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>
This commit is contained in:
jack
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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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@@ -0,0 +1,28 @@
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sym,notional,budget_bp,budget_bp_old,maker_share,w_stop,w_time,net_bp,n
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BTC,5000.0,10.971497515124305,10.971497515124295,0.4230769230769231,0.3516483516483517,0.22527472527472528,15.724284543080632,91
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BTC,10000.0,10.971497515124305,10.971497515124295,0.4230769230769231,0.3516483516483517,0.22527472527472528,15.724284543080632,91
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BTC,20000.0,10.971497515124305,10.971497515124295,0.4230769230769231,0.3516483516483517,0.22527472527472528,15.724284543080632,91
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BTC,50000.0,10.971497515124305,10.971497515124295,0.4230769230769231,0.3516483516483517,0.22527472527472528,15.724284543080632,91
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BTC,100000.0,10.971497515124305,10.971497515124295,0.4230769230769231,0.3516483516483517,0.22527472527472528,15.724284543080632,91
|
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BTC,200000.0,10.971497515124305,10.971497515124295,0.4230769230769231,0.3516483516483517,0.22527472527472528,15.724284543080632,91
|
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BTC,320000.0,10.971497515124305,10.971497515124295,0.4230769230769231,0.3516483516483517,0.22527472527472528,15.724284543080632,91
|
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BTC,530000.0,10.908448857487189,10.971497515124295,0.4214204503001539,0.3524912405185926,0.22608830918125347,15.641274735676083,91
|
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BTC,1000000.0,10.690710190313572,10.971497515124295,0.41456048099090254,0.35467750033199696,0.23076201867710053,15.36403490298731,91
|
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ETH,5000.0,15.336810540229132,15.336810540229127,0.4722222222222222,0.3253968253968254,0.20238095238095238,21.90346054757228,126
|
||||
ETH,10000.0,15.336810540229132,15.336810540229127,0.4722222222222222,0.3253968253968254,0.20238095238095238,21.90346054757228,126
|
||||
ETH,20000.0,15.336810540229132,15.336810540229127,0.4722222222222222,0.3253968253968254,0.20238095238095238,21.90346054757228,126
|
||||
ETH,50000.0,15.336810540229132,15.336810540229127,0.4722222222222222,0.3253968253968254,0.20238095238095238,21.90346054757228,126
|
||||
ETH,100000.0,15.336810540229132,15.336810540229127,0.4722222222222222,0.3253968253968254,0.20238095238095238,21.90346054757228,126
|
||||
ETH,200000.0,15.336810540229132,15.336810540229127,0.4722222222222222,0.3253968253968254,0.20238095238095238,21.90346054757228,126
|
||||
ETH,320000.0,15.336810540229132,15.336810540229127,0.4722222222222222,0.3253968253968254,0.20238095238095238,21.90346054757228,126
|
||||
ETH,530000.0,15.336810540229132,15.336810540229127,0.4722222222222222,0.3253968253968254,0.20238095238095238,21.90346054757228,126
|
||||
ETH,1000000.0,15.19839735083419,15.336810540229127,0.46778822061185865,0.3266775971833763,0.20553418220476508,21.75495166938152,126
|
||||
SOL,5000.0,17.20729581305582,17.207295813055815,0.49310344827586206,0.2655172413793103,0.2413793103448276,24.42271817346687,145
|
||||
SOL,10000.0,17.20729581305582,17.207295813055815,0.49310344827586206,0.2655172413793103,0.2413793103448276,24.42271817346687,145
|
||||
SOL,20000.0,17.20729581305582,17.207295813055815,0.49310344827586206,0.2655172413793103,0.2413793103448276,24.42271817346687,145
|
||||
SOL,50000.0,17.12169148018871,17.207295813055815,0.49112524587410755,0.26723407222095347,0.24164068190493895,24.325613268263233,145
|
||||
SOL,100000.0,17.024617862994077,17.207295813055815,0.4874068783573466,0.2680997947311664,0.24449332691148706,24.238726756516837,145
|
||||
SOL,200000.0,16.83524812354264,17.207295813055815,0.4813611824188418,0.26935037627246794,0.24928844130869035,24.048022486441045,145
|
||||
SOL,320000.0,16.524586307202075,17.207295813055815,0.4732031722818229,0.27277272324336915,0.254024104474808,23.702889125473174,145
|
||||
SOL,530000.0,16.275686387067427,17.207295813055815,0.4660589094844057,0.2753631016069268,0.2585779889086674,23.432003034952427,145
|
||||
SOL,1000000.0,15.829170691791866,17.207295813055815,0.4547125585779213,0.27881565544002057,0.2664717859820582,22.915331236730324,145
|
||||
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@@ -0,0 +1,161 @@
|
||||
"""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。
|
||||
|
||||
step42 的预算（BTC 8.58 / ETH 20.64 / SOL 16.83bp）建立在一个假设上：挂在
|
||||
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——
|
||||
真实仓位下全额成交率只有 30%/16%/1.5%（32 万仓位）。
|
||||
|
||||
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，
|
||||
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
|
||||
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）。
|
||||
|
||||
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，
|
||||
本机 15GB 跑不动；210 天 30 万根峰值约 2GB。
|
||||
|
||||
python research/step43_fill_aware_budget.py --syms BTC,ETH,SOL
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import sys
|
||||
import warnings
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
warnings.filterwarnings("ignore")
|
||||
for v in ("OMP_NUM_THREADS", "OPENBLAS_NUM_THREADS", "MKL_NUM_THREADS"):
|
||||
os.environ.setdefault(v, "1")
|
||||
|
||||
HERE = Path(__file__).resolve().parent
|
||||
sys.path.insert(0, str(HERE))
|
||||
sys.path.insert(0, str(HERE.parent))
|
||||
pd.set_option("display.width", 400)
|
||||
|
||||
LTF, HTF = "1m", "5m"
|
||||
SL, SCALE_AT, RUNNER, RUNNER_STOP, MAXB = 2.0, 3.0, 8.0, 2.0, 48
|
||||
NOTIONALS = [5e3, 1e4, 2e4, 5e4, 1e5, 2e5, 3.2e5, 5.3e5, 1e6]
|
||||
|
||||
|
||||
def signals_for(sym: str, cache: Path) -> tuple[pd.DataFrame, pd.DataFrame]:
|
||||
"""跑缠论链路，返回 (cdf, 过完三滤网的信号)。口径抄 step42.run_one。"""
|
||||
from chanlun import TF_DF
|
||||
from lib.fast_bsp3 import find_fast_bsp3
|
||||
from lib.fx_signal import extract_fx_signals, signals_to_frame
|
||||
from lib.nested_bsp import attach_htf_context, htf_fx_timeline
|
||||
from lib.nested_level import build_htf_zones
|
||||
from lib.shadow_budget import ATR_GATE_BP
|
||||
|
||||
def load(tf: str) -> 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])
|
||||
|
||||
chan_l = TF_DF(load(LTF), 1, LTF)
|
||||
cdf = chan_l.dataframe
|
||||
zones = build_htf_zones(cdf, LTF, chan=chan_l).reset_index(drop=True)
|
||||
if zones.empty:
|
||||
raise RuntimeError("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))
|
||||
|
||||
chan_h = TF_DF(load(HTF), 1, HTF)
|
||||
tl = htf_fx_timeline(
|
||||
signals_to_frame(extract_fx_signals(chan_h, chan_h.dataframe)),
|
||||
chan_h.dataframe)
|
||||
del chan_h
|
||||
|
||||
sig = find_fast_bsp3(cdf, zones)
|
||||
if sig.empty:
|
||||
raise RuntimeError("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].copy()
|
||||
|
||||
# ATR 门控：与 shadow_signal 同源，分母取次根开盘价
|
||||
idx = sig["entry_idx"].astype(int).to_numpy()
|
||||
atr = cdf["atr"].to_numpy(float)
|
||||
op = cdf["open"].to_numpy(float)
|
||||
ref = op[np.minimum(idx + 1, len(op) - 1)]
|
||||
with np.errstate(invalid="ignore", divide="ignore"):
|
||||
atr_bp = atr[idx] / ref * 1e4
|
||||
sig = sig[np.isfinite(atr_bp) & (atr_bp >= ATR_GATE_BP)]
|
||||
return cdf, sig
|
||||
|
||||
|
||||
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("--save", default="research/out/step43_fill_budget.csv")
|
||||
a = ap.parse_args()
|
||||
|
||||
from lib.exit_fill import (assert_converges, budget_bp, net_bp,
|
||||
walk_filled)
|
||||
from lib.exit_model import cfg_name, slip_budget
|
||||
from lib.exit_model import walk_exits
|
||||
|
||||
cache = Path(a.cache)
|
||||
rows = []
|
||||
for sym in a.syms.split(","):
|
||||
print(f"\n{'=' * 74}\n{sym}")
|
||||
try:
|
||||
cdf, sig = signals_for(sym, cache)
|
||||
except Exception as e:
|
||||
print(f" 跳过：{e!r}")
|
||||
continue
|
||||
print(f" {len(cdf):,} 根 1m · 过三滤网 {len(sig)} 笔信号")
|
||||
if len(sig) < 25:
|
||||
print(" 样本不足 25 笔，不出统计")
|
||||
continue
|
||||
|
||||
# 老口径：假定限价全额成交
|
||||
old = walk_exits(cdf, sig, [SL], [SCALE_AT], [MAXB], SCALE_AT,
|
||||
[RUNNER], [RUNNER_STOP])
|
||||
c = cfg_name(SL, RUNNER, MAXB, RUNNER_STOP)
|
||||
b_old = slip_budget(old[f"{c}_g"].to_numpy(),
|
||||
old[f"{c}_r"].to_numpy(),
|
||||
old[f"{c}_c"].to_numpy())
|
||||
# 先证明两套实现在「仓位趋近 0」这个极限上逐笔一致，
|
||||
# 否则后面看到的差异分不清是成交量效应还是实现 bug
|
||||
assert_converges(cdf, sig, SL, SCALE_AT, RUNNER, RUNNER_STOP, MAXB)
|
||||
print(f"\n 老口径（限价全额成交）预算 {b_old:.2f}bp"
|
||||
f" [极限一致性断言通过]")
|
||||
print(f"\n {'仓位':>10} {'预算bp':>9} {'maker占比':>10} "
|
||||
f"{'止损占比':>9} {'超时占比':>9} {'净收益bp':>10}")
|
||||
for nt in NOTIONALS:
|
||||
r = walk_filled(cdf, sig, nt, SL, SCALE_AT, RUNNER,
|
||||
RUNNER_STOP, MAXB)
|
||||
if r.empty:
|
||||
continue
|
||||
b = budget_bp(r)
|
||||
print(f" {nt:>10,.0f} {b:>9.2f} "
|
||||
f"{r['maker_share'].mean() * 100:>9.1f}% "
|
||||
f"{r['w_stop'].mean() * 100:>8.1f}% "
|
||||
f"{r['w_time'].mean() * 100:>8.1f}% "
|
||||
f"{net_bp(r):>10.2f}")
|
||||
rows.append({"sym": sym, "notional": nt, "budget_bp": b,
|
||||
"budget_bp_old": b_old,
|
||||
"maker_share": r["maker_share"].mean(),
|
||||
"w_stop": r["w_stop"].mean(),
|
||||
"w_time": r["w_time"].mean(),
|
||||
"net_bp": net_bp(r), "n": len(r)})
|
||||
del cdf
|
||||
if rows:
|
||||
out = pd.DataFrame(rows)
|
||||
Path(a.save).parent.mkdir(parents=True, exist_ok=True)
|
||||
out.to_csv(a.save, index=False)
|
||||
print(f"\n已存 {a.save}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user