出场模型改为按成交量结算止盈限价单，并出预算对仓位规模的曲线
exit_model.walk_exits 给止盈记的毛收益是 target*a/entry，即假定限价单全额 成交在目标价。新增 lib/exit_fill.py：两张挂单常驻（半仓 3ATR、半仓 8ATR）， 每根按该根在限价之上的可成交量逐步吃进，未成交部分继续持有，止损触发时 市价平掉剩余。可成交量 = 形状函数 f(k) × 该根主动买成交额，f 由影子成交流 实测（近似线性，即区间内均匀分布，故结论对形状假设不敏感）。 结果：预算对仓位规模远比预期稳健。到 100 万名义额，BTC 10.97→10.69、 ETH 15.34→15.20、SOL 17.21→15.83bp。原因是挂单常驻多根而非只在首次触及 那一根成交，且价格决定性穿过限价时整根成交量都可用。 首版实现有个静默 bug 值得记：avail_above 里有个 `hi <= 0` 的守卫，而空头 用「价格取负」处理，负价格空间里 hi 恒为负——所有空头挂单的可成交量一律 判 0，空头全被拖到 48 根超时收盘。下跌段里那比 3ATR 目标赚得多，于是预算 反而偏高 0.76bp，表现为「一个看似合理的模型差异」。已改为显式方向参数， 并加 assert_converges：仓位趋近 0 时必须逐笔收敛到 walk_exits，不符即抛错。 Co-authored-by: Cursor <cursoragent@cursor.com>
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"""Step 43：把「限价单全额成交」的假设换成按成交量结算，重算滑点预算。
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step42 的预算（BTC 8.58 / ETH 20.64 / SOL 16.83bp）建立在一个假设上：挂在
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3ATR 与 8ATR 的止盈限价单全额成交在目标价。影子交易的成交流数据推翻了它——
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真实仓位下全额成交率只有 30%/16%/1.5%（32 万仓位）。
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预算因此不再是一个常数，而是**仓位规模的函数**。规模越大，止盈越难成交，
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越多仓位被拖到止损或超时（taker，且吃滑点），预算越低。这条曲线与「冲击
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反推的容量」是两个不同的约束，而后者宽松得多（100~500 万 vs 数万）。
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数据用 Bitget 210 天 1m，与影子测量同源同交易所。全量 366 万根峰值 24.5GB，
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本机 15GB 跑不动；210 天 30 万根峰值约 2GB。
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python research/step43_fill_aware_budget.py --syms BTC,ETH,SOL
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"""
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from __future__ import annotations
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import argparse
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import os
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import sys
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import warnings
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from pathlib import Path
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import numpy as np
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import pandas as pd
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warnings.filterwarnings("ignore")
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for v in ("OMP_NUM_THREADS", "OPENBLAS_NUM_THREADS", "MKL_NUM_THREADS"):
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os.environ.setdefault(v, "1")
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HERE = Path(__file__).resolve().parent
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sys.path.insert(0, str(HERE))
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sys.path.insert(0, str(HERE.parent))
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pd.set_option("display.width", 400)
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LTF, HTF = "1m", "5m"
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SL, SCALE_AT, RUNNER, RUNNER_STOP, MAXB = 2.0, 3.0, 8.0, 2.0, 48
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NOTIONALS = [5e3, 1e4, 2e4, 5e4, 1e5, 2e5, 3.2e5, 5.3e5, 1e6]
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def signals_for(sym: str, cache: Path) -> tuple[pd.DataFrame, pd.DataFrame]:
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"""跑缠论链路，返回 (cdf, 过完三滤网的信号)。口径抄 step42.run_one。"""
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from chanlun import TF_DF
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from lib.fast_bsp3 import find_fast_bsp3
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from lib.fx_signal import extract_fx_signals, signals_to_frame
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from lib.nested_bsp import attach_htf_context, htf_fx_timeline
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from lib.nested_level import build_htf_zones
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from lib.shadow_budget import ATR_GATE_BP
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def load(tf: str) -> pd.DataFrame:
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c = sorted(cache.glob(f"bitget_{sym}_{tf}_*.feather"),
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key=lambda p: p.stat().st_size, reverse=True)
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if not c:
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raise FileNotFoundError(f"没有 {sym} {tf} 缓存")
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return pd.read_feather(c[0])
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chan_l = TF_DF(load(LTF), 1, LTF)
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cdf = chan_l.dataframe
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zones = build_htf_zones(cdf, LTF, chan=chan_l).reset_index(drop=True)
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if zones.empty:
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raise RuntimeError("无中枢")
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z = zones.copy()
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pg, pdn = z["zg"].shift(), z["zd"].shift()
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z["z_above"], z["z_below"] = z["zd"] > pg, z["zg"] < pdn
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z["zone_i"] = np.arange(len(z))
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chan_h = TF_DF(load(HTF), 1, HTF)
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tl = htf_fx_timeline(
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signals_to_frame(extract_fx_signals(chan_h, chan_h.dataframe)),
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chan_h.dataframe)
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del chan_h
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sig = find_fast_bsp3(cdf, zones)
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if sig.empty:
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raise RuntimeError("无信号")
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sig = sig.merge(z[["zone_i", "z_above", "z_below"]], on="zone_i",
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how="left")
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sig = attach_htf_context(sig, cdf, tl, "h1")
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push = np.where(sig["direction"] == 1, sig["z_above"], sig["z_below"])
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keep = ((sig["h1_agree"] == 1)
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& pd.Series(push, index=sig.index).fillna(False).astype(bool))
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sig = sig[keep].copy()
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# ATR 门控：与 shadow_signal 同源，分母取次根开盘价
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idx = sig["entry_idx"].astype(int).to_numpy()
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atr = cdf["atr"].to_numpy(float)
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op = cdf["open"].to_numpy(float)
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ref = op[np.minimum(idx + 1, len(op) - 1)]
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with np.errstate(invalid="ignore", divide="ignore"):
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atr_bp = atr[idx] / ref * 1e4
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sig = sig[np.isfinite(atr_bp) & (atr_bp >= ATR_GATE_BP)]
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return cdf, sig
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def main() -> None:
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ap = argparse.ArgumentParser()
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ap.add_argument("--syms", default="BTC,ETH,SOL")
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ap.add_argument("--cache", default="research/live/cache")
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ap.add_argument("--save", default="research/out/step43_fill_budget.csv")
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a = ap.parse_args()
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from lib.exit_fill import (assert_converges, budget_bp, net_bp,
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walk_filled)
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from lib.exit_model import cfg_name, slip_budget
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from lib.exit_model import walk_exits
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cache = Path(a.cache)
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rows = []
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for sym in a.syms.split(","):
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print(f"\n{'=' * 74}\n{sym}")
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try:
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cdf, sig = signals_for(sym, cache)
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except Exception as e:
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print(f" 跳过：{e!r}")
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continue
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print(f" {len(cdf):,} 根 1m · 过三滤网 {len(sig)} 笔信号")
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if len(sig) < 25:
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print(" 样本不足 25 笔，不出统计")
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continue
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# 老口径：假定限价全额成交
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old = walk_exits(cdf, sig, [SL], [SCALE_AT], [MAXB], SCALE_AT,
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[RUNNER], [RUNNER_STOP])
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c = cfg_name(SL, RUNNER, MAXB, RUNNER_STOP)
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b_old = slip_budget(old[f"{c}_g"].to_numpy(),
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old[f"{c}_r"].to_numpy(),
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old[f"{c}_c"].to_numpy())
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# 先证明两套实现在「仓位趋近 0」这个极限上逐笔一致，
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# 否则后面看到的差异分不清是成交量效应还是实现 bug
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assert_converges(cdf, sig, SL, SCALE_AT, RUNNER, RUNNER_STOP, MAXB)
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print(f"\n 老口径（限价全额成交）预算 {b_old:.2f}bp"
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f" [极限一致性断言通过]")
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print(f"\n {'仓位':>10} {'预算bp':>9} {'maker占比':>10} "
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f"{'止损占比':>9} {'超时占比':>9} {'净收益bp':>10}")
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for nt in NOTIONALS:
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r = walk_filled(cdf, sig, nt, SL, SCALE_AT, RUNNER,
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RUNNER_STOP, MAXB)
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if r.empty:
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continue
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b = budget_bp(r)
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print(f" {nt:>10,.0f} {b:>9.2f} "
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f"{r['maker_share'].mean() * 100:>9.1f}% "
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f"{r['w_stop'].mean() * 100:>8.1f}% "
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f"{r['w_time'].mean() * 100:>8.1f}% "
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f"{net_bp(r):>10.2f}")
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rows.append({"sym": sym, "notional": nt, "budget_bp": b,
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"budget_bp_old": b_old,
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"maker_share": r["maker_share"].mean(),
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"w_stop": r["w_stop"].mean(),
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"w_time": r["w_time"].mean(),
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"net_bp": net_bp(r), "n": len(r)})
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del cdf
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if rows:
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out = pd.DataFrame(rows)
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Path(a.save).parent.mkdir(parents=True, exist_ok=True)
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out.to_csv(a.save, index=False)
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print(f"\n已存 {a.save}")
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if __name__ == "__main__":
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main()
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