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docs: add backtest engine design spec
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
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co-authored by
Claude Opus 4.8
Claude Opus 4.8
parent
626d0aae44
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8d68e9c094
@@ -17,7 +17,62 @@
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"Bash(pip list *)",
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"Bash(uv run *)",
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"Bash(git stash *)",
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"Bash(dir /s /b src\\\\xmtdx)"
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"Bash(dir /s /b src\\\\xmtdx)",
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"Bash(git remote *)",
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"Bash(uv --version)",
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"Bash(git rm *)",
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"mcp__plugin_context7_context7__resolve-library-id",
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"mcp__plugin_context7_context7__query-docs",
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"Bash(grep -E \"\\\\.py$\")",
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"Bash(awk '{added+=$1; deleted+=$2} END {print \"+\" added \" -\" deleted}')",
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"Bash(find D:/python/easty_tdx/examples/1[1-7]* -name \"*.py\")",
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"Bash(easy-tdx ping *)",
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"Bash(easy-tdx version *)",
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"Bash(easy-tdx kline *)",
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"Bash(easy-tdx quote *)",
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"Bash(easy-tdx tick *)",
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"Bash(easy-tdx transaction *)",
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"Bash(easy-tdx auction *)",
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"Bash(easy-tdx quote-list *)",
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"Bash(easy-tdx board-list *)",
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"Bash(easy-tdx board-members *)",
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"Bash(easy-tdx belong-board *)",
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"Bash(easy-tdx capital-flow *)",
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"Bash(echo \"EXIT CODE: $?\")",
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"Bash(easy-tdx unusual *)",
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"Bash(easy-tdx market-stat *)",
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"Bash(easy-tdx server-info *)",
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"Bash(easy-tdx symbol-info *)",
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"Bash(easy-tdx ex *)",
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"WebSearch",
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"mcp__zread__search_doc",
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"mcp__zread__read_file",
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"Bash(git status *)",
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"Bash(pip show *)",
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"Bash(git tag *)",
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"Bash(twine upload *)",
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"Bash(pip index *)",
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"Bash(curl -s https://pypi.org/pypi/easy-tdx/json)",
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"Bash(curl -sI \"https://files.pythonhosted.org/packages/py3/e/easy-tdx/easy_tdx-1.2.0-py3-none-any.whl\")",
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"Bash(gh run *)",
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"Bash(easy-tdx *)",
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"mcp__web-search-prime__web_search_prime",
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"mcp__plugin_compound-engineering_context7__resolve-library-id",
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"mcp__plugin_compound-engineering_context7__query-docs",
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"Bash(pip uninstall *)",
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"Bash(xargs grep -l \"Command\\\\|Request\\\\|Response\")",
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"Bash(quotes/stock_xdxr_info.go)",
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"Bash(mypy src/)",
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"Bash(ruff format *)",
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"mcp__plugin_episodic-memory_episodic-memory__search",
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"Bash(echo \"FK/\")",
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"Bash(git pull *)",
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"mcp__playwright__browser_navigate",
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"mcp__playwright__browser_snapshot",
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"Bash(gh auth *)",
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"Bash(rm -rf easy_tdx.wiki)",
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"Bash(gh repo *)",
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"Bash(gh api *)"
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]
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}
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}
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@@ -13,3 +13,4 @@ venv/
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.omc/
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uv.lock
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docs/_build/
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FK/
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@@ -39,6 +39,8 @@ easy-tdx 要做的事很简单：**把机构的数据锁砸开，扔到每个普
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随便用，随便改，随便分发。
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**数据面前，人人平等。**
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📖 **详细用法请查看 [GitHub Wiki](https://github.com/handsomejustin/easy_tdx/wiki)**
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## 安装
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```bash
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@@ -0,0 +1,424 @@
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# Backtest Engine Design — easy-tdx
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**Date:** 2026-06-09
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**Status:** Approved
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**Module:** `easy_tdx.backtest`
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**Priority:** P0 — 量化工具链全栈的第一块拼图
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---
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## 1. 目标
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为 easy-tdx 新增自建回测引擎模块，让用户能基于 easy-tdx 获取的 K 线数据执行策略回测、查看绩效报告。
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### 核心约束
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- 纯计算模块，与 `chanlun` 同级，零网络依赖
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- 仅依赖 `pandas`/`numpy`（项目已有），不引入第三方回测库
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- 接收 easy-tdx 标准 DataFrame（`datetime, open, close, high, low, vol, amount`）
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- 双模式策略定义：Python 类继承 + DSL 公式语法
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- v1 只实现向量化执行路径（日级策略），架构预留事件驱动扩展点
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---
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## 2. 架构
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### 2.1 文件结构
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```
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src/easy_tdx/backtest/
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├── __init__.py # 公开 API 导出
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├── strategy.py # Strategy 基类 + StrategyDataProxy + IndicatorProvider
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├── dsl.py # DSL 解析器 + @dsl_strategy 装饰器 + 字符串 DSL 编译
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├── engine.py # BacktestEngine（向量化执行路径）
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├── orders.py # OrderSimulator（撮合规则）
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├── portfolio.py # PortfolioTracker（持仓/资金曲线）
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├── performance.py # PerformanceAnalyzer（绩效指标计算）
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├── types.py # Trade / Position / Signal / BacktestResult 数据类
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└── cli.py # CLI 集成（easy-tdx backtest ...）
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```
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### 2.2 模块交互流
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```
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easy_tdx MacClient.get_stock_kline() → DataFrame
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│
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▼
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BacktestEngine(strategy, cash=100000)
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│
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┌───────────┼───────────┐
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▼ ▼ ▼
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Strategy DSL Parser OrderSimulator
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(Python类) (公式语法) (撮合规则)
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│ │ │
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└─────┬─────┘ │
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▼ │
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Signal (bool mask) │
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│ │
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▼ ▼
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PortfolioTracker ←────┘
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│
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▼
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PerformanceAnalyzer
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│
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▼
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BacktestResult
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(绩效指标 + 资金曲线 + 交易记录)
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```
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---
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## 3. 核心数据类型
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### 3.1 Signal
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```python
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@dataclass
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class Signal:
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datetime: int
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direction: Literal["BUY", "SELL"]
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size: float # 0 = 全仓/清仓
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price: float | None # None = 市价
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stop_loss: float | None
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take_profit: float | None
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```
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### 3.2 Trade
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```python
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@dataclass
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class Trade:
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datetime: int
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direction: Literal["BUY", "SELL"]
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size: float
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price: float
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commission: float
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slippage: float
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pnl: float # 仅平仓时计算
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```
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### 3.3 Position
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```python
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@dataclass
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class Position:
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datetime: int
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size: float # 正=多头，负=空头，0=空仓
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avg_price: float
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market_value: float
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unrealized_pnl: float
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```
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### 3.4 BacktestResult
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```python
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@dataclass
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class BacktestResult:
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performance: dict[str, float]
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equity_curve: pd.DataFrame # datetime, cash, position_value, total, drawdown, drawdown_pct
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trades: pd.DataFrame # datetime, direction, size, price, commission, pnl
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positions: pd.DataFrame # datetime, size, avg_price, market_value, unrealized_pnl
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config: dict
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```
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方法：
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- `to_json() → str`
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- `to_dict() → dict`
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- `summary() → None`（打印概要）
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---
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## 4. Strategy 基类
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### 4.1 接口定义
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```python
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class Strategy(ABC):
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def init(self) -> None:
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"""注册指标。策略初始化时调用一次。"""
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pass
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def next(self) -> None:
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"""每根 K 线调用。在此生成买卖信号。"""
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pass
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def I(self, func: Callable, *args, **kwargs) -> np.ndarray:
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"""注册指标函数。init() 后一次性计算，返回完整数组。"""
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...
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def buy(self, size: float = 0, price: float | None = None,
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stop_loss: float | None = None, take_profit: float | None = None) -> None:
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"""买入。size=0 全仓。"""
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...
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def sell(self, size: float = 0, price: float | None = None,
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stop_loss: float | None = None, take_profit: float | None = None) -> None:
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"""卖出。size=0 清仓。"""
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...
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@property
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def data(self) -> StrategyDataProxy: ...
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@property
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def position(self) -> Position: ...
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```
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### 4.2 StrategyDataProxy
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```python
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class StrategyDataProxy:
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"""K 线数据代理。支持 .close[0]（当前）、.close[-1]（前一根）。"""
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@property
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def open(self) -> _SeriesAccessor: ...
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@property
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def close(self) -> _SeriesAccessor: ...
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@property
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def high(self) -> _SeriesAccessor: ...
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@property
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def low(self) -> _SeriesAccessor: ...
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@property
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def vol(self) -> _SeriesAccessor: ...
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@property
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def amount(self) -> _SeriesAccessor: ...
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class _SeriesAccessor:
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"""[0] 当前值、[-1] 前一根、切片。"""
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def __getitem__(self, key: int) -> float: ...
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def __len__(self) -> int: ...
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```
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### 4.3 Python 类策略示例
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```python
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class MACrossStrategy(Strategy):
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def init(self):
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self.ma5 = self.I(MA, self.data.close, 5)
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self.ma20 = self.I(MA, self.data.close, 20)
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def next(self):
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if crossover(self.ma5, self.ma20):
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self.buy(size=100)
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elif crossover(self.ma20, self.ma5):
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self.sell(size=100)
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engine = BacktestEngine(strategy=MACrossStrategy, cash=100000)
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result = engine.run(df)
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```
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---
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## 5. DSL 策略定义
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### 5.1 设计边界
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| 能做 | 不做 |
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|------|------|
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| 指标交叉、比较、逻辑组合 | 循环、变量赋值、函数定义 |
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| 内置常用函数（CROSS, ABOVE, BELOW, BETWEEN） | 自定义控制流 |
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| 参数化（可调窗口期） | 图灵完备 |
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超出 DSL 能力的——直接用 Python 类。
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### 5.2 两种 DSL 模式
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**Python 装饰器模式**：
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```python
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from easy_tdx.backtest import dsl_strategy
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@dsl_strategy
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def dual_ma(df):
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buy = CROSS(MA(df.close, 5), MA(df.close, 20))
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sell = CROSS(MA(df.close, 20), MA(df.close, 5))
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return buy, sell
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```
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**字符串模式**（CLI 用）：
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```bash
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easy-tdx backtest SH 600519 --strategy "CROSS(MA(5),MA(20))" --cash 100000 --table
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```
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### 5.3 内置 DSL 函数
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复用 `MyTT.py` 已有实现：
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| 函数 | 签名 | 含义 |
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|------|------|------|
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| `MA(series, n)` | `(ndarray, int) → ndarray` | 简单移动平均 |
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| `EMA(series, n)` | `(ndarray, int) → ndarray` | 指数移动平均 |
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| `RSI(series, n)` | `(ndarray, int) → ndarray` | 相对强弱 |
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| `BOLL(series, n, k)` | `(ndarray, int, float) → tuple` | 布林带 |
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| `MACD(series, fast, slow, signal)` | `(ndarray, ...) → tuple` | MACD |
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| `CROSS(a, b)` | `(ndarray, ndarray) → ndarray[bool]` | 上穿检测 |
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| `REF(series, n)` | `(ndarray, int) → ndarray` | 前 n 期值 |
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| `HHV(series, n)` | `(ndarray, int) → ndarray` | n 期最高 |
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| `LLV(series, n)` | `(ndarray, int) → ndarray` | n 期最低 |
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| `BETWEEN(x, a, b)` | `(ndarray, ...) → ndarray[bool]` | 区间判断 |
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| `COUNT(cond, n)` | `(ndarray[bool], int) → ndarray` | n 期满足条件次数 |
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### 5.4 DSL 编译器
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`DSLCompiler.compile(func)` 流程：
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1. 调用 `func(mock_df)` 捕获 DSL 函数调用
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2. 记录 `(buy_mask, sell_mask)` 信号生成规则
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3. 动态生成 Strategy 子类
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引擎侧优化：DSL 策略不逐 Bar 调用 `next()`，直接用 bool mask 一次性生成全部 Signal。
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---
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## 6. 引擎执行流
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### 6.1 BacktestEngine 构造参数
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```python
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class BacktestEngine:
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def __init__(
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self,
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strategy: type[Strategy] | Strategy,
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cash: float = 100000.0,
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commission: float = 0.0003,
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min_commission: float = 5.0,
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stamp_tax: float = 0.001,
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slippage: float = 0.0,
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execution: str = "next_open", # "next_open" | "next_close" | "this_close" | "worst" | "best"
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position_mode: str = "full", # "full" | "fixed" | "percent" | "signal_only"
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benchmark: pd.DataFrame | None = None,
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):
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...
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```
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### 6.2 四步执行管道
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1. **信号生成**：DSL → bool mask；Python 类 → trace next() 生成 mask
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2. **信号→订单**（OrderSimulator）：根据 execution 规则确定成交价，根据仓位模式确定量
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3. **持仓追踪**（PortfolioTracker）：逐 Bar 更新现金/持仓/市值/回撤
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4. **绩效分析**（PerformanceAnalyzer）：从资金曲线计算全部指标
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Step 2 是唯一需要逐行处理的步骤（仓位依赖前一 Bar 状态）。其余步骤全向量化。
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### 6.3 OrderSimulator 成交价规则
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| 模式 | 说明 |
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|------|------|
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| `next_open`（默认） | 下一根 K 线开盘价成交，最真实 |
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| `next_close` | 下一根 K 线收盘价成交 |
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| `this_close` | 当根 K 线收盘价成交（有未来函数风险，标注警告） |
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| `worst` | 对投资者最差价格（买入取 high，卖出取 low） |
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| `best` | 对投资者最优价格（买入取 low，卖出取 high） |
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### 6.4 仓位管理规则
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| 模式 | 说明 |
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|------|------|
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| `full`（默认） | 买入用全部现金，卖出清仓 |
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| `fixed` | 每次固定股数 |
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| `percent` | 每次用总资产的 N% |
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| `signal_only` | 只生成信号，不模拟仓位 |
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### 6.5 费用模型
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- 佣金：`max(size * price * commission_rate, min_commission)`，买卖双向
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- 印花税：`size * price * stamp_tax_rate`，仅卖出
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- 滑点：`size * slippage_per_share`
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### 6.6 多策略批量回测
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```python
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# 多只股票
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results = engine.run_many({
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"SH600519": df_519,
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"SZ000858": df_858,
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})
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# → dict[str, BacktestResult]
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# 参数扫描
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results = engine.run_grid(df, params={
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"short": [5, 10, 15],
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"long": [20, 30, 60],
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})
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# → list[GridResult]，支持 .sort_by("sharpe").to_table()
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```
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---
|
||||
|
||||
## 7. 绩效指标
|
||||
|
||||
| 指标 | key | 算法 |
|
||||
|------|-----|------|
|
||||
| 总收益率 | `total_return` | `(total[-1] / total[0]) - 1` |
|
||||
| 年化收益率 | `annual_return` | `(1 + r) ** (252/n) - 1` |
|
||||
| 最大回撤 | `max_drawdown` | `max((peak - total) / peak)` |
|
||||
| 最大回撤天数 | `max_dd_duration` | 首次新高 - 回撤起点 |
|
||||
| 夏普比率 | `sharpe` | `(mean(ret) - rf/252) / std(ret) * sqrt(252)` |
|
||||
| 索提诺比率 | `sortino` | 分母只用负收益标准差 |
|
||||
| 卡玛比率 | `calmar` | `annual_return / max_drawdown` |
|
||||
| 总交易次数 | `total_trades` | `len(trades)` |
|
||||
| 盈利/亏损次数 | `win_trades` / `lose_trades` | `trade_pnl > 0 / <= 0` |
|
||||
| 胜率 | `win_rate` | `win_trades / total_trades` |
|
||||
| 盈亏比 | `profit_factor` | `sum(win_pnl) / abs(sum(lose_pnl))` |
|
||||
| 平均盈利/亏损 | `avg_win` / `avg_loss` | 盈利/亏损交易均值 |
|
||||
| 最大单笔盈亏 | `max_win` / `max_loss` | 单笔极值 |
|
||||
| 平均持仓天数 | `avg_holding_days` | 买入到卖出的 Bar 数均值 |
|
||||
| 收益波动率 | `volatility` | `std(daily_ret) * sqrt(252)` |
|
||||
| 基准超额收益 | `alpha` | 策略收益 - 基准收益（需 benchmark） |
|
||||
| 信息比率 | `information_ratio` | 超额收益均值 / 跟踪误差（需 benchmark） |
|
||||
|
||||
---
|
||||
|
||||
## 8. CLI 集成
|
||||
|
||||
### 8.1 命令
|
||||
|
||||
```bash
|
||||
# DSL 字符串模式
|
||||
easy-tdx backtest SH 600519 --strategy "CROSS(MA(5),MA(20))" --cash 100000 --table
|
||||
|
||||
# DSL 文件模式
|
||||
easy-tdx backtest SH 600519 --strategy-file my_strategy.py --cash 100000
|
||||
|
||||
# 参数化
|
||||
easy-tdx backtest SH 600519 --strategy "CROSS(MA({short}),MA({long}))" --params short=5,long=20
|
||||
|
||||
# 指定周期/复权
|
||||
easy-tdx backtest SH 600519 --strategy "CROSS(MA(5),MA(20))" --period 5MIN --adjust QFQ
|
||||
|
||||
# 参数扫描
|
||||
easy-tdx backtest SH 600519 --strategy "CROSS(MA({short}),MA({long}))" \
|
||||
--grid short=5,10,15 --grid long=20,30,60 --sort-by sharpe --table
|
||||
|
||||
# 输出 CSV
|
||||
easy-tdx backtest SH 600519 --strategy "CROSS(MA(5),MA(20))" --output csv
|
||||
```
|
||||
|
||||
### 8.2 输出格式
|
||||
|
||||
默认 JSON，`--table` 切换表格，`--output csv` 输出 CSV。与现有 CLI 行为一致。
|
||||
|
||||
---
|
||||
|
||||
## 9. 测试计划
|
||||
|
||||
```
|
||||
tests/unit/test_backtest_strategy.py # Strategy 基类 + 指标注入
|
||||
tests/unit/test_backtest_dsl.py # DSL 解析 + 编译
|
||||
tests/unit/test_backtest_engine.py # 引擎核心（信号→成交→持仓）
|
||||
tests/unit/test_backtest_orders.py # 撮合规则（5 种 execution 模式）
|
||||
tests/unit/test_backtest_portfolio.py # 持仓追踪 + 资金曲线
|
||||
tests/unit/test_backtest_performance.py # 绩效计算（手工验证已知结果）
|
||||
tests/unit/test_backtest_cli.py # CLI 命令（click test runner）
|
||||
```
|
||||
|
||||
全部离线测试，使用手工构造的 DataFrame fixture，零网络依赖。
|
||||
|
||||
---
|
||||
|
||||
## 10. 未来扩展点（v1 不实现，架构不堵死）
|
||||
|
||||
- 事件驱动执行路径（支持日内策略、逐 tick 推演）
|
||||
- 多品种组合回测（Portfolio 级别，同时持有多只股票）
|
||||
- 风控模块（最大回撤止损、单笔止损、仓位上限）
|
||||
- 实时模拟交易（Strategy 基类接口可直接迁移）
|
||||
- 与缠论模块深度集成（策略可直接引用笔/中枢/买卖点信号）
|
||||
- 可视化（K 线 + 买卖点标注 + 资金曲线）
|
||||
@@ -321,4 +321,14 @@ def BIAS_SIGNAL(CLOSE, P=10, M=30): #乖离率信号指标：M日乖离 + 短/
|
||||
X_LMA = MA(X, M) #长周期信号线 MA(X,M)
|
||||
return RD(X), RD(S_SMA), RD(X_LMA)
|
||||
|
||||
def FK(CLOSE): #FK趋势指标：快线EMA(2)与斜率外推慢线EMA(42)比较
|
||||
fast = EMA(CLOSE, 2)
|
||||
slow = EMA(SLOPE(CLOSE, 21) * 20 + CLOSE, 42)
|
||||
return fast > slow
|
||||
|
||||
def OUTPERFORM_20D(CLOSE, INDEX_CLOSE): #20日相对强度：个股涨幅跑赢大盘返回1，否则返回0
|
||||
stock_ret = (CLOSE - REF(CLOSE, 20)) / REF(CLOSE, 20)
|
||||
index_ret = (INDEX_CLOSE - REF(INDEX_CLOSE, 20)) / REF(INDEX_CLOSE, 20)
|
||||
return IF(stock_ret > index_ret, 1, 0)
|
||||
|
||||
#望大家能提交更多指标和函数 https://github.com/mpquant/MyTT
|
||||
|
||||
+14
-10
@@ -1,12 +1,16 @@
|
||||
"""响应帧头解析与 zlib 解压。
|
||||
|
||||
响应帧格式（16 字节固定头 + body）：
|
||||
struct "<IIIHH"
|
||||
偏移 0: I (4字节) — 未知
|
||||
偏移 4: I (4字节) — 未知
|
||||
偏移 8: I (4字节) — 未知
|
||||
响应帧格式（16 字节固定头 + body），字节级结构（gotdx 交叉验证）：
|
||||
偏移 0: I (4字节) — magic = 7654321 (0x0074CBB1)，协议标识
|
||||
偏移 4: B (1字节) — ZipFlag：bit4=1 表示 body 已压缩，0x0C=未压缩, 0x1C=已压缩
|
||||
偏移 5: I (4字节) — SeqID：请求 bytes 1-4 的回显（命令标识）
|
||||
偏移 9: B (1字节) — 保留（观察到恒为 0x00）
|
||||
偏移 10: H (2字节) — Method：请求 bytes 10-11 的回显
|
||||
偏移 12: H (2字节) — zipsize（body 实际长度）
|
||||
偏移 14: H (2字节) — unzipsize（解压后长度；等于 zipsize 表示未压缩）
|
||||
|
||||
兼容说明：使用 IIIHH 解码可正确提取 zipsize/unzipsize。前三个 uint32 中：
|
||||
u0 = magic, u1 = ZipFlag(1B) + SeqID(3B 低字节), u2 = SeqID(1B 高字节) + 保留(1B) + Method(2B)
|
||||
"""
|
||||
|
||||
import zlib
|
||||
@@ -21,22 +25,22 @@ _HEADER_FMT = "<IIIHH"
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class FrameHeader:
|
||||
unknown_0: int
|
||||
unknown_1: int
|
||||
unknown_2: int
|
||||
magic: int # 协议魔数，恒为 7654321
|
||||
seq_id: int # ZipFlag(1B) + 请求 bytes 1-4 回显(3B)
|
||||
method: int # 请求回显(1B) + 保留(1B) + Method(2B)
|
||||
zipsize: int
|
||||
unzipsize: int
|
||||
|
||||
|
||||
def parse_header(buf: bytes) -> FrameHeader:
|
||||
"""解析 16 字节响应帧头。"""
|
||||
u0, u1, u2, zipsize, unzipsize = unpack_from(
|
||||
magic, seq_id, method, zipsize, unzipsize = unpack_from(
|
||||
_HEADER_FMT,
|
||||
buf,
|
||||
0,
|
||||
"frame header",
|
||||
)
|
||||
return FrameHeader(u0, u1, u2, zipsize, unzipsize)
|
||||
return FrameHeader(magic, seq_id, method, zipsize, unzipsize)
|
||||
|
||||
|
||||
def decompress_body(header: FrameHeader, raw_body: bytes) -> bytes:
|
||||
|
||||
@@ -39,10 +39,10 @@ class GetSecurityListCmd(BaseCommand[list[SecurityInfo]]):
|
||||
code_bytes,
|
||||
volunit,
|
||||
name_bytes,
|
||||
_unknown1, # 4字节，含义未明
|
||||
_unknown1, # 4字节，排序/分组字段（非用户可见数据）
|
||||
decimal_point,
|
||||
pre_close_raw,
|
||||
_unknown2, # 4字节，含义未明
|
||||
_unknown2, # 4字节，私有时间戳（非用户可见数据）
|
||||
) = struct.unpack("<6sH8s4sBI4s", raw)
|
||||
|
||||
code = code_bytes.decode("utf-8", errors="replace").rstrip("\x00")
|
||||
|
||||
@@ -100,8 +100,8 @@ class GetSecurityQuotesCmd(BaseCommand[list[SecurityQuote]]):
|
||||
s_vol, pos = get_price(body, pos)
|
||||
b_vol, pos = get_price(body, pos)
|
||||
|
||||
unknown_2, pos = get_price(body, pos)
|
||||
unknown_3, pos = get_price(body, pos)
|
||||
unknown_2, pos = get_price(body, pos) # IndexOpenAmount(指数)/舍入残差(个股)
|
||||
unknown_3, pos = get_price(body, pos) # StockOpenAmount(个股)/负值(指数)
|
||||
|
||||
# 五档买盘
|
||||
bid1_d, pos = get_price(body, pos)
|
||||
@@ -129,8 +129,8 @@ class GetSecurityQuotesCmd(BaseCommand[list[SecurityQuote]]):
|
||||
bv5, pos = get_price(body, pos)
|
||||
av5, pos = get_price(body, pos)
|
||||
|
||||
# 尾部：2字节 H + 4个 get_price + 2字节 h + 2字节 H
|
||||
(unknown_4,) = unpack_from("<H", body, pos, "security_quotes tail flag")
|
||||
# 尾部：2字节 H（交易状态标志，0x8020=停牌）+ 4个 get_price + 2字节 h + 2字节 H
|
||||
(trading_status,) = unpack_from("<H", body, pos, "security_quotes tail flag")
|
||||
pos += 2
|
||||
unknown_5, pos = get_price(body, pos)
|
||||
unknown_6, pos = get_price(body, pos)
|
||||
@@ -196,6 +196,8 @@ class GetSecurityQuotesCmd(BaseCommand[list[SecurityQuote]]):
|
||||
unknown_7=unknown_7,
|
||||
unknown_8=unknown_8,
|
||||
server_time=_format_server_time(unknown_0),
|
||||
trading_status=trading_status,
|
||||
open_amount=unknown_3 * 100.0,
|
||||
_raw=body[record_start:pos],
|
||||
)
|
||||
)
|
||||
|
||||
@@ -9,7 +9,10 @@ from .enums import Market
|
||||
class SecurityQuote:
|
||||
"""单只股票实时五档行情。
|
||||
|
||||
带 unknown_ 前缀的字段为协议中尚未明确含义的字段，保留以供逆向分析。
|
||||
带 unknown_ 前缀的字段保留原始协议值，其含义已在逆向分析中确认：
|
||||
unknown_2: 指数→集合竞价成交金额/100；个股→舍入残差≈0
|
||||
unknown_3: 个股→集合竞价成交金额/100；指数→负值/无意义
|
||||
unknown_5-8: 保留字段，恒为 0
|
||||
_raw 为该股票记录的原始字节切片。
|
||||
"""
|
||||
|
||||
@@ -17,18 +20,18 @@ class SecurityQuote:
|
||||
code: str
|
||||
|
||||
# 价格
|
||||
price: float # 现价
|
||||
price: float # 现价
|
||||
pre_close: float # 昨收
|
||||
open: float # 今开
|
||||
high: float # 最高
|
||||
low: float # 最低
|
||||
open: float # 今开
|
||||
high: float # 最高
|
||||
low: float # 最低
|
||||
|
||||
# 量额
|
||||
vol: float # 总成交量（手）
|
||||
cur_vol: float # 当前成交量
|
||||
amount: float # 成交额（元）
|
||||
s_vol: float # 内盘（主动卖）
|
||||
b_vol: float # 外盘（主动买）
|
||||
vol: float # 总成交量（手）
|
||||
cur_vol: float # 当前成交量
|
||||
amount: float # 成交额（元）
|
||||
s_vol: float # 内盘（主动卖）
|
||||
b_vol: float # 外盘（主动买）
|
||||
|
||||
# 活跃度指标（含义来自社区逆向，仅供参考）
|
||||
active1: int
|
||||
@@ -60,21 +63,25 @@ class SecurityQuote:
|
||||
|
||||
# 价格指标
|
||||
rise_speed: float # 涨速（原 reversed_bytes9 / 100）
|
||||
limit_up: float | None # 涨停价（业务规则计算）
|
||||
limit_up: float | None # 涨停价（业务规则计算）
|
||||
limit_down: float | None # 跌停价（业务规则计算）
|
||||
|
||||
# 未知字段：买卖量之后的两个变长整数（保留供进一步分析）
|
||||
unknown_2: int = field(default=0, repr=False) # 未知变长整数 2
|
||||
unknown_3: int = field(default=0, repr=False) # 未知变长整数 3
|
||||
# 协议原始值（含义已确认，保留以供高级分析）
|
||||
unknown_2: int = field(default=0, repr=False) # 指数: IndexOpenAmount/100; 个股: 舍入残差
|
||||
unknown_3: int = field(default=0, repr=False) # 个股: StockOpenAmount/100; 指数: 负值
|
||||
|
||||
# 未知字段：尾部四个变长整数
|
||||
unknown_5: int = field(default=0, repr=False) # 原 reversed_bytes5
|
||||
unknown_6: int = field(default=0, repr=False) # 原 reversed_bytes6
|
||||
unknown_7: int = field(default=0, repr=False) # 原 reversed_bytes7
|
||||
unknown_8: int = field(default=0, repr=False) # 原 reversed_bytes8
|
||||
# 尾部保留字段
|
||||
unknown_5: int = field(default=0, repr=False) # 保留，恒为 0
|
||||
unknown_6: int = field(default=0, repr=False) # 保留，恒为 0
|
||||
unknown_7: int = field(default=0, repr=False) # 保留，恒为 0
|
||||
unknown_8: int = field(default=0, repr=False) # 保留，恒为 0
|
||||
|
||||
# 服务器时间字符串（从 unknown_0 原始整数解析，格式 HH:MM:SS.mmm）
|
||||
server_time: str = field(default="", repr=True)
|
||||
|
||||
# 已确认语义的新字段
|
||||
trading_status: int = field(default=0, repr=False) # 交易状态标志，0x8020=停牌
|
||||
open_amount: float = field(default=0.0, repr=False) # 集合竞价成交金额（元），个股有效
|
||||
|
||||
# 原始字节（该股票记录切片）
|
||||
_raw: bytes = field(default=b"", repr=False, compare=False)
|
||||
|
||||
@@ -158,6 +158,11 @@ def test_security_quotes_parse():
|
||||
assert q.unknown_2 == -1
|
||||
assert q.unknown_3 == 22694
|
||||
|
||||
# confirmed semantic fields
|
||||
assert isinstance(q.trading_status, int)
|
||||
assert isinstance(q.open_amount, float)
|
||||
assert q.open_amount == 22694 * 100.0
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# minute_time
|
||||
|
||||
Reference in New Issue
Block a user