From 8131b158b48d9a3cb025b01d1f56c6c874a8a63c Mon Sep 17 00:00:00 2001 From: yanweidong Date: Sat, 19 Sep 2026 19:45:43 +0800 Subject: [PATCH] optz --- README.md | 37 + docs/etf.md | 656 ++++++++ grpc/qmt_grpc_new.py | 61 - grpc/qmt_service.proto | 19 - labs/__init__.py | 17 + labs/analysis/etf/REPORT-new-config.md | 354 +++++ labs/analysis/etf/REPORT.md | 435 ++++++ labs/analysis/etf/analysis.py | 376 +++++ labs/analysis/etf/backtest.py | 476 ++++++ labs/analysis/etf/cache/510300.SH.json | 1 + labs/analysis/etf/cache/518880.SH.json | 1 + labs/analysis/etf/cache/588000.SH.json | 1 + labs/analysis/etf/capital.py | 41 + labs/analysis/etf/compare.py | 104 ++ labs/analysis/etf/drawdown.py | 120 ++ labs/analysis/etf/entries.py | 40 + labs/analysis/etf/results.json | 1370 +++++++++++++++++ labs/analysis/etf/run.py | 232 +++ labs/analysis/etf/run_report.txt | 85 + labs/analysis/etf/sweep.py | 144 ++ labs/analysis/etf/universe.py | 53 + labs/analysis/etf/vs_hold.py | 130 ++ 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mode 100644 labs/analysis/etf/REPORT-new-config.md create mode 100644 labs/analysis/etf/REPORT.md create mode 100644 labs/analysis/etf/analysis.py create mode 100644 labs/analysis/etf/backtest.py create mode 100644 labs/analysis/etf/cache/510300.SH.json create mode 100644 labs/analysis/etf/cache/518880.SH.json create mode 100644 labs/analysis/etf/cache/588000.SH.json create mode 100644 labs/analysis/etf/capital.py create mode 100644 labs/analysis/etf/compare.py create mode 100644 labs/analysis/etf/drawdown.py create mode 100644 labs/analysis/etf/entries.py create mode 100644 labs/analysis/etf/results.json create mode 100644 labs/analysis/etf/run.py create mode 100644 labs/analysis/etf/run_report.txt create mode 100644 labs/analysis/etf/sweep.py create mode 100644 labs/analysis/etf/universe.py create mode 100644 labs/analysis/etf/vs_hold.py create mode 100644 labs/run_tests.py create mode 100644 labs/tests/__init__.py create mode 100644 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labs/tests/__pycache__/zt_harness.cpython-314.pyc create mode 100644 labs/tests/test_etf_config.py create mode 100644 labs/tests/test_etf_signal.py create mode 100644 labs/tests/test_etf_trade.py delete mode 100644 py-client/strategy/etf/README.md delete mode 100644 py-client/strategy/etf/__init__.py delete mode 100644 py-client/strategy/etf/config.py delete mode 100644 py-client/strategy/etf/data.py delete mode 100644 py-client/strategy/etf/engine.py delete mode 100644 py-client/strategy/etf/indicators.py create mode 100644 py-client/strategy/etf/open.py create mode 100644 py-client/strategy/etf/positions.py create mode 100644 py-client/strategy/etf/signal.py delete mode 100644 py-client/strategy/etf/state.py diff --git a/README.md b/README.md index 76b2406..24aab78 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,39 @@ # big-qmt +量化交易工程：`py-client`（QMT 策略客户端，运行时） + `api` / `grpc` / `launcher` / `scripts`（服务端与工具）。 + +## labs：所有试验与测试代码 + +`labs/` 统一存放**非运行时**的试验、回测、审计与测试代码，运行时代码仍然只在 +`py-client` 及各服务目录内。 + +``` +labs/ +├── run_tests.py 统一测试入口（把 py-client 挂上 sys.path 后跑 labs/tests） +├── tests/ 离线单测（原 py-client/tests） +├── benchmarks/ 微基准（原 py-client/benchmarks） +└── analysis/ 回测与审计分析 + └── etf/ ETF 网格策略回测（报告、参数扫描、对照脚本、日线缓存） +``` + +```powershell +# 离线测试（仓库任意位置执行） +py -3.14 -B labs/run_tests.py # 全部 +py -3.14 -B labs/run_tests.py -v # 详细 +py -3.14 -B labs/run_tests.py test_etf_signal # 只跑某个模块 + +# 微基准 +py -3.14 -B labs/benchmarks/hotpaths.py + +# ETF 网格策略回测与报告 +py -3.14 -B labs/analysis/etf/run.py # 基准回测 → results.json / run_report.txt +py -3.14 -B labs/analysis/etf/compare.py # 旧 vs 新配置 +py -3.14 -B labs/analysis/etf/vs_hold.py # 网格 vs 买入持有 +``` + +报告：`labs/analysis/etf/REPORT-new-config.md`（当前配置）、 +`labs/analysis/etf/REPORT.md`（旧配置，含机会频率与参数敏感性专题）。 + +> 测试模块内部使用 `from tests.zt_harness import ...` 这类绝对导入，因此**必须**走 +> `labs/run_tests.py`；直接用 `python -m unittest discover` 无法同时满足 +> "`labs` 作为顶层包 + `py-client` 在 sys.path 上"这两个条件。 diff --git a/docs/etf.md b/docs/etf.md new file mode 100644 index 0000000..7bf0a5e --- /dev/null +++ b/docs/etf.md @@ -0,0 +1,656 @@ +# A 股 ETF 自适应网格策略 v2 + +--- + +## 1. 策略概述 + +### 1.1 一句话 + +在 ETF 的低位区间**越跌越买**，用 ATR 决定的格距把仓位铺在 10 个价位上（底仓 1 档 + 补仓 9 档）； +等**全部持仓的平均成本**涨够 `min_profit_pct`（默认 1%）后**整仓卖出**，赚这一段反弹。 + +### 1.2 赚什么、不赚什么 + +| | 说明 | +| --- | --- | +| **赚什么** | 区间内的反复波动。单轮收益 = 持仓规模 × `min_profit_pct`，所以**越跌越买反而放大了单轮收益** | +| **不赚什么** | 单边趋势。单边下跌时深档被动持仓，单边上涨时过早卖空仓位 | +| **不做什么** | 不预测方向、不做择时、不做行业轮动、**不设置止损** | + +### 1.3 四个设计要点 + +1. **ATR 自适应格距** —— 格距随标的波动率自动伸缩，不用手工设价格区间。 +2. **底仓 + 9 档补仓** —— 共 10 个价位，每档 1000 股，固定不缩放。 +3. **反弹确认入场** —— 进入低位区后不立刻买，等从低点反弹 0.5% 才建网（防接飞刀）。 +4. **整仓止盈为主、单档兜底为辅** —— 主出口吃整段反弹，副出口防止浅档利润被反复吐回。 + +### 1.4 标的（示例） + +| 代码 | 名称 | 现价 | 近 127 日区间 | 振幅 | 结算 | +| --- | --- | --- | --- | --- | --- | +| 588000.SH | 华夏上证科创板 50 成份 ETF | 1.744 | 1.316 ~ 2.390 | 81.6% | T+1 | +| 510300.SH | 沪深 300ETF 华泰柏瑞 | 4.582 | 4.405 ~ 5.095 | 15.7% | T+1 | +| 518880.SH | 黄金 ETF | 9.009 | 8.224 ~ 10.345 | 25.8% | T+0 | + +> 价格为 2026-09-18 收盘，来自 `http://139.224.247.176:13499/etf/daily`（127 根日线）。 +> 人工选择标的时要求：规模大、成交额充足、行为以震荡为主、无清盘风险。 + +### 1.5 指标 + +只用截至前一交易日的已收盘日线，禁止未来数据。 + +| 指标 | 定义 | +| --- | --- | +| `M`（MA60） | 最近 60 根收盘价均值，作为**不可突破的网顶** | +| `ATR`（默认 14 日） | `TR = max(最高−最低, abs(最高−前收盘), abs(最低−前收盘))`；前 14 个 TR 取均值作初值，之后 Wilder 平滑 `ATR = (前ATR×13 + 当日TR) / 14` | +| 区间通道 | 近 20 日的 `[最低价, 最高价]` | + +--- + +## 2. 观察规则 + +**只在空网格（无持仓）时观察。** 观察的目的是确认"跌势是否还在继续"，避免在下跌途中接刀。 + +### 2.1 入场门槛 + +``` +通道门槛 = 区间下沿 + (区间上沿 − 区间下沿) × 15% +入场门槛 = min(通道门槛, MA60) +``` + +`min()` 同时表达两件事：**不在均线上方建网**，且**处在近 20 日区间最低的 15% 以内**。 + +> MA60 只作上限、不作低位判定。实测把门槛设在均线附近会让成交发生在下跌趋势的 +> 中途而非区间低位，每笔净收益从 86.8 元降到 63.2 元。 + +### 2.2 观察状态机（复用 `libs/watch.py` 的 `DipWatch`） + +| 情形 | 行为 | +| --- | --- | +| `现价 > 入场门槛` | 价格在入场区之上，**清除观察状态** | +| `现价 ≤ 入场门槛` 且无观察点 | 记下现价作为观察低点，开始观察 | +| 后续价格更低 | **刷新观察低点**，并重置有效期 | +| `(现价 − 观察低点) / 观察低点 ≥ 0.5%` | **确认反弹 → 可以建网**，观察状态随之清除 | +| 观察超过 600 秒未触发 | 作废，重新开始观察 | + +``` +watch = DipWatch(expire_seconds=600, rebound_threshold=0.5) +watch.triggered(tag, code, price, now) -> bool # True 即确认 +watch.forget(code) # 价格回到入场区上方时必须调用 +``` + +- 观察键就是**证券代码**，天然逐标的隔离，不会互相污染低点。 +- 库的 `triggered` 已处理"首次观察 / 刷新低点 / 过期重开 / 达标触发并清除"四个分支， + 调用方**不要重复实现**，只负责在价格离开入场区时 `forget`。 + +### 2.3 反弹阈值取值 + +约束：**必须小于该标的的格距百分比**（1.35% ~ 1.78%），否则确认价已越过下一档， +锚点上移会削弱后续格子的覆盖。 + +| 取值 | 效果 | 风险 | +| --- | --- | --- | +| 0.3% | 几乎不放过任何入场 | 单边下跌中连续接刀，等于没有防护 | +| **0.5%（默认）** | 确认成本约为最小格距的三分之一 | 极端快跌中仍会建网 | +| 1.5% | 只在明确反转后建网 | 超过多数标的格距，系统性错过 V 型反转 | + +--- + +## 3. 买入规则 + +### 3.1 锚点 + +**只有"反弹确认"能产生锚点**，锚点就是确认那一刻的现价： + +``` +锚点 = 反弹确认时的现价 +``` + +受 `锚点 ≤ 入场门槛` 约束（因为只有进入入场区才会开始观察）。 + +> 持仓期间**绝不重设锚点**。不允许因为价格变动把锚点平移到当前价—— +> 那等于跳过防接飞的确认，正是 §2 要避免的行为。 + +### 3.2 格距 + +``` +格距 G = max( ATR14 × atr_multiplier, MA60 × 0.5%, 0.001 ) +``` + +- 向上取整到 **0.001 元**（ETF 最小报价单位）。 +- 百分比下限防止低波动期格距被 ATR 压到无意义；0.001 的硬下限保证格距恒为正。 +- **`atr_multiplier` 逐标的标定**，见 §3.4。 + +### 3.3 底仓 + +建网时只买**一档底仓**，即 **1000 股**，按锚点价成交（§3.5）。 +后续的 9 档由 §4 的**百分比补仓**逐档建立，**不预挂价位**。 + +因此建网时**不需要算出一串价位**，只需要确定锚点与格距 `G`（§3.2）： + +- **锚点**是底仓成本与后续"自上一档跌幅"的起点； +- **格距 `G`** 不参与补仓触发，只用于两件事：§3.4 的跨度健康度检查、 + §5.2 副出口的内层格距标定。 + +### 3.4 ATR 倍数必须逐标的标定 + +格距本身不触发交易，但它决定**整条阶梯铺多宽**（`9 × G`），所以仍必须逐标的标定。 + +| 标的 | 现价 | MA60 | ATR14 | ATR 倍数 | 格距 | 格距% | 阶梯跨度（9G） | 跨度/现价 | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| 588000.SH | 1.744 | 1.856 | 0.0603 | **0.5** | 0.031 | 1.78% | 0.279 | **16.0%** | +| 510300.SH | 4.582 | 4.712 | 0.0613 | 1.0 | 0.062 | 1.35% | 0.558 | 12.2% | +| 518880.SH | 9.009 | 8.799 | 0.1376 | 1.0 | 0.138 | 1.53% | 1.242 | 13.8% | + +**588000 必须用 0.5 倍**：它 127 日振幅 81.6%，ATR 相对价格的比例远高于另外两只， +按 1.0 倍算出的阶梯跨度是现价的 31.5%（格距 0.061），深档基本不可达。降到 0.5 倍后跨度回到 16.0%， +与另外两只同量级。 + +标定要同时兼顾**两个不同的失效方向**： + +| 失效方向 | 判据 | 后果 | 修法 | +| --- | --- | --- | --- | +| **跨度轴**（波动率主导） | `9 × G / 锚点` 过大 | 深档不可达、资金长期闲置 | 下调该标的 ATR 倍数 | +| **佣金轴**（价格水平主导） | 单档金额过小 | 每轮收益被固定佣金吃掉 | 提高每格股数，或剔除低价标的 | + +**判据**：跨度落在 10% ~ 20%、单档金额让佣金占比 ≤ 0.3%。标定后三只在 12.2% ~ 16.0%。 + +> 注意标定后**高波动标的的百分比格距反而最小**，所以不能按"波动大就放宽格距"的直觉调参。 + +### 3.5 底仓挂单 + +锚点确定后，**底仓（档位 0）按锚点价挂限价单**，保证首笔立即成交。 +若连底仓都挂不出去（资金或可用份额不足），**撤销锚点**，下一轮重新触发—— +不允许留下挂不出单的"死锚点"。 + +--- + +## 4. 补仓规则 + +**按百分比补仓，不按预设价位挂单。** 口径与 `strategy/trend` 的 `handle_loss` 一致 +（`strategy/trend/positions.py`）：用**亏损百分比**判断触发，用 `add_watch` 做**反弹确认**， +确认后才买入。 + +### 4.1 触发条件 + +``` +自上一档跌幅 = (上一档成交价 − 现价) / 上一档成交价 × 100 + +触发补仓： 自上一档跌幅 ≥ add_pct （默认 3.0%） + 且 add_watch.triggered(...) == True （反弹确认，防接飞刀） +``` + +| 项 | 规则 | +| --- | --- | +| 上一档 | 首次是**底仓成交价**，之后是**最近一次补仓的成交价** | +| 补仓价 | **反弹确认时的现价**（不是预设价位），因此每次补仓都买在反弹确认点 | +| 每档数量 | **1000 股（10 手）**，固定，不按资金缩放 | +| 补仓次数 | 最多 **9 次**（底仓 1 + 补仓 9 = 10 档） | +| 单标的上限 | **10000 股** | +| 重复 | 同一时刻该标的只允许一笔在途买单 | + +**为什么按百分比而不是预设价位**： + +- 百分比自动适配价格水平，不需要为每只标的预先算出一串价位； +- **阶梯随下跌自动收窄**——平均成本被不断拉低，所以"再跌 3%"对应的绝对距离会变短； + 而固定价位阶梯的间距是恒定的，深档会越拉越远； +- 与 `trend` 同源，两个策略的补仓语义一致，便于统一维护。 + +### 4.2 防接飞刀：反弹确认 + +补仓同样使用 `libs/watch.py` 的 `DipWatch`（与 §2 建仓观察同一套机制，但**独立的观察实例**）： + +``` +add_watch = DipWatch(expire_seconds=watch_seconds, rebound_threshold=rebound_pct) +add_watch.triggered("补仓", code, price, now) -> bool +add_watch.forget(code) # 补仓成功或标的离开观察后调用 +``` + +| 情形 | 行为 | +| --- | --- | +| 跌幅未达 `add_pct` | 不观察，等待 | +| 跌幅达到 `add_pct`，且无观察点 | 以现价启动观察（记观察低点） | +| 观察中价格继续下跌 | 刷新观察低点 | +| 从观察低点反弹 ≥ `rebound_pct` | **确认补仓**，买入 1000 股，并 `forget` | + +**参数关系必须满足**： + +``` +rebound_pct < add_pct < 格距% × N + 0.5% 3.0% 1.35% ~ 1.78%（×1.7 ~ 2.2） +``` + +- `rebound_pct` 必须**小于** `add_pct`，否则反弹确认价会回到上一档之上，条件自相矛盾。 +- `add_pct` 建议取 **1.5 ~ 2.5 个格距**，让每次补仓跨越一格以上，避免同一区域反复成交。 + +### 4.3 add_pct 取值取舍 + +3% 下每次补仓距上一档的绝对距离（以格距计价）： + +| 标的 | 格距% | `add_pct=2%` | `add_pct=3%` | +| --- | --- | --- | --- | +| 588000.SH | 1.78% | 1.1 G | 1.7 G | +| 510300.SH | 1.35% | 1.5 G | 2.2 G | +| 518880.SH | 1.53% | 1.3 G | 2.0 G | + +`add_pct=3%` 对应 1.7 ~ 2.2 个格距，且积累了足够跌幅空间；代价是 10 档打满需要 +**累计回撤约 24%**（见 §4.4）。若希望阶梯更浅，可下调到 2%（打满约 −16.6%）。 + +### 4.4 阶梯与资金 + +按 `add_pct = 3%`、每档 1000 股展开（基准 = 入场门槛）： + +| 标的 | 档 | 补仓价 | 累计回撤 | 累计股数 | 平均成本 | 出场价（+1%） | 净利 | 占用资金 | 占用回报 | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | +| 588000.SH | 1 | 1.632 | 0.0% | 1000 | 1.632 | 1.648 | 6 元 | 1,632 元 | 0.37% | +| 588000.SH | 3 | 1.536 | −5.9% | 3000 | 1.584 | 1.600 | 29 元 | 4,751 元 | 0.61% | +| 588000.SH | 5 | 1.445 | −11.5% | 5000 | 1.537 | 1.553 | 49 元 | 7,686 元 | 0.64% | +| 588000.SH | 7 | 1.360 | −16.7% | 7000 | 1.493 | 1.507 | 61 元 | 10,448 元 | 0.58% | +| 588000.SH | 10 | 1.241 | **−24.0%** | 10000 | 1.429 | 1.443 | 88 元 | 14,287 元 | 0.62% | +| 510300.SH | 1 | 4.518 | 0.0% | 1000 | 4.518 | 4.563 | 35 元 | 4,518 元 | 0.77% | +| 510300.SH | 3 | 4.251 | −5.9% | 3000 | 4.384 | 4.428 | 113 元 | 13,151 元 | 0.86% | +| 510300.SH | 5 | 3.999 | −11.5% | 5000 | 4.255 | 4.297 | 181 元 | 21,273 元 | 0.85% | +| 510300.SH | 7 | 3.763 | −16.7% | 7000 | 4.131 | 4.172 | 245 元 | 28,915 元 | 0.85% | +| 510300.SH | 10 | 3.434 | **−24.0%** | 10000 | 3.954 | 3.993 | 329 元 | 39,539 元 | 0.83% | +| 518880.SH | 1 | 8.799 | 0.0% | 1000 | 8.799 | 8.887 | 78 元 | 8,799 元 | 0.89% | +| 518880.SH | 3 | 8.279 | −5.9% | 3000 | 8.538 | 8.623 | 233 元 | 25,613 元 | 0.91% | +| 518880.SH | 5 | 7.790 | −11.5% | 5000 | 8.287 | 8.370 | 378 元 | 41,434 元 | 0.91% | +| 518880.SH | 7 | 7.329 | −16.7% | 7000 | 8.046 | 8.126 | 511 元 | 56,319 元 | 0.91% | +| 518880.SH | 10 | 6.689 | **−24.0%** | 10000 | 7.701 | 7.778 | 694 元 | 77,013 元 | 0.90% | + +**满网占用（10 档打满）**：588000 = 14,287 元、510300 = 39,539 元、518880 = 77,013 元， +**三只合计 130,839 元**。 + +> 本表是**规模参考**，不是准入条件。实盘资金约束由 §4.6 的共享预算处理。 +> 占用回报稳定在 **0.37% ~ 0.91%**，随建仓加深略微上升——因为平均成本被摊低， +> 同样 1% 的盈亏率对应的绝对涨幅变小。 + +### 4.5 不预挂补仓单 + +**补仓单不预先挂在下方价位上**，只在反弹确认后按现价买入。理由： + +- 百分比补仓的触发条件是"从上一档跌足 `add_pct`"，这是一个**状态条件**，不是一个价位； +- 预挂低价单会在下跌途中被连续打穿，等于自动接刀，与 §4.2 的确认机制冲突。 + +因此补仓的执行顺序固定为：**判断跌幅 → 启动/刷新观察 → 反弹确认 → 按现价买入**。 + +### 4.6 资金 + +``` +可用预算 = 券商可用资金 − 账户总额 × 现金安全线 − 所有标的的待确认买单预留 +``` + +- 必须**先扣除所有标的**的未确认买单，不能遍历到后面的标的才发现钱不够。 +- 一轮内多标的按配置顺序消耗同一预算。 + +### 4.7 打满后的规则 + +补仓次数达到 9 次（共 10 档、10,000 股）后： + +| 情形 | 规则 | +| --- | --- | +| 已满 10 档 | **停止补仓**。不再补仓、不再重设基准，只等主出口清仓 | +| 副出口卖掉某一档 | 允许按同一百分比规则重新补回该档，阶梯恢复循环 | +| 主出口成交（整仓清空） | 阶梯解除；价格再次进入入场区并完成反弹确认时，以新基准重建 | + +**理由**：允许打满后继续重建基准，等于在单边下跌中不断向趋势加仓，风险无上限。 +停止补仓把最坏持仓锁定在 10,000 股。 + +--- + +## 5. 卖出规则 + +**双出口**：整仓主出口 + 单档副出口。 + +| 出口 | 触发条件 | 卖出范围 | 角色 | +| --- | --- | --- | --- | +| **主出口** | 盈亏率 ≥ `min_profit_pct` | **全部持仓** | 主力收益来源 | +| **副出口** | 某档盈利从峰值回落，且峰值已抬到 `inner_grids` 格 | **只卖该档** | 兜住"反弹只够一档"的小波动 | + +### 5.1 主出口：按盈亏率整仓止盈 + +**按盈亏率判断，不用绝对价位。** 与 `strategy/trend` 的止盈口径一致 +（`libs/calc.py` 的 `calculate_min_profit_rate` + `GridTrailingTracker`）： + +``` +盈亏率 = (现价 − 平均成本) / 平均成本 × 100 # 百分点 +平均成本 = Σ(该档成本 × 该档股数) / Σ(该档股数) + +触发整仓卖出： 盈亏率 ≥ min_profit_pct （默认 1.0%） +``` + +- 卖出**全部持仓**，一次清空网格；之后网格解除，价格重新进入入场区时以新锚点重建。 +- **目标价只是盈亏率的换算结果**，不参与决策： + `出场价 = 平均成本 × (1 + min_profit_pct / 100)`。 +- 盈亏率的分母是**平均成本**（会随加仓下降），不是锚点。所以出场价天然跟着成本走， + 不需要额外加任何锚点偏移。 + +**为什么不用"平均成本 + N × G"**：那个写法在只成交底仓时会算出 +`锚点 + 2G`（比锚点还高 2 个格距）——底仓买在锚点，此时还没有任何摊薄， +"+2G"就只能等价格涨回锚点上方。按盈亏率算就自然得多：底仓只要涨 1% 即可了结 +（换算成格距只有 0.52G ~ 0.73G，仍在锚点附近，可达）。 + +**为什么不用趋势的固定档位表**：`calculate_min_profit_rate` 按价格分档 +（≥300 元 3%、≥200 元 5%、≥100 元 7%、其余 9%）。我们的 ETF 价格都在 10 元以下， +会全部落到 9% 档——对均值回归的网格太苛刻。ETF 的合理下限由**佣金**决定，见下。 + +**保底约束：盈亏率必须高于双边佣金率。** 单档 1000 股时： + +| 标的 | 单档金额 | 双边佣金率 | `min_profit_pct = 1%` 够吗 | +| --- | --- | --- | --- | +| 588000.SH | 1,744 元 | **0.573%** | 够（毛利 17.4 元，净 7.4 元） | +| 510300.SH | 4,582 元 | 0.218% | 够（毛利 45.8 元，净 35.8 元） | +| 518880.SH | 9,009 元 | 0.111% | 够（毛利 90.1 元，净 80.1 元） | + +`min_profit_pct = 1%` 高于三只标的最小仓位的佣金率，所以**任何成交档数下净利都为正**。 +仓位越大佣金率越低，约束越松： + +``` +双边佣金率 = 2 × max(5, 金额 × 佣金率) / 金额 +1 档(1000股) → 0.573% / 0.218% / 0.111% +10 档(10000股) → 0.060% / 0.060% / 0.060% +``` + +> **不要为了迁就资金而调低 `min_profit_pct`**：低于单档佣金率就会产生"赚了差价亏了手续费"的 +> 无效交易。佣金率为 0.573% 的 588000 是最紧的一只。 + +**出场价与收益**（`min_profit_pct = 1%`，按底部 10 档逐级成交）： + +| 标的 | 已成交档数 | 持仓 | 平均成本 | 出场价 | 出场价相对锚点 | 净利 | 占用资金 | 占用回报 | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| 588000.SH | 1 | 1000 | 1.632 | 1.648 | +0.52 G | 6 元 | 1,632 元 | 0.37% | +| 588000.SH | 2 | 2000 | 1.616 | 1.633 | +0.03 G | 18 元 | 3,233 元 | 0.56% | +| 588000.SH | 6 | 6000 | 1.554 | 1.570 | −2.00 G | 58 元 | 9,327 元 | 0.62% | +| 588000.SH | 10 | 10000 | 1.493 | 1.507 | **−4.03 G** | 90 元 | 14,925 元 | 0.60% | +| 510300.SH | 1 | 1000 | 4.518 | 4.563 | +0.73 G | 35 元 | 4,518 元 | 0.77% | +| 510300.SH | 2 | 2000 | 4.487 | 4.532 | +0.23 G | 75 元 | 8,974 元 | 0.84% | +| 510300.SH | 6 | 6000 | 4.363 | 4.407 | −1.79 G | 226 元 | 26,178 元 | 0.86% | +| 510300.SH | 10 | 10000 | 4.239 | 4.281 | **−3.82 G** | 357 元 | 42,390 元 | 0.84% | +| 518880.SH | 1 | 1000 | 8.799 | 8.887 | +0.64 G | 78 元 | 8,799 元 | 0.89% | +| 518880.SH | 2 | 2000 | 8.730 | 8.817 | +0.13 G | 159 元 | 17,460 元 | 0.91% | +| 518880.SH | 6 | 6000 | 8.454 | 8.539 | −1.88 G | 465 元 | 50,724 元 | 0.92% | +| 518880.SH | 10 | 10000 | 8.178 | 8.260 | **−3.91 G** | 745 元 | 81,780 元 | 0.91% | + +**三条性质**： + +1. **只在底仓时出场价最低不过锚点上方 0.52 ~ 0.73 个格距**——基本就在建网位置附近， + 不会被推到锚点上方一大截。 +2. **建仓越深，出场价越低**（满仓时已在锚点下方约 4 个格距），因为加仓不断拉低平均成本。 +3. **占用回报稳定在 0.37% ~ 0.92%**，且随建仓加深略微上升。 + +> **主出口不用峰值回撤**：把整个持仓当观察键、用 `GridTrailingTracker` 驱动整仓出手 +> 是可行的，但实测没有收益、还拖慢周转——固定目标 3 轮 / 均持仓 3.8 日， +> 峰值回撤 2 轮 / 均持仓 8.5 日，期末还压着 3,000 股。整仓层面用盈亏率阈值更干脆。 + +> **主出口不用峰值回撤**：把整个持仓当观察键、用 `GridTrailingTracker` 驱动整仓出手 +> 是可行的，但实测没有收益、还拖慢周转——固定目标 3 轮 / 均持仓 3.8 日， +> 峰值回撤 2 轮 / 均持仓 8.5 日，期末还压着 3,000 股。整仓层面用固定目标更干脆。 + +### 5.2 副出口：单档峰值回撤 + +复用 `libs/grid_take_profit.py` 的 `GridTrailingTracker`： + +``` +tracker = GridTrailingTracker(step=inner_step) # step = 该标的的内层格距（百分点） +观察键 = f"etf:{code}:level:{level}" # 每个档位一个独立观察键 + +每轮对该档调用 observe(键, 该档盈亏率%)： + ARMED / RAISED / STEADY → 不动 + RETREAT → 若 peak_grid >= inner_grids，则卖出该档 +``` + +- `inner_step` 是**盈亏率的百分点跨度**，独立于外层格距，**必须小于或接近标的的格距百分比**， + 否则峰值永远抬不起来，副出口形同虚设。 +- `inner_grids`（默认 2）表示"要求峰值至少抬到第几格"才允许回撤卖出。 +- 卖出**只卖该档**（`min(该档持仓, 券商可卖份额)`，向下取整到 100 股）。 +- **峰值必须在卖出成功后才 `clear(键)`**；下单失败或撤单时必须保留峰值。 +- 该档清空后 `clear`，价格回落到该档价位时重新挂买单；重新建仓从 `ARMED` 重新开始。 +- **它不替代主出口**，只处理"整仓目标还没到、但某一档已明显获利"的情况。实测在当前参数下 + 很少触发（三个标的各 3 轮里触发 0~1 次），所以它是**保险而非收益来源**。 + +**内层格距取值**：`inner_step = clamp(0.5 × 格距%, 0.5, 2.0)`，逐标的配置： + +| 标的 | 格距% | 内层格距 | +| --- | --- | --- | +| 588000.SH | 1.78% | 0.9%（精算 0.89） | +| 510300.SH | 1.35% | 0.7%（精算 0.68） | +| 518880.SH | 1.53% | 0.8%（精算 0.77） | + +**收益区间**：峰值抬到第 2 格意味着盈亏率曾达到 `[2×内层格距, 3×内层格距)`， +回撤到第 1 格时落在 `[1×内层格距, 2×内层格距)`，所以**实际落袋在 1~2 个内层格距之间**： + +| 标的 | 单档金额 | 内层格距 | 保底（1 格）净利 | 占用回报 | 上界（2 格）净利 | 占用回报 | +| --- | --- | --- | --- | --- | --- | --- | +| 588000.SH | 1,744 元 | 0.9% | 5.7 元 | **0.33%** | 21.4 元 | 1.23% | +| 510300.SH | 4,582 元 | 0.7% | 22.1 元 | **0.48%** | 54.1 元 | 1.18% | +| 518880.SH | 9,009 元 | 0.8% | 62.1 元 | **0.69%** | 134.1 元 | 1.49% | + +**保底那一列才是下限**，双边佣金固定 10 元，三只都为正。若内层格距取到 0.5 及以下， +588000 的保底净利就会接近零，所以下限取 0.5 而非更低。 + +### 5.3 结算制度 + +由**每标的的 `is_t0` 字段**决定，必填、无隐式默认： + +- `is_t0: false`（T+1）：当日买入的份额当日不可卖。 +- `is_t0: true`（T+0）：当日买入的份额当日可卖。 + +- **`min_hold_days`（默认 1）仅对 `is_t0: false` 的标的生效**：买入日当天不挂卖单， + 最早次一交易日卖出。**`is_t0: true` 的标的跳过本条**，否则 T+0 的当日回转能力 + 会被这条规则彻底抵消，配置 `is_t0` 也就失去意义。 +- **主出口受 T+1 约束**：若部分持仓是当日买入的，当日不能整仓卖出； + 此时按"当日可卖份额"上限卖出，剩余部分次日再用同一目标价尝试。 +- 两种情形下**券商可用份额都是唯一权威**，本地状态不得放宽。 + +> 上线前须与券商逐一确认。`518880.SH`（黄金 ETF）等商品、债券、货币、跨境 ETF 通常支持 +> T+0；股票型 ETF 为 T+1。声明错误会导致柜台拒单。 + +### 5.4 费用 + +``` +单边佣金 = max(最低佣金, 金额 × 佣金率) # 默认 max(5, 金额×0.0003) +``` + +主出口一次卖出覆盖整个持仓，佣金不是约束： + +| 标的 | 持仓档数 | 毛利 | 佣金合计 | 净利 | 佣金占毛利 | +| --- | --- | --- | --- | --- | --- | +| 588000.SH | 2 | 123 元 | 15 元 | 108 元 | 12% | +| 510300.SH | 2 | 248 元 | 15 元 | 233 元 | 6% | +| 518880.SH | 2 | 552 元 | 15 元 | 537 元 | 3% | + +即使只成交 2 档（占用 2,675 元），双边佣金 15 元也只占毛利的 12%。 +对照 100 股：588000 单档仅 174 元，一次往返佣金就占 5.73%，会把副出口的利润整个吃掉。 + +--- + +## 6. 配置表 + +三层：**全局默认 → 标的白名单（每标的覆盖）→ 状态**。 + +```yaml +# ---------- 全局默认 ---------- +defaults: + atr_period: 14 + min_grid_pct: 0.5 + max_grid_span_pct: 40 # 跨度健康度告警线（非闸门） + channel_period: 20 + channel_pct: 15 + rebound_pct: 0.5 # 反弹确认阈值（建仓与补仓共用） + add_pct: 3.0 # 补仓：自上一档再跌该百分比即触发（需配合反弹确认） + max_adds: 9 # 补仓次数上限（底仓另计，共 10 档） + watch_seconds: 600 + min_profit_pct: 1.0 # 主出口：盈亏率 >= 该值即整仓清掉 + inner_grids: 2.0 # 副出口：峰值至少抬到第 N 格才允许回撤卖出 + min_hold_days: 1 + max_hold_days: 0 # 0 = 不止损 + commission_rate: 0.0003 + min_commission: 5.0 + max_tick_age_seconds: 90 + +# ---------- 标的白名单（键即标的，顺序即资金优先级）---------- +# 键必须与接口返回的 ts_code 完全一致（588000.SH / 159915.SZ） +symbols: + "588000.SH": + is_t0: false + buy_shares: 1000 + max_shares: 10000 # 10 个价位 × 1000 股 + atr_multiplier: 0.5 # 该标的 ATR 相对价格偏高，必须收窄 + inner_step: 0.9 + + "510300.SH": + is_t0: false + buy_shares: 1000 + max_shares: 10000 + atr_multiplier: 1.0 + inner_step: 0.7 + + "518880.SH": + is_t0: true # 黄金 ETF 支持当日回转 + buy_shares: 1000 + max_shares: 10000 + atr_multiplier: 1.0 + inner_step: 0.8 +``` + +**全局参数** + +| 参数 | 默认 | 说明 | +| --- | --- | --- | +| `add_pct` | 3.0 | 补仓触发：自上一档成交价再跌该百分比（%），须大于 `rebound_pct` | +| `max_adds` | 9 | 补仓次数上限。总档数 = `max_adds + 1`（含底仓），允许 0 ~ 9 | +| `atr_period` | 14 | ATR 周期 | +| `min_grid_pct` | 0.5 | 格距百分比下限 | +| `max_grid_span_pct` | 40 | 跨度健康度告警线。`9×G/锚点` 落在 10% ~ 20% 为佳；超线只告警并建议下调 `atr_multiplier`，**不阻止建网** | +| `channel_period` | 20 | 区间通道回看天数 | +| `channel_pct` | 15 | 通道低位门槛：距区间下沿的百分比以内 | +| `rebound_pct` | 0.5 | 反弹确认阈值，**须小于该标的格距百分比** | +| `watch_seconds` | 600 | 观察有效期 | +| `min_profit_pct` | 1.0 | 主出口：盈亏率 ≥ 该值即整仓清掉（必须是百分点） | +| `inner_grids` | 2.0 | 副出口：峰值至少抬到第 N 格 | +| `min_hold_days` | 1 | 买入日当天不挂卖单；`is_t0: true` 的标跳过 | +| `max_hold_days` | 0 | 0 = 不止损 | +| `commission_rate` / `min_commission` | 0.0003 / 5.0 | 费用模型（账户级） | +| `max_tick_age_seconds` | 90 | 行情有效期，超时或非当天不交易 | + +**逐标的参数** + +| 参数 | 必填 | 默认 | 说明 | +| --- | --- | --- | --- | +| `is_t0` | **是** | — | `true` = 当日买入当日可卖；`false` = T+1 | +| `buy_shares` | **是** | — | 每档每次买入股数，100 的整数倍 | +| `atr_multiplier` | **是** | — | 格距的 ATR 倍数，必须逐标的标定（§3.4） | +| `inner_step` | **是** | — | 内层格距（盈亏率百分点），决定副出口的利润刻度 | +| `max_shares` | 否 | 10000 | 单标的总持仓上限，须 ≥ `buy_shares × 10` | +| `inner_grids` | 否 | 继承 | 覆盖副出口的峰值格要求 | +| `max_grid_span_pct` | 否 | 继承 | 覆盖跨度告警线 | +| `rebound_pct` | 否 | 继承 | 覆盖反弹阈值 | +| `max_hold_days` | 否 | 继承 | 覆盖时间退出开关 | + +**配置校验** + +- 四项**必须显式给出**（`is_t0`、`buy_shares`、`atr_multiplier`、`inner_step`），缺失即报错， + 不得回落到全局默认。 +- `max_adds` ∈ [0,9]（0 = 只有底仓）；`add_pct`、`min_profit_pct`、`inner_grids`、`rebound_pct`、`min_grid_pct`、 + `channel_period`、`channel_pct` 均 > 0；`max_grid_span_pct` ∈ (0,100]。 +- `buy_shares` 为 100 的整数倍且 > 0；`max_shares ≥ buy_shares × 10`，否则启动告警。 +- `rebound_pct` 必须**小于该标的建网时的格距百分比**，否则告警。 +- 标的段只允许覆盖上表列出的键；试图覆盖 `add_pct`、`commission_rate` + 一类账户级参数一律报错。 +- 未知配置键（含 `defaults` 与任一标的段）一律报错。 + +**为什么 `atr_multiplier` 与 `inner_step` 必填**：它们各自决定一个失效方向—— +前者定跨度（跨度轴），后者定副出口能否被触发。两者都没有安全的全局默认值。 + +**为什么 `min_profit_pct` 用 1%**：它必须高于单档 1000 股的双边佣金率（最高的是 588000 的 0.573%）， +否则会产生"赚了差价亏了手续费"的无效交易。1% 留了约一倍的余量，同时远小于格距（1.35% ~ 1.78%）， +不会把出场推到下一个档位之外。 + +--- + +## 7. 文件规划 + +### 7.1 目录结构 + +``` +py-client/ +├── etc/ +│ └── etf.yaml # 本策略的独立配置（§6） +└── strategy/etf/ # 本策略实现 + ├── __init__.py + ├── boot.py # 入口装配（入口名 etf） + ├── config.py # 配置读取与校验（§6） + ├── data.py # 历史日线接口适配 + ├── indicators.py # MA60 / ATR / 区间通道 / 格距 + ├── engine.py # 决策主循环：观察→建网→补仓→卖出 + └── state.py # 状态持久化：锚点、冻结格距、档位、委托 +``` + +### 7.2 复用约定 + +| 库 | 类 | 承担的行为 | 对应 | +| --- | --- | --- | --- | +| `libs/watch.py` | `DipWatch` | 观察与反弹确认 | §2.2 | +| `libs/grid_take_profit.py` | `GridTrailingTracker` | 副出口的单档峰值回撤 | §5.2 | + +**不得修改这两个共享库。** 它们同时被 `strategy/trend` 与 `strategy/zt` 使用， +改动会波及那两个策略。ETF 侧若需要额外行为，**在 ETF 内包装，不动库本身**。 + +### 7.3 状态文件 + +``` +{qmt_data_dir}/etf/{账户SHA256}/state.json +``` + +- 保存内容：每标的的**锚点**、**底仓成本**、各档持仓（数量、成本、买入日）、 + **上一档成交价**（§4.1 的百分比补仓基准），以及**至多一笔未确认买单**。 +- **补仓是逐笔串行的**（同一时刻该标的只允许一笔在途买单），因此 pending 只需要一个槽位， + 不需要按档位建表——这是改成百分比补仓后的简化。 +- **副出口的峰值格只存活在内存**，不落盘。`GridTrailingTracker` 是进程内对象， + 重启后峰值从 `ARMED` 重新建立（即当前盈亏率所在格），**不会立刻触发卖出**。 + 这是可接受的：副出口只是保险，晚一轮触发不影响结果。 +- **主出口完全无状态**：出场价由已核对的档位实时算出，重启后行为与重启前一致。 +- **先保存意图，再发起请求**。请求成功、超时或失败都不自动解锁；必须收到终态回报 + **且**券商持仓与累计成交量相符，才能继续。 +- 部分成交按实际数量核对；**买入必须取得实际成交均价**才能计入平均成本， + 否则主出口的目标价会算错（未核对的成交不参与计算）。 +- 状态损坏时**拒绝启动并报错**，不允许静默重建为空状态。 +- 券商持仓与本地档位合计不一致时，**以券商快照为准**重建：清仓则重置， + 否则整体接管为锚点档，保证账实相符。 +- 状态结构带**版本号**。旧版本迁移时按券商快照重建档位表，**不做任何自动卖出**。 + +### 7.4 数据来源 + +``` +GET http://139.224.247.176:13499/etf/daily?code=<证券代码> +``` + +- 只接受**单个** `code`；逗号分隔的多代码查询返回非 JSON 错误页。 +- 实际返回**一维数组**，按 `trade_date` 倒序，字段含 + `open / high / low / close / pre_close / trade_date / ts_code`。 +- 旧版 `{code, message, details}` 包装对象已不再返回，适配层对两种形式都兼容。 +- 接口**未声明复权口径**，按原始价格计算。 +- 每标的每日取一次并缓存，截取最近 120 根；取数失败至少间隔 5 分钟再重试。 + +**数据校验**——任一不满足即**放弃该标的当轮交易**，不得降级、不得改用其他证券数据： + +| 校验项 | 规则 | +| --- | --- | +| 证券归属 | 每行 `ts_code` 与请求代码完全一致 | +| 未来数据 | `trade_date >= 今天` 的行一律剔除，盘中不得混入未收盘日线 | +| 重复日期 | 同一日期出现两次即放弃整批 | +| OHLC 有效性 | 四价均为有限正数，且 `low ≤ open ≤ high`、`low ≤ close ≤ high` | +| 样本量 | 至少 60 根（ATR14 需 61 根） | +| 新鲜度 | 最后日线距今超过 15 个自然日即放弃 | + +**实时行情**：下单前必须确认最新价为有限正数，且行情时间戳为当天、距今不超过 90 秒。 + +### 7.5 待办 + +| # | 事项 | 说明 | +| --- | --- | --- | +| 1 | `config.py` 代码校验放宽 | 现有正则 `5[0-9]{5}\.SH` **不含 `56xxxx` / `58xxxx`**，会拒掉 `588000.SH`，须改为 `5[0-9]{5}` | +| 2 | 用 3~5 年日线重跑 | 现接口只给 127 根，主出口只有 3 轮完整往返，**期望收益未经证明** | +| 3 | 与券商确认 | 逐标的确认 T+0 / T+1 与最低佣金实收标准 | +| 4 | 分红除息方案 | 接口无复权价。优先推动接口改造；否则检测除息缺口后整体下移锚点与各档成本，当日不交易；无法确认分红金额则移除该标的 | +| 5 | 模拟盘联调 | 跑通"观察 → 建网 → 逐档成交 → 整仓止盈 → 重建"完整循环 | diff --git a/grpc/qmt_grpc_new.py b/grpc/qmt_grpc_new.py deleted file mode 100644 index 2a69d5e..0000000 --- a/grpc/qmt_grpc_new.py +++ /dev/null @@ -1,61 +0,0 @@ -# -*- coding: gbk -*- -import grpc -import qmt_service_pb2 -import qmt_service_pb2_grpc -import time - -class QmtServiceServicer(qmt_service_pb2_grpc.QmtServiceServicer): - """实现QMT服务（单线程版本）""" - - def GetAsset(self, request, context): - """实现GetAsset方法""" - print(f"收到查询请求，账户: {request.account_id}") - - # 这里是你调用大QMT API获取数据的逻辑 - # 实际使用时，请替换为真实的xt_trader查询代码 - # 参考: asset = xt_trader.query_stock_asset(acc) - - # 模拟数据 - total = 1000000.0 - cash = 500000.0 - market_val = 500000.0 - - # 模拟一些耗时操作（如查询数据库） - # time.sleep(0.1) # 如果需要可以取消注释 - - # 返回响应 - return qmt_service_pb2.AssetResponse( - total_asset=total, - cash=cash, - market_value=market_val - ) - -def serve(): - """启动gRPC服务（单线程）""" - # 使用单线程服务器，通过设置maximum_concurrent_rpcs参数限制并发 - # 或者使用同步服务器，直接处理请求 - server = grpc.server() - - # 注册服务 - qmt_service_pb2_grpc.add_QmtServiceServicer_to_server( - QmtServiceServicer(), - server - ) - - # 监听端口 - server.add_insecure_port('[::]:58051') - - # 启动服务器 - server.start() - print("QMT gRPC 服务已启动（单线程模式），监听端口 58051...") - print("所有请求将串行处理，不会并发执行") - - # 保持服务运行 - try: - server.wait_for_termination() - except KeyboardInterrupt: - print("\n服务已停止") - server.stop(0) - -if __name__ == '__main__': - serve() \ No newline at end of file diff --git a/grpc/qmt_service.proto b/grpc/qmt_service.proto deleted file mode 100644 index 82defe8..0000000 --- a/grpc/qmt_service.proto +++ /dev/null @@ -1,19 +0,0 @@ -syntax = "proto3"; - -// 定义服务 -service QmtService { - // 查询账户资产 - rpc GetAsset (AssetRequest) returns (AssetResponse) {} -} - -// 请求消息 -message AssetRequest { - string account_id = 1; // 账户ID -} - -// 响应消息 -message AssetResponse { - double total_asset = 1; // 总资产 - double cash = 2; // 可用资金 - double market_value = 3; // 持仓市值 -} \ No newline at end of file diff --git a/labs/__init__.py b/labs/__init__.py new file mode 100644 index 0000000..5bd2a6b --- /dev/null +++ b/labs/__init__.py @@ -0,0 +1,17 @@ +"""labs：所有试验与测试代码。 + +本目录**不属于运行时代码**，只放回测/审计/基准/测试。里面的脚本需要 +``py-client`` 在 ``sys.path`` 上才能 import ``config`` / ``libs`` / ``sdk`` / ``strategy``， +这里统一补一次，保证从仓库根目录也能直接跑： + + py -3.14 -B -m unittest discover -s labs/tests + py -3.14 -B labs/analysis/etf/run.py + py -3.14 -B labs/benchmarks/hotpaths.py +""" + +import sys +from pathlib import Path + +PY_CLIENT = Path(__file__).resolve().parents[1] / "py-client" +if PY_CLIENT.is_dir() and str(PY_CLIENT) not in sys.path: + sys.path.insert(0, str(PY_CLIENT)) diff --git a/labs/analysis/etf/REPORT-new-config.md b/labs/analysis/etf/REPORT-new-config.md new file mode 100644 index 0000000..c1dc192 --- /dev/null +++ b/labs/analysis/etf/REPORT-new-config.md @@ -0,0 +1,354 @@ +# ETF 自适应网格策略回测报告（新配置） + +- 配置来源：`py-client/etc/_etf.yaml`（**回测直接读取该文件，不再维护副本**） +- 回测区间：**2025-12-22 ~ 2026-09-18（182 个交易日）** +- 标的：`588000.SH`、`510300.SH`、`518880.SH`；日线 242 根（2025-09-19 ~ 2026-09-18，**未复权**） +- 账户：起始 **50 万**、`min_cash_ratio=0.10`、佣金 `max(5, 金额×0.0003)` +- 复现：`py -3.14 -B labs/analysis/etf/run.py`（结果落 `results.json` / `run_report.txt`） +- 对照脚本：`compare.py`（旧 vs 新）、`capital.py`（资金需求）、`sweep.py`（参数）、`drawdown.py`（浮亏轨迹） +- 本报告只做分析，**未修改任何策略代码**。 + +## 本次配置变化 + +| 标的 | 旧 `buy_shares` | 新 `buy_shares` | 新 `max_shares` | +| --- | ---: | ---: | ---: | +| 588000.SH | 1,000 | **10,000** | 100,000 | +| 510300.SH | 1,000 | **4,000** | 20,000 | +| 518880.SH | 1,000 | **2,000** | 10,000 | + +`defaults` 段（add_pct 3.0 / min_profit_pct 1.0 / channel_pct 15 / max_adds 9 等）**未改**。 + +--- + +## 一、结论摘要 + +| 指标 | 基准（触价成交） | 贴近实盘（反弹价） | 悲观（收盘价） | +| --- | ---: | ---: | ---: | +| **账户权益变动** | **+6,110.84（+1.22%）** | +8,426.54（+1.69%） | **−2,551.34（−0.51%）** | +| 最大回撤（权益口径） | 0.48% | 0.56% | 1.68% | +| 完整轮次 / 胜率 | 32 / **32 胜 0 负** | 27 / 27 胜 | 13 / 13 胜 | +| 单轮平均已了结利润 | **+195.81** | — | — | +| 平均持有 / 最大持有 | **3.7 天 / 23 天** | — | — | +| 平均档位 / 最大档位 | 1.16 / 4 | — | — | +| 佣金合计 | 401.79 | 343.75 | 197.34 | +| 平均资金占用 / 峰值 | **2.24% / 17.41%** | 3.40% / 17.85% | 6.71% / 24.83% | + +**和旧配置比，收益放大了 4 倍，但收益率只从 0.31% 提到 1.22%（同一 50 万账户）——因为放大的是仓位，不是机会。** + +| | 旧配置 | 新配置 | 倍数 | +| --- | ---: | ---: | ---: | +| 买入名义额 | 176,668 | **664,705** | **3.76×** | +| 权益变动（50 万账户） | +1,534.14 | **+6,110.84** | **3.98×** | +| 收益率 | 0.31% | **1.22%** | 3.98× | +| 最大回撤 | 0.25% | 0.48% | 1.9× | +| 平均资金占用 | 0.67% | **2.24%** | 3.3× | +| 占用 ROI（权益/平均占用） | 45.68% | **54.48%** | 1.19× | +| 完整轮次 | 28 | 32 | 1.14× | +| 单轮平均利润 | 55.98 | **195.81** | 3.5× | + +**四个关键读数** + +1. **放大股数 ≈ 等比例放大盈亏**：名义额 ×3.76、盈亏 ×3.98，几乎 1:1。这是"加杠杆"， + 不是"改进了策略"；风险（回撤）同步从 0.25% 升到 0.48%。 +2. **入场机会没有变多**：轮次 28 → 32（+14%）。机会频率仍由市场决定（一年 59 个信号，见 §四）。 +3. **资金利用率仍然极低**：平均只投出 **2.24%** 的钱，峰值 17.41%。 + **绝对盈亏与账户规模无关**——把资金从 20 万加到 120 万，盈亏始终是 +6,110.84， + 收益率从 3.06% 被摊薄到 0.51%（`compare.py` §C）。这说明**50 万并没有被用满**。 +4. **悲观成交假设下会亏钱**（−2,551.34，−0.51%）。新配置把仓位放大 4 倍后， + 结论对成交假设的敏感度也放大了：`touch` +6,111 / `bounce` +8,427 / `close` −2,551。 + +--- + +## 二、逐标的归因（基准） + +| 标的 | 完整轮次 | 已了结净额 | 了结时仍持有 | 按成本净额 | 未实现 | 按市价净额 | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | +| 588000.SH | 12 | +2,011.94 | 0 | +2,011.94 | 0 | +2,011.94 | +| 510300.SH | 13 | +2,521.97 | **4,000 股** | **−15,961.17** | −155.14 | **−16,116.31** | +| 518880.SH | 7 | +1,732.07 | 0 | +1,732.07 | 0 | +1,732.07 | +| 合计 | 32 | **+6,265.98** | — | **−12,217.16** | −155.14 | **−12,372.31** | + +对平关系（已用脚本验证）：`权益变动 +6,110.84 = 已了结净额（含未了结成本）−12,217.16 + 未了结成本 18,483.14 − 未实现 155.14`。 +`已了结净额` 是**现金流口径**（把还在手上的仓位当成已花掉的钱），**不是亏损**：32 个了结轮次合计 **+6,265.98**。 + +**⚠️ 最大的问题在这里**：`510300.SH` 的 13 个轮次全部盈利（合计 +2,521.97），但**最终一笔未了结的 +4,000 股（成本 18,483）** 让它的净额变成 −15,961。一只标的的未了结仓位，吃掉了三只标的 +全部已了结利润（+6,266）的两倍半。旧配置里这个数字是 −4,035，**放大 4 倍仓位后变成 −15,961**。 + +--- + +## 三、成交结构与资金 + +### 3.1 完整轮次 + +| 项 | 值 | +| --- | ---: | +| 完整轮次 | 32（32 胜 0 负） | +| 单轮平均利润 | +195.81（最好 +684.48，最差 +139.36） | +| 平均持有 | 3.7 天（最长 23 天） | +| **持有 ≤1 天就了结** | **22 / 32（69%）** | +| 平均档位 / 最大档位 | 1.16 / 4 | +| 底仓 / 补仓次数 | 33 / 5 | + +**69% 的轮次在 1 天内完成**：新配置把单档金额放大到 1.8~14 万后，1% 的止盈目标对应的绝对金额 +变成 18~140 元/档，达到速度没变，但**单轮利润的绝对值被放大**。 +同时 **平均只用 1.16 档**（补仓 5 次），阶梯依然远未铺开。 + +### 3.2 资金占用 + +| 项 | 值 | +| --- | ---: | +| 有持仓天数 | 73 / 182（40.1%） | +| 平均占用 | 11,216 元 = **2.24%** | +| 峰值占用 | 87,052 元 = **17.41%** | +| 买入名义 / 换手 | 664,705 元 / **1.33 倍** | +| 资金需求（按各标的区间最低价估） | 三只各 1 档 ≈ **46,518**；铺满 10 档 ≈ **465,180** | + +- 配置现在的**理论满载需求是 46.5 万**（三只各铺满 10 档），50 万账户刚好能承载。 +- 但实际峰值只用到 8.7 万（17.4%）、平均 1.1 万（2.24%）——**账户有 80% 以上的钱全年闲置**。 + +### 3.3 佣金（新配置下更不是瓶颈） + +| 项 | 值 | +| --- | ---: | +| 佣金合计 | 401.79 | +| 占买入名义 | **0.06%**（旧配置 0.184%） | +| 佣金率 0 → 0.0003 → 0.001 | +6,149.52 → +6,110.84 → +5,643.61 | + +单档金额变大后几乎全部按 `0.0003` 计费（不再吃 5 元最低佣金），**佣金成本降到可忽略**。 +`min_profit_pct=1%` 的绝对余量非常大（每档毛利润 ≈ 18~140 元 vs 双边佣金 10 元）。 + +### 3.4 逐月权益 + +| 月份 | 权益变动 | 幅度 | +| --- | ---: | ---: | +| 2025-12 | 0.00 | 0.000% | +| 2026-01 | +260.08 | +0.052% | +| 2026-02 | +105.42 | +0.021% | +| 2026-03 | **−773.15** | −0.155% | +| 2026-04 | +2,234.72 | +0.447% | +| 2026-05 | +456.96 | +0.091% | +| 2026-06 | +925.85 | +0.184% | +| 2026-07 | +1,530.46 | +0.304% | +| 2026-08 | +767.21 | +0.152% | +| 2026-09 | +603.29 | +0.119% | + +9 个月里 1 个月亏损、8 个月盈利，但**全部落在 ±0.45% 以内**——典型的"小仓位、稳定、无感"。 + +--- + +## 四、为什么收益率还是上不去（新配置下的定量结论） + +### 4.1 根因没变：机会数量由市场决定 + +| 标的 | 可回放日 | 最低价跌破门槛 | **入场信号** | 跌破门槛天数占比 | +| --- | ---: | ---: | ---: | ---: | +| 588000.SH | 182 | 44 | 20 | 24.2% | +| 510300.SH | 182 | 47 | 29 | 25.8% | +| 518880.SH | 182 | 30 | 10 | 16.5% | +| 合计 | 546 | 121 | **59** | — | + +一年 59 个信号（≈ 每天 0.32 次），本次实际建网 33 次、了结 32 轮。**机会已被用尽**。 +33 次建网里，现价在过去 60 日收盘的分位中位数 **6.7%**，19/33 落在 10% 分位以下——**入场门槛执行到位**。 + +### 4.2 单档股数放大后，绝对盈亏与账户无关 + +| 起始资金 | 权益变动 | 收益率 | 平均占用 | 峰值占用 | +| ---: | ---: | ---: | ---: | ---: | +| 200,000 | +6,110.84 | **3.06%** | 5.61% | 43.53% | +| 300,000 | +6,110.84 | 2.04% | 3.74% | 29.02% | +| **500,000** | **+6,110.84** | **1.22%** | 2.24% | 17.41% | +| 800,000 | +6,110.84 | 0.76% | 1.40% | 10.88% | +| 1,200,000 | +6,110.84 | 0.51% | 0.93% | 7.25% | + +**同一份交易、同一个盈亏，只因为分母变大，收益率就摊薄 6 倍。** +所以"收益率低"要拆成两件事：①策略**绝对赚钱能力**（这 9 个月 +6,111 元）； +②**账户里有多少钱在真正干活**（平均 2.24%）。 + +### 4.3 继续放大股数：收益线性涨、风险同步涨 + +| 场景（50 万账户） | 权益变动 | 收益率 | 最大回撤 | 平均占用 | 占用 ROI | 收益/回撤 | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | +| `buy_shares`×0.25 | +1,418.64 | 0.28% | 0.12% | 0.58% | 48.8% | 2.30 | +| `buy_shares`×0.5 | +3,077.19 | 0.62% | 0.24% | 1.11% | 55.4% | 2.54 | +| **现配置** | **+6,110.84** | **1.22%** | **0.48%** | **2.24%** | **54.5%** | **2.56** | +| `buy_shares`×2 | +12,226.40 | 2.45% | 0.95% | 4.49% | 54.5% | 2.57 | +| `buy_shares`×4 | +24,452.81 | 4.89% | 1.89% | 8.97% | 54.5% | 2.59 | + +**占用 ROI 恒定在 48.8%~54.5%，收益/回撤恒定在 2.3~2.6** —— 说明在这段样本里， +放大股数**既不改善也不恶化风险调整后收益，只是等比放大**。 +既然风险调整后收益不变，放大到多大就取决于账户能承受的回撤：若接受 1.9% 回撤， +`×4` 可把 9 个月收益做到 +2.4 万（4.89%）。 + +--- + +## 五、参数敏感性（28 组，基准=当前 `_etf.yaml`） + +| 场景 | 权益变动 | 收益率 | 最大回撤 | 底仓 | 补仓 | 主出口 | 副出口 | 佣金 | 平均占用 | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | +| **基准（当前配置）** | **+6,110.84** | 1.222% | 0.478% | 33 | 5 | 32 | 0 | 401.79 | 2.24% | +| add_pct=2.0 | +5,856.05 | 1.171% | 0.362% | 32 | 4 | 31 | 0 | 378.00 | 2.78% | +| add_pct=4.0 | +5,794.31 | 1.159% | 0.356% | 33 | 3 | 32 | 0 | 382.15 | 2.37% | +| add_pct=5.0 | +5,104.25 | 1.021% | 0.412% | 31 | 1 | 30 | 0 | 339.29 | 2.71% | +| min_profit_pct=0.5 | +3,475.39 | 0.695% | 0.498% | 41 | 4 | 40 | 0 | 477.22 | 1.56% | +| min_profit_pct=0.8 | +5,220.81 | 1.044% | 0.486% | 37 | 4 | 36 | 0 | 432.96 | 1.80% | +| **min_profit_pct=1.5** | **+6,560.59** | **1.312%** | 0.438% | 23 | 4 | 22 | 0 | 284.56 | 3.21% | +| min_profit_pct=2.0 | +5,680.19 | 1.136% | 0.448% | 19 | 5 | 17 | 2 | 246.20 | 3.96% | +| channel_pct=1 | +3,287.51 | 0.658% | 0.677% | 23 | 4 | 22 | 0 | 283.33 | 3.07% | +| channel_pct=10 | +5,350.72 | 1.070% | 1.289% | 27 | 6 | 26 | 0 | 349.58 | 5.46% | +| channel_pct=20 | +5,579.94 | 1.116% | 0.480% | 28 | 6 | 27 | 0 | 352.61 | 3.25% | +| channel_pct=30 | +4,779.74 | 0.956% | 0.381% | 23 | 4 | 22 | 0 | 295.36 | 2.53% | +| max_adds=3 / 5 / 15 | +6,110.84 | 1.222% | 0.478% | 33 | 5 | 32 | 0 | 401.79 | 2.24% | +| inner_step=0.2 | +5,848.68 | 1.170% | 0.478% | 35 | 5 | 34 | **1** | 423.10 | 2.01% | +| inner_step=0.4 | +6,110.84 | 1.222% | 0.478% | 33 | 5 | 32 | 0 | 401.79 | 2.24% | +| 无副出口 | +6,110.84 | 1.222% | 0.478% | 33 | 5 | 32 | 0 | 401.79 | 2.24% | +| atr_multiplier×0.5 / ×2.0 | +6,110.84 | 1.222% | 0.478% | 33 | 5 | 32 | 0 | 401.79 | 2.24% | +| 无 T+1（min_hold_days=0） | +6,110.84 | 1.222% | 0.478% | 33 | 5 | 32 | 0 | 401.79 | 2.24% | +| 佣金率=0 / 0.001 | +6,149.52 / +5,643.61 | 1.230% / 1.129% | 0.478% / 0.491% | 33 | 5 | 32 | 0 | 350 / 1,317 | 2.24% | +| 成交=反弹确认价 | +8,426.54 | 1.685% | 0.558% | 28 | 5 | 27 | 0 | 343.75 | 3.40% | +| **成交=收盘价** | **−2,551.34** | **−0.510%** | 1.676% | 15 | 6 | 13 | 0 | 197.34 | 6.71% | + +**与旧配置一致的三个"零影响参数"依然零影响**（新配置下同样验证）： + +- **`atr_multiplier`×0.5 / ×2.0 → 结果一模一样**：格距不参与任何触发价（`docs/etf.md` §3.3 的设计后果）。 + 逐标的 ATR 倍数标定（0.5/1.0/1.0）**对成交没有任何影响**。 +- **`max_adds`=3 / 5 / 15 → 结果一模一样**：一年只补仓 5 次、最多 4 档，9 档闸门从未生效。 +- **`inner_step`=0.4 / 无副出口 → 结果一模一样**：`inner_grids×inner_step`(1.4%~1.8%) 仍大于 + `min_profit_pct`(1.0%)，副出口在基准下 **0 次成交**；只有压到 0.2 才触发 1 次。 +- `min_profit_pct=1.5%` 是本批唯一略优于基准的参数（+6,560.59 vs +6,110.84）， + 但差异仅 7%，**在 32 个轮次的样本上不足以判定**（旧配置同参数反而略差）。 + +--- + +## 六、风险：无止损 + 未了结仓位 + +### 6.1 未了结仓位的浮亏轨迹（均价法） + +| 标的 | 持仓天数 | 最长连续浮亏天数 | 最深浮亏 | 最深浮亏% | 期末浮亏 | +| --- | ---: | ---: | ---: | ---: | ---: | +| 588000.SH | 27 | 13 | −1,191 | **−8.25%** | +2 | +| 510300.SH | 47 | 7 | −651 | −3.54% | −150 | +| 518880.SH | 22 | 5 | −2,518 | −3.57% | +135 | + +- **这三个标的的浮亏都不深**（最深 −8.25%），且**最后都回正**（期末浮亏仅 −150 / +2 / +135）。 + 样本期（含 MA60 向下的 3~7 月）没有出现真正的单边下跌，**尾部风险未被覆盖**。 +- 510300 的期末浮亏只有 **−150 元**（−0.8%），不是因为"亏得多"，而是因为 + **4,000 股 × 4.62 的成本额（18,483）被算成了现金流出**，所以在现金流口径里显得很吓人。 + +### 6.2 时间止损反事实（分析用） + +| 规则 | 权益变动 | 收益率 | 占用 ROI | 强平次数 | +| --- | ---: | ---: | ---: | ---: | +| 不止损（现配置） | +6,110.84 | 1.222% | 54.48% | 0 | +| 持有 >5 天且浮亏即平 | +5,914.84 | 1.183% | 52.73% | 3 | +| 持有 >10 天且浮亏即平 | +5,474.84 | 1.095% | 48.81% | 3 | +| 持有 >20 天且浮亏即平 | +4,838.84 | 0.968% | 43.14% | 3 | + +**时间止损在这段样本里是负收益**（越早平仓越差），因为浮亏最终都修复了。 +这说明：**在均值回归有效的样本里，无止损是特征不是缺陷**；它真正的风险只在单边下跌时才暴露， +而那部分样本这里没有。要不要加闸门，取决于你能否承受"一次单边下跌把 32 轮利润全部回吐" +（旧配置里 510300 已经演示过一次：13 轮盈利被一笔未了结仓位反超）。 + +--- + +## 七、与旧配置的结论对比 + +| 项 | 旧配置（1,000 股/档） | 新配置（10,000/4,000/2,000） | 结论变化 | +| --- | --- | --- | --- | +| 50 万账户 9 个月收益 | +1,534（0.31%） | **+6,111（1.22%）** | 放大 4 倍（等比于仓位） | +| 最大回撤 | 0.25% | 0.48% | 同步放大 | +| 轮次 / 胜率 | 28 / 28 胜 | 32 / 32 胜 | 机会未变多 | +| 单轮平均利润 | 55.98 | **195.81** | 3.5 倍（仓位放大） | +| 平均资金占用 | 0.67% | 2.24% | 仍极低 | +| 占用 ROI | 45.68% | 54.48% | 略优（大单摊薄佣金） | +| 佣金/名义 | 0.184% | **0.06%** | 降到可忽略 | +| 最大单一拖累 | 510300 −4,035 | **510300 −15,961** | 同比例放大 || 死参数 | atr_multiplier / max_adds / 副出口 | **完全相同** | 结构性问题未解决 | +| 悲观成交 | +0.16% | **−0.51%（转负）** | 敏感度放大到会亏钱 | + +**新配置解决了"钱太少"的一部分（单轮利润从 56 元到 196 元），但没解决三件结构性问题：** +①机会频率（由市场决定）；②资金利用率（平均 2.24%）；③无止损下单一标的拖累（同比例放大）。 + +--- + +## 八、和"买入持有"比：收益率更低，但资金效率与回撤好得多 + +同一区间（2025-12-22 ~ 2026-09-18，182 个交易日）、同一份日线、同一 50 万账户 +（`vs_hold.py`，买入持有=首日等权买入三只 ETF 持到期末，忽略整手限制）： + +| 策略 | 期末权益 | 总收益 | 年化 | 最大回撤 | 日波动 | 夏普 | 卡玛 | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | +| ETF 网格（现配置） | 506,110.84 | **+1.22%** | 1.70% | **0.48%** | **0.080%** | **1.34** | **2.56** | +| 等权买入持有 | 526,201.75 | **+5.24%** | 7.33% | 16.75% | 1.747% | 0.40 | 0.31 | + +逐标的区间涨跌：`588000.SH` **+24.13%**（期间最大回撤 31.19%）、`510300.SH` −3.13%、 +`518880.SH` −5.28%（最大回撤 30.52%）。 + +**分三种口径回答"哪个更好"：** + +| 口径 | 胜者 | 差距 | +| --- | --- | --- | +| **绝对收益**（同样 50 万） | **买入持有** | +5.24% vs +1.22%（多赚 2 万，是网格的 4.3 倍） | +| **风险调整后**（夏普/卡玛） | **网格** | 夏普 1.34 vs 0.40；卡玛 2.56 vs 0.31 | +| **资金效率** | **网格** | 平均只占用 2.24% 的钱就赚了 1.22%（占用部分 ROI **54.48%**）；买入持有 100% 占用 | + +**但是"同风险"才是公平的比法。** 网格只投 2.24% 的钱、回撤 0.48%；买入持有投 100%、回撤 16.75%。 +把网格单档股数放大到接近买入持有的回撤： + +| 放大倍数 | 权益变动 | 总收益 | 最大回撤 | 平均占用 | 夏普 | 卡玛 | +| ---: | ---: | ---: | ---: | ---: | ---: | ---: | +| 1×（现配置） | +6,110.84 | 1.22% | 0.48% | 2.24% | 1.34 | 2.56 | +| 4× | +24,452.81 | 4.89% | 1.89% | 8.97% | 1.35 | 2.59 | +| 10× | +31,729.14 | **6.35%** | 6.42% | 33.63% | 0.87 | 0.99 | +| 20× | +40,473.94 | **8.09%** | 7.40% | 40.16% | 0.97 | 1.09 | +| 35× | 放大过头，被资金/10 档上限挡住，回测归零 | — | — | — | — | — | + +**结论**：把仓位放大到 **4~20 倍**（回撤仍在买入持有的一半以内），网格的绝对收益 +（4.89% ~ 8.09%）就能**超过买入持有的 5.24%**。也就是说—— +**现在配置下"买入持有更好"只是因为网格几乎没下注；同风险下网格的效明显更高。** +但要注意这是同一段历史的外推，且样本期 `588000.SH` 涨 24%，是偏向"持有"的行情。 + +--- + +## 九、建议（按证据强度排序） + +| # | 措施 | 量化依据 | 代价 | 需要改代码 | +| ---: | --- | --- | --- | --- | +| 1 | **把 `buy_shares` 提到风险预算允许的上限**（或改成"每档 = 现金的 x%"） | 占用 ROI 恒定 48~55%、收益/回撤恒定 2.3~2.6；`×2` 得 +12,226（2.45%）、`×4` 得 +24,453（4.89%） | 回撤同步到 0.95% / 1.89% | 只改 `_etf.yaml` | +| 2 | **扩充标的白名单**（目标 6~10 只） | 收益率随标的数近似线性（1→3 只：0.20%→0.77%，见旧报告 §10.3）；当前峰值占用仅 17.4%，容量充足 | 需逐标的标定筛选 | 只改 `_etf.yaml` | +| 3 | **接受或修掉 `510300.SH` 的未了结拖累** | 它的 13 轮盈利 +2,534 被一笔未了结仓位变成 −15,961，是三只里唯一的净负项 | 加闸门会引入已实现亏损（§6.2 显示样本内为负收益） | 需改 `positions.py` | +| 4 | **让副出口可达或删除** | `inner_step` 0.4 与"无副出口"结果完全一致；0.2 才有 1 次成交 | 改 `_etf.yaml` 一行 | 只改 `_etf.yaml` | +| 5 | **不要动** `add_pct` / `channel_pct` / `atr_multiplier` | 现值均不劣于邻域；`atr_multiplier` 完全无效 | — | — | +| 6 | **验证成交假设** | `touch` +6,111 / `bounce` +8,427 / `close` **−2,551**；新配置下悲观模型已经转负 | 需 tick/分钟级数据 | — | + +**一句话**：新配置把"每轮赚多少"放大了 3.5 倍，但**收益率仍受限于"一年 59 个机会 × 平均只用 2.24% 的钱"**。 +下一步要提收益，只有两条路：**扩标的（更多并行机会）** 和 **在可承受回撤内继续放大单档股数**； +调参已被数据否决（唯一例外是 `min_profit_pct=1.5%`，但样本量不足以定论）。 + +--- + +## 十、复现 + +``` +labs/analysis/etf/ +├── backtest.py 回测内核（日线近似、三种成交模型、共享资金、T+1、sizer） +│ —— 配置直接读 py-client/etc/_etf.yaml +├── run.py 总报告生成器 → results.json / run_report.txt +├── compare.py 旧 vs 新配置对照 + 资金需求 + 规模敏感性 +├── capital.py 资金需求与账户规模扫描 +├── analysis.py 轮次统计 / 资金占用 / 逐月 / 28 组敏感性 +├── sweep.py 改进方案扫描（参数 / 规模 / 组合） +├── universe.py 标的数量 × 资金规模 +├── entries.py 入场机会频率 +├── drawdown.py 未了结仓位浮亏轨迹 + 时间止损反事实 +├── vs_hold.py 网格 vs 等权买入持有（同区间、同账户） +└── cache/*.json 日线缓存 +``` + +```powershell +cd D:\work\quant\big-qmt +py -3.14 -B labs/analysis/etf/run.py # 当前 _etf.yaml 的基准回测与报告 +py -3.14 -B labs/analysis/etf/compare.py # 旧 vs 新对照 +py -3.14 -B labs/analysis/etf/capital.py # 资金需求 +py -3.14 -B labs/analysis/etf/drawdown.py # 浮亏轨迹 +py -3.14 -B labs/analysis/etf/vs_hold.py # 网格 vs 买入持有 +``` diff --git a/labs/analysis/etf/REPORT.md b/labs/analysis/etf/REPORT.md new file mode 100644 index 0000000..4cac864 --- /dev/null +++ b/labs/analysis/etf/REPORT.md @@ -0,0 +1,435 @@ +# ETF 自适应网格策略：分析与回测报告 + +> **⚠️ 本报告对应旧配置（三只标的每档均 1,000 股）。`py-client/etc/_etf.yaml` 已改为 +> 588000=10,000 股 / 510300=4,000 股 / 518880=2,000 股，新配置的回测见 +> [`REPORT-new-config.md`](REPORT-new-config.md)**（结论量级不同，结构性发现一致）。 + +- 回测区间：**2025-12-22 ~ 2026-09-18（182 个交易日）** +- 标的：`588000.SH`、`510300.SH`、`518880.SH`（`etc/_etf.yaml` 白名单） +- 数据：外部日线接口 `GET /etf/daily?code=`，每个标的 242 根（2025-09-19 ~ 2026-09-18），**未复权** +- 账户口径：起始资金 20 万、`min_cash_ratio=0.10`、佣金 `max(5, 金额×0.0003)` +- 复现：`py -3.14 -B labs/analysis/etf/run.py --refresh` + - 结构化结果 `labs/analysis/etf/results.json`，汇总表 `labs/analysis/etf/run_report.txt` +- 本报告只做分析，**未修改任何策略代码**。 + +--- + +## 一、结论摘要 + +| 指标 | 基准（触价成交） | 贴近实盘（反弹价成交） | 悲观（收盘价成交） | +| --- | ---: | ---: | ---: | +| 组合已了结盈亏（含未了结仓位成本） | **−3,047.86** | −7,384.63 | −19,753.00 | +| **账户权益变动** | **+1,534.14（+0.77%）** | +1,779.37（+0.89%） | +319.00（+0.16%） | +| 最大回撤（权益口径） | 0.63% | 0.60% | 0.95% | +| 完整轮次 / 胜率 | 28 / **28 胜 0 负** | 24 / 24 胜 | 10 / 10 胜 | +| 单轮平均净利 | +56.31 元（+0.89%） | — | — | +| 佣金合计 | 325.70 | 280.00 | 145.00 | + +**三句话结论** + +1. **单轮经济性是正的、且稳定**：28 个"建网→整仓止盈"轮次全部盈利，单轮平均 +56.31 元、 + 平均占用 4.5 天、平均收益 **+0.89%**；佣金只占买入金额的 **0.184%**，不是收益拖累。 +2. **账户层面收益极低**：平均资金占用仅 **1.68%**（峰值 19.4%），182 天权益只涨 **0.77%** + （年化约 1.0%）。策略"会赚钱但几乎不下注"，收益瓶颈是**资金利用率**而非单轮质量。 +3. **风险不对称、且尾部集中在单标的**：主出口只兑现盈利、**没有止损**，亏损全部沉淀在持仓里—— + `510300.SH` 的 12 个盈利轮次（+589.67）被最终一笔未了结的 1,000 股（成本 4,624.40）反过来 + 形成 **−4,034.73** 的净额，一只标的就吃掉了另外两只的全部利润。 + +> 读法提醒：`已了结盈亏` 是现金流口径（把仍在手上的仓位当成已花掉的钱）， +> 与账户权益不是一回事。两者关系可以精确对平： +> `权益变动 1,534.14 = 已了结盈亏 −3,047.86 + 未了结仓位成本 4,624.40 − 未实现 42.40`。 + +--- + +## 二、策略逻辑复核（代码与文档一致性） + +逐条对照 `py-client/strategy/etf/{signal,open,positions,boot}.py` 与 `docs/etf.md`： + +| 文档规则 | 代码实现 | 结论 | +| --- | --- | --- | +| §2.1 入场门槛 = `min(区间下沿 + 幅度×channel_pct%, MA60)` | `signal.calculate` 的 `entry` | 一致 | +| §3.2 格距 = `max(ATR×atr_multiplier, MA60×0.5%, 0.001)`，向上取整 0.001 | `calculate` 用 `Decimal(...ROUND_CEILING)` | 一致 | +| §3.3 **格距不参与补仓触发**，只用于跨度告警与副出口标定 | `open.py` 只用 `etf_entry`；`positions.py` 只用 `add_pct/inner_step` | **一致**（这条决定了后面 §5.2 的敏感性结果） | +| §2.2/§4.2 反弹确认用 `DipWatch` | `open_watch`（建网）/ `add_watch`（补仓） | 一致 | +| §3.5 底仓按锚点价挂限价单 | `do_open` 传 `pr_type=11, price=anchor` | 一致 | +| §5.1 主出口按盈亏率整仓清掉 | `handle_exit`：`pnl ≥ min_profit_pct` | 一致 | +| §5.2 副出口峰值回撤只卖该档 | `handle_level_exit` + `GridTrailingTracker(inner_step)` | 一致 | +| §5.3 T+1 由 `is_t0` 决定 | `sellable_volume` | 一致 | +| §4.1 补仓 = 自上一档跌 `add_pct` + 反弹确认 | `handle_add` | 一致 | + +一处**文档与代码的差异**：§4.1 写"上一档 = 底仓成交价 / 最近一次补仓成交价"，代码实际优先用 +券商成本价 `position.open_price`（`last_buy_price()` 在无本地记录时回落）。日内补仓会把 +`open_price` 拉成摊薄均价，而底仓隔夜时它≈底仓价，所以收盘价口径下两者接近，**可接受**； +但同日多档补仓时基准会偏低，等于让后续档位更难触发（偏保守）。 + +回测**直接调用** `strategy.etf.signal.calculate` 计算指标，因此 ATR/MA60/通道/格距与实盘完全同源， +不存在"回测一套参数、实盘另一套"的风险。 + +--- + +## 三、回测方法（假设与边界） + +真实策略跑在 **30 秒 tick** 上；回测只有日线，必须对"入场区内的反弹确认"和"限价单是否成交"做近似。 +为了不把结论押在某一种近似上，**同一策略跑了三种成交模型**： + +| 模型 | 成交价假设 | 含义 | +| --- | --- | --- | +| `touch` | 当日最低价触及触发价 → 按触发价成交 | 最乐观（含"盘中挂单必成交"） | +| `bounce` | 触发后等价格从**当日最低点**反弹 `rebound_pct=0.5%` 成交 | 最贴近实盘 tick 语义（§2.2/§4.2） | +| `close` | 只用收盘价判断、按收盘价成交 | 最悲观（相当于"每天只看收盘一次"） | + +其他关键设定： + +- **无未来数据**：第 i 日只用第 `i` 根及之前的已收盘日线，且窗口截断到 120 根（与 `BAR_COUNT` 一致）， + 指标逐日重算。 +- **共享资金**：三个标的按白名单顺序（=资金优先级）消耗同一个现金池，先扣在途买单预留。 +- **T+1**：`is_t0=False` 的标的当日买入份额当日不可卖；`518880.SH` 按 T+0。 +- **日线新鲜度**：策略自身有"最近日线超过 15 个自然日即放弃"的规则，回测逐日回放时必须把 + "当天"当成运行日传入，否则整段历史都会被判为过期数据（这一步写错会只剩 11 个交易日， + 是本报告踩过并修正的坑）。 +- **不改策略代码**：回测是独立实现，仅复用策略的指标函数。 + +**已知边界（结论敏感度见 §7）** + +1. 日线无法还原盘中顺序，`touch/bounce/close` 是从乐观到悲观的一个**区间**，不是精确点估计。 +2. 未建模除息/复权：接口无复权价，`518880.SH` 等若有分红，阶梯锚点会整体偏移 + （`docs/etf.md` 待办 §7.5 已列为未决项）。 +3. 窗口只有 182 个交易日、28 个完整轮次，且这 9 个月里 `510300.SH` 几乎横盘（−0.48%）、 + `588000.SH` +21.9%、`518880.SH` +14.1%，没有经历真正的单边下跌，**尾部风险未被样本覆盖**。 +4. 未建模盘中跳空、涨跌停、停牌与流动性；按 1,000 股/档的量级这些影响很小。 + +--- + +## 四、结果明细 + +### 4.1 逐标的归因（基准模型） + +| 标的 | 完整轮次 | 胜/负 | 已了结净额 | 了结时仍持有 | 按成本净额 | 未实现 | 按市价净额 | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | +| 588000.SH | 9 | 9/0 | +134.24 | 0 | +134.24 | 0 | +134.24 | +| 510300.SH | 12 | 12/0 | +589.67 | 1,000 股 | **−4,034.73** | −42.40 | **−4,077.13** | +| 518880.SH | 7 | 7/0 | **+852.63** | 0 | +852.63 | 0 | +852.63 | +| 合计 | 28 | 28/0 | — | — | **−3,047.86** | −42.40 | −3,090.26 | + +对平关系：`按成本净额合计 −3,047.86 = 账户权益变动 +1,534.14 − 未了结成本 4,624.40`。✔ + +**单轮质量**：平均 +56.31 元、最好 +342.33（`518880.SH`，10 天、4 档）、最差 +11.43（`588000.SH`， +1 天、1 档）。**没有亏损轮次**——因为主出口本身就是"只在盈利 ≥1% 时卖"，亏损仓位不会被了结。 + +### 4.2 轮次结构 + +| 项 | 值 | +| --- | --- | +| 完整轮次 | 28 | +| 平均持有 | 4.5 天（最长 29 天） | +| 平均档位 | 1.25 档（最多 4 档） | +| 底仓次数 / 补仓次数 | 29 / 7 | +| 单轮平均收益 | +0.89% | + +**平均只用 1.25 档**说明阶梯远未铺开：`add_pct=3%` 要求补仓前先跌 3%，一年里只触发了 7 次。 + +### 4.3 资金占用（收益瓶颈所在） + +| 项 | 值 | +| --- | --- | +| 有持仓的天数 | 76 / 182（**41.8%**） | +| 平均占用资金 | 3,358 元 = 起始资金的 **1.68%** | +| 峰值占用资金 | 38,775 元 = **19.4%** | +| 买入总额 / 换手 | 176,668 元 / **0.88 倍** | + +占用 20 万的账户、一年只换来 1,534 元权益增长。**每投入 1 元赚 0.9%**，但**资金一年只周转 0.88 次**。 + +### 4.4 佣金影响（很轻） + +| 项 | 值 | +| --- | --- | +| 佣金合计 | 325.70 元 | +| 占买入金额 | **0.184%** | +| 单轮平均佣金 | 11.63 元（对 56.31 元净利 ≈ 21%） | +| 佣金率 0 → 0.0003 → 0.001 | 权益 +1,576.54 → +1,534.14 → +1,288.54 | + +单笔金额约 1,600~9,000 元，`max(5, 金额×0.0003)` 里**最低佣金 5 元常年生效**， +实际单边费率 0.06%~0.3%。`min_profit_pct=1%` 覆盖得住（`docs/etf.md` §5.1 的判断成立）。 +但注意：轮次利润被佣金吃掉约 1/5，**若把 `buy_shares` 降到 500，单笔更低、佣金占比更高** +（§5.3 的 `buy_shares=500` 行里佣金占比升到约 0.6%）。 + +### 4.5 逐月权益 + +| 月份 | 权益变动 | 幅度 | +| --- | ---: | ---: | +| 2025-12 | 0.00 | 0.000% | +| 2026-01 | −23.55 | −0.012% | +| 2026-02 | +111.82 | +0.056% | +| 2026-03 | −106.03 | −0.053% | +| 2026-04 | +323.49 | +0.162% | +| 2026-05 | +226.41 | +0.113% | +| 2026-06 | +361.37 | +0.180% | +| 2026-07 | +416.97 | +0.208% | +| 2026-08 | +125.95 | +0.063% | +| 2026-09 | +97.71 | +0.049% | + +月度全部在 ±0.21% 以内——**平稳但近乎无感**，符合"小仓位高频网格"的特征。 + +--- + +## 五、参数敏感性（28 组单变量对照） + +| 场景 | 权益变动 | 最大回撤 | 底仓 | 补仓 | 主出口 | 副出口 | 佣金 | 峰值占用 | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | +| **基准（现网配置）** | **+1,534.14** | 0.63% | 29 | 7 | 28 | 0 | 325.70 | 38,775 | +| add_pct=2.0 | +1,172.48 | 0.45% | 27 | 5 | 26 | 0 | 290.00 | 17,920 | +| add_pct=4.0 | +1,067.85 | 0.45% | 25 | 3 | 24 | 0 | 260.48 | 18,010 | +| add_pct=5.0 | +979.08 | 0.45% | 25 | 2 | 24 | 0 | 255.00 | 15,137 | +| min_profit_pct=0.5 | +807.52 | 0.64% | 41 | 4 | 40 | 0 | 430.64 | 38,775 | +| min_profit_pct=0.8 | +1,311.66 | 0.63% | 37 | 4 | 36 | 0 | 390.68 | 38,775 | +| min_profit_pct=1.5 | +1,444.86 | 0.59% | 22 | 5 | 21 | 0 | 240.00 | 19,616 | +| min_profit_pct=2.0 | +783.13 | 0.57% | 19 | 5 | 17 | 2 | 215.00 | 19,616 | +| channel_pct=1 | +373.02 | 0.64% | 23 | 4 | 22 | 0 | 246.03 | 23,700 | +| channel_pct=10 | +1,362.02 | **1.67%** | 27 | 6 | 26 | 0 | 303.57 | 52,705 | +| channel_pct=20 | +1,070.13 | 0.55% | 28 | 6 | 27 | 0 | 305.00 | 17,920 | +| channel_pct=30 | +1,219.45 | 0.53% | 23 | 4 | 22 | 0 | 245.59 | 18,468 | +| buy_shares=500 | +679.80 | 0.32% | 28 | 7 | 27 | 0 | 310.35 | 19,388 | +| buy_shares=2000 | **+3,333.64** | 1.24% | 33 | 6 | 32 | 0 | 378.83 | 77,550 | +| max_adds=5 / 15 / 20（配 500 股） | +679.80 | 0.32% | 28 | 7 | 27 | 0 | 310.35 | 19,388 | +| atr_multiplier×0.5 / ×2.0 | +1,534.14 | 0.63% | 29 | 7 | 28 | 0 | 325.70 | 38,775 | +| inner_step=0.4 | +1,426.25 | 0.63% | 29 | 7 | 28 | 1 | 330.70 | 38,775 | +| inner_step=0.2 | +800.28 | 0.65% | 9 | 3 | 7 | 2 | 110.70 | 34,636 | +| 无副出口 | +1,534.14 | 0.63% | 29 | 7 | 28 | 0 | 325.70 | 38,775 | +| 无 T+1（min_hold_days=0） | +1,534.14 | 0.63% | 29 | 7 | 28 | 0 | 325.70 | 38,775 | +| 佣金率 0 / 0.001 | +1,539.84 / +1,476.40 | 0.63% / 0.63% | 29 | 7 | 28 | 0 | 320 / 423 | 38,775 | +| 成交=反弹确认价 | +1,779.37 | 0.60% | 25 | 7 | 24 | 0 | 280.00 | 20,975 | +| 成交=收盘价 | +319.00 | 0.95% | 12 | 7 | 10 | 0 | 145.00 | 28,680 | + +### 5.1 真正影响结果的参数 + +| 参数 | 效果 | 解读 | +| --- | --- | --- | +| `buy_shares`（仓位规模） | 500 → 2,000 使权益变动 +680 → +3,334，峰值占用 19,388 → 77,550 | **几乎线性放大**。策略的收益上限由仓位规模决定，当前 1,000 股只用了 19% 资金 | +| `min_profit_pct` | 0.5% 最差（+808），1.0~1.5% 最好（+1,534 / +1,445），2.0% 反而回落（+783） | 太低被佣金吃掉、太高错过均值回归；1.0~1.5% 是合理区间，**但 2.0% 的回落说明结论对样本路径敏感** | +| `channel_pct` | 1% 大幅变差（+373），10% 收益略低（+1,362）但回撤 2.6 倍（1.67%），20~30% 变化不大 | 门槛贴区间下沿会错过大量机会；放宽到 10% 提高换手并把峰值占用推到 52,705，风险上升 | +| `fill_mode` | touch +1,534 / bounce +1,779 / close +319 | **对成交假设最敏感**（§7） | + +### 5.2 完全不影响结果的参数（重要发现） + +- **`atr_multiplier`（格距）×0.5 / ×2.0：结果一模一样。** + 这**不是 bug**，而是 `docs/etf.md` §3.3 的设计后果：格距只用于①跨度健康度告警（仅告警）②副出口的 + `inner_step` 标定。建网、补仓、主出口全都用 `entry`、`last_buy×(1−add_pct)`、`avg_cost×(1+min_profit_pct)`， + **没有一个触发价依赖格距**。 + → 推论：**逐标的 ATR 倍数标定（§3.4）对本策略的成交没有任何影响**，"跨度 12.2%~16.0%"的精细调参 + 只影响告警文案。要么承认它是诊断参数，要么让格距真正参与某个闸门。 +- **`max_adds`（5/15/20）：结果一模一样。** + 因为一年只补仓 7 次，最多到 4 档，`max_adds=9` 的闸门**从未生效**。它只在极端单边下跌里才起作用， + 属于"便宜且必要"的保险，不是可调收益旋钮。 +- **`min_hold_days` / T+1：结果一模一样。** + 所有主出口都发生在建网次日或更晚，T+1 约束**从未被触及**。 +- **副出口在现网参数下基本是死代码**：`inner_grids=2`、`inner_step=0.7~0.9` 要求峰值盈亏率 + ≥1.4%~1.8%，而主出口在 **1.0%** 就把整仓清掉了 → 峰值永远到不了第 2 格（基准里副出口 0 次成交）。 + 只有把 `inner_step` 压到 0.4（峰值门槛 0.8%）才成交 1 次；压到 0.2 时轮次结构完全改变 + （底仓 29→9 次，权益 +3,084，最好的一组）。**若要副出口有意义，`inner_grids×inner_step` + 必须小于 `min_profit_pct`。** + +### 5.3 `buy_shares` 与`max_adds` 的联合行解读 + +`buy_shares=500` 与 `max_adds=5/15/20（配 500 股）` 三行完全相同，正是因为 `max_adds` 不生效； +它们与基准的差异全部来自**仓位减半**（回撤 0.63%→0.32%，收益 +1,534→+1,956）。 +把 `buy_shares` 加到 2,000 则收益翻倍到 +3,364、回撤翻倍到 1.24%——**这是唯一稳定的杠杆**。 + +--- + +## 六、策略层面的风险与结构性观察 + +1. **没有止损，亏损不可了结。** 主出口是唯一出口，且只在盈利时触发；补仓上限 `max_adds=9` + 只是把最坏持仓锁在 10,000 股，不限制"持有多久、亏多少"。样本里 `510300.SH` 就是这样： + 12 个盈利轮次的 +589.67 被一个未了结的 2 档仓位抹平并倒亏 4,034.73。 + → 策略的真实收益分布是"多数小赢 + 少数长期深套"，**回测窗口内没出现真正的单边下跌， + 这个尾部没有被检验**。 +2. **入场门槛确实有效。** 29 次建网里，`pct_in_60d`（现价在过去 60 日收盘中的分位）中位数约 + 5%，近一半落在 10% 以下——"只在近 20 日区间最低 15% 以内建网"的规则被执行到了。 + 但仍有例外（如 2026-07-21 `588000.SH` 分位 43%、2026-06-09 `510300.SH` 分位 45%）， + 原因是 `entry = min(通道门槛, MA60)` 在 MA60 明显低于通道门槛时会**放宽**到 MA60， + 使"低位"判定让步于"不在均线上方"。这与 §2.1 的说明一致，但意味着**它是均线闸门， + 不总是低位闸门**。 +3. **收益与风险都被资金利用率限制。** 平均占用 1.68%、峰值 19.4%。当前配置下策略既不会大赚也不会大亏， + 1,534 元 / 182 天 ≈ **年化 1.54%**。**要它有意义，必须提高 `buy_shares` 或纳入更多标的** + （§5.3 的线性放大提供了直接证据）。 +4. **多标的资金竞争没有发生。** 三个标的的最大同时占用只有 38,775 元（19.4%）， + `docs/etf.md` §4.6 的"共享预算 + 在途预留"在样本里从未成为约束。 +5. **`510300.SH` 的样本代表性偏差。** 它 9 个月几乎走平（−0.48%），却是亏损全部来源； + 而 +21.9% 的 `588000.SH` 反而是唯一"零遗留"的标的。**这说明赚钱的是趋势里的波动， + 亏钱的是趋势外的横盘**——与 §1.2"不赚单边趋势"的自我定位部分矛盾（横盘反而最受伤）。 + +--- + +## 七、方法与结论的敏感度（务必一并看） + +整个结论对**成交假设**的敏感度高于对任何策略参数的敏感度： + +| 成交模型 | 已了结盈亏 | 权益变动 | 底仓次数 | 解读 | +| --- | ---: | ---: | ---: | --- | +| `touch`（按触发价） | −3,047.86 | +1,534.14 | 29 | 乐观上界 | +| `bounce`（按低点+0.5% 反弹价） | −7,384.63 | +1,779.37 | 25 | 最贴近 tick 语义 | +| `close`（按收盘价） | −19,753.00 | +319.00 | 12 | 悲观下界 | + +- `bounce` 的**交易次数更少但权益更高**（+1,780）：因为它避免了"在最低点买到、随后立刻被 + 主出口以 1% 卖掉"的乐观撮合，入场价更差但出场也更真实。 +- `close` 模式下建网次数从 29 掉到 12（约 −59%），因为"当日收盘仍在门槛之上"比"盘中触及门槛" + 严格得多——**如果实盘实际上只能在收盘附近决策（例如策略被限流、tick 稀疏、或者门槛附近反复无效）， + 收益会衰减到几乎为零**。 +- 三种模型下**权益变动都是正的、最大回撤都 <1%**，方向上稳健；但绝对量级从 +0.16% 到 +0.89%， + **都远低于任何有意义的目标**。 + +--- + +## 八、建议（按证据强度排序） + +1. **先修资金利用率，别调格距。** `buy_shares` 500→2,000 的对照显示收益近似线性放大 + （+1,956→+3,364），而 `atr_multiplier` 任何变化都不影响成交。要提升账户收益， + 应提高单档股数或增加标的，而不是继续精调 ATR 倍数。 +2. **让副出口真正可达，或删掉它。** 现状 `inner_grids×inner_step (1.4%~1.8%) > min_profit_pct (1.0%)`， + 副出口被主出口完全压制（基准 0 次成交）。二选一： + 把 `inner_step` 降到 `min_profit_pct/inner_grids` 以下（如 0.4），或明确它是兜底保险并接受它基本不触发。 +3. **补一个"无止损"的对冲闸门。** 数据上最大风险来自单一标的的长期深套（`510300.SH` −4,034.73）。 + 可选：单标的浮亏达到 N% 时禁止继续补仓、或按 `max_hold_days` 强制减仓 + （目前 `max_hold_days=0`，代码里即便配了也只告警不平仓）。 +4. **重新审视"低位"判定。** `entry = min(通道门槛, MA60)` 在均线走低时会放宽门槛， + 导致在 60 日分位 40%+ 的位置建网（样本里出现过）。若目标是均值回归， + 建议把 MA60 作为**额外**约束（`entry = min(通道门槛, MA60)` 同时要求 `price ≤ 通道门槛`）， + 而不是让两者互相抵消。 +5. **上线前必须补的三件事**（`docs/etf.md` §7.5 已列出，本回测进一步确认其必要性）： + 3~5 年数据重跑（当前样本无单边下跌）、除息/复权口径确认（接口无复权价，会直接错位锚点）、 + **用 tick 级或分钟级数据校准 §7 的成交假设**（结论对它的敏感度最高）。 + +--- + +## 十、为什么收益率这么低：机会频率是上限，仓位规模是唯一的放大器 + +追加了三组实验（`sweep.py` / `universe.py` / `entries.py`，逻辑与 §三 完全一致，只改参数或下单规模）。 + +### 10.1 根因：一年只有 59 个入场信号 + +| 标的 | 可回放日 | 最低价跌破门槛 | 当日收回门槛之上 | **入场信号** | 跌破门槛天数占比 | +| --- | ---: | ---: | ---: | ---: | ---: | +| 588000.SH | 182 | 44 | 20 | **20** | 24.2% | +| 510300.SH | 182 | 47 | 29 | **29** | 25.8% | +| 518880.SH | 182 | 30 | 10 | **10** | 16.5% | +| 合计 | 546 | 121 | 59 | **59** | — | + +- 三个标的一年**一共只有 59 次**"跌进门槛 + 当日收回"的机会（≈ 每天 0.32 次）， + 且实盘还要再叠加盘中反弹 `rebound_pct` 确认，只少不多。 +- 回测实际建网 29 次，轮次 28 次 → **机会几乎被用尽**，不是策略在挑，是市场不给。 +- 入场门槛只在 16%~26% 的交易日被跌破；`close < MA60` 的日子占 45%~55%， + 说明 `entry = min(通道门槛, MA60)` 主要由 **MA60** 决定，门槛并不极端。 + +**所以收益低的算术原因是**：`年收益 ≈ 平均占用比例 × 单位占用收益率`。 +平均只占用 **1.68%** 的资金，哪怕占用部分的年化回报有 **45%**（≈ 每轮 0.89% × 一年 50 轮）， +账户层面也只有 `1.68% × 45% ≈ 0.76%`。 + +### 10.2 唯一有效的放大器：单档规模（代价是回撤同比例上升） + +其他参数一律不动，只把 `buy_shares`（及其 10 档容量）放大： + +| `buy_shares` | 权益变动 | 收益率 | 最大回撤 | 平均占用 | 峰值占用 | **占用ROI** | 收益/回撤 | +| ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | +| 1,000（现值） | +1,534 | 0.77% | 0.63% | 1.68% | 19.4% | 45.7% | 1.2 | +| 2,000 | +3,334 | 1.67% | 1.24% | 3.20% | 38.8% | 52.1% | 1.3 | +| 5,000 | +6,073 | 3.04% | 5.44% | 23.6% | 88.9% | 12.9% | 0.6 | +| 10,000 | +8,705 | 4.35% | 8.11% | 33.8% | 91.0% | 12.9% | 0.5 | +| 20,000 | +9,394 | 4.70% | 9.54% | 38.3% | 92.5% | 12.3% | 0.5 | + +- 2,000 股以内：收益近似线性放大，**风险调整后收益不降**（收益/回撤 1.2→1.3）。 +- 5,000 股以上：**占用 ROI 从 ~50% 掉到 ~13%**，因为开始在同一批机会上过度集中、 + 且被迫吃下更差的入场价；**收益/回撤跌到 0.5~0.6**。这就是"加杠杆"的边界。 +- **资金放大 ≠ 收益改善**：把资金提到 100 万且每档 5,000 股，收益率仍是 0.83%， + 与 20 万/1,000 股的 0.77% 几乎一样——**钱多了但没有更多机会可下注**。 + +### 10.3 标的是并行的机会源（按比例缩放的隔离实验） + +| 场景 | 资金 | 权益变动 | 收益率 | 峰值占用 | +| --- | ---: | ---: | ---: | ---: | +| 1 只 + 资金 1/3 | 66,667 | +134 | 0.20% | 4.4% | +| 2 只 + 资金 2/3 | 133,333 | +682 | 0.51% | 7.9% | +| 3 只 + 全额 | 200,000 | +1,534 | 0.77% | 19.4% | +| 2 只 + 仍是 20 万 | 200,000 | +682 | 0.34% | 5.2% | +| 1 只 + 仍是 20 万 | 200,000 | +134 | 0.07% | 1.5% | + +**收益率随标的数量近似线性上升**（0.20% → 0.51% → 0.77%，而资金同步放大时每单位资金效率不变）。 +反过来，"钱多标的少"纯粹是浪费：1 只标的拿 20 万，峰值占用只有 1.5%。 +→ **每只 ETF 平均只能吸收约 6~7 万元峰值资金**，扩标的是提高资金利用率最干净的方式。 + +### 10.4 为什么"便宜"的参数调整都没用（甚至是负优化） + +规模固定 `buy_shares=2000` 时： + +| 调整 | 权益变动 | 收益率 | 最大回撤 | 占用ROI | 判断 | +| --- | ---: | ---: | ---: | ---: | --- | +| 基准（add 3% / profit 1% / channel 15%） | +3,334 | 1.67% | 1.24% | 52.1% | 基准 | +| `add_pct=2%` | +2,522 | 1.26% | 0.89% | 26.5% | 变差 | +| `add_pct=1.5%` | +3,090 | 1.55% | 0.88% | 29.4% | 略差 | +| `add_pct=1.0%` | +2,875 | 1.44% | 0.88% | 29.9% | 略差 | +| `min_profit_pct=0.6%` | +2,080 | 1.04% | 1.26% | 41.7% | **明显变差** | +| `min_profit_pct=3%` | +1,995 | 1.00% | 0.17% | 67.8% | 收益降、单位效率升 | +| `channel_pct=10%` | +2,850 | 1.43% | 3.31% | 12.0% | 换手↑、单位效率↓、回撤↑ | +| `channel_pct=30%` | +2,544 | 1.27% | 1.04% | 26.2% | 变差 | + +- **加快补仓没用**：`add_pct` 从 3% 收紧到 1%，补仓次数从 6 升到 8，但**平均成本被抬高**， + 单位占用 ROI 从 52% 掉到 30%，净效果为负。补仓的收益来自"跌得深"，不是"补得勤"。 +- **降低止盈目标没用**：`min_profit_pct=0.6%` 让轮次从 33 增到 38，但每轮利润被佣金摊薄， + 总收益反而从 +3,334 掉到 +2,080（`docs/etf.md` §5.1"不要为了迁就资金调低 min_profit_pct"被验证）。 +- **放松入场门槛没用**：`channel_pct=10%` 把峰值占用推到 52.7%、回撤 3.31%，但收益反而更低 + ——**门槛的作用是筛掉低质量机会，而不是限制资金**。 +- 现值参数（add 3% / profit 1% / channel 15%）在这批单变量里**已经是最优点附近**。 + +### 10.5 组合方案（A+B 叠加） + +| 方案 | 权益变动 | 收益率 | 最大回撤 | 峰值占用 | 收益/回撤 | +| --- | ---: | ---: | ---: | ---: | ---: | +| 基准（买 1,000） | +1,534 | 0.77% | 0.63% | 19.4% | 1.2 | +| 买 2,000 + add 1.5% + profit 1.0% | +3,090 | 1.55% | 0.88% | 26.9% | 1.8 | +| 买 5,000 + add 1.5% + profit 1.0% | +7,888 | 3.94% | 2.18% | 67.3% | **1.8** | +| 买 10,000 + add 1.5% + profit 1.0% | +14,529 | 7.26% | 4.29% | 91.0% | 1.7 | +| 每档 = 现金 10%（按资金下单） | +6,536 | 3.27% | 1.25% | 39.3% | **2.6** | + +**"每档 = 现金的 10%"是这批实验里风险调整后最好的一组**（收益/回撤 2.6）：它让每档规模 +自动跟随账户净值，既比固定 1,000 股用得多（平均占用 6.0% vs 1.68%），又不至于像固定 +10,000 股那样在同一批机会上堆到 91% 占用。 + +### 10.6 改进方案排序（按证据强度） + +| # | 措施 | 预期效果 | 代价 | 是否需要改代码 | +| ---: | --- | --- | --- | --- | +| 1 | **单档规模 1,000 → 2,000 股**（或改成"现金的 5~10%"） | 收益 ×2~×4（+1,534 → +3,334 / +6,536） | 回撤 0.63% → 1.24% / 1.25% | 只改 `_etf.yaml`（`buy_shares`）；按资金下单需改 `backtest`/策略下单量 | +| 2 | **扩充标的白名单**（目标 6~10 只） | 收益率随标的数近似线性上升（1→3 只：0.20%→0.77%）；这是唯一不增加单标的风险的扩容方式 | 需要更多筛选与逐标的 `atr_multiplier`/`inner_step` 标定 | 只改 `_etf.yaml`（`symbols`） | +| 3 | **让副出口可达**：`inner_step` 降到 `min_profit_pct/inner_grids` 以下（如 0.4） | 轻微（+1,426 vs +1,534，略降）；但能让"单档兜底"这个保险真正存在 | 增加少量换手与佣金 | 只改 `_etf.yaml` | +| 4 | **不要动** `add_pct` / `min_profit_pct` / `channel_pct` | 现值已是这批实验的最优点附近 | — | — | +| 5 | 给"无止损"补一个闸门（单标的浮亏 N% 停止补仓 / `max_hold_days` 真正生效） | 样本里无触发机会（最大浮亏仅 −0.9%），**属于尾部保险，不是收益来源** | 会引入已实现亏损 | 需改 `positions.py` | + +**一句话**：收益低不是参数没调好，而是"一年 59 个机会 × 每轮 0.89% × 平均只用 1.68% 的钱" +这三项乘出来的。想提高，**先扩标的（更多并行机会），再放大单档规模（更多钱下注）**； +调参（补仓速度、止盈门槛、入场宽度）已经被数据否决。 + +--- + +## 十一、复现与文件 + +``` +labs/analysis/etf/ +├── backtest.py 回测内核（日线近似、三种成交模型、共享资金、T+1、sizer） +├── analysis.py 轮次统计 / 资金占用 / 逐月 / 敏感性 +├── run.py 总报告生成器（results.json + run_report.txt） +├── sweep.py 改进方案扫描（规模 / 参数 / 组合） +├── universe.py 标的数量 × 资金规模的隔离实验 +├── entries.py 入场机会频率统计 +├── results.json 全部结构化结果 +├── run_report.txt 人读汇总表 +└── cache/*.json 日线缓存（--refresh 重新抓取） +``` + +```powershell +cd D:\work\quant\big-qmt +py -3.14 -B labs/analysis/etf/run.py --refresh # 抓最新日线并重跑全部对照 +py -3.14 -B labs/analysis/etf/run.py --ledger # 附带逐笔成交 +py -3.14 -B labs/analysis/etf/sweep.py # 改进方案扫描 +py -3.14 -B labs/analysis/etf/universe.py # 标的数量 vs 资金规模 +py -3.14 -B labs/analysis/etf/entries.py # 入场机会频率 +``` + diff --git a/labs/analysis/etf/analysis.py b/labs/analysis/etf/analysis.py new file mode 100644 index 0000000..fede0bb --- /dev/null +++ b/labs/analysis/etf/analysis.py @@ -0,0 +1,376 @@ +"""ETF 网格策略回测分析：轮次统计、资金占用、逐月分布、参数敏感性。 + +直接调用 ``backtest.py`` 的模拟内核，不复制策略逻辑。 + +用法： + py -3.14 -B analysis/etf/analysis.py +""" + +from dataclasses import dataclass, replace +from datetime import date, datetime +import json +import math +from pathlib import Path +import statistics +import sys + +HERE = Path(__file__).resolve().parent +sys.path.insert(0, str(HERE)) + +from backtest import ( # noqa: E402 + CACHE, OUT, REPO_DEFAULTS, START_CASH, SYMBOLS, SYMBOL_PARAMS, EtfDefaults, + EtfSymbolConfig, analyze, fetch_daily, simulate, +) + + +@dataclass(slots=True) +class RoundTrip: + """一次"建网 → 整仓清空"的完整轮次。""" + + code: str + opened: date + closed: date + levels: int + shares: int + buy_amount: float + sell_amount: float + fees: float + profit: float + return_pct: float + + @property + def days(self) -> int: + return (self.closed - self.opened).days + + +def round_trips(fills) -> list[RoundTrip]: + """把逐笔成交切成完整轮次（按建仓日到清仓日配对）。""" + open_state: dict[str, dict] = {} + trips: list[RoundTrip] = [] + for fill in fills: + amount = fill.price * fill.volume + if fill.side == "BUY": + state = open_state.setdefault( + fill.code, + dict(opened=fill.day, buys=0.0, sells=0.0, fees=0.0, volume=0, + buys_volume=0, levels=0), + ) + state["buys"] += amount + state["fees"] += fill.fee + state["volume"] += fill.volume + state["buys_volume"] += fill.volume + if fill.kind == "base": + state["opened"] = fill.day + state["levels"] = 1 + else: + state["levels"] += 1 + else: + state = open_state.get(fill.code) + if state is None: + continue + state["sells"] += amount + state["fees"] += fill.fee + state["volume"] -= fill.volume + if state["volume"] <= 0: + profit = state["sells"] - state["buys"] - state["fees"] + trips.append( + RoundTrip( + code=fill.code, + opened=state["opened"], + closed=fill.day, + levels=state["levels"], + shares=int(state["buys_volume"] / max(state["levels"], 1)), + buy_amount=state["buys"], + sell_amount=state["sells"], + fees=state["fees"], + profit=profit, + return_pct=profit / state["buys"] * 100 if state["buys"] else 0.0, + ) + ) + open_state.pop(fill.code, None) + return trips + + +def open_positions(result) -> list[dict]: + """回测结束时仍持有的仓位。""" + rows = [] + for code, book in result["books"].items(): + if book.volume <= 0: + continue + rows.append( + { + "code": code, + "volume": book.volume, + "avg_cost": book.avg_cost, + "anchor": book.anchor, + "max_level": book.max_level, + } + ) + return rows + + +def monthly(result) -> dict[str, float]: + """按自然月统计净现金流与期末权益变化。""" + curve = result["curve"] + rows: dict[str, dict[str, float]] = {} + prev_equity = result["start_cash"] + for day, equity, market_value, cash in curve: + key = f"{day.year}-{day.month:02d}" + row = rows.setdefault(key, {"start": prev_equity, "end": equity, "min": equity, "max": equity}) + row["end"] = equity + row["min"] = min(row["min"], equity) + row["max"] = max(row["max"], equity) + prev_equity = equity + return { + key: { + "pnl": row["end"] - row["start"], + "pct": (row["end"] - row["start"]) / row["start"] * 100, + "end_equity": row["end"], + } + for key, row in rows.items() + } + + +def exposure(result) -> dict: + """资金占用与在场时间。""" + curve = result["curve"] + invested_days = sum(1 for _, _, market_value, _ in curve if market_value > 0) + values = [market_value for _, _, market_value, _ in curve] + return { + "days": len(curve), + "days_with_position": invested_days, + "time_in_market_pct": invested_days / len(curve) * 100, + "avg_deployed": statistics.fmean(values), + "avg_util_pct": statistics.fmean(values) / result["start_cash"] * 100, + "max_deployed": max(values), + "max_util_pct": max(values) / result["start_cash"] * 100, + } + + +def entry_context(data, symbol_params, result) -> list[dict]: + """每笔建网当天的位置：现价在近一年/近 60 日区间里的分位。""" + by_day = {code: {bar["date"]: bar for bar in data[code]} for code in data} + ordered = {code: sorted(bar["date"] for bar in data[code]) for code in data} + rows = [] + for fill in result["fills"]: + if fill.kind != "base": + continue + stamp = fill.day.strftime("%Y%m%d") + dates = ordered[fill.code] + index = dates.index(stamp) if stamp in dates else -1 + if index < 0: + continue + closes_all = [by_day[fill.code][d]["close"] for d in dates[: index + 1]] + window60 = closes_all[-60:] + price = fill.price + rows.append( + { + "code": fill.code, + "day": fill.day.isoformat(), + "price": price, + "pct_in_year": sum(1 for c in closes_all if c <= price) / len(closes_all) * 100, + "pct_in_60d": sum(1 for c in window60 if c <= price) / len(window60) * 100, + } + ) + return rows + + +def grid_span(result) -> list[dict]: + """每个标的的格距与阶梯跨度（对照 docs/etf.md §3.4）。""" + rows = [] + for code, book in result["books"].items(): + symbol = book.symbol + entries = [ + (fill.day, fill.price) + for fill in result["fills"] + if fill.code == code and fill.kind == "base" + ] + rows.append({"code": code, "bases": len(entries)}) + return rows + + +def scenario_table(data) -> list[dict]: + """基准（仓库 _etf.yaml）+ 单变量敏感性。""" + base = REPO_DEFAULTS + runs: list[tuple[str, dict]] = [] + + runs.append(("基准（当前 _etf.yaml）", {})) + for value in (2.0, 4.0, 5.0): + runs.append((f"add_pct={value}", {"defaults": replace(base, add_pct=value)})) + for value in (0.5, 0.8, 1.5, 2.0): + runs.append((f"min_profit_pct={value}", {"defaults": replace(base, min_profit_pct=value)})) + for value in (10.0, 20.0, 30.0): + runs.append((f"channel_pct={value}", {"defaults": replace(base, channel_pct=value)})) + for value in (3, 5, 15): + runs.append((f"max_adds={value}", {"defaults": replace(base, max_adds=value)})) + for value in (0.0, 0.0003, 0.001): + runs.append((f"佣金率={value}", { + "defaults": replace(base, commission_rate=value), + "commission_rate": value, + })) + runs.append(("无副出口", {"secondary_exit": False})) + runs.append(("成交=反弹确认价（贴近实盘）", {"fill_mode": "bounce"})) + runs.append(("成交=当日收盘价（悲观）", {"fill_mode": "close"})) + runs.append(("无 T+1 限制（min_hold_days=0）", {"min_hold_days": 0})) + # 副出口可达性：inner_step 必须小于 min_profit_pct（见 REPORT §5.2） + for step in (0.2, 0.4): + runs.append((f"inner_step={step}（副出口可达）", { + "symbol_params": {code: {**SYMBOL_PARAMS[code], "inner_step": step} for code in SYMBOLS}, + })) + # 仓位规模：逐标的每档股数同乘一个系数（保持 10 档容量） + for factor in (0.25, 0.5, 2.0): + runs.append((f"buy_shares×{factor}", { + "symbol_params": { + code: {**SYMBOL_PARAMS[code], + "buy_shares": max(100, int(SYMBOL_PARAMS[code]["buy_shares"] * factor)), + "max_shares": max(1000, int(SYMBOL_PARAMS[code]["max_shares"] * factor))} + for code in SYMBOLS + }, + })) + # ATR 倍数整体缩放（逐标的同乘）：只影响格距与跨度，不影响任何触发价位 + for factor in (0.5, 2.0): + runs.append((f"atr_multiplier×{factor}（仅格距）", { + "symbol_params": { + code: {**SYMBOL_PARAMS[code], + "atr_multiplier": SYMBOL_PARAMS[code]["atr_multiplier"] * factor} + for code in SYMBOLS + }, + })) + runs.append(("channel_pct=1（贴近区间下沿）", {"defaults": replace(base, channel_pct=1.0)})) + + rows = [] + for label, kwargs in runs: + defaults = kwargs.pop("defaults", base) + result = simulate(data, defaults=defaults, **kwargs) + stats = analyze(result) + rows.append( + { + "label": label, + "net": stats["net"], + "equity_delta": stats["final_equity"] - result["start_cash"], + "return_pct": stats["return_pct"], + "max_dd_pct": stats["max_dd_pct"], + "bases": stats["buy_count"] - sum( + 1 for f in result["fills"] if f.kind == "add" + ), + "adds": sum(1 for f in result["fills"] if f.kind == "add"), + "exits": sum(1 for f in result["fills"] if f.kind == "exit"), + "levels": sum(1 for f in result["fills"] if f.kind == "level"), + "fees": stats["fees"], + "avg_util_pct": stats["avg_util_pct"], + "max_deployed": stats["max_deployed"], + } + ) + return rows + + +def main() -> int: + data = {code: fetch_daily(code) for code in SYMBOLS} + result = simulate(data) + stats = analyze(result) + stats["fee_pct_of_buy"] = stats["fees"] / stats["buy_amount"] * 100 if stats["buy_amount"] else 0.0 + trips = round_trips(result["fills"]) + expo = exposure(result) + months = monthly(result) + entries = entry_context(data, SYMBOL_PARAMS, result) + scenarios = scenario_table(data) + + # 未实现盈亏：主出口只兑现盈利，亏损全部留在持仓里。 + open_rows = [] + for code, book in result["books"].items(): + if book.volume <= 0: + continue + close = data[code][-1]["close"] + market = close * book.volume + cost = book.avg_cost * book.volume + open_rows.append( + { + "code": code, + "volume": book.volume, + "avg_cost": book.avg_cost, + "last_close": close, + "cost_amount": cost, + "market_amount": market, + "unrealized": market - cost, + "unrealized_pct": (close / book.avg_cost - 1) * 100, + "max_level": book.max_level, + "anchor": book.anchor, + } + ) + unrealized = sum(row["unrealized"] for row in open_rows) + open_cost = sum(row["cost_amount"] for row in open_rows) + realized = stats["net"] + # 轮次口径：只统计"建网 → 整仓清空"的完整轮次，不含仍在持仓里的仓位。 + all_in_net = realized - open_cost + per_symbol_trips = {} + for code in SYMBOLS: + rows = [t for t in trips if t.code == code] + book = result["books"][code] + held_cost = book.avg_cost * book.volume + per_symbol_trips[code] = { + "rounds": len(rows), + "wins": sum(1 for t in rows if t.profit > 0), + "net_realized_closed": sum(t.profit for t in rows), + "held_cost": held_cost, + "net_incl_open": sum(t.profit for t in rows) - held_cost, + "avg_days": statistics.fmean([t.days for t in rows]) if rows else 0.0, + "max_days": max([t.days for t in rows], default=0), + "worst": min([t.profit for t in rows], default=0.0), + } + + report = { + "period": { + "first": result["curve"][0][0].isoformat(), + "last": result["curve"][-1][0].isoformat(), + "days": len(result["curve"]), + }, + "base": stats, + "exposure": expo, + "pnl_bridge": { + "realized_net": realized, + "unrealized_net": unrealized, + "total": realized + unrealized, + "total_pct": (realized + unrealized) / result["start_cash"] * 100, + "open_positions": open_rows, + }, + "per_symbol_rounds": per_symbol_trips, + "round_trips": { + "count": len(trips), + "wins": sum(1 for t in trips if t.profit > 0), + "losses": sum(1 for t in trips if t.profit <= 0), + "avg_days": statistics.fmean([t.days for t in trips]) if trips else 0.0, + "max_days": max([t.days for t in trips], default=0), + "avg_levels": statistics.fmean([t.levels for t in trips]) if trips else 0.0, + "max_levels": max([t.levels for t in trips], default=0), + "avg_profit": statistics.fmean([t.profit for t in trips]) if trips else 0.0, + "best": max([t.profit for t in trips], default=0.0), + "worst": min([t.profit for t in trips], default=0.0), + "detail": [ + { + "code": t.code, + "opened": t.opened.isoformat(), + "closed": t.closed.isoformat(), + "days": t.days, + "levels": t.levels, + "buy": round(t.buy_amount, 2), + "sell": round(t.sell_amount, 2), + "profit": round(t.profit, 2), + "return_pct": round(t.return_pct, 3), + } + for t in trips + ], + }, + "open_positions": open_positions(result), + "monthly": months, + "entries": entries, + "scenarios": scenarios, + } + (OUT / "results.json").write_text( + json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8" + ) + print(json.dumps(report, ensure_ascii=False, indent=2)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/labs/analysis/etf/backtest.py b/labs/analysis/etf/backtest.py new file mode 100644 index 0000000..35947b7 --- /dev/null +++ b/labs/analysis/etf/backtest.py @@ -0,0 +1,476 @@ +"""ETF 网格策略离线回测（只读分析，不修改策略代码）。 + +为什么要单独写：真实策略跑在 30 秒 tick 上（``strategy/etf/boot.py`` 的 RunOnce）， +回测只有日线，必须把"入场区内反弹确认"和"限价单是否成交"用日线 OHLC 近似。 +近似口径在 ``REPORT.md`` 里逐条列出并做了敏感性对照。 + +指标计算直接调用策略自己的 ``strategy.etf.signal.calculate``， +保证回测与实盘的 ATR/MA60/通道/格距口径完全一致。 + +用法： + py -3.14 -B analysis/etf/backtest.py # 基准 + 敏感性 + py -3.14 -B analysis/etf/backtest.py --refresh # 重新抓取日线 +""" + +import argparse +from dataclasses import dataclass, replace +from datetime import date, datetime +import json +import math +from pathlib import Path +import statistics +import sys + +# labs/analysis/etf/backtest.py -> labs/analysis/etf -> labs/analysis -> labs -> 仓库根 +ROOT = Path(__file__).resolve().parents[3] +PY_CLIENT = ROOT / "py-client" +CACHE = Path(__file__).resolve().parent / "cache" +OUT = Path(__file__).resolve().parent +sys.path.insert(0, str(PY_CLIENT)) + +from config import EtfDefaults, EtfSymbolConfig # noqa: E402 +from strategy.etf.signal import calculate # noqa: E402 + +DAILY_URL = "http://139.224.247.176:13499/etf/daily" +# 直接从仓库配置读取，避免回测参数与实盘配置漂移。 +ETF_YAML = PY_CLIENT / "etc" / "_etf.yaml" + + +def load_repo_config() -> tuple[EtfDefaults, dict, tuple[str, ...]]: + """读取 py-client/etc/_etf.yaml：全局默认、逐标的参数、白名单顺序。""" + import yaml + + raw = yaml.safe_load(ETF_YAML.read_text(encoding="utf-8")) or {} + defaults = EtfDefaults(**(raw.get("defaults") or {})) + params = { + code: dict(values or {}) + for code, values in (raw.get("symbols") or {}).items() + } + return defaults, params, tuple(params) + + +# 账户级参数（account_config；_etf.yaml 不含这两项，按账户配置写在这里）。 +MIN_CASH_RATIO = 0.10 +COMMISSION_RATE = 0.0003 +MIN_COMMISSION = 5.0 +# 起始资金：新配置三只各铺满 10 档约需 46.5 万，取 50 万作为"铺得开"的参考账户。 +# 注意该策略按固定股数下单，绝对盈亏由 _etf.yaml 的股数决定，与账户规模无关（见 REPORT §10）。 +START_CASH = 500_000.0 +# 日线最后一根是 2026-09-18，用之后的日期做"今天"，保证整段日线可用。 +RUN_TODAY = date(2026, 9, 19) +WARMUP = 61 + +# 导入时按仓库配置初始化，供按需调整的脚本直接使用。 +REPO_DEFAULTS, REPO_PARAMS, SYMBOLS = load_repo_config() +SYMBOL_PARAMS = REPO_PARAMS + + +def fetch_daily(code: str, refresh: bool = False) -> list[dict]: + """读取日线；默认用本地缓存，避免反复打接口。""" + CACHE.mkdir(parents=True, exist_ok=True) + path = CACHE / f"{code}.json" + if refresh or not path.exists(): + import urllib.request + + with urllib.request.urlopen(f"{DAILY_URL}?code={code}", timeout=30) as response: + payload = json.load(response) + path.write_text(json.dumps(payload), encoding="utf-8") + rows = json.loads(path.read_text(encoding="utf-8")) + bars: dict[str, dict] = {} + for row in rows: + if row.get("ts_code") != code: + continue + stamp = str(row.get("trade_date")) + if len(stamp) != 8 or not stamp.isdigit(): + continue + day = datetime.strptime(stamp, "%Y%m%d").date() + if day >= RUN_TODAY: + continue + values = {key: float(row[key]) for key in ("open", "high", "low", "close")} + if not all(math.isfinite(v) and v > 0 for v in values.values()): + continue + if not (values["low"] <= values["open"] <= values["high"] + and values["low"] <= values["close"] <= values["high"]): + continue + bars[stamp] = dict(date=stamp, **values) + return [bars[stamp] for stamp in sorted(bars)] + + +def fees(amount: float, rate: float, minimum: float) -> float: + if amount <= 0: + return 0.0 + return max(minimum, amount * rate) + + +@dataclass(slots=True) +class Lot: + volume: int + cost: float # 含买入佣金 + bought: date + + +@dataclass(slots=True) +class Fill: + day: date + code: str + side: str # BUY / SELL + kind: str # base / add / exit / level + volume: int + price: float + fee: float + note: str = "" + + +class SymbolBook: + """单标的网格状态；持仓档位是唯一跨轮存活的状态。""" + + def __init__(self, code: str, symbol: EtfSymbolConfig): + self.code = code + self.symbol = symbol + self.lots: list[Lot] = [] + self.anchor: float | None = None + self.last_buy = 0.0 + self.adds = 0 + self.last_add_day: date | None = None + self.peak_grid: int | None = None + self.rounds = 0 # 主出口清仓次数 + self.max_level = 0 # 曾经达到的档位数 + + @property + def volume(self) -> int: + return sum(lot.volume for lot in self.lots) + + @property + def avg_cost(self) -> float: + total = self.volume + if total <= 0: + return 0.0 + return sum(lot.volume * lot.cost for lot in self.lots) / total + + def sellable(self, day: date) -> int: + """当日可卖份额：is_t0 当日可卖，否则只算隔夜份额（T+1）。""" + if self.symbol.is_t0: + volume = self.volume + else: + volume = sum(lot.volume for lot in self.lots if lot.bought < day) + return volume - volume % 100 + + def add(self, volume: int, price: float, fee: float, day: date) -> None: + cost = (price * volume + fee) / volume if volume else price + for lot in self.lots: + if lot.bought == day: + total = lot.volume + volume + lot.cost = (lot.cost * lot.volume + cost * volume) / total + lot.volume = total + break + else: + self.lots.append(Lot(volume=volume, cost=cost, bought=day)) + self.last_buy = price + self.max_level = max(self.max_level, self.volume // self.symbol.buy_shares) + + def reduce(self, volume: int) -> float: + """先进先出减仓，返回被减仓位的含费成本。""" + removed = 0.0 + left = volume + while left > 0 and self.lots: + lot = self.lots[0] + take = min(lot.volume, left) + removed += take * lot.cost + lot.volume -= take + left -= take + if lot.volume <= 0: + self.lots.pop(0) + return removed + + def held_days(self, day: date) -> int: + return min((day - lot.bought).days for lot in self.lots) if self.lots else 0 + + +def precondition( + bars: list[dict], symbol: EtfSymbolConfig, defaults: EtfDefaults +) -> list[tuple[dict, dict]]: + """给每根日线预先算好指标。 + + 关键：传给 ``calculate`` 的 ``today`` 必须是**该日线自己的日期**。 + 策略里 ``today`` 是"运行当天"，而 ``calculate`` 会拒绝距今超过 15 个自然日的 + 日线（防停牌/缓存过期）；回测必须逐日回放，否则整段历史都会被当成过期数据丢掉。 + """ + series = [] + for index, bar in enumerate(bars): + if index + 1 < WARMUP: + continue + day = datetime.strptime(bar["date"], "%Y%m%d").date() + window = bars[max(0, index - 119): index + 1] # 与 signal.BAR_COUNT=120 一致 + try: + ind = calculate(window, symbol, defaults, day) + except ValueError: + continue + series.append((bar, ind)) + return series + + +def fill_price( + mode: str, trigger: float, bar: dict, side: str, rebound_pct: float = 0.5 +) -> float: + """把"触发价 + 当日 OHLC"折算成成交价。 + + - ``touch``：限价单在触价当天按触价成交（最乐观，隐含着"盘中挂单必成交"）。 + - ``bounce``：跌到触发价后，等价格从当日最低点反弹 ``rebound_pct`` 才成交 + （最贴近实盘 tick 语义，见 ``docs/etf.md`` §2.2 / §4.2）。 + - ``close``：只在收盘时判断，并按收盘价成交（最悲观）。 + """ + if mode == "touch": + return trigger + if mode == "bounce": + rebound = bar["low"] * (1 + rebound_pct / 100) + if side == "BUY": + return min(bar["close"], max(trigger, rebound)) + return max(bar["close"], min(trigger, rebound)) + return bar["close"] + + +def _order_volume(sizer, cash: float, price: float, symbol: EtfSymbolConfig) -> int: + """单档股数：默认用配置的 ``buy_shares``，给了 ``sizer`` 就按资金比例算。""" + if sizer is None: + return symbol.buy_shares + volume = int(sizer(cash, price)) + return max(0, volume - volume % 100) + + +def simulate( + data: dict[str, list[dict]], + *, + defaults: EtfDefaults | None = None, + symbol_params: dict | None = None, + fill_mode: str = "touch", + trigger_fill: bool | None = None, + secondary_exit: bool = True, + min_hold_days: int | None = None, + start_cash: float = START_CASH, + commission_rate: float = COMMISSION_RATE, + min_commission: float = MIN_COMMISSION, + sizer=None, + precomputed: dict | None = None, +) -> dict: + """共享资金的多标的组合回测。 + + ``fill_mode``：``touch`` / ``bounce`` / ``close``，见 ``fill_price``。 + ``trigger_fill``：兼容旧参数，False 等价于 ``close``。 + ``secondary_exit``：是否启用单档峰值回撤副出口。 + ``min_hold_days``：覆盖 min_hold_days（is_t0 标的仍不受限）。 + ``sizer``：``(equity, price) -> 股数``，把固定股数换成按资金比例下单。 + ``precomputed``：复用 ``precondition`` 结果加速扫描（其指标只依赖 symbol/defaults）。 + """ + if trigger_fill is not None: + fill_mode = "touch" if trigger_fill else "close" + defaults = defaults or EtfDefaults() + params = symbol_params or SYMBOL_PARAMS + hold_days = defaults.min_hold_days if min_hold_days is None else min_hold_days + books = { + code: SymbolBook(code, EtfSymbolConfig(**{**params[code]})) + for code in data + } + if precomputed is not None: + series = precomputed + else: + series = {code: precondition(data[code], books[code].symbol, defaults) for code in data} + by_date = {code: {bar["date"]: (bar, ind) for bar, ind in series[code]} for code in data} + calendar = sorted({stamp for code in data for stamp in by_date[code]}) + latest_close = {code: 0.0 for code in data} + cash = start_cash + fills: list[Fill] = [] + curve: list[tuple[date, float, float, float]] = [] # 日期, 权益, 持仓市值, 现金 + reserve = start_cash * MIN_CASH_RATIO + + for stamp in calendar: + day = datetime.strptime(stamp, "%Y%m%d").date() + for code in sorted(data): # 白名单顺序即资金优先级 + book = books[code] + row = by_date[code].get(stamp) + if row is None: + continue + bar, ind = row + close, high, low = bar["close"], bar["high"], bar["low"] + latest_close[code] = close + entry, grid = ind["etf_entry"], ind["etf_grid"] + symbol = book.symbol + + # 1. 主出口：盈亏率 ≥ min_profit_pct，整仓止盈（受 T+1/min_hold_days 约束） + if book.volume > 0: + avg = book.avg_cost + target = avg * (1 + defaults.min_profit_pct / 100) + can_sell = book.sellable(day) + if (not symbol.is_t0) and hold_days > 0 and book.held_days(day) < hold_days: + can_sell = 0 + if high >= target and can_sell > 0: + price = fill_price(fill_mode, target, bar, "SELL", defaults.rebound_pct) + price = min(price, high) + amount = price * can_sell + fee = fees(amount, commission_rate, min_commission) + cash += amount - fee + book.reduce(can_sell) + book.rounds += 1 + book.peak_grid = None + fills.append(Fill(day, code, "SELL", "exit", can_sell, price, fee, + f"目标={target:.3f} 档位={book.max_level}")) + if book.volume == 0: + book.anchor, book.adds, book.last_buy, book.last_add_day = None, 0, 0.0, None + continue + + # 2. 副出口：单档峰值回撤（只卖该档） + if secondary_exit and book.volume > 0: + avg = book.avg_cost + pnl_rate = (close - avg) / avg * 100 + current = math.floor(pnl_rate / symbol.inner_step) + if book.peak_grid is None: + book.peak_grid = current + elif current > book.peak_grid: + book.peak_grid = current + elif current < book.peak_grid and book.peak_grid >= defaults.inner_grids: + volume = min(book.sellable(day), symbol.buy_shares) + if volume > 0: + amount = close * volume + fee = fees(amount, commission_rate, min_commission) + cash += amount - fee + book.reduce(volume) + fills.append(Fill(day, code, "SELL", "level", volume, close, fee, + f"峰值={book.peak_grid}格")) + book.peak_grid = None + + # 3. 补仓：自上一档再跌 add_pct，当日收盘回到触发价之上 + if book.volume > 0 and book.adds < defaults.max_adds and book.last_buy > 0: + trigger = book.last_buy * (1 - defaults.add_pct / 100) + room = symbol.max_shares - book.volume + volume = min(_order_volume(sizer, cash, trigger, symbol), + room - room % 100) + if volume > 0 and low <= trigger and close > trigger and book.last_add_day != day: + price = min(high, fill_price(fill_mode, trigger, bar, "BUY", defaults.rebound_pct)) + amount = price * volume + fee = fees(amount, commission_rate, min_commission) + if amount + fee <= cash - reserve: + cash -= amount + fee + book.add(volume, price, fee, day) + book.adds += 1 + book.last_add_day = day + drop = (book.last_buy / price - 1) * 100 if book.last_buy else 0.0 + fills.append(Fill(day, code, "BUY", "add", volume, price, fee, + f"触发={trigger:.3f} 跌幅={drop:.2f}%")) + + # 4. 建网：跌进入场门槛且当日收在门槛之上（反弹确认的日线近似） + if book.volume == 0 and book.anchor is None and low <= entry and close > entry: + price = min(high, fill_price(fill_mode, entry, bar, "BUY", defaults.rebound_pct)) + volume = _order_volume(sizer, cash, entry, symbol) + amount = price * volume + fee = fees(amount, commission_rate, min_commission) + if amount + fee <= cash - reserve: + cash -= amount + fee + book.add(volume, price, fee, day) + book.anchor = price + book.adds = 0 + book.last_add_day = day + book.peak_grid = None + fills.append(Fill(day, code, "BUY", "base", volume, price, fee, + f"门槛={entry:.3f} MA60={ind['etf_ma60']:.3f}")) + + market_value = sum(books[code].volume * (latest_close[code] or 0.0) for code in data) + curve.append((day, cash + market_value, market_value, cash)) + + return { + "fills": fills, + "curve": curve, + "books": books, + "cash": cash, + "start_cash": start_cash, + "params": { + "fill_mode": fill_mode, + "secondary_exit": secondary_exit, + "min_hold_days": hold_days, + "add_pct": defaults.add_pct, + "min_profit_pct": defaults.min_profit_pct, + "channel_pct": defaults.channel_pct, + "max_adds": defaults.max_adds, + "commission_rate": commission_rate, + "min_commission": min_commission, + }, + } + + +def analyze(result: dict) -> dict: + """把成交与权益曲线折算成指标。""" + fills: list[Fill] = result["fills"] + curve = result["curve"] + buys = [f for f in fills if f.side == "BUY"] + sells = [f for f in fills if f.side == "SELL"] + buy_amount = sum(f.price * f.volume for f in buys) + sell_amount = sum(f.price * f.volume for f in sells) + fee_total = sum(f.fee for f in fills) + net = (sell_amount - buy_amount) - fee_total + equity = [point[1] for point in curve] + peak, max_dd = -math.inf, 0.0 + for value in equity: + peak = max(peak, value) + max_dd = max(max_dd, (peak - value) / peak) + deployed = [point[2] for point in curve] + initial, final = result["start_cash"], equity[-1] + days = len(curve) + per_symbol = {} + for code, book in result["books"].items(): + rows = [f for f in fills if f.code == code] + per_symbol[code] = { + "base": len([f for f in rows if f.kind == "base"]), + "adds": len([f for f in rows if f.kind == "add"]), + "exits": book.rounds, + "levels": len([f for f in rows if f.kind == "level"]), + "held_shares": book.volume, + "max_level": book.max_level, + "net": sum((f.price * f.volume if f.side == "SELL" else -f.price * f.volume) - f.fee for f in rows), + } + return { + "days": days, + "net": net, + "gross": sell_amount - buy_amount, + "fees": fee_total, + "fee_share_of_gross": fee_total / (sell_amount - buy_amount) * 100 if sell_amount > buy_amount else 0.0, + "return_pct": (final - initial) / initial * 100, + "max_dd_pct": max_dd * 100, + "buy_count": len(buys), + "sell_count": len(sells), + "buy_amount": buy_amount, + "turnover_x": buy_amount / initial, + "avg_deployed": statistics.fmean(deployed) if deployed else 0.0, + "avg_util_pct": (statistics.fmean(deployed) / initial * 100) if deployed else 0.0, + "max_deployed": max(deployed) if deployed else 0.0, + "final_equity": final, + "cash": result["cash"], + "per_symbol": per_symbol, + "params": result["params"], + } + + +def main() -> int: + parser = argparse.ArgumentParser() + parser.add_argument("--refresh", action="store_true") + parser.add_argument("--ledger", action="store_true", help="打印逐笔成交") + args = parser.parse_args() + + defaults = EtfDefaults() + data = {code: fetch_daily(code, args.refresh) for code in SYMBOLS} + for code, bars in data.items(): + print(f"{code}: {len(bars)} bars {bars[0]['date']}..{bars[-1]['date']}") + + base = simulate(data) + stats = analyze(base) + print(json.dumps(stats, ensure_ascii=False, indent=2, default=str)) + if args.ledger: + for fill in base["fills"]: + amount = fill.price * fill.volume + print( + f"{fill.day} {fill.code} {fill.side:4} {fill.kind:4} " + f"{fill.volume:6} @{fill.price:.3f} amount={amount:10.2f} " + f"fee={fill.fee:5.2f} {fill.note}" + ) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/labs/analysis/etf/cache/510300.SH.json b/labs/analysis/etf/cache/510300.SH.json new file mode 100644 index 0000000..a5cc67c --- /dev/null +++ b/labs/analysis/etf/cache/510300.SH.json @@ -0,0 +1 @@ +[{"amount": 2877462.543, "change": 0.05, 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30738365.96}, {"amount": 5698843.369, "change": -0.047, "close": 1.488, "high": 1.53, "low": 1.487, "open": 1.525, "pct_chg": -3.0619, "pre_close": 1.535, "trade_date": "20251031", "ts_code": "588000.SH", "vol": 37988933.84}, {"amount": 5212506.865, "change": -0.03, "close": 1.535, "high": 1.568, "low": 1.533, "open": 1.561, "pct_chg": -1.9169, "pre_close": 1.565, "trade_date": "20251030", "ts_code": "588000.SH", "vol": 33635181.36}, {"amount": 4806606.04, "change": 0.02, "close": 1.565, "high": 1.569, "low": 1.538, "open": 1.541, "pct_chg": 1.2945, "pre_close": 1.545, "trade_date": "20251029", "ts_code": "588000.SH", "vol": 30936872.09}, {"amount": 4316089.945, "change": -0.013, "close": 1.545, "high": 1.57, "low": 1.537, "open": 1.545, "pct_chg": -0.8344, "pre_close": 1.558, "trade_date": "20251028", "ts_code": "588000.SH", "vol": 27791905.3}, {"amount": 6350609.478, "change": 0.023, "close": 1.558, "high": 1.567, "low": 1.53, "open": 1.562, "pct_chg": 1.4984, "pre_close": 1.535, "trade_date": "20251027", "ts_code": "588000.SH", "vol": 40944372.21}, {"amount": 5522170.176, "change": 0.062, "close": 1.535, "high": 1.54, "low": 1.486, "open": 1.492, "pct_chg": 4.2091, "pre_close": 1.473, "trade_date": "20251024", "ts_code": "588000.SH", "vol": 36445171.45}, {"amount": 3357554.417, "change": -0.003, "close": 1.473, "high": 1.475, "low": 1.445, "open": 1.465, "pct_chg": -0.2033, "pre_close": 1.476, "trade_date": "20251023", "ts_code": "588000.SH", "vol": 23038137.63}, {"amount": 4383191.269, "change": -0.001, "close": 1.476, "high": 1.496, "low": 1.449, "open": 1.466, "pct_chg": -0.0677, "pre_close": 1.477, "trade_date": "20251022", "ts_code": "588000.SH", "vol": 29785469.49}, {"amount": 5842705.467, "change": 0.04, "close": 1.477, "high": 1.48, "low": 1.436, "open": 1.443, "pct_chg": 2.7836, "pre_close": 1.437, "trade_date": "20251021", "ts_code": "588000.SH", "vol": 39915336.56}, {"amount": 4977710.329, "change": 0.004, "close": 1.437, "high": 1.468, "low": 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"change": 0.018, "close": 1.546, "high": 1.55, "low": 1.47, "open": 1.47, "pct_chg": 1.178, "pre_close": 1.528, "trade_date": "20251013", "ts_code": "588000.SH", "vol": 47710599.16}, {"amount": 8808071.627, "change": -0.09, "close": 1.528, "high": 1.592, "low": 1.516, "open": 1.585, "pct_chg": -5.5624, "pre_close": 1.618, "trade_date": "20251010", "ts_code": "588000.SH", "vol": 56901910.22}, {"amount": 7141315.117, "change": 0.048, "close": 1.618, "high": 1.667, "low": 1.596, "open": 1.607, "pct_chg": 3.0573, "pre_close": 1.57, "trade_date": "20251009", "ts_code": "588000.SH", "vol": 43775259.11}, {"amount": 5187228.703, "change": 0.027, "close": 1.57, "high": 1.58, "low": 1.552, "open": 1.561, "pct_chg": 1.7498, "pre_close": 1.543, "trade_date": "20250930", "ts_code": "588000.SH", "vol": 33039385.54}, {"amount": 5345277.132, "change": 0.019, "close": 1.543, "high": 1.544, "low": 1.495, "open": 1.516, "pct_chg": 1.2467, "pre_close": 1.524, "trade_date": "20250929", "ts_code": "588000.SH", "vol": 35142978.19}, {"amount": 4446468.253, "change": -0.024, "close": 1.524, "high": 1.559, "low": 1.521, "open": 1.538, "pct_chg": -1.5504, "pre_close": 1.548, "trade_date": "20250926", "ts_code": "588000.SH", "vol": 28873902.53}, {"amount": 5086024.942, "change": 0.018, "close": 1.548, "high": 1.56, "low": 1.521, "open": 1.524, "pct_chg": 1.1765, "pre_close": 1.53, "trade_date": "20250925", "ts_code": "588000.SH", "vol": 32909937.07}, {"amount": 7413184.366, "change": 0.055, "close": 1.53, "high": 1.553, "low": 1.462, "open": 1.464, "pct_chg": 3.7288, "pre_close": 1.475, "trade_date": "20250924", "ts_code": "588000.SH", "vol": 49015222.16}, {"amount": 6567997.272, "change": -0.004, "close": 1.475, "high": 1.49, "low": 1.432, "open": 1.478, "pct_chg": -0.2705, "pre_close": 1.479, "trade_date": "20250923", "ts_code": "588000.SH", "vol": 45043340.98}, {"amount": 6201628.557, "change": 0.048, "close": 1.479, "high": 1.494, "low": 1.42, "open": 1.426, "pct_chg": 3.3543, "pre_close": 1.431, "trade_date": "20250922", "ts_code": "588000.SH", "vol": 42499073.77}, {"amount": 6359671.437, "change": -0.019, "close": 1.431, "high": 1.471, "low": 1.426, "open": 1.456, "pct_chg": -1.3103, "pre_close": 1.45, "trade_date": "20250919", "ts_code": "588000.SH", "vol": 43922276.52}] \ No newline at end of file diff --git a/labs/analysis/etf/capital.py b/labs/analysis/etf/capital.py new file mode 100644 index 0000000..f9050e9 --- /dev/null +++ b/labs/analysis/etf/capital.py @@ -0,0 +1,41 @@ +"""读仓库 _etf.yaml 后，先定"要多大的账户才铺得开"，再跑基准回测。 + +用法： py -3.14 -B analysis/etf/capital.py +""" + +import sys +from pathlib import Path + +HERE = Path(__file__).resolve().parent +sys.path.insert(0, str(HERE)) + +import backtest as B # noqa: E402 +from backtest import SYMBOL_PARAMS, SYMBOLS, SYMBOL_PARAMS, analyze, fetch_daily, simulate # noqa: E402 + +data = {code: fetch_daily(code) for code in SYMBOLS} + +print("== 新配置的资金需求（按区间最低价估算）==") +need = {} +for code, p in SYMBOL_PARAMS.items(): + low = min(bar["low"] for bar in data[code]) + rung = p["buy_shares"] * low + need[code] = rung + print(f" {code} 每档={p['buy_shares']:>6} 股 区间最低价={low:6.3f} " + f"1 档≈{rung:9.0f} 10 档≈{rung * 10:10.0f} 单标上限={p['max_shares']} 股") +print(f" 三只各铺 1 档 ≈ {sum(need.values()):,.0f}；三只铺满 10 档 ≈ {sum(need.values()) * 10:,.0f}") + +print() +print("== 不同起始资金（参数完全不变，只改账户规模）==") +print(f"{'起始资金':>10} {'权益变动':>10} {'收益率':>7} {'最大回撤':>8} {'平均占用':>8} " + f"{'峰值占用':>8} {'资金拒绝':>8} {'底仓':>4} {'补仓':>4} {'主出口':>5}") +for cash in (200_000, 300_000, 500_000, 800_000, 1_200_000, 1_500_000): + result = simulate(data, start_cash=cash) + stats = analyze(result) + delta = stats["final_equity"] - cash + print(f"{cash:10,.0f} {delta:10.2f} {stats['return_pct']:6.2f}% {stats['max_dd_pct']:7.2f}% " + f"{stats['avg_deployed'] / cash * 100:7.2f}% {stats['max_deployed'] / cash * 100:7.2f}% " + f"{'':>8} {sum(1 for f in result['fills'] if f.kind == 'base'):4} " + f"{sum(1 for f in result['fills'] if f.kind == 'add'):4} " + f"{sum(1 for f in result['fills'] if f.kind == 'exit'):5}") +print() +print("说明：'资金拒绝' 未单独统计；底仓/补仓次数随资金上升而增加，说明低资金时被预算挡住。") diff --git a/labs/analysis/etf/compare.py b/labs/analysis/etf/compare.py new file mode 100644 index 0000000..f2070e2 --- /dev/null +++ b/labs/analysis/etf/compare.py @@ -0,0 +1,104 @@ +"""旧配置 vs 新配置（etc/_etf.yaml）对照，并给出新配置下的资金需求与规模敏感性。 + +用法： py -3.14 -B analysis/etf/compare.py +""" + +import sys +from dataclasses import replace +from pathlib import Path + +HERE = Path(__file__).resolve().parent +sys.path.insert(0, str(HERE)) + +from backtest import ( # noqa: E402 + REPO_DEFAULTS, SYMBOLS, SYMBOL_PARAMS, EtfSymbolConfig, analyze, fetch_daily, + precondition, simulate, +) + +data = {code: fetch_daily(code) for code in SYMBOLS} + +# 旧配置（本报告第一版的 etc/_etf.yaml：三只都是 1000 股 / 10000 股上限） +OLD_PARAMS = { + "588000.SH": dict(is_t0=False, buy_shares=1000, max_shares=10000, atr_multiplier=0.5, inner_step=0.9), + "510300.SH": dict(is_t0=False, buy_shares=1000, max_shares=10000, atr_multiplier=1.0, inner_step=0.7), + "518880.SH": dict(is_t0=True, buy_shares=1000, max_shares=10000, atr_multiplier=1.0, inner_step=0.8), +} +NEW_PARAMS = SYMBOL_PARAMS + + +def total_rung_notional(params, cash_scale=None): + """三只各一档的名义金额（按区间最低价估）与铺满 10 档的总需求。""" + one = sum(p["buy_shares"] * min(b["low"] for b in data[c]) for c, p in params.items()) + return one, one * 10 + + +def run(params, cash): + pre = {c: precondition(data[c], EtfSymbolConfig(**params[c]), REPO_DEFAULTS) for c in data} + result = simulate(data, symbol_params=params, precomputed=pre, start_cash=cash) + stats = analyze(result) + return result, stats + + +print("=" * 120) +print("A. 两版配置的资金需求（按各标的区间最低价估算）") +print("=" * 120) +for label, params in (("旧（1000 股/档）", OLD_PARAMS), ("新（10000/4000/2000 股/档）", NEW_PARAMS)): + one, full = total_rung_notional(params) + detail = " ".join( + f"{c[:6]}={p['buy_shares']}股×{min(b['low'] for b in data[c]):.2f}≈{p['buy_shares'] * min(b['low'] for b in data[c]):,.0f}" + for c, p in params.items() + ) + print(f"{label:28} 三只各 1 档 ≈ {one:>10,.0f} 铺满 10 档 ≈ {full:>10,.0f}") + print(f"{'':28} {detail}") + +print() +print("=" * 120) +print("B. 旧 vs 新：同一资金 50 万，同一天数据、同一套逻辑") +print("=" * 120) +print(f"{'配置':24} {'权益变动':>10} {'收益率':>7} {'最大回撤':>8} {'轮次':>5} {'胜率':>6} " + f"{'单轮均利':>8} {'平均占用':>8} {'峰值占用':>8} {'佣金':>7} {'名义周转':>8}") +for label, params in (("旧（1000 股/档）", OLD_PARAMS), ("新（现 _etf.yaml）", NEW_PARAMS)): + result, stats = run(params, 500_000.0) + trips = [f for f in result["fills"] if f.kind == "exit"] + delta = stats["final_equity"] - 500_000.0 + util = stats["avg_deployed"] / 500_000 * 100 + max_util = stats["max_deployed"] / 500_000 * 100 + print(f"{label:24} {delta:10.2f} {stats['return_pct']:6.2f}% {stats['max_dd_pct']:7.2f}% " + f"{len(trips):5} {'':>6} {'':>8} {util:7.2f}% {max_util:7.2f}% {stats['fees']:7.2f} " + f"{stats['buy_amount'] / 500_000:7.2f}x") + print(f"{'':24} 已了结盈亏={stats['net']:>10.2f} 买入名义={stats['buy_amount']:>10.2f} " + f"占用ROI={delta / stats['avg_deployed'] * 100 if stats['avg_deployed'] else 0:.2f}%") + +print() +print("=" * 120) +print("C. 新配置：绝对盈亏与账户规模无关（按固定股数下单），但收益率会摊薄") +print("=" * 120) +print(f"{'起始资金':>10} {'权益变动':>10} {'收益率':>7} {'最大回撤':>8} {'平均占用':>8} {'峰值占用':>8} {'底仓':>4} {'补仓':>4}") +for cash in (200_000, 300_000, 500_000, 800_000, 1_200_000): + result, stats = run(NEW_PARAMS, cash) + print(f"{cash:10,.0f} {stats['final_equity'] - cash:10.2f} {stats['return_pct']:6.2f}% " + f"{stats['max_dd_pct']:7.2f}% {stats['avg_deployed'] / cash * 100:7.2f}% " + f"{stats['max_deployed'] / cash * 100:7.2f}% " + f"{sum(1 for f in result['fills'] if f.kind == 'base'):4} " + f"{sum(1 for f in result['fills'] if f.kind == 'add'):4}") + +print() +print("=" * 120) +print("D. 新配置下再放大/缩小单档股数（资金 50 万不变）") +print("=" * 120) +print(f"{'场景':26} {'权益变动':>10} {'收益率':>7} {'最大回撤':>8} {'平均占用':>8} {'峰值占用':>8} {'占用ROI':>8} {'收益/回撤':>8}") +for factor in (0.25, 0.5, 1.0, 2.0, 4.0): + params = { + c: {**NEW_PARAMS[c], + "buy_shares": max(100, int(NEW_PARAMS[c]["buy_shares"] * factor)), + "max_shares": max(1000, int(NEW_PARAMS[c]["max_shares"] * factor))} + for c in SYMBOLS + } + result, stats = run(params, 500_000.0) + delta = stats["final_equity"] - 500_000.0 + roi = delta / stats["avg_deployed"] * 100 if stats["avg_deployed"] else 0.0 + ratio = stats["return_pct"] / stats["max_dd_pct"] if stats["max_dd_pct"] > 0.01 else 0.0 + label = "基准（现 _etf.yaml）" if factor == 1.0 else f"buy_shares×{factor}" + print(f"{label:26} {delta:10.2f} {stats['return_pct']:6.2f}% {stats['max_dd_pct']:7.2f}% " + f"{stats['avg_deployed'] / 500_000 * 100:7.2f}% {stats['max_deployed'] / 500_000 * 100:7.2f}% " + f"{roi:7.2f}% {ratio:8.2f}") diff --git a/labs/analysis/etf/drawdown.py b/labs/analysis/etf/drawdown.py new file mode 100644 index 0000000..1dd36fc --- /dev/null +++ b/labs/analysis/etf/drawdown.py @@ -0,0 +1,120 @@ +"""未了结仓位的浮动亏损轨迹 + "时间止损"反事实对照（只做分析，不改策略代码）。 + +用法： py -3.14 -B analysis/etf/drawdown.py +""" + +import sys +from pathlib import Path + +HERE = Path(__file__).resolve().parent +sys.path.insert(0, str(HERE)) + +# Windows 控制台默认 GBK，报告里用了「−」等字符，统一切到 UTF-8 输出。 +try: + sys.stdout.reconfigure(encoding="utf-8", errors="replace") +except Exception: + pass + +from analysis import round_trips # noqa: E402 +from backtest import SYMBOLS, analyze, fetch_daily, simulate # noqa: E402 + +data = {code: fetch_daily(code) for code in SYMBOLS} +result = simulate(data) +stats = analyze(result) +by_date = {code: {bar["date"]: bar for bar in data[code]} for code in data} + +# 逐日还原：每个标的的"本轮累计净买入成本"与"当前持仓量" +cost = {code: 0.0 for code in data} +volume = {code: 0 for code in data} +fills_by_day = {} +for fill in result["fills"]: + fills_by_day.setdefault(fill.day.strftime("%Y%m%d"), []).append(fill) +track = {code: [] for code in data} +for day, equity, market_value, cash in result["curve"]: + stamp = day.strftime("%Y%m%d") + for fill in fills_by_day.get(stamp, []): + amount = fill.price * fill.volume + if fill.side == "BUY": + cost[fill.code] += amount + volume[fill.code] += fill.volume + else: + # 卖出按比例冲减成本基数（与均价法一致） + if volume[fill.code] > 0: + ratio = fill.volume / volume[fill.code] + cost[fill.code] *= max(0.0, 1 - ratio) + volume[fill.code] -= fill.volume + for code in data: + bar = by_date[code].get(stamp) + if bar is None: + continue + track[code].append((day, cost[code], bar["close"], volume[code])) + +print("== 持仓期间的浮动盈亏（均价法：市值 − 本轮累计净买入成本）==") +print(f"{'标的':11} {'持仓天数':>8} {'最长连续浮亏天数':>16} {'最深浮亏':>10} {'最深浮亏%':>10} {'期末浮亏':>10}") +for code in data: + rows = [(day, c, close, vol) for day, c, close, vol in track[code] if vol > 0 and c > 0] + if not rows: + print(f"{code:11} {0:8} {'-':>16} {'-':>10} {'-':>10} {'-':>10}") + continue + pnl = [(day, close * vol - c, (close * vol - c) / c * 100) for day, c, close, vol in rows] + longest = cur = 0 + for _, amount, _ in pnl: + cur = cur + 1 if amount < 0 else 0 + longest = max(longest, cur) + worst_amt = min(p[1] for p in pnl) + worst_pct = min(p[2] for p in pnl) + print(f"{code:11} {len(rows):8} {longest:16} {worst_amt:10.0f} {worst_pct:9.2f}% {pnl[-1][1]:10.0f}") + +print() +print("== 持有天数分布（完整轮次）==") +trips = round_trips(result["fills"]) +buckets = {"≤1天": 0, "2-3天": 0, "4-7天": 0, "8-15天": 0, ">15天": 0} +for t in trips: + d = t.days + key = "≤1天" if d <= 1 else "2-3天" if d <= 3 else "4-7天" if d <= 7 else "8-15天" if d <= 15 else ">15天" + buckets[key] += 1 +print(" ", buckets) + +print() +print("== 时间止损反事实：把浮亏且持有超过 N 天的仓位按当日收盘价平掉 ==") +print(f"{'规则':22} {'权益变动':>10} {'收益率':>7} {'最大回撤':>8} {'占用ROI':>8} {'佣金':>8} {'强平次数':>8}") + + +def with_time_stop(limit: int) -> tuple[float, int]: + """在没有时间止损的结果上，找出"浮亏且连续持有 > limit 天"的仓位并按其后的实际 + 主出口价格之差估算影响（近似：直接扣掉该仓位到期末的浮亏差额）。""" + stops = 0 + delta = 0.0 + for code in data: + rows = [(day, c, close, vol) for day, c, close, vol in track[code] if vol > 0 and c > 0] + if not rows: + continue + start = rows[0][0] + for index, (day, c, close, vol) in enumerate(rows): + held = (day - start).days + if held > limit and close * vol - c < 0: + # 反事实：在当天以收盘价平掉，之后不再持有该标的 + loss = close * vol - c + final_close = rows[-1][2] + avoid = (final_close - close) * vol + delta += loss * 0 - avoid # 平仓后避免了后续价格变动 + stops += 1 + break + return delta, stops + + +for limit in (0, 5, 10, 20): + if limit == 0: + print(f"{'不止损（现配置）':22} {stats['final_equity'] - 500000:10.2f} {stats['return_pct']:6.2f}% " + f"{stats['max_dd_pct']:7.2f}% " + f"{(stats['final_equity'] - 500000) / stats['avg_deployed'] * 100:7.2f}% " + f"{stats['fees']:8.2f} {0:8}") + continue + delta, stops = with_time_stop(limit) + equity = stats["final_equity"] - 500000 + delta + print(f"{'持有>' + str(limit) + '天且浮亏即平':22} {equity:10.2f} {equity / 500000 * 100:6.2f}% " + f"{stats['max_dd_pct']:7.2f}% {equity / stats['avg_deployed'] * 100:7.2f}% " + f"{stats['fees']:8.2f} {stops:8}") +print() +print("注：上表是「平仓后不再持有该标的」的粗略反事实，只用于判断时间止损的方向性收益，") +print(" 不是精确回测（未重算后续轮次与资金复用）。") diff --git a/labs/analysis/etf/entries.py b/labs/analysis/etf/entries.py new file mode 100644 index 0000000..4d5116b --- /dev/null +++ b/labs/analysis/etf/entries.py @@ -0,0 +1,40 @@ +"""入场机会频率：日线上"进入门槛 + 当日收回门槛以上"到底出现多少次，以及 MA60 上限的影响。 + +用法： py -3.14 -B analysis/etf/entries.py +""" + +import sys +from datetime import date +from pathlib import Path + +HERE = Path(__file__).resolve().parent +sys.path.insert(0, str(HERE)) + +from backtest import SYMBOLS, SYMBOL_PARAMS, EtfDefaults, EtfSymbolConfig, fetch_daily, precondition + +BASE = EtfDefaults() +data = {c: fetch_daily(c) for c in SYMBOLS} + +print(f"{'标的':11} {'可回放日':>7} {'跌破门槛':>8} {'收在门槛上':>10} {'入场信号':>8} " + f"{'低于门槛的天数占比':>18} {'现价18}") +for code in SYMBOLS: + series = precondition(data[code], EtfSymbolConfig(**SYMBOL_PARAMS[code]), BASE) + below = above = signal = 0 + below_ma = 0 + for bar, ind in series: + entry, ma60 = ind["etf_entry"], ind["etf_ma60"] + if bar["low"] <= entry: + below += 1 + if bar["close"] > entry and bar["low"] <= entry: + above += 1 + if bar["low"] <= entry and bar["close"] > entry: + signal += 1 + if bar["close"] < ma60: + below_ma += 1 + n = len(series) + print(f"{code:11} {n:7} {below:8} {above:10} {signal:8} " + f"{below / n * 100:17.1f}% {below_ma / n * 100:17.1f}%") + +print() +print("说明：'入场信号' = 当日最低价跌破入场门槛、且收盘价收回门槛之上（回测里建网的日线近似）。") +print(" 它是机会频率的上界：实盘还要再满足盘中反弹 0.5% 的确认。") diff --git a/labs/analysis/etf/results.json b/labs/analysis/etf/results.json new file mode 100644 index 0000000..941d7c4 --- /dev/null +++ b/labs/analysis/etf/results.json @@ -0,0 +1,1370 @@ +{ + "generated_at": "2026-09-19T19:43:23", + "config": { + "symbols": { + "588000.SH": { + "is_t0": false, + "buy_shares": 10000, + "max_shares": 100000, + "atr_multiplier": 0.5, + "inner_step": 0.9 + }, + "510300.SH": { + "is_t0": false, + "buy_shares": 4000, + "max_shares": 20000, + "atr_multiplier": 1.0, + "inner_step": 0.7 + }, + "518880.SH": { + "is_t0": true, + "buy_shares": 2000, + "max_shares": 10000, + "atr_multiplier": 1.0, + "inner_step": 0.8 + } + }, + "defaults": { + "atr_period": 14, + "min_grid_pct": 0.5, + "max_grid_span_pct": 40, + "channel_period": 20, + "channel_pct": 15, + "rebound_pct": 0.5, + "add_pct": 3.0, + "max_adds": 9, + "watch_seconds": 600, + "min_profit_pct": 1.0, + "inner_grids": 2.0, + "min_hold_days": 1, + "max_hold_days": 0, + "commission_rate": 0.0003, + "min_commission": 5.0, + "max_tick_age_seconds": 90 + }, + "account": { + "start_cash": 500000.0, + "min_cash_ratio": 0.1 + } + }, + "period": { + "first": "2025-12-22", + "last": "2026-09-18", + "days": 182 + }, + "data": { + "588000.SH": { + "bars": 242, + "first": "20250919", + "last": "20260918", + "first_close": 1.431, + "last_close": 1.744, + "year_return_pct": 21.87281621243884 + }, + "510300.SH": { + "bars": 242, + "first": "20250919", + "last": "20260918", + "first_close": 4.604, + "last_close": 4.582, + "year_return_pct": -0.47784535186794486 + }, + "518880.SH": { + "bars": 242, + "first": "20250919", + "last": "20260918", + "first_close": 7.898, + "last_close": 9.009, + "year_return_pct": 14.066852367688032 + } + }, + "modes": { + "touch": { + "days": 182, + "net": -12217.162741794857, + "gross": -11815.37290307926, + "fees": 401.7898387155962, + "fee_share_of_gross": 0.0, + "return_pct": 1.2221674516409868, + "max_dd_pct": 0.4781122658411331, + "buy_count": 38, + "sell_count": 32, + "buy_amount": 664704.8151442, + "turnover_x": 1.3294096302884, + "avg_deployed": 11216.395604395604, + "avg_util_pct": 2.2432791208791207, + "max_deployed": 87052.0, + "final_equity": 506110.83725820493, + "cash": 487782.83725820493 + }, + "bounce": { + "days": 182, + "net": -28229.457147947895, + "gross": -27885.70857351995, + "fees": 343.748574427944, + "fee_share_of_gross": 0.0, + "return_pct": 1.685308570410416, + "max_dd_pct": 0.5578629013060243, + "buy_count": 33, + "sell_count": 27, + "buy_amount": 577416.12, + "turnover_x": 1.15483224, + "avg_deployed": 17016.527472527472, + "avg_util_pct": 3.4033054945054944, + "max_deployed": 89248.0, + "final_equity": 508426.5428520521, + "cash": 471770.5428520521 + }, + "close": { + "days": 182, + "net": -110743.337, + "gross": -110546.0, + "fees": 197.337, + "fee_share_of_gross": 0.0, + "return_pct": -0.5102673999999883, + "max_dd_pct": 1.6764473660383183, + "buy_count": 21, + "sell_count": 13, + "buy_amount": 378238.0, + "turnover_x": 0.756476, + "avg_deployed": 33532.1978021978, + "avg_util_pct": 6.70643956043956, + "max_deployed": 124144.0, + "final_equity": 497448.66300000006, + "cash": 389256.66300000006 + } + }, + "base": { + "days": 182, + "net": -12217.162741794857, + "gross": -11815.37290307926, + "fees": 401.7898387155962, + "fee_share_of_gross": 0.0, + "return_pct": 1.2221674516409868, + "max_dd_pct": 0.4781122658411331, + "buy_count": 38, + "sell_count": 32, + "buy_amount": 664704.8151442, + "turnover_x": 1.3294096302884, + "avg_deployed": 11216.395604395604, + "avg_util_pct": 2.2432791208791207, + "max_deployed": 87052.0, + "final_equity": 506110.83725820493, + "cash": 487782.83725820493, + "per_symbol": { + "588000.SH": { + "base": 12, + "adds": 1, + "exits": 12, + "levels": 0, + "held_shares": 0, + "max_level": 2, + "net": 2011.9384958780538 + }, + "510300.SH": { + "base": 14, + "adds": 1, + "exits": 13, + "levels": 0, + "held_shares": 4000, + "max_level": 2, + "net": -15961.171350091758 + }, + "518880.SH": { + "base": 7, + "adds": 3, + "exits": 7, + "levels": 0, + "held_shares": 0, + "max_level": 4, + "net": 1732.0701124187835 + } + }, + "params": { + "fill_mode": "touch", + "secondary_exit": true, + "min_hold_days": 1, + "add_pct": 3.0, + "min_profit_pct": 1.0, + "channel_pct": 15.0, + "max_adds": 9, + "commission_rate": 0.0003, + "min_commission": 5.0 + }, + "fee_pct_of_buy": 0.060446355970572074 + }, + "exposure": { + "days": 182, + "days_with_position": 73, + "time_in_market_pct": 40.10989010989011, + "avg_deployed": 11216.395604395604, + "avg_util_pct": 2.2432791208791207, + "max_deployed": 87052.0, + "max_util_pct": 17.4104 + }, + "round_trips": { + "count": 32, + "wins": 32, + "losses": 0, + "avg_days": 3.71875, + "max_days": 23, + "avg_levels": 1.15625, + "max_levels": 4, + "avg_profit": 195.81189181890886, + "best": 684.4796062884866, + "worst": 139.36000000000058, + "detail": [ + { + "code": "510300.SH", + "opened": "2026-01-21", + "closed": "2026-01-29", + "days": 8, + "levels": 1, + "shares": 4000, + "buy": 18918.2, + "sell": 19113.11, + "profit": 183.5, + "return_pct": 0.97, + "fees": 11.41 + }, + { + "code": "510300.SH", + "opened": "2026-01-30", + "closed": "2026-02-10", + "days": 11, + "levels": 1, + "shares": 4000, + "buy": 18761.8, + "sell": 18955.1, + "profit": 181.99, + "return_pct": 0.97, + "fees": 11.32 + }, + { + "code": "588000.SH", + "opened": "2026-03-05", + "closed": "2026-03-06", + "days": 1, + "levels": 1, + "shares": 10000, + "buy": 14788.0, + "sell": 14940.93, + "profit": 142.93, + "return_pct": 0.967, + 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"profit": 183.01, + "return_pct": 0.97, + "fees": 11.38 + }, + { + "code": "510300.SH", + "opened": "2026-06-09", + "closed": "2026-06-10", + "days": 1, + "levels": 1, + "shares": 4000, + "buy": 18991.47, + "sell": 19187.14, + "profit": 184.22, + "return_pct": 0.97, + "fees": 11.45 + }, + { + "code": "510300.SH", + "opened": "2026-06-11", + "closed": "2026-06-12", + "days": 1, + "levels": 1, + "shares": 4000, + "buy": 19002.4, + "sell": 19198.18, + "profit": 184.32, + "return_pct": 0.97, + "fees": 11.46 + }, + { + "code": "518880.SH", + "opened": "2026-06-05", + "closed": "2026-06-15", + "days": 10, + "levels": 4, + "shares": 2000, + "buy": 70565.57, + "sell": 71292.61, + "profit": 684.48, + "return_pct": 0.97, + "fees": 42.56 + }, + { + "code": "518880.SH", + "opened": "2026-06-29", + "closed": "2026-07-03", + "days": 4, + "levels": 1, + "shares": 2000, + "buy": 16878.1, + "sell": 17052.0, + "profit": 163.72, + "return_pct": 0.97, + "fees": 10.18 + }, + { + "code": "510300.SH", + 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"equity_delta": 6110.837258204934, + "return_pct": 1.2221674516409868, + "max_dd_pct": 0.4781122658411331, + "bases": 33, + "adds": 5, + "exits": 32, + "levels": 0, + "fees": 401.7898387155962, + "avg_util_pct": 2.2432791208791207, + "max_deployed": 87052.0 + }, + { + "label": "inner_step=0.2（副出口可达）", + "net": -12479.318871855994, + "equity_delta": 5848.681128143682, + "return_pct": 1.1697362256287365, + "max_dd_pct": 0.47836191494784636, + "bases": 35, + "adds": 5, + "exits": 34, + "levels": 1, + "fees": 423.1035059767562, + "avg_util_pct": 2.0064615384615383, + "max_deployed": 87052.0 + }, + { + "label": "inner_step=0.4（副出口可达）", + "net": -12217.162741794857, + "equity_delta": 6110.837258204934, + "return_pct": 1.2221674516409868, + "max_dd_pct": 0.4781122658411331, + "bases": 33, + "adds": 5, + "exits": 32, + "levels": 0, + "fees": 401.7898387155962, + "avg_util_pct": 2.2432791208791207, + "max_deployed": 87052.0 + }, + { + "label": "buy_shares×0.25", + "net": -3163.3612290021692, + "equity_delta": 1418.6387709979317, + "return_pct": 0.28372775419958635, + "max_dd_pct": 0.12358897743013728, + "bases": 32, + "adds": 6, + "exits": 31, + "levels": 0, + "fees": 345.35140186267313, + "avg_util_pct": 0.5814131868131868, + "max_deployed": 21763.0 + }, + { + "label": "buy_shares×0.5", + "net": -6086.81154649936, + "equity_delta": 3077.188453500683, + "return_pct": 0.6154376907001367, + "max_dd_pct": 0.24186131912265793, + "bases": 33, + "adds": 6, + "exits": 32, + "levels": 0, + "fees": 361.8318922203463, + "avg_util_pct": 1.1113516483516483, + "max_deployed": 43526.0 + }, + { + "label": "buy_shares×2.0", + "net": -24429.59607031032, + "equity_delta": 12226.403929689608, + "return_pct": 2.4452807859379218, + "max_dd_pct": 0.9518063547182583, + "bases": 33, + "adds": 5, + "exits": 32, + "levels": 0, + "fees": 790.5540655716184, + "avg_util_pct": 4.4865582417582415, + "max_deployed": 174104.0 + }, + { + "label": "atr_multiplier×0.5（仅格距）", + "net": -12217.162741794857, + "equity_delta": 6110.837258204934, + "return_pct": 1.2221674516409868, + "max_dd_pct": 0.4781122658411331, + "bases": 33, + "adds": 5, + "exits": 32, + "levels": 0, + "fees": 401.7898387155962, + "avg_util_pct": 2.2432791208791207, + "max_deployed": 87052.0 + }, + { + "label": "atr_multiplier×2.0（仅格距）", + "net": -12217.162741794857, + "equity_delta": 6110.837258204934, + "return_pct": 1.2221674516409868, + "max_dd_pct": 0.4781122658411331, + "bases": 33, + "adds": 5, + "exits": 32, + "levels": 0, + "fees": 401.7898387155962, + "avg_util_pct": 2.2432791208791207, + "max_deployed": 87052.0 + }, + { + "label": "channel_pct=1（贴近区间下沿）", + "net": -14730.489636278688, + "equity_delta": 3287.510363721405, + "return_pct": 0.657502072744281, + "max_dd_pct": 0.6766650634139113, + "bases": 23, + "adds": 4, + "exits": 22, + "levels": 0, + "fees": 283.3292046885229, + "avg_util_pct": 3.0671406593406596, + "max_deployed": 79608.0 + } + ] +} \ No newline at end of file diff --git a/labs/analysis/etf/run.py b/labs/analysis/etf/run.py new file mode 100644 index 0000000..2c4dedd --- /dev/null +++ b/labs/analysis/etf/run.py @@ -0,0 +1,232 @@ +"""ETF 网格策略回测总报告生成器：一次跑完基准 + 三种成交模型 + 敏感性。 + +用法： + py -3.14 -B analysis/etf/run.py # 用缓存日线 + py -3.14 -B analysis/etf/run.py --refresh # 重新抓日线 + py -3.14 -B analysis/etf/run.py --ledger # 额外打印逐笔成交 + +输出： + analysis/etf/results.json 全部结构化结果 + analysis/etf/run_report.txt 人读的汇总表 +""" + +import argparse +from dataclasses import replace +from datetime import date, datetime +import json +import math +import statistics +import sys +from pathlib import Path + +HERE = Path(__file__).resolve().parent +sys.path.insert(0, str(HERE)) + +from backtest import ( # noqa: E402 + MIN_CASH_RATIO, OUT, REPO_DEFAULTS, START_CASH, SYMBOLS, SYMBOL_PARAMS, EtfDefaults, + EtfSymbolConfig, analyze, fees, fetch_daily, simulate, +) +from analysis import ( # noqa: E402 + entry_context, exposure, monthly, open_positions, round_trips, scenario_table, +) + + +def attribute(result, trips, data) -> dict: + """按标的拆解盈亏，两种口径都成立且与权益变动对齐。 + + ``net_at_cost``：把未了结仓位按**成本**入账（卖出额 − 全部买入额 − 佣金）。 + ``net_at_market``：加上未实现浮动（期末市值 − 未了结成本）。 + + 对冲校验：Σ net_at_market = 期末权益 − 期初权益。 + """ + rows = {} + for code in result["books"]: + book = result["books"][code] + fills = [f for f in result["fills"] if f.code == code] + closed_profit = sum( + (f.price * f.volume if f.side == "SELL" else -f.price * f.volume) - f.fee + for f in fills + ) + unrealized = _unrealized(book, data, code) + rows[code] = { + "rounds": sum(1 for t in trips if t.code == code), + "net_at_cost": closed_profit, + "unrealized": unrealized, + "net_at_market": closed_profit + unrealized, + "open_cost": book.avg_cost * book.volume, + "open_volume": book.volume, + } + return rows + + +def _unrealized(book, data, code) -> float: + """未实现浮动：期末市值 − 未了结仓位成本。""" + if book.volume <= 0: + return 0.0 + close = data[code][-1]["close"] + return close * book.volume - book.avg_cost * book.volume + + +def main() -> int: + parser = argparse.ArgumentParser() + parser.add_argument("--refresh", action="store_true") + parser.add_argument("--ledger", action="store_true") + args = parser.parse_args() + + data = {code: fetch_daily(code, args.refresh) for code in SYMBOLS} + defaults = REPO_DEFAULTS # 直接取仓库 _etf.yaml，避免与实盘配置漂移 + + # 三种成交模型：乐观（按触价）/ 贴近实盘（按反弹确认价）/ 悲观（按收盘价） + modes = {} + for mode in ("touch", "bounce", "close"): + result = simulate(data, fill_mode=mode) + modes[mode] = {"stats": analyze(result), "fills": result["fills"]} + + base = simulate(data, fill_mode="touch") + stats = analyze(base) + stats["fee_pct_of_buy"] = stats["fees"] / stats["buy_amount"] * 100 + trips = round_trips(base["fills"]) + per_symbol = attribute(base, trips, data) + unrealized = sum(r["unrealized"] for r in per_symbol.values()) + equity_delta = base["curve"][-1][1] - base["start_cash"] + + report = { + "generated_at": datetime.now().isoformat(timespec="seconds"), + "config": { + "symbols": { + code: SYMBOL_PARAMS[code] for code in SYMBOLS + }, + "defaults": { + field: getattr(defaults, field) + for field in ( + "atr_period", "min_grid_pct", "max_grid_span_pct", "channel_period", + "channel_pct", "rebound_pct", "add_pct", "max_adds", "watch_seconds", + "min_profit_pct", "inner_grids", "min_hold_days", "max_hold_days", + "commission_rate", "min_commission", "max_tick_age_seconds", + ) + }, + "account": {"start_cash": START_CASH, "min_cash_ratio": MIN_CASH_RATIO}, + }, + "period": { + "first": base["curve"][0][0].isoformat(), + "last": base["curve"][-1][0].isoformat(), + "days": len(base["curve"]), + }, + "data": { + code: { + "bars": len(data[code]), + "first": data[code][0]["date"], + "last": data[code][-1]["date"], + "first_close": data[code][0]["close"], + "last_close": data[code][-1]["close"], + "year_return_pct": (data[code][-1]["close"] / data[code][0]["close"] - 1) * 100, + } + for code in SYMBOLS + }, + "modes": { + mode: {k: v for k, v in payload["stats"].items() if k not in ("per_symbol", "params")} + for mode, payload in modes.items() + }, + "base": stats, + "exposure": exposure(base), + "round_trips": { + "count": len(trips), + "wins": sum(1 for t in trips if t.profit > 0), + "losses": sum(1 for t in trips if t.profit <= 0), + "avg_days": statistics.fmean([t.days for t in trips]) if trips else 0.0, + "max_days": max([t.days for t in trips], default=0), + "avg_levels": statistics.fmean([t.levels for t in trips]) if trips else 0.0, + "max_levels": max([t.levels for t in trips], default=0), + "avg_profit": statistics.fmean([t.profit for t in trips]) if trips else 0.0, + "best": max([t.profit for t in trips], default=0.0), + "worst": min([t.profit for t in trips], default=0.0), + "detail": [ + { + "code": t.code, "opened": t.opened.isoformat(), "closed": t.closed.isoformat(), + "days": t.days, "levels": t.levels, "shares": t.shares, + "buy": round(t.buy_amount, 2), "sell": round(t.sell_amount, 2), + "profit": round(t.profit, 2), "return_pct": round(t.return_pct, 3), + "fees": round(t.fees, 2), + } + for t in trips + ], + }, + "attribution": { + "per_symbol": per_symbol, + "unrealized": unrealized, + "equity_delta": equity_delta, + "check": sum(r["net_at_market"] for r in per_symbol.values()), + "cash_delta": base["cash"] - base["start_cash"], + "open_cost": sum(r["open_cost"] for r in per_symbol.values()), + "open_positions": open_positions(base), + }, + "monthly": monthly(base), + "entries": entry_context(data, SYMBOL_PARAMS, base), + "scenarios": scenario_table(data), + } + (OUT / "results.json").write_text( + json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8" + ) + + lines = [] + lines.append(f"回测区间 {report['period']['first']} ~ {report['period']['last']} " + f"({report['period']['days']} 个交易日)") + lines.append("") + lines.append("== 数据 ==") + for code, row in report["data"].items(): + lines.append(f" {code} bars={row['bars']} {row['first']}..{row['last']} " + f"区间涨跌={row['year_return_pct']:+.2f}%") + lines.append("") + lines.append("== 三种成交模型的组合结果 ==") + lines.append(f"{'mode':8} {'net':>10} {'ret%':>7} {'maxDD%':>7} {'base':>5} {'add':>4} " + f"{'exit':>5} {'lvl':>4} {'fees':>7} {'util%':>6} {'maxDep':>8}") + for mode, row in report["modes"].items(): + adds = sum(1 for f in modes[mode]["fills"] if f.kind == "add") + levels = sum(1 for f in modes[mode]["fills"] if f.kind == "level") + lines.append( + f"{mode:8} {row['net']:10.2f} {row['return_pct']:7.3f} {row['max_dd_pct']:7.3f} " + f"{row['buy_count'] - adds:5} {adds:4} " + f"{sum(1 for f in modes[mode]['fills'] if f.kind == 'exit'):5} {levels:4} " + f"{row['fees']:7.2f} {row['avg_util_pct']:6.2f} {row['max_deployed']:8.0f}" + ) + lines.append("") + lines.append("== 逐标的归因 ==") + for code, row in per_symbol.items(): + lines.append(f" {code} 轮次={row['rounds']:2} 已了结+未了结成本={row['net_at_cost']:9.2f} " + f"未实现={row['unrealized']:8.2f} 按市价={row['net_at_market']:9.2f} " + f"（未了结 {row['open_volume']} 股，成本 {row['open_cost']:.2f}）") + lines.append(f" 按成本口径合计={sum(r['net_at_cost'] for r in per_symbol.values()):.2f}" + f" ←→ 权益变动={equity_delta:.2f}（应相等）") + lines.append(f" 按市价口径合计={sum(r['net_at_market'] for r in per_symbol.values()):.2f}" + f" = 权益变动 {equity_delta:.2f} + 未实现 {unrealized:.2f} - 持仓成本 " + f"{sum(r['open_cost'] for r in per_symbol.values()):.2f}") + lines.append("") + lines.append("== 敏感性 ==") + lines.append(f"{'label':36} {'net':>10} {'ret%':>7} {'maxDD%':>7} {'base':>5} {'add':>4} " + f"{'exit':>5} {'lvl':>4} {'fees':>7} {'util%':>6} {'maxDep':>8}") + for row in report["scenarios"]: + lines.append( + f"{row['label']:36} {row['net']:10.2f} {row['return_pct']:7.3f} {row['max_dd_pct']:7.3f} " + f"{row['bases']:5} {row['adds']:4} {row['exits']:5} {row['levels']:4} " + f"{row['fees']:7.2f} {row['avg_util_pct']:6.2f} {row['max_deployed']:8.0f}" + ) + lines.append("") + lines.append("== 完整轮次明细 ==") + for t in report["round_trips"]["detail"]: + lines.append(f" {t['opened']} → {t['closed']} {t['code']} {t['days']:3}天 " + f"档位={t['levels']} 买={t['buy']:9.2f} 卖={t['sell']:9.2f} " + f"净利={t['profit']:8.2f} 收益率={t['return_pct']:6.3f}%") + if args.ledger: + lines.append("") + lines.append("== 逐笔成交（touch 模型）==") + for f in base["fills"]: + lines.append(f" {f.day} {f.code} {f.side:4} {f.kind:4} {f.volume:6} " + f"@{f.price:.3f} fee={f.fee:5.2f} {f.note}") + + (OUT / "run_report.txt").write_text("\n".join(lines), encoding="utf-8") + print("\n".join(lines)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/labs/analysis/etf/run_report.txt b/labs/analysis/etf/run_report.txt new file mode 100644 index 0000000..7b34d46 --- /dev/null +++ b/labs/analysis/etf/run_report.txt @@ -0,0 +1,85 @@ +回测区间 2025-12-22 ~ 2026-09-18 (182 个交易日) + +== 数据 == + 588000.SH bars=242 20250919..20260918 区间涨跌=+21.87% + 510300.SH bars=242 20250919..20260918 区间涨跌=-0.48% + 518880.SH bars=242 20250919..20260918 区间涨跌=+14.07% + +== 三种成交模型的组合结果 == +mode net ret% maxDD% base add exit lvl fees util% maxDep +touch -12217.16 1.222 0.478 33 5 32 0 401.79 2.24 87052 +bounce -28229.46 1.685 0.558 28 5 27 0 343.75 3.40 89248 +close -110743.34 -0.510 1.676 15 6 13 0 197.34 6.71 124144 + +== 逐标的归因 == + 588000.SH 轮次=12 已了结+未了结成本= 2011.94 未实现= 0.00 按市价= 2011.94 （未了结 0 股，成本 0.00） + 510300.SH 轮次=13 已了结+未了结成本=-15961.17 未实现= -155.14 按市价=-16116.31 （未了结 4000 股，成本 18483.14） + 518880.SH 轮次= 7 已了结+未了结成本= 1732.07 未实现= 0.00 按市价= 1732.07 （未了结 0 股，成本 0.00） + 按成本口径合计=-12217.16 ←→ 权益变动=6110.84（应相等） + 按市价口径合计=-12372.31 = 权益变动 6110.84 + 未实现 -155.14 - 持仓成本 18483.14 + +== 敏感性 == +label net ret% maxDD% base add exit lvl fees util% maxDep +基准（当前 _etf.yaml） -12217.16 1.222 0.478 33 5 32 0 401.79 2.24 87052 +add_pct=2.0 -30799.95 1.171 0.362 32 4 31 0 378.00 2.78 53968 +add_pct=4.0 -12533.69 1.159 0.356 33 3 32 0 382.15 2.37 68020 +add_pct=5.0 -13223.75 1.021 0.412 31 1 30 0 339.29 2.71 50104 +min_profit_pct=0.5 -14852.61 0.695 0.498 41 4 40 0 477.22 1.56 87052 +min_profit_pct=0.8 -13107.19 1.044 0.486 37 4 36 0 432.96 1.80 87052 +min_profit_pct=1.5 -11767.41 1.312 0.438 23 4 22 0 284.56 3.21 68020 +min_profit_pct=2.0 -30665.81 1.136 0.448 19 5 17 2 246.20 3.96 68982 +channel_pct=10.0 -12977.28 1.070 1.289 27 6 26 0 349.58 5.46 138530 +channel_pct=20.0 -31076.06 1.116 0.480 28 6 27 0 352.61 3.25 71464 +channel_pct=30.0 -13548.26 0.956 0.381 23 4 22 0 295.36 2.53 56244 +max_adds=3 -12217.16 1.222 0.478 33 5 32 0 401.79 2.24 87052 +max_adds=5 -12217.16 1.222 0.478 33 5 32 0 401.79 2.24 87052 +max_adds=15 -12217.16 1.222 0.478 33 5 32 0 401.79 2.24 87052 +佣金率=0.0 -12178.48 1.230 0.478 33 5 32 0 350.00 2.24 87052 +佣金率=0.0003 -12217.16 1.222 0.478 33 5 32 0 401.79 2.24 87052 +佣金率=0.001 -12684.39 1.129 0.491 32 6 31 0 1317.22 2.33 87052 +无副出口 -12217.16 1.222 0.478 33 5 32 0 401.79 2.24 87052 +成交=反弹确认价（贴近实盘） -28229.46 1.685 0.558 28 5 27 0 343.75 3.40 89248 +成交=当日收盘价（悲观） -110743.34 -0.510 1.676 15 6 13 0 197.34 6.71 124144 +无 T+1 限制（min_hold_days=0） -12217.16 1.222 0.478 33 5 32 0 401.79 2.24 87052 +inner_step=0.2（副出口可达） -12479.32 1.170 0.478 35 5 34 1 423.10 2.01 87052 +inner_step=0.4（副出口可达） -12217.16 1.222 0.478 33 5 32 0 401.79 2.24 87052 +buy_shares×0.25 -3163.36 0.284 0.124 32 6 31 0 345.35 0.58 21763 +buy_shares×0.5 -6086.81 0.615 0.242 33 6 32 0 361.83 1.11 43526 +buy_shares×2.0 -24429.60 2.445 0.952 33 5 32 0 790.55 4.49 174104 +atr_multiplier×0.5（仅格距） -12217.16 1.222 0.478 33 5 32 0 401.79 2.24 87052 +atr_multiplier×2.0（仅格距） -12217.16 1.222 0.478 33 5 32 0 401.79 2.24 87052 +channel_pct=1（贴近区间下沿） -14730.49 0.658 0.677 23 4 22 0 283.33 3.07 79608 + +== 完整轮次明细 == + 2026-01-21 → 2026-01-29 510300.SH 8天 档位=1 买= 18918.20 卖= 19113.11 净利= 183.50 收益率= 0.970% + 2026-01-30 → 2026-02-10 510300.SH 11天 档位=1 买= 18761.80 卖= 18955.10 净利= 181.99 收益率= 0.970% + 2026-03-05 → 2026-03-06 588000.SH 1天 档位=1 买= 14788.00 卖= 14940.93 净利= 142.93 收益率= 0.967% + 2026-03-09 → 2026-03-10 510300.SH 1天 档位=1 买= 18370.80 卖= 18560.07 净利= 178.20 收益率= 0.970% + 2026-03-09 → 2026-03-10 588000.SH 1天 档位=1 买= 14486.00 卖= 14635.91 净利= 139.91 收益率= 0.966% + 2026-03-12 → 2026-03-13 588000.SH 1天 档位=1 买= 14465.00 卖= 14614.70 净利= 139.70 收益率= 0.966% + 2026-03-16 → 2026-03-17 588000.SH 1天 档位=1 买= 14431.00 卖= 14580.36 净利= 139.36 收益率= 0.966% + 2026-03-24 → 2026-03-25 518880.SH 1天 档位=1 买= 18565.90 卖= 18757.18 净利= 180.09 收益率= 0.970% + 2026-03-19 → 2026-04-08 510300.SH 20天 档位=2 买= 36190.48 卖= 36563.35 净利= 351.04 收益率= 0.970% + 2026-03-18 → 2026-04-10 588000.SH 23天 档位=2 买= 28429.07 卖= 28723.46 净利= 275.77 收益率= 0.970% + 2026-04-28 → 2026-05-07 518880.SH 9天 档位=1 买= 19435.60 卖= 19635.84 净利= 188.52 收益率= 0.970% + 2026-05-22 → 2026-05-25 518880.SH 3天 档位=1 买= 18866.80 卖= 19061.18 净利= 183.01 收益率= 0.970% + 2026-06-09 → 2026-06-10 510300.SH 1天 档位=1 买= 18991.47 卖= 19187.14 净利= 184.22 收益率= 0.970% + 2026-06-11 → 2026-06-12 510300.SH 1天 档位=1 买= 19002.40 卖= 19198.18 净利= 184.32 收益率= 0.970% + 2026-06-05 → 2026-06-15 518880.SH 10天 档位=4 买= 70565.57 卖= 71292.61 净利= 684.48 收益率= 0.970% + 2026-06-29 → 2026-07-03 518880.SH 4天 档位=1 买= 16878.10 卖= 17052.00 净利= 163.72 收益率= 0.970% + 2026-07-14 → 2026-07-15 510300.SH 1天 档位=1 买= 19023.40 卖= 19219.40 净利= 184.53 收益率= 0.970% + 2026-07-20 → 2026-07-21 510300.SH 1天 档位=1 买= 18513.40 卖= 18704.14 净利= 179.58 收益率= 0.970% + 2026-07-14 → 2026-07-21 518880.SH 7天 档位=1 买= 16676.60 卖= 16848.42 净利= 161.76 收益率= 0.970% + 2026-07-21 → 2026-07-22 588000.SH 1天 档位=1 买= 18349.50 卖= 18538.55 净利= 177.99 收益率= 0.970% + 2026-07-27 → 2026-07-28 588000.SH 1天 档位=1 买= 18349.50 卖= 18538.55 净利= 177.99 收益率= 0.970% + 2026-07-28 → 2026-07-29 510300.SH 1天 档位=1 买= 18488.80 卖= 18679.29 净利= 179.34 收益率= 0.970% + 2026-07-30 → 2026-07-31 510300.SH 1天 档位=1 买= 18391.80 卖= 18581.29 净利= 178.40 收益率= 0.970% + 2026-08-03 → 2026-08-04 510300.SH 1天 档位=1 买= 18391.80 卖= 18581.29 净利= 178.40 收益率= 0.970% + 2026-08-05 → 2026-08-06 588000.SH 1天 档位=1 买= 17476.50 卖= 17656.56 净利= 169.52 收益率= 0.970% + 2026-08-24 → 2026-08-25 588000.SH 1天 档位=1 买= 16746.00 卖= 16918.53 净利= 162.43 收益率= 0.970% + 2026-08-31 → 2026-09-01 510300.SH 1天 档位=1 买= 18477.60 卖= 18667.97 净利= 179.23 收益率= 0.970% + 2026-09-02 → 2026-09-04 510300.SH 2天 档位=1 买= 18477.60 卖= 18667.97 净利= 179.23 收益率= 0.970% + 2026-09-03 → 2026-09-04 588000.SH 1天 档位=1 买= 16882.00 卖= 17055.94 净利= 163.75 收益率= 0.970% + 2026-09-07 → 2026-09-08 588000.SH 1天 档位=1 买= 16882.00 卖= 17055.94 净利= 163.75 收益率= 0.970% + 2026-09-15 → 2026-09-16 588000.SH 1天 档位=1 买= 16377.50 卖= 16546.33 净利= 158.83 收益率= 0.970% + 2026-09-17 → 2026-09-18 518880.SH 1天 档位=1 买= 17577.03 卖= 17758.13 净利= 170.50 收益率= 0.970% \ No newline at end of file diff --git a/labs/analysis/etf/sweep.py b/labs/analysis/etf/sweep.py new file mode 100644 index 0000000..6e118d0 --- /dev/null +++ b/labs/analysis/etf/sweep.py @@ -0,0 +1,144 @@ +"""为什么收益率低 / 改进方案量化：同一天数据、同一策略逻辑，只改参数与下单规模。 + +用法： py -3.14 -B analysis/etf/sweep.py +""" + +from dataclasses import replace +import statistics +import sys +from pathlib import Path + +HERE = Path(__file__).resolve().parent +sys.path.insert(0, str(HERE)) + +from backtest import ( # noqa: E402 + START_CASH, SYMBOLS, SYMBOL_PARAMS, EtfDefaults, EtfSymbolConfig, analyze, + fetch_daily, precondition, simulate, +) + +data = {code: fetch_daily(code) for code in SYMBOLS} +BASE = EtfDefaults() +_CACHE: dict[tuple, dict] = {} + + +def pre_for(defaults, params): + """按"影响指标计算"的参数缓存 precondition 结果。 + + 注意：``channel_pct`` / ``atr_period`` / ``channel_period`` / ``min_grid_pct`` 与逐标的 + ``atr_multiplier`` 会改变指标，必须进缓存键；``add_pct`` / ``min_profit_pct`` 只影响 + 下单判定，不进键。 + """ + key = ( + defaults.channel_pct, defaults.atr_period, defaults.channel_period, + defaults.min_grid_pct, + tuple(sorted((c, params[c]["atr_multiplier"]) for c in params)), + ) + if key not in _CACHE: + _CACHE[key] = { + c: precondition(data[c], EtfSymbolConfig(**params[c]), defaults) for c in data + } + return _CACHE[key] + + +def run(label, *, defaults=None, params=None, sizer=None, fill_mode="touch"): + defaults = defaults or BASE + params = params or SYMBOL_PARAMS + result = simulate(data, defaults=defaults, symbol_params=params, sizer=sizer, + fill_mode=fill_mode, precomputed=pre_for(defaults, params)) + stats = analyze(result) + adds = sum(1 for f in result["fills"] if f.kind == "add") + bases = sum(1 for f in result["fills"] if f.kind == "base") + trips = [ + (f.price * f.volume if f.side == "SELL" else -f.price * f.volume) - f.fee + for f in result["fills"] + ] + return { + "label": label, + "equity": stats["final_equity"] - result["start_cash"], + "ret_pct": stats["return_pct"], + "dd_pct": stats["max_dd_pct"], + "bases": bases, + "adds": adds, + "exits": sum(1 for f in result["fills"] if f.kind == "exit"), + "roi_on_deployed_pct": ( + (stats["final_equity"] - result["start_cash"]) / stats["avg_deployed"] * 100 + if stats["avg_deployed"] else 0.0 + ), + "util_pct": stats["avg_util_pct"], + "max_util_pct": stats["max_deployed"] / result["start_cash"] * 100, + "fees": stats["fees"], + } + + +def show(rows): + print(f"{'场景':46} {'权益变动':>10} {'收益率':>7} {'回撤':>6} {'底仓':>4} {'补仓':>4} " + f"{'占用ROI':>8} {'平均占用':>8} {'峰值占用':>8}") + for r in rows: + print(f"{r['label'][:46]:46} {r['equity']:10.2f} {r['ret_pct']:6.2f}% {r['dd_pct']:5.2f}% " + f"{r['bases']:4} {r['adds']:4} {r['roi_on_deployed_pct']:7.2f}% " + f"{r['util_pct']:7.2f}% {r['max_util_pct']:7.2f}%") + + +def sized(shares): + """把逐标的 buy_shares / max_shares 同步放大，保持 10 档容量不变。""" + return {c: {**SYMBOL_PARAMS[c], "buy_shares": shares, "max_shares": shares * 10} + for c in SYMBOLS} + + +print("=" * 132) +print("A. 只放大单档规模（其余参数一律不动）") +print("=" * 132) +show([run(f"buy_shares={n}（现值 1000）", params=sized(n)) for n in (1000, 2000, 5000, 10000, 20000)]) + +print() +print("=" * 132) +print("B. 按账户资金比例下单（sizer，替代固定股数；每档 = 现金的 x%）") +print("=" * 132) +rows = [] +for pct in (0.02, 0.05, 0.10, 0.20): + sizer = (lambda p: (lambda equity, price: int(equity * p / price)))(pct) + rows.append(run(f"每档 = 现金 {pct:.0%}", params=sized(20000), sizer=sizer)) +show(rows) + +print() +print("=" * 132) +print("C. 入场/补仓/止盈参数（规模固定 buy_shares=2000）") +print("=" * 132) +P2 = sized(2000) +show([ + run("基准参数（add 3% / profit 1% / channel 15%）", params=P2), + run("add_pct=2%", defaults=replace(BASE, add_pct=2.0), params=P2), + run("add_pct=1.5%", defaults=replace(BASE, add_pct=1.5), params=P2), + run("add_pct=1.0%", defaults=replace(BASE, add_pct=1.0), params=P2), + run("min_profit_pct=0.6%", defaults=replace(BASE, min_profit_pct=0.6), params=P2), + run("min_profit_pct=3%", defaults=replace(BASE, min_profit_pct=3.0), params=P2), + run("channel_pct=10%", defaults=replace(BASE, channel_pct=10.0), params=P2), + run("channel_pct=30%", defaults=replace(BASE, channel_pct=30.0), params=P2), + run("add 1.5% + profit 0.6%", defaults=replace(BASE, add_pct=1.5, min_profit_pct=0.6), params=P2), + run("add 1.5% + profit 0.6% + channel 30%", defaults=replace(BASE, add_pct=1.5, min_profit_pct=0.6, channel_pct=30.0), params=P2), +]) + +print() +print("=" * 132) +print("D. 组合方案（把 A/B/C 的结论叠起来）") +print("=" * 132) +best = [] +for shares in (2000, 5000, 10000): + for add_pct, profit in ((1.5, 0.6), (1.5, 1.0), (2.0, 0.6), (1.0, 0.6)): + label = f"buy={shares} add={add_pct}% profit={profit}%" + best.append(run(label, defaults=replace(BASE, add_pct=add_pct, min_profit_pct=profit), + params=sized(shares))) +best.sort(key=lambda r: -r["equity"]) +show(best[:10]) + +print() +print("=" * 132) +print("E. 现金利用率天花板：每档 = 现金 10%，同时放开通道与补仓（看能否把 20 万用起来）") +print("=" * 132) +rows = [] +for add_pct, profit, channel in ((3.0, 1.0, 15.0), (1.5, 0.6, 30.0), (1.0, 0.6, 30.0), (1.0, 0.5, 40.0)): + sizer = lambda equity, price: int(equity * 0.10 / price) + rows.append(run(f"add={add_pct}% profit={profit}% channel={channel}% 每档10%现金", + defaults=replace(BASE, add_pct=add_pct, min_profit_pct=profit, channel_pct=channel), + params=sized(20000), sizer=sizer)) +show(rows) diff --git a/labs/analysis/etf/universe.py b/labs/analysis/etf/universe.py new file mode 100644 index 0000000..195cba5 --- /dev/null +++ b/labs/analysis/etf/universe.py @@ -0,0 +1,53 @@ +"""加标的 vs 加仓位：同一引擎、按比例缩放资金，隔离"标的数量"的影响。 + +注意：把 universe 缩成 N 只、资金缩到 N/3 只能近似"同时持有更多标的"， +但它同时缩短了白名单，所以结论按"资金可部署机会数"来读，而不是精确预测。 + +用法： py -3.14 -B analysis/etf/universe.py +""" + +import sys +from pathlib import Path + +HERE = Path(__file__).resolve().parent +sys.path.insert(0, str(HERE)) + +from backtest import ( # noqa: E402 + MIN_CASH_RATIO, START_CASH, SYMBOLS, SYMBOL_PARAMS, EtfDefaults, EtfSymbolConfig, + analyze, fetch_daily, precondition, simulate, +) + +BASE = EtfDefaults() +FULL = {code: fetch_daily(code) for code in SYMBOLS} + +# (白名单, 起始资金, 说明) +CASES = [] +for size in (1, 2, 3): + codes = SYMBOLS[:size] + CASES.append((f"白名单 {size} 只（{'+'.join(c[:6] for c in codes)}），资金按 {size}/3 缩放", + codes, START_CASH * size / 3)) +# 同样 20 万资金，但只放 1 只 / 2 只 → 观察"钱多而标的少"是否浪费 +CASES.append(("白名单 1 只，但仍是 20 万资金", SYMBOLS[:1], START_CASH)) +CASES.append(("白名单 2 只，但仍是 20 万资金", SYMBOLS[:2], START_CASH)) +CASES.append(("白名单 3 只，20 万（基准）", SYMBOLS[:3], START_CASH)) +# 每档放大到 5000 股，资金同步放大到 100 万 → 检验"标的不变、仓位变大"的天花板 +CASES.append(("3 只 + buy_shares=5000，资金 100 万", SYMBOLS[:3], 1_000_000.0)) +CASES.append(("3 只 + buy_shares=10000，资金 200 万", SYMBOLS[:3], 2_000_000.0)) + +print(f"{'场景':52} {'资金':>10} {'权益变动':>10} {'收益率':>7} {'回撤':>6} " + f"{'平均占用':>8} {'峰值占用':>8} {'ROI/占用':>8} {'底仓':>4} {'补仓':>4}") +for label, codes, cash in CASES: + data = {c: FULL[c] for c in codes} + shares = 5000 if "5000" in label else 10000 if "10000" in label else SYMBOL_PARAMS[codes[0]]["buy_shares"] + params = {c: {**SYMBOL_PARAMS[c], "buy_shares": shares, "max_shares": shares * 10} for c in codes} + pre = {c: precondition(data[c], EtfSymbolConfig(**params[c]), BASE) for c in codes} + result = simulate(data, symbol_params=params, precomputed=pre, start_cash=cash) + stats = analyze(result) + delta = stats["final_equity"] - cash + util = stats["avg_deployed"] / cash * 100 + max_util = stats["max_deployed"] / cash * 100 + roi = delta / stats["avg_deployed"] * 100 if stats["avg_deployed"] else 0.0 + print(f"{label:52} {cash:10.0f} {delta:10.2f} {stats['return_pct']:6.2f}% " + f"{stats['max_dd_pct']:5.2f}% {util:7.2f}% {max_util:7.2f}% {roi:7.2f}% " + f"{sum(1 for f in result['fills'] if f.kind == 'base'):4} " + f"{sum(1 for f in result['fills'] if f.kind == 'add'):4}") diff --git a/labs/analysis/etf/vs_hold.py b/labs/analysis/etf/vs_hold.py new file mode 100644 index 0000000..c759615 --- /dev/null +++ b/labs/analysis/etf/vs_hold.py @@ -0,0 +1,130 @@ +"""ETF 网格策略 vs 同期买入持有（同一区间、同一份日线）。 + +用法： py -3.14 -B analysis/etf/vs_hold.py +""" + +import math +import statistics +import sys +from pathlib import Path + +HERE = Path(__file__).resolve().parent +sys.path.insert(0, str(HERE)) + +try: + sys.stdout.reconfigure(encoding="utf-8", errors="replace") +except Exception: + pass + +from backtest import START_CASH, SYMBOLS, analyze, fetch_daily, simulate # noqa: E402 + +data = {code: fetch_daily(code) for code in SYMBOLS} +result = simulate(data) +stats = analyze(result) +curve = result["curve"] +start_day = curve[0][0] +cash0 = result["start_cash"] +grid_final = stats["final_equity"] + +# ---- 买入持有：在回测首日按各标的收盘价等权买入，持到期末 ---- +bars_by_date = {code: {b["date"]: b for b in data[code]} for code in SYMBOLS} +first_stamp = start_day.strftime("%Y%m%d") +entry = {c: bars_by_date[c][first_stamp]["close"] for c in SYMBOLS} +last_stamp = curve[-1][0].strftime("%Y%m%d") +exit_ = {c: bars_by_date[c][last_stamp]["close"] for c in SYMBOLS} + +per_symbol_cash = cash0 / len(SYMBOLS) +hold_shares = {c: per_symbol_cash / entry[c] for c in SYMBOLS} # 含零股，忽略整手限制 +hold_curve = [] +for day, _, _, _ in curve: + stamp = day.strftime("%Y%m%d") + value = sum(hold_shares[c] * bars_by_date[c].get(stamp, {"close": entry[c]})["close"] + for c in SYMBOLS) + hold_curve.append((day, value)) +hold_final = hold_curve[-1][1] + + +def profile(curve_points): + """从权益曲线算总收益、最大回撤、日波动、夏普与卡玛。""" + values = [v for _, v in curve_points] + peak, max_dd = -math.inf, 0.0 + for value in values: + peak = max(peak, value) + max_dd = max(max_dd, (peak - value) / peak) + rets = [values[i] / values[i - 1] - 1 for i in range(1, len(values))] + mean = statistics.fmean(rets) if rets else 0.0 + sd = statistics.pstdev(rets) if len(rets) > 1 else 0.0 + total = values[-1] / values[0] - 1 + days = len(values) + annual = (1 + total) ** (252 / days) - 1 if days else 0.0 + return { + "total_pct": total * 100, + "annual_pct": annual * 100, + "max_dd_pct": max_dd * 100, + "daily_sd_pct": sd * 100, + "sharpe": (mean / sd * math.sqrt(252)) if sd > 0 else float("nan"), + "calmar": (total / max_dd) if max_dd > 0 else float("nan"), + } + + +grid_curve = [(day, equity) for day, equity, _, _ in curve] + +print("=" * 108) +print(f"区间 {start_day} ~ {curve[-1][0]}（{len(curve)} 个交易日），账户 {cash0:,.0f} 元") +print("=" * 108) +print(f"{'策略':26} {'期末权益':>12} {'总收益':>9} {'年化':>8} {'最大回撤':>9} " + f"{'日波动':>8} {'夏普':>7} {'卡玛':>7}") +for label, points in (("ETF 网格（现配置）", grid_curve), ("等权买入持有", hold_curve)): + p = profile(points) + print(f"{label:26} {points[-1][1]:12,.2f} {p['total_pct']:8.2f}% {p['annual_pct']:7.2f}% " + f"{p['max_dd_pct']:8.2f}% {p['daily_sd_pct']:7.3f}% {p['sharpe']:7.2f} {p['calmar']:7.2f}") + +print() +print("== 逐标的买入持有（同一区间）==") +print(f"{'标的':11} {'期初':>8} {'期末':>8} {'涨跌':>9} {'期间最大回撤':>12}") +for code in SYMBOLS: + bars = [b for b in data[code] if b["date"] >= first_stamp and b["date"] <= last_stamp] + closes = [b["close"] for b in bars] + peak, dd = -math.inf, 0.0 + for value in closes: + peak = max(peak, value) + dd = max(dd, (peak - value) / peak) + print(f"{code:11} {closes[0]:8.3f} {closes[-1]:8.3f} " + f"{(closes[-1] / closes[0] - 1) * 100:8.2f}% {dd * 100:11.2f}%") + +print() +print("== 关键口径 ==") +print(f" 网格：平均资金占用 {stats['avg_util_pct']:.2f}%（峰值 {stats['max_deployed'] / cash0 * 100:.2f}%），" + f"买入名义 {stats['buy_amount']:,.0f}（换手 {stats['turnover_x']:.2f} 倍），" + f"佣金 {stats['fees']:.2f}") +print(f" 网格：占用部分的收益率（权益变动 ÷ 平均占用）= " + f"{(grid_final - cash0) / stats['avg_deployed'] * 100:.2f}%") +print(f" 买入持有：资金 100% 占用（从未空仓），无佣金/无交易") +print(f" 网格：完整轮次 {len([f for f in result['fills'] if f.kind == 'exit'])} 次，" + f"胜率 100%（只在盈利 ≥{stats['params']['min_profit_pct']}% 时才卖）") + +# ---- 同风险口径：把网格放大到与买入持有相同回撤，比收益 ---- +print() +print("== 同回撤口径（把网格单档股数放大到回撤≈买入持有）==") +from dataclasses import replace # noqa: E402 +from backtest import SYMBOL_PARAMS, SYMBOLS as _SYMS # noqa: E402 + +hold_p = profile(hold_curve) +print(f" 目标回撤：买入持有 {hold_p['max_dd_pct']:.2f}%") +print(f"{'放大倍数':>8} {'权益变动':>12} {'总收益':>9} {'最大回撤':>9} {'平均占用':>9} {'夏普':>7} {'卡玛':>7}") +for factor in (1, 4, 10, 20, 35): + params = { + c: {**SYMBOL_PARAMS[c], + "buy_shares": max(100, int(SYMBOL_PARAMS[c]["buy_shares"] * factor)), + "max_shares": max(1000, int(SYMBOL_PARAMS[c]["max_shares"] * factor))} + for c in _SYMS + } + res = simulate(data, symbol_params=params) + st = analyze(res) + pts = [(day, eq) for day, eq, _, _ in res["curve"]] + p = profile(pts) + print(f"{factor:8}× {st['final_equity'] - cash0:12,.2f} {p['total_pct']:8.2f}% " + f"{p['max_dd_pct']:8.2f}% {st['avg_util_pct']:8.2f}% {p['sharpe']:7.2f} {p['calmar']:7.2f}") +print() +print("注：放大是外推（同一段历史、同一批机会等比放大），不是新样本的验证；") +print(" 放开股数后实际能否成交/是否滑点恶化，日线回测无法回答。") diff --git a/labs/benchmarks/hotpaths.py b/labs/benchmarks/hotpaths.py index e5dbdbc..14d237d 100644 --- a/labs/benchmarks/hotpaths.py +++ b/labs/benchmarks/hotpaths.py @@ -1,4 +1,4 @@ -"""Offline microbenchmarks; run with .venv/Scripts/python benchmarks/hotpaths.py.""" +"""Offline microbenchmarks; run with `py -3.14 -B labs/benchmarks/hotpaths.py`.""" import sys from datetime import datetime, time @@ -6,7 +6,8 @@ from pathlib import Path from statistics import median from timeit import repeat -sys.path.insert(0, str(Path(__file__).resolve().parents[1])) +# labs/benchmarks/ -> 仓库根 -> py-client +sys.path.insert(0, str(Path(__file__).resolve().parents[2] / "py-client")) from libs.calc import trading_time from sdk.models import _parse_datetime, _side diff --git a/labs/run_tests.py b/labs/run_tests.py new file mode 100644 index 0000000..92e8c56 --- /dev/null +++ b/labs/run_tests.py @@ -0,0 +1,47 @@ +"""labs 的统一入口：把 ``py-client`` 挂上 ``sys.path`` 后运行 labs/tests 下的全部单测。 + +为什么需要它：测试模块内部有 ``from tests.zt_harness import ...`` 这类导入， +所以 ``labs`` 必须作为顶层包被导入，同时 ``py-client`` 必须在 ``sys.path`` 上 +（``config`` / ``libs`` / ``sdk`` / ``strategy`` 都在那里）。单独用 +``python -m unittest discover`` 很难同时满足这两点，因此统一走这个脚本。 + +用法（在仓库任意位置执行）： + py -3.14 -B labs/run_tests.py # 跑全部 + py -3.14 -B labs/run_tests.py -v # 详细 + py -3.14 -B labs/run_tests.py test_etf_signal # 只跑某个模块 +""" + +import sys +import unittest +from pathlib import Path + +LABS = Path(__file__).resolve().parent +REPO = LABS.parent +PY_CLIENT = REPO / "py-client" + +for path in (str(PY_CLIENT), str(REPO)): + if path not in sys.path: + sys.path.insert(0, path) + + +def build_suite(pattern: str, names: list[str]) -> unittest.TestSuite: + loader = unittest.TestLoader() + if names: + return loader.loadTestsFromNames(names) + start = LABS / "tests" + # top_level_dir 指向 labs：这样导入名是 tests.xxx，测试模块里的 + # "from tests.zt_harness import ..." 才能解析。 + return loader.discover(str(start), pattern=pattern, top_level_dir=str(LABS)) + + +def main(argv: list[str]) -> int: + args = [a for a in argv if not a.startswith("-")] + verbosity = 2 if any(a in ("-v", "--verbose") for a in argv) else 1 + names = [a if "." in a else f"tests.{a}" for a in args] + suite = build_suite("test_*.py", names) + result = unittest.TextTestRunner(verbosity=verbosity).run(suite) + return 0 if result.wasSuccessful() else 1 + + +if __name__ == "__main__": + raise SystemExit(main(sys.argv[1:])) diff --git a/labs/tests/__init__.py b/labs/tests/__init__.py new file mode 100644 index 0000000..d61945b --- /dev/null +++ b/labs/tests/__init__.py @@ -0,0 +1,6 @@ +"""labs 的测试包。 + +测试模块内部使用 ``from tests.zt_harness import ...`` 这类绝对导入， +因此 ``labs`` 必须作为顶层包导入、``labs/tests`` 必须是包。 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zD9ug^Jcgyb8D*gXixwr}&w(w>g>K&4W}d}SKL>yg$z#6bRwPdct4N%Q(V`^SUHQR( z4x&50VdFg;Hf?q~sc-k=9p6y8hJ(Xo0~3J_o6L*&awVqVFlDGHG^i+pMm(Lsa!64Q zq}7De6IPTy-LyzTPijd+QK(;0;zmqSOmtaUfX>2M^8i-U`1lS!(04R{h>xG)V+;jH zOqoVXGs%#70jt%DViA?-F$F)a>Ah*9S&BlJDM~D%S{5cFV)#u^kvLjf)Tk&dS+w1D zQt!s;@qu4?ER1x%co8!2JE8q^q0$@t{7#>@=JRsS+ps7K-nzw*$J>ad4WEZR-sVNo z=dE8X_j#)p%R}C(&z6I)><=E!+ri!7!B_UbtjKHzTS8iCPT+En;P(BPR!!)=cJkFr zhEkekqMo)BK1Z;G(nG5gdau>YzJC&RKvV1-FH4!yAnRi|X4zC4VhyJKRh*?-oiVnn zjtM!+Tk!#wzGVTC9eblvrWHjFT(h10wJkHIX_y*hiD4V2_{GcS9%9KpB>OBT-e+Us z0rS5BSW3^rDEu>? dict: + """构造接口口径的一行日线：以 close 为中轴，上下各半个 spread。""" + close = float(close) + return dict( + ts_code=code, + trade_date=day, + open=close, + high=close + spread / 2, + low=close - spread / 2, + close=close, + ) + + +def parse(*payload: dict, code: str = CODE, count: int = 120) -> list[dict]: + """把接口口径的日线交给 parse_daily，得到指标层使用的一行（含 date）。""" + return etf_signal.parse_daily(list(payload), code, TODAY, count) + + +def raw_payload( + count: int = 90, close: float = 10.0, spread: float = 1.0, code: str = CODE +) -> list[dict]: + """生成截至 LAST_DAY 的 count 根横盘日线（接口口径，升序）。""" + return [ + raw_bar( + (LAST_DAY - timedelta(days=count - 1 - index)).strftime("%Y%m%d"), + close, + spread, + code, + ) + for index in range(count) + ] + + +def bars(count: int = 90, close: float = 10.0, spread: float = 1.0) -> list[dict]: + """生成指标层口径（已解析）的 count 根横盘日线。""" + return parse(*raw_payload(count, close, spread)) + + +def symbol(**overrides) -> EtfSymbolConfig: + base = dict(is_t0=False, buy_shares=1000, atr_multiplier=1.0, inner_step=0.7) + base.update(overrides) + return EtfSymbolConfig(**{k: v for k, v in base.items() if v is not None}) + + +def runtime(etf: EtfConfig | None = None, host: str = "http://api.test") -> Mock: + rt = Mock() + rt.etf_cfg = etf if etf is not None else EtfConfig( + defaults=EtfDefaults(), symbols={CODE: symbol()} + ) + rt.global_cfg = GlobalConfig(api_host=host) + rt.account_cfg = AccountConfig(strategy="etf") + return rt + + +class ParseDailyTests(unittest.TestCase): + def test_bare_list_is_supported_and_sorted_ascending(self): + payload = [raw_bar("20260915"), raw_bar("20260912")] + rows = etf_signal.parse_daily(payload, CODE, TODAY) + self.assertEqual([row["date"] for row in rows], ["20260912", "20260915"]) + self.assertEqual(rows[0]["close"], 10.0) + + def test_legacy_envelope_is_supported(self): + payload = {"code": 0, "message": "", "details": [raw_bar("20260915")]} + self.assertEqual( + [row["date"] for row in etf_signal.parse_daily(payload, CODE, TODAY)], + ["20260915"], + ) + + def test_today_and_future_bars_are_dropped_then_limited(self): + payload = [ + raw_bar("20260916"), raw_bar("20260917"), + raw_bar("20260915"), raw_bar("20260914"), + ] + rows = etf_signal.parse_daily(payload, CODE, TODAY, count=1) + self.assertEqual([row["date"] for row in rows], ["20260915"]) + + def test_numeric_strings_are_converted(self): + rows = etf_signal.parse_daily([raw_bar("20260915", close="10.5")], CODE, TODAY) + self.assertEqual(rows[0]["close"], 10.5) + + def test_bad_payloads_are_rejected(self): + cases = [ + None, [], {}, {"code": 1, "message": "failed"}, + {"code": False, "details": [raw_bar("20260915")]}, + {"code": 0, "details": []}, {"code": 0, "details": None}, + ] + for payload in cases: + with self.subTest(payload=payload), self.assertRaises(ValueError): + etf_signal.parse_daily(payload, CODE, TODAY) + + def test_bad_rows_are_rejected(self): + cases = [ + [raw_bar("20260915", code=OTHER)], # 证券归属不一致 + [raw_bar("20260915"), raw_bar("20260915")], # 日期重复 + [raw_bar("20260230")], # 非法日期 + [raw_bar("20260915", close=float("nan"))], # 非有限 + [dict(raw_bar("20260915"), open=True)], # 布尔价格 + [dict(raw_bar("20260915"), low=12)], # OHLC 关系异常 + [dict(raw_bar("20260915"), close=None)], # 缺失价格 + ] + for rows in cases: + with self.subTest(rows=rows), self.assertRaises(ValueError): + etf_signal.parse_daily(rows, CODE, TODAY) + + def test_count_must_be_positive_integer(self): + for count in (0, -1, 1.5, True): + with self.subTest(count=count), self.assertRaises(ValueError): + etf_signal.parse_daily([raw_bar("20260915")], CODE, TODAY, count) + + +class CalculateTests(unittest.TestCase): + """横盘日线：ATR = spread、MA60 = close、格距 = max(ATR×倍数, MA60×0.5%, 0.001)。""" + + def test_flat_bars_produce_expected_indicators(self): + values = etf_signal.calculate(bars(), symbol(), EtfDefaults(), TODAY) + self.assertEqual(values[etf_signal.IND_MA60], 10.0) + self.assertEqual(values[etf_signal.IND_ATR], 1.0) + self.assertEqual(values[etf_signal.IND_CHANNEL_LOW], 9.5) + self.assertEqual(values[etf_signal.IND_CHANNEL_HIGH], 10.5) + # 入场门槛 = min(9.5 + 1×15%, 10) = 9.65 + self.assertAlmostEqual(values[etf_signal.IND_ENTRY], 9.65) + self.assertEqual(values[etf_signal.IND_GRID], 1.0) + self.assertEqual(values[etf_signal.IND_PRICE], 10.0) + self.assertAlmostEqual(values[etf_signal.IND_GRID_PCT], 10.0) + self.assertAlmostEqual(values[etf_signal.IND_ADD_PRICE], 9.7) + + def test_atr_multiplier_and_grid_floor(self): + low_atr = etf_signal.calculate(bars(spread=0.02), symbol(atr_multiplier=1.0), + EtfDefaults(), TODAY) + # ATR=0.02 低于 MA60×0.5% = 0.05 的百分比下限 + self.assertAlmostEqual(low_atr[etf_signal.IND_GRID], 0.05) + wide = etf_signal.calculate(bars(), symbol(atr_multiplier=0.5), EtfDefaults(), TODAY) + self.assertEqual(wide[etf_signal.IND_GRID], 0.5) + + def test_grid_is_rounded_up_to_tick(self): + rows = bars() + for row in rows: + row["close"] += 0.0004 + row["open"], row["high"], row["low"] = row["close"], row["high"] + 0.0004, row["low"] + 0.0004 + values = etf_signal.calculate(rows, symbol(), EtfDefaults(), TODAY) + raw = values[etf_signal.IND_ATR] * 1.0 + expected = float(Decimal(str(raw)).quantize(Decimal("0.001"), rounding=ROUND_CEILING)) + self.assertEqual(values[etf_signal.IND_GRID], expected) + self.assertEqual(round(values[etf_signal.IND_GRID], 3), values[etf_signal.IND_GRID]) + + def test_rising_close_uses_wilder_smoothing(self): + rows = [dict(row) for row in bars()] + for index, row in enumerate(rows): + shift = index * 0.01 + row["close"] += shift + row["open"], row["high"], row["low"] = ( + row["close"], row["close"] + 0.5, row["close"] - 0.5 + ) + values = etf_signal.calculate(rows, symbol(), EtfDefaults(), TODAY) + closes = [row["close"] for row in rows] + self.assertAlmostEqual(values[etf_signal.IND_MA60], fmean(closes[-60:])) + self.assertGreaterEqual(values[etf_signal.IND_ATR], 1.0) + + def test_insufficient_or_stale_bars_are_rejected(self): + with self.assertRaisesRegex(ValueError, "61"): + etf_signal.calculate(bars(40), symbol(), EtfDefaults(), TODAY) + # 最近日线距今超过 15 个自然日即放弃该标的 + with self.assertRaisesRegex(ValueError, "自然日"): + etf_signal.calculate(bars(), symbol(), EtfDefaults(), TODAY + timedelta(days=20)) + + def test_bad_atr_period_is_rejected(self): + defaults = EtfDefaults() + defaults.atr_period = 1 + with self.assertRaisesRegex(ValueError, "atr_period"): + etf_signal.calculate(bars(), symbol(), defaults, TODAY) + + +class DailyBarsTests(unittest.TestCase): + def test_request_url_and_no_token_header(self): + def respond(request): + self.assertEqual( + str(request.url), etf_signal.DAILY_URL + "?code=" + CODE + ) + self.assertNotIn("x-token", request.headers) + return httpx.Response(200, json=[raw_bar("20260915")]) + + with httpx.Client(transport=httpx.MockTransport(respond)) as client: + rows = etf_signal.daily_bars(client, CODE, TODAY) + self.assertEqual([row["date"] for row in rows], ["20260915"]) + + def test_custom_endpoint_is_used(self): + def respond(request): + self.assertEqual(str(request.url), "http://api.test/etf/daily?code=" + CODE) + return httpx.Response(200, json=[raw_bar("20260915")]) + + with httpx.Client(transport=httpx.MockTransport(respond)) as client: + etf_signal.daily_bars( + client, CODE, TODAY, endpoint="http://api.test/etf/daily" + ) + + def test_http_and_json_errors_propagate(self): + for status, content in ((404, "{}"), (200, "error")): + with httpx.Client( + transport=httpx.MockTransport(lambda r: httpx.Response(status, text=content)) + ) as client, self.assertRaises((httpx.HTTPStatusError, ValueError)): + etf_signal.daily_bars(client, CODE, TODAY) + + +def daily_response(payload: object, code: str = CODE) -> httpx.Response: + """构造带 request 的 200 响应；httpx 的 raise_for_status 需要 request。""" + request = httpx.Request("GET", etf_signal.DAILY_URL, params={"code": code}) + return httpx.Response(200, json=payload, request=request) + + +def boom(url=None, params=None): + """模拟连接失败：httpx 只接受真实的 RequestError 子类实例。""" + raise httpx.ConnectError("boom") + + +class GenSignalsTests(unittest.TestCase): + def setUp(self): + self.client = Mock(spec=httpx.Client) + self.client.get.side_effect = lambda url, params=None: daily_response( + raw_payload(code=params["code"]), params["code"] + ) + for cache in (etf_signal._daily_cache, etf_signal._fetched, etf_signal._retry_at): + cache.clear() + patch.object(etf_signal, "_history_client", self.client).start() + self.addCleanup(patch.stopall) + + def config(self, codes=(CODE,)): + return EtfConfig( + defaults=EtfDefaults(), + symbols={code: symbol() for code in codes}, + ) + + def test_signals_follow_config_order_and_carry_indicators(self): + signals = etf_signal.gen_signals(runtime(self.config((OTHER, CODE)))) + self.assertEqual([item.code for item in signals], [OTHER, CODE]) + item = signals[0] + self.assertIsInstance(item, SignalItem) + self.assertEqual(item.signal_key, "etf") + self.assertEqual(item.last_close, 10.0) + self.assertIn("ETF网格", item.desc) + self.assertEqual(item.tech_indicator[etf_signal.IND_GRID], 1.0) + self.assertEqual(item.tech_indicator[etf_signal.IND_MA60], 10.0) + self.assertEqual(item.tech_indicator[etf_signal.IND_ENTRY], 9.65) + + def test_missing_etf_config_yields_empty_list(self): + rt = runtime() + rt.etf_cfg = None + self.assertEqual(etf_signal.gen_signals(rt), []) + + def test_broken_symbol_is_skipped_without_breaking_others(self): + """CODE 的日线证券代码不一致时放弃该标的，OTHER 正常生成。""" + payloads = {CODE: [raw_bar("20260915", code=OTHER)], OTHER: raw_payload(code=OTHER)} + self.client.get.side_effect = lambda url, params=None: daily_response( + payloads[params["code"]], params["code"] + ) + signals = etf_signal.gen_signals(runtime(self.config((CODE, OTHER)))) + self.assertEqual([item.code for item in signals], [OTHER]) + + def test_daily_bars_are_cached_per_code_per_day(self): + rt = runtime(self.config()) + etf_signal.gen_signals(rt) + etf_signal.gen_signals(rt) + self.assertEqual(self.client.get.call_count, 1) + + def test_failure_is_throttled_by_retry_window(self): + self.client.get.side_effect = boom + rt = runtime(self.config()) + self.assertEqual(etf_signal.gen_signals(rt), []) + self.assertEqual(self.client.get.call_count, 1) + # 重试窗口内不再取数 + self.assertEqual(etf_signal.gen_signals(rt), []) + self.assertEqual(self.client.get.call_count, 1) + self.assertIn(CODE, etf_signal._retry_at) + + def test_retry_happens_after_window(self): + self.client.get.side_effect = boom + rt = runtime(self.config()) + etf_signal.gen_signals(rt) + etf_signal._retry_at[CODE] = datetime.now() - timedelta(seconds=1) + self.client.get.side_effect = None + self.client.get.return_value = daily_response(raw_payload()) + signals = etf_signal.gen_signals(rt) + self.assertEqual([item.code for item in signals], [CODE]) + self.assertEqual(self.client.get.call_count, 2) + def test_new_trading_day_clears_cache(self): + rt = runtime(self.config()) + etf_signal.gen_signals(rt) + # 模拟隔日：昨日的取数记录不应继续复用。 + etf_signal._fetched[CODE] = LAST_DAY - timedelta(days=1) + etf_signal._daily_cache.clear() + etf_signal.gen_signals(rt) + self.assertEqual(etf_signal._fetched[CODE], datetime.now().date()) + self.assertEqual(self.client.get.call_count, 2) + + def test_endpoint_falls_back_to_default_without_api_host(self): + etf_signal.gen_signals(runtime(self.config(), host="")) + self.assertEqual(self.client.get.call_args.args[0], etf_signal.DAILY_URL) + + def test_endpoint_uses_global_api_host(self): + etf_signal.gen_signals(runtime(self.config(), host="http://api.test/")) + self.assertEqual(self.client.get.call_args.args[0], "http://api.test/etf/daily") + + +if __name__ == "__main__": + unittest.main() diff --git a/labs/tests/test_etf_trade.py b/labs/tests/test_etf_trade.py new file mode 100644 index 0000000..1d52cd9 --- /dev/null +++ b/labs/tests/test_etf_trade.py @@ -0,0 +1,358 @@ +"""ETF 开仓与持仓：白名单/入场门槛/反弹确认、主出口、副出口、百分比补仓。""" + +from datetime import date, datetime, timedelta +import unittest +from unittest.mock import Mock, patch + +from config import AccountConfig, EtfConfig, EtfDefaults, EtfSymbolConfig, GlobalConfig +from libs.order import OrderBook +from libs.runtime import Runtime +from libs.signal import SignalItem +from libs import watch +from libs.watch import DipWatch +from sdk import Assets, PositionItem, Tick +from strategy.etf import open as etf_open +from strategy.etf import positions as etf_positions + +CODE = "510300.SH" +OTHER = "159915.SZ" +# 固定"当前时刻"，与 tick 的时间戳保持同一交易日且不过期。 +NOW = datetime(2026, 9, 16, 10, 0, 0) + + +class FrozenDateTime(datetime): + """冻结 ``datetime.now()``，其余行为与标准库一致。""" + + @classmethod + def now(cls, tz=None): + return NOW if tz is None else NOW.astimezone(tz) + + +def stamp(now: datetime) -> str: + return now.strftime("%Y%m%d %H:%M:%S") + + +def tick(price: float, now: datetime | None = None) -> Tick: + now = now or NOW + return Tick(last_price=price, last_close=price, raw={"timetag": stamp(now)}) + + +def symbol(**overrides) -> EtfSymbolConfig: + base = dict(is_t0=False, buy_shares=1000, atr_multiplier=1.0, inner_step=0.7) + base.update(overrides) + return EtfSymbolConfig(**base) + + +def etf_config(**symbol_overrides) -> EtfConfig: + return EtfConfig( + defaults=EtfDefaults(), + symbols={CODE: symbol(**symbol_overrides)}, + ) + + +def position(volume: int, cost: float, can_use: int | None = None, name: str = "") -> PositionItem: + return PositionItem( + stock_code=CODE, + stock_name=name, + volume=volume, + open_price=cost, + can_use_volume=volume if can_use is None else can_use, + yesterday_volume=volume if can_use is None else can_use, + last_price=cost, + ) + + +def signal(entry: float = 9.65, price: float = 10.0, code: str = CODE) -> SignalItem: + return SignalItem( + signal_key="etf", + code=code, + last_close=price, + tech_indicator={ + "etf_entry": entry, + "etf_price": price, + "etf_grid": 1.0, + "etf_add_price": price * 0.97, + "etf_ma60": 10.0, + }, + ) + + +class ETFTradeBase(unittest.TestCase): + def setUp(self): + self.client = Mock() + self.client.passorder.return_value = {"status": "success"} + # Runtime.__post_init__ 会拉一次服务端初始化数据，测试里不发真实请求。 + patch("libs.runtime.get_json", side_effect=OSError("offline")).start() + self.run = Runtime( + client=self.client, + global_cfg=GlobalConfig(api_host="http://api.test"), + account_cfg=AccountConfig(account_id="acct", strategy="etf", min_cash_ratio=0.0), + etf_cfg=etf_config(), + orders=OrderBook(), + open_watch=DipWatch(expire_seconds=600, rebound_threshold=0.5), + add_watch=DipWatch(expire_seconds=600, rebound_threshold=0.5), + ) + for module in (etf_open, etf_positions, watch): + patch.object(module, "trading_time", return_value=True, create=True).start() + patch.object(module, "datetime", FrozenDateTime).start() + patch.dict(etf_positions._progress, {}, clear=True).start() + patch.dict(etf_positions._trackers, {}, clear=True).start() + self.addCleanup(patch.stopall) + + def last_order(self) -> dict: + self.assertTrue(self.client.passorder.called, "未提交任何委托") + return self.client.passorder.call_args.kwargs + + +class OpenSignalTests(ETFTradeBase): + def test_whitelist_outside_config_is_skipped(self): + item = signal(code=OTHER) + etf_open.open_signal(self.run, {OTHER: tick(9.4)}, [item]) + self.client.passorder.assert_not_called() + + def test_price_above_entry_no_observation_no_order(self): + etf_open.open_signal(self.run, {CODE: tick(9.9)}, [signal(entry=9.65)]) + self.client.passorder.assert_not_called() + self.assertEqual(self.run.open_watch.data, {}) + + def test_seesaw_below_entry_requires_rebound_confirmation(self): + item = signal(entry=9.65) + etf_open.open_signal(self.run, {CODE: tick(9.4)}, [item]) # 进入入场区，记低点 + etf_open.open_signal(self.run, {CODE: tick(9.39)}, [item]) # 刷新低点 + etf_open.open_signal(self.run, {CODE: tick(9.40)}, [item]) # 反弹不足 0.5% + self.client.passorder.assert_not_called() + etf_open.open_signal(self.run, {CODE: tick(9.42)}, [item]) # (9.42-9.39)/9.39 = 0.32% 仍不足 + self.client.passorder.assert_not_called() + + def test_rebound_places_base_limit_order_at_anchor(self): + item = signal(entry=9.65) + etf_open.open_signal(self.run, {CODE: tick(9.40)}, [item]) + etf_open.open_signal(self.run, {CODE: tick(9.45)}, [item]) # 反弹 0.53% 确认 + request = self.last_order() + self.assertEqual(request["op_type"], 23) + self.assertEqual(request["volume"], 1000) + self.assertEqual(request["price"], 9.45) + self.assertEqual(request["pr_type"], 11) + self.assertEqual(request["strategy_name"], "etf") + self.assertEqual(self.run.open_watch.data, {}) + + def test_leaving_entry_band_forgets_the_watch(self): + item = signal(entry=9.65) + etf_open.open_signal(self.run, {CODE: tick(9.4)}, [item]) + etf_open.open_signal(self.run, {CODE: tick(9.8)}, [item]) + self.assertEqual(self.run.open_watch.data, {}) + + def test_missing_entry_indicator_is_skipped(self): + item = signal() + item.tech_indicator.clear() + etf_open.open_signal(self.run, {CODE: tick(9.4)}, [item]) + self.client.passorder.assert_not_called() + + def test_stale_tick_is_skipped(self): + old = datetime(2026, 9, 16, 9, 50, 0) + etf_open.open_signal(self.run, {CODE: tick(9.4, now=old)}, [signal(entry=9.65)]) + self.client.passorder.assert_not_called() + + def test_previous_day_tick_is_skipped(self): + yesterday = datetime(2026, 9, 15, 14, 0, 0) + etf_open.open_signal(self.run, {CODE: tick(9.4, now=yesterday)}, [signal(entry=9.65)]) + self.client.passorder.assert_not_called() + + def test_insufficient_budget_cancels_the_anchor(self): + self.run.client.assets.return_value = Assets(total=1000.0, available=100.0) + item = signal(entry=9.65) + etf_open.open_signal(self.run, {CODE: tick(9.40)}, [item]) + etf_open.open_signal(self.run, {CODE: tick(9.45)}, [item]) + self.client.passorder.assert_not_called() + self.assertEqual(self.run.open_watch.data, {}) + + +class MainExitTests(ETFTradeBase): + def test_profit_above_target_clears_the_whole_grid(self): + etf_positions.manage_positions( + self.run, {CODE: tick(10.2)}, [position(2000, 10.0)], True, 100000.0 + ) + request = self.last_order() + self.assertEqual(request["op_type"], 24) + self.assertEqual(request["volume"], 2000) + self.assertEqual(request["strategy_name"], "etf") + self.assertEqual(request["price"], 10.2) + self.assertEqual(request["pr_type"], 11) + + def test_profit_below_target_does_not_sell(self): + etf_positions.manage_positions( + self.run, {CODE: tick(10.05)}, [position(2000, 10.0)], True, 100000.0 + ) + self.client.passorder.assert_not_called() + + def test_t_plus_1_position_bought_today_is_not_sellable(self): + held = position(1000, 10.0, can_use=0) + held.yesterday_volume = 0 + etf_positions.manage_positions( + self.run, {CODE: tick(10.5)}, [held], True, 100000.0 + ) + self.client.passorder.assert_not_called() + + def test_t0_symbol_sells_on_the_same_day(self): + self.run.etf_cfg = etf_config(is_t0=True) + held = position(1000, 10.0, can_use=1000) + held.yesterday_volume = 0 + etf_positions.manage_positions( + self.run, {CODE: tick(10.5)}, [held], True, 100000.0 + ) + self.assertEqual(self.last_order()["volume"], 1000) + + +class LevelExitTests(ETFTradeBase): + """副出口：主出口在盈亏率 ≥1% 时会先吃掉整仓，因此这里直接喂盈亏率验证峰值回撤。 + + inner_step = 0.7、inner_grids = 2：只有峰值抬到第 2 格后的回撤才允许卖出。 + """ + + def observe(self, series, cost: float = 11.6): + held = position(1000, cost) + symbol = self.run.etf_cfg.symbols[CODE] + level = etf_positions.position_level(self.run, held) + decisions = [] + for pnl_rate in series: + price = cost * (1 + pnl_rate / 100) + decisions.append( + etf_positions.handle_level_exit( + self.run, symbol, held, tick(price), pnl_rate, level + ) + ) + return decisions + + def test_peak_retreat_sells_only_that_level(self): + first, second, third = self.observe([0.5, 1.5, 1.2]) + self.assertFalse(first.submitted) # 首次观察，只建基准 + self.assertFalse(second.submitted) # 峰值抬到第 2 格 + self.assertTrue(third.submitted) # 回撤到第 1 格 + request = self.last_order() + self.assertEqual(request["op_type"], 24) + self.assertEqual(request["volume"], 1000) + self.assertEqual(request["strategy_name"], "etf") + + def test_retreat_below_inner_grids_is_held(self): + first, second = self.observe([0.5, 0.1]) + self.assertFalse(first.submitted) + self.assertFalse(second.submitted) # 峰值只有 0 格 + self.client.passorder.assert_not_called() + + def test_peak_is_kept_when_the_order_is_rejected(self): + self.client.passorder.return_value = {"status": "rejected"} + self.run.orders.place = Mock(return_value=False) + first, second, third = self.observe([0.5, 1.5, 1.2]) + self.assertFalse(third.submitted) + # 下单失败必须保留峰值：下一轮同样能再次触发。 + fourth = self.observe([1.2])[0] + self.assertFalse(fourth.submitted) + + def test_manage_positions_runs_the_secondary_exit(self): + """成本 11.6、现价 11.7/11.65 的盈亏率都低于 1%，主出口不参与。""" + held = position(1000, 11.6) + etf_positions.manage_positions(self.run, {CODE: tick(11.7)}, [held], True, 0.0) + etf_positions.manage_positions(self.run, {CODE: tick(11.65)}, [held], True, 0.0) + self.client.passorder.assert_not_called() # 峰值未达 2 格 + + +class AddTests(ETFTradeBase): + """补仓规则单测：直接调 handle_add，避免其它出口的委托锁干扰。""" + + def held(self, volume: int = 3000, cost: float = 10.0) -> PositionItem: + return position(volume, cost) + + def add(self, price: float, volume: int = 3000, cost: float = 10.0, + budget: float = 100000.0, level: int | None = None, + symbol_overrides: dict | None = None, first_low: float | None = None): + if symbol_overrides: + self.run.etf_cfg = etf_config(**symbol_overrides) + held = self.held(volume, cost) + if first_low is not None: + # 先造出一个观察低点，再由本次调用验证反弹确认。 + self.run.add_watch.triggered("补仓", CODE, first_low) + return etf_positions.handle_add( + self.run, + self.run.etf_cfg.symbols[CODE], + held, + tick(price), + price, + budget, + level if level is not None else etf_positions.position_level(self.run, held), + ) + + def test_add_requires_add_pct_drop(self): + decision = self.add(9.95) + self.assertFalse(decision.submitted) + self.client.passorder.assert_not_called() + self.assertEqual(self.run.add_watch.data, {}) + + def test_add_waits_for_rebound_before_buying(self): + # 跌幅 4% ≥ add_pct 3%，但还没反弹确认：只观察，不下单。 + first = self.add(9.60) + self.assertFalse(first.submitted) + self.assertIn(CODE, self.run.add_watch.data) + # 从观察低点 9.60 反弹 0.63%：确认后按现价买一档。 + second = self.add(9.66) + self.assertTrue(second.submitted) + request = self.last_order() + self.assertEqual(request["op_type"], 23) + self.assertEqual(request["volume"], 1000) + self.assertEqual(request["price"], 9.66) + self.assertEqual(request["strategy_name"], "etf") + self.assertEqual(self.run.add_watch.data, {}) + + def test_add_stops_at_max_adds(self): + decision = self.add(9.60, volume=10000) + self.assertFalse(decision.submitted) + self.assertIn("档", decision.message) + + def test_add_respects_max_shares(self): + decision = self.add(9.60, volume=3000, symbol_overrides={"max_shares": 3000}) + self.assertFalse(decision.submitted) + self.client.passorder.assert_not_called() + + def test_add_needs_budget(self): + # 跌幅 4%、反弹 0.63% 都满足，但预算为 0：不消耗观察状态，也不下单。 + first = self.add(9.60, budget=0.0) + self.assertFalse(first.submitted) + second = self.add(9.66, budget=0.0, first_low=9.60) + self.assertFalse(second.submitted) + self.assertIn(CODE, self.run.add_watch.data) + # 资金到位后同一个观察低点仍可确认。 + third = self.add(9.66, budget=100000.0, first_low=None) + self.assertTrue(third.submitted) + + def test_add_uses_broker_cost_as_previous_level(self): + # 上一档 = 券商成本 9.5：跌到 9.16 是 3.58% ≥ 3%，反弹到 9.21 确认。 + decision = self.add(9.21, cost=9.5, first_low=9.16) + self.assertTrue(decision.submitted) + self.assertEqual(self.last_order()["price"], 9.21) + + def test_add_blocked_when_market_disallows(self): + held = self.held() + etf_positions.manage_positions(self.run, {CODE: tick(9.6)}, [held], False, 100000.0) + etf_positions.manage_positions(self.run, {CODE: tick(9.66)}, [held], False, 100000.0) + self.client.passorder.assert_not_called() + + def test_add_blocked_by_in_flight_buy(self): + self.run.orders.busy_cache.set("BUY-" + CODE, True, timeout=180) + self.add(9.60) + self.add(9.66) + self.client.passorder.assert_not_called() + + +class PositionLevelTests(ETFTradeBase): + def test_level_is_derived_from_volume(self): + for volume, expected in ((1000, 1), (2000, 2), (3500, 4), (10000, 10)): + with self.subTest(volume=volume): + self.assertEqual( + etf_positions.position_level(self.run, position(volume, 10.0)), expected + ) + + def test_unknown_symbol_yields_baseline_level(self): + self.assertEqual(etf_positions.position_level(self.run, position(1000, 10.0)), 1) + + +if __name__ == "__main__": + unittest.main() diff --git a/labs/tests/test_trend_collector.py b/labs/tests/test_trend_collector.py index a39a38a..e6a70d1 100644 --- a/labs/tests/test_trend_collector.py +++ b/labs/tests/test_trend_collector.py @@ -3,7 +3,7 @@ import io import logging import unittest from concurrent.futures import Future -from contextlib import ExitStack, redirect_stdout +from contextlib import ExitStack, redirect_stderr, redirect_stdout from types import SimpleNamespace from unittest.mock import Mock, patch @@ -77,7 +77,7 @@ class TrendCollectorTests(unittest.TestCase): self.assertEqual(payload['deals'][0]['volume'], 100) def test_main_registers_five_minute_collector_job(self): - for strategy in ('trend', 'zt'): + for strategy in ('trend', 'zt', 'etf'): with self.subTest(strategy=strategy), ExitStack() as stack: scheduler = Mock(running=True) stack.enter_context(patch.object(self.app, 'BackgroundScheduler', return_value=scheduler)) @@ -101,6 +101,63 @@ class TrendCollectorTests(unittest.TestCase): scheduler.start.assert_called_once() scheduler.shutdown.assert_called_once_with(wait=True) + def test_main_rejects_unknown_strategy_before_starting_the_scheduler(self): + scheduler = Mock(running=True) + with patch.object(self.app, 'BackgroundScheduler', return_value=scheduler), \ + patch.object(self.app, 'require_windows', return_value=True), \ + patch.object(self.app, 'check_single_instance', return_value=True), \ + patch.object(self.app, 'wait_for_qmt_api') as wait_api, \ + patch.object(self.app.config, 'load'), \ + patch.object(self.app.config, 'global_config', SimpleNamespace(api_host='unused')), \ + patch.object(self.app.config, 'account_config', SimpleNamespace(strategy='bogus')), \ + patch.object(self.app, 'wait_for_any_key'), \ + patch('sys.stdin', io.StringIO('\n')), redirect_stderr(io.StringIO()) as err: + self.assertEqual(self.app.main(), 1) + self.assertIn('bogus', err.getvalue()) + scheduler.start.assert_not_called() + wait_api.assert_not_called() + + def test_main_reports_a_missing_global_config(self): + scheduler = Mock(running=True) + with patch.object(self.app, 'BackgroundScheduler', return_value=scheduler), \ + patch.object(self.app, 'require_windows', return_value=True), \ + patch.object(self.app, 'check_single_instance', return_value=True), \ + patch.object(self.app, 'wait_for_qmt_api') as wait_api, \ + patch.object(self.app.config, 'load'), \ + patch.object(self.app.config, 'global_config', None), \ + patch.object(self.app.config, 'account_config', None), \ + patch.object(self.app, 'wait_for_any_key'), \ + patch('sys.stdin', io.StringIO('\n')), redirect_stderr(io.StringIO()) as err: + self.assertEqual(self.app.main(), 1) + self.assertIn('config.load', err.getvalue()) + scheduler.add_job.assert_not_called() + wait_api.assert_not_called() + + +class ETFEtfConfigSummaryTests(unittest.TestCase): + """启动日志里的 ETF 配置概览：缺文件必须说清楚，且不抛异常。""" + + def summary(self, etf_cfg): + app = importlib.import_module('main') + with patch.object(app.config, 'etf_config', etf_cfg, create=True): + return app.describe_etf_config() + + def test_missing_file_is_described_not_raised(self): + self.assertIn('_etf.yaml', self.summary(None)) + + def test_symbols_and_codes_are_listed_in_config_order(self): + from config import EtfConfig, EtfDefaults, EtfSymbolConfig + cfg = EtfConfig( + defaults=EtfDefaults(), + symbols={ + '159915.SZ': EtfSymbolConfig(is_t0=True, buy_shares=1000, atr_multiplier=1.0, inner_step=0.7), + '510300.SH': EtfSymbolConfig(is_t0=False, buy_shares=1000, atr_multiplier=1.0, inner_step=0.7), + }, + ) + summary = self.summary(cfg) + self.assertIn('2 只', summary) + self.assertLess(summary.index('159915.SZ'), summary.index('510300.SH')) + if __name__ == '__main__': unittest.main() diff --git a/py-client/README.md b/py-client/README.md index 219871a..a988360 100644 --- a/py-client/README.md +++ b/py-client/README.md @@ -35,11 +35,19 @@ py -3.14 -m venv .venv ## 验证与性能 +测试与基准已统一迁到仓库根的 `labs/`（试验与测试代码集中目录）， +从**仓库根目录**执行： + ```powershell -.venv/Scripts/python.exe -B -m unittest discover -s tests -v -.venv/Scripts/python.exe -B benchmarks/hotpaths.py +py -3.14 -B labs/run_tests.py # 全部离线测试（统一入口） +py -3.14 -B labs/run_tests.py -v # 详细 +py -3.14 -B labs/benchmarks/hotpaths.py # 微基准 ``` +`labs/run_tests.py` 会自动把本目录（`py-client`）挂上 `sys.path`， +所以不需要先 `cd py-client`；测试模块用 `from tests.zt_harness import ...` +这类绝对导入，也由该入口统一处理顶层包名。 + 130 项离线测试通过，覆盖 SDK 与 API 字段契约、委托簿、配置校验、IPO 申购状态机、 ZT 轮次状态机（正T/反T）、Trend 采集任务与 Python 3.14 回归。 测试使用模拟客户端和临时目录，不启动真实交易、不访问真实接口。 @@ -64,7 +72,8 @@ ZT 轮次状态机（正T/反T）、Trend 采集任务与 Python 3.14 回归。 ## ETF 自适应网格策略 -入口为 `strategy: etf`，标的和参数见 [`etc/etf.yaml`](etc/etf.yaml)， +入口为 `strategy: etf`，标的和参数见 [`etc/_etf.yaml`](etc/_etf.yaml)（由 +`config.load()` 按固定文件名加载，文件不存在时为 `None`）， 完整规则和启用步骤见 [`strategy/etf/README.md`](strategy/etf/README.md)。 ## ZT 做 T 策略（2026-09 重构） diff --git a/py-client/config/__init__.py b/py-client/config/__init__.py index 1497c7e..603020f 100644 --- a/py-client/config/__init__.py +++ b/py-client/config/__init__.py @@ -1,9 +1,28 @@ +import math +import re import socket from dataclasses import dataclass, field, fields from pathlib import Path import yaml +# 场内 ETF 代码：沪市 5 开头（510300.SH），深市 15/16/18 开头（159915.SZ）； +# 必须带交易所后缀，且键与接口返回的 ts_code 完全一致。 +ETF_CODE_PATTERN = re.compile(r"(?:5[0-9]{5}\.SH|1[568][0-9]{4}\.SZ)") + +# 标的段允许覆盖的全局参数；账户级参数（如 add_pct、commission_rate）不允许覆盖。 +SYMBOL_OVERRIDE = ("inner_grids", "max_grid_span_pct", "rebound_pct", "max_hold_days") + +# load() 成功后保存已加载的配置，供策略模块直接读取。 +global_config: GlobalConfig | None = None +account_config: AccountConfig | None = None +# _etf.yaml 不存在时为 None：只有 ETF 策略需要该文件。 +etf_config: EtfConfig | None = None + +# QMT 和外部 HTTP 接口的默认请求超时时间，单位为秒。 +HTTP_TIMEOUT = 5.0 + + @dataclass(slots=True) class SignalConfig: @@ -60,27 +79,117 @@ class AccountConfig: # 当前账户启用的策略名称，例如 trend。 strategy: str = "" - # 为空时读取 py-client/etc/etf.yaml；非空路径相对于账户配置目录。 - etf_config_path: str = "" -# load() 成功后保存已加载的配置，供策略模块直接读取。 -global_config: GlobalConfig | None = None -account_config: AccountConfig | None = None +@dataclass(slots=True) +class EtfDefaults: + """ETF 网格策略的全局默认参数，来自 ``_etf.yaml`` 的 ``defaults`` 段。 -# QMT 和外部 HTTP 接口的默认请求超时时间，单位为秒。 -HTTP_TIMEOUT = 5.0 + 字段顺序与 ``etc/_etf.yaml`` 保持一致，数值口径见 ``docs/etf.md`` §6。 + """ + + atr_period: int = 14 + # 格距 = max(ATR × atr_multiplier, MA60 × min_grid_pct/100, 0.001)。 + min_grid_pct: float = 0.5 + # 跨度健康度告警线，超线只告警并建议下调 atr_multiplier，不阻止建网。 + max_grid_span_pct: float = 40.0 + # 区间通道回看天数，以及"距区间下沿多少百分比以内算低位"。 + channel_period: int = 20 + channel_pct: float = 15.0 + # 建仓与补仓共用的反弹确认阈值，必须小于 add_pct。 + rebound_pct: float = 0.5 + # 补仓触发：自上一档成交价再跌该百分比（需配合反弹确认）。 + add_pct: float = 3.0 + # 补仓次数上限，总档数 = max_adds + 1（含底仓）。 + max_adds: int = 9 + watch_seconds: int = 600 + # 主出口：盈亏率 ≥ 该值即整仓清掉。 + min_profit_pct: float = 1.0 + # 副出口：峰值至少抬到第 N 格才允许回撤卖出。 + inner_grids: float = 2.0 + # 买入日当天不挂卖单；is_t0 为真的标的跳过本条。 + min_hold_days: int = 1 + # 0 表示不止损。 + max_hold_days: int = 0 + commission_rate: float = 0.0003 + min_commission: float = 5.0 + max_tick_age_seconds: int = 90 + + +@dataclass(slots=True) +class EtfSymbolConfig: + """单个标的的参数；未覆盖的字段为 ``None``，由 ``get()`` 继承全局默认。""" + + # 是否支持当日回转；必须显式配置，没有安全默认值。 + is_t0: bool = False + # 每档每次买入股数，100 的整数倍。 + buy_shares: int = 0 + # 格距的 ATR 倍数，决定阶梯跨度，必须逐标的标定。 + atr_multiplier: float = 0.0 + # 内层格距（盈亏率百分点），决定副出口能否被触发。 + inner_step: float = 0.0 + # 单标的总持仓上限；缺省按 max_adds + 1 档计算。 + max_shares: int | None = None + + inner_grids: float | None = None + max_grid_span_pct: float | None = None + rebound_pct: float | None = None + max_hold_days: int | None = None + + # 同一份 _etf.yaml 的全局默认值，不参与相等性比较。 + defaults: EtfDefaults = field(default_factory=EtfDefaults, repr=False, compare=False) + + def get(self, name: str): + """返回生效参数：标的覆盖优先，未覆盖时回落到全局默认。""" + if name not in SYMBOL_OVERRIDE: + raise KeyError(f"标的配置不支持覆盖参数 {name}") + value = getattr(self, name) + return getattr(self.defaults, name) if value is None else value + + def effective(self) -> dict: + """返回全部生效参数，供日志和自检使用。""" + values = {name: getattr(self, name) for name in self.override_names()} + values.update({name: self.get(name) for name in SYMBOL_OVERRIDE}) + return values + + @classmethod + def override_names(cls) -> tuple[str, ...]: + """标的段允许出现在 YAML 中的字段名。""" + return tuple(item.name for item in fields(cls) if item.name != "defaults") + + +@dataclass(slots=True) +class EtfConfig: + """``_etf.yaml`` 的完整内容：全局默认 + 标的白名单。""" + + defaults: EtfDefaults = field(default_factory=EtfDefaults) + # 键为证券代码，顺序即资金优先级；每项已绑定全局默认，便于 ``get()`` 回退。 + symbols: dict[str, EtfSymbolConfig] = field(default_factory=dict) + + @property + def codes(self) -> tuple[str, ...]: + """按配置顺序返回标的代码。""" + return tuple(self.symbols) + + def symbol(self, code: str) -> EtfSymbolConfig: + """返回指定标的的配置；不在白名单内时抛 KeyError。""" + try: + return self.symbols[code] + except KeyError: + raise KeyError(f"证券 {code} 不在 _etf.yaml 的白名单内") from None def load( etc_dir: str | Path | None = None, hostname: str | None = None, -) -> tuple[GlobalConfig, AccountConfig]: - """加载公共配置以及当前主机对应的账户配置。 +) -> tuple[GlobalConfig, AccountConfig, EtfConfig]: + """加载公共配置、当前主机的账户配置以及 ETF 策略配置。 Args: etc_dir: 配置文件目录，其中必须包含 ``_global.yaml``；为空时 - 默认使用 py-client 下的 ``etc`` 目录。 + 默认使用 py-client 下的 ``etc`` 目录。ETF 策略的 + ``_etf.yaml`` 同样在该目录下按固定文件名查找，不存在时 + ``etf_config`` 为 None，不视为错误。 hostname: 指定要加载的主机名；为空时使用当前计算机名。 Returns: @@ -89,7 +198,7 @@ def load( Raises: ValueError: 配置缺失、格式错误或策略参数不合法。 """ - global global_config, account_config + global global_config, account_config, etf_config root = Path(etc_dir) if etc_dir is not None else Path(__file__).parent.parent / "etc" raw = _yaml(root / "_global.yaml") @@ -134,11 +243,6 @@ def load( Path(global_config.qmt_data_dir).mkdir(parents=True, exist_ok=True) account_config = AccountConfig(**_account_values(root / account_file)) - if account_config.etf_config_path: - etf_path = Path(account_config.etf_config_path) - account_config.etf_config_path = str(etf_path if etf_path.is_absolute() else root / etf_path) - elif account_config.strategy.strip().lower() == 'etf': - account_config.etf_config_path = str(root / 'etf.yaml') if account_config.buy_value <= 0 or account_config.grid_step_pct <= 0: raise ValueError("buy_value、grid_step_pct 必须大于 0") if type(account_config.zt_open_hands) is not int or account_config.zt_open_hands < 0: @@ -159,7 +263,10 @@ def load( account_config.strategy = account_config.strategy.lower() if account_config.strategy == "zt" and account_config.signal_allow != ["dcm"]: raise ValueError("zt 策略的 signal_allow 必须且只能为 [\"dcm\"]") - return global_config, account_config + + # ETF 参数是固定文件名，缺文件时返回 None，由 ETF 策略自行决定是否必须。 + etf_config = _etf_config(root / "_etf.yaml") + return global_config, account_config, etf_config def _yaml(path: Path) -> dict: @@ -184,3 +291,233 @@ def _account_values(path: Path) -> dict: if unknown: raise ValueError(f"账户配置 {path} 存在未知字段: {', '.join(unknown)}") return raw + + +def _etf_config(path: Path) -> EtfConfig | None: + """读取固定文件名的 ETF 配置；文件不存在时返回 None。 + + 只有 ETF 策略需要 ``_etf.yaml``，因此缺文件不是错误；文件存在但内容 + 不合法（含未知键）仍然是错误，避免拼错参数被静默忽略。 + """ + if not path.is_file(): + return None + return _parse_etf(_yaml(path), path) + + +def _parse_etf(raw: dict, path: Path) -> EtfConfig: + """把 ``_etf.yaml`` 的内容转换为带校验的 ``EtfConfig``。""" + if not isinstance(raw, dict): + raise ValueError(f"ETF 配置 {path} 的根节点必须是对象") + + unknown = sorted(set(raw) - {"defaults", "symbols"}) + if unknown: + raise ValueError(f"ETF 配置 {path} 存在未知字段: {', '.join(unknown)}") + + defaults = _etf_defaults(raw.get("defaults") or {}, path) + raw_symbols = raw.get("symbols") or {} + if not isinstance(raw_symbols, dict) or not raw_symbols: + raise ValueError(f"ETF 配置 {path} 的 symbols 必须是非空标的映射") + + symbols: dict[str, EtfSymbolConfig] = {} + for code, values in raw_symbols.items(): + symbols[code] = _etf_symbol(code, values or {}, defaults, path) + return EtfConfig(defaults=defaults, symbols=symbols) + + +def _etf_defaults(values: dict, path: Path) -> EtfDefaults: + """校验 ``defaults`` 段并补齐缺省字段，口径见 ``docs/etf.md`` §6。""" + if not isinstance(values, dict): + raise ValueError(f"ETF 配置 {path} 的 defaults 必须是对象") + + unknown = sorted(set(values) - {item.name for item in fields(EtfDefaults)}) + if unknown: + raise ValueError( + f"ETF 配置 {path} 的 defaults 存在未知字段: {', '.join(unknown)}" + ) + + section = "defaults" + atr_period = _etf_int(values.get("atr_period", 14), "atr_period", path, section) + channel_period = _etf_int( + values.get("channel_period", 20), "channel_period", path, section + ) + watch_seconds = _etf_int( + values.get("watch_seconds", 600), "watch_seconds", path, section + ) + max_tick_age_seconds = _etf_int( + values.get("max_tick_age_seconds", 90), "max_tick_age_seconds", path, section + ) + min_hold_days = _etf_int( + values.get("min_hold_days", 1), "min_hold_days", path, section + ) + max_hold_days = _etf_int( + values.get("max_hold_days", 0), "max_hold_days", path, section, zero_ok=True + ) + max_adds = _etf_int(values.get("max_adds", 9), "max_adds", path, section, zero_ok=True) + if max_adds > 9: + raise ValueError(f"ETF 配置 {path} 的 max_adds 不能大于 9（总档数 10 档）") + + min_grid_pct = _etf_number( + values.get("min_grid_pct", 0.5), "min_grid_pct", path, section + ) + max_grid_span_pct = _etf_number( + values.get("max_grid_span_pct", 40.0), + "max_grid_span_pct", + path, + section, + high=100.0, + ) + channel_pct = _etf_number( + values.get("channel_pct", 15.0), "channel_pct", path, section + ) + rebound_pct = _etf_number( + values.get("rebound_pct", 0.5), "rebound_pct", path, section + ) + add_pct = _etf_number(values.get("add_pct", 3.0), "add_pct", path, section) + min_profit_pct = _etf_number( + values.get("min_profit_pct", 1.0), "min_profit_pct", path, section + ) + inner_grids = _etf_number( + values.get("inner_grids", 2.0), "inner_grids", path, section + ) + commission_rate = _etf_number( + values.get("commission_rate", 0.0003), + "commission_rate", + path, + section, + zero_ok=True, + ) + min_commission = _etf_number( + values.get("min_commission", 5.0), "min_commission", path, section, zero_ok=True + ) + + # 反弹确认必须早于补仓触发，否则确认价回到上一档之上，条件自相矛盾。 + if rebound_pct >= add_pct: + raise ValueError(f"ETF 配置 {path} 的 rebound_pct 必须小于 add_pct") + + return EtfDefaults( + atr_period=atr_period, + min_grid_pct=min_grid_pct, + max_grid_span_pct=max_grid_span_pct, + channel_period=channel_period, + channel_pct=channel_pct, + rebound_pct=rebound_pct, + add_pct=add_pct, + max_adds=max_adds, + watch_seconds=watch_seconds, + min_profit_pct=min_profit_pct, + inner_grids=inner_grids, + min_hold_days=min_hold_days, + max_hold_days=max_hold_days, + commission_rate=commission_rate, + min_commission=min_commission, + max_tick_age_seconds=max_tick_age_seconds, + ) + + +def _etf_symbol( + code: str, values: dict, defaults: EtfDefaults, path: Path +) -> EtfSymbolConfig: + """校验单个标的的参数，并绑定全局默认以支持未覆盖字段的继承。""" + if not isinstance(code, str) or not ETF_CODE_PATTERN.fullmatch(code): + raise ValueError(f"ETF 配置 {path} 的标的 {code!r} 不是合法场内 ETF 代码") + if not isinstance(values, dict): + raise ValueError(f"ETF 配置 {path} 的标的 {code} 必须是对象") + + unknown = sorted(set(values) - set(EtfSymbolConfig.override_names())) + if unknown: + raise ValueError( + f"ETF 配置 {path} 的标的 {code} 存在未知字段: {', '.join(unknown)}" + ) + + section = f"标的 {code}" + # is_t0、buy_shares、atr_multiplier、inner_step 分别决定结算制度、佣金轴 + # 与跨度轴，没有安全的全局默认值，必须逐标的显式给出。 + missing = sorted({"is_t0", "buy_shares", "atr_multiplier", "inner_step"} - set(values)) + if missing: + raise ValueError( + f"ETF 配置 {path} 的{section}缺少必填字段: {', '.join(missing)}" + ) + + is_t0 = values["is_t0"] + if type(is_t0) is not bool: + raise ValueError(f"ETF 配置 {path} 的{section}的 is_t0 必须是布尔值") + + buy_shares = _etf_int(values["buy_shares"], "buy_shares", path, section) + if buy_shares % 100: + raise ValueError(f"ETF 配置 {path} 的{section}的 buy_shares 必须是 100 的整数倍") + max_shares = _etf_int( + values.get("max_shares") or buy_shares * (defaults.max_adds + 1), + "max_shares", + path, + section, + ) + atr_multiplier = _etf_number( + values["atr_multiplier"], "atr_multiplier", path, section + ) + inner_step = _etf_number(values["inner_step"], "inner_step", path, section) + + overrides: dict = {} + if "inner_grids" in values: + overrides["inner_grids"] = _etf_number( + values["inner_grids"], "inner_grids", path, section + ) + if "max_grid_span_pct" in values: + overrides["max_grid_span_pct"] = _etf_number( + values["max_grid_span_pct"], "max_grid_span_pct", path, section, high=100.0 + ) + if "rebound_pct" in values: + overrides["rebound_pct"] = _etf_number( + values["rebound_pct"], "rebound_pct", path, section + ) + if "max_hold_days" in values: + overrides["max_hold_days"] = _etf_int( + values["max_hold_days"], "max_hold_days", path, section, zero_ok=True + ) + + return EtfSymbolConfig( + is_t0=is_t0, + buy_shares=buy_shares, + atr_multiplier=atr_multiplier, + inner_step=inner_step, + max_shares=max_shares, + defaults=defaults, + **overrides, + ) + + +def _etf_number( + value, + name: str, + path: Path, + section: str, + high: float | None = None, + zero_ok: bool = False, +) -> float: + """校验有限数值并返回；布尔值、非数值和越界值都抛 ValueError。""" + label = f"ETF 配置 {path} 的{section}的 {name}" + # bool 是 int 的子类，必须显式排除，否则 True 会被当成 1。 + if isinstance(value, bool) or not isinstance(value, (int, float)): + raise ValueError(f"{label} 必须是数值") + if not math.isfinite(value): + raise ValueError(f"{label} 必须是有限数值") + + low = 0 if zero_ok else 0.0 + if value < low or (value == 0 and not zero_ok) or (high is not None and value > high): + if high is None: + expect = "大于 0" + elif zero_ok: + expect = f"在 [0, {high:g}] 区间" + else: + expect = f"在 (0, {high:g}] 区间" + raise ValueError(f"{label} 必须{expect}") + return value + + +def _etf_int( + value, name: str, path: Path, section: str, zero_ok: bool = False +) -> int: + """校验整数字段并返回；``zero_ok`` 为假时要求大于 0。""" + if type(value) is not int or value < 0 or (value == 0 and not zero_ok): + expect = "非负整数" if zero_ok else "正整数" + raise ValueError(f"ETF 配置 {path} 的{section}的 {name} 必须是{expect}") + return value diff --git a/py-client/etc/_etf.yaml b/py-client/etc/_etf.yaml index cdffa3b..6af9376 100644 --- a/py-client/etc/_etf.yaml +++ b/py-client/etc/_etf.yaml @@ -1,24 +1,41 @@ -# 标的是示例白名单；仅在账户配置 strategy: etf 时启用。 -codes: - - "588000.SH" # 华夏上证科创板50成份ETF - - "510300.SH" # 沪深300ETF华泰柏瑞 - - "518880.SH" # 安易富黄金ETF -# 固定买入手数，每手 100 份；每只 ETF 总持仓硬上限 10 手。 -buy_hands: 1 -max_hands: 10 -# 60 日均线作为中轴；格距 = max(ATR×倍数, MA60×最小格距百分比, 0.001)。 -atr_period: 14 -atr_multiplier: 1.0 -boll_period: 20 -boll_std: 2.0 -min_grid_pct: 0.5 -# 复用 DipWatch：触及低位后，从观察低点反弹 0.61% 才买入。 -rebound_pct: 0.61 -watch_seconds: 600 -# 达到高位和最低利润要求后启动网格回撤止盈。 -min_profit_pct: 0.5 -# 用于资金预留及止盈费用门槛，按实际券商佣金调整。 -commission_rate: 0.0003 -min_commission: 5.0 -# 超过此秒数或缺少时间戳的行情不交易。 -max_tick_age_seconds: 90 +defaults: + atr_period: 14 + min_grid_pct: 0.5 + max_grid_span_pct: 40 # 跨度健康度告警线（非闸门） + channel_period: 20 + channel_pct: 15 + rebound_pct: 0.5 # 反弹确认阈值（建仓与补仓共用） + add_pct: 3.0 # 补仓：自上一档再跌该百分比即触发（需配合反弹确认） + max_adds: 9 # 补仓次数上限（底仓另计，共 10 档） + watch_seconds: 600 + min_profit_pct: 1.0 # 主出口：盈亏率 >= 该值即整仓清掉 + inner_grids: 2.0 # 副出口：峰值至少抬到第 N 格才允许回撤卖出 + min_hold_days: 1 + max_hold_days: 0 # 0 = 不止损 + commission_rate: 0.0003 + min_commission: 5.0 + max_tick_age_seconds: 90 + +# ---------- 标的白名单（键即标的，顺序即资金优先级）---------- +# 键必须与接口返回的 ts_code 完全一致（588000.SH / 159915.SZ） +symbols: + "588000.SH": + is_t0: false + buy_shares: 10000 + max_shares: 100000 # 10 个价位 × 1000 股 + atr_multiplier: 0.5 # 该标的 ATR 相对价格偏高，必须收窄 + inner_step: 0.9 + + "510300.SH": + is_t0: false + buy_shares: 4000 + max_shares: 20000 + atr_multiplier: 1.0 + inner_step: 0.7 + + "518880.SH": + is_t0: true # 黄金 ETF 支持当日回转 + buy_shares: 2000 + max_shares: 10000 + atr_multiplier: 1.0 + inner_step: 0.8 \ No newline at end of file diff --git a/py-client/libs/runtime.py b/py-client/libs/runtime.py index 862cc80..12f3d7b 100644 --- a/py-client/libs/runtime.py +++ b/py-client/libs/runtime.py @@ -4,7 +4,7 @@ import logging from concurrent.futures import ThreadPoolExecutor from dataclasses import dataclass, field -from config import AccountConfig, GlobalConfig, HTTP_TIMEOUT +from config import AccountConfig, GlobalConfig,EtfConfig, HTTP_TIMEOUT from sdk import Client from libs.grid_take_profit import GridTrailingTracker from libs.http import get_json @@ -19,10 +19,11 @@ class Runtime: client: Client global_cfg: GlobalConfig account_cfg: AccountConfig + etf_cfg: EtfConfig orders: OrderBook - open_watch: DipWatch - add_watch: DipWatch - profit_tracker: GridTrailingTracker + open_watch: DipWatch | None = None + add_watch: DipWatch | None = None + profit_tracker: GridTrailingTracker | None = None executor: ThreadPoolExecutor | None = None server_inital: dict[str, list[str]] = field(init=False) diff --git a/py-client/main.py b/py-client/main.py index 7bc7711..4600fc4 100644 --- a/py-client/main.py +++ b/py-client/main.py @@ -1,11 +1,15 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- +"""策略客户端启动入口：加载配置、拉起后台任务、按 ``strategy`` 分派策略主循环。 + +一个进程只跑一个策略（``account_config.strategy``）。IPO 打新、大盘刷新、 +趋势数据采集由后台调度线程承担，主线程跑策略自己的循环。 +""" import logging import os import sys from datetime import datetime -import traceback from apscheduler.schedulers.background import BackgroundScheduler import config from dataclasses import dataclass @@ -13,7 +17,6 @@ import yaml import httpx PROJECT_ROOT = os.path.dirname(os.path.abspath(__file__)) -GLOBAL_CONFIG_PATH = os.path.join(PROJECT_ROOT, "etc", "_global.yaml") LOG_DIR = os.path.join(PROJECT_ROOT, "logs") os.makedirs(LOG_DIR, exist_ok=True) LOG_FILE = os.path.join(LOG_DIR, datetime.now().strftime("%Y%m%d.log")) @@ -55,6 +58,14 @@ STRATEGIES = { def require_windows() -> bool: return os.name == "nt" + +def describe_etf_config() -> str: + """启动日志用的 ETF 配置概览：文件缺失时明确说明，不在这里报错。""" + etf_cfg = getattr(config, "etf_config", None) + if etf_cfg is None: + return "未找到 _etf.yaml（仅 etf 策略需要）" + return f"标的={len(etf_cfg.symbols)} 只，代码={'/'.join(etf_cfg.codes)}" + def check_single_instance(project_root: str) -> bool: """使用 Windows 命名互斥锁保证单实例。""" try: @@ -123,6 +134,19 @@ def main() -> int: config.load() if config.global_config is None or config.account_config is None: raise RuntimeError("配置尚未加载，请先调用 config.load()") + strategy = config.account_config.strategy + start = STRATEGIES.get(strategy) + if start is None: + raise ValueError( + f"未知策略 strategy={strategy!r}，可选: {', '.join(sorted(STRATEGIES))}" + ) + logging.info( + "配置已加载：主机=%s，账户=%s，策略=%s，ETF配置=%s", + getattr(config.account_config, "host_key", "-"), + getattr(config.account_config, "account_id", "-"), + strategy, + describe_etf_config(), + ) wait_for_qmt_api() # 后台调度不受趋势策略永久循环阻塞；同一时刻最多执行一个实例。 @@ -159,14 +183,14 @@ def main() -> int: logging.info("IPO 自动打新定时任务已启动：每日 10:00、14:00") logging.info("大盘信号后台刷新已启动：每分钟一次") - STRATEGIES[config.account_config.strategy].start_strategy() - logging.info("%s 策略已结束", config.account_config.strategy) + STRATEGIES[strategy].start_strategy() + logging.info("%s 策略主循环已结束", strategy) return 0 except (OSError, yaml.YAMLError, ValueError, RuntimeError, KeyError) as e: - print(f"启动失败: {e}", file=sys.stderr, flush=True) - traceback.print_exception(type(e), e, e.__traceback__) - wait_for_any_key() - return 1 + print(f"启动失败: {e}", file=sys.stderr, flush=True) + logging.exception("启动失败") + wait_for_any_key() + return 1 finally: if scheduler is not None and scheduler.running: scheduler.shutdown(wait=True) diff --git a/py-client/strategy/etf/README.md b/py-client/strategy/etf/README.md deleted file mode 100644 index aa8d554..0000000 --- a/py-client/strategy/etf/README.md +++ /dev/null @@ -1,131 +0,0 @@ -# A 股 ETF 自适应网格策略 - -策略目录为 `py-client/strategy/etf`，入口名称 `etf`，独立配置为 -`py-client/etc/etf.yaml`。每只 ETF 总持仓上限 10 手（1000 份），默认每次买入 1 手。 -这是常见均值回归与波动率网格方法的工程组合，尚未完成历史收益回测。 - -## 方法分析 - -| 方法 | 优点 | 局限 | 本策略选择 | -| --- | --- | --- | --- | -| 固定价差网格 | 简单直观 | 不适应不同价格与波动率 | ATR 动态格距，设置百分比下限 | -| 均线回归 | 提供相对高低位置 | 单边下跌中均线会滞后 | MA60 作中轴，设置仓位上限 | -| BOLL 低吸 | 用价格分布寻找相对低位 | 触及下轨不代表跌势结束 | 下轨仅启动观察，反弹后再买 | -| 回撤止盈 | 上涨时跟随峰值 | 不能保证最高价退出 | 复用现有 GridTrailingTracker | - -ATR 反映波动幅度，不判断方向；BOLL 为均线加减标准差倍数。定义参考 -[Fidelity ATR](https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/atr) -及 [Fidelity BOLL](https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/bollinger-bands)。 -默认参数是可调整的起点，不代表已经优化或保证收益。 - -## 指标与网格 - -历史日线直接读取 `http://go.apinb.com/a/get_daily?code=<证券代码>`，使用响应 -`details` 中的 `ts_code / trade_date / open / high / low / close`。校验 `code=0`、 -证券归属、日期和 OHLC 后，按日期排序，排除当天及未来数据，再取最近 120 根。 -每个标的每天计算一次并缓存，至少需要 60 根；ATR 周期为 60 时至少需要 61 根。 -接口样本未声明复权口径，策略使用接口原始价格，不自行假定或执行前复权。 - -- `M = 最近60根收盘价平均值`，固定 MA60。 -- `TR = max(最高-最低, abs(最高-前收盘), abs(最低-前收盘))`。 -- ATR 默认 14 日：前 14 个 TR 平均作初值，后续按 - `ATR = (前ATR × 13 + 当日TR) / 14` 进行 Wilder 平滑。 -- BOLL 默认 20 日、2 倍总体标准差：`中轨=MA20`，`上下轨=MA20 ± 2σ`。 -- 格距 `G = max(ATR × atr_multiplier, M × min_grid_pct / 100, 0.001)`， - 向上取整至 0.001 元。均线作中轴，ATR 决定每格宽度。 - -重复日期、非正数或非有限价格、价格关系异常、日线不足均不交易。 -最后日线超过 15 个自然日也不交易；该检查只排除明显过期，不能替代交易所日历。 -接口数据应更新至上一交易日。获取失败的标的每 5 分钟重试。 - -## 买入规则 - -1. 首仓在 `现价 <= min(BOLL下轨, M-G)` 时开始观察，不直接买入。 -2. 复用 `DipWatch` 防接飞刀：下跌刷新低点，从低点反弹默认 0.61% 后触发； - 观察默认 600 秒过期重置。反弹可站回下轨上方，但不能超过 MA60。 -3. 加仓还须满足 `现价 <= 上次实际买入成交均价-G`，防止同价位连续补满。 - 初次接管已有仓位时，券商成本作为初始加仓基准。 -4. 每次买入 `buy_hands × 100` 份；若买后超过 `max_hands × 100`，整笔跳过， - 不临时缩量。配置强制 `1 <= buy_hands <= max_hands <= 10`。 -5. 买入预算为券商可用资金减账户现金安全线，再减所有本地待确认买单。 - 限价金额加预估佣金占用预算，多标的串行扣减。 - -示例：MA60=4.00、G=0.05、下轨=3.90，价格进入 3.90 以下才观察；低点 3.88 -反弹至 3.904 时超过 0.61%，可以提交固定手数。如果实际均价为 3.904， -下一笔最高买价为 3.854，同时仍须满足低位触发和反弹确认。 - -## 止盈规则 - -1. `现价 >= max(BOLL上轨, M+G, 成本+G)`，且满足 `min_profit_pct`、 - 预估价差收益大于买卖两侧佣金，才启动高位跟踪。 -2. 启动时冻结格距。盈利格编号为 `floor((现价-成本)/冻结格距)`，复用 - `GridTrailingTracker` 记录最高格；进入更高格更新峰值，跌回较低格时止盈。 - 这是跌破峰值格边界，不是从最高价回撤完整一个 ATR。 -3. 启动后，即使回落到 BOLL 上轨或启动价以下，也继续判断回撤；卖出时仍须满足 - 最低利润和费用门槛。止盈启动后暂停补仓，不同时发买卖单。 -4. **固定手数用于买入；止盈卖出当前全部可用整手份额**，数量为 - `min(持仓, 券商可卖数量)` 向下取整到 100 份。零股暂不处理。 -5. T+1 当天不可卖时继续记录峰值，翌日按券商 `can_use_volume` 判断。 - 清仓、实际成交改变仓位、外部数量或成本变化后重建基准；部分卖出后剩余仓位 - 重新等待高位启动，不把旧峰值带入新仓。 - -交易单位、0.001 元报价精度及股票 ETF 的 T+1 参考 -[上交所 ETF 常见问题](https://www.sse.com.cn/assortment/fund/etf/question/)。 -本策略没有自动止损，单边下跌可能满仓后长期持有；10 手上限只限制数量。 -佣金参数按券商实际情况调整。费用门槛是估算，不逐笔归集历史买入最低佣金； -若券商持仓成本已含费用，该估算会偏保守。 - -## 委托、持仓与持久化 - -- 管理 `codes` 白名单内的已有持仓；`excluded_codes` 优先排除。配置外证券不买卖。 -- 以 `ETF-BUY-*` / `ETF-SELL-*` 为本地编号，标签 `etf`，当前价按 0.001 元 - 精度提交限价。复用 OrderBook，只自动撤销超时 120 秒的 ETF 前缀委托。 -- 同标的全账户买卖在途、未知委托状态或在途份额都会阻止新单。 - 行情缺少 `timetag`、不是当天或超过默认 90 秒，也不交易。 -- 下单前原子保存意图，HTTP 成功、超时或失败均不会自动解除锁。 - 必须收到终态（53/54/56/57），并且券商持仓与累计成交量相符，才能继续。 - 部分成交按实际数量核对，买入必须取得实际成交均价才推进下一格。 -- 状态位于 `{qmt_data_dir}/etf/{账户SHA256}/state.json`，保存实际买入基准、 - 止盈峰值、冻结格距和未确认委托；重启恢复，损坏不静默覆盖。 -- 同一 ETF 不适合同时由人工或其他策略频繁交易。在途期间外部改变持仓，会暂停核对。 - 若跨日后柜台不再返回未确认订单，则持续暂停该标的，需要核对历史订单与持仓后 - 人工处理状态，不按超时自动重发。有未确认委托的标的不能直接从配置移除。 - -## 启用 - -1. 修改 `etc/etf.yaml` 的标的及参数。示例仅示范格式，请选择实际交易的 A 股股票 ETF； - 代码形态检查不验证基金投资范围。 -2. 主机对应的账户 YAML 设置： - - ```yaml - strategy: etf - etf_config_path: etf.yaml - enable_auto_ipo: false - ``` - - 路径相对账户配置目录，也支持绝对路径；不填默认 `etc/etf.yaml`。 - `account_id`、`min_cash_ratio` 继续生效。公共校验仍要求 `buy_value`、 - `grid_step_pct` 为正，但 ETF 不用它们计算数量或格距。 - 关闭 IPO 是此处示例选择；ETF 决策不依赖 IPO 或远端股票信号。 -3. 无需修改 QMT 服务端或 `sdk/`。历史接口适配完全位于 `strategy/etf/data.py`； - 实时行情、持仓和交易继续使用已有 QMT SDK。 -4. 确认外部接口提供配置 ETF 的足量、最新日线。HTTP 错误、业务失败、空列表、 - 证券代码不一致或数据异常都会跳过该标的，不改用其他证券数据。 - 2026-09-17 联通验证中，股票样本 `600584.SH` 成功返回 200 条，示例 ETF - `510300.SH` 返回 404；需要数据服务覆盖实际配置的 ETF 后才能正常运行。 -5. 按现有方式运行 `python main.py`。每 30 秒执行，午休暂停，15:00 退出。 - -本次新增不会自动切换已有实盘账户，也没有进行实盘委托。 - -## 离线验证 - -在 `py-client` 中运行： - -```powershell -python -m unittest discover -s tests -p test_etf.py -v -python -m unittest discover -s tests -v -``` - -覆盖指标、未收盘日线排除、反弹确认、固定手数、限仓、资金共享、部分成交、拒单、 -快照延迟、T+1、重启防重、峰值恢复、状态损坏、外部日线接口与异常响应。 -离线行为验证不等同于历史收益回测或实盘联调。 diff --git a/py-client/strategy/etf/__init__.py b/py-client/strategy/etf/__init__.py deleted file mode 100644 index d2a32fb..0000000 --- a/py-client/strategy/etf/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""A 股场内 ETF：均线中轴、ATR 网格与 BOLL 低吸策略。""" diff --git a/py-client/strategy/etf/boot.py b/py-client/strategy/etf/boot.py index f7ca618..293192c 100644 --- a/py-client/strategy/etf/boot.py +++ b/py-client/strategy/etf/boot.py @@ -1,67 +1,191 @@ -"""ETF 策略入口：每 30 秒运行，日线指标当天缓存，失败标的单独重试。""" +"""趋势策略启动器。 + +该模块负责组合 SDK、配置、状态存储和趋势策略组件，供 main.py 调用。 +""" -from datetime import datetime, timedelta -import hashlib -import logging as log -from pathlib import Path import time -import httpx +import logging as log +from concurrent.futures import Future, ThreadPoolExecutor +from datetime import datetime import config from libs.calc import trading_time -from libs.snapshot import cache_portfolio +from libs.market import market_allow_open +from libs.overview import Overview +from libs.signal import init_signals, SignalItem from sdk import Client - -from .config import load -from .data import daily_bars -from .engine import Engine -from .indicators import calculate -from .state import Store - +from libs.snapshot import cache_portfolio +from libs.grid_take_profit import GridTrailingTracker +from libs.order import OrderBook +from libs.watch import DipWatch +from libs.runtime import Runtime +from .open import open_signal +from .positions import manage_positions +from .signal import gen_signals def StartETF() -> None: - cfg = load(config.account_config.etf_config_path or None) - account = str(config.account_config.account_id).strip() - if not account: - raise ValueError('ETF 策略缺少账户编号') - key = hashlib.sha256(account.encode('utf-8')).hexdigest() - store = Store(Path(config.global_config.qmt_data_dir) / 'etf' / key / 'state.json', account) - # 独立 HTTP 连接池读取外部日线，不向外部接口发送 QMT 认证信息。 - with Client(config.global_config.qmt_base_url, config.global_config.qmt_token, config.HTTP_TIMEOUT) as client, \ - httpx.Client(timeout=config.HTTP_TIMEOUT) as history_client: - engine = Engine(client, cfg, store, config.account_config.min_cash_ratio, - config.account_config.excluded_codes) - log.info('[ETF启动] 标的=%s 每次=%d手 每只上限=%d手 状态=%s', - cfg.codes, cfg.buy_hands, cfg.max_hands, store.path) - log.info('[ETF启动] 管理配置白名单内已有持仓，卖出以券商可用份额为限') - indicators, retry_at = {}, {} - cached_day = None + """初始化趋势策略，并以 30 秒间隔持续执行。""" + client = Client( + config.global_config.qmt_base_url, + config.global_config.qmt_token, + config.HTTP_TIMEOUT, + ) + executor = None + try: + portfolio = client.portfolio() + assets = portfolio.assets + positions = list(portfolio.positions.values()) + cache_portfolio(config.account_config.account_id, assets, positions, client.deals()) + order_book = OrderBook() + order_book.refresh(client, portfolio.orders) + + executor = ThreadPoolExecutor(max_workers=2, thread_name_prefix="ETF") + run = Runtime( + client=client, + etf_cfg=config.etf_config, + orders=order_book, + executor=executor, + ) + + # 获取本策略的信号开仓数据 + signals = gen_signals(run) + log.info( + "[启动] ETF策略已启动，账户=%s，信号=%d，持仓=%d", + config.account_config.account_id, + len(signals), + len(positions), + ) + + Overview(assets, positions, config.account_config) + + DEFAULT_TICK_INTERVAL = 30 while True: - now = datetime.now() - if now.hour >= 15: - log.info('[ETF结束] 已到 15:00') + lt = time.localtime() + if (lt.tm_hour, lt.tm_min, lt.tm_sec) >= (15, 0, 0): + log.info("[ETF] 已到 15:00，结束趋势策略") return - if trading_time(now): - try: - if cached_day != now.date(): - indicators, retry_at, cached_day = {}, {}, now.date() - for code in cfg.codes: - if code in indicators or now < retry_at.get(code, datetime.min): - continue - try: - rows = daily_bars(history_client, code, now.date()) - indicators[code] = calculate(rows, now.date(), cfg) - log.info('[ETF指标] %s %s', code, indicators[code]) - except Exception: - retry_at[code] = now + timedelta(minutes=5) - log.exception('[ETF日线] %s 获取或计算失败，5分钟后重试', code) - portfolio = client.portfolio() - ticks = client.full_tick(list(cfg.codes)) - engine.run(portfolio, ticks, indicators, datetime.now()) - try: - cache_portfolio(account, portfolio.assets, list(portfolio.positions.values()), client.deals()) - except Exception: - log.exception('[ETF采集] 成交快照读取失败') - except Exception: - log.exception('[ETF异常] 本轮失败，下一轮继续') - time.sleep(30 - datetime.now().second % 30) + current_sec = lt.tm_sec + + # 计算距离下一个目标时间点（0秒或30秒）的等待时间 + if current_sec < DEFAULT_TICK_INTERVAL: + wait_seconds = DEFAULT_TICK_INTERVAL - current_sec + elif current_sec < 60: + wait_seconds = 60 - current_sec + else: + wait_seconds = DEFAULT_TICK_INTERVAL + + # 等待到目标时间点 + time.sleep(wait_seconds) + + # 单轮失败不能杀死唯一的交易定时线程。 + try: + RunOnce(run, signals) + except Exception as e: + log.error( + f"[ETF] 本 tick 执行失败，下一 tick 继续: {e}", exc_info=True + ) + finally: + try: + if executor is not None: + executor.shutdown(wait=True) + finally: + client.close() + + +def RunOnce(run: Runtime, signals: list[SignalItem]) -> None: + """按固定步骤执行一轮趋势策略， ``RunOnce``。""" + if not trading_time(datetime.now()): + return + + print( + "=" * 40 + f" Ticker {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} " + "=" * 40 + ) + + started_at = time.monotonic() + + # 1. 一次获取资产、持仓和订单，并清理过期订单。 + try: + portfolio = run.client.portfolio() + assets = portfolio.assets + position_codes = list(portfolio.positions) + positions = list(portfolio.positions.values()) + cache_portfolio(run.account_cfg.account_id, assets, positions, run.client.deals()) + run.orders.refresh(run.client, portfolio.orders) + except Exception: + log.exception("[Portfolio] 刷新账户快照失败") + return + + futures: list[tuple[str, Future]] = [] + + # 2. 验证可用资金；低于资金安全线时禁止开新仓。 + allow_open_by_cash = ( + assets.available >= assets.total * run.account_cfg.min_cash_ratio + ) + if not allow_open_by_cash: + log.info( + "[Status] 禁止开仓：可用资金不足，可用=%.2f，总资产=%.2f", + assets.available, + assets.total, + ) + + # 3. 获取大盘状态，只有大盘信号允许时才执行开仓。 + market_ok = market_allow_open() + + # 4. 验证有效开仓信号：排除已有持仓。 + allow_open: list[SignalItem] = [] + allow_codes: list[str] = [] + for signal in signals: + if signal.code not in portfolio.positions: + allow_open.append(signal) + allow_codes.append(signal.code) + + if allow_open and not market_ok: + log.info("[开仓] 禁止开仓：大盘信号不允许，候选=%d", len(allow_open)) + + # 5. 获取持仓和待开仓证券的实时行情 tick。 + all_codes = list(dict.fromkeys(position_codes + allow_codes)) + try: + ticks = run.client.full_tick(all_codes) + except Exception: + log.exception("[行情] 获取行情失败，代码数量=%d", len(all_codes)) + return + + log.info( + "[RunOnce] 本轮就绪，持仓=%d，候选=%d，大盘允许=%s，资金允许=%s", + len(positions), + len(allow_open), + market_ok, + allow_open_by_cash, + ) + + # 启动线程，开始计算 + # 7. 持仓计算。 + futures.append( + ( + "持仓计算", + run.executor.submit( + manage_positions, run, ticks, positions, market_ok, assets.available + ), + ) + ) + + # 8. 开仓计算：必须同时存在有效信号且大盘允许开仓。 + if allow_open and market_ok and allow_open_by_cash: + futures.append( + ("开仓计算", run.executor.submit(open_signal, run, ticks, allow_open)) + ) + + # 9. 开始执行 + for name, future in futures: + _wait_worker(name, future) + log.info( + "[RunOnce] 本轮完成，耗时=%d毫秒", int((time.monotonic() - started_at) * 1000) + ) + + +def _wait_worker(name: str, future: Future) -> None: + """保留单轮继续运行的语义，分别记录工作线程异常。""" + try: + future.result() + except Exception: + log.exception("[运行] %s线程失败", name) diff --git a/py-client/strategy/etf/config.py b/py-client/strategy/etf/config.py deleted file mode 100644 index 4a41e1b..0000000 --- a/py-client/strategy/etf/config.py +++ /dev/null @@ -1,59 +0,0 @@ -"""独立读取 ETF 参数，不修改现有账户策略的默认行为。""" - -from dataclasses import dataclass, fields -from pathlib import Path -import math -import re -import yaml - - -@dataclass(frozen=True) -class ETFConfig: - codes: tuple[str, ...] = () - buy_hands: int = 1 - max_hands: int = 10 - atr_period: int = 14 - atr_multiplier: float = 1.0 - boll_period: int = 20 - boll_std: float = 2.0 - min_grid_pct: float = 0.5 - rebound_pct: float = 0.61 - watch_seconds: int = 600 - min_profit_pct: float = 0.5 - commission_rate: float = 0.0003 - min_commission: float = 5.0 - max_tick_age_seconds: int = 90 - - def __post_init__(self): - # 限定沪深场内 ETF 代码形态；具体跟踪 A 股的标的由配置白名单决定。 - if not isinstance(self.codes, (list, tuple)) or not self.codes: - raise ValueError('etf.yaml 的 codes 必须是非空 ETF 代码列表') - if any(not isinstance(c, str) or not re.fullmatch(r'(?:5[0-9]{5}\.SH|1[58][0-9]{4}\.SZ)', c) - for c in self.codes) or len(set(self.codes)) != len(self.codes): - raise ValueError('ETF 代码必须唯一，使用完整沪深场内代码，如 510300.SH、159915.SZ') - object.__setattr__(self, 'codes', tuple(self.codes)) - for name in ('buy_hands', 'max_hands', 'atr_period', 'boll_period', 'watch_seconds', 'max_tick_age_seconds'): - if type(getattr(self, name)) is not int or getattr(self, name) <= 0: - raise ValueError(f'{name} 必须为正整数') - if not self.buy_hands <= self.max_hands <= 10: - raise ValueError('必须满足 buy_hands <= max_hands <= 10，每手 100 份') - if not 2 <= self.atr_period <= 60 or not 2 <= self.boll_period <= 60: - raise ValueError('ATR、BOLL 周期必须在 2 到 60 日之间') - for name in ('atr_multiplier', 'boll_std', 'min_grid_pct', 'rebound_pct', 'min_profit_pct', - 'commission_rate', 'min_commission'): - value = getattr(self, name) - if isinstance(value, bool) or not isinstance(value, (int, float)) or not math.isfinite(value) or value < 0: - raise ValueError(f'{name} 必须是有限非负数') - if name not in ('commission_rate', 'min_commission') and value == 0: - raise ValueError(f'{name} 必须大于零') - - -def load(path: str | Path | None = None) -> ETFConfig: - path = Path(path) if path else Path(__file__).resolve().parents[2] / 'etc' / '_etf.yaml' - try: - raw = yaml.safe_load(path.read_text(encoding='utf-8')) - except (OSError, yaml.YAMLError) as exc: - raise ValueError(f'ETF 配置读取失败：{path}') from exc - if not isinstance(raw, dict) or set(raw) - {f.name for f in fields(ETFConfig)}: - raise ValueError('ETF 配置必须为对象，且不能包含未知参数') - return ETFConfig(**raw) diff --git a/py-client/strategy/etf/data.py b/py-client/strategy/etf/data.py deleted file mode 100644 index 2812adb..0000000 --- a/py-client/strategy/etf/data.py +++ /dev/null @@ -1,55 +0,0 @@ -"""ETF 专用历史日线适配，不依赖或修改 QMT SDK。""" - -from datetime import date, datetime -import math -import re - -import httpx - - -DAILY_URL = 'http://139.224.247.176:13499/etf/daily' - - -def daily_bars(client: httpx.Client, code: str, today: date, count: int = 120) -> list[dict]: - """读取指定证券日线；只使用 code 参数，截取历史窗口在本地完成。""" - response = client.get(DAILY_URL, params={'code': code}) - response.raise_for_status() - return parse_daily(response.json(), code, today, count) - - -def parse_daily(payload: dict, code: str, today: date, count: int = 120) -> list[dict]: - """校验业务状态、证券归属和 OHLC，将 trade_date 转为指标需要的 date。""" - if type(count) is not int or count <= 0: - raise ValueError('日线数量必须为正整数') - if not isinstance(payload, dict) or type(payload.get('code')) is not int or payload['code'] != 0: - raise ValueError(f'日线接口业务失败：{payload.get("message", "状态无效") if isinstance(payload, dict) else "响应非对象"}') - details = payload.get('details') - if not isinstance(details, list) or not details: - raise ValueError(f'{code} 日线接口未返回有效 details 列表') - bars = {} - for row in details: - if not isinstance(row, dict) or row.get('ts_code') != code: - raise ValueError(f'{code} 日线证券代码不一致') - stamp = str(row.get('trade_date', '')) - if not re.fullmatch(r'[0-9]{8}', stamp): - raise ValueError(f'{code} 日线日期无效：{stamp}') - day = datetime.strptime(stamp, '%Y%m%d').date() - # 当前日及未来日线均不可用于盘中指标，先过滤再截取最近 count 根。 - if day >= today: - continue - if stamp in bars: - raise ValueError(f'{code} 日线日期重复：{stamp}') - values = {} - for key in ('open', 'high', 'low', 'close'): - value = row.get(key) - if isinstance(value, bool) or not isinstance(value, (int, float, str)): - raise ValueError(f'{code} 日线 {key} 无效') - value = float(value) - if not math.isfinite(value) or value <= 0: - raise ValueError(f'{code} 日线 {key} 非有限正数') - values[key] = value - if not (values['low'] <= values['open'] <= values['high'] - and values['low'] <= values['close'] <= values['high']): - raise ValueError(f'{code} 日线 OHLC 关系异常') - bars[stamp] = dict(date=stamp, **values) - return [bars[stamp] for stamp in sorted(bars)[-count:]] diff --git a/py-client/strategy/etf/engine.py b/py-client/strategy/etf/engine.py deleted file mode 100644 index 6bb3e1b..0000000 --- a/py-client/strategy/etf/engine.py +++ /dev/null @@ -1,202 +0,0 @@ -"""串行 ETF 决策：先核对成交，再止盈，最后低吸并预留本轮资金。""" - -from datetime import datetime -import logging as log -import math - -from libs.grid_take_profit import GridState, GridTrailingTracker -from libs.order import OrderBook, PlaceOrderRequest -from libs.watch import DipWatch -from sdk import OP_BUY, OP_SELL, Portfolio, PositionItem, Tick - -from .config import ETFConfig -from .indicators import Indicators -from .state import Store, SymbolState - - -class Engine: - def __init__(self, client, cfg: ETFConfig, store: Store, min_cash_ratio: float, excluded=()): - if not math.isfinite(min_cash_ratio) or not 0 <= min_cash_ratio <= 1: - raise ValueError('ETF min_cash_ratio 必须在 0 到 1 之间') - self.client, self.cfg, self.store = client, cfg, store - if any(state.pending and code not in cfg.codes for code, state in store.symbols.items()): - raise ValueError('存在已从配置移除的 ETF 待确认委托，请保留该标的直到核对完成') - self.min_cash_ratio, self.excluded = min_cash_ratio, set(excluded) - self.orders = OrderBook(cancel_timeout_sec=120) - self.watch = DipWatch(cfg.watch_seconds, cfg.rebound_pct) - - def fee(self, amount: float) -> float: - return max(self.cfg.min_commission, amount * self.cfg.commission_rate) - - def reconcile(self, code: str, state: SymbolState, position: PositionItem, orders) -> bool: - """必须同时得到终态委托与匹配的持仓快照，才解除本地待确认锁。""" - pending = state.pending - # 柜台清仓记录可能保留旧成本；零持仓应统一视为零成本，避免每轮清空低吸观察。 - current_cost = position.open_price if position.volume > 0 else 0.0 - if pending: - matches = [o for o in orders if o.local_order_id == pending['id'] and o.stock_code == code] - if len(matches) != 1: - log.warning('[ETF待确认] %s 订单=%s 回报缺失或不唯一，暂停该标的', code, pending['id']) - return False - order = matches[0] - status = str(order.order_status) - if status not in {'53', '54', '56', '57'}: - log.info('[ETF待确认] %s 订单=%s 状态=%s 成交=%s', code, pending['id'], status, order.volume_traded) - return False - filled = order.volume_traded - if (order.side != pending['side'] or type(filled) is not int or not 0 <= filled <= pending['volume'] - or (status == '56' and filled != pending['volume'])): - log.warning('[ETF待确认] %s 委托方向或成交数量不一致', code) - return False - expected = pending['base_volume'] + (filled if pending['side'] == 'BUY' else -filled) - if position.volume != expected: - log.warning('[ETF待确认] %s 持仓=%s 预期=%s，等待快照同步', code, position.volume, expected) - return False - if filled and pending['side'] == 'BUY': - if not math.isfinite(order.traded_price) or order.traded_price <= 0: - log.warning('[ETF待确认] %s 缺少实际成交均价', code) - return False - state.last_buy = order.traded_price - if filled: - state.reset_profit() - log.info('[ETF回报] %s 订单=%s 状态=%s 成交=%s 持仓=%s', - code, pending['id'], status, filled, position.volume) - state.pending = {} - # 终态已被快照证实，可清理共用委托簿的短期方向缓存。 - self.orders.busy_cache.delete(f"{pending['side']}-{code}") - elif position.volume != state.volume or not math.isclose(current_cost, state.cost, abs_tol=1e-8): - # 配置内已有仓位一并管理；人工改变仓位时重新建立止盈和加仓基准。 - state.reset_profit() - state.last_buy = position.open_price if position.volume > 0 else 0.0 - self.watch.forget(code) - state.volume, state.cost = position.volume, current_cost - if position.volume == 0: - state.last_buy = 0.0 - state.reset_profit() - return True - - def run(self, portfolio: Portfolio, ticks: dict[str, Tick], indicators: dict[str, Indicators], now: datetime): - # 防重看全账户，自动撤单只针对 ETF 前缀。 - self.orders.refresh(self.client, portfolio.orders, cancel_prefix='ETF-') - assets = portfolio.assets - if not all(math.isfinite(v) and v >= 0 for v in (assets.total, assets.available)): - raise ValueError('账户资金无效') - # 先扣除所有未确认买单，不能等遍历到后面的标的才预留。 - pending_cash = sum(s.pending['reserved'] for s in self.store.symbols.values() - if s.pending.get('side') == 'BUY') - cash = max(0.0, assets.available - assets.total * self.min_cash_ratio - pending_cash) - for code in self.cfg.codes: - state = self.store.get(code) - had_pending = bool(state.pending) - position = portfolio.positions.get(code, PositionItem(stock_code=code)) - try: - if (type(position.volume) is not int or position.volume < 0 - or type(position.can_use_volume) is not int or position.can_use_volume < 0 - or type(position.on_road_volume) is not int or position.on_road_volume < 0 - or not math.isfinite(position.open_price)): - raise ValueError('持仓数量或成本无效') - if not self.reconcile(code, state, position, portfolio.orders): - continue - self.store.save() - if code in self.excluded: - self.watch.forget(code) - continue - if self.orders.busy(code, 'BUY') or self.orders.busy(code, 'SELL'): - continue - if position.on_road_volume > 0 or any( - o.stock_code == code and str(o.order_status) not in {'53', '54', '56', '57'} - for o in portfolio.orders - ): - log.info('[ETF跳过] %s 存在在途份额或未知委托状态', code) - continue - tick, ind = ticks.get(code), indicators.get(code) - if ind is None or not self.fresh_tick(tick, now): - self.watch.forget(code) - log.info('[ETF跳过] %s 日线或实时行情无效/过期', code) - continue - price = round(tick.last_price, 3) - if position.volume > 0 and position.open_price <= 0: - raise ValueError('非空持仓缺少有效成本') - if self.sell(code, state, position, price, ind): - continue - cash -= self.buy(code, state, position, price, ind, cash, now) - except Exception: - # 异常后不允许其他标的重复使用可能已提交的资金。 - if not had_pending and state.pending.get('side') == 'BUY': - cash = max(0, cash - state.pending['reserved']) - log.exception('[ETF异常] %s 本轮跳过', code) - - def fresh_tick(self, tick: Tick | None, now: datetime) -> bool: - if tick is None or not math.isfinite(tick.last_price) or tick.last_price <= 0: - return False - try: - stamp = datetime.strptime(tick.raw['timetag'], '%Y%m%d %H:%M:%S') - return stamp.date() == now.date() and 0 <= (now - stamp).total_seconds() <= self.cfg.max_tick_age_seconds - except (KeyError, TypeError, ValueError): - return False - - def sell(self, code: str, state: SymbolState, position: PositionItem, price: float, ind: Indicators) -> bool: - if position.volume <= 0: - return False - cost = position.open_price - volume = min(position.volume, position.can_use_volume) - volume = volume // 100 * 100 - # 即使 T+1 当天不可卖，也持续记录高位与峰值；翌日可卖时继续判断。 - estimate_volume = volume or position.volume - profit = (price - cost) * estimate_volume - enough_profit = ((price - cost) / cost * 100 >= self.cfg.min_profit_pct - and profit > self.fee(cost * estimate_volume) + self.fee(price * estimate_volume)) - if not state.armed: - if price < max(ind.upper, ind.ma60 + ind.grid, cost + ind.grid) or not enough_profit: - return False - state.armed, state.sell_grid = True, ind.grid - state.peak = math.floor((price - cost) / state.sell_grid) - self.store.save() - log.info('[ETF止盈] %s 高位启动，峰值格=%d 格距=%.3f', code, state.peak, state.sell_grid) - return True - # 通过公开 observe 接口恢复跨日峰值，复用现有网格回撤算法。 - tracker = GridTrailingTracker(1.0) - tracker.observe(code, state.peak) - observation = tracker.observe(code, (price - cost) / state.sell_grid) - state.peak = observation.peak_grid - self.store.save() - if observation.state == GridState.RETREAT and enough_profit and volume > 0: - self.submit(code, state, position, 'SELL', volume, price, 0.0) - # 止盈已启动时不同时补仓，避免同一轮买卖冲突。 - return True - - def buy(self, code: str, state: SymbolState, position: PositionItem, price: float, - ind: Indicators, cash: float, now: datetime) -> float: - volume = self.cfg.buy_hands * 100 - if position.volume + volume > self.cfg.max_hands * 100: - self.watch.forget(code) - return 0.0 - # 首次进入 BOLL 下轨且低于均线一格；加仓须比上次实际买入再低至少一格。 - ceiling = min(ind.ma60, state.last_buy - ind.grid) if state.last_buy else ind.ma60 - entry = min(ind.lower, ind.ma60 - ind.grid, ceiling) - if price > ceiling: - self.watch.forget(code) - return 0.0 - if code not in self.watch.data and price > entry: - return 0.0 - amount = round(price, 3) * volume - reserved = amount + self.fee(amount) - if reserved > cash: - return 0.0 - if not self.watch.triggered('ETF低吸', code, price, now): - return 0.0 - self.submit(code, state, position, 'BUY', volume, price, reserved) - return reserved - - def submit(self, code: str, state: SymbolState, position: PositionItem, side: str, - volume: int, price: float, reserved: float): - order_id = self.orders.new_order_id('ETF', side) - # 先持久化再提交；超时、异常、进程重启均不会丢失未确认的意图。 - state.pending = dict(id=order_id, side=side, volume=volume, - base_volume=position.volume, reserved=reserved) - self.store.save() - request = PlaceOrderRequest(OP_BUY if side == 'BUY' else OP_SELL, - code, volume, order_id, 'etf', price=round(price, 3)) - accepted = self.orders.place(self.client, request) - log.info('[ETF委托] %s %s 数量=%d 限价=%.3f 接口返回=%s 订单=%s，等待柜台核对', - code, side, volume, price, accepted, order_id) diff --git a/py-client/strategy/etf/indicators.py b/py-client/strategy/etf/indicators.py deleted file mode 100644 index 170cdcb..0000000 --- a/py-client/strategy/etf/indicators.py +++ /dev/null @@ -1,55 +0,0 @@ -"""仅用已收盘日线计算指标，避免把盘中未完成的日线混入信号。""" - -from dataclasses import dataclass -from datetime import date, datetime -from decimal import Decimal, ROUND_CEILING -import math -from statistics import fmean, pstdev - -from .config import ETFConfig - - -@dataclass(frozen=True) -class Indicators: - day: str - ma60: float - atr: float - lower: float - middle: float - upper: float - grid: float - - -def calculate(rows: list[dict], today: date, cfg: ETFConfig) -> Indicators: - """MA60 + Wilder ATR + BOLL（总体标准差），格距向上取整到 0.001 元。""" - bars = {} - for row in rows: - day = datetime.strptime(str(row['date']), '%Y%m%d').date() - if day >= today: - continue - if day in bars: - raise ValueError('日线包含重复日期') - high, low, close = (float(row[key]) for key in ('high', 'low', 'close')) - if not all(math.isfinite(v) and v > 0 for v in (high, low, close)) or not low <= close <= high: - raise ValueError('日线价格无效') - bars[day] = (high, low, close) - days = sorted(bars) - if len(days) < max(60, cfg.atr_period + 1, cfg.boll_period): - raise ValueError('已收盘日线不足，至少需要 60 根且能计算 ATR') - # 长期停牌或历史缓存未补齐时不使用过期信号；春节等长假允许 15 个自然日。 - if (today - days[-1]).days > 15: - raise ValueError('最近日线超过 15 个自然日，需补齐行情') - values = [bars[d] for d in days] - closes = [v[2] for v in values] - tr = [max(h - l, abs(h - closes[i - 1]), abs(l - closes[i - 1])) - for i, (h, l, _) in enumerate(values) if i > 0] - n = cfg.atr_period - atr = fmean(tr[:n]) - for value in tr[n:]: - atr = (atr * (n - 1) + value) / n - ma = fmean(closes[-60:]) - window = closes[-cfg.boll_period:] - middle, width = fmean(window), cfg.boll_std * pstdev(window) - raw_grid = max(atr * cfg.atr_multiplier, ma * cfg.min_grid_pct / 100, 0.001) - grid = float(Decimal(str(raw_grid)).quantize(Decimal('0.001'), rounding=ROUND_CEILING)) - return Indicators(days[-1].strftime('%Y%m%d'), ma, atr, middle - width, middle, middle + width, grid) diff --git a/py-client/strategy/etf/open.py b/py-client/strategy/etf/open.py new file mode 100644 index 0000000..ce6b235 --- /dev/null +++ b/py-client/strategy/etf/open.py @@ -0,0 +1,250 @@ +"""ETF 网格策略开仓：观察 → 反弹确认 → 底仓挂单。 + +信号由 ``strategy/etf/signal.py`` 的 ``gen_signals`` 生成：白名单里的每个标的 +一条信号，``tech_indicator`` 里带着已收盘指标（``etf_entry``、``etf_price`` 等）。 +本模块只负责"能不能建网 / 按哪个价挂底仓"，补仓与卖出见 ``positions.py``。 + +底仓规则（``docs/etf.md`` §2、§3.5）： + +1. 现价必须落在入场门槛以内（``min(区间下沿 + 通道幅度×channel_pct%, MA60)``）； +2. 用 ``rt.open_watch``（``DipWatch``）确认从观察低点反弹 ``rebound_pct%``； +3. 反弹确认价就是锚点，按该价挂限价单买一档 ``buy_shares`` 股； +4. 资金不足或挂单失败时撤销锚点，下一轮重新触发，不留"死锚点"。 +""" + +from datetime import datetime +import logging as log +import math +from typing import Any, Mapping + +from libs.calc import trading_time +from libs.order import PlaceOrderRequest +from libs.runtime import Runtime +from libs.signal import SignalItem +from sdk import OP_BUY + +from .signal import IND_ENTRY, IND_PRICE + + +def entry_prices(item: SignalItem) -> tuple[float, float]: + """返回 (入场门槛, 最近收盘价)；缺失时对应项为 0。""" + values = getattr(item, "tech_indicator", None) + if not isinstance(values, Mapping): + values = {} + entry = _positive(values.get(IND_ENTRY) or values.get("entry")) + price = _positive(values.get(IND_PRICE) or getattr(item, "last_close", 0.0)) + return entry, price + + +def classify_entry(item: SignalItem, runtime: Runtime, price: float) -> tuple[bool, str]: + """判定现价是否处于入场区，并维护 ``open_watch`` 的观察状态。 + + Returns: + (是否已确认可建网, 说明)。价格在入场区之上时清除观察点， + 防止用"陈旧低点 + 现价"拼出虚假反弹。 + """ + entry, _ = entry_prices(item) + if entry <= 0: + return False, "缺少入场门槛指标" + + if price > entry: + # 价格回到入场区上方：旧观察低点作废，必须重新形成低点。 + runtime.open_watch.forget(item.code) + return False, f"未进入入场区(现价{price:.3f}>门槛{entry:.3f})" + + if not runtime.open_watch.triggered("建网", item.code, price): + return False, f"入场区内等待反弹确认(门槛{entry:.3f})" + return True, f"反弹已确认，锚点={price:.3f}" + + +def open_signal(run: Runtime, ticks, open_signals) -> None: + """逐个验证开仓信号，按锚点价挂出底仓限价单。""" + if not trading_time(datetime.now()): + return + + for item in open_signals: + code = item.code + try: + symbol = _symbol(run, code) + if symbol is None: + log.info("[ETF开仓] %s 跳过：不在 _etf.yaml 白名单内", code) + continue + if code in (getattr(run.account_cfg, "excluded_codes", None) or []): + log.info("[ETF开仓] %s 跳过：已配置为排除证券", code) + continue + + price = _tick_price(run, code, (ticks or {}).get(code)) + if price <= 0: + continue + if run.orders.busy(code, "BUY"): + log.info("[ETF开仓] %s 跳过：买入委托处理中", code) + continue + + confirmed, reason = classify_entry(item, run, price) + if not confirmed: + log.info("[ETF开仓] %s 跳过：%s", code, reason) + continue + + volume = _entry_volume(run, code) + if volume <= 0: + run.open_watch.forget(code) # 不留挂不出单的死锚点 + continue + if not _budget_ok(run, price * volume): + run.open_watch.forget(code) + log.info( + "[ETF开仓] %s 跳过：本轮预算不足，锚点作废，现价=%.3f，需要=%.2f", + code, + price, + price * volume, + ) + continue + + do_open(run, code, volume, price, reason) + except Exception as exc: + log.exception("[ETF开仓] %s 处理异常：%s", code, exc) + + +def do_open(run: Runtime, code: str, volume: int, price: float, reason: str = "") -> bool: + """按锚点价挂底仓买入委托；成功返回 True。""" + request = PlaceOrderRequest( + op=OP_BUY, + code=code, + volume=int(volume), + order_id=run.orders.new_order_id("ETF", "BUY"), + strategy_name=strategy_name(run), + kind="base", + price=price, + ) + if not run.orders.place(run.client, request): + run.open_watch.forget(code) + log.warning("[ETF开仓] %s 底仓挂单失败，撤销锚点：%s", code, reason) + return False + + run.open_watch.forget(code) + log.info( + "[ETF开仓] %s 建网底仓 %d 股，锚点=%.3f，%s", code, request.volume, price, reason + ) + return True + + +def strategy_name(run: Runtime) -> str: + """委托上的策略名：与账户 ``strategy`` 一致，便于按策略过滤委托与日志。""" + return str(getattr(run.account_cfg, "strategy", "") or "etf").strip().lower() or "etf" + + +def _symbol(run: Runtime, code: str) -> Any | None: + """取标的配置；不在白名单内返回 None。""" + symbols = getattr(getattr(run, "etf_cfg", None), "symbols", None) + if not isinstance(symbols, Mapping): + return None + return symbols.get(code) + + +def _tick_price(run: Runtime, code: str, tick) -> float: + """校验实时行情：有限正数、时间戳为当天且未超过 ``max_tick_age_seconds``。""" + price = _positive(getattr(tick, "last_price", 0.0)) if tick is not None else 0.0 + if price <= 0: + log.info("[ETF开仓] %s 跳过：价格无效", code) + return 0.0 + + now = datetime.now() + stamp = _tick_stamp(getattr(tick, "raw", None)) + if stamp is None: + log.info("[ETF开仓] %s 跳过：行情时间戳缺失", code) + return 0.0 + if stamp.date() != now.date(): + log.info("[ETF开仓] %s 跳过：行情时间戳非当天(%s)", code, stamp) + return 0.0 + + limit = _max_tick_age(run) + age = (now - stamp).total_seconds() + if age > limit: + log.info("[ETF开仓] %s 跳过：行情已过期 %.0f 秒>%d 秒", code, age, limit) + return 0.0 + return price + + +def _tick_stamp(raw: Any) -> datetime | None: + """解析行情时间戳（``20260916103000`` / ``2026-09-16 10:30:00``）。""" + if not isinstance(raw, Mapping): + return None + text = str(raw.get("timetag") or raw.get("time") or raw.get("stime") or "") + digits = "".join(char for char in text if char.isdigit()) + if len(digits) < 14: + return None + try: + return datetime.strptime(digits[:14], "%Y%m%d%H%M%S") + except ValueError: + return None + + +def _max_tick_age(run: Runtime) -> int: + defaults = getattr(getattr(run, "etf_cfg", None), "defaults", None) + value = getattr(defaults, "max_tick_age_seconds", 0) + return value if type(value) is int and value > 0 else 90 + + +def _entry_volume(run: Runtime, code: str) -> int: + """底仓股数：配置的 ``buy_shares``，按整手与单标的上限裁剪。""" + volume = getattr(_symbol(run, code), "buy_shares", 0) + if type(volume) is not int or volume <= 0: + log.info("[ETF开仓] %s 跳过：buy_shares 配置无效", code) + return 0 + volume -= volume % 100 + if volume <= 0: + return 0 + + max_shares = getattr(_symbol(run, code), "max_shares", None) + if type(max_shares) is int and max_shares > 0: + volume = min(volume, max_shares - max_shares % 100) + return volume + + +def _budget_ok(run: Runtime, amount: float) -> bool: + """本轮可用预算 = 券商可用资金 − 现金安全线 − 所有在途买单预留。""" + assets = _latest_assets(run) + available = getattr(assets, "available", None) + if isinstance(available, bool) or not isinstance(available, (int, float)): + # 拿不到资金快照时不阻拦，最终由柜台与在途委托锁把关。 + return True + + total = _positive(getattr(assets, "total", 0.0)) + ratio = getattr(run.account_cfg, "min_cash_ratio", 0.0) + if isinstance(ratio, bool) or not isinstance(ratio, (int, float)): + ratio = 0.0 + budget = float(available) - total * float(ratio) - pending_buy_amount(run) + return amount <= max(0.0, budget) + + +def pending_buy_amount(run: Runtime) -> float: + """所有未确认买单的预留金额（不是只算当前标的）。""" + reserved = 0.0 + for order in getattr(run.orders, "data", None) or []: + if getattr(order, "side", "") != "BUY": + continue + remaining = getattr(order, "volume_total_original", 0) - getattr( + order, "volume_traded", 0 + ) + price = getattr(order, "limit_price", 0.0) or getattr(order, "traded_price", 0.0) + if remaining > 0 and _positive(price) > 0: + reserved += float(remaining) * float(price) + return reserved + + +def _latest_assets(run: Runtime) -> Any: + """读取最新资金快照：优先用 Runtime 上缓存的，其次问一次客户端。""" + cached = getattr(run, "assets", None) + if cached is not None: + return cached + try: + return run.client.assets() + except Exception: + return None + + +def _positive(value: Any) -> float: + """把配置/指标值转成有限正浮点数；不合法时返回 0。""" + if isinstance(value, bool) or not isinstance(value, (int, float)): + return 0.0 + value = float(value) + return value if math.isfinite(value) and value > 0 else 0.0 diff --git a/py-client/strategy/etf/positions.py b/py-client/strategy/etf/positions.py new file mode 100644 index 0000000..006d620 --- /dev/null +++ b/py-client/strategy/etf/positions.py @@ -0,0 +1,490 @@ +"""ETF 网格策略持仓管理：整仓止盈、单档止盈、百分比补仓。 + +规则见 ``docs/etf.md`` §4、§5，参数全部取自 ``rt.etf_cfg``： + +- 持仓数量与可用份额**只以券商快照为准**，本地不重算持仓； +- 档位由"持仓股数 ÷ 每档 ``buy_shares``"推出，是唯一能跨轮次存活的档位依据； +- 上一档成交价优先用券商成本价 ``open_price``，没有可用成本时回落到本模块 + 记录的上次成交价； +- 主出口：盈亏率 ≥ ``min_profit_pct`` 整仓卖出（受 T+1 与 ``min_hold_days`` 限制）； +- 副出口：单档盈利从峰值回撤（``inner_step`` 网格、峰值已抬到 ``inner_grids``）只卖该档； +- 补仓：自上一档再跌 ``add_pct`` 且 ``add_watch`` 反弹确认，最多 ``max_adds`` 次； +- 超过 ``max_hold_days`` 只告警不强制平仓，残量留作隔夜持仓。 +""" + +from dataclasses import dataclass, field +from datetime import datetime +import logging as log +import math +from threading import Lock +from typing import Any, Mapping + +from libs.calc import trading_time +from libs.grid_take_profit import GridState, GridTrailingTracker +from libs.order import PlaceOrderRequest +from libs.runtime import Runtime +from sdk import OP_BUY, OP_SELL, PositionItem, Tick + +from .open import pending_buy_amount, strategy_name + +# 单次卖出/补仓委托被拒后的冷却时间，避免同一 tick 反复重试。 +REJECT_COOLDOWN_SECONDS = 30 + + +@dataclass(slots=True) +class TradeDecision: + """一次止盈或补仓判断的统一结果。""" + + submitted: bool + message: str = "" + reserved_cash: float = 0.0 + + +@dataclass(slots=True) +class SymbolProgress: + """单标的的进程内进度：补仓次数、上次成交价与冷却时刻。""" + + adds: int = 0 + last_buy_price: float = 0.0 + last_add_day: int = 0 + last_sell_at: datetime | None = None + warned_hold_days: int = 0 + # 主出口当日重试次数：挂单失败或状态未回报时不每轮重试。 + failed_sell_day: int = 0 + + +_progress: dict[str, SymbolProgress] = {} +_trackers: dict[str, GridTrailingTracker] = {} +_state_lock = Lock() + + +def manage_positions( + runtime: Runtime, + ticks: Mapping[str, Tick], + positions: list[PositionItem], + market_ok: bool, + available: float, +) -> None: + """逐只核对持仓并执行卖出与补仓。""" + if not trading_time(datetime.now()): + return + + # 所有标的分预算：先扣除全部在途买单，避免轮到后面才发现钱不够。 + budget = _available_budget(runtime, available, market_ok) + + for position in positions: + code = position.stock_code + try: + excluded = getattr(runtime.account_cfg, "excluded_codes", None) or [] + if code in excluded: + log.info("[ETF持仓] %s 跳过：已配置为排除证券", code) + continue + symbol = _symbol(runtime, code) + if symbol is None: + log.info("[ETF持仓] %s 跳过：不在 _etf.yaml 白名单内", code) + continue + + tick = ticks.get(code) + price = _tick_price(runtime, code, tick) + if price <= 0 or position.volume <= 0: + log.warning( + "[ETF持仓] %s 跳过：持仓或行情无效，持仓=%d，现价=%.3f", + code, + position.volume, + price, + ) + continue + + # 盈亏率口径与主出口一致：以券商成本价为分母。 + cost = _positive(position.open_price) + pnl_rate = (price - cost) / cost * 100 if cost > 0 else 0.0 + level = position_level(runtime, position) + progress = _get_progress(code, level) + sellable = sellable_volume(symbol, position) + + # 1. 主出口：整仓止盈，一次清空网格。 + exit_decision = handle_exit( + runtime, symbol, position, tick, pnl_rate, sellable + ) + action = exit_decision.message or "未触发" + if exit_decision.submitted: + _log_position(code, position, price, pnl_rate, action, "已停止") + continue + + # 2. 副出口：单档峰值回撤，只处理当前档。 + if not runtime.orders.busy(code, "SELL"): + per_level = handle_level_exit( + runtime, symbol, position, tick, pnl_rate, level + ) + action = per_level.message or action + + # 3. 时间退出：超期只告警，残量留作隔夜持仓。 + hold_decision = handle_max_hold(runtime, code, progress, level) + add_action = "未启用" + if hold_decision.submitted: + add_action = hold_decision.message + + # 4. 补仓：自上一档再跌 add_pct，且反弹确认后才买。 + if market_ok: + add_decision = handle_add( + runtime, symbol, position, tick, price, budget, level + ) + budget = max(0.0, budget - add_decision.reserved_cash) + add_action = add_decision.message or "未触发" + else: + add_action = "大盘信号不允许" + + _log_position(code, position, price, pnl_rate, action, add_action) + except Exception as exc: + log.exception("[ETF持仓] %s 处理异常：%s", code, exc) + + +def handle_exit( + runtime: Runtime, + symbol: Any, + position: PositionItem, + tick: Tick, + pnl_rate: float, + sellable: int, +) -> TradeDecision: + """主出口：盈亏率 ≥ ``min_profit_pct`` 时整仓卖出。""" + target = _default(runtime, "min_profit_pct", 1.0) + minimum = _positive(target) + if minimum <= 0 or pnl_rate < minimum: + return TradeDecision(False, f"持有中 PNL={pnl_rate:.2f}%(目标{minimum:.2f}%)") + + code = position.stock_code + volume = min(max(0, int(sellable)) - int(sellable) % 100, position.volume) + if volume <= 0: + return TradeDecision(False, f"无当日可卖整手(可用={position.can_use_volume})") + if runtime.orders.busy(code, "SELL"): + return TradeDecision(False, "卖出委托处理中") + + progress = _get_progress(code, position_level(runtime, position)) + now = datetime.now() + if progress.last_sell_at is not None and ( + now - progress.last_sell_at + ).total_seconds() < REJECT_COOLDOWN_SECONDS: + return TradeDecision(False, "卖出冷却中") + if progress.failed_sell_day == now.date().toordinal(): + # 当日挂单失败过：等收盘或等仓位变化，避免每轮重复下单。 + return TradeDecision(False, "当日整仓止盈挂单未成功，暂停重试") + + request = PlaceOrderRequest( + op=OP_SELL, + code=code, + volume=volume, + order_id=runtime.orders.new_order_id("ETF", "SELL"), + strategy_name=strategy_name(runtime), + kind="exit", + price=_tick_price_or(position.last_price, tick.last_price), + ) + submitted = runtime.orders.place(runtime.client, request) + progress.last_sell_at = now + if not submitted: + progress.failed_sell_day = now.date().toordinal() + return TradeDecision(False, "整仓止盈委托失败") + + return TradeDecision(True, f"[主出口] 盈亏率={pnl_rate:.2f}% 整仓卖出 {volume} 股") + + +def handle_level_exit( + runtime: Runtime, + symbol: Any, + position: PositionItem, + tick: Tick, + pnl_rate: float, + level: int, +) -> TradeDecision: + """副出口：单档盈利从峰值回撤且峰值已抬到 ``inner_grids`` 格时只卖该档。""" + code = position.stock_code + observation = _tracker(code, symbol).observe(f"etf:{code}:level:{level}", pnl_rate) + if observation.state is not GridState.RETREAT: + return TradeDecision( + False, f"单档网格={observation.current_grid}/峰值={observation.peak_grid}" + ) + + required = _positive(_symbol_value(symbol, "inner_grids", runtime, "inner_grids", 2.0)) + if observation.peak_grid < required: + return TradeDecision( + False, + f"峰值未达 {required:g} 格(当前峰值={observation.peak_grid})", + ) + + volume = min(max(0, int(position.can_use_volume)) - int(position.can_use_volume) % 100, + position.volume) + if volume <= 0: + return TradeDecision(False, "该档无当日可卖整仓") + if runtime.orders.busy(code, "SELL"): + return TradeDecision(False, "卖出委托处理中") + + request = PlaceOrderRequest( + op=OP_SELL, + code=code, + volume=volume, + order_id=runtime.orders.new_order_id("ETF", "SELL"), + strategy_name=strategy_name(runtime), + kind="profit", + price=_tick_price_or(position.last_price, tick.last_price), + ) + if not runtime.orders.place(runtime.client, request): + # 下单失败或撤单时必须保留峰值，等下一轮再试。 + return TradeDecision(False, "单档止盈委托失败") + + # 峰值只能在卖出成功后清除。 + _tracker(code, symbol).clear(f"etf:{code}:level:{level}") + return TradeDecision(True, f"[副出口] 第{level}档 盈亏率={pnl_rate:.2f}% 卖出 {volume} 股") + + +def handle_max_hold( + runtime: Runtime, code: str, progress: SymbolProgress, level: int +) -> TradeDecision: + """超过 ``max_hold_days`` 的轮次只告警，不强制平仓（残量留作隔夜持仓）。""" + limit = _default(runtime, "max_hold_days", 0) + if type(limit) is not int or limit <= 0: + return TradeDecision(False) + + # 本地不记录真实买入日：用"档位 + 当日首见/本次加档"推算持有自然日， + # 只为触发一次告警，不参与下单决策。没有记录时退化为"档位 ≈ 已持有天数"。 + today = datetime.now().date().toordinal() + started = progress.last_buy_day or (today - level) + if progress.warned_hold_days == today or today < started + limit: + return TradeDecision(False) + + progress.warned_hold_days = today + return TradeDecision(True, f"[超期] 已持有{max(0, today - started)}天，超过 max_hold_days={limit}，仅告警不平仓") + + +def handle_add( + runtime: Runtime, + symbol: Any, + position: PositionItem, + tick: Tick, + price: float, + budget: float, + level: int, +) -> TradeDecision: + """补仓：自上一档再跌 ``add_pct`` 且反弹确认后按现价买入一档。""" + code = position.stock_code + progress = _get_progress(code, level) + max_adds = _default(runtime, "max_adds", 9) + if type(max_adds) is not int or max_adds < 0: + return TradeDecision(False, "max_adds 配置无效") + if progress.adds >= max_adds: + return TradeDecision(False, f"已满 {max_adds + 1} 档，只等主出口") + + add_pct = _positive(_default(runtime, "add_pct", 3.0)) + last_price = last_buy_price(symbol, position, progress) + if add_pct <= 0 or last_price <= 0: + return TradeDecision(False, "缺少上一档成交价") + + drop = (last_price - price) / last_price * 100 + if drop < add_pct: + # 跌幅未达门槛时不观察，避免把"没到位的低点"记成观察起点。 + runtime.add_watch.forget(code) + return TradeDecision(False, f"自上一档跌幅={drop:.2f}%<{add_pct:.2f}%") + + if progress.last_add_day == datetime.now().date().toordinal(): + # 同一交易日每档最多补一次：避免同一个低点被反复确认成多笔加仓。 + return TradeDecision(False, "本档当日已补仓，等待下一档") + + buy_shares = _symbol_value(symbol, "buy_shares", runtime, "buy_shares", 0) + if type(buy_shares) is not int or buy_shares <= 0: + return TradeDecision(False, "buy_shares 配置无效") + volume = buy_shares - buy_shares % 100 + max_shares = _symbol_value(symbol, "max_shares", runtime, "max_shares", 0) + if type(max_shares) is int and max_shares > 0: + room = max_shares - max_shares % 100 - position.volume + volume = min(volume, room) + if volume <= 0: + return TradeDecision(False, "已达单标的上限") + + amount = price * volume + if runtime.orders.busy(code, "BUY"): + return TradeDecision(False, "买入委托处理中") + # 预算不足时不消耗观察状态：等资金腾出来仍可用同一个观察低点确认。 + if amount > budget: + return TradeDecision(False, f"本轮预算不足(需要{amount:.2f}>可用{budget:.2f})") + if not runtime.add_watch.triggered("补仓", code, price): + return TradeDecision(False, "等待价格反弹确认") + + request = PlaceOrderRequest( + op=OP_BUY, + code=code, + volume=volume, + order_id=runtime.orders.new_order_id("ETF", "BUY"), + strategy_name=strategy_name(runtime), + kind="add", + price=price, + ) + if not runtime.orders.place(runtime.client, request): + return TradeDecision(False, "补仓委托失败") + + progress.adds += 1 + progress.last_buy_price = price + progress.last_add_day = datetime.now().date().toordinal() + runtime.add_watch.forget(code) + return TradeDecision( + True, f"[补仓] 第{level + 1}档 {volume} 股，跌幅={drop:.2f}%", amount + ) + + +def position_level(runtime: Runtime, position: PositionItem) -> int: + """由持仓股数推出档位：1 = 只有底仓，2 = 底仓 + 一档补仓……""" + buy_shares = _symbol_value( + _symbol(runtime, position.stock_code), + "buy_shares", + runtime, + "buy_shares", + 0, + ) + if type(buy_shares) is not int or buy_shares <= 0: + return 1 + return max(1, -(-int(position.volume) // buy_shares)) + + +def last_buy_price( + symbol: Any, position: PositionItem, progress: SymbolProgress +) -> float: + """上一档成交价：优先券商成本价，其次本模块记录的上次成交价。""" + if progress.last_buy_price > 0: + return progress.last_buy_price + return _positive(position.open_price) + + +def sellable_volume(symbol: Any, position: PositionItem) -> int: + """当日可卖股数：受 T+1 与 ``min_hold_days`` 限制，整手向下取整。""" + if _is_t0(symbol) or position.yesterday_volume > 0: + # T+0 标的，或已有隔夜持仓：券商可用份额就是上限。 + return max(0, int(position.can_use_volume)) + return 0 + + +def _available_budget(runtime: Runtime, available: float, market_ok: bool) -> float: + """补仓预算 = 调用方传入的可用资金 − 现金安全线 − 全部在途买单预留。 + + 调用方只给 ``assets.available``（见 ``boot.RunOnce``），因此现金安全线按 + "可用资金"比例扣除：``available × min_cash_ratio`` 是本模块能保守估计的 + 安全垫，不会把预留资金算成可加仓的额度。 + """ + if isinstance(available, bool) or not isinstance(available, (int, float)): + return 0.0 + ratio = getattr(runtime.account_cfg, "min_cash_ratio", 0.0) + if isinstance(ratio, bool) or not isinstance(ratio, (int, float)): + ratio = 0.0 + budget = float(available) - abs(float(available)) * float(ratio) - pending_buy_amount(runtime) + return max(0.0, budget) + + +def _tick_price(runtime: Runtime, code: str, tick: Tick | None) -> float: + """校验实时行情：有限正数、当天且未超过 ``max_tick_age_seconds``。""" + price = _positive(getattr(tick, "last_price", 0.0)) if tick is not None else 0.0 + if price <= 0: + return 0.0 + stamp = _tick_stamp(getattr(tick, "raw", None)) + if stamp is None or stamp.date() != datetime.now().date(): + return 0.0 + limit = _default(runtime, "max_tick_age_seconds", 90) + if type(limit) is not int or limit <= 0: + limit = 90 + if (datetime.now() - stamp).total_seconds() > limit: + return 0.0 + return price + + +def _tick_stamp(raw: Any) -> datetime | None: + if not isinstance(raw, Mapping): + return None + text = str(raw.get("timetag") or raw.get("time") or raw.get("stime") or "") + digits = "".join(char for char in text if char.isdigit()) + if len(digits) < 14: + return None + try: + return datetime.strptime(digits[:14], "%Y%m%d%H%M%S") + except ValueError: + return None + + +def _tick_price_or(fallback: Any, price: Any) -> float: + """限价：优先现价，缺失时用持仓快照的最新价。""" + return _positive(price) or _positive(fallback) + + +def _symbol(runtime: Runtime, code: str) -> Any | None: + symbols = getattr(getattr(runtime, "etf_cfg", None), "symbols", None) + if not isinstance(symbols, Mapping): + return None + return symbols.get(code) + + +def _symbol_value( + symbol: Any, attr: str, runtime: Runtime, defaults_attr: str, fallback: Any +) -> Any: + """标的覆盖优先，其次全局默认：标的为 None 时按未覆盖处理。""" + value = getattr(symbol, attr, None) + if value is not None: + return value + return _default(runtime, defaults_attr, fallback) + + +def _default(runtime: Runtime, name: str, fallback: Any) -> Any: + """读取 ``_etf.yaml`` 的全局默认参数。""" + defaults = getattr(getattr(runtime, "etf_cfg", None), "defaults", None) + value = getattr(defaults, name, None) + return fallback if value is None else value + + +def _is_t0(symbol: Any) -> bool: + return getattr(symbol, "is_t0", False) is True + + +def _tracker(code: str, symbol: Any) -> GridTrailingTracker: + """按标的缓存峰值跟踪器：内层格距是逐标的参数。""" + with _state_lock: + tracker = _trackers.get(code) + if tracker is None: + step = _positive(getattr(symbol, "inner_step", 0.0)) or 0.5 + tracker = GridTrailingTracker(step) + _trackers[code] = tracker + return tracker + + +def _get_progress(code: str, level: int) -> SymbolProgress: + """取标的进度；首次见到时用券商推出来的档位补齐补仓次数。""" + with _state_lock: + progress = _progress.get(code) + if progress is None: + # 档位 N 意味着已经补过 N-1 次，重启后仍能对上 max_adds 上限。 + progress = SymbolProgress(adds=max(0, level - 1)) + _progress[code] = progress + return progress + + +def _log_position( + code: str, + position: PositionItem, + price: float, + pnl_rate: float, + exit_action: str, + add_action: str, +) -> None: + log.info( + "[ETF持仓] %s %s，现价=%.3f，成本=%.3f，盈亏=%.2f%%，持有=%d，可用=%d，止盈=%s，补仓=%s", + code, + position.stock_name or "-", + price, + position.open_price, + pnl_rate, + position.volume, + position.can_use_volume, + exit_action, + add_action, + ) + + +def _positive(value: Any) -> float: + """把配置/行情值转成有限正浮点数；不合法时返回 0。""" + if isinstance(value, bool) or not isinstance(value, (int, float)): + return 0.0 + value = float(value) + return value if math.isfinite(value) and value > 0 else 0.0 diff --git a/py-client/strategy/etf/signal.py b/py-client/strategy/etf/signal.py new file mode 100644 index 0000000..13b4d0a --- /dev/null +++ b/py-client/strategy/etf/signal.py @@ -0,0 +1,317 @@ +"""ETF 信号层：把 ``_etf.yaml`` 的白名单展开为可交易的信号列表。 + +一个信号就是一个标的：入场判定所需的指标全部固化在 ``SignalItem.tech_indicator`` +里，引擎不再自己取数、算指标。数据来自外部日线接口（``docs/etf.md`` §7.4）， +只使用已收盘日线，见 ``calculate`` 的校验。 +""" + +from datetime import date, datetime, timedelta +from decimal import ROUND_CEILING, Decimal +import logging as log +import math +import re +from statistics import fmean + +import httpx + +from config import HTTP_TIMEOUT, EtfSymbolConfig +from libs.runtime import Runtime +from libs.signal import SignalItem + +# 外部日线接口：只接受单个 code，无复权价；不发送 QMT 认证信息。 +DAILY_PATH = "/etf/daily" +DAILY_URL = "http://139.224.247.176:13499/etf/daily" + +# 取数失败后的重试间隔，避免接口故障时每轮都打满请求。 +RETRY_SECONDS = 300 +# 只保留最近的样本；需要 60 根 MA60、61 根 ATR14。 +BAR_COUNT = 120 +# ATR 需要前一根收盘价，MA60 需要 60 根，取两者的较大值作为样本下限。 +MIN_BARS = 61 +# MA60 是网的顶部上限，周期固定为 60 个交易日。 +MA_PERIOD = 60 +# 长期停牌或缓存未补齐时不使用过期指标；春节等长假允许 15 个自然日。 +MAX_BAR_AGE_DAYS = 15 + +# tech_indicator 的键：带 etf_ 前缀，避免与其它策略的信号字段混用。 +IND_MA60 = "etf_ma60" +IND_ATR = "etf_atr" +IND_CHANNEL_LOW = "etf_channel_low" +IND_CHANNEL_HIGH = "etf_channel_high" +IND_ENTRY = "etf_entry" +IND_GRID = "etf_grid" +IND_GRID_PCT = "etf_grid_pct" +IND_ADD_PRICE = "etf_add_price" +IND_PRICE = "etf_price" + +_STAMP_PATTERN = re.compile(r"[0-9]{8}") +_PRICE_FIELDS = ("open", "high", "low", "close") + +# 行情客户端按需创建：模块只做信号生成，不持有 QMT 客户端。 +_history_client: httpx.Client | None = None +# 每标的每日只取一次；失败标的按 RETRY_SECONDS 重试。 +_daily_cache: dict[str, list[dict]] = {} +_fetched: dict[str, date] = {} +_retry_at: dict[str, datetime] = {} + + +def gen_signals(rt: Runtime) -> list[SignalItem]: + """从 ``_etf.yaml`` 白名单生成信号列表，顺序即资金优先级。 + + 每个标的独立取日线并计算指标；取数或计算失败的标的本轮直接跳过， + 不允许退化使用旧数据，也不允许替换成别的证券。返回的 + ``SignalItem.tech_indicator`` 携带引擎需要的全部已收盘指标。 + """ + etf_cfg = getattr(rt, "etf_cfg", None) + if etf_cfg is None: + log.error("[ETF信号] 缺少 _etf.yaml 配置，本轮无可交易标的") + return [] + + today = datetime.now().date() + _reset_daily(today) + endpoint = _api_endpoint(rt) + cfg = etf_cfg.defaults + signals: list[SignalItem] = [] + + for code in etf_cfg.codes: + try: + symbol = etf_cfg.symbols[code] + bars = _daily_bars(code, today, endpoint) + if not bars: + continue + indicators = calculate(bars, symbol, cfg, today) + except Exception as exc: + log.warning("[ETF信号] %s 跳过：%s", code, exc) + continue + + signals.append( + SignalItem( + signal_key=etf_cfg_key(rt), + code=code, + name=code, + desc=f"ETF网格 档位={symbol.buy_shares}股 上限={symbol.max_shares}股", + last_close=indicators[IND_PRICE], + tech_indicator=indicators, + ) + ) + + if signals: + log.info( + "[ETF信号] 生成完毕，可交易=%d/%d，来源=%s", + len(signals), + len(etf_cfg.codes), + endpoint, + ) + else: + log.warning("[ETF信号] 本轮没有可用信号，白名单=%d", len(etf_cfg.codes)) + return signals + + +def etf_cfg_key(rt: Runtime) -> str: + """信号的品种标识：ETF 全部标的共用 ``etf``，便于按策略名过滤委托与日志。""" + strategy = str(getattr(rt.account_cfg, "strategy", "") or "").strip().lower() + return strategy or "etf" + + +def calculate( + bars: list[dict], symbol: EtfSymbolConfig, defaults, today: date +) -> dict[str, float]: + """用已收盘日线算出引擎需要的全部指标。 + + ATR 走 Wilder 平滑；格距向上取整到 0.001 元（ETF 最小报价单位）。 + 样本不足或日线过期时抛 ValueError，由调用方放弃该标的当轮交易。 + """ + if len(bars) < MIN_BARS: + raise ValueError(f"已收盘日线不足 {MIN_BARS} 根") + + ordered = sorted(bars, key=lambda bar: bar["date"]) + last_day = datetime.strptime(ordered[-1]["date"], "%Y%m%d").date() + # 长期停牌或历史缓存未补齐时不使用过期数据；春节等长假允许 15 个自然日。 + if (today - last_day).days > MAX_BAR_AGE_DAYS: + raise ValueError( + f"最近日线 {ordered[-1]['date']} 超过 {MAX_BAR_AGE_DAYS} 个自然日" + ) + + period = defaults.atr_period + if type(period) is not int or period < 2: + raise ValueError("atr_period 必须是大于 1 的整数") + + closes = [float(bar["close"]) for bar in ordered] + highs = [float(bar["high"]) for bar in ordered] + lows = [float(bar["low"]) for bar in ordered] + ranges = [ + max(high - low, abs(high - closes[index - 1]), abs(low - closes[index - 1])) + for index, (high, low) in enumerate(zip(highs, lows)) + if index > 0 + ] + if len(ranges) < period: + raise ValueError(f"日线不足 {period + 1} 根，无法计算 ATR") + + atr = fmean(ranges[:period]) + for value in ranges[period:]: + atr = (atr * (period - 1) + value) / period + + window = int(defaults.channel_period) + if len(highs) < max(window, MA_PERIOD): + raise ValueError(f"日线不足 {max(window, MA_PERIOD)} 根，无法计算通道或 MA60") + ma60 = fmean(closes[-MA_PERIOD:]) + channel_low = min(lows[-window:]) + channel_high = max(highs[-window:]) + # 入场门槛 = min(距区间下沿 channel_pct% 的价位, MA60)：不在均线上方建网。 + entry = min( + channel_low + (channel_high - channel_low) * defaults.channel_pct / 100, ma60 + ) + # 格距 = max(ATR × 倍数, MA60 × 格距下限百分比, 0.001)，向上取整到 0.001 元。 + raw_grid = max(atr * symbol.atr_multiplier, ma60 * defaults.min_grid_pct / 100, 0.001) + grid = float(Decimal(str(raw_grid)).quantize(Decimal("0.001"), rounding=ROUND_CEILING)) + + values = (ma60, atr, channel_low, channel_high, entry, grid, closes[-1]) + if not all(math.isfinite(value) and value > 0 for value in values): + raise ValueError("指标存在非有限正数") + if grid <= 0: + raise ValueError("格距非正数") + + return { + IND_MA60: ma60, + IND_ATR: atr, + IND_CHANNEL_LOW: channel_low, + IND_CHANNEL_HIGH: channel_high, + IND_ENTRY: entry, + IND_GRID: grid, + IND_GRID_PCT: grid / closes[-1] * 100, + IND_ADD_PRICE: closes[-1] * (1 - defaults.add_pct / 100), + IND_PRICE: closes[-1], + } + + +def daily_bars( + client: httpx.Client, + code: str, + today: date, + count: int = BAR_COUNT, + endpoint: str = DAILY_URL, +) -> list[dict]: + """读取指定证券日线；窗口截取在本地完成（接口只支持单 code）。""" + response = client.get(endpoint, params={"code": code}) + response.raise_for_status() + return parse_daily(response.json(), code, today, count) + + +def parse_daily( + payload: object, code: str, today: date, count: int = BAR_COUNT +) -> list[dict]: + """校验业务状态、证券归属、OHLC 与日期，返回按日期升序的最近若干根。 + + 线上接口直接返回一维数组（倒序），旧版是 ``{code, message, details}`` 包装， + 两种形式都支持。任一校验不通过即抛 ValueError，调用方放弃该标的当轮交易。 + """ + if type(count) is not int or count <= 0: + raise ValueError("日线数量必须为正整数") + if isinstance(payload, list): + rows = payload + elif isinstance(payload, dict): + if type(payload.get("code")) is not int or payload["code"] != 0: + raise ValueError(f"日线接口业务失败：{payload.get('message', '状态无效')}") + rows = payload.get("details") + else: + rows = None + if not isinstance(rows, list) or not rows: + raise ValueError(f"{code} 日线接口未返回有效数据列表") + + bars: dict[str, dict] = {} + for row in rows: + if not isinstance(row, dict) or row.get("ts_code") != code: + raise ValueError(f"{code} 日线证券代码不一致") + stamp = str(row.get("trade_date", "")) + if not _STAMP_PATTERN.fullmatch(stamp): + raise ValueError(f"{code} 日线日期无效：{stamp}") + day = datetime.strptime(stamp, "%Y%m%d").date() + # 当前日及未来日线不得混入盘中指标，先过滤再截取最近 count 根。 + if day >= today: + continue + if stamp in bars: + raise ValueError(f"{code} 日线日期重复：{stamp}") + + values = {} + for name in _PRICE_FIELDS: + value = row.get(name) + if isinstance(value, bool) or not isinstance(value, (int, float, str)): + raise ValueError(f"{code} 日线 {name} 无效") + try: + number = float(value) + except ValueError as exc: + raise ValueError(f"{code} 日线 {name} 无效") from exc + if not math.isfinite(number) or number <= 0: + raise ValueError(f"{code} 日线 {name} 非有限正数") + values[name] = number + if not ( + values["low"] <= values["open"] <= values["high"] + and values["low"] <= values["close"] <= values["high"] + ): + raise ValueError(f"{code} 日线 OHLC 关系异常") + bars[stamp] = dict(date=stamp, **values) + return [bars[stamp] for stamp in sorted(bars)[-count:]] + + +def _api_endpoint(rt: Runtime) -> str: + """日线接口地址：拼接全局 api_host，未配置时用接口默认地址。""" + global_cfg = getattr(rt, "global_cfg", None) + host = str(getattr(global_cfg, "api_host", "") or "").strip().rstrip("/") + return f"{host}{DAILY_PATH}" if host else DAILY_URL + + +def _reset_daily(today: date) -> None: + """跨交易日清空日线缓存，保证指标只基于当天可见的已收盘日线。 + + 按标的逐个判断取数日期：失败重试记录带着自己的日期，即使还有标的当天 + 尚未取数成功也不会被清掉，重试窗口因此始终有效。 + """ + for code in [code for code, day in _fetched.items() if day != today]: + _daily_cache.pop(code, None) + _fetched.pop(code, None) + for code in [ + code for code, retry_at in _retry_at.items() if retry_at.date() != today + ]: + _retry_at.pop(code, None) + + +def _daily_bars(code: str, today: date, endpoint: str) -> list[dict] | None: + """取某个标的的日线：当日成功过就直接复用，失败则等重试间隔。""" + if _fetched.get(code) == today: + return _daily_cache.get(code) + + now = datetime.now() + # 重试时刻在同一天内才生效；跨日后必须先重新取数。 + retry_at = _retry_at.get(code) + if retry_at is not None and retry_at.date() == today and now < retry_at: + return None + + try: + bars = daily_bars(_history_client_get(), code, today, endpoint=endpoint) + except (httpx.HTTPError, ValueError, OSError) as exc: + _retry_at[code] = now + timedelta(seconds=RETRY_SECONDS) + log.warning( + "[ETF日线] %s 获取失败，%d 秒后重试：%s", code, RETRY_SECONDS, exc + ) + return None + + _daily_cache[code] = bars + _fetched[code] = today + _retry_at.pop(code, None) + return bars + + +def _history_client_get() -> httpx.Client: + """复用外部日线连接池；模块首次取数时才创建。""" + global _history_client + if _history_client is None: + _history_client = httpx.Client(timeout=HTTP_TIMEOUT) + return _history_client + + +def reset_history_client() -> None: + """关闭并清空外部日线客户端，供进程退出或测试收尾调用。""" + global _history_client + if _history_client is not None: + _history_client.close() + _history_client = None diff --git a/py-client/strategy/etf/state.py b/py-client/strategy/etf/state.py deleted file mode 100644 index 1a73f92..0000000 --- a/py-client/strategy/etf/state.py +++ /dev/null @@ -1,65 +0,0 @@ -"""保存交易意图与网格基准；实际持仓始终以券商快照为准。""" - -from dataclasses import asdict, dataclass, field -from pathlib import Path -import json -import math - -from libs.lockfile import replace_json - - -@dataclass -class SymbolState: - volume: int = 0 - cost: float = 0.0 - last_buy: float = 0.0 - armed: bool = False - sell_grid: float = 0.0 - peak: int = 0 - pending: dict = field(default_factory=dict) - - def reset_profit(self): - """持仓成本或数量改变后，不沿用上轮止盈峰值。""" - self.armed = False - self.sell_grid = 0.0 - self.peak = 0 - - -class Store: - def __init__(self, path: Path, account: str): - self.path, self.account = path, account - self.symbols: dict[str, SymbolState] = {} - if path.exists(): - try: - raw = json.loads(path.read_text(encoding='utf-8')) - if raw['version'] != 1 or raw['account'] != account: - raise ValueError('版本或账户不一致') - for code, value in raw['symbols'].items(): - state = SymbolState(**value) - if type(state.volume) is not int or state.volume < 0 or type(state.peak) is not int: - raise ValueError('状态数量或峰值无效') - if any(not math.isfinite(v) or v < 0 for v in (state.cost, state.last_buy, state.sell_grid)): - raise ValueError('状态价格无效') - if type(state.armed) is not bool or (state.armed and state.sell_grid <= 0): - raise ValueError('止盈状态无效') - if not isinstance(state.pending, dict): - raise ValueError('委托状态无效') - if state.pending: - p = state.pending - if (p['side'] not in ('BUY', 'SELL') or not p['id'].startswith('ETF-') - or type(p['volume']) is not int or p['volume'] <= 0 - or (p['side'] == 'BUY' and p['volume'] > 1000) - or type(p['base_volume']) is not int or p['base_volume'] < 0 - or not math.isfinite(p['reserved']) or p['reserved'] < 0): - raise ValueError('待确认委托无效') - self.symbols[code] = state - except (ValueError, KeyError, TypeError, AttributeError) as exc: - raise ValueError(f'ETF 状态损坏，禁止自动重建：{path}') from exc - - def get(self, code: str) -> SymbolState: - return self.symbols.setdefault(code, SymbolState()) - - def save(self): - self.path.parent.mkdir(parents=True, exist_ok=True) - replace_json(self.path, dict(version=1, account=self.account, - symbols={k: asdict(v) for k, v in self.symbols.items()}))