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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"""出场模拟，但止盈按限价单的**真实成交量**结算，而非假定全额成交。
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## 为什么要另写一份
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`exit_model.walk_exits` 给止盈记的毛收益是 `target * a / entry`，即假定挂在
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目标价的限价单全额成交在目标价。影子交易的成交流数据显示这个假定在真实
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仓位上不成立：止盈位被首次触及那一根，限价落在该根价格区间中的位置中位
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k≈0.28，而该位置之上可供成交的主动买量，对 32 万仓位只够覆盖 30%/16%/1.5%
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（BTC/ETH/SOL）。
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不成交不等于仓位消失——它继续持有，结果从「继续走下去」的分布里抽，其中
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包含反转打到止损。所以这不是给预算打折能修的事，是出场规则变了。
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## 模型
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两张挂单常驻：半仓在 `scale_at`、半仓在 `runner`。每根按该根在限价之上的
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可成交量逐步吃进，未成交部分继续持有；整仓止损始终有效，触发时未成交的
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部分市价平掉；到 `maxb` 根仍未了结的按收盘市价平。
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某根在限价 P 之上的可成交量：
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P ≤ low 整根成交量都在限价之上 avail = V
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P > high 该根没到限价 avail = 0
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否则 k = (high−P)/(high−low) avail = f(k) × V
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`V` 是该根的**主动买**成交额（多头出场靠主动买盘打上来）。实测买卖大致
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均衡，取总成交额的一半；以 BTC 校验，历史 `volume×close` 中位与影子成交流
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实测差 0.3%。`f` 由成交流定，实测几乎是线性（f(k)≈k，即区间内均匀分布），
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所以结论对形状假设不敏感。
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## 仍然乐观的两处
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1. **未计排队**。我们的单排在该价位既有挂单之后，真实成交更少。
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2. **未计自身的流动性效应**。大单挂在 3ATR 会吸收本该冲到 8ATR 的买盘，
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即两张挂单在真实市场里互相竞争，此处按独立处理。
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两处都指向同一方向：真实成交率比本模型更低。
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"""
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from __future__ import annotations
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import numpy as np
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import pandas as pd
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from lib.exit_model import FEE_MAKER, FEE_TAKER, SLIP
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# 成交流实测的区间内成交分布形状（BTC/ETH/SOL 均值，见 shadow_depth.tape_shape）
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SHAPE_K = np.linspace(0.0, 1.0, 21)
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SHAPE_F = np.array([0.044, 0.088, 0.110, 0.179, 0.204, 0.240, 0.282, 0.316,
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0.390, 0.430, 0.465, 0.537, 0.583, 0.617, 0.662, 0.714,
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0.761, 0.804, 0.857, 0.904, 1.000])
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TAKER_SHARE = 0.5
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def avail_at(price: float, hi: float, lo: float, vol_notional: float,
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is_long: bool, kgrid=SHAPE_K, f=SHAPE_F) -> float:
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"""该根里能打到限价 `price` 的对手方成交额。
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多头在 `price` 挂卖出，靠价格 ≥ price 的主动买成交；空头挂买回，靠
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价格 ≤ price 的主动卖成交。方向用显式参数而非「把价格取负」——取负会
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让所有价格变成负数，任何对价格正负的假设都会静默失效。
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"""
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if vol_notional <= 0 or not (np.isfinite(hi) and np.isfinite(lo)):
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return 0.0
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if hi <= lo:
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# 该根无波动：只要限价被覆盖就算整根可成交
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return vol_notional if (hi >= price if is_long else lo <= price) \
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else 0.0
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if is_long:
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if price > hi:
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return 0.0 # 该根没涨到限价
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if price <= lo:
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return vol_notional # 整根都在限价之上
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k = (hi - price) / (hi - lo)
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else:
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if price < lo:
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return 0.0 # 该根没跌到限价
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if price >= hi:
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return vol_notional # 整根都在限价之下
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k = (price - lo) / (hi - lo)
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return float(np.interp(k, kgrid, f)) * vol_notional
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def walk_filled(cdf: pd.DataFrame, sig: pd.DataFrame, notional: float,
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sl: float = 2.0, scale_at: float = 3.0, runner: float = 8.0,
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runner_stop: float = 2.0, maxb: int = 48,
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taker_share: float = TAKER_SHARE) -> pd.DataFrame:
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"""前推每笔信号，返回按成交量结算的出场权重与毛收益。
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每行的 `w_*` 是各出场去向占**全仓名义额**的比例，四者相加为 1。
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"""
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high = cdf["high"].to_numpy(float)
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low = cdf["low"].to_numpy(float)
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close = cdf["close"].to_numpy(float)
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open_ = cdf["open"].to_numpy(float)
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vol = cdf["volume"].to_numpy(float)
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atr = cdf["atr"].to_numpy(float)
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n = len(cdf)
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out = []
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for s, d in zip(sig["entry_idx"].astype(int), sig["direction"].astype(int)):
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e = s + 1
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if e >= n - 1:
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continue
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a = atr[s]
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if not np.isfinite(a) or a <= 0:
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continue
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entry = open_[e]
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cap = min(e + maxb, n - 1)
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p_stop = entry - d * sl * a
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p_scale = entry + d * scale_at * a
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p_run = entry + d * runner * a
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# 两张挂单各半仓，单位是「占全仓的比例」
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rem_scale, rem_run = 0.5, 0.5
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w_scale = w_run = w_stop = w_time = 0.0
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scaled_any = False
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stop_bar = None
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for j in range(e, cap + 1):
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hi, lo = high[j], low[j]
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# 同根内止损优先，与 walk_exits 一致，宁可低估
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hit_stop = (lo <= p_stop) if d == 1 else (hi >= p_stop)
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if hit_stop:
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stop_bar = j
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w_stop = rem_scale + rem_run
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rem_scale = rem_run = 0.0
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break
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v = vol[j] * close[j] * taker_share
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is_long = d == 1
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if rem_scale > 0:
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got = avail_at(p_scale, hi, lo, v, is_long)
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fill = min(rem_scale, got / notional) if notional > 0 else \
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rem_scale
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if fill > 0:
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rem_scale -= fill
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w_scale += fill
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scaled_any = True
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if rem_run > 0:
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got = avail_at(p_run, hi, lo, v, is_long)
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fill = min(rem_run, got / notional) if notional > 0 else \
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rem_run
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if fill > 0:
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rem_run -= fill
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w_run += fill
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if rem_scale <= 1e-12 and rem_run <= 1e-12:
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break
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# 减仓成交后，剩余半仓的止损位可以另设；此处 runner_stop 等于初始 SL
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# 即止损不动，与 step42 的 k=2.0 一致，故上面那个统一止损已覆盖
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left = rem_scale + rem_run
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if left > 1e-12 and stop_bar is None:
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w_time = left
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r_scale = d * (p_scale - entry) / entry
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r_run = d * (p_run - entry) / entry
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r_stop = d * (p_stop - entry) / entry
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r_time = d * (close[cap] - entry) / entry
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gross = (w_scale * r_scale + w_run * r_run
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+ w_stop * r_stop + w_time * r_time)
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# 入场整仓 taker；两张挂单成交的部分是 maker；止损与超时是 taker
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fee = (FEE_TAKER * 1.0 + FEE_MAKER * (w_scale + w_run)
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+ FEE_TAKER * (w_stop + w_time))
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tk = 1.0 + w_stop + w_time
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out.append({"sig_idx": s, "direction": d, "atr_pct": a / entry,
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"w_scale": w_scale, "w_run": w_run,
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"w_stop": w_stop, "w_time": w_time,
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"maker_share": w_scale + w_run,
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"gross": gross, "fee": fee, "taker_notional": tk,
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"scaled": int(scaled_any)})
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return pd.DataFrame(out)
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def budget_bp(r: pd.DataFrame) -> float:
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"""盈亏平衡的单边滑点上限（bp）。与 exit_model.slip_budget 同口径。"""
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if r.empty:
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return float("nan")
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net = r["gross"].mean() - r["fee"].mean()
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return net / r["taker_notional"].mean() * 1e4
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def assert_converges(cdf: pd.DataFrame, sig: pd.DataFrame, sl: float = 2.0,
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scale_at: float = 3.0, runner: float = 8.0,
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runner_stop: float = 2.0, maxb: int = 48,
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tol: float = 1e-9) -> None:
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"""仓位趋近 0 时必须逐笔收敛到 exit_model.walk_exits，否则抛错。
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这个断言是必需的。首版实现里空头的可成交量恒为 0（负价格空间踩到了一个
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`hi <= 0` 的守卫），后果是空头全被拖到超时收盘——而下跌段里那比 3ATR
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目标赚得多，于是预算反而**偏高** 0.76bp，看上去像个合理的模型差异。
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没有这条断言，这种错只会表现为「数字有点不一样」。
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"""
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from lib.exit_model import cfg_name, walk_exits
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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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new = walk_filled(cdf, sig, 1e-12, sl, scale_at, runner, runner_stop, maxb)
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j = old[["sig_idx"]].copy()
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j["g_old"] = old[f"{c}_g"].to_numpy()
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j = j.merge(new[["sig_idx", "gross"]], on="sig_idx")
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d = (j["gross"] - j["g_old"]).abs()
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bad = int((d > tol).sum())
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if bad:
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worst = j.loc[d.idxmax()]
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raise AssertionError(
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f"仓位趋近 0 时应与 walk_exits 一致，但 {bad}/{len(j)} 笔不符；"
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f"最大差 {d.max():.3e}（sig_idx {int(worst['sig_idx'])}："
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f"老 {worst['g_old']:.6f} 新 {worst['gross']:.6f}）")
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def net_bp(r: pd.DataFrame, slip: float = SLIP) -> float:
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"""扣掉手续费与滑点后的净均收益（bp）。"""
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if r.empty:
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return float("nan")
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net = r["gross"] - r["fee"] - slip * r["taker_notional"]
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return float(net.mean() * 1e4)
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