diff --git a/research/live/deploy/start.sh b/research/live/deploy/start.sh
index 086dbe4..1084dd4 100755
--- a/research/live/deploy/start.sh
+++ b/research/live/deploy/start.sh
@@ -13,6 +13,10 @@
 set -euo pipefail
 
 NAME="${NAME:-shadow}"
+# lean 模式让 TF_DF 只构建到中枢，跳过线段/走势中枢/MACD 状态机。等价性由
+# verify_lean_parity.py 在影子这条路径上逐根验过（180 窗口 / 90 命中零分歧）。
+# 设 0 可退回 full，用来复量两模式的耗时差。
+SHADOW_LEAN="${SHADOW_LEAN:-1}"
 IMAGE="${SHADOW_IMAGE:-hummingbot/hummingbot:latest}"
 HOURS="${HOURS:-168}"
 WORKERS="${WORKERS:-2}"
@@ -99,6 +103,7 @@ docker run -d --name "$NAME" -w /home/hummingbot \
 	--restart unless-stopped \
 	-e PYTHONPATH=/home/hummingbot:/repo/research:/repo/research/live:/repo \
 	-e SHADOW_SITE="$SHADOW_SITE" \
+	-e SHADOW_LEAN="$SHADOW_LEAN" \
 	-v "$REPO_ROOT:/repo:ro" \
 	-v "$OUT:/out" \
 	--entrypoint /opt/conda/envs/hummingbot/bin/python \
diff --git a/research/live/shadow_signal.py b/research/live/shadow_signal.py
index 2299ab6..1c22f40 100644
--- a/research/live/shadow_signal.py
+++ b/research/live/shadow_signal.py
@@ -39,12 +39,22 @@ for _v in ("OMP_NUM_THREADS", "OPENBLAS_NUM_THREADS", "MKL_NUM_THREADS"):
     os.environ.setdefault(_v, "1")
 
 
-def compute(df_l, df_h, entry_px: float | None = None) -> dict:
+LEAN = os.environ.get("SHADOW_LEAN", "1") not in ("0", "", "false")
+
+
+def compute(df_l, df_h, entry_px: float | None = None,
+            lean: bool | None = None) -> dict:
     """在 df_l 的最后一根上找信号。df_l/df_h 都只含已收盘 K 线。
 
     entry_px 是次根开盘价（回测 entry_delay=1 的成交价），用作 atr_pct 的
     分母。取不到时退回用信号根收盘价，并在返回里标 atr_ref="close"。
 
+    lean=True 让 TF_DF 只构建到中枢，跳过线段/走势中枢/MACD 状态机。本路径
+    只读 chan.dataframe 与 chan.klc_list，不碰 bsp_list/seg_list/chanmacd，
+    所以可以跳。但静态检查会漏间接依赖，等价性由 verify_lean_parity.py 在
+    这条路径上逐根实测，不套用 step46 那 5 个对拍用例——那些用例走的是
+    bsp_list，覆盖不到 fast_bsp3 + 嵌套上下文这条链。
+
     返回 dict：
       last_idx    最后一根在 chanlun 处理后 dataframe 里的下标
       n_bars      实际参与计算的根数
@@ -55,6 +65,8 @@ def compute(df_l, df_h, entry_px: float | None = None) -> dict:
     import numpy as np
     import pandas as pd
 
+    lean = LEAN if lean is None else lean
+
     try:
         from chanlun import TF_DF
         from lib.fast_bsp3 import find_fast_bsp3
@@ -63,7 +75,7 @@ def compute(df_l, df_h, entry_px: float | None = None) -> dict:
         from lib.nested_level import build_htf_zones
         from lib.shadow_budget import ATR_GATE_BP
 
-        chan_l = TF_DF(df_l, 1, "1m")
+        chan_l = TF_DF(df_l, 1, "1m", lean=lean)
         cdf = chan_l.dataframe
         last = len(cdf) - 1
         base = {"last_idx": last, "n_bars": int(len(df_l)), "hits": [],
@@ -99,7 +111,7 @@ def compute(df_l, df_h, entry_px: float | None = None) -> dict:
 
         # 5m 同向。算不出时 h1_agree 记 0，该信号自然不会通过 pass_all
         if df_h is not None and len(df_h) > 0:
-            chan_h = TF_DF(df_h, 1, "5m")
+            chan_h = TF_DF(df_h, 1, "5m", lean=lean)
             hdf = chan_h.dataframe
             tl = htf_fx_timeline(
                 signals_to_frame(extract_fx_signals(chan_h, hdf)), hdf)
diff --git a/research/live/verify_lean_parity.py b/research/live/verify_lean_parity.py
new file mode 100644
index 0000000..9a5ca2b
--- /dev/null
+++ b/research/live/verify_lean_parity.py
@@ -0,0 +1,200 @@
+"""在影子信号路径上实测 lean 与 full 是否等价，并量提速。
+
+## 为什么不能直接引用 step46 的对拍结论
+
+step46 固化的是 klc/笔/中枢/`bsp_list`/被消费列的哈希，走的是 `bsp_list` 那条
+链。影子路径走的是另一条：`find_fast_bsp3` + `build_htf_zones` +
+`htf_fx_timeline` + `attach_htf_context`。两条链读的东西不完全一样，所以
+「lean ≡ full」在 step46 用例上成立，不等于在这条路径上成立。
+
+静态检查显示这条链只读 `chan.dataframe` 与 `chan.klc_list`（lean 都不跳），
+但静态检查漏不掉间接依赖——`build_htf_zones` 收的是 chan 对象本体。所以逐根
+实测：同一个窗口分别用 full 和 lean 跑 `compute()`，比对返回的每一个字段。
+
+判据是**逐字段完全相同**，不是「信号数量相同」。数量相同而方向或标志不同，
+会让影子测的是另一批信号，且不报错。
+
+    python research/live/verify_lean_parity.py --syms BTC,ETH,SOL --n 150
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import sys
+import time
+import warnings
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+warnings.filterwarnings("ignore")
+for _v in ("OMP_NUM_THREADS", "OPENBLAS_NUM_THREADS", "MKL_NUM_THREADS"):
+    os.environ.setdefault(_v, "1")
+
+HERE = Path(__file__).resolve()
+sys.path.insert(0, str(HERE.parents[1]))
+sys.path.insert(0, str(HERE.parents[2]))
+sys.path.insert(0, str(HERE.parent))
+
+LTF_BARS, HTF_BARS = 2001, 801       # 与 shadow_hb 同窗口
+SKIP = ("inner_ms", "queue_ms")      # 计时字段本就不同，不参与比对
+
+
+def load(sym: str, tf: str, cache: Path) -> pd.DataFrame:
+    c = sorted(cache.glob(f"bitget_{sym}_{tf}_*.feather"),
+               key=lambda p: p.stat().st_size, reverse=True)
+    if not c:
+        raise FileNotFoundError(f"没有 {sym} {tf} 缓存")
+    return pd.read_feather(c[0])
+
+
+def canon(d: dict) -> str:
+    """把返回值规范化成可比较的字符串。
+
+    浮点直接比会被末位差异误判，但 lean 走的是同一段算术、不该有任何差异，
+    所以这里**不设容差**：round 到 12 位只是为了消掉 repr 差异，真有数值
+    分歧一定会被抓到。
+    """
+    def norm(v):
+        if isinstance(v, float):
+            return None if not np.isfinite(v) else round(v, 12)
+        if isinstance(v, dict):
+            return {k: norm(x) for k, x in sorted(v.items())}
+        if isinstance(v, (list, tuple)):
+            return [norm(x) for x in v]
+        if isinstance(v, (np.integer, np.floating, np.bool_)):
+            return norm(v.item())
+        return v
+    return json.dumps({k: norm(v) for k, v in sorted(d.items())
+                       if k not in SKIP}, sort_keys=True, ensure_ascii=False)
+
+
+def signal_bars(sym: str, cache: Path) -> np.ndarray:
+    """全量历史里过三滤网的信号根下标。
+
+    随机取窗口几乎测不到信号分支——信号密度约 1/2000 根，12 个窗口命中 0 个。
+    只测早退路径的「一致」是很弱的证据：lean 若真影响了中枢或笔，分歧恰恰
+    出现在有信号的那些根上。所以把窗口对齐到这些根。
+
+    注意这些下标来自**全量历史**建的中枢，而 compute 只看 2000 根窗口，所以
+    对齐后未必真的命中（这正是尚未收口的窗口左边界效应）。但命中率会从
+    1/2000 提到可用水平。
+    """
+    from step43_fill_aware_budget import signals_for
+    _, sig = signals_for(sym, cache)
+    return sig["entry_idx"].astype(int).to_numpy()
+
+
+def run_one(sym: str, cache: Path, n: int, step: int,
+            at_signals: bool = True) -> dict:
+    from shadow_signal import NUM_COLS, compute
+
+    l_all, h_all = load(sym, "1m", cache), load(sym, "5m", cache)
+    for df in (l_all, h_all):
+        for c in NUM_COLS:
+            if c not in df.columns:
+                raise RuntimeError(f"{sym} 缺列 {c}")
+
+    l_ts = l_all["timestamp"].to_numpy("int64")
+    h_ts = h_all["timestamp"].to_numpy("int64")
+    # 一半窗口对齐到已知信号根（测信号分支），一半均匀铺开（测早退路径）
+    ends: list[int] = []
+    if at_signals:
+        try:
+            sb = signal_bars(sym, cache)
+            sb = sb[(sb > LTF_BARS) & (sb < len(l_all) - 1)]
+            ends += list(sb[-(n // 2 or 1):])
+            print(f"  对齐到信号根 {len(ends)} 个")
+        except Exception as e:
+            print(f"  取信号根失败，只用均匀窗口：{e!r}")
+    ends += list(range(len(l_all) - 1, LTF_BARS, -step))[:max(n - len(ends), 1)]
+    ends = sorted(set(ends))
+    if not ends:
+        raise RuntimeError("数据不足一个窗口")
+
+    n_same = n_diff = 0
+    t_full = t_lean = 0.0
+    first_diff = None
+    n_hits_full = n_hits_lean = 0
+
+    for e in ends:
+        df_l = l_all.iloc[e - LTF_BARS + 1:e + 1]
+        # 5m 只取已收盘且不晚于 1m 窗口末尾的根，与实盘一致
+        hi = int(np.searchsorted(h_ts, l_ts[e], side="right"))
+        df_h = h_all.iloc[max(0, hi - HTF_BARS):hi]
+        # 次根开盘价：窗口末尾的下一根，与实盘 entry_px 同义
+        entry_px = float(l_all["open"].to_numpy(float)[e + 1]) \
+            if e + 1 < len(l_all) else None
+
+        t0 = time.perf_counter()
+        rf = compute(df_l.copy(), df_h.copy(), entry_px, lean=False)
+        t_full += time.perf_counter() - t0
+
+        t0 = time.perf_counter()
+        rl = compute(df_l.copy(), df_h.copy(), entry_px, lean=True)
+        t_lean += time.perf_counter() - t0
+
+        n_hits_full += len(rf.get("hits") or [])
+        n_hits_lean += len(rl.get("hits") or [])
+        if canon(rf) == canon(rl):
+            n_same += 1
+        else:
+            n_diff += 1
+            if first_diff is None:
+                first_diff = (e, canon(rf), canon(rl))
+
+    k = len(ends)
+    print(f"  窗口 {k} 个 · 完全一致 {n_same} · 不一致 {n_diff}")
+    print(f"  命中数 full {n_hits_full} / lean {n_hits_lean}")
+    print(f"  单窗耗时 full {t_full / k * 1000:.0f}ms · "
+          f"lean {t_lean / k * 1000:.0f}ms · "
+          f"提速 {t_full / max(t_lean, 1e-9):.2f}x")
+    if first_diff:
+        e, a, b = first_diff
+        print(f"  ⚠ 首个分歧在窗口末尾 idx={e}")
+        print(f"    full: {a[:400]}")
+        print(f"    lean: {b[:400]}")
+    return {"sym": sym, "n": k, "same": n_same, "diff": n_diff,
+            "full_ms": t_full / k * 1000, "lean_ms": t_lean / k * 1000,
+            "hits_full": n_hits_full, "hits_lean": n_hits_lean}
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--syms", default="BTC,ETH,SOL")
+    ap.add_argument("--cache", default="research/live/cache")
+    ap.add_argument("--n", type=int, default=150, help="每币比对多少个窗口")
+    ap.add_argument("--step", type=int, default=37,
+                    help="窗口间隔根数。取质数避免与任何周期共振")
+    ap.add_argument("--no-signals", action="store_true",
+                    help="不对齐信号根（快，但测不到信号分支）")
+    a = ap.parse_args()
+
+    rows = []
+    for sym in a.syms.split(","):
+        print(f"\n{'=' * 70}\n{sym}")
+        try:
+            rows.append(run_one(sym, Path(a.cache), a.n, a.step,
+                                at_signals=not a.no_signals))
+        except Exception as e:
+            print(f"  跳过：{e!r}")
+
+    if not rows:
+        return
+    d = pd.DataFrame(rows)
+    print(f"\n\n{'=' * 70}\n汇总\n")
+    print(f"  比对窗口 {int(d['n'].sum()):,} 个 · "
+          f"不一致 {int(d['diff'].sum())} 个")
+    print(f"  单窗耗时 full {d['full_ms'].mean():.0f}ms → "
+          f"lean {d['lean_ms'].mean():.0f}ms "
+          f"（{d['full_ms'].sum() / max(d['lean_ms'].sum(), 1e-9):.2f}x）")
+    if int(d["diff"].sum()) == 0:
+        print("\n  逐字段完全一致，可以开 lean。")
+    else:
+        print("\n  ⛔ 存在分歧，不要开 lean。lean 跳掉的东西这条路径确实在用。")
+
+
+if __name__ == "__main__":
+    main()