diff --git a/.gitignore b/.gitignore index 0dc5c6c..2861ef6 100644 --- a/.gitignore +++ b/.gitignore @@ -31,6 +31,10 @@ research/live/cache/ # Scratch outputs from short shakedown runs, superseded by the real collection.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/ diff --git a/research/HANDOFF.md b/research/HANDOFF.md index 305dde1..1c9a086 100644 --- a/research/HANDOFF.md +++ b/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 | -| 刷交易额换 VIP 费率 | 成本收益不划算，见 5.3 | step23 | +| 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。**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」， -> 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。 +> **重要判断：1m 若失败，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。 + +#### ⚠️ 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） - -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 +### ~~第 2 步：向用户确认交易所~~ ✅ 已定：Bitget + Hummingbot 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， -这样两边数字直接可比。代码量约两三百行。 +#### 形态已定：Hummingbot V2 controller（不再写独立 shadow.py） -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': ...}` - 和 `options={'defaultType':'swap'}` -- 持仓也要影子管理：SL/TP/48 根超时，出场同样记录真实盘口 +- 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 + / 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 -- 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 -- > 3.9bp → 1m 放弃，**但 5m/30m 不受影响**（余量 26bp / 89bp） +~~旧阈值（2.5 / 3.9bp）已作废~~：那是 TP=3.0、全 taker、原始费率记错 +三重错误叠加出来的，见 5.3。**新判据如下。** + +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 | -| `nested_level.py` | `build_htf_zones`：构建中枢，可传入已建好的 `chan` 复用 | +| `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` 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） -- [ ] 写 `research/live/shadow.py` 影子交易器，测 1m 真实滑点。 - **务必用 WebSocket 而非 REST 轮询**——代理下单次 REST 往返约 2.4s， - 而信号计算只要 0.20s，网络是延迟主项 +- [ ] **对齐 step23 与 step42 的 1m 数字**（3.91bp vs 11.06bp）。差距可能来自 + 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）。 + §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 +- [ ] **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 -- [ ] 若可行：选框架（Bitget→Hummingbot）做真实 dry run +- [ ] 按 §6 第 4 步的四档判据决策 1m 是否可行 +- [ ] 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） diff --git a/research/lib/exit_model.py b/research/lib/exit_model.py new file mode 100644 index 0000000..bbe136c --- /dev/null +++ b/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}%"} diff --git a/research/lib/shadow_budget.py b/research/lib/shadow_budget.py new file mode 100644 index 0000000..bbf8cd8 --- /dev/null +++ b/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" diff --git a/research/out/step17_fast_bsp3_trades.csv b/research/out/step17_fast_bsp3_trades.csv new file mode 100644 index 0000000..e9c89f1 --- /dev/null +++ b/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 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+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 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+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 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+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 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+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 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+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 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+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 diff --git a/research/step40_filter_ablation.py b/research/step40_filter_ablation.py new file mode 100644 index 0000000..357ed34 --- /dev/null +++ b/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()) diff --git a/research/step41_exit_tp.py b/research/step41_exit_tp.py new file mode 100644 index 0000000..b9a865b --- /dev/null +++ b/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() diff --git a/research/step42_exit_tp_1m.py b/research/step42_exit_tp_1m.py new file mode 100644 index 0000000..4ec4394 --- /dev/null +++ b/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()