{"repo":"akfamily/akquant","free":true,"listed":false,"github":"https://github.com/akfamily/akquant","clone":"git clone https://github.com/akfamily/akquant.git","description":"AKQuant is a high-performance quantitative research and trading framework built on Rust and Python! 开源量化回测框架","language":"Python","stars":2028,"topics":["akshare","backtest","finance","quant"],"license":"MIT","category":"data_api","readme_excerpt":"

\n \"AKQuant\"\n

\n\n

\n \n \"PyPI\n \n \n \"Python\n \n \n \"License\"\n \n \n \"AKShare\"\n \n \n \"Downloads\"\n \n

\n\n**AKQuant** 是一款专为量化投研设计的**下一代高性能混合框架**。核心引擎采用 **Rust** 编写以确保极致的执行效率，同时提供优雅的 **Python** 接口以维持灵活的策略开发体验。\n\n🚀 **核心亮点：**\n\n* **高性能内核**：得益于 Rust 的零开销抽象与 **Zero-Copy 数据导入**（`add_arrays` 借用 NumPy 缓冲入引擎；策略侧 `get_history` 等读取则返回安全快照拷贝），AKQuant 在部分回测场景下可显著降低 Python 层开销；实际运行速度取决于策略逻辑、数据规模、回调频率与运行环境。\n* **原生 ML 支持**：内置 **Walk-forward Validation**（滚动训练）框架，无缝集成 PyTorch/Scikit-learn，让 AI 策略开发从实验到回测一气呵成。\n* **TA-Lib 指标生态**：内置 `akquant.talib` 双后端（`python/rust`）兼容能力，支持 **103 个指标**。\n* **因子表达式引擎**：内置 **Polars** 驱动的高性能因子计算引擎，支持 `Rank(Ts_Mean(Close, 5))` 等 Alpha101 风格公式，自动处理并行计算与数据对齐。\n* **参数优化**：内置多进程网格搜索（Grid Search）框架，支持策略参数的高效并行优化。\n* **专业级风控**：内置完善的订单流管理与即时风控模块，支持多资产组合回测。\n\n👉 **[阅读完整文档](https://akquant.akfamily.xyz/)** | **[English Documentation](https://akquant.akfamily.xyz/en/)**\n\n## 安装说明\n\n**AKQuant** 已发布至 PyPI，无需安装 Rust 环境即可直接使用。\n\n```bash\npip install akquant\n```\n\n## 快速开始\n\n以下是一个简单的策略示例：\n\n```python\nimport akquant as aq\nimport akshare as ak\nfrom akquant import Strategy\n\n# 1. 准备数据\n# 使用 akshare 获取 A 股历史数据 (需安装: pip install akshare)\ndf = ak.stock_zh_a_daily(symbol=\"sh600000\", start_date=\"20250212\", end_date=\"20260212\")\n\n\nclass MyStrategy(Strategy):\n def on_bar(self, bar):\n # 简单策略示例:\n # 当收盘价 > 开盘价 (阳线) -> 买入\n # 当收盘价 < 开盘价 (阴线) -> 卖出\n\n # 获取当前持仓\n current_pos = self.get_position(bar.symbol)\n\n if current_pos == 0 and bar.close > bar.open:\n self.buy(symbol=bar.symbol, quantity=100)\n print(f\"[{bar.timestamp_iso}] Buy 100 at {bar.close:.2f}\") # UTC ISO 8601\n\n elif current_pos > 0 and bar.close < bar.open:\n self.close_position(symbol=bar.symbol)\n print(f\"[{bar.timestamp_iso}] Sell 100 at {bar.close:.2f}\") # UTC ISO 8601\n\n\n# 运行回测\nresult = aq.run_backtest(\n data=df,\n strategy=MyStrategy,\n initial_cash=100000.0,\n symbols=\"sh600000\"\n)\n\n# 打印回测结果\nprint(\"\\n=== Backtest Result ===\")\nprint(result)\n\n# 生成最小基准对比报告\nbenchmark_returns = (\n df.set_index(\"date\")[\"close\"].pct_change().fillna(0.0).rename(\"SIMPLE_BENCH\")\n)\nresult.viz.report(\n filename=\"quickstart_report.html\",\n show=False,\n benchmark=benchmark_returns,\n)\n```\n\n调用 `result.viz.report(..., benchmark=...)` 后，报告会新增“基准对比 (Benchmark Comparison)”区块，展示策略/基准/超额累计收益曲线，以及累计超额收益、年化超额收益、跟踪误差、信息比率、Beta、Alpha 等相对指标。\n\n**运行结果示例:**\n\n```text\n=== Backtest Result ===\nBacktestResult:\n Value\nstart_time 2025-02-12 00:00:00+08:00\nend_time 2026-02-12 00:00:00+08:00\nduration 365 days, 0:00:00\ntotal_bars 249\nclosed_trade_count 62.0\nexecution_count 124.0\nopen_position_count 0.0\ninitial_market_value 100000.0\nend_market_value 99804.0\ntotal_pnl -196.0\nunrealized_pnl 0.0\ntotal_return_pct -0.196\nannualized_return -0.00196\nvolatility 0.002402\ntotal_profit 548.0\ntotal_loss -744.0\ntotal_commission 0.0\nmax_drawdown 345.0\nmax_drawdown_pct 0.344487\nwin_rate 22.580645\nloss_rate 77.419355\nwinning_trades 14.0\nlosing_trades 48.0\navg_pnl -3.16129\navg_return_pct -0.199577\navg_trade_bars 1.967742\navg_profit 39.142857\navg_profit_pct 3.371156\navg_winning_trade_bars 4.5\navg_loss -15.5\navg_loss_pct -1.241041\navg_losing_trade_bars 1.229167\nlargest_win 120.0\nlargest_win_pct 10.178117\nlargest_win_bars 7.0\nlargest_loss -70.0\nlargest_loss_pct -5.380477\nlargest_loss_bars 1.0\nmax_wins 2.0\nmax_losses 9.0\nsharpe_ratio -0.816142\nsortino_ratio -1.066016\nprofit_factor 0.736559\nulcer_index 0.001761\nupi -1.113153\nequity_r2 0.399577\nstd_error 68.64863\ncalmar_ratio -0.568962\nexposure_time_pct 48.995984\nvar_95 -0.00023\nvar_99 -0.00062\ncvar_95 -0.000405\ncvar_99 -0.00069\nsqn -0.743693\nkelly_criterion -0.080763\nmax_leverage 0.01458\nmin_margin_level 68.587671\n```\n\n## 复杂订单助手 (OCO / Bracket)\n\nAKQuant 提供了两组复杂订单助手，减少手写订单联动逻辑：\n\n* `place_oco(first_order_id, second_order_id, group_id=None)`：将两个订单绑定为 OCO，任一成交后自动撤销另一单。\n* `place_bracket(symbol, quantity, entry_price=None, stop_trigger_price=None, take_profit_price=None, ...)`：一次性提交 Bracket 结构；进场成交后自动挂出止损/止盈，并在双退出单场景下自动绑定 OCO。\n\n```python\nfrom akquant import OrderStatus, Strategy\n\nclass BracketHelperStrategy(Strategy):\n def __init__(self):\n self.entry_order_id = \"\"\n\n def on_bar(self, bar):\n if self.get_position(bar.symbol) > 0 or self.entry_order_id:\n return\n\n self.entry_order_id = self.place_bracket(\n symbol=bar.symbol,\n quantity=100,\n stop_trigger_price=bar.close * 0.98,\n take_profit_price=bar.close * 1.04,\n entry_tag=\"entry\",\n stop_tag=\"stop\",\n take_profit_tag=\"take\",\n )\n\n def on_order(self, order):\n if order.id == self.entry_order_id and order.status in (\n OrderStatus.Cancelled,\n OrderStatus.Rejected,\n ):\n self.entry_order_id = \"\"\n```\n\n可直接运行完整示例：\n\n```bash\npython examples/06_complex_orders.py\n```\n\n## 流式回测 (Streaming)\n\n如果你希望在回测执行过程中实时消费事件，可直接使用 `run_backtest` 并传入 `on_event`：\n\n```python\ndef on_event(event):\n if event[\"event_type\"] == \"finished\":\n payload = event[\"payload\"]\n print(\"status:\", payload.get(\"status\"))\n print(\"callback_error_count:\", payload.get(\"callback_error_count\"))\n\nresult = aq.run_backtest(\n data=df,\n strategy=MyStrategy,\n symbols=\"sh600000\",\n on_event=on_event,\n show_progress=False,\n stream_progress_interval=10,\n stream_equity_interval=10,\n stream_batch_size=32,\n stream_max_buffer=256,\n stream_error_mode=\"continue\",\n)\n```\n\n`on_event` 为可选参数：不传时保持传统阻塞语义，传入时可实时消费事件。\n\n关键参数：\n\n* `stream_progress_interval` / `stream_equity_interval`: 进度与权益事件采样间隔\n* `stream_batch_size` / `stream_max_buffer`: 缓冲与批量刷新控制\n* `stream_error_mode`: 回调异常策略，支持 `\"continue\"` 与 `\"fail_fast\"`\n\n## 可视化 (Visualization)\n\nAKQuant 内置了基于 **Plotly** 的强大可视化模块，仅需一行代码即可生成包含权益曲线、回撤分析、月度热力图等详细指标的交互式 HTML 报告。\n\n```python\n# 生成交互式 HTML 报告，自动在浏览器中打开\nresult.viz.report(\n show=True,\n compact_currency=True, # 金额列按 K/M/B 紧凑显示（默认 True）\n)\n\n# 如果你希望金额列保留原始数值精度（不缩写），可关闭：\nresult.viz.report(\n show=False,\n filename=\"report_raw_amount.html\",\n compact_currency=False,\n)\n```\n\n你也可以直接复用结构化分析结果做二次研究：\n\n```python\nexposure = result.exposure_df() # 暴露分解（净暴露/总暴露/杠杆）\nattr_by_symbol = result.attribution_df(by=\"symbol\")\nattr_by_tag = result.attribution_df(by=\"tag\")\ncapacity = result.capacity_df() # 容量代理（成交率/换手等）\norders_by_strategy = result.orders_by_strategy() # 按策略归属聚合订单\nexecutions_by_strategy = result.executions_by_strategy() # 按策略归属聚合成交\n```\n\n

\n \"Strategy\n
\n 👉 点击查看交互式报表示例 (Interactive Demo)\n

\n\n## 文档索引\n\n* 📖 **[核心特性与架构](docs/zh/index.md#核心特性)**: 了解 AKQuant 的设计理念与性能优势。\n* 🛠️ **[安装指南](docs/zh/start/installation.md)**: 详细的安装步骤（含源码编译）。\n* 🚀 **[快速入门](docs/zh/start/quickstart.md)**: 更多示例与基础用法。\n* 🤖 **[机器学习指南](docs/zh/advanced/ml.md)**: 如何使用内置的 ML 框架进行滚动训练。\n* 📚 **[API 参考](docs/zh/reference/api.md)**: 详细的类与函数文档。\n* 💻 **[贡献指南](CONTRIBUTING.md)**: 如何参与项目开发。\n\n## 🧪 测试与质量保证\n\nAKQuant 采用严格的测试流程以确保回测引擎的准确性：\n\n* **单元测试**: 覆盖核心 Rust 组件与 Python 接口。\n* **黄金测试 (Golden Tests)**: 使用合成数据验证关键业务逻辑（如 T+1、涨跌停、保证金、期权希腊值），并与锁定的基线结果进行比对，防止算法回退。\n\n运行测试：\n```bash\n# 1. 使用 uv 环境运行命令\nuv sync\n\n# 2. 构建并绑定 Rust 扩展\n# 注意：不要使用 `uv run maturin develop`，它会先尝试安装当前项目，\n# 对于以 maturin 作为 build backend 的仓库可能卡在 Preparing packages。\nuvx maturin develop\n\n# 3. 运行所有测试\nuv run pytest\n\n# 4. 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