
+
+单独抽出来是因为两边必须用同一份实现——`fast_bsp3` 当初分散在两处的教训。
+
+## 费率模型（用户 2026-08-27 指出）
+
+原先所有数字都按「双边 taker」算（§5.3 的 6bp），这在两个方向上都错了：
+费率档位记高了（见下方 FEE_TAKER 注释），且没有区分出场性质——
+
+  入场   信号在收盘出现，次根开盘市价单进场   → **taker + 滑点**
+  止盈   挂在目标价的限价单被动成交           → **maker，无滑点**
+  止损   stop-market，触发后市价成交          → **taker + 滑点**
+  超时   到点市价平                           → **taker + 滑点**
+
+止损做不成 maker：stop-limit 在急跌里可能不成交，那种情况下的损失远大于
+省下的 2bp。所以按出场原因分别计费，不是一刀切。
+
+分批离场不额外增加费用：手续费按名义额收，入场 1.0、出场 0.5+0.5，总额不变。
+但**第一批必然是止盈成交（maker）**，这正是分批在成本上占便宜的地方。
+
+## 出场配置
+
+  整仓    `s{SL}_tp{TP}_m{MAXB}`
+  分批    `s{SL}_so{目标}_k{剩余半仓止损}_m{MAXB}`
+          到 3 ATR 平一半，剩余半仓止损挪到「开仓价下方 k 个 ATR」。
+          k=0 即保本损，k 等于原 SL 即止损不动，k 更大则是主动放宽。
+
+## 实现
+
+每笔只前推两遍：第一遍记录各价位的首次触及根，第二遍从减仓根起记录剩余
+半仓各止损位的首次触及根。之后所有配置都是解析推导，不再重复走 K 线。
+同一根内止损与目标并存时一律判止损先到，宁可低估。
+"""
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+
+# 单边费率。用户 2026-08-27 给的实际档位：返佣前 taker 0.040% / maker 0.016%，
+# API 返 50% → taker 0.020% / maker 0.008%。
+# 注意：此前这里写的是 0.030/0.010，隐含「原始 taker 6bp」的错误前提，
+# 所以 step41/42 首轮跑出来的所有数字都偏保守（taker 高估 50%）。
+FEE_TAKER = 0.00020
+FEE_MAKER = 0.00008
+SLIP = 0.00010
+
+TP, SL_, TIME = 0, 1, 2   # 出场原因编码
+
+
+def taker_notional(reason: np.ndarray, scaled: np.ndarray) -> np.ndarray:
+	"""每笔走 taker 的名义额（入场 1.0，加上非止盈出场的部分）。滑点只发生在这上面。"""
+	exit_taker = np.where(reason == TP, 0.0, 1.0)
+	exit_taker = np.where(scaled == 1, 0.5 * exit_taker, exit_taker)
+	return 1.0 + exit_taker
+
+
+def fee_of(reason: np.ndarray, scaled: np.ndarray,
+           fee_taker: float = FEE_TAKER, fee_maker: float = FEE_MAKER) -> np.ndarray:
+	"""只算手续费，不含滑点。分批时第一半必然是止盈成交（maker）。"""
+	exit_fee = np.where(reason == TP, fee_maker, fee_taker)
+	exit_fee = np.where(scaled == 1, 0.5 * fee_maker + 0.5 * exit_fee, exit_fee)
+	return fee_taker + exit_fee
+
+
+def cost_of(reason: np.ndarray, scaled: np.ndarray, slip: float = SLIP) -> np.ndarray:
+	"""新口径：入场 taker、止盈 maker、止损/超时 taker，滑点只加在 taker 腿上。"""
+	return fee_of(reason, scaled) + slip * taker_notional(reason, scaled)
+
+
+def all_taker_cost(reason: np.ndarray, scaled: np.ndarray) -> np.ndarray:
+	"""旧口径：进出都当 taker，双边费 + 双边滑点。用来对照新旧差多少。"""
+	return np.full(len(reason), 2.0 * (FEE_TAKER + SLIP))
+
+
+def flat_cost(reason: np.ndarray, scaled: np.ndarray) -> np.ndarray:
+	"""研究口径的固定 5bp，用于和 step28/35/37 的历史数字对齐。"""
+	return np.full(len(reason), 0.0005)
+
+
+def slip_budget(gross: np.ndarray, reason: np.ndarray, scaled: np.ndarray) -> float:
+	"""盈亏平衡的单边滑点上限（bp）：毛收益扣掉手续费后，摊到走 taker 的名义额上。"""
+	net_of_fee = gross.mean() - fee_of(reason, scaled).mean()
+	return net_of_fee / taker_notional(reason, scaled).mean() * 10000
+
+
+def cfg_name(sl: float, target, maxb: int, rstop=None) -> str:
+	"""整仓 s{SL}_tp{T}_m{B}；分批 s{SL}_so{T}_k{K}_m{B}。target=None 表示不设目标。"""
+	t = f"{target:g}" if target is not None else "R"
+	if rstop is None:
+		return f"s{sl:g}_tp{t}_m{maxb}"
+	return f"s{sl:g}_so{t}_k{rstop:g}_m{maxb}"
+
+
+def walk_exits(cdf: pd.DataFrame, sig: pd.DataFrame, sls, tps, maxbs,
+               scale_at: float = 3.0, runners=(5.0, 6.0, 8.0, None),
+               runner_stops=(0.0, 0.5, 1.0, 1.5, 2.0)) -> pd.DataFrame:
+	"""前推每笔信号，解析出全部出场配置的结果。
+
+	每个配置四列：`{cfg}_g` 毛收益率、`{cfg}_r` 出场原因、`{cfg}_c` 是否分批、
+	`{cfg}_b` 持仓根数。另有 `s{SL}_mfe` 各初始止损下的最大有利偏移。
+	"""
+	high = cdf["high"].to_numpy(float)
+	low = cdf["low"].to_numpy(float)
+	open_ = cdf["open"].to_numpy(float)
+	close = cdf["close"].to_numpy(float)
+	atr = cdf["atr"].to_numpy(float)
+	n = len(cdf)
+	horizon = max(maxbs)
+	ups = sorted(set(list(tps) + [scale_at] + [r for r in runners if r]))
+	downs = sorted(set(list(sls) + list(runner_stops)))
+	out = []
+
+	for s, d in zip(sig["entry_idx"].astype(int), sig["direction"].astype(int)):
+		e = s + 1
+		if e >= n - 1:
+			continue
+		a = atr[s]
+		if not np.isfinite(a) or a <= 0:
+			continue
+		entry = open_[e]
+		end = min(e + horizon, n - 1)
+
+		# 第一遍：各价位的首次触及根（与止损无关，纯价格事件）
+		up_bar = {t: None for t in ups}
+		dn_bar = {L: None for L in downs}
+		mfe = {sl: 0.0 for sl in sls}
+		alive = {sl: True for sl in sls}
+		for j in range(e, end + 1):
+			adv = (high[j] - entry) / a if d == 1 else (entry - low[j]) / a
+			ret = (entry - low[j]) / a if d == 1 else (high[j] - entry) / a
+			for L in downs:
+				if dn_bar[L] is None and ret >= L:
+					dn_bar[L] = j
+			for sl in sls:
+				if alive[sl]:
+					if dn_bar[sl] is not None and dn_bar[sl] == j:
+						alive[sl] = False      # 同根内止损优先，不更新 MFE
+					elif adv > mfe[sl]:
+						mfe[sl] = adv
+			for t in ups:
+				if up_bar[t] is None and adv >= t:
+					up_bar[t] = j
+		for sl in sls:
+			mfe[sl] = mfe[sl]
+
+		# 第二遍：从减仓根起，剩余半仓各止损位的首次触及根
+		j0 = up_bar[scale_at]
+		dn_after = {L: None for L in runner_stops}
+		if j0 is not None:
+			for j in range(j0, end + 1):
+				ret = (entry - low[j]) / a if d == 1 else (high[j] - entry) / a
+				for L in runner_stops:
+					if dn_after[L] is None and ret >= L:
+						dn_after[L] = j
+				if all(v is not None for v in dn_after.values()):
+					break
+
+		row = {"sig_idx": s, "direction": d, "atr_pct": a / entry}
+		row.update({f"s{sl:g}_mfe": mfe[sl] for sl in sls})
+
+		def resolve(target, stop_bar, stop_ret, cap, start):
+			"""在 cap 根之前，目标与止损谁先到。返回 (毛收益率, 原因, 出场根)。"""
+			tb = up_bar[target] if target is not None else None
+			if tb is not None and tb <= cap and (stop_bar is None or tb < stop_bar):
+				return target * a / entry, TP, tb
+			if stop_bar is not None and stop_bar <= cap:
+				return stop_ret, SL_, stop_bar
+			k = min(cap, n - 1)
+			return d * (close[k] - entry) / entry, TIME, k
+
+		for sl in sls:
+			dead = dn_bar[sl]
+			for b in maxbs:
+				cap = e + b
+				for t in tps:
+					g, r, xb = resolve(t, dead, -sl * a / entry, cap, e)
+					c = cfg_name(sl, t, b)
+					row[f"{c}_g"], row[f"{c}_r"], row[f"{c}_c"], row[f"{c}_b"] = g, r, 0, xb - e + 1
+				# 分批：先看能否走到减仓点
+				scaled_ok = (j0 is not None and j0 <= cap
+				             and (dead is None or j0 < dead))
+				for rn in runners:
+					for k in runner_stops:
+						c = cfg_name(sl, rn, b, k)
+						if not scaled_ok:
+							g, r, xb = resolve(scale_at, dead, -sl * a / entry, cap, e)
+							row[f"{c}_g"], row[f"{c}_r"] = g, r
+							row[f"{c}_c"], row[f"{c}_b"] = 0, xb - e + 1
+						else:
+							rg, rr, rxb = resolve(rn, dn_after[k], -k * a / entry, cap, j0)
+							row[f"{c}_g"] = 0.5 * (scale_at * a / entry) + 0.5 * rg
+							row[f"{c}_r"], row[f"{c}_c"] = rr, 1
+							row[f"{c}_b"] = rxb - e + 1
+		out.append(row)
+	return pd.DataFrame(out)
+
+
+def stat(g: pd.DataFrame, cfg: str, label: str, cost_fn=cost_of, minn: int = 25) -> dict:
+	"""给一个配置出统计。`滑点余量bp` 是滑点的盈亏平衡上限。"""
+	gross = g[f"{cfg}_g"].to_numpy()
+	if len(gross) < minn:
+		return {}
+	reason, scaled = g[f"{cfg}_r"].to_numpy(), g[f"{cfg}_c"].to_numpy()
+	r = gross - cost_fn(reason, scaled)
+	w, o = r[r > 0], r[r <= 0]
+	sd = r.std(ddof=1)
+	t10 = r[r <= np.quantile(r, 0.90)]
+	return {"口径": label, "笔数": len(r),
+	        "胜率": f"{(r > 0).mean() * 100:.1f}%",
+	        "毛bp": f"{gross.mean() * 10000:.2f}",
+	        "净均收益": f"{r.mean() * 100:+.3f}%",
+	        "中位": f"{np.median(r) * 100:+.3f}%",
+	        "PF": f"{w.sum() / abs(o.sum()):.2f}" if len(o) else "inf",
+	        "t值": f"{r.mean() / (sd / np.sqrt(len(r))):+.2f}",
+	        "剔10%PF": f"{t10[t10 > 0].sum() / abs(t10[t10 <= 0].sum()):.2f}" if (t10 <= 0).any() else "inf",
+	        "滑点余量bp": f"{slip_budget(gross, reason, scaled):.2f}",
+	        "止盈占比": f"{(reason == TP).mean() * 100:.0f}%",
+	        "均持仓": f"{g[f'{cfg}_b'].mean():.1f}"}
+
+
+def rstat(g: pd.DataFrame, cfg: str, label: str, sl: float, cost_fn=cost_of) -> dict:
+	"""R 倍数口径：固定风险仓位下，不同初始止损之间唯一可比的量。
+
+	SL 越宽，每笔风险越大、仓位越小，直接比百分比收益会把仓位差异算成策略优势。
+	"""
+	gross = g[f"{cfg}_g"].to_numpy()
+	reason, scaled = g[f"{cfg}_r"].to_numpy(), g[f"{cfg}_c"].to_numpy()
+	net = gross - cost_fn(reason, scaled)
+	risk = sl * g["atr_pct"].to_numpy()
+	r = net / risk
+	return {"口径": label, "笔数": len(r), "风险%": f"{risk.mean() * 100:.2f}%",
+	        "净均收益": f"{net.mean() * 100:+.3f}%",
+	        "均R": f"{r.mean():+.3f}", "中位R": f"{np.median(r):+.3f}",
+	        "R夏普": f"{r.mean() / r.std(ddof=1):.3f}",
+	        "最差1%R": f"{np.percentile(r, 1):.2f}",
+	        "胜率": f"{(r > 0).mean() * 100:.1f}%"}

research/lib/fast_bsp3.py

@@ -1,170 +1,15 @@
-"""快速三类买卖点：不等笔确认，突破回抽当根即入场。
+"""快速三类买卖点 —— 实现已移入引擎 `chanlun.analysis.fast_bsp`。
 
-引擎的 B3/S3 要等 pullback_bi.sure_time（回拉笔被确认），滞后 9~10 根，
+这里只做转发，保证 step 脚本里的 `from lib.fast_bsp3 import find_fast_bsp3` 不用改，
-此时价格已从回抽低点反弹完毕，入场价被吃掉。
+同时让回测与 web 图表共用同一份代码。设计说明见引擎模块的 docstring。
-
-但三买的形态条件本身是实时可判的：
-  中枢已成 -> 收盘突破 zg -> 收盘未跌回中枢 -> 重新上行
-最后一步发生的当根就能下单，滞后约 2 根。
-
-require_touch 控制是否强求回抽碰到中枢边界。step32/33 的实测表明
-强求反而更差：这等于排除掉「突破后一去不回头」的强势段，而那正是
-缠论里最强的趋势形态。故默认 False（笔数 +21%、滞后 -1 根、PF 2.26→2.37）。
-
-判据本身（收盘越过前一根极值）没有独立预测力：裸用 15 万笔样本 PF 0.95，
-把中枢换成「近20根高点」这类伪阻力位后 PF 0.90。alpha 全部来自中枢结构，
-判据只负责在这个已知价位上确认动能恢复。它也不能用于识别笔端点——
-入场前的回抽极值命中笔端点±2根的比例 19.3%，低于随机基准 22%。
-
-全部判定只使用当根及之前的数据，无未来函数。
 """
 from __future__ import annotations
 
-import numpy as np
+import sys
-import pandas as pd
+from pathlib import Path
 
+sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
 
-def find_fast_bsp3(
+from chanlun.analysis.fast_bsp import find_fast_bsp3  # noqa: F401,E402
-    df: pd.DataFrame,
-    zones: pd.DataFrame,
-    scan: int = 200,
-    pullback_win: int = 30,
-    tol: float = -1.0,
-    max_per_zone: int = 1,
-    diag: dict | None = None,
-    require_touch: bool = False,
-) -> pd.DataFrame:
-    """扫描每个中枢，找突破后回抽不回中枢的入场点。
 
-    zones 需含 zg / zd / available_ts，且 available_ts 已是可用时刻。
+__all__ = ["find_fast_bsp3"]
-    max_per_zone > 1 时，同一中枢在首次入场后继续往后找二次、三次突破回抽，
-    用来检验「趋势里同一中枢反复给机会」是否值得做。
-
-    tol 是「算作回抽中」的边界容差。require_touch=False 时它不再是入场门槛，
-    仅决定哪些K线被视为回抽中而跳过转强判定，故 tol 越大入场越晚。
-    默认负值 = 完全禁用该跳过，突破后每根都检查转强，滞后压到 2.2 根。
-    step34 实测滞后与收益严格单调：9.9根 PF1.88 / 5.8根 2.37 / 2.2根 2.86。
-
-    返回列：
-      entry_idx   实时可下单的K线
-      direction   +1 三买 / -1 三卖
-      bo_idx      突破根
-      pb_idx      回抽极值根
-      lag         entry_idx - bo_idx
-      depth       回抽深度（相对中枢边界，负值表示曾插入中枢）
-      occ         这是该中枢的第几次入场
-    """
-    if zones.empty:
-        return pd.DataFrame()
-
-    ts = df["timestamp"].to_numpy()
-    close = df["close"].to_numpy(dtype=float)
-    high = df["high"].to_numpy(dtype=float)
-    low = df["low"].to_numpy(dtype=float)
-    n = len(df)
-    rows = []
-
-    def note(key: str) -> None:
-        if diag is not None:
-            diag[key] = diag.get(key, 0) + 1
-
-    for zone_i, (_, z) in enumerate(zones.iterrows()):
-        note("中枢总数")
-        zg, zd = float(z["zg"]), float(z["zd"])
-        if zg <= zd:
-            note("×无效中枢")
-            continue
-        start = int(np.searchsorted(ts, z["available_ts"], side="left"))
-        if start >= n - 2:
-            note("×中枢太靠后")
-            continue
-        # 允许多次入场时按比例放宽扫描窗口，否则后几次机会会被窗口截断
-        scan_end = min(start + scan * max_per_zone, n)
-        cursor = start
-
-        for occ in range(1, max_per_zone + 1):
-            if cursor >= n - 2:
-                break
-            # 第一步：找突破。要求突破前确实待在中枢内，避免把远处的价格当突破。
-            was_inside = False
-            bo_idx, d = None, 0
-            for j in range(cursor, scan_end):
-                c = close[j]
-                if zd <= c <= zg:
-                    was_inside = True
-                    continue
-                if not was_inside:
-                    continue
-                bo_idx, d = j, (1 if c > zg else -1)
-                break
-            if bo_idx is None:
-                if occ == 1:
-                    note("×窗口内未突破")
-                break
-
-            edge = zg if d == 1 else zd
-            # 第二步：突破后监控回抽，回抽不跌回中枢且重新顺势 -> 入场
-            touched = False
-            pb_idx = None
-            pb_ext = None
-            entry_idx = None
-            fell_back = False
-            for j in range(bo_idx + 1, min(bo_idx + pullback_win + 1, n)):
-                # 收盘跌回中枢 -> 突破失效
-                if zd <= close[j] <= zg:
-                    fell_back = True
-                    break
-                # 回抽触及边界附近（允许 tol 的毛刺）
-                near = (low[j] <= edge * (1 + tol)) if d == 1 else (high[j] >= edge * (1 - tol))
-                if near:
-                    touched = True
-                    ext = low[j] if d == 1 else high[j]
-                    if pb_ext is None or ((ext < pb_ext) if d == 1 else (ext > pb_ext)):
-                        pb_ext, pb_idx = ext, j
-                    continue
-                # 回抽后重新顺势：收盘创出前一根之上（三买）/ 之下（三卖）
-                # require_touch=False 时不强求回抽碰到中枢边界，
-                # 这样「突破后一去不回头」的强势段也能收进来。
-                if (touched and pb_idx is not None) or not require_touch:
-                    go = close[j] > high[j - 1] if d == 1 else close[j] < low[j - 1]
-                    if go:
-                        entry_idx = j
-                        break
-            if entry_idx is None:
-                if occ == 1:
-                    note("×突破后跌回中枢" if fell_back
-                         else "×回抽未触及边界" if not touched
-                         else "×触及边界但未转强")
-                # 这次突破没走成，从突破点之后继续找下一次
-                cursor = bo_idx + 1
-                continue
-            if pb_ext is None:
-                # 未触及边界就转强（require_touch=False）：取突破至入场间的实际极值
-                seg = slice(bo_idx + 1, entry_idx + 1)
-                pb_ext = float(low[seg].min() if d == 1 else high[seg].max())
-                pb_idx = int((low[seg].argmin() if d == 1 else high[seg].argmax())
-                             + bo_idx + 1)
-            if occ == 1:
-                note("√成交")
-
-            # 回抽深度：>0 表示未插入中枢，越大表示回抽越浅
-            depth = (pb_ext - zg) / zg if d == 1 else (zd - pb_ext) / zd
-            rows.append({
-                "entry_idx": entry_idx, "direction": d,
-                "bo_idx": bo_idx, "pb_idx": pb_idx,
-                "lag": entry_idx - bo_idx,
-                "depth": depth,
-                "zg": zg, "zd": zd,
-                "width_pct": (zg - zd) / close[bo_idx],
-                "occ": occ,
-                "zone_i": zone_i,
-            })
-            cursor = entry_idx + 1
-
-    out = pd.DataFrame(rows)
-    if out.empty:
-        return out
-    # 相邻中枢可能突破到同一根K线，同一时刻只能有一个仓位，保留最早成型的那个
-    return (out.sort_values(["entry_idx", "occ", "zone_i"])
-               .drop_duplicates("entry_idx", keep="first")
-               .reset_index(drop=True))

research/lib/nested_level.py

@@ -17,43 +17,8 @@ import pandas as pd
 
 sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
 
-from chanlun import TF_DF
+# build_htf_zones 已移入引擎，与 web 共用同一份实现；annotate_position 仍是研究专用
-
+from chanlun.analysis.fast_bsp import build_htf_zones  # noqa: F401
-
-def build_htf_zones(df_htf: pd.DataFrame, tf: str, chan: TF_DF | None = None) -> pd.DataFrame:
-    """算 pure 笔中枢，返回带生效时间的区间表。
-
-    available_ts —— 该中枢最早可被使用的时间戳（其确认时刻）。
-    传入已构建好的 chan 可避免重复跑一遍 pipeline（大数据集上省一半时间）。
-    """
-    if chan is None:
-        chan = TF_DF(df_htf, 1, tf)
-    zs_list = chan.cal_bi_zs_list_pure(chan.bi_list)
-    # 用引擎自己的 dataframe 对齐，避免调用方传入的 df 与引擎内部行数不一致
-    src = chan.dataframe if getattr(chan, "dataframe", None) is not None else df_htf
-    ts_of = dict(zip(src["date"].dt.strftime("%Y-%m-%d %H:%M:%S"), src["timestamp"]))
-
-    rows = []
-    for zs in zs_list:
-        bis = getattr(zs, "bi_list", [])
-        if not bis:
-            continue
-        # 中枢可用时刻：构成它的最后一笔被确认之时
-        last_bi = bis[-1]
-        sure_key = str(getattr(last_bi, "sure_time", "") or "")
-        end_key = str(getattr(last_bi, "end_time", "") or "")
-        avail = ts_of.get(sure_key) or ts_of.get(end_key)
-        if avail is None:
-            continue
-        start_key = str(bis[0].start_time)
-        rows.append({
-            "zg": float(zs.zg), "zd": float(zs.zd),
-            "gg": float(getattr(zs, "gg", zs.zg)), "dd": float(getattr(zs, "dd", zs.zd)),
-            "start_ts": ts_of.get(start_key, avail),
-            "available_ts": int(avail),
-        })
-    out = pd.DataFrame(rows)
-    return out.sort_values("available_ts").reset_index(drop=True) if not out.empty else out
 
 
 def annotate_position(

research/lib/shadow_budget.py

@@ -0,0 +1,128 @@
+"""影子测量要对照的那条线：逐币滑点预算、ATR 门控、lag 阈值。
+
+记录与报表由 `research/live/shadow_hb.py` / `shadow_report.py` 负责，
+**这里只放从回测推出来的常数和判据**。分工的理由是这些数会变——
+2026-08-27 一天之内预算就动了四次（3.91 → 11.06 → 14.25 → 15.19bp），
+费率也从 3/1bp 改成 2/0.8bp。写死在 live 侧的任何一份都会静默过期。
+
+## 预算的定义
+
+    余量 = (毛均收益 − 实际手续费) / taker 名义额
+
+分母是 **taker 名义额**（入场 1.0 + 非止盈出场的部分），所以这个数的含义是
+**每条 taker 腿能承受多少 bp 滑点**。止盈挂限价是 maker、不吃滑点，
+不能混进分母——否则会被那 60% 零滑点的止盈腿稀释，低估真实成本。
+
+对照时用入场滑点即可（入场必然是 taker），无需等出场腿。
+
+## 口径必须一致，否则数字不可比
+
+预算算在这套口径上，live 侧的信号路径要对齐，缺一项数就不作数：
+
+  滤网    同向（h1_agree）**+ 中枢阶梯**。只有同向会显著偏低，见 §1.4
+  门控    ATR ≥ ATR_GATE_BP
+  出场    SL 2.0 / 3 ATR 减半 / 剩余半仓止损保持 2.0 / 目标 8 ATR / 48 根
+  费率    taker 2.0bp、maker 0.8bp（返佣后）
+
+> 曾经的 `毛均收益 − 6.0bp` 是错的：6bp 假设双边 taker 且原始费率 0.12%，
+> 而实际是原始 taker 0.040%、返 50% 后 2.0bp，且止盈是 maker。
+"""
+from __future__ import annotations
+
+import numpy as np
+
+from .exit_model import FEE_TAKER, FEE_MAKER
+
+# ATR 门控。阈值是**费率的函数**，不是市场常数——低 ATR 的信号毛质量其实
+# 更好（毛R 1.16 vs 高 ATR 桶的 0.94），断崖只在扣费之后出现。见 §5.3。
+ATR_GATE_BP = 8.0
+
+# lag 探针。补丁后实测 506~642ms，理论下限约 500ms。
+# 超阈值要**停止开新仓**，不能只打日志——退化是静默的，不崩也不报错。
+LAG_ALARM_MS = 800.0
+LAG_WINDOW = 30
+
+# 逐币滑点预算（bp / taker 腿）。取 2026 年——那是七年里 ATR 最低的一年，
+# 也是当前所处的环境，**不要用全样本平均值**，低波动是现状不是尾部情形。
+BUDGET_BP = {
+	"BTC": 8.58,    # 全期 14.61。ATR 中位 2026 仅 6.5bp，11 个币里最低，
+	                # 门控要刷掉 58.5% 的信号 —— **不建议作为 1m 交易标的**，
+	                # 但流动性最好，适合做滑点测量的乐观边界
+	"BNB": 15.48,   # 全期 22.11。2026 门控只保留 25.2%
+	"ETH": 20.64,   # 全期 20.80
+	"SOL": 16.83,   # 全期 28.29
+	"LINK": 9.18,   # 全期 26.24。2026 波动压缩得厉害
+	"LTC": 9.63,    # 全期 23.63
+	"AVAX": 15.02,  # 全期 29.00
+	"XRP": 17.04,   # 全期 24.52
+	"DOGE": 18.55,  # 全期 28.48
+	"ADA": 22.28,   # 全期 23.53。2026 唯一没被波动压缩的
+	"TRX": np.nan,  # 2026 门控后只剩 4.2% 的信号，当前环境基本不能做
+}
+
+# 组合口径（8 个样本外币合计）。单币样本薄时用这个判整体。
+BUDGET_PORTFOLIO_2026 = 15.19
+BUDGET_PORTFOLIO_RECENT = 19.17   # 2025+2026
+BUDGET_PORTFOLIO_FULL = 25.39     # 全样本外
+
+# 腿 → 是否 taker。这不是日志字段，**这就是成本模型本身**：
+# 止盈挂限价是 maker 不吃滑点，止损是 stop-market、超时是市价，都吃。
+# 止损做不成 maker——stop-limit 在急跌里可能不成交，损失远大于省下的费。
+LEG_IS_TAKER = {
+	"entry": True,
+	"scale": False,      # 3 ATR 减半，限价
+	"runner_tp": False,  # 剩余半仓到 8 ATR，限价
+	"stop": True,        # stop-market
+	"timeout": True,     # 48 根到点市价平
+}
+
+
+def gate_threshold_bp(fee_taker: float = FEE_TAKER) -> float:
+	"""换费率档位时的粗略指引，**不是** ATR_GATE_BP 的定义式。
+
+	拟合自 taker 0/1.5/3/6bp 上实测的最优阈值 5/8/9/12bp。当前费率下
+	公式给 7.2 而实测峰值是 8——不必调和：均R 在 5~12bp 之间只在
+	0.972~0.987 之间动，**曲线是平的，精确值不重要**。
+	要记住的是阈值随费率上移，换 VIP 档 / 换交易所要重扫，别抄 8bp。
+	"""
+	return 5.0 + 1.1 * fee_taker * 1e4
+
+
+def fee_rate(leg: str) -> float:
+	return FEE_TAKER if LEG_IS_TAKER[leg] else FEE_MAKER
+
+
+def budget_of(symbol: str) -> float:
+	"""取该币的预算。未收录或当前环境不可做的返回 nan。"""
+	return BUDGET_BP.get(symbol.upper(), np.nan)
+
+
+def lag_healthy(recent_lag_ms, alarm_ms: float = LAG_ALARM_MS,
+                window: int = LAG_WINDOW) -> bool:
+	"""滚动中位数是否还在阈值内。False 时应告警并**停止开新仓**。
+
+	用中位数而非均值：单根的网络抖动不该触发停机，持续退化才该。
+	"""
+	x = np.asarray(recent_lag_ms, dtype=float)[-window:]
+	if len(x) < window:
+		return True
+	return bool(np.median(x) <= alarm_ms)
+
+
+def verdict(measured_slip_bp: float, symbol: str | None = None,
+            budget_bp: float | None = None) -> str:
+	"""把实测滑点翻成 §6 第 4 步的四档判断。
+
+	`measured_slip_bp` 用**每条 taker 腿**的中位滑点，不是每笔的总滑点。
+	"""
+	if budget_bp is None:
+		budget_bp = budget_of(symbol) if symbol else BUDGET_PORTFOLIO_2026
+	if not np.isfinite(budget_bp):
+		return "该币当前环境预算不足，不作交易标的"
+	if measured_slip_bp <= 5:
+		return "可行，进真实 dry run"
+	if measured_slip_bp <= 10:
+		return "可行但仅中高波动期，门控阈值要上调"
+	if measured_slip_bp <= budget_bp:
+		return "仅 2021 型牛市能做，常态不要开"
+	return "放弃该腿"

research/out/step17_fast_bsp3_trades.csv

@@ -0,0 +1,919 @@
+entry_idx,exit_idx,direction,entry,exit,ret,reason,bars_held,gross,risk_pct,boosted,symbol,date,h1_agree,h2_agree,depth,year
+1076,1081,-1,10207.64,10265.816831912982,-0.006499342052911582,sl,5,-0.005699342052911582,0.005699342052911594,False,BTC,2019-09-20 06:45:00+08:00,0.0,0.0,-0.00022793377062850747,2019
+1720,1731,-1,7921.82,8061.213708522294,-0.018396172157697878,sl,11,-0.01759617215769788,0.017596172157697886,False,BTC,2019-09-26 23:45:00+08:00,1.0,0.0,-0.0031707317073170734,2019
+2632,2644,-1,8039.21,7968.878481466982,0.007948560932357586,tp,12,0.008748560932357586,0.004374280466178768,False,BTC,2019-10-06 11:45:00+08:00,1.0,0.0,0.0027294088548107482,2019
+3793,3818,-1,7894.44,7944.063776651064,-0.007085914726195141,sl,25,-0.0062859147261951406,0.0062859147261951406,False,BTC,2019-10-18 14:00:00+08:00,1.0,0.0,0.002735257683185616,2019
+4678,4679,1,9596.62,9487.593235342762,-0.012160954654580388,sl,1,-0.011360954654580388,0.011360954654580454,False,BTC,2019-10-27 19:15:00+08:00,1.0,0.0,-0.0035162493340436867,2019
+5457,5460,1,9251.3,9315.143722242545,0.006101054148340826,tp,3,0.006901054148340826,0.003450527074170432,False,BTC,2019-11-04 22:00:00+08:00,1.0,1.0,0.000569745535365462,2019
+5818,5821,-1,9098.38,9009.422675516824,0.008977270732061686,tp,3,0.009777270732061687,0.004888635366030857,False,BTC,2019-11-08 16:15:00+08:00,1.0,0.0,-0.0016420361247947851,2019
+7384,7391,-1,6979.96,7064.0593971633025,-0.01284869328238306,sl,7,-0.012048693282383059,0.012048693282383071,False,BTC,2019-11-24 23:45:00+08:00,1.0,1.0,0.001269876945552616,2019
+7856,7904,1,7740.96,7741.36,-0.000748326822513017,time,48,5.1673177486983035e-05,0.011394280259283098,False,BTC,2019-11-29 21:45:00+08:00,0.0,1.0,0.003416197828077937,2019
+10079,10084,1,7332.2,7425.97228255147,0.011989105937027114,tp,5,0.012789105937027114,0.006394552968513537,False,BTC,2019-12-23 01:30:00+08:00,1.0,1.0,0.004200553250345767,2019
+10274,10280,-1,7209.73,7270.1049899911195,-0.009174098612724741,sl,6,-0.00837409861272474,0.00837409861272471,False,BTC,2019-12-25 02:15:00+08:00,0.0,0.0,0.0025006885155604316,2019
+11367,11388,1,7464.83,7426.749186460953,-0.005901363800521524,sl,21,-0.0051013638005215235,0.0051013638005214836,False,BTC,2020-01-05 11:30:00+08:00,1.0,1.0,0.0016641821385011174,2020
+11619,11634,1,8075.8,7995.727128844512,-0.010715162727592119,sl,15,-0.009915162727592118,0.009915162727592087,False,BTC,2020-01-08 02:30:00+08:00,1.0,1.0,0.008810390262266286,2020
+12749,12782,-1,8626.41,8721.018150269878,-0.011767267990957772,sl,33,-0.010967267990957772,0.010967267990957834,False,BTC,2020-01-19 21:00:00+08:00,1.0,0.0,0.00025581901841543683,2020
+13661,13666,1,9398.32,9308.838054993004,-0.010321057487614317,sl,5,-0.009521057487614316,0.009521057487614277,False,BTC,2020-01-29 09:00:00+08:00,1.0,1.0,0.017908437474119195,2020
+15589,15595,-1,9706.69,9763.331547984493,-0.0066353102844010385,sl,6,-0.005835310284401038,0.005835310284400997,False,BTC,2020-02-18 11:00:00+08:00,0.0,0.0,-0.0003476103073122481,2020
+16312,16334,-1,9404.39,9251.008840602248,0.015509527720325466,tp,22,0.016309527720325467,0.008154763860162739,False,BTC,2020-02-25 23:45:00+08:00,1.0,1.0,0.0012479079497908253,2020
+21097,21107,-1,6722.26,6770.2498846602875,-0.007938950986764469,sl,10,-0.0071389509867644686,0.007138950986764446,False,BTC,2020-04-15 20:00:00+08:00,1.0,1.0,0.002820166686230816,2020
+21554,21570,-1,7154.66,7091.797835123753,0.007986184790925973,tp,16,0.008786184790925973,0.004393092395463005,False,BTC,2020-04-20 14:15:00+08:00,0.0,1.0,0.001208623087621641,2020
+23221,23223,1,9543.19,9462.47933494767,-0.009257409425184977,sl,2,-0.008457409425184977,0.008457409425184918,False,BTC,2020-05-07 23:00:00+08:00,1.0,1.0,0.00650975825004068,2020
+24754,24762,1,9188.94,9137.518263459233,-0.006396046610465146,sl,8,-0.005596046610465146,0.005596046610465112,False,BTC,2020-05-23 22:15:00+08:00,1.0,0.0,-0.004954999397068737,2020
+25227,25239,1,9399.28,9519.7192959904,0.012013672535598454,tp,12,0.012813672535598454,0.0064068362677992706,False,BTC,2020-05-28 20:30:00+08:00,0.0,0.0,0.00011484290357524277,2020
+25652,25653,1,9734.51,9842.399131341195,0.01028315994756747,tp,1,0.011083159947567471,0.005541579973783751,False,BTC,2020-06-02 06:45:00+08:00,1.0,1.0,-0.0013809919070346015,2020
+27310,27325,-1,9295.24,9342.240513261428,-0.0058564066405415935,sl,15,-0.005056406640541593,0.005056406640541632,False,BTC,2020-06-19 13:15:00+08:00,1.0,1.0,0.0006844634287981182,2020
+29183,29213,1,9423.01,9386.960793482587,-0.00462565724937287,sl,30,-0.0038256572493728692,0.0038256572493728033,False,BTC,2020-07-09 01:30:00+08:00,0.0,1.0,0.0035986307231492746,2020
+30399,30410,1,9329.6,9396.770255534977,0.006399692970221296,tp,11,0.007199692970221296,0.0035998464851106082,False,BTC,2020-07-21 17:30:00+08:00,1.0,1.0,0.002425500164238022,2020
+30547,30548,1,9519.17,9483.77061582041,-0.004518746926422254,sl,1,-0.003718746926422254,0.003718746926422191,False,BTC,2020-07-23 06:30:00+08:00,0.0,1.0,0.004210889526450547,2020
+31440,31471,1,11614.52,11516.386978725643,-0.009249167186793583,sl,31,-0.008449167186793583,0.008449167186793538,False,BTC,2020-08-01 13:45:00+08:00,1.0,0.0,-0.0011689668588715841,2020
+32289,32291,1,12045.48,11967.321375819023,-0.0072886267862282295,sl,2,-0.006488626786228229,0.006488626786228205,False,BTC,2020-08-10 10:00:00+08:00,1.0,1.0,0.004017607720308061,2020
+32614,32617,1,11590.73,11497.374187577876,-0.008854351401691142,sl,3,-0.008054351401691142,0.00805435140169111,False,BTC,2020-08-13 19:15:00+08:00,1.0,1.0,0.0013693500093622973,2020
+33008,33015,1,12132.85,12348.075032635035,0.016939033502848463,tp,7,0.017739033502848462,0.008869516751424222,False,BTC,2020-08-17 21:45:00+08:00,1.0,0.0,0.000806667783548266,2020
+33251,33266,-1,11729.04,11791.088936728875,-0.006090197384344648,sl,15,-0.005290197384344647,0.00529019738434463,False,BTC,2020-08-20 10:30:00+08:00,0.0,1.0,-0.0011532599491956648,2020
+33562,33576,-1,11585.89,11525.904777228985,0.004377437622057035,tp,14,0.005177437622057035,0.002588718811028518,False,BTC,2020-08-23 16:15:00+08:00,0.0,1.0,-0.00038783054814803595,2020
+34543,34591,-1,11377.81,11419.12,-0.004430751436348587,time,48,-0.0036307514363485864,0.009790163199069786,False,BTC,2020-09-02 21:30:00+08:00,1.0,1.0,0.00038898155830655886,2020
+34634,34647,-1,10800.53,10486.698631217772,0.028257034125383506,tp,13,0.029057034125383505,0.014528517062691723,False,BTC,2020-09-03 20:15:00+08:00,1.0,1.0,0.013479036574487098,2020
+37188,37194,-1,10770.77,10694.352277439513,0.006294917314220539,tp,6,0.007094917314220539,0.0035474586571102406,False,BTC,2020-09-30 10:45:00+08:00,1.0,0.0,0.00017632939438698805,2020
+37816,37828,1,10726.87,10683.389599060092,-0.004853409889362748,sl,12,-0.0040534098893627475,0.004053409889362815,False,BTC,2020-10-06 23:45:00+08:00,0.0,0.0,0.0026164537941397994,2020
+38004,38019,1,10909.99,10841.707532243687,-0.007058710388947474,sl,15,-0.006258710388947474,0.006258710388947481,False,BTC,2020-10-08 22:45:00+08:00,1.0,0.0,0.007699742537247636,2020
+38748,38760,-1,11305.01,11355.440575602132,-0.005260904997176587,sl,12,-0.004460904997176587,0.0044609049971766545,False,BTC,2020-10-16 16:45:00+08:00,1.0,0.0,-2.377863674437319e-05,2020
+40192,40193,1,13890.98,14123.513302889363,0.015939877452085006,tp,1,0.016739877452085005,0.008369938726042508,False,BTC,2020-10-31 17:45:00+08:00,1.0,0.0,-0.0005294236531551147,2020
+40583,40598,1,13724.19,13915.580553715448,0.01314548994989488,tp,15,0.01394548994989488,0.006972744974947408,False,BTC,2020-11-04 19:30:00+08:00,0.0,1.0,-0.00037214063974355324,2020
+41006,41010,-1,15270.18,15405.71648986001,-0.009675893398768657,sl,4,-0.008875893398768656,0.008875893398768639,False,BTC,2020-11-09 05:15:00+08:00,1.0,0.0,-0.00022092646582441559,2020
+42032,42069,1,18023.42,17836.425950929774,-0.011175059176905646,sl,37,-0.010375059176905645,0.01037505917690573,False,BTC,2020-11-19 21:45:00+08:00,1.0,0.0,-0.002335121277279981,2020
+42100,42102,1,18184.86,18436.877563745216,0.013058647454267765,tp,2,0.013858647454267766,0.006929323727133889,False,BTC,2020-11-20 14:45:00+08:00,1.0,0.0,0.0009583054058395293,2020
+42480,42481,-1,18346.99,18096.862068936833,0.012833186210008776,tp,1,0.013633186210008776,0.006816593105004437,False,BTC,2020-11-24 13:45:00+08:00,1.0,0.0,0.00039180621037227355,2020
+43183,43231,1,18835.87,18888.39,0.0019882970099071844,time,48,0.0027882970099071844,0.024303498699666477,False,BTC,2020-12-01 21:30:00+08:00,0.0,0.0,0.022509068101773343,2020
+43264,43279,-1,19047.14,19268.013068084387,-0.01239612771704242,sl,15,-0.01159612771704242,0.011596127717042357,False,BTC,2020-12-02 17:45:00+08:00,0.0,0.0,-0.001976581524273429,2020
+43893,43928,-1,18279.99,17933.28421643968,0.018166409913808638,tp,35,0.018966409913808636,0.009483204956904353,False,BTC,2020-12-09 07:00:00+08:00,1.0,1.0,0.009795425257731975,2020
+44092,44101,-1,18010.0,17763.67023581231,0.012877388350232584,tp,9,0.013677388350232585,0.006838694175116267,False,BTC,2020-12-11 08:45:00+08:00,1.0,0.0,0.0003353893600616888,2020
+45925,45929,1,28105.9,27809.188372304994,-0.011356916081499163,sl,4,-0.010556916081499163,0.010556916081499142,False,BTC,2020-12-30 11:00:00+08:00,1.0,1.0,-0.0037737410071942393,2020
+46667,46679,1,35420.8,36783.71933707012,0.03767793773912835,tp,12,0.03847793773912835,0.01923896886956415,False,BTC,2021-01-07 04:30:00+08:00,1.0,0.0,0.01878550724637677,2021
+47222,47258,1,34089.0,33172.56252237664,-0.027683671495888993,sl,36,-0.026883671495888994,0.026883671495889046,False,BTC,2021-01-12 23:15:00+08:00,0.0,1.0,0.002818706687763171,2021
+47335,47344,1,36091.71,37141.56635886247,0.02828857349409234,tp,9,0.02908857349409234,0.014544286747046173,False,BTC,2021-01-14 03:30:00+08:00,1.0,0.0,0.0005140514893735835,2021
+48027,48035,-1,35270.73,34463.53032250836,0.022085822819421105,tp,8,0.022885822819421104,0.011442911409710568,False,BTC,2021-01-21 08:30:00+08:00,1.0,0.0,-0.003024313850878869,2021
+49565,49568,1,39040.04,38653.93107527527,-0.010690075028732787,sl,3,-0.009890075028732787,0.009890075028732848,False,BTC,2021-02-06 09:00:00+08:00,1.0,1.0,-0.000878643289319631,2021
+51993,51996,1,50062.81,50929.72385339253,0.016516524050338544,tp,3,0.017316524050338542,0.008658262025169275,False,BTC,2021-03-03 16:00:00+08:00,1.0,1.0,0.0002551814781114674,2021
+52528,52530,1,51889.85,51458.41333810557,-0.00911447117103684,sl,2,-0.00831447117103684,0.008314471171036873,False,BTC,2021-03-09 05:45:00+08:00,1.0,1.0,-0.0010700389105058367,2021
+52961,53000,1,59426.74,60774.81724973707,0.021884691264186306,tp,39,0.022684691264186305,0.01134234563209314,False,BTC,2021-03-13 18:00:00+08:00,1.0,1.0,-0.0018071427325226678,2021
+53504,53529,1,57421.0,58609.7045970198,0.019901565577398538,tp,25,0.020701565577398537,0.010350782788699239,False,BTC,2021-03-19 09:45:00+08:00,0.0,0.0,-0.0011226530970346235,2021
+54066,54114,-1,52479.97,52897.4,-0.008754082290824486,time,48,-0.007954082290824485,0.015501297899183097,False,BTC,2021-03-25 06:15:00+08:00,1.0,1.0,0.009115551718152448,2021
+54332,54338,1,56180.01,55677.8566561866,-0.009738292175693797,sl,6,-0.008938292175693797,0.008938292175693826,False,BTC,2021-03-28 00:45:00+08:00,1.0,0.0,0.0009064370383564383,2021
+54495,54522,1,57739.99,57234.66007001838,-0.009551818799788862,sl,27,-0.008751818799788862,0.008751818799788891,False,BTC,2021-03-29 17:30:00+08:00,1.0,1.0,0.012202486678507993,2021
+54797,54816,1,58955.0,58516.51194379823,-0.008237673754588611,sl,19,-0.007437673754588611,0.007437673754588562,False,BTC,2021-04-01 21:00:00+08:00,1.0,0.0,-0.0011983222462738363,2021
+55274,55276,1,58981.71,58631.69100744089,-0.0067343649507467865,sl,2,-0.005934364950746786,0.005934364950746821,False,BTC,2021-04-06 20:15:00+08:00,0.0,0.0,0.00013139376967699187,2021
+55364,55397,-1,56857.17,55656.60261686567,0.020315496658281272,tp,33,0.02111549665828127,0.010557748329140665,False,BTC,2021-04-07 18:45:00+08:00,1.0,0.0,-0.005345631298392798,2021
+56334,56352,-1,60840.0,59916.07894578944,0.014386079129036118,tp,18,0.015186079129036118,0.0075930395645180775,False,BTC,2021-04-17 21:15:00+08:00,1.0,1.0,-0.001276967208600098,2021
+56841,56845,-1,52709.99,51191.765085689156,0.02800336183540999,tp,4,0.028803361835409987,0.014401680917704987,False,BTC,2021-04-23 04:00:00+08:00,1.0,1.0,-0.001648304095225283,2021
+57589,57592,1,56225.48,57155.83599109152,0.015746875030529107,tp,3,0.016546875030529106,0.008273437515264577,False,BTC,2021-04-30 23:00:00+08:00,1.0,1.0,0.006636154655310527,2021
+58365,58409,1,58941.0,58309.52841333695,-0.011513621870396675,sl,44,-0.010713621870396674,0.010713621870396633,False,BTC,2021-05-09 01:00:00+08:00,0.0,1.0,0.0013847083189506939,2021
+59126,59133,-1,47963.58,46982.95095819804,0.01964528456387041,tp,7,0.02044528456387041,0.010222642281935188,False,BTC,2021-05-16 23:15:00+08:00,1.0,0.0,-0.0024861855670103455,2021
+59699,59703,-1,37502.82,38301.70761431125,-0.02210206779946815,sl,4,-0.021302067799468152,0.021302067799468055,False,BTC,2021-05-22 22:30:00+08:00,0.0,1.0,-0.004450649350649313,2021
+60534,60545,1,35741.01,36682.512625772186,0.025542362058939682,tp,11,0.02634236205893968,0.013171181029469866,False,BTC,2021-05-31 15:15:00+08:00,1.0,0.0,0.00516325820221998,2021
+61262,61283,-1,33962.61,32919.92657592727,0.02990092151553526,tp,21,0.03070092151553526,0.015350460757767683,False,BTC,2021-06-08 05:15:00+08:00,1.0,1.0,0.015897443181818082,2021
+62008,62016,1,40056.8,40786.70679963437,0.017421795041899644,tp,8,0.018221795041899642,0.009110897520949861,False,BTC,2021-06-15 23:45:00+08:00,0.0,0.0,-0.0026410681955340684,2021
+63792,63800,1,35185.64,35634.457851252046,0.01195571088807952,tp,8,0.01275571088807952,0.0063778554440397555,False,BTC,2021-07-04 13:45:00+08:00,1.0,0.0,0.010076853801801759,2021
+63941,63947,1,34131.61,33819.611111829494,-0.009941053942972707,sl,6,-0.009141053942972707,0.009141053942972608,False,BTC,2021-07-06 03:00:00+08:00,1.0,0.0,0.0015063231296537998,2021
+64155,64183,-1,33209.11,32630.652126925877,0.01661865027620804,tp,28,0.01741865027620804,0.00870932513810404,False,BTC,2021-07-08 08:30:00+08:00,1.0,1.0,0.009093824989902265,2021
+64430,64442,1,33352.04,33776.58356795167,0.011929163432031946,tp,12,0.012729163432031947,0.0063645817160159985,False,BTC,2021-07-11 05:15:00+08:00,0.0,1.0,0.00195812622382889,2021
+64850,64867,-1,32472.62,32057.033682456735,0.011998053176591977,tp,17,0.012798053176591977,0.00639902658829599,False,BTC,2021-07-15 14:15:00+08:00,1.0,1.0,-0.0005500581974399967,2021
+65263,65269,-1,30664.6,30919.196413407863,-0.009102616483106384,sl,6,-0.008302616483106384,0.00830261648310633,False,BTC,2021-07-19 21:30:00+08:00,1.0,1.0,0.00905908798972378,2021
+65600,65607,1,32741.29,32511.521873253692,-0.007817687047343234,sl,7,-0.007017687047343233,0.007017687047343278,False,BTC,2021-07-23 09:45:00+08:00,1.0,0.0,-0.001011128284389463,2021
+65686,65689,1,33275.89,33781.04269187153,0.014380741728366446,tp,3,0.015180741728366446,0.007590370864183251,False,BTC,2021-07-24 07:15:00+08:00,1.0,0.0,0.006056183206106763,2021
+66068,66069,1,39147.03,38693.5679849357,-0.0123835611300347,sl,1,-0.0115835611300347,0.011583561130034711,False,BTC,2021-07-28 06:45:00+08:00,1.0,0.0,0.004502622422063525,2021
+66351,66361,1,41372.0,42389.111929317645,0.02378454822869683,tp,10,0.02458454822869683,0.012292274114348455,False,BTC,2021-07-31 05:30:00+08:00,1.0,1.0,0.0032446882944667825,2021
+67117,67120,1,44144.01,43672.23177391669,-0.01148725351601071,sl,3,-0.010687253516010709,0.010687253516010737,False,BTC,2021-08-08 05:00:00+08:00,1.0,0.0,0.00010622119815669544,2021
+67269,67309,1,45777.6,45271.956194020575,-0.011845660016676795,sl,40,-0.011045660016676794,0.01104566001667672,False,BTC,2021-08-09 19:00:00+08:00,1.0,0.0,-0.0018288059235469915,2021
+68044,68050,-1,46667.09,45889.42014251797,0.01586420292077406,tp,6,0.016664202920774057,0.008332101460387029,False,BTC,2021-08-17 20:45:00+08:00,0.0,0.0,-0.001446193109315185,2021
+69511,69521,-1,48221.01,48576.9561827438,-0.00818155801265463,sl,10,-0.007381558012654629,0.007381558012654619,False,BTC,2021-09-02 03:30:00+08:00,0.0,0.0,0.0012715758520986018,2021
+69871,69893,1,50258.52,50748.11381391446,0.00894150878128651,tp,22,0.00974150878128651,0.0048707543906432406,False,BTC,2021-09-05 21:30:00+08:00,1.0,1.0,-3.727963676005848e-05,2021
+70581,70587,1,46364.37,45987.10650684472,-0.008936926979818382,sl,6,-0.008136926979818382,0.008136926979818368,False,BTC,2021-09-13 07:00:00+08:00,1.0,1.0,0.0057219415769821395,2021
+71412,71415,-1,42419.5,42893.07347947735,-0.01196405142628632,sl,3,-0.01116405142628632,0.011164051426286285,False,BTC,2021-09-21 22:45:00+08:00,1.0,0.0,0.002176648799813597,2021
+71689,71695,-1,41100.0,41851.97781320563,-0.019096297158287853,sl,6,-0.018296297158287855,0.018296297158287907,False,BTC,2021-09-24 20:00:00+08:00,1.0,0.0,0.003299389741966901,2021
+71904,71911,-1,43130.78,43406.66505689027,-0.007196477339159431,sl,7,-0.006396477339159431,0.006396477339159489,False,BTC,2021-09-27 01:45:00+08:00,0.0,1.0,-0.0041060183156097895,2021
+72666,72699,1,48647.42,49664.47174044002,0.02010659156107402,tp,33,0.02090659156107402,0.010453295780537004,False,BTC,2021-10-05 00:15:00+08:00,0.0,1.0,0.005290678249248258,2021
+72843,72845,1,52481.5,53481.49751932635,0.01825428616419791,tp,2,0.01905428616419791,0.009527143082098954,False,BTC,2021-10-06 20:30:00+08:00,1.0,0.0,0.009200077324569848,2021
+73166,73183,1,54740.56,54447.443470409475,-0.006154649817073903,sl,17,-0.005354649817073903,0.005354649817073925,False,BTC,2021-10-10 05:15:00+08:00,0.0,1.0,-0.0005972515040597309,2021
+73454,73488,1,55703.73,55109.9379214904,-0.011459826164416757,sl,34,-0.010659826164416757,0.010659826164416807,False,BTC,2021-10-13 05:15:00+08:00,0.0,0.0,-0.0014646887029316292,2021
+74300,74348,-1,62870.17,63091.75,-0.004324405930507294,time,48,-0.003524405930507294,0.015566868078392158,False,BTC,2021-10-22 00:45:00+08:00,1.0,1.0,-0.002712576634209825,2021
+75629,75631,1,61983.83,61622.91471183104,-0.006622732931620373,sl,2,-0.005822732931620373,0.005822732931620409,False,BTC,2021-11-04 21:00:00+08:00,0.0,0.0,0.00022648253452481443,2021
+76054,76057,1,67728.53,67241.48934327549,-0.007991070834174434,sl,3,-0.0071910708341744334,0.007191070834174466,False,BTC,2021-11-09 07:15:00+08:00,1.0,1.0,-0.0015485947157099493,2021
+76739,76742,-1,61865.45,60580.20295073717,0.01997487594873758,tp,3,0.02077487594873758,0.010387437974368786,False,BTC,2021-11-16 10:30:00+08:00,1.0,1.0,0.0016926143712607895,2021
+77184,77186,1,57939.75,58626.563577809844,0.011053927188326563,tp,2,0.011853927188326563,0.005926963594163271,False,BTC,2021-11-21 01:45:00+08:00,0.0,1.0,0.0011878040305768349,2021
+77732,77780,-1,54450.0,54107.37,0.005492561983471026,time,48,0.006292561983471026,0.01332970724508316,False,BTC,2021-11-26 18:45:00+08:00,1.0,1.0,0.0042290013376463705,2021
+78681,78682,1,48458.3,47894.694770639726,-0.012430726405182951,sl,1,-0.01163072640518295,0.01163072640518289,False,BTC,2021-12-06 16:00:00+08:00,0.0,0.0,0.002924625256930757,2021
+78745,78793,1,50631.8,51300.37,0.012404547339814102,time,48,0.013204547339814103,0.010363965964304916,False,BTC,2021-12-07 08:00:00+08:00,1.0,1.0,0.014422289766970577,2021
+78907,78955,-1,50475.6,49653.32,0.015490643399979373,time,48,0.016290643399979373,0.011288509705299248,False,BTC,2021-12-09 00:30:00+08:00,0.0,1.0,-0.002296643334124606,2021
+79913,79921,1,47862.22,47522.80524462923,-0.007891496286022117,sl,8,-0.007091496286022117,0.007091496286022088,False,BTC,2021-12-19 12:00:00+08:00,1.0,1.0,0.004164242040902426,2021
+80830,80878,-1,48028.85,47777.6,0.0044312308123138485,time,48,0.005231230812313849,0.008872129272696094,False,BTC,2021-12-29 01:15:00+08:00,1.0,1.0,0.0018340274874357107,2021
+81417,81419,-1,46301.16,45697.92481036935,0.012228511372731265,tp,2,0.013028511372731266,0.006514255686365652,False,BTC,2022-01-04 04:00:00+08:00,1.0,1.0,-0.001497752728920836,2022
+81608,81615,-1,44673.22,43468.117941887824,0.026175939010265603,tp,7,0.0269759390102656,0.013487969505132829,False,BTC,2022-01-06 03:45:00+08:00,1.0,1.0,-0.004248054879852951,2022
+82583,82631,-1,43161.77,42994.0,0.003087004633961879,time,48,0.003887004633961879,0.005437820510440227,False,BTC,2022-01-16 07:30:00+08:00,1.0,0.0,-0.0021937345099888044,2022
+83028,83033,1,42941.36,43460.64039839311,0.011292779511247618,tp,5,0.012092779511247619,0.006046389755623836,False,BTC,2022-01-20 22:45:00+08:00,1.0,1.0,0.004895424278339533,2022
+83060,83071,-1,41137.96,40202.33210609536,0.02194366288227804,tp,11,0.02274366288227804,0.011371831441139065,False,BTC,2022-01-21 06:45:00+08:00,1.0,0.0,0.0026045424738279418,2022
+83481,83483,1,36329.48,36020.28674259676,-0.009310808781277349,sl,2,-0.008510808781277348,0.008510808781277305,False,BTC,2022-01-25 16:00:00+08:00,0.0,0.0,0.002587817429242612,2022
+83638,83647,1,36788.89,36293.02071201717,-0.014278778185012662,sl,9,-0.013478778185012662,0.013478778185012575,False,BTC,2022-01-27 07:15:00+08:00,0.0,1.0,-0.0015921052631579426,2022
+84096,84144,1,38449.98,38433.9,-0.0012182056791707498,time,48,-0.00041820567917074975,0.008890608935480116,False,BTC,2022-02-01 01:45:00+08:00,1.0,1.0,0.002868421052631579,2022
+84695,84697,1,42548.99,42224.373170683524,-0.00842924876281373,sl,2,-0.00762924876281373,0.007629248762813804,False,BTC,2022-02-07 07:30:00+08:00,1.0,0.0,0.009257179097950206,2022
+85170,85218,-1,42279.83,42285.95,-0.0009447498724568038,time,48,-0.00014474987245680372,0.011229631504703502,False,BTC,2022-02-12 06:15:00+08:00,1.0,1.0,-0.0019253337073661216,2022
+86776,86803,1,40792.8,41959.17106448568,0.027792571838306673,tp,27,0.02859257183830667,0.014296285919153346,False,BTC,2022-02-28 23:45:00+08:00,1.0,1.0,-0.003448362720403059,2022
+87452,87457,-1,38823.3,37992.7902015409,0.020592045458760713,tp,5,0.021392045458760712,0.010696022729380396,False,BTC,2022-03-08 00:45:00+08:00,1.0,0.0,0.0035628636452834273,2022
+88512,88521,1,41634.8,42320.72248007735,0.01567473940255134,tp,9,0.016474739402551338,0.008237369701275699,False,BTC,2022-03-19 01:45:00+08:00,1.0,1.0,-0.0013737010779839211,2022
+88841,88842,1,42363.4,42977.1386653557,0.01368747421962583,tp,1,0.01448747421962583,0.007243737109812901,False,BTC,2022-03-22 12:00:00+08:00,1.0,1.0,0.002191102219681814,2022
+90538,90586,-1,42646.1,42361.9,0.005864149828471938,time,48,0.006664149828471938,0.009921124548351003,False,BTC,2022-04-09 04:15:00+08:00,1.0,1.0,-0.000299303944315579,2022
+91446,91466,-1,38918.3,39152.50069460389,-0.00681775243532963,sl,20,-0.0060177524353296295,0.006017752435329659,False,BTC,2022-04-18 15:15:00+08:00,0.0,1.0,0.005210191082802548,2022
+92248,92263,-1,38546.9,37823.53005647777,0.017965969339226496,tp,15,0.018765969339226495,0.009382984669613202,False,BTC,2022-04-26 23:45:00+08:00,1.0,1.0,0.0010010443465193436,2022
+92840,92888,1,38551.9,38553.1,-0.0007688731294696996,time,48,3.112687053030044e-05,0.008835824578962832,False,BTC,2022-05-03 03:45:00+08:00,0.0,0.0,-0.0002908881259990709,2022
+93328,93329,-1,34972.2,35215.044344354115,-0.0077439252993554385,sl,1,-0.006943925299355438,0.006943925299355509,False,BTC,2022-05-08 05:45:00+08:00,1.0,1.0,-0.00013966480446927373,2022
+93848,93854,-1,30172.3,30585.948453546087,-0.014509543307805097,sl,6,-0.013709543307805097,0.013709543307805126,False,BTC,2022-05-13 15:45:00+08:00,1.0,0.0,-0.0025823913597761493,2022
+94012,94056,-1,29949.7,30277.700000709945,-0.011751695700122014,sl,44,-0.010951695700122013,0.010951695700122,False,BTC,2022-05-15 08:45:00+08:00,0.0,1.0,-0.003638112293967457,2022
+94552,94600,-1,29142.0,29246.8,-0.004396184201496098,time,48,-0.0035961842014960977,0.01079242666801459,False,BTC,2022-05-20 23:45:00+08:00,1.0,0.0,-0.0015151669918317177,2022
+94993,95015,-1,29791.0,29414.42246072892,0.011840647822197254,tp,22,0.012640647822197254,0.0063203239110985986,False,BTC,2022-05-25 14:00:00+08:00,1.0,0.0,0.0015778908858491757,2022
+96787,96792,-1,27072.7,26541.605272136523,0.01881735356515891,tp,5,0.01961735356515891,0.009808676782579438,False,BTC,2022-06-13 06:30:00+08:00,1.0,0.0,0.0032315321414847216,2022
+97302,97343,-1,19230.0,18615.582206014828,0.03115100332736206,tp,41,0.03195100332736206,0.01597550166368103,False,BTC,2022-06-18 15:15:00+08:00,1.0,1.0,0.03538925222598685,2022
+98451,98460,-1,19495.3,19170.86404828558,0.015841752202552333,tp,9,0.016641752202552332,0.008320876101276175,False,BTC,2022-06-30 14:30:00+08:00,1.0,1.0,0.006716867469879481,2022
+98964,98970,-1,19453.4,19634.236886535342,-0.010095901309557221,sl,6,-0.00929590130955722,0.009295901309557255,False,BTC,2022-07-05 22:45:00+08:00,1.0,1.0,0.0016430436877203508,2022
+100309,100315,1,22747.4,23356.194752026975,0.025963267539453896,tp,6,0.026763267539453895,0.013381633769726965,False,BTC,2022-07-19 23:00:00+08:00,1.0,1.0,0.0035827972456414065,2022
+100606,100616,-1,23012.3,22584.04608818948,0.017809783107751928,tp,10,0.018609783107751927,0.009304891553875955,False,BTC,2022-07-23 01:15:00+08:00,1.0,0.0,-0.0015666244608021976,2022
+101092,101124,1,22544.9,23101.91637630246,0.023906979241533895,tp,32,0.024706979241533894,0.012353489620766944,False,BTC,2022-07-28 02:45:00+08:00,1.0,1.0,-0.00275,2022
+101668,101716,-1,22973.0,23028.6,-0.003220232446785293,time,48,-0.0024202324467852932,0.009687316548625776,False,BTC,2022-08-03 02:45:00+08:00,0.0,0.0,0.0003315821204030974,2022
+103063,103087,-1,23845.7,23500.08680089094,0.01369373258529053,tp,24,0.01449373258529053,0.007246866292645288,False,BTC,2022-08-17 15:30:00+08:00,0.0,1.0,0.011260844727746475,2022
+103804,103826,1,21467.0,21696.09498280411,0.009871960814464453,tp,22,0.010671960814464454,0.005335980407232216,False,BTC,2022-08-25 08:45:00+08:00,0.0,0.0,0.004163934426229576,2022
+105256,105257,1,19936.0,20224.647274399034,0.013678695545697951,tp,1,0.014478695545697952,0.007239347772848965,False,BTC,2022-09-09 11:45:00+08:00,1.0,1.0,-0.0009034589571502323,2022
+105680,105699,-1,21188.3,20551.91190510968,0.029234882217559724,tp,19,0.030034882217559723,0.01501744110877988,False,BTC,2022-09-13 21:45:00+08:00,0.0,0.0,-0.0032587260665452125,2022
+108292,108305,-1,19216.6,19001.972283115065,0.010368870501802284,tp,13,0.011168870501802285,0.005584435250901149,False,BTC,2022-10-11 02:45:00+08:00,1.0,0.0,0.0001501812532367403,2022
+109067,109070,1,19378.8,19269.272905004735,-0.006451902852357454,sl,3,-0.005651902852357454,0.0056519028523573855,False,BTC,2022-10-19 04:30:00+08:00,0.0,0.0,-0.00020376175548596944,2022
+109245,109251,-1,19135.9,18973.47634339909,0.0076879026646728445,tp,6,0.008487902664672845,0.004243951332336405,False,BTC,2022-10-21 01:00:00+08:00,1.0,0.0,-0.00019195849546047874,2022
+109956,109995,-1,20214.5,19999.08374995535,0.009856521311170246,tp,39,0.010656521311170246,0.005328260655585102,False,BTC,2022-10-28 10:45:00+08:00,0.0,1.0,0.00010821925220482411,2022
+110299,110328,-1,20350.1,20491.67898182089,-0.007757163936338912,sl,29,-0.0069571639363389115,0.006957163936338859,False,BTC,2022-11-01 00:30:00+08:00,0.0,0.0,0.005244356984640176,2022
+111014,111017,-1,20364.3,20107.657457969843,0.011802571265899472,tp,3,0.012602571265899472,0.006301285632949752,False,BTC,2022-11-08 11:15:00+08:00,0.0,1.0,-0.0004505464692660894,2022
+111305,111346,1,17147.5,16888.40719891155,-0.015909654532057095,sl,41,-0.015109654532057096,0.015109654532057079,False,BTC,2022-11-11 12:00:00+08:00,0.0,1.0,0.0032408500590319633,2022
+113359,113363,-1,16877.5,16958.523460095155,-0.005600679016154959,sl,4,-0.004800679016154959,0.004800679016154854,False,BTC,2022-12-02 21:30:00+08:00,1.0,0.0,0.0036651053864167767,2022
+114166,114173,-1,17115.7,17140.87942708444,-0.00227113042904703,sl,7,-0.0014711304290470299,0.0014711304290470426,False,BTC,2022-12-11 07:15:00+08:00,1.0,0.0,-0.000517956805894105,2022
+115294,115305,-1,16617.4,16668.46025574574,-0.00387269824074396,sl,11,-0.00307269824074396,0.0030726982407439556,False,BTC,2022-12-23 01:15:00+08:00,1.0,1.0,-0.00042514970059871526,2022
+116035,116052,-1,16498.4,16427.70412087335,0.003485014251482056,tp,17,0.004285014251482056,0.00214250712574104,False,BTC,2022-12-30 18:30:00+08:00,1.0,1.0,0.0008503914212995034,2022
+118081,118089,1,21384.4,21610.108082225728,0.009754800799916138,tp,8,0.010554800799916138,0.005277400399958091,False,BTC,2023-01-21 02:00:00+08:00,1.0,1.0,0.0017325800376647492,2023
+118576,118577,1,23593.3,23370.586666674877,-0.010239685560100633,sl,1,-0.009439685560100632,0.009439685560100677,False,BTC,2023-01-26 05:45:00+08:00,1.0,1.0,0.00986446143082492,2023
+119604,119610,-1,23159.8,22997.490952364056,0.006208223198643468,tp,6,0.007008223198643468,0.0035041115993217674,False,BTC,2023-02-05 22:45:00+08:00,1.0,0.0,-0.00016288878982199137,2023
+122690,122691,-1,21314.0,21048.164692206414,0.01167233310470049,tp,1,0.01247233310470049,0.006236166552350265,False,BTC,2023-03-10 02:15:00+08:00,1.0,0.0,-0.0008491879350347691,2023
+123058,123061,1,23345.1,24082.90320702327,0.0308041998973349,tp,3,0.0316041998973349,0.015802099948667436,False,BTC,2023-03-13 22:15:00+08:00,1.0,0.0,0.014541744299529735,2023
+123383,123390,1,25025.9,25426.486969660546,0.015206895642536097,tp,7,0.016006895642536097,0.008003447821268059,False,BTC,2023-03-17 07:30:00+08:00,0.0,1.0,-0.0011348088531187416,2023
+123810,123826,1,28033.6,27790.922948079296,-0.009456649589089596,sl,16,-0.008656649589089595,0.008656649589089538,False,BTC,2023-03-21 18:15:00+08:00,1.0,0.0,-0.0007971014492753623,2023
+124307,124337,-1,27797.3,27977.35387349352,-0.007277387138086134,sl,30,-0.006477387138086134,0.006477387138086164,False,BTC,2023-03-26 22:30:00+08:00,0.0,0.0,0.00044162060516273914,2023
+124405,124407,-1,26705.6,26971.27059594981,-0.010748123088408813,sl,2,-0.009948123088408813,0.009948123088408874,False,BTC,2023-03-27 23:00:00+08:00,1.0,0.0,0.012709691597685152,2023
+125538,125568,-1,28001.4,27893.070955892927,0.003068700997345664,tp,30,0.003868700997345664,0.0019343504986728604,False,BTC,2023-04-08 18:15:00+08:00,1.0,0.0,-0.0005298813990291953,2023
+126410,126423,-1,29511.5,29223.0063425817,0.00897563517334941,tp,13,0.00977563517334941,0.004887817586674696,False,BTC,2023-04-17 20:15:00+08:00,1.0,1.0,-0.00031898890590163054,2023
+126593,126609,-1,29150.3,29396.470947182304,-0.009244885547740679,sl,16,-0.008444885547740678,0.00844488554774071,False,BTC,2023-04-19 18:00:00+08:00,1.0,1.0,0.0002281239359891293,2023
+127003,127008,1,27616.4,27471.473570774368,-0.006047839299316128,sl,5,-0.005247839299316127,0.0052478392993161585,False,BTC,2023-04-24 00:30:00+08:00,0.0,1.0,-0.001179357232052201,2023
+129056,129074,1,27191.5,27480.173107698836,0.009816299494284473,tp,18,0.010616299494284474,0.005308149747142255,False,BTC,2023-05-15 09:45:00+08:00,0.0,0.0,-0.00034106791329449696,2023
+129186,129194,-1,27016.7,27117.788699475655,-0.004541711588597228,sl,8,-0.0037417115885972274,0.0037417115885972014,False,BTC,2023-05-16 18:15:00+08:00,1.0,1.0,-0.00032352941176467913,2023
+129798,129806,-1,26834.6,26928.943831752174,-0.004315753234711004,sl,8,-0.003515753234711004,0.003515753234710947,False,BTC,2023-05-23 03:15:00+08:00,0.0,1.0,-0.001380190555341216,2023
+130370,130384,1,27590.3,27855.924859760453,0.008827472690056053,tp,14,0.009627472690056054,0.004813736345028018,False,BTC,2023-05-29 02:15:00+08:00,1.0,0.0,-0.0013181083859598126,2023
+131092,131125,-1,26758.5,26572.302549688367,0.006158441254615638,tp,33,0.006958441254615639,0.0034792206273078123,False,BTC,2023-06-05 14:45:00+08:00,1.0,1.0,0.0008648207260039816,2023
+131565,131613,-1,25603.5,25670.3,-0.0034090182982795037,time,48,-0.002609018298279504,0.007347255185746186,False,BTC,2023-06-10 13:00:00+08:00,1.0,1.0,0.010297482837528604,2023
+132012,132014,-1,24920.1,25164.32229256468,-0.0106002131839231,sl,2,-0.0098002131839231,0.009800213183923101,False,BTC,2023-06-15 04:45:00+08:00,1.0,0.0,0.0012050313973277754,2023
+132489,132537,1,26805.5,26772.6,-0.002027360056704835,time,48,-0.001227360056704835,0.007342813016896116,False,BTC,2023-06-20 04:00:00+08:00,0.0,0.0,0.0013835012760630562,2023
+133474,133479,1,30559.2,30795.6674289838,0.006938011105781575,tp,5,0.007738011105781576,0.0038690055528908048,False,BTC,2023-06-30 10:15:00+08:00,0.0,0.0,-0.0003393996909155973,2023
+134890,134893,-1,30007.7,30241.389320048496,-0.008587645172688863,sl,3,-0.0077876451726888625,0.007787645172688915,False,BTC,2023-07-15 04:15:00+08:00,1.0,1.0,-0.0007014594989858857,2023
+138717,138727,1,26489.4,26795.70440670202,0.010763282169547821,tp,10,0.011563282169547821,0.0057816410847739376,False,BTC,2023-08-24 01:00:00+08:00,1.0,0.0,0.0009864874165500192,2023
+139774,139777,-1,25821.5,25888.30960427859,-0.003387363409507232,sl,3,-0.002587363409507232,0.002587363409507176,False,BTC,2023-09-04 01:15:00+08:00,1.0,0.0,0.00021972345682822356,2023
+140012,140049,-1,25741.9,25651.43536384662,0.00271429522115234,tp,37,0.00351429522115234,0.0017571476105761796,False,BTC,2023-09-06 12:45:00+08:00,0.0,0.0,0.001536645313618886,2023
+140934,140947,1,26414.0,26576.553076240158,0.005354049982590962,tp,13,0.0061540499825909626,0.003077024991295492,False,BTC,2023-09-16 03:15:00+08:00,1.0,1.0,-0.00011409447022134328,2023
+145249,145294,1,34365.1,34170.82846669413,-0.0064531636254766,sl,45,-0.0056531636254765994,0.005653163625476689,False,BTC,2023-10-31 02:00:00+08:00,0.0,1.0,0.001137916004832316,2023
+145845,145849,1,35186.2,34965.05225881053,-0.007085070317040961,sl,4,-0.0062850703170409606,0.006285070317040952,False,BTC,2023-11-06 07:00:00+08:00,0.0,0.0,0.0002794905779979568,2023
+147356,147359,1,37198.0,36895.86457906982,-0.008922356603316818,sl,3,-0.008122356603316817,0.008122356603316783,False,BTC,2023-11-22 00:45:00+08:00,0.0,0.0,-0.002720386996989046,2023
+147684,147694,1,37822.1,37708.65977764861,-0.0037993105182258474,sl,10,-0.0029993105182258475,0.002999310518225834,False,BTC,2023-11-25 10:45:00+08:00,1.0,1.0,2.6560424966412963e-06,2023
+148039,148047,1,38286.1,38043.5221696032,-0.007135924275306087,sl,8,-0.006335924275306087,0.006335924275306073,False,BTC,2023-11-29 03:30:00+08:00,1.0,1.0,-0.0019556025369978857,2023
+149761,149777,-1,42463.8,42147.36592978803,0.006651854761278392,tp,16,0.007451854761278393,0.0037259273806392315,False,BTC,2023-12-17 02:00:00+08:00,1.0,0.0,0.0011834874210520149,2023
+150094,150098,-1,42644.8,42842.59570985099,-0.005438214034325097,sl,4,-0.0046382140343250965,0.004638214034325146,False,BTC,2023-12-20 13:15:00+08:00,0.0,1.0,0.0033053066979145417,2023
+150681,150688,-1,42747.4,42342.78819473823,0.008665179291881491,tp,7,0.009465179291881492,0.004732589645940704,False,BTC,2023-12-26 16:00:00+08:00,1.0,1.0,0.0047884094032011465,2023
+151228,151239,1,42503.5,42312.16224363724,-0.005301694127842607,sl,11,-0.004501694127842607,0.004501694127842551,False,BTC,2024-01-01 08:45:00+08:00,0.0,0.0,9.467455621336218e-06,2024
+151945,151946,1,45148.1,44829.941451816536,-0.007846997507834498,sl,1,-0.007046997507834499,0.007046997507834448,False,BTC,2024-01-08 20:00:00+08:00,1.0,1.0,-0.004790497826722144,2024
+152343,152347,-1,44564.1,43704.14199427953,0.01849710250449276,tp,4,0.01929710250449276,0.009648551252246347,False,BTC,2024-01-12 23:30:00+08:00,1.0,1.0,0.0003600912820354858,2024
+153806,153818,1,42091.0,41983.693189269616,-0.003349400364220007,sl,12,-0.002549400364220007,0.002549400364219945,False,BTC,2024-01-28 05:15:00+08:00,1.0,1.0,0.00010982189753136,2024
+155150,155153,1,48061.3,47822.03626334921,-0.005778303471832671,sl,3,-0.004978303471832671,0.004978303471832678,False,BTC,2024-02-11 05:15:00+08:00,1.0,1.0,0.0016129745482723389,2024
+156224,156230,-1,51268.5,51548.47065061496,-0.0062608707220800225,sl,6,-0.005460870722080022,0.005460870722080072,False,BTC,2024-02-22 09:45:00+08:00,0.0,1.0,-0.00030977135002226483,2024
+156583,156586,1,51743.0,52031.77725883251,0.004780991802417906,tp,3,0.005580991802417906,0.0027904959012089374,False,BTC,2024-02-26 03:30:00+08:00,1.0,0.0,3.4978964039738246e-05,2024
+156663,156665,1,52583.0,53152.15644229141,0.010023962921313141,tp,2,0.010823962921313142,0.005411981460656577,False,BTC,2024-02-26 23:30:00+08:00,1.0,0.0,0.002424151257781668,2024
+156825,156828,1,58495.9,59189.07885053749,0.011050041636037502,tp,3,0.011850041636037503,0.005925020818018758,False,BTC,2024-02-28 16:00:00+08:00,1.0,1.0,0.002850232751670896,2024
+157862,157865,1,69027.1,69678.0885127553,0.00863091210199036,tp,3,0.00943091210199036,0.004715456050995138,False,BTC,2024-03-10 11:15:00+08:00,1.0,1.0,0.0006209125081805343,2024
+158473,158496,-1,68262.3,67313.408096704,0.013100672894057138,tp,23,0.013900672894057138,0.00695033644702859,False,BTC,2024-03-16 20:00:00+08:00,1.0,1.0,-0.0015612244897960032,2024
+158746,158777,-1,63768.6,64605.311732780574,-0.013921061663272762,sl,31,-0.013121061663272761,0.013121061663272765,False,BTC,2024-03-19 16:15:00+08:00,1.0,1.0,-0.0005806949806949583,2024
+159993,160013,-1,69525.4,69894.83145339607,-0.006113618524971758,sl,20,-0.005313618524971758,0.005313618524971686,False,BTC,2024-04-01 16:00:00+08:00,0.0,1.0,0.0005338310031300374,2024
+160067,160102,-1,66683.2,65213.74020307516,0.02123643191875668,tp,35,0.022036431918756677,0.011018215959378333,False,BTC,2024-04-02 10:30:00+08:00,1.0,0.0,0.009158937909913033,2024
+160533,160535,1,69572.0,69253.81258034009,-0.0053734982415326454,sl,2,-0.004573498241532645,0.0045734982415325505,False,BTC,2024-04-07 07:00:00+08:00,1.0,1.0,0.0036198647711562196,2024
+161193,161203,-1,62563.1,63851.32064028765,-0.021390741831649237,sl,10,-0.02059074183164924,0.02059074183164929,False,BTC,2024-04-14 04:00:00+08:00,1.0,1.0,0.013030267083975916,2024
+161557,161569,-1,60776.9,61553.85298318127,-0.013583688920976042,sl,12,-0.012783688920976042,0.012783688920975998,False,BTC,2024-04-17 23:00:00+08:00,1.0,1.0,0.00012391513905055842,2024
+161848,161893,1,64771.0,65469.998267565556,0.009991839983411643,tp,45,0.010791839983411643,0.005395919991705823,False,BTC,2024-04-20 23:45:00+08:00,0.0,0.0,-0.0009026006180306782,2024
+162790,162809,-1,61276.1,60452.86775201552,0.012634801627134897,tp,19,0.013434801627134898,0.006717400813567467,False,BTC,2024-04-30 19:15:00+08:00,1.0,1.0,0.000249396621078037,2024
+163600,163648,-1,61464.0,61272.7,0.0023123909931017004,time,48,0.0031123909931017003,0.006722316877457217,False,BTC,2024-05-09 05:45:00+08:00,1.0,0.0,-0.00033599341517213564,2024
+165982,165986,-1,67985.4,67543.94093402439,0.005693439267484001,tp,4,0.006493439267484001,0.0032467196337420358,False,BTC,2024-06-03 01:15:00+08:00,1.0,0.0,-0.000576843116241123,2024
+167128,167129,-1,66380.0,65701.75872870094,0.009417554554068433,tp,1,0.010217554554068434,0.005108777277034206,False,BTC,2024-06-14 23:45:00+08:00,1.0,0.0,-0.0021861924686192905,2024
+167454,167502,-1,65408.2,64874.0,0.007367171700184336,time,48,0.008167171700184337,0.006659503400758167,False,BTC,2024-06-18 09:15:00+08:00,1.0,0.0,0.002047978622428313,2024
+168035,168046,-1,63045.7,62505.79684376458,0.007763679303035955,tp,11,0.008563679303035955,0.004281839651517971,False,BTC,2024-06-24 10:30:00+08:00,1.0,1.0,0.0010686488684615498,2024
+169081,169084,-1,56876.7,57400.66644200281,-0.010012321425167302,sl,3,-0.009212321425167301,0.009212321425167308,False,BTC,2024-07-05 08:00:00+08:00,0.0,0.0,0.004912256339935082,2024
+170481,170490,1,65522.1,66435.7511528946,0.013144167737215483,tp,9,0.013944167737215483,0.006972083868607724,False,BTC,2024-07-19 22:00:00+08:00,1.0,0.0,0.0037884640343124384,2024
+170596,170599,1,67429.1,67128.60249533739,-0.005256495855092542,sl,3,-0.0044564958550925415,0.004456495855092587,False,BTC,2024-07-21 02:45:00+08:00,1.0,1.0,-0.0014536408458091683,2024
+170968,170974,-1,66155.7,66554.18528718704,-0.006823446009747293,sl,6,-0.006023446009747292,0.006023446009747276,False,BTC,2024-07-24 23:45:00+08:00,0.0,0.0,0.00017215620406049118,2024
+171183,171200,1,68042.1,67664.05364821361,-0.006356065315244492,sl,17,-0.005556065315244492,0.0055560653152444935,False,BTC,2024-07-27 05:30:00+08:00,0.0,1.0,0.0027893713391355566,2024
+171451,171458,-1,66874.9,67448.74515215623,-0.009380874919532416,sl,7,-0.008580874919532416,0.008580874919532444,False,BTC,2024-07-30 00:30:00+08:00,1.0,0.0,0.007733022126345365,2024
+176246,176260,1,61132.7,60640.55984824491,-0.00885035851115825,sl,14,-0.00805035851115825,0.008050358511158283,False,BTC,2024-09-17 23:15:00+08:00,1.0,1.0,0.01273571360993488,2024
+176753,176755,-1,62616.8,62896.2582852714,-0.005262992124659738,sl,2,-0.004462992124659737,0.004462992124659688,False,BTC,2024-09-23 06:00:00+08:00,0.0,1.0,0.0037507866981821296,2024
+177091,177105,1,64351.6,64846.17132072827,0.00688545491842111,tp,14,0.00768545491842111,0.0038427274592105613,False,BTC,2024-09-26 18:30:00+08:00,1.0,1.0,0.0030123024495474055,2024
+177752,177754,-1,60898.6,60348.69548879006,0.008229838308433053,tp,2,0.009029838308433054,0.004514919154216538,False,BTC,2024-10-03 15:45:00+08:00,1.0,0.0,-0.0012132083163473536,2024
+177880,177928,1,61843.1,61835.5,-0.0009228916402961453,time,48,-0.00012289164029614531,0.007304401318062403,False,BTC,2024-10-04 23:45:00+08:00,0.0,1.0,0.000142512445267802,2024
+178218,178222,1,62709.5,62430.40988863478,-0.005250523626647026,sl,4,-0.004450523626647026,0.004450523626647009,False,BTC,2024-10-08 12:15:00+08:00,1.0,0.0,-0.0010843064288463772,2024
+178564,178578,1,62889.4,62552.76115756157,-0.006152870951836637,sl,14,-0.0053528709518366364,0.005352870951836622,False,BTC,2024-10-12 02:45:00+08:00,1.0,1.0,0.0017939198587888382,2024
+178780,178782,-1,62483.4,62733.70318114216,-0.004805914869263846,sl,2,-0.004005914869263846,0.004005914869263843,False,BTC,2024-10-14 08:45:00+08:00,0.0,0.0,0.00039586645469000725,2024
+180706,180714,-1,68232.7,68499.38632304428,-0.004708482634342232,sl,8,-0.003908482634342232,0.00390848263434231,False,BTC,2024-11-03 10:15:00+08:00,1.0,1.0,-2.4558808233226183e-05,2024
+180845,180847,1,69132.0,68843.48176715054,-0.004973439692898451,sl,2,-0.0041734396928984506,0.0041734396928984644,False,BTC,2024-11-04 21:00:00+08:00,1.0,1.0,-0.0016915588297912675,2024
+181367,181368,1,76898.5,76642.0744680315,-0.0041345973194341044,sl,1,-0.0033345973194341045,0.003334597319434044,False,BTC,2024-11-10 07:30:00+08:00,1.0,0.0,-0.00039030677064372997,2024
+182370,182373,1,93369.9,92947.78490347612,-0.005320890528145341,sl,3,-0.0045208905281453405,0.0045208905281453405,False,BTC,2024-11-20 18:15:00+08:00,1.0,1.0,-0.00032620982935891596,2024
+182868,182870,-1,95444.4,96085.25019705418,-0.007514382373970436,sl,2,-0.006714382373970436,0.006714382373970418,False,BTC,2024-11-25 22:45:00+08:00,1.0,0.0,6.054038967497743e-05,2024
+183400,183418,1,96404.0,97141.73405766377,0.006852525389649446,tp,18,0.007652525389649447,0.003826262694824744,False,BTC,2024-12-01 11:45:00+08:00,0.0,0.0,0.0006730211251537089,2024
+183717,183722,-1,96467.6,95853.50727738645,0.005565792479688036,tp,5,0.006365792479688036,0.0031828962398440267,False,BTC,2024-12-04 19:00:00+08:00,1.0,0.0,-0.0001465379884255328,2024
+185521,185533,-1,95038.0,95755.45307481795,-0.008349117982469676,sl,12,-0.007549117982469677,0.0075491179824696535,False,BTC,2024-12-23 14:00:00+08:00,1.0,1.0,4.064748313905066e-05,2024
+187010,187058,-1,97049.6,96172.6,0.008236616328145607,time,48,0.009036616328145607,0.00829259231994448,False,BTC,2025-01-08 02:15:00+08:00,1.0,1.0,-0.0001319529756418303,2025
+187311,187359,-1,94681.7,94297.5,0.003257806313152353,time,48,0.004057806313152353,0.007657150648256152,False,BTC,2025-01-11 05:30:00+08:00,1.0,1.0,0.0006332570204364843,2025
+187906,187908,1,101821.4,101194.10973435506,-0.0069606918157178125,sl,2,-0.006160691815717812,0.006160691815717847,False,BTC,2025-01-17 10:15:00+08:00,1.0,1.0,0.005038799351075878,2025
+189608,189629,1,101831.9,100720.60598169944,-0.011713024487420502,sl,21,-0.010913024487420502,0.010913024487420535,False,BTC,2025-02-04 03:45:00+08:00,1.0,1.0,0.0068866259713088185,2025
+190374,190382,-1,94988.2,95472.80296429759,-0.005901717521729982,sl,8,-0.005101717521729982,0.0051017175217299765,False,BTC,2025-02-12 03:15:00+08:00,1.0,1.0,-0.0029838770341767368,2025
+190655,190668,1,97930.0,97306.11503689933,-0.007170723609728115,sl,13,-0.006370723609728114,0.006370723609728047,False,BTC,2025-02-15 01:30:00+08:00,1.0,1.0,7.36228127025879e-05,2025
+192604,192606,-1,85477.9,86929.42483018567,-0.017781287914018444,sl,2,-0.016981287914018445,0.01698128791401843,False,BTC,2025-03-07 08:45:00+08:00,0.0,1.0,0.005097998331943353,2025
+192836,192848,-1,84816.7,84138.40518395319,0.0071971847059223906,tp,12,0.007997184705922391,0.003998592352961217,False,BTC,2025-03-09 18:45:00+08:00,1.0,1.0,-0.0003927298229209961,2025
+193063,193091,1,83302.6,82256.77624391673,-0.013354515178197062,sl,28,-0.012554515178197062,0.012554515178197006,False,BTC,2025-03-12 03:30:00+08:00,1.0,1.0,0.0005268181873105185,2025
+196114,196139,1,84856.7,85858.6247987474,0.011007256218394166,tp,25,0.011807256218394166,0.005903628109197083,False,BTC,2025-04-12 22:15:00+08:00,1.0,1.0,0.001260730795284328,2025
+197031,197069,1,88201.5,89205.60662424867,0.010584235236914003,tp,38,0.011384235236914004,0.005692117618457019,False,BTC,2025-04-22 11:30:00+08:00,1.0,0.0,0.00014138739804992774,2025
+197353,197354,1,94764.2,94372.66119691111,-0.004931716440268437,sl,1,-0.004131716440268437,0.00413171644026844,False,BTC,2025-04-25 20:00:00+08:00,1.0,1.0,8.919280511365902e-05,2025
+197586,197593,-1,93766.3,93250.59144769945,0.004699934969179242,tp,7,0.005499934969179243,0.002749967484589584,False,BTC,2025-04-28 06:15:00+08:00,0.0,1.0,0.0011556958539014621,2025
+197798,197837,1,94608.6,94263.48322420545,-0.004447837255752134,sl,39,-0.003647837255752134,0.0036478372557522077,False,BTC,2025-04-30 11:15:00+08:00,1.0,1.0,-0.00063096978143386,2025
+198452,198458,1,96206.2,97098.44813744376,0.008474330941703996,tp,6,0.009274330941703997,0.004637165470851987,False,BTC,2025-05-07 06:45:00+08:00,0.0,1.0,0.0032650628861776174,2025
+198561,198567,1,98020.0,98991.95195153284,0.009115853412903926,tp,6,0.009915853412903927,0.004957926706451959,False,BTC,2025-05-08 10:00:00+08:00,1.0,1.0,0.0010590393938025371,2025
+199768,199776,1,105294.5,106226.8910610943,0.008055078480778183,tp,8,0.008855078480778183,0.0044275392403890605,False,BTC,2025-05-20 23:45:00+08:00,1.0,0.0,-0.0005838629484792118,2025
+199891,199894,1,108885.3,110461.51455866646,0.013675916938893088,tp,3,0.014475916938893088,0.00723795846944654,False,BTC,2025-05-22 06:30:00+08:00,1.0,1.0,-0.0006076887447893082,2025
+200622,200623,-1,108250.0,107540.90362596668,0.005750543870977593,tp,1,0.006550543870977594,0.003275271935488802,False,BTC,2025-05-29 21:15:00+08:00,1.0,0.0,-0.0002766366134532321,2025
+200643,200668,-1,106154.9,104913.69129460344,0.01089242969845529,tp,25,0.01169242969845529,0.005846214849227625,False,BTC,2025-05-30 02:30:00+08:00,1.0,1.0,-0.001131775700934634,2025
+201297,201300,-1,104223.8,104655.27269831063,-0.00493986726938207,sl,3,-0.00413986726938207,0.0041398672693820455,False,BTC,2025-06-05 22:00:00+08:00,0.0,0.0,0.001619503023893537,2025
+203896,203897,1,108695.3,109533.58650140539,0.0069122608006545105,tp,1,0.007712260800654511,0.0038561304003272667,False,BTC,2025-07-02 23:45:00+08:00,1.0,1.0,0.0008891696359683938,2025
+204581,204582,1,109623.5,110299.03591477411,0.0053623275554430475,tp,1,0.006162327555443048,0.003081163777721519,False,BTC,2025-07-10 03:00:00+08:00,1.0,1.0,-0.0006910622010972142,2025
+206789,206837,-1,113645.1,113628.6,-0.0006548111621178564,time,48,0.0001451888378821436,0.006213249374798341,False,BTC,2025-08-02 03:00:00+08:00,1.0,1.0,-0.0009707718029744149,2025
+207134,207147,1,114545.6,114217.44664812618,-0.0036648272118163312,sl,13,-0.0028648272118163313,0.0028648272118162806,False,BTC,2025-08-05 17:15:00+08:00,1.0,0.0,0.0015666666666667177,2025
+207330,207333,1,115824.9,116414.53929108783,0.004290781784295388,tp,3,0.005090781784295388,0.0025453908921477184,False,BTC,2025-08-07 18:15:00+08:00,1.0,0.0,9.32548830261043e-05,2025
+207589,207590,1,117875.2,117588.60053309865,-0.003231380535526941,sl,1,-0.0024313805355269413,0.002431380535526967,False,BTC,2025-08-10 11:00:00+08:00,1.0,0.0,-0.0002381205266746632,2025
+207899,207909,1,119903.4,120652.74672726975,0.005449586978098702,tp,10,0.006249586978098702,0.0031247934890493386,False,BTC,2025-08-13 16:30:00+08:00,1.0,0.0,-0.000783279953322248,2025
+207929,207949,1,121507.4,123055.1588856803,0.011937980449588296,tp,20,0.012737980449588297,0.00636899022479413,False,BTC,2025-08-14 00:00:00+08:00,0.0,1.0,-0.0008113937997862913,2025
+208352,208356,-1,116214.0,115396.5379931616,0.006234109546512431,tp,4,0.007034109546512431,0.0035170547732562246,False,BTC,2025-08-18 09:45:00+08:00,1.0,1.0,0.0011350039314895466,2025
+210104,210109,1,112900.1,112511.66075216966,-0.004240557163637135,sl,5,-0.003440557163637135,0.0034405571636371528,False,BTC,2025-09-05 15:45:00+08:00,1.0,1.0,0.0007076201060360189,2025
+210474,210481,1,111902.3,112522.95980719093,0.004746443703042071,tp,7,0.0055464437030420715,0.0027732218515210336,False,BTC,2025-09-09 12:15:00+08:00,1.0,0.0,-0.0008116401602787343,2025
+214412,214427,1,110369.3,111609.92710848266,0.010440690196301493,tp,15,0.011240690196301494,0.005620345098150739,False,BTC,2025-10-20 12:45:00+08:00,1.0,1.0,0.004886560996957244,2025
+214761,214780,-1,109852.1,110497.89049211083,-0.006678726871045956,sl,19,-0.005878726871045956,0.005878726871045967,False,BTC,2025-10-24 04:00:00+08:00,1.0,0.0,-0.00024784649437410326,2025
+215747,215753,-1,108816.9,107839.30579514877,0.008183845384781431,tp,6,0.008983845384781431,0.0044919226923907166,False,BTC,2025-11-03 10:30:00+08:00,1.0,0.0,-0.0003884359162801267,2025
+216814,216843,-1,97224.3,95416.97222635138,0.017789259821347336,tp,29,0.018589259821347334,0.009294629910673646,False,BTC,2025-11-14 13:15:00+08:00,1.0,1.0,0.0066868037746941125,2025
+218015,218045,1,89473.2,90832.33128656783,0.014390373056600566,tp,30,0.015190373056600566,0.007595186528300254,False,BTC,2025-11-27 01:30:00+08:00,1.0,0.0,-0.00020463691238332472,2025
+218847,218850,-1,91014.3,91398.90399932429,-0.005025753528009208,sl,3,-0.004225753528009208,0.004225753528009262,False,BTC,2025-12-05 17:30:00+08:00,1.0,1.0,0.00346264765571028,2025
+219408,219443,1,90262.0,89727.7200991646,-0.006719211859203264,sl,35,-0.005919211859203264,0.005919211859203253,False,BTC,2025-12-11 13:45:00+08:00,0.0,1.0,-0.0013546935228716586,2025
+220024,220072,-1,87191.4,86575.1,0.006268357659126799,time,48,0.007068357659126799,0.011555679676356753,False,BTC,2025-12-17 23:45:00+08:00,0.0,1.0,0.012104448871921018,2025
+221536,221553,1,89429.9,89176.69387413036,-0.0036313363413090476,sl,17,-0.0028313363413090477,0.0028313363413091258,False,BTC,2026-01-02 17:45:00+08:00,1.0,1.0,0.0006593055464923576,2026
+221603,221614,1,90259.9,89984.60328067763,-0.0038500445859386,sl,11,-0.0030500445859386,0.0030500445859385755,False,BTC,2026-01-03 10:30:00+08:00,1.0,1.0,-0.0002803114571746061,2026
+221692,221695,1,91301.4,90984.1406078585,-0.004274857911724196,sl,3,-0.0034748579117241957,0.003474857911724195,False,BTC,2026-01-04 08:45:00+08:00,0.0,1.0,0.0006418151417470873,2026
+221952,221956,-1,91547.6,92150.0246186455,-0.007380452340044943,sl,4,-0.006580452340044942,0.00658045234004502,False,BTC,2026-01-07 01:45:00+08:00,1.0,0.0,-0.0013081519488761246,2026
+223840,223855,-1,87630.4,87999.34714240358,-0.005010264273626361,sl,15,-0.00421026427362636,0.004210264273626427,False,BTC,2026-01-26 17:45:00+08:00,0.0,0.0,0.0005833636932141885,2026
+224115,224121,1,88293.5,87976.64630026092,-0.004388641290005228,sl,6,-0.0035886412900052274,0.0035886412900052954,False,BTC,2026-01-29 14:30:00+08:00,1.0,0.0,-0.0006710493391870067,2026
+224339,224340,-1,80964.0,81506.99302773151,-0.007506598336686853,sl,1,-0.006706598336686852,0.006706598336686825,False,BTC,2026-01-31 22:30:00+08:00,1.0,1.0,0.006191814722975898,2026
+224727,224734,-1,74327.8,72567.42040872827,0.02288399967807109,tp,7,0.02368399967807109,0.011841999839035553,False,BTC,2026-02-04 23:30:00+08:00,1.0,1.0,-0.005135498352952298,2026
+225321,225355,-1,68728.6,67494.30305779116,0.017159000215468522,tp,34,0.01795900021546852,0.008979500107734262,False,BTC,2026-02-11 04:00:00+08:00,0.0,0.0,0.001663449325075805,2026
+225593,225641,1,69020.1,68863.2,-0.00307325083562627,time,48,-0.00227325083562627,0.009204546930098624,False,BTC,2026-02-14 00:00:00+08:00,1.0,1.0,0.001049675835403687,2026
+225871,225879,-1,68393.1,67253.06983498827,0.015868789176272644,tp,8,0.016668789176272642,0.008334394588136356,False,BTC,2026-02-16 21:30:00+08:00,1.0,1.0,0.00511903970687253,2026
+226359,226361,1,68593.2,68366.81402141534,-0.0041004143061507264,sl,2,-0.0033004143061507265,0.0033004143061508328,False,BTC,2026-02-21 23:30:00+08:00,1.0,1.0,-8.813290441986516e-05,2026
+226494,226515,-1,65006.7,65449.132798644314,-0.007605956903585582,sl,21,-0.0068059569035855815,0.0068059569035855555,False,BTC,2026-02-23 09:15:00+08:00,1.0,0.0,0.017745957348488755,2026
+227592,227600,-1,70185.6,69499.2826932912,0.008978605678498244,tp,8,0.009778605678498245,0.004889302839249119,False,BTC,2026-03-06 19:45:00+08:00,1.0,1.0,-0.0001161262547299794,2026
+227860,227866,-1,67229.8,67737.70750184539,-0.008354797156103213,sl,6,-0.007554797156103212,0.0075547971561031255,False,BTC,2026-03-09 14:45:00+08:00,0.0,0.0,0.00018165089651367493,2026
+228752,228756,-1,72104.4,71239.53802673102,0.011194579710377862,tp,4,0.011994579710377862,0.005997289855188955,False,BTC,2026-03-18 21:45:00+08:00,1.0,1.0,-0.00012904660641758014,2026
+229232,229239,1,71450.0,70667.75185422362,-0.011748189583994106,sl,7,-0.010948189583994105,0.010948189583994157,False,BTC,2026-03-23 21:45:00+08:00,1.0,1.0,0.0002126603813709506,2026
+229868,229907,1,67579.1,67126.21917851601,-0.007501492347249305,sl,39,-0.006701492347249305,0.006701492347249284,False,BTC,2026-03-30 12:45:00+08:00,1.0,1.0,-0.00020566318926979003,2026
+230961,230987,1,72667.3,73385.94961448296,0.00908958740015052,tp,26,0.00988958740015052,0.004944793700075223,False,BTC,2026-04-10 22:00:00+08:00,0.0,0.0,-0.0010633122524150392,2026
+232783,232792,-1,76529.9,75885.75852340233,0.007616860293789303,tp,9,0.008416860293789304,0.004208430146894688,False,BTC,2026-04-29 21:30:00+08:00,1.0,0.0,0.0002751557601210545,2026
+232807,232815,-1,75395.6,75861.89582903204,-0.006984655723040051,sl,8,-0.00618465572304005,0.006184655723040077,False,BTC,2026-04-30 03:30:00+08:00,0.0,1.0,-0.0009192383453709783,2026
+232971,232975,1,77912.1,78445.06208910997,0.006040556076783478,tp,4,0.006840556076783478,0.003420278038391729,False,BTC,2026-05-01 20:30:00+08:00,1.0,1.0,0.0009970025351242842,2026
+234242,234246,1,81813.1,81343.94755157444,-0.006534441653299584,sl,4,-0.0057344416532995835,0.005734441653299625,False,BTC,2026-05-15 02:15:00+08:00,1.0,1.0,-0.0030454791553871144,2026
+235184,235186,-1,76765.0,76309.75880190425,0.005130322387751644,tp,2,0.005930322387751644,0.0029651611938758095,False,BTC,2026-05-24 21:45:00+08:00,1.0,0.0,0.0013624700948812645,2026
+236157,236181,-1,65694.5,64623.063486656254,0.015509379222670787,tp,24,0.016309379222670787,0.008154689611335397,False,BTC,2026-06-04 01:00:00+08:00,1.0,1.0,0.00040067482075082576,2026
+236336,236346,-1,60700.4,61431.58121365706,-0.012845739627038024,sl,10,-0.012045739627038024,0.012045739627038074,False,BTC,2026-06-05 21:45:00+08:00,1.0,1.0,-0.0025615116780475407,2026
+236556,236561,1,61843.0,62649.84573831126,0.012246678497344272,tp,5,0.013046678497344272,0.00652333924867214,False,BTC,2026-06-08 04:45:00+08:00,0.0,0.0,-0.0008052053680357869,2026
+236830,236871,-1,61871.3,62396.64192880755,-0.009290882344601546,sl,41,-0.008490882344601545,0.008490882344601597,False,BTC,2026-06-11 01:15:00+08:00,0.0,1.0,-0.0006011426519528319,2026
+240726,240739,1,65932.8,66382.3091249815,0.006017685961789903,tp,13,0.006817685961789903,0.0034088429808949105,False,BTC,2026-07-21 15:15:00+08:00,1.0,1.0,-0.00045098302109855917,2026
+240867,240879,-1,65751.1,66029.6212712818,-0.005035994094118401,sl,12,-0.0042359940941184004,0.004235994094118487,False,BTC,2026-07-23 02:30:00+08:00,0.0,1.0,-0.0005576376715226105,2026
+241018,241022,-1,65363.1,64932.4818685733,0.005788092232876064,tp,4,0.006588092232876064,0.00329404611643802,False,BTC,2026-07-24 16:15:00+08:00,0.0,0.0,-5.177512540231068e-05,2026
+241040,241088,-1,64129.1,64039.2,0.0006018596861643381,time,48,0.0014018596861643381,0.004506589247593315,False,BTC,2026-07-24 21:45:00+08:00,1.0,1.0,0.003361489930978208,2026
+241465,241470,1,63987.2,63763.8643542493,-0.004290317528360299,sl,5,-0.0034903175283602992,0.003490317528360311,False,BTC,2026-07-29 08:00:00+08:00,0.0,1.0,0.0023553554088325272,2026
+242158,242165,1,64408.2,64199.95623946291,-0.004033187087002695,sl,7,-0.0032331870870026943,0.003233187087002695,False,BTC,2026-08-05 13:15:00+08:00,0.0,0.0,-9.990820933269832e-05,2026
+242207,242242,1,64664.5,64408.45377069678,-0.004759610440090279,sl,35,-0.003959610440090279,0.003959610440090236,False,BTC,2026-08-06 01:30:00+08:00,1.0,1.0,-0.0008671894669116385,2026
+242772,242787,-1,63747.0,63344.14312977136,0.00551962084848923,tp,15,0.00631962084848923,0.0031598104242445864,False,BTC,2026-08-11 22:45:00+08:00,1.0,1.0,0.00040691886317437663,2026
+242779,242795,-1,63470.3,63694.426541703375,-0.004331203440087284,sl,16,-0.0035312034400872836,0.0035312034400872432,False,BTC,2026-08-12 00:30:00+08:00,1.0,0.0,0.0002849810299716172,2026
+243613,243631,1,71910.0,71355.22042567248,-0.008514915510047561,sl,18,-0.007714915510047561,0.007714915510047584,False,BTC,2026-08-20 17:00:00+08:00,1.0,1.0,0.01359349704917467,2026
+244011,244015,1,78711.4,78195.51508335938,-0.007354132141476514,sl,4,-0.0065541321414765135,0.00655413214147659,False,BTC,2026-08-24 20:30:00+08:00,1.0,1.0,0.001006785528998944,2026
+490,492,1,149.91,148.82273445348744,-0.008052788649940329,sl,2,-0.007252788649940329,0.007252788649940293,False,ETH,2019-12-02 18:15:00+08:00,1.0,1.0,0.0011437029063510221,2019
+1867,1915,-1,133.03,131.13,0.013482492670826171,time,48,0.014282492670826171,0.012989060467203517,False,ETH,2019-12-17 02:30:00+08:00,1.0,0.0,0.00021269053527118848,2019
+4583,4593,1,147.5,149.625664934136,0.013611287689057706,tp,10,0.014411287689057707,0.007205643844528891,False,ETH,2020-01-14 09:30:00+08:00,1.0,1.0,-0.00020668274199105158,2020
+5333,5356,1,169.79,168.31174356607346,-0.009506381023184712,sl,23,-0.008706381023184712,0.008706381023184651,False,ETH,2020-01-22 05:00:00+08:00,1.0,0.0,0.009832901007419891,2020
+5634,5667,-1,159.76,161.17032990551002,-0.009627803614859948,sl,33,-0.008827803614859948,0.008827803614859978,False,ETH,2020-01-25 08:15:00+08:00,0.0,0.0,0.00030695561421807937,2020
+6018,6057,1,176.83,175.32886569659658,-0.00928913817453731,sl,39,-0.00848913817453731,0.008489138174537251,False,ETH,2020-01-29 08:15:00+08:00,1.0,1.0,0.005506481587702236,2020
+6430,6443,1,187.89,191.442311571979,0.018106336537223933,tp,13,0.01890633653722393,0.009453168268611964,False,ETH,2020-02-02 15:15:00+08:00,1.0,1.0,0.00786803407672699,2020
+6732,6737,1,195.52,198.38342430806813,0.013845173425061981,tp,5,0.014645173425061981,0.0073225867125309724,False,ETH,2020-02-05 18:45:00+08:00,0.0,1.0,0.006718346253230033,2020
+7162,7180,1,227.44,226.06620282104276,-0.006840261954613229,sl,18,-0.006040261954613229,0.006040261954613246,False,ETH,2020-02-10 06:15:00+08:00,0.0,0.0,0.0006211180124222997,2020
+8168,8175,1,258.89,255.72364266277214,-0.013030512330440924,sl,7,-0.012230512330440924,0.012230512330440937,False,ETH,2020-02-20 17:45:00+08:00,1.0,1.0,0.004094365373367172,2020
+14392,14404,1,194.4,192.7269063419686,-0.00940644885818624,sl,12,-0.00860644885818624,0.00860644885818617,False,ETH,2020-04-25 13:45:00+08:00,1.0,0.0,0.00031670625494854723,2020
+15246,15251,-1,203.3,198.9867414333097,0.020416225118988236,tp,5,0.021216225118988234,0.010608112559494114,False,ETH,2020-05-04 11:15:00+08:00,1.0,0.0,9.740417863931344e-05,2020
+15815,15863,-1,190.91,190.24,0.002709507097585184,time,48,0.003509507097585184,0.02427671910481727,False,ETH,2020-05-10 09:30:00+08:00,1.0,1.0,0.052266171221825634,2020
+17587,17596,1,209.63,211.76393895527846,0.009379549469438853,tp,9,0.010179549469438854,0.005089774734719414,False,ETH,2020-05-28 20:30:00+08:00,1.0,1.0,0.003469210754553334,2020
+18010,18013,1,244.19,248.29574012003522,0.016013711126726,tp,3,0.016813711126726,0.008406855563363026,False,ETH,2020-06-02 06:15:00+08:00,1.0,0.0,-0.003489821354382946,2020
+18231,18246,-1,243.34,240.28614568309436,0.011749742405299754,tp,15,0.012549742405299755,0.00627487120264985,False,ETH,2020-06-04 13:30:00+08:00,1.0,0.0,-0.0011778563015311808,2020
+18577,18579,1,243.2,241.3693986998394,-0.008327143503949815,sl,2,-0.007527143503949814,0.007527143503949838,False,ETH,2020-06-08 04:00:00+08:00,1.0,1.0,0.0002088031403991604,2020
+21485,21489,1,241.86,240.38185089815016,-0.00691158977032106,sl,4,-0.006111589770321059,0.006111589770321087,False,ETH,2020-07-08 11:00:00+08:00,1.0,0.0,-0.00045772303595200244,2020
+22134,22161,-1,240.68,238.8580181912769,0.006770142133634323,tp,27,0.007570142133634323,0.0037850710668171673,False,ETH,2020-07-15 05:15:00+08:00,0.0,0.0,0.001735537190082593,2020
+23080,23091,1,279.3,284.9805278213683,0.019538445475718893,tp,11,0.02033844547571889,0.01016922273785944,False,ETH,2020-07-25 01:45:00+08:00,1.0,1.0,-0.0008322477927341808,2020
+23435,23456,1,312.76,321.78050352015254,0.02804161504077422,tp,21,0.02884161504077422,0.014420807520387073,False,ETH,2020-07-28 18:30:00+08:00,0.0,0.0,0.0038785998254629713,2020
+23660,23661,1,331.13,336.61566824273183,0.015766509355032265,tp,1,0.016566509355032264,0.008283254677516123,False,ETH,2020-07-31 02:45:00+08:00,1.0,0.0,0.0031813037227370333,2020
+24968,24973,1,386.78,395.59181166390863,0.021982490469798485,tp,5,0.022782490469798484,0.011391245234899218,False,ETH,2020-08-13 17:45:00+08:00,0.0,1.0,0.0038743455497382675,2020
+26013,26017,1,397.95,402.59633688641134,0.010875680076420022,tp,4,0.011675680076420022,0.005837840038209991,False,ETH,2020-08-24 15:00:00+08:00,1.0,0.0,0.0008632728196013075,2020
+26767,26776,1,443.51,451.44835956343434,0.017098941542320008,tp,9,0.017898941542320006,0.008949470771160014,False,ETH,2020-09-01 11:30:00+08:00,1.0,0.0,0.004031247864805115,2020
+26994,27000,-1,405.6,415.1531858804414,-0.024353219626334785,sl,6,-0.023553219626334786,0.023553219626334755,False,ETH,2020-09-03 20:15:00+08:00,1.0,1.0,0.009121140142517756,2020
+30845,30856,1,380.11,376.62052015713306,-0.009980184269992781,sl,11,-0.00918018426999278,0.009180184269992713,False,ETH,2020-10-13 23:00:00+08:00,0.0,0.0,0.0036436170212766077,2020
+32095,32099,-1,396.39,389.66237362064163,0.016172240418169875,tp,4,0.016972240418169873,0.008486120209084931,False,ETH,2020-10-26 23:30:00+08:00,1.0,1.0,-2.483854942869077e-05,2020
+33412,33424,1,448.22,456.8519478508656,0.01845828354572659,tp,12,0.01925828354572659,0.009629141772863278,False,ETH,2020-11-09 16:45:00+08:00,0.0,1.0,0.006423838074200887,2020
+33612,33616,1,464.61,460.60041348450227,-0.009430004768510674,sl,4,-0.008630004768510674,0.008630004768510682,False,ETH,2020-11-11 18:45:00+08:00,0.0,1.0,0.00037069341474042534,2020
+35624,35672,-1,595.53,598.34,-0.005518486054439003,time,48,-0.004718486054439003,0.012857815438655508,False,ETH,2020-12-02 17:45:00+08:00,0.0,0.0,0.004585174602119986,2020
+35872,35883,-1,567.68,575.1237789938186,-0.013912632105796722,sl,11,-0.013112632105796721,0.013112632105796784,False,ETH,2020-12-05 07:45:00+08:00,1.0,1.0,-0.002181818181818166,2020
+36591,36601,1,557.86,554.7581043076638,-0.006360347923020573,sl,10,-0.0055603479230205725,0.005560347923020512,False,ETH,2020-12-12 19:30:00+08:00,0.0,1.0,-0.0006668228594085183,2020
+36977,36983,1,595.15,602.4223497929802,0.011419356116912052,tp,6,0.012219356116912052,0.006109678058456015,False,ETH,2020-12-16 20:00:00+08:00,1.0,1.0,0.00010141473556117695,2020
+37398,37399,-1,646.16,635.2289127713298,0.01611699769201159,tp,1,0.01691699769201159,0.008458498846005752,False,ETH,2020-12-21 05:15:00+08:00,0.0,1.0,0.011564211807668931,2020
+39581,39589,1,1045.3,1110.0642885886984,0.0611576089052889,tp,8,0.061957608905288904,0.0309788044526445,False,ETH,2021-01-12 23:00:00+08:00,0.0,0.0,-0.006985365853658505,2021
+40196,40198,1,1265.0,1294.5496024057197,0.02255936949068753,tp,2,0.02335936949068753,0.011679684745343722,False,ETH,2021-01-19 08:45:00+08:00,1.0,1.0,0.0049546953542727685,2021
+40768,40770,1,1394.24,1434.5875552103632,0.028138744556434492,tp,2,0.02893874455643449,0.014469372278217266,False,ETH,2021-01-25 07:45:00+08:00,1.0,1.0,0.009065901561760523,2021
+42860,42861,-1,1809.73,1826.2858428874397,-0.009948239177910333,sl,1,-0.009148239177910333,0.00914823917791029,False,ETH,2021-02-16 02:45:00+08:00,0.0,0.0,-0.00189779265384701,2021
+43552,43554,1,1778.3,1745.553087507009,-0.01921472895067817,sl,2,-0.01841472895067817,0.0184147289506782,False,ETH,2021-02-23 07:45:00+08:00,0.0,0.0,0.004837904671158586,2021
+44843,44878,1,1714.46,1759.6811903896007,0.02557634613207697,tp,35,0.02637634613207697,0.013188173066038464,False,ETH,2021-03-08 18:30:00+08:00,0.0,1.0,-0.004397724784109039,2021
+45330,45343,1,1895.0,1868.5599030729577,-0.014752557745141058,sl,13,-0.013952557745141057,0.01395255774514105,False,ETH,2021-03-13 20:15:00+08:00,1.0,1.0,-0.001952701236710734,2021
+46227,46229,-1,1704.37,1664.2495537808331,0.022739751473662854,tp,2,0.023539751473662853,0.01176987573683145,False,ETH,2021-03-23 04:30:00+08:00,1.0,1.0,-0.0005301864203865593,2021
+46417,46426,-1,1608.18,1565.3001919768767,0.02586356255091057,tp,9,0.02666356255091057,0.013331781275455307,False,ETH,2021-03-25 04:00:00+08:00,1.0,1.0,-0.002326141979028504,2021
+47082,47085,1,1875.36,1909.7672736723678,0.017547023330116814,tp,3,0.018347023330116813,0.009173511665058413,False,ETH,2021-04-01 02:15:00+08:00,1.0,1.0,-0.0027100271002710027,2021
+47651,47699,1,2101.77,2085.75,-0.008422147047488537,time,48,-0.007622147047488537,0.011507349173438774,False,ETH,2021-04-07 00:30:00+08:00,0.0,0.0,-0.0033536921799957693,2021
+47891,47913,-1,2074.96,2086.9700028976445,-0.006588064780836463,sl,22,-0.005788064780836462,0.005788064780836482,False,ETH,2021-04-09 12:30:00+08:00,1.0,0.0,-0.0005747126436780739,2021
+49226,49231,-1,2195.45,2284.646830450568,-0.04142804001483436,sl,5,-0.04062804001483436,0.04062804001483438,False,ETH,2021-04-23 10:15:00+08:00,1.0,1.0,0.005791556728232222,2021
+49993,50021,1,2836.47,2811.582453682315,-0.009574126402776906,sl,28,-0.008774126402776906,0.008774126402776968,False,ETH,2021-05-01 10:00:00+08:00,1.0,1.0,-0.0018529867695302182,2021
+50408,50425,1,3382.11,3329.3683246279547,-0.016394311057903316,sl,17,-0.015594311057903317,0.015594311057903376,False,ETH,2021-05-05 17:45:00+08:00,1.0,0.0,0.005715551086852409,2021
+50861,50868,1,4018.34,4099.759423190904,0.019461954735264842,tp,7,0.02026195473526484,0.010130977367632391,False,ETH,2021-05-10 11:00:00+08:00,1.0,1.0,0.003967383216934905,2021
+51718,51725,-1,3256.18,3106.3719181058145,0.04520730975995962,tp,7,0.04600730975995962,0.023003654879979827,False,ETH,2021-05-19 09:15:00+08:00,0.0,0.0,0.01045400238948626,2021
+52246,52250,-1,2382.43,2450.5218312897864,-0.029380831877447215,sl,4,-0.028580831877447217,0.028580831877447272,False,ETH,2021-05-24 21:15:00+08:00,0.0,0.0,-0.00612844596293273,2021
+52390,52394,1,2722.28,2844.075263788218,0.0439401677227242,tp,4,0.044740167722724204,0.022370083861362085,False,ETH,2021-05-26 09:15:00+08:00,1.0,1.0,0.005971984203859698,2021
+53265,53268,1,2761.54,2720.3073683661455,-0.015731028206672527,sl,3,-0.014931028206672529,0.014931028206672551,False,ETH,2021-06-04 12:00:00+08:00,0.0,0.0,-0.0008498802441475628,2021
+53622,53644,-1,2600.0,2503.884701523607,0.0361674224909204,tp,22,0.0369674224909204,0.018483711245460183,False,ETH,2021-06-08 05:15:00+08:00,1.0,1.0,0.007794117647058851,2021
+54078,54126,-1,2404.87,2346.467989077706,0.02348489312199571,tp,48,0.024284893121995707,0.01214244656099789,False,ETH,2021-06-12 23:15:00+08:00,0.0,0.0,-0.005203394163292524,2021
+54376,54382,1,2576.68,2552.4296696903734,-0.010211463708969064,sl,6,-0.009411463708969064,0.009411463708968995,False,ETH,2021-06-16 01:45:00+08:00,1.0,0.0,-0.0021742543171114456,2021
+54898,54901,-1,2097.67,2129.6270733931037,-0.01603455710054663,sl,3,-0.015234557100546633,0.01523455710054666,False,ETH,2021-06-21 12:15:00+08:00,1.0,1.0,0.002067746387213212,2021
+55310,55325,-1,1854.8,1801.7448262677722,0.027804255840105558,tp,15,0.028604255840105557,0.014302127920052775,False,ETH,2021-06-25 19:15:00+08:00,1.0,1.0,0.001015733285368189,2021
+55820,55825,1,2213.51,2273.812420894392,0.026442895173001982,tp,5,0.02724289517300198,0.013621447586500982,False,ETH,2021-07-01 02:45:00+08:00,1.0,0.0,0.010755316548521219,2021
+56358,56368,-1,2266.84,2296.413906363624,-0.013846313971706777,sl,10,-0.013046313971706776,0.013046313971706873,False,ETH,2021-07-06 17:15:00+08:00,0.0,0.0,0.003480459212827468,2021
+56972,56973,-1,2023.23,2045.084852375851,-0.011601961406192522,sl,1,-0.010801961406192521,0.010801961406192486,False,ETH,2021-07-13 02:45:00+08:00,1.0,1.0,-0.0021906654388600694,2021
+57195,57213,-1,1989.31,1944.2280219616105,0.02186211804011918,tp,18,0.022662118040119177,0.011331059020059561,False,ETH,2021-07-15 10:30:00+08:00,0.0,0.0,0.00013893850978522723,2021
+57623,57666,-1,1814.53,1834.9738393233329,-0.012066740876884315,sl,43,-0.011266740876884314,0.011266740876884286,False,ETH,2021-07-19 21:30:00+08:00,1.0,1.0,0.0020035978585201527,2021
+57959,57989,1,2060.96,2039.168536597647,-0.011373452858062735,sl,30,-0.010573452858062734,0.010573452858062705,False,ETH,2021-07-23 09:30:00+08:00,1.0,0.0,0.0007462354644697976,2021
+58705,58708,1,2394.28,2430.962124917967,0.014520733129778711,tp,3,0.015320733129778712,0.0076603665648893255,False,ETH,2021-07-31 04:00:00+08:00,1.0,1.0,-0.0008096651123356737,2021
+59361,59374,1,2869.48,2936.4788393033014,0.022548773751098226,tp,13,0.023348773751098225,0.011674386875549073,False,ETH,2021-08-07 00:00:00+08:00,1.0,0.0,8.900756564307967e-05,2021
+60405,60417,-1,3229.85,3153.7960342986985,0.022747212935988182,tp,12,0.02354721293598818,0.011773606467994082,False,ETH,2021-08-17 21:00:00+08:00,0.0,0.0,0.00039327949367550587,2021
+60786,60814,1,3265.26,3239.852455106583,-0.008581170532642807,sl,28,-0.007781170532642807,0.007781170532642776,False,ETH,2021-08-21 20:15:00+08:00,1.0,1.0,-0.0009970826101407722,2021
+60943,60954,1,3357.68,3325.1226893998796,-0.010496370887076856,sl,11,-0.009696370887076856,0.009696370887076911,False,ETH,2021-08-23 11:30:00+08:00,1.0,1.0,0.0015690440060698249,2021
+61238,61246,-1,3133.11,3076.797464659927,0.017173366827233416,tp,8,0.017973366827233415,0.008986683413616693,False,ETH,2021-08-26 13:15:00+08:00,1.0,1.0,-0.0005177813699736918,2021
+61614,61635,-1,3174.33,3196.8420849799727,-0.007891917028151695,sl,21,-0.007091917028151695,0.007091917028151686,False,ETH,2021-08-30 11:15:00+08:00,1.0,0.0,-0.0016704858070806581,2021
+63781,63795,-1,2916.39,2816.2494128641483,0.0335371727155324,tp,14,0.0343371727155324,0.017168586357766236,False,ETH,2021-09-22 01:00:00+08:00,1.0,1.0,0.007853543836076624,2021
+65207,65251,1,3586.59,3528.7878953053114,-0.016916172937160016,sl,44,-0.016116172937160018,0.01611617293716005,False,ETH,2021-10-06 21:30:00+08:00,1.0,0.0,0.0025772386728505742,2021
+66222,66232,1,3884.71,3858.5711909744255,-0.007528638437766159,sl,10,-0.006728638437766159,0.006728638437766136,False,ETH,2021-10-17 11:15:00+08:00,1.0,0.0,0.000783289817232376,2021
+69344,69373,-1,4070.65,3958.1241252882865,0.026843220299390427,tp,29,0.027643220299390425,0.013821610149695233,False,ETH,2021-11-18 23:45:00+08:00,1.0,1.0,-0.0018481134342705119,2021
+70460,70477,1,4414.4,4497.898387709964,0.018115002652673965,tp,17,0.018915002652673964,0.009457501326337027,False,ETH,2021-11-30 14:45:00+08:00,0.0,1.0,0.003484184959400625,2021
+70478,70480,1,4524.52,4618.1111420989555,0.01988531956957977,tp,2,0.02068531956957977,0.010342659784789847,False,ETH,2021-11-30 19:15:00+08:00,1.0,0.0,-0.0011210762331838565,2021
+71742,71744,-1,3863.41,3788.1233399215166,0.018687100793983356,tp,2,0.019487100793983355,0.009743550396991683,False,ETH,2021-12-13 23:15:00+08:00,1.0,1.0,0.0018045855307365318,2021
+72717,72762,1,4107.76,4073.674834711315,-0.009097749938819552,sl,45,-0.008297749938819552,0.008297749938819604,False,ETH,2021-12-24 03:00:00+08:00,1.0,1.0,0.0025076870347401246,2021
+73791,73796,1,3771.2,3742.7537426002264,-0.008343025402994659,sl,5,-0.007543025402994659,0.007543025402994604,False,ETH,2022-01-04 07:30:00+08:00,1.0,0.0,0.0013937113809021407,2022
+74234,74243,-1,3190.39,3145.1643774894487,0.013375578067430987,tp,9,0.014175578067430987,0.007087789033715519,False,ETH,2022-01-08 22:15:00+08:00,1.0,0.0,0.0018446740868396148,2022
+74846,74849,-1,3299.17,3321.7111014449497,-0.007632355242363873,sl,3,-0.006832355242363873,0.00683235524236394,False,ETH,2022-01-15 07:15:00+08:00,0.0,0.0,-0.0010817405204975898,2022
+74902,74907,1,3297.26,3338.9479561116063,0.011843211670176487,tp,5,0.012643211670176487,0.0063216058350882695,False,ETH,2022-01-15 21:15:00+08:00,0.0,0.0,-0.0005101775845688305,2022
+75392,75399,1,3260.67,3234.641636164943,-0.008782520106314655,sl,7,-0.007982520106314655,0.007982520106314698,False,ETH,2022-01-20 23:45:00+08:00,1.0,1.0,0.0041249263406009755,2022
+75893,75904,-1,2426.46,2464.3348806181734,-0.016409109821787043,sl,11,-0.015609109821787044,0.015609109821787074,False,ETH,2022-01-26 05:00:00+08:00,1.0,0.0,-0.004048780487804893,2022
+75998,76007,1,2468.87,2417.097002345887,-0.021770321505025666,sl,9,-0.020970321505025667,0.020970321505025726,False,ETH,2022-01-27 07:15:00+08:00,0.0,1.0,0.0007300745335978576,2022
+76799,76801,1,2803.44,2855.393756421412,0.01773214494385897,tp,2,0.018532144943858967,0.009266072471929463,False,ETH,2022-02-04 15:30:00+08:00,1.0,1.0,-0.00028922736524706423,2022
+77225,77237,1,3060.19,3125.8176461840826,0.020645611607149412,tp,12,0.02144561160714941,0.010722805803574676,False,ETH,2022-02-09 02:00:00+08:00,0.0,0.0,0.0041636197139487425,2022
+77269,77271,-1,3064.67,3087.488773191379,-0.00824575213363234,sl,2,-0.0074457521336323395,0.007445752133632294,False,ETH,2022-02-09 13:00:00+08:00,1.0,0.0,-0.008391203703703675,2022
+78435,78443,-1,2724.44,2678.8076526890286,0.015949257576225374,tp,8,0.016749257576225373,0.008374628788112667,False,ETH,2022-02-21 16:30:00+08:00,1.0,0.0,-0.002909090909090909,2022
+78691,78700,-1,2567.46,2503.7643768354283,0.024008808380489577,tp,9,0.024808808380489576,0.012404404190244822,False,ETH,2022-02-24 08:30:00+08:00,1.0,1.0,9.62186086789185e-05,2022
+79323,79325,1,3025.25,2982.041874525572,-0.015082497471094296,sl,2,-0.014282497471094295,0.014282497471094283,False,ETH,2022-03-02 22:30:00+08:00,0.0,1.0,0.002102486863716622,2022
+79806,79809,-1,2601.83,2628.0859263030525,-0.010891330449357778,sl,3,-0.010091330449357777,0.0100913304493578,False,ETH,2022-03-07 23:15:00+08:00,0.0,1.0,0.001980934454597817,2022
+80093,80133,1,2602.52,2572.273722978709,-0.012421919148091468,sl,40,-0.011621919148091468,0.011621919148091398,False,ETH,2022-03-10 23:00:00+08:00,0.0,0.0,-6.648338111014448e-05,2022
+80439,80446,-1,2564.46,2586.6282061446323,-0.00944439536769231,sl,7,-0.00864439536769231,0.008644395367692372,False,ETH,2022-03-14 13:30:00+08:00,1.0,1.0,0.00017425313171593568,2022
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+210375,210423,1,3013.29,3022.2,0.0021569009288849913,time,48,0.002956900928884991,0.010232575321608525,False,ETH,2025-11-27 01:30:00+08:00,1.0,0.0,-0.0025328161175498043,2025
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+227386,227424,-1,2072.0,2048.9540502654822,0.010322562613184249,tp,38,0.011122562613184249,0.005561281306592116,False,ETH,2026-05-23 06:15:00+08:00,1.0,1.0,-7.194244604320909e-05,2026
+227583,227599,1,2105.2,2091.6525524973363,-0.007235230620683805,sl,16,-0.006435230620683805,0.006435230620683901,False,ETH,2026-05-25 07:30:00+08:00,1.0,0.0,0.0070493940418134855,2026
+229591,229639,1,1715.59,1714.79,-0.0012663118810438128,time,48,-0.0004663118810438127,0.006835832251427009,False,ETH,2026-06-15 05:30:00+08:00,1.0,0.0,0.009769062455406022,2026
+231363,231364,1,1730.7,1720.1369286787033,-0.00690335200860737,sl,1,-0.006103352008607369,0.006103352008607328,False,ETH,2026-07-03 16:30:00+08:00,1.0,1.0,0.0014708553815140967,2026
+231565,231569,1,1763.11,1755.6629293909732,-0.005023826425479236,sl,4,-0.004223826425479236,0.0042238264254792855,False,ETH,2026-07-05 19:00:00+08:00,0.0,0.0,0.0005247068485650824,2026
+235132,235135,-1,1872.67,1856.016076872,0.008093143547982339,tp,3,0.00889314354798234,0.004446571773991141,False,ETH,2026-08-11 22:45:00+08:00,1.0,1.0,0.0007974144759772241,2026
+236426,236428,1,2528.89,2509.200817369884,-0.008585701485677803,sl,2,-0.007785701485677803,0.007785701485677821,False,ETH,2026-08-25 10:15:00+08:00,1.0,0.0,-0.0005352845057271124,2026
+1668,1669,-1,2.8056,2.744257819009535,0.02106419339551792,tp,1,0.021864193395517918,0.010932096697758976,False,SOL,2020-10-02 00:00:00+08:00,1.0,1.0,0.016059551297515164,2020
+2914,2915,-1,2.3256,2.267357744775424,0.02424396939481254,tp,1,0.02504396939481254,0.012521984697406237,False,SOL,2020-10-14 23:30:00+08:00,0.0,1.0,0.010877782444350942,2020
+3060,3064,-1,2.1464,2.172960835900414,-0.013174597419126976,sl,4,-0.012374597419126976,0.012374597419126984,False,SOL,2020-10-16 12:00:00+08:00,1.0,1.0,0.004432108161346455,2020
+3331,3332,1,2.2186,2.2005401613688553,-0.0089401959033375,sl,1,-0.0081401959033375,0.008140195903337491,False,SOL,2020-10-19 07:45:00+08:00,0.0,1.0,0.005331996536480801,2020
+3589,3610,-1,1.9596,1.8700272299298175,0.044909721407523216,tp,21,0.04570972140752322,0.0228548607037616,False,SOL,2020-10-22 00:15:00+08:00,0.0,1.0,0.02278358027690063,2020
+3721,3743,1,1.9474,1.9241946958938134,-0.012716044010571324,sl,22,-0.011916044010571324,0.01191604401057131,False,SOL,2020-10-23 09:15:00+08:00,0.0,0.0,0.0006238627501950039,2020
+4789,4797,-1,1.4314,1.4483317379639915,-0.01262879555958607,sl,8,-0.01182879555958607,0.0118287955595861,False,SOL,2020-11-03 12:15:00+08:00,1.0,1.0,0.0023189196562541587,2020
+5502,5504,1,2.2171,2.1763858614409974,-0.019163690658518973,sl,2,-0.018363690658518974,0.018363690658519,False,SOL,2020-11-10 22:30:00+08:00,1.0,1.0,-0.0025015919221322938,2020
+6015,6026,1,2.0263,2.1281001785380282,0.04943944062479803,tp,11,0.05023944062479803,0.02511972031239899,False,SOL,2020-11-16 06:45:00+08:00,0.0,1.0,0.00020408163265303876,2020
+6173,6174,1,2.3224,2.274642150217469,-0.02136400696802051,sl,1,-0.02056400696802051,0.020564006968020552,False,SOL,2020-11-17 22:15:00+08:00,0.0,0.0,0.01872607982504107,2020
+6627,6630,1,2.2048,2.1435057777776882,-0.028600354781527504,sl,3,-0.027800354781527505,0.0278003547815276,False,SOL,2020-11-22 15:45:00+08:00,0.0,0.0,0.021920766053323305,2020
+7762,7768,-1,2.0321,2.0530529673278597,-0.011110992238501993,sl,6,-0.010310992238501992,0.010310992238502006,False,SOL,2020-12-04 11:30:00+08:00,1.0,1.0,0.010625452789181259,2020
+8814,8816,1,1.6566,1.7016383495706418,0.026387220554534394,tp,2,0.027187220554534392,0.013593610277267201,False,SOL,2020-12-15 10:30:00+08:00,0.0,1.0,0.012734501180270957,2020
+8995,8998,1,1.7869,1.7558390946557862,-0.018182564969619834,sl,3,-0.017382564969619835,0.01738256496961984,False,SOL,2020-12-17 07:45:00+08:00,1.0,1.0,-0.0006826715212197576,2020
+9563,9569,-1,1.4804,1.5003033696431902,-0.014244589059166634,sl,6,-0.013444589059166633,0.01344458905916658,False,SOL,2020-12-23 05:45:00+08:00,0.0,0.0,0.0015199577055248418,2020
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+179184,179232,1,192.47,194.08,0.007564939990647964,time,48,0.008364939990647965,0.007128323800709541,False,SOL,2025-10-25 03:00:00+08:00,1.0,1.0,-0.0003663578793112115,2025
+179700,179714,-1,194.04,196.0325982239398,-0.011069007544525914,sl,14,-0.010269007544525914,0.010269007544525869,False,SOL,2025-10-30 12:00:00+08:00,0.0,0.0,0.0015150749962122264,2025
+181301,181306,-1,140.94,138.82312581698068,0.014219683432803462,tp,5,0.015019683432803462,0.007509841716401723,False,SOL,2025-11-16 04:15:00+08:00,1.0,0.0,0.0009816295049783674,2025
+182256,182260,-1,135.33,136.79082622012015,-0.011594548290254487,sl,4,-0.010794548290254486,0.01079454829025451,False,SOL,2025-11-26 03:00:00+08:00,0.0,1.0,-0.003280600714442058,2025
+182348,182396,1,142.48,143.46,0.00607815833801248,time,48,0.0068781583380124805,0.01181269464395312,False,SOL,2025-11-27 02:00:00+08:00,1.0,0.0,0.005075053609721286,2025
+182513,182516,-1,140.38,141.41199949464016,-0.00815147096908506,sl,3,-0.00735147096908506,0.007351470969084973,False,SOL,2025-11-28 19:15:00+08:00,0.0,1.0,-0.001971830985915501,2025
+183727,183775,-1,130.74,130.57,0.0005002906532049555,time,48,0.0013002906532049556,0.012339264382811589,False,SOL,2025-12-11 10:45:00+08:00,1.0,0.0,-0.001062296077092456,2025
+184354,184358,-1,129.01,125.01411336751298,0.03017346432437032,tp,4,0.030973464324370318,0.015486732162185159,False,SOL,2025-12-17 23:30:00+08:00,0.0,1.0,0.005809566923192861,2025
+184464,184471,-1,120.96,117.05782548189684,0.03146004065892156,tp,7,0.03226004065892156,0.016130020329460766,False,SOL,2025-12-19 03:00:00+08:00,1.0,0.0,0.0047490379104233055,2025
+184952,184974,-1,124.21,121.93740866872876,0.017496363668555105,tp,22,0.018296363668555103,0.009148181834277572,False,SOL,2025-12-24 05:00:00+08:00,1.0,1.0,0.003913738019169401,2025
+184972,184988,-1,122.56,120.99931559207093,0.011934043798376906,tp,16,0.012734043798376907,0.00636702189918843,False,SOL,2025-12-24 10:00:00+08:00,1.0,0.0,-0.0012947078815342013,2025
+185647,185650,1,125.95,125.26792566010916,-0.006215437394925337,sl,3,-0.005415437394925337,0.005415437394925344,False,SOL,2025-12-31 10:45:00+08:00,1.0,0.0,0.0018402944471114558,2025
+185826,185828,1,127.06,126.52576747118292,-0.005004568934496187,sl,2,-0.004204568934496187,0.004204568934496239,False,SOL,2026-01-02 07:30:00+08:00,1.0,1.0,0.0007139457401237776,2026
+188297,188303,-1,124.42,125.5769899515917,-0.01009906728493568,sl,6,-0.00929906728493568,0.009299067284935727,False,SOL,2026-01-28 01:15:00+08:00,0.0,1.0,0.0022123893805309825,2026
+188915,188924,1,104.1,103.09648270332127,-0.01043993560690416,sl,9,-0.00963993560690416,0.009639935606904187,False,SOL,2026-02-03 11:45:00+08:00,0.0,0.0,-0.005454368364663507,2026
+189145,189148,-1,88.4,90.026559126961,-0.019199990123993194,sl,3,-0.018399990123993196,0.018399990123993234,False,SOL,2026-02-05 21:15:00+08:00,1.0,1.0,-0.0015569395017793657,2026
+189819,189821,1,81.9,81.18708572873759,-0.009504691956806106,sl,2,-0.008704691956806105,0.00870469195680605,False,SOL,2026-02-12 21:45:00+08:00,1.0,1.0,0.003206709422792367,2026
+191249,191254,-1,84.42,82.87722151413323,0.017475035369187118,tp,5,0.018275035369187117,0.00913751768459355,False,SOL,2026-02-27 19:15:00+08:00,1.0,0.0,0.002818886539816712,2026
+191484,191486,1,83.38,82.44866553165969,-0.01196975855529276,sl,2,-0.01116975855529276,0.011169758555292768,False,SOL,2026-03-02 06:00:00+08:00,0.0,0.0,0.0019459985405010532,2026
+191554,191562,1,88.73,87.40467884224768,-0.0157365621295202,sl,8,-0.0149365621295202,0.01493656212952023,False,SOL,2026-03-02 23:30:00+08:00,0.0,0.0,0.01798435127875752,2026
+193568,193570,1,91.68,90.47419416913385,-0.013952332361105522,sl,2,-0.013152332361105521,0.013152332361105534,False,SOL,2026-03-23 23:00:00+08:00,1.0,1.0,0.0021003758567323893,2026
+195038,195040,1,84.0,85.93148380955554,0.022193854875661244,tp,2,0.022993854875661243,0.011496927437830623,False,SOL,2026-04-08 06:30:00+08:00,1.0,1.0,0.003013863773357444,2026
+195869,195871,-1,84.35,85.04561967180088,-0.009046824799062053,sl,2,-0.008246824799062052,0.008246824799062082,False,SOL,2026-04-16 22:15:00+08:00,0.0,0.0,0.007597895967270503,2026
+196373,196397,1,85.43,87.02247028439646,0.017840644789844963,tp,24,0.01864064478984496,0.00932032239492246,False,SOL,2026-04-22 04:15:00+08:00,0.0,1.0,0.007921494443130785,2026
+199474,199476,-1,85.63,86.04649015764232,-0.005663834609860202,sl,2,-0.0048638346098602014,0.004863834609860145,False,SOL,2026-05-24 11:30:00+08:00,0.0,0.0,-0.0005804504295332848,2026
+200544,200553,-1,69.96,67.96752294060155,0.027680232409926234,tp,9,0.028480232409926233,0.014240116204963071,False,SOL,2026-06-04 15:00:00+08:00,0.0,1.0,0.0067758328627894404,2026
+200939,200960,1,66.32,67.48707056039154,0.016797565747761624,tp,21,0.017597565747761622,0.00879878287388084,False,SOL,2026-06-08 17:45:00+08:00,1.0,0.0,0.0022872827081428133,2026
+201541,201547,1,67.65,67.30664639801576,-0.005875441271016228,sl,6,-0.005075441271016228,0.00507544127101615,False,SOL,2026-06-15 00:15:00+08:00,0.0,0.0,0.00014878738283001215,2026
+202140,202154,1,73.35,72.84968542400783,-0.007620921281420092,sl,14,-0.006820921281420092,0.006820921281420031,False,SOL,2026-06-21 06:00:00+08:00,1.0,0.0,0.0,2026
+204165,204170,-1,76.31,76.73188107400745,-0.006328516236501698,sl,5,-0.005528516236501697,0.00552851623650171,False,SOL,2026-07-12 08:15:00+08:00,1.0,1.0,0.0006503642039541774,2026
+204267,204315,-1,76.28,75.37,0.011129732564236976,time,48,0.011929732564236977,0.008472508147320335,False,SOL,2026-07-13 09:45:00+08:00,0.0,0.0,-0.0005148005148004124,2026
+205367,205371,-1,74.81,74.13773389574973,0.008186313383909495,tp,4,0.008986313383909495,0.0044931566919547816,False,SOL,2026-07-24 20:45:00+08:00,1.0,1.0,-0.0013267878466232494,2026
+205670,205693,1,75.79,75.2052615487194,-0.008515245431859086,sl,23,-0.007715245431859087,0.0077152454318591334,False,SOL,2026-07-28 00:30:00+08:00,0.0,1.0,-0.002260638297872363,2026
+205716,205760,-1,73.14,73.57900772858036,-0.006802293253764892,sl,44,-0.006002293253764892,0.006002293253764905,False,SOL,2026-07-28 12:00:00+08:00,1.0,1.0,0.0006802721088434988,2026
+206771,206776,1,74.81,74.54287010869565,-0.004370777854623046,sl,5,-0.0035707778546230457,0.003570777854622987,False,SOL,2026-08-08 11:45:00+08:00,0.0,0.0,0.0009451795841208909,2026
+207165,207172,-1,76.15,76.37601560044723,-0.003768031522616237,sl,7,-0.002968031522616237,0.0029680315226162293,False,SOL,2026-08-12 14:15:00+08:00,1.0,0.0,-0.0007842112142202073,2026
+207529,207533,-1,75.32,75.46147566195486,-0.0026783279601018477,sl,4,-0.0018783279601018478,0.0018783279601018502,False,SOL,2026-08-16 09:15:00+08:00,1.0,0.0,0.00013217023526289858,2026

research/step40_filter_ablation.py

@@ -0,0 +1,97 @@
+"""一次性诊断：未过滤的原始第四类信号本身能不能当开单信号。
+
+口径严格按 HANDOFF §1.3：SL/TP/超时 = 1.5/3.0/48 ATR，次根开盘入场，5bp 成本。
+不调任何参数，只是把同一批信号按两个过滤标志切开对比。
+"""
+from __future__ import annotations
+
+import sys
+import warnings
+from pathlib import Path
+
+warnings.filterwarnings("ignore")
+HERE = Path(__file__).resolve().parent
+sys.path.insert(0, str(HERE))
+sys.path.insert(0, str(HERE.parent))
+
+import numpy as np  # noqa: E402
+import pandas as pd  # noqa: E402
+
+from chanlun import TF_DF  # noqa: E402
+from chanlun.analysis.fast_bsp import (  # noqa: E402
+    add_zone_ladder,
+    attach_htf_agree,
+    attach_zone_ladder,
+    find_fast_bsp3,
+    htf_fx_timeline,
+    zones_from_zs_list,
+)
+from lib.breakout import run_trades, summarize_trades  # noqa: E402
+from lib.data import fetch_ohlcv  # noqa: E402
+
+SL, TP, MAXB, FEE = 1.5, 3.0, 48, 0.0005
+BEST = {"15m": "1h", "30m": "2h"}
+SYMBOLS = ["BTC", "ETH", "SOL"]
+
+
+def collect(sym: str, ltf: str):
+    df = fetch_ohlcv(f"{sym}/USDT:USDT", ltf, 10**9)
+    chan = TF_DF(df, 1, ltf)
+    cdf = chan.dataframe
+    zones = add_zone_ladder(zones_from_zs_list(chan.bi_zs_list, cdf))
+    if zones.empty:
+        return None
+    sig = find_fast_bsp3(cdf, zones)
+    if sig.empty:
+        return None
+    sig = attach_zone_ladder(sig, zones)
+
+    df_h = fetch_ohlcv(f"{sym}/USDT:USDT", BEST[ltf], 10**9)
+    chan_h = TF_DF(df_h, 1, BEST[ltf])
+    sig = attach_htf_agree(sig, cdf, htf_fx_timeline(chan_h))
+    return cdf, sig
+
+
+def trades(cdf, sig, label, sym, ltf):
+    if sig.empty:
+        return pd.DataFrame()
+    entries = list(zip(sig["entry_idx"].astype(int), sig["direction"].astype(int)))
+    tr = run_trades(cdf, entries, SL, TP, MAXB, fee=FEE, entry_delay=1)
+    if not tr.empty:
+        tr["组"] = label
+        tr["symbol"], tr["ltf"] = sym, ltf
+    return tr
+
+
+def main():
+    for ltf in ("30m", "15m"):
+        buckets: dict[str, list] = {}
+        for sym in SYMBOLS:
+            got = collect(sym, ltf)
+            if got is None:
+                continue
+            cdf, sig = got
+            agree = sig["htf_agree"] == 1
+            ladder = sig["ladder_ok"].fillna(False).astype(bool)
+            groups = {
+                "全部(图上所有 B4/S4)": sig,
+                "仅大级别同向": sig[agree],
+                "仅中枢阶梯": sig[ladder],
+                "同向+阶梯(实盘口径)": sig[agree & ladder],
+                "两个都不满足(对照)": sig[~agree & ~ladder],
+                "大级别反向(对照)": sig[sig["htf_agree"] == 0],
+            }
+            for k, s in groups.items():
+                tr = trades(cdf, s, k, sym, ltf)
+                if not tr.empty:
+                    buckets.setdefault(k, []).append(tr)
+
+        rows = [summarize_trades(pd.concat(v, ignore_index=True), k)
+                for k, v in buckets.items()]
+        print(f"\n########## {ltf} / 大级别 {BEST[ltf]}  BTC+ETH+SOL 全量，已扣 5bp ##########")
+        print(pd.DataFrame(rows).to_string(index=False))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

research/step41_exit_tp.py

@@ -0,0 +1,234 @@
+"""Step 41：出场口径全扫（5m/15m/30m）——SL × TP × MAX_BARS × 分批 + 真实费率模型。
+
+起因有三，都来自用户：
+  1. 「止盈没做好」→ 量 MFE，确认 TP=3.0 截在中位数以下
+  2. 「可以测 1pt/1.5pt/2pt 损啊」→ SL 一起扫，不再钉死 1.5
+  3. 「只有开仓才是 taker，止盈止损都是 maker 才对」→ 费率按出场原因分别计
+
+第 3 条只对了一半：止盈挂限价确实是 maker，但止损是 stop-market，
+成交时仍是 taker（做成 stop-limit 会有急跌不成交的风险，损失远大于省下的费）。
+所以模型是「入场 taker + 止盈 maker + 止损/超时 taker」，见 lib/exit_model.py。
+
+信号口径与 step37 完全一致（pure 笔中枢 + 大级别分型同向 + 中枢阶梯），只换出场。
+样本内 BTC/ETH/SOL，样本外是 step37 那 8 个没参与过调参的币。
+1m 另见 step42——它的右尾量纲和这三个级别不同，不能混用参数。
+"""
+from __future__ import annotations
+
+import argparse
+import os
+import sys
+import warnings
+from concurrent.futures import ProcessPoolExecutor, as_completed
+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
+sys.path.insert(0, str(HERE))
+sys.path.insert(0, str(HERE.parent))
+pd.set_option("display.width", 400)
+
+SLS = [1.0, 1.5, 2.0]
+TPS = [2.0, 3.0, 4.0, 5.0, 6.0, 8.0]
+MAXBS = [48, 96]
+RUNNERS = [5.0, 6.0, 8.0, None]
+SCALE_AT = 3.0
+# 减仓后剩余半仓的止损位（开仓价下方几个 ATR）。0 = 保本损，1.5 = 与原止损同位
+RUNNER_STOPS = [0.0, 0.5, 1.0, 1.5, 2.0]
+PAIRS = [("5m", "30m"), ("15m", "1h"), ("30m", "2h")]
+IS_SYMS = ["BTC", "ETH", "SOL"]
+OOS_SYMS = ["BNB", "XRP", "DOGE", "ADA", "AVAX", "LINK", "LTC", "TRX"]
+
+
+def cfgs() -> list[tuple[str, str, float]]:
+	"""所有配置的 (列名, 可读标签, 初始 SL)。"""
+	from lib.exit_model import cfg_name
+	out = []
+	for sl in SLS:
+		for b in MAXBS:
+			for t in TPS:
+				out.append((cfg_name(sl, t, b), f"SL{sl:g} 整仓TP{t:g} {b}根", sl))
+			for rn in RUNNERS:
+				tgt = f"{rn:g}ATR" if rn else "不设目标"
+				for k in RUNNER_STOPS:
+					kk = "保本" if k == 0 else f"留损{k:g}"
+					out.append((cfg_name(sl, rn, b, k),
+					            f"SL{sl:g} 分批→{tgt} {kk} {b}根", sl))
+	return out
+
+
+def run_one(task: tuple) -> dict | None:
+	import warnings as _w
+	_w.filterwarnings("ignore")
+	sys.path.insert(0, str(HERE))
+	sys.path.insert(0, str(HERE.parent))
+
+	from chanlun import TF_DF
+	from lib.data import fetch_ohlcv
+	from lib.exit_model import walk_exits
+	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
+
+	sym, ltf, htf, group = task
+	pair = f"{sym}/USDT:USDT"
+	try:
+		df_l = fetch_ohlcv(pair, ltf, 10 ** 9)
+		if df_l is None or len(df_l) < 3000:
+			return {"task": f"{sym} {ltf}", "error": "小级别数据不足"}
+		chan_l = TF_DF(df_l, 1, ltf)
+		cdf = chan_l.dataframe
+		zones = build_htf_zones(cdf, ltf, chan=chan_l).reset_index(drop=True)
+		if zones.empty:
+			return {"task": f"{sym} {ltf}", "error": "无中枢"}
+		z = zones.copy()
+		pg, pdn = z["zg"].shift(), z["zd"].shift()
+		z["z_above"], z["z_below"] = z["zd"] > pg, z["zg"] < pdn
+		z["zone_i"] = np.arange(len(z))
+
+		df_h = fetch_ohlcv(pair, htf, 10 ** 9)
+		if df_h is None or len(df_h) < 300:
+			return {"task": f"{sym} {ltf}", "error": "大级别数据不足"}
+		chan_h = TF_DF(df_h, 1, htf)
+		tl = htf_fx_timeline(
+			signals_to_frame(extract_fx_signals(chan_h, chan_h.dataframe)), chan_h.dataframe)
+
+		sig = find_fast_bsp3(cdf, zones)
+		if sig.empty:
+			return {"task": f"{sym} {ltf}", "error": "无信号"}
+		sig = sig.merge(z[["zone_i", "z_above", "z_below"]], on="zone_i", how="left")
+		sig = attach_htf_context(sig, cdf, tl, "h1")
+		push = np.where(sig["direction"] == 1, sig["z_above"], sig["z_below"])
+		keep = (sig["h1_agree"] == 1) & pd.Series(push, index=sig.index).fillna(False).astype(bool)
+		sig = sig[keep]
+		if len(sig) < 25:
+			return {"task": f"{sym} {ltf}", "error": f"过滤后仅 {len(sig)} 笔"}
+
+		r = walk_exits(cdf, sig, SLS, TPS, MAXBS, SCALE_AT, RUNNERS, RUNNER_STOPS)
+		if r.empty:
+			return {"task": f"{sym} {ltf}", "error": "无成交"}
+		r["symbol"], r["ltf"], r["group"] = sym, ltf, group
+		r["date"] = cdf["date"].to_numpy()[r["sig_idx"].to_numpy()]
+		return {"task": f"{sym} {ltf}", "rows": r}
+	except Exception as e:
+		return {"task": f"{sym} {ltf}", "error": repr(e)[:200]}
+
+
+def main() -> None:
+	ap = argparse.ArgumentParser()
+	ap.add_argument("--workers", type=int, default=6)
+	ap.add_argument("--reuse", action="store_true")
+	args = ap.parse_args()
+
+	from lib.exit_model import all_taker_cost, cfg_name, cost_of, flat_cost, rstat, stat
+
+	cache = HERE / "out" / "step41_exit_tp.feather"
+	if args.reuse and cache.exists():
+		allr = pd.read_feather(cache)
+		print(f"[复用] {len(allr)} 笔\n")
+	else:
+		tasks = ([(s, l, h, "样本内") for s in IS_SYMS for l, h in PAIRS]
+		         + [(s, l, h, "样本外") for s in OOS_SYMS for l, h in PAIRS])
+		print(f"[出场口径全扫] {len(tasks)} 个任务，{len(cfgs())} 个配置\n", flush=True)
+		res = []
+		with ProcessPoolExecutor(max_workers=args.workers) as ex:
+			futs = {ex.submit(run_one, t): t for t in tasks}
+			for i, f in enumerate(as_completed(futs), 1):
+				r = f.result()
+				if not r or "error" in r:
+					print(f"  [{i}/{len(tasks)}] 跳过 {(r or {}).get('task', '')} "
+					      f"{(r or {}).get('error', '')}", flush=True)
+					continue
+				res.append(r["rows"])
+				print(f"  [{i}/{len(tasks)}] {r['task']} n={len(r['rows'])}", flush=True)
+		if not res:
+			return
+		allr = pd.concat(res, ignore_index=True)
+		allr.to_feather(cache)
+
+	oos = allr[allr["group"] == "样本外"]
+	ins = allr[allr["group"] == "样本内"]
+	base = f"s1.5_tp{SCALE_AT:g}_m48"
+
+	print("=" * 165)
+	print("########## 1. 费率模型的影响（样本外，同一配置 SL1.5/TP3/48根）##########")
+	print("  旧：进出都当 taker，双边 6bp + 双边滑点")
+	print("  新：入场 taker + 止盈 maker + 止损/超时 taker，滑点只加在 taker 腿上")
+	rows = [stat(oos, base, "旧 全taker", all_taker_cost),
+	        stat(oos, base, "新 混合费率", cost_of),
+	        stat(oos, base, "研究口径 固定5bp", flat_cost)]
+	print(pd.DataFrame([r for r in rows if r]).to_string(index=False))
+
+	print("\n########## 2. 减仓后剩余半仓的止损位（样本外，48根）##########")
+	print("  0 = 保本损；1.5 = 与原止损同位（不动）；更大 = 减仓后主动放宽")
+	for sl in SLS:
+		rows = []
+		for rn in (6.0, 8.0):
+			for k in RUNNER_STOPS:
+				kk = "保本" if k == 0 else f"留损{k:g}ATR"
+				rows.append(stat(oos, cfg_name(sl, rn, 48, k),
+				                 f"SL{sl:g} 分批→{rn:g}ATR {kk}"))
+		rows = [r for r in rows if r]
+		if rows:
+			print(pd.DataFrame(rows).to_string(index=False))
+			print()
+
+	print("########## 3. 初始 SL 扫描：R 倍数口径（唯一可比）##########")
+	print("  SL 越宽每笔风险越大、固定风险下仓位越小，直接比百分比会把仓位差异算成策略优势")
+	for name, frame in (("样本外", oos), ("样本内", ins)):
+		rows = []
+		for sl in SLS:
+			rows.append(rstat(frame, cfg_name(sl, 3.0, 48), f"SL{sl:g} 整仓TP3/48", sl))
+			rows.append(rstat(frame, cfg_name(sl, 8.0, 48, sl),
+			                  f"SL{sl:g} 分批→8ATR 留损{sl:g} /48", sl))
+		print(f"  【{name}】")
+		print(pd.DataFrame(rows).to_string(index=False))
+		print()
+
+	print("########## 4. 全网格排名（样本外，按剔10%PF）##########")
+	rank = [r for r in (stat(oos, c, lab) for c, lab, _ in cfgs()) if r]
+	rk = pd.DataFrame(rank)
+	rk["_k"] = rk["剔10%PF"].astype(float)
+	print(rk.sort_values("_k", ascending=False).drop(columns="_k").head(15).to_string(index=False))
+
+	print("\n########## 5. 现用 vs 最优：样本内外对照 ##########")
+	best = rk.sort_values("_k", ascending=False).iloc[0]["口径"]
+	lut = {lab: (c, sl) for c, lab, sl in cfgs()}
+	best_cfg, best_sl = lut[best]
+	rows = []
+	for name, frame in (("样本内", ins), ("样本外", oos)):
+		rows.append(stat(frame, base, f"{name} 现用 SL1.5/TP3/48根"))
+		rows.append(stat(frame, best_cfg, f"{name} 最优 {best}"))
+	print(pd.DataFrame([r for r in rows if r]).to_string(index=False))
+	print("\n  同一对比的 R 倍数口径：")
+	rows = []
+	for name, frame in (("样本内", ins), ("样本外", oos)):
+		rows.append(rstat(frame, base, f"{name} 现用 SL1.5/TP3/48根", 1.5))
+		rows.append(rstat(frame, best_cfg, f"{name} 最优 {best}", best_sl))
+	print(pd.DataFrame(rows).to_string(index=False))
+
+	print(f"\n########## 6. {best} 的逐币与分年稳定性（样本外）##########")
+	f = oos.assign(year=pd.to_datetime(oos["date"]).dt.year)
+	for key, name in (("symbol", "币"), ("year", "年")):
+		rows = []
+		for k, g in f.groupby(key):
+			a, bst = stat(g, base, str(k)), stat(g, best_cfg, str(k))
+			if a and bst:
+				rows.append({name: k, "笔数": a["笔数"],
+				             "现用 净收益": a["净均收益"], "最优 净收益": bst["净均收益"],
+				             "现用 PF": a["PF"], "最优 PF": bst["PF"],
+				             "现用 剔10%": a["剔10%PF"], "最优 剔10%": bst["剔10%PF"],
+				             "现用 余量bp": a["滑点余量bp"], "最优 余量bp": bst["滑点余量bp"]})
+		print(pd.DataFrame(rows).to_string(index=False))
+		print()
+
+
+if __name__ == "__main__":
+	main()

research/step42_exit_tp_1m.py

@@ -0,0 +1,240 @@
+"""Step 42：1m 单独定出场（SL × TP × MAX_BARS × 分批 + 真实费率模型）。
+
+用户指出的问题：`SL1.5/TP3.0/MAXB48` 是在 5m/15m/30m 上定的，1m 不该混用。
+查证成立，而且原因比成本更根本——**1m 以 ATR 计的右尾是其他级别的两倍**
+（MFE 中位 5.39 ATR vs 3.94~4.29），因为 1m 的 ATR 量的是分钟级噪声，
+信号一旦成立抓到的却是几小时级别的趋势。所以「TP=3 ATR」在两个级别上
+根本不是一回事，这是量纲问题不是调参问题。
+
+费率按出场原因分别计（入场 taker / 止盈 maker / 止损超时 taker），
+实现在 lib/exit_model.py，与 step41 共用。
+
+配对按 §1.5：1m 的大级别是 5m。
+⚠️ 1m 全量 366 万根，单币 TF_DF 约 370s / 峰值 24.5GB，最多开 3 并行。
+"""
+from __future__ import annotations
+
+import argparse
+import os
+import sys
+import warnings
+from concurrent.futures import ProcessPoolExecutor, as_completed
+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
+sys.path.insert(0, str(HERE))
+sys.path.insert(0, str(HERE.parent))
+pd.set_option("display.width", 400)
+
+LTF, HTF = "1m", "5m"
+SLS = [1.0, 1.5, 2.0]
+TPS = [2.0, 3.0, 4.0, 5.0, 6.0, 8.0, 10.0, 12.0]
+MAXBS = [48, 96]
+RUNNERS = [5.0, 6.0, 8.0, None]
+SCALE_AT = 3.0
+# 减仓后剩余半仓的止损位（开仓价下方几个 ATR）。0 = 保本损，等于初始 SL 即不动
+RUNNER_STOPS = [0.0, 0.5, 1.0, 1.5, 2.0]
+IS_SYMS = ["BTC", "ETH", "SOL"]
+OOS_SYMS = ["BNB", "XRP", "DOGE", "ADA", "AVAX", "LINK", "LTC", "TRX"]
+
+
+def cfgs() -> list[tuple[str, str, float]]:
+	"""所有配置的 (列名, 可读标签, 初始 SL)。"""
+	from lib.exit_model import cfg_name
+	out = []
+	for sl in SLS:
+		for b in MAXBS:
+			for t in TPS:
+				out.append((cfg_name(sl, t, b), f"SL{sl:g} 整仓TP{t:g} {b}根", sl))
+			for rn in RUNNERS:
+				tgt = f"{rn:g}ATR" if rn else "不设目标"
+				for k in RUNNER_STOPS:
+					kk = "保本" if k == 0 else f"留损{k:g}"
+					out.append((cfg_name(sl, rn, b, k),
+					            f"SL{sl:g} 分批→{tgt} {kk} {b}根", sl))
+	return out
+
+
+def run_one(task: tuple) -> dict | None:
+	import warnings as _w
+	_w.filterwarnings("ignore")
+	sys.path.insert(0, str(HERE))
+	sys.path.insert(0, str(HERE.parent))
+
+	from chanlun import TF_DF
+	from lib.data import fetch_ohlcv
+	from lib.exit_model import walk_exits
+	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
+
+	sym, group = task
+	pair = f"{sym}/USDT:USDT"
+	try:
+		df_l = fetch_ohlcv(pair, LTF, 10 ** 9)
+		if df_l is None or len(df_l) < 3000:
+			return {"task": sym, "error": "1m 数据不足"}
+		chan_l = TF_DF(df_l, 1, LTF)
+		cdf = chan_l.dataframe
+		del df_l
+		zones = build_htf_zones(cdf, LTF, chan=chan_l).reset_index(drop=True)
+		if zones.empty:
+			return {"task": sym, "error": "无中枢"}
+		z = zones.copy()
+		pg, pdn = z["zg"].shift(), z["zd"].shift()
+		z["z_above"], z["z_below"] = z["zd"] > pg, z["zg"] < pdn
+		z["zone_i"] = np.arange(len(z))
+
+		df_h = fetch_ohlcv(pair, HTF, 10 ** 9)
+		chan_h = TF_DF(df_h, 1, HTF)
+		tl = htf_fx_timeline(
+			signals_to_frame(extract_fx_signals(chan_h, chan_h.dataframe)), chan_h.dataframe)
+		del df_h, chan_h
+
+		sig = find_fast_bsp3(cdf, zones)
+		if sig.empty:
+			return {"task": sym, "error": "无信号"}
+		sig = sig.merge(z[["zone_i", "z_above", "z_below"]], on="zone_i", how="left")
+		sig = attach_htf_context(sig, cdf, tl, "h1")
+		push = np.where(sig["direction"] == 1, sig["z_above"], sig["z_below"])
+		keep = (sig["h1_agree"] == 1) & pd.Series(push, index=sig.index).fillna(False).astype(bool)
+		sig = sig[keep]
+		if len(sig) < 25:
+			return {"task": sym, "error": f"过滤后仅 {len(sig)} 笔"}
+
+		r = walk_exits(cdf, sig, SLS, TPS, MAXBS, SCALE_AT, RUNNERS, RUNNER_STOPS)
+		r["symbol"], r["group"] = sym, group
+		r["date"] = cdf["date"].to_numpy()[r["sig_idx"].to_numpy()]
+		return {"task": sym, "rows": r}
+	except Exception as e:
+		return {"task": sym, "error": repr(e)[:200]}
+
+
+def main() -> None:
+	ap = argparse.ArgumentParser()
+	ap.add_argument("--symbols", default="all")
+	ap.add_argument("--workers", type=int, default=3)
+	ap.add_argument("--reuse", action="store_true")
+	args = ap.parse_args()
+
+	from lib.exit_model import (all_taker_cost, cfg_name, cost_of, flat_cost,
+	                           rstat, stat)
+
+	cache = HERE / "out" / "step42_exit_tp_1m.feather"
+	if args.reuse and cache.exists():
+		allr = pd.read_feather(cache)
+		print(f"[复用] {len(allr)} 笔\n")
+	else:
+		if args.symbols == "all":
+			tasks = [(s, "样本内") for s in IS_SYMS] + [(s, "样本外") for s in OOS_SYMS]
+		elif args.symbols == "is":
+			tasks = [(s, "样本内") for s in IS_SYMS]
+		else:
+			picked = [s.strip() for s in args.symbols.split(",")]
+			tasks = [(s, "样本内" if s in IS_SYMS else "样本外") for s in picked]
+		print(f"[1m 出场全扫] {len(tasks)} 个币 × {len(cfgs())} 个配置，"
+		      f"{args.workers} 并行（单币约 6.5 分钟 / 24GB）\n", flush=True)
+		res = []
+		with ProcessPoolExecutor(max_workers=args.workers) as ex:
+			futs = {ex.submit(run_one, t): t for t in tasks}
+			for i, f in enumerate(as_completed(futs), 1):
+				r = f.result()
+				if not r or "error" in r:
+					print(f"  [{i}/{len(tasks)}] 跳过 {(r or {}).get('task', '')} "
+					      f"{(r or {}).get('error', '')}", flush=True)
+					continue
+				res.append(r["rows"])
+				print(f"  [{i}/{len(tasks)}] {r['task']} n={len(r['rows'])}", flush=True)
+		if not res:
+			return
+		allr = pd.concat(res, ignore_index=True)
+		allr.to_feather(cache)
+
+	oos = allr[allr["group"] == "样本外"]
+	ins = allr[allr["group"] == "样本内"]
+	base = "s1.5_tp3_m48"
+
+	print("=" * 170)
+	print(f"1m 出场全扫  样本内 {len(ins)} 笔 / 样本外 {len(oos)} 笔")
+
+	print("\n########## 1. 费率模型的影响（样本外，SL1.5/TP3/48根）##########")
+	gr = oos[f"{base}_g"].to_numpy()
+	rs, sc = oos[f"{base}_r"].to_numpy(), oos[f"{base}_c"].to_numpy()
+	for fn, name in ((all_taker_cost, "旧 全taker双边"),
+	                 (cost_of, "新 入场taker+止盈maker"),
+	                 (flat_cost, "研究口径 固定5bp")):
+		print(f"  {name:26s} 平均成本 {fn(rs, sc).mean() * 10000:5.2f}bp   "
+		      f"净均收益 {(gr - fn(rs, sc)).mean() * 100:+.3f}%")
+	print(f"  止盈成交占比 {(rs == 0).mean() * 100:.0f}%（只有这部分是 maker）")
+
+	print("\n########## 2. 全网格排名（样本外，按剔10%PF）##########")
+	rank = [r for r in (stat(oos, c, lab) for c, lab, _ in cfgs()) if r]
+	rk = pd.DataFrame(rank)
+	rk["_k"] = rk["剔10%PF"].astype(float)
+	print(rk.sort_values("_k", ascending=False).drop(columns="_k").head(15).to_string(index=False))
+
+	print("\n########## 3. 减仓后剩余半仓的止损位（样本外，分批→8ATR，48根）##########")
+	print("  0 = 保本损；等于初始 SL 即不动；更大 = 减仓后主动放宽")
+	for sl in SLS:
+		rows = []
+		for k in RUNNER_STOPS:
+			kk = "保本损" if k == 0 else f"留损{k:g}ATR"
+			rows.append(stat(oos, cfg_name(sl, 8.0, 48, k), f"初始SL{sl:g} → {kk}"))
+		rows = [r for r in rows if r]
+		if rows:
+			print(pd.DataFrame(rows).to_string(index=False))
+			print()
+
+	print("########## 4. 初始 SL：R 倍数口径（唯一可比）##########")
+	for name, frame in (("样本外", oos), ("样本内", ins)):
+		rows = []
+		for sl in SLS:
+			rows.append(rstat(frame, cfg_name(sl, 3.0, 48), f"SL{sl:g} 整仓TP3/48", sl))
+			rows.append(rstat(frame, cfg_name(sl, 8.0, 48, sl),
+			                  f"SL{sl:g} 分批→8ATR 留损{sl:g}/48", sl))
+		print(f"  【{name}】")
+		print(pd.DataFrame(rows).to_string(index=False))
+		print()
+
+	print("########## 5. 现用 vs 最优：样本内外对照 ##########")
+	best_lab = rk.sort_values("_k", ascending=False).iloc[0]["口径"]
+	lut = {lab: (c, sl) for c, lab, sl in cfgs()}
+	best_cfg, best_sl = lut[best_lab]
+	rows = []
+	for name, frame in (("样本内", ins), ("样本外", oos)):
+		rows += [stat(frame, base, f"{name} 现用 SL1.5/TP3/48根"),
+		         stat(frame, best_cfg, f"{name} 最优 {best_lab}")]
+	print(pd.DataFrame([r for r in rows if r]).to_string(index=False))
+	print("\n  同一对比的 R 倍数口径：")
+	rows = []
+	for name, frame in (("样本内", ins), ("样本外", oos)):
+		rows += [rstat(frame, base, f"{name} 现用 SL1.5/TP3/48根", 1.5),
+		         rstat(frame, best_cfg, f"{name} 最优 {best_lab}", best_sl)]
+	print(pd.DataFrame(rows).to_string(index=False))
+
+	print(f"\n########## 6. {best_lab} 的逐币与分年稳定性（样本外）##########")
+	f = oos.assign(year=pd.to_datetime(oos["date"]).dt.year)
+	for key, name in (("symbol", "币"), ("year", "年")):
+		rows = []
+		for k, g in f.groupby(key):
+			a, b = stat(g, base, str(k)), stat(g, best_cfg, str(k))
+			if a and b:
+				rows.append({name: k, "笔数": a["笔数"],
+				             "现用 净收益": a["净均收益"], "最优 净收益": b["净均收益"],
+				             "现用 PF": a["PF"], "最优 PF": b["PF"],
+				             "现用 剔10%": a["剔10%PF"], "最优 剔10%": b["剔10%PF"],
+				             "现用 余量bp": a["滑点余量bp"], "最优 余量bp": b["滑点余量bp"]})
+		print(pd.DataFrame(rows).to_string(index=False))
+		print()
+
+
+if __name__ == "__main__":
+	main()

web/api/analyze.py

@@ -241,7 +241,9 @@ def analyze():
 				'is_sure': bool(bsp.is_sure),
 				'sure_time': format_time_safely(bsp.sure_time, client_tz) if bsp.sure_time else None,
 				'zs_count': int(bsp.zs_count) if hasattr(bsp, 'zs_count') else 0
-			} for bsp in analysis_result.get('bsp_list', [])]
+			} for bsp in analysis_result.get('bsp_list', [])],
+			# 添加主周期第四类买卖点（B4/S4，低滞后三类买卖点）
+			'fast_bsp_list': serialize_fast_bsp_list(analysis_result.get('fast_bsp_list', []), client_tz)
 		})
 		
 	
@@ -425,6 +427,7 @@ def analyze():
 				'sure_time': format_time_safely(bsp.sure_time, client_tz) if bsp.sure_time else None,
 				'zs_count': int(bsp.zs_count) if hasattr(bsp, 'zs_count') else 0
 			} for bsp in element_analysis.get('bsp_list', [])]
+			result['element_fast_bsp_list'] = serialize_fast_bsp_list(element_analysis.get('fast_bsp_list', []), client_tz)
 
 			# 次次周期：仅当已指定次周期且次次周期有效时获取
 			if sub_sub_timeframe and is_smaller_or_equal_timeframe(sub_sub_timeframe, element_timeframe):
@@ -526,6 +529,7 @@ def analyze():
 						'sure_time': format_time_safely(bsp.sure_time, client_tz) if bsp.sure_time else None,
 						'zs_count': int(bsp.zs_count) if hasattr(bsp, 'zs_count') else 0
 					} for bsp in sub_sub_analysis.get('bsp_list', [])]
+					result['sub_sub_fast_bsp_list'] = serialize_fast_bsp_list(sub_sub_analysis.get('fast_bsp_list', []), client_tz)
 					result['sub_sub_chan_macd'] = serialize_chan_macd_data(sub_sub_analysis.get('chan_macd', {}), client_tz)
 					try:
 						sub_sub_klc_trend = []

web/services/runtime/__init__.py

@@ -67,6 +67,7 @@ from .serialize import (  # noqa: F401
 	convert_direction,
 	format_time_safely,
 	serialize_chan_macd_data,
+	serialize_fast_bsp_list,
 	clean_dataframe_for_json,
 	get_uncompleted_seg_list,
 )

web/services/runtime/analyze.py

@@ -30,6 +30,15 @@ def analyze_chan(df, symbol=None, timeframe=None):
 	bsp_list = []
 	if len(bi_zs_list) > 0:
 		bsp_list = chan.find_all_bsp(bi_list, bi_zs_list)
+	# 第四类买卖点（B4/S4）：复用上面刚算好的 bi_zs_list，不重复建中枢。
+	# 大级别由同一份 df 重采样得到，失败时退化为空列表，不拖垮主分析。
+	fast_bsp_list = []
+	try:
+		if len(bi_zs_list) > 0:
+			fast_bsp_list = chan.cal_fast_bsp(df=df, bi_zs_list=bi_zs_list, timeframe=timeframe)
+	except Exception as e:
+		print(f"第四类买卖点计算出错: {e}")
+		fast_bsp_list = []
 	#bsp_state_list = chan.get_bsp_state(df)
 	#for bsp in bsp_list:
 		#print(bsp.end_time, bsp.type, bsp.dir)
@@ -177,6 +186,7 @@ def analyze_chan(df, symbol=None, timeframe=None):
 		'zs_list': zs_list,
 		'bi_zs_list': bi_zs_list,  # 添加BI中枢列表
 		'bsp_list': bsp_list,  # 添加买卖点列表
+		'fast_bsp_list': fast_bsp_list,  # 第四类买卖点（B4/S4）
 		'klc_fx_info': klc_fx_info,  # KLC分型信息
 		'chan_macd': chan_macd_data,   # 添加ChanMACD分析数据
 		'ema52_dict': ema52_dict  # 添加多时间周期EMA52数据

web/services/runtime/serialize.py

@@ -31,6 +31,36 @@ def format_time_safely(time_obj, client_tz):
 		# 已经是datetime对象
 		return time_obj.astimezone(client_tz).isoformat()
 
+def serialize_fast_bsp_list(fast_bsp_list, client_tz):
+	"""序列化第四类买卖点（ChanFastBSP）。
+
+	htf_agree（大级别分型同向）与 ladder_ok（中枢顺向推进）分别输出，
+	由前端决定要显示全部还是只显示两者都满足的。
+	"""
+	out = []
+	for bsp in fast_bsp_list or []:
+		try:
+			out.append({
+				'time': format_time_safely(bsp.time, client_tz),
+				'price': float(bsp.price),
+				'type': str(bsp.type).split('.')[-1].split('(')[0],
+				'dir': str(bsp.dir).split('.')[-1].split('(')[0],
+				'is_sure': True,
+				'lag': int(bsp.lag),
+				'depth': float(bsp.depth),
+				'zg': bsp.zg,
+				'zd': bsp.zd,
+				'occ': int(bsp.occ),
+				'htf_agree': bsp.htf_agree,
+				'ladder_ok': bsp.ladder_ok,
+				'bo_time': format_time_safely(bsp.bo_time, client_tz) if bsp.bo_time else None,
+				'pb_time': format_time_safely(bsp.pb_time, client_tz) if bsp.pb_time else None,
+			})
+		except Exception as e:
+			print(f"序列化fast_bsp出错: {e}")
+			continue
+	return out
+
 def serialize_chan_macd_data(chan_macd_data, client_tz):
 	"""序列化ChanMACD数据为JSON可序列化格式"""
 	serialized_data = {

web/static/js/app/chart_tv_overlays.js

@@ -852,6 +852,8 @@ function chartTvRenderOverlays(ctx) {
                 'BSP1_SELL': { color: '#00E676', text: 'S1', position: 'aboveBar', size: 0.5 },
                 'BSP2_SELL': { color: '#00B0FF', text: 'S2', position: 'aboveBar', size: 0.5 },
                 'BSP3_SELL': { color: '#8B4513', text: 'S3', position: 'aboveBar', size: 0.5 },
+                'BSP4_BUY':  { color: '#FF6D00', text: 'B4', position: 'belowBar', size: 0.5 },
+                'BSP4_SELL': { color: '#0091EA', text: 'S4', position: 'aboveBar', size: 0.5 },
             };
             
             const getBspStyleKey = (bsp) => {
@@ -988,6 +990,56 @@ function chartTvRenderOverlays(ctx) {
             window.bspMarkers = [];
         }
 
+        // 第四类买卖点（B4/S4）：中枢突破回抽后当根入场，位置同 B3/S3 但早 7~8 根。
+        // 与 BSP 分开收集，因为它数量远多于 B1/B2/B3，混在一个开关里图会糊掉。
+        if ($('#showMainFastBsp').is(':checked') || $('#showElementFastBsp').is(':checked') || $('#showSubSubFastBsp').is(':checked')) {
+            // 深色 = 区间套(大级别分型同向) + 中枢顺向推进都满足；浅色 = 未通过过滤
+            const FAST_BSP_STYLE = {
+                'BUY':  { strong: '#FF6D00', weak: '#FFCC80', text: 'B4', position: 'belowBar' },
+                'SELL': { strong: '#0091EA', weak: '#81D4FA', text: 'S4', position: 'aboveBar' },
+            };
+            const onlyFiltered = ($('#fastBspFilterMode').val() || 'all') === 'filtered';
+            const allFastBspMarkers = [];
+
+            const collectFastBsp = function(list, prefix, label) {
+                (list || []).forEach(function(bsp) {
+                    try {
+                        const ts = Math.floor(new Date(bsp.time).getTime() / 1000);
+                        if (isNaN(ts)) return;
+                        const style = FAST_BSP_STYLE[(bsp.dir || '').toUpperCase()];
+                        if (!style) return;
+                        const passed = !!(bsp.htf_agree && bsp.ladder_ok);
+                        if (onlyFiltered && !passed) return;
+                        allFastBspMarkers.push({
+                            time: ts,
+                            position: style.position,
+                            color: passed ? style.strong : style.weak,
+                            text: prefix + (passed ? style.text : style.text.toLowerCase()),
+                            size: passed ? 2 : 1
+                        });
+                    } catch (e) {
+                        console.error(label + '第四类买卖点处理出错:', e);
+                    }
+                });
+            };
+
+            if ($('#showMainFastBsp').is(':checked')) {
+                collectFastBsp(currentData.fast_bsp_list, '', '主周期');
+            }
+            if ($('#showElementFastBsp').is(':checked')) {
+                collectFastBsp(currentData.element_fast_bsp_list, 'e', '次周期');
+            }
+            if ($('#showSubSubFastBsp').is(':checked')) {
+                collectFastBsp(currentData.sub_sub_fast_bsp_list, 's', '次次周期');
+            }
+
+            allFastBspMarkers.sort((a, b) => a.time - b.time);
+            window.fastBspMarkers = allFastBspMarkers;
+            console.log(`绘制第四类买卖点，共${allFastBspMarkers.length}个标记（${onlyFiltered ? '仅过滤后' : '全部'}）`);
+        } else {
+            window.fastBspMarkers = [];
+        }
+
         // 添加买卖点标记（旧版，保留兼容）
         // 这里为了与主面板上的「买卖点」开关保持一致，
         // 同时响应顶部的 `#showMainBsp` 复选框
@@ -2113,7 +2165,8 @@ function chartTvRenderOverlays(ctx) {
                 ...(window.kluDivMarkersElement || []),
                 ...(window.kluDivMarkersSubSub || []),
                 ...trendMarkersToUse,
-                ...(window.bspMarkers || [])
+                ...(window.bspMarkers || []),
+                ...(window.fastBspMarkers || [])
             ];
             if (combinedMarkers.length > 0) {
                 console.log(
@@ -2122,6 +2175,7 @@ function chartTvRenderOverlays(ctx) {
                     '个，小周期分型:', allElementFxMarkers.length,
                     '个，UnitTF:', (window.unittfMarkers || []).length,
                     '个，BSP标记:', (window.bspMarkers || []).length,
+                    '个，第四类标记:', (window.fastBspMarkers || []).length,
                     '个）'
                 );
                 
@@ -2223,14 +2277,15 @@ function chartTvRenderOverlays(ctx) {
             // 这里的 onlyMainAndU 实际上是「最终要挂到主K线上」的一组标记
             // 之前没有把 window.bspMarkers 合进去，导致上面已经合并了 BSP 标记，
             // 但在这里再次调用 setMarkers 时把 BSP 覆盖掉了，从而前端看不到买卖点。
-            // 修复：把 BSP 标记一并合并进来。
+            // 修复：把 BSP 标记一并合并进来。第四类买卖点同理，两处都要带上。
             const onlyMainAndU = [
                 ...(window.mainFxMarkers || []),
                 ...(window.kluDivMarkersMain || []),
                 ...(window.kluDivMarkersElement || []),
                 ...(window.kluDivMarkersSubSub || []),
                 ...trendMarkersToUse,
-                ...(window.bspMarkers || [])
+                ...(window.bspMarkers || []),
+                ...(window.fastBspMarkers || [])
             ];
             if (onlyMainAndU.length > 0) {
                 console.log('仅设置', onlyMainAndU.length, '个主周期/UnitTF标记（主周期分型:', (window.mainFxMarkers || []).length, '，UnitTF:', (window.unittfMarkers || []).length, '）');

web/static/js/app/ui.js

@@ -649,6 +649,11 @@ $('#showElementBsp').change(function() {
     updateChartDisplay();
 });
 
+// 第四类买卖点（B4/S4）显示开关与过滤模式
+$('#showMainFastBsp, #showElementFastBsp, #showSubSubFastBsp, #fastBspFilterMode').change(function() {
+    updateChartDisplay();
+});
+
 // 在控制台输出当前显示状态
 console.log('当前显示状态:', {
     'showOriginalKline': $('#showOriginalKline').is(':checked'),

web/templates/index.html

@@ -1017,6 +1017,17 @@
                             <input class="form-check-input" type="checkbox" id="showMainBsp">
                             <label class="form-check-label" for="showMainBsp">买卖点</label>
                         </div>
+                        <div class="form-check form-check-inline">
+                            <input class="form-check-input" type="checkbox" id="showMainFastBsp">
+                            <label class="form-check-label" for="showMainFastBsp">第四类</label>
+                        </div>
+                        <div class="form-check form-check-inline">
+                            <select id="fastBspFilterMode" class="form-select form-select-sm" style="width: 130px;"
+                                    title="深色为区间套(大级别分型同向)+中枢顺向推进都满足的信号，浅色为未通过过滤">
+                                <option value="all" selected>第四类:全部</option>
+                                <option value="filtered">第四类:仅过滤后</option>
+                            </select>
+                        </div>
                     </div>
                     <div class="d-flex align-items-center mt-1">
                         <label class="form-label me-0 mb-0">次周期:</label>
@@ -1059,6 +1070,10 @@
                             <input class="form-check-input" type="checkbox" id="showElementBsp">
                             <label class="form-check-label" for="showElementBsp">买卖点</label>
                         </div>
+                        <div class="form-check form-check-inline">
+                            <input class="form-check-input" type="checkbox" id="showElementFastBsp">
+                            <label class="form-check-label" for="showElementFastBsp">第四类</label>
+                        </div>
                     </div>
                     <div class="d-flex align-items-center mt-1">
                         <label class="form-label me-0 mb-0">次次周期:</label>
@@ -1101,6 +1116,10 @@
                             <input class="form-check-input" type="checkbox" id="showSubSubBsp">
                             <label class="form-check-label" for="showSubSubBsp">买卖点</label>
                         </div>
+                        <div class="form-check form-check-inline">
+                            <input class="form-check-input" type="checkbox" id="showSubSubFastBsp">
+                            <label class="form-check-label" for="showSubSubFastBsp">第四类</label>
+                        </div>
                     </div>
                 </div>
             </div>
@@ -1269,12 +1288,12 @@
     <script defer src="{{ url_for('static', filename='js/app/chart_tv_shell.js') }}?v=20260810e"></script>
     <script defer src="{{ url_for('static', filename='js/app/chart_tv_indicators.js') }}?v=20260808i"></script>
     <script defer src="{{ url_for('static', filename='js/app/chart_tv_chan.js') }}?v=20260810d"></script>
-    <script defer src="{{ url_for('static', filename='js/app/chart_tv_overlays.js') }}?v=20260810e"></script>
+    <script defer src="{{ url_for('static', filename='js/app/chart_tv_overlays.js') }}?v=20260827a"></script>
     <script defer src="{{ url_for('static', filename='js/app/chart_tv_finalize.js') }}?v=20260810a"></script>
     <script defer src="{{ url_for('static', filename='js/app/chart_tv.js') }}?v=20260808i"></script>
     <script defer src="{{ url_for('static', filename='js/app/chart_sync.js') }}?v=20260809z"></script>
     <script defer src="{{ url_for('static', filename='js/app/chart_tables.js') }}?v=20260808i"></script>
-    <script defer src="{{ url_for('static', filename='js/app/ui.js') }}?v=20260810e"></script>
+    <script defer src="{{ url_for('static', filename='js/app/ui.js') }}?v=20260827a"></script>
     <script defer src="{{ url_for('static', filename='js/app/overlays.js') }}?v=20260808i"></script>
     <script defer src="{{ url_for('static', filename='js/app/main.js') }}?v=20260808i"></script>
 

web/tests/fixtures/analyze_contract_keys.json

@@ -4,6 +4,7 @@
     "bi_zs_list",
     "bsp_list",
     "chan_macd",
+    "fast_bsp_list",
     "klc_fx_info",
     "klc_list",
     "klc_trend",

web/tests/test_analyze_contract.py

@@ -35,6 +35,7 @@ ANALYZE_CHAN_KEYS = {
     "zs_list",
     "bi_zs_list",
     "bsp_list",
+    "fast_bsp_list",
     "klc_fx_info",
     "chan_macd",
     "ema52_dict",
@@ -87,7 +88,7 @@ def test_klines_recent_returns_tail_only():
 
 def test_contract_keys_stable():
     assert "bi_list" in CONTRACT_KEYS and "seg_list" in CONTRACT_KEYS
-    for k in ("kline_data", "macd", "zs_list", "bsp_list", "chan_macd"):
+    for k in ("kline_data", "macd", "zs_list", "bsp_list", "fast_bsp_list", "chan_macd"):
         assert k in CONTRACT_KEYS