Metadata-Version: 2.4
Name: akquant
Version: 0.1.53
Classifier: Programming Language :: Rust
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: License :: OSI Approved :: MIT License
Requires-Dist: pandas>=3.0.0
Requires-Dist: numpy>=2.4.1
Requires-Dist: pyarrow>=14.0.0
Requires-Dist: tqdm>=4.0.0
Requires-Dist: plotly>=5.0.0
Requires-Dist: polars>=0.20.0
Requires-Dist: ruff>=0.1.0 ; extra == 'dev'
Requires-Dist: pre-commit>=3.0.0 ; extra == 'dev'
Requires-Dist: mypy>=1.0.0 ; extra == 'dev'
Requires-Dist: pytest>=7.0.0 ; extra == 'dev'
Requires-Dist: mkdocs>=1.5.0 ; extra == 'dev'
Requires-Dist: mkdocs-material>=9.5.0 ; extra == 'dev'
Requires-Dist: mkdocs-static-i18n>=1.2.0 ; extra == 'dev'
Requires-Dist: mkdocs>=1.5.0 ; extra == 'docs'
Requires-Dist: mkdocs-material>=9.5.0 ; extra == 'docs'
Requires-Dist: mkdocs-static-i18n>=1.2.0 ; extra == 'docs'
Requires-Dist: akshare>=1.0.0 ; extra == 'docs'
Requires-Dist: matplotlib>=3.0.0 ; extra == 'full'
Requires-Dist: plotly>=5.0.0 ; extra == 'full'
Requires-Dist: scikit-learn>=1.0.0 ; extra == 'full'
Requires-Dist: torch>=2.0.0 ; extra == 'full'
Requires-Dist: quantstats>=0.0.60 ; extra == 'full'
Requires-Dist: scikit-learn>=1.0.0 ; extra == 'ml'
Requires-Dist: torch>=2.0.0 ; extra == 'ml'
Requires-Dist: matplotlib>=3.0.0 ; extra == 'plot'
Requires-Dist: plotly>=5.0.0 ; extra == 'plot'
Requires-Dist: quantstats>=0.0.60 ; extra == 'quantstats'
Provides-Extra: dev
Provides-Extra: docs
Provides-Extra: full
Provides-Extra: ml
Provides-Extra: plot
Provides-Extra: quantstats
License-File: LICENSE
Summary: High-performance quantitative trading framework based on Rust and Python
Author: AKQuant Developers
Author-email: albertandking@gmail.com
License: MIT License
Requires-Python: >=3.10
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM
Project-URL: Documentation, https://akquant.akfamily.xyz
Project-URL: Homepage, https://github.com/akfamily/akquant
Project-URL: Repository, https://github.com/akfamily/akquant

  AKQuant

**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。

🚀 **核心亮点：**

*   **极致性能**：得益于 Rust 的零开销抽象与 **Zero-Copy** 数据架构，回测速度较传统纯 Python 框架（如 Backtrader）提升 **X倍+**。
*   **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。
*   **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。
*   **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。
*   **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。

👉 **[阅读完整文档](https://akquant.akfamily.xyz/)** | **[English Documentation](https://akquant.akfamily.xyz/en/)**

## 安装说明

**AKQuant** 已发布至 PyPI，无需安装 Rust 环境即可直接使用。

```bash
pip install akquant
```

## 快速开始

以下是一个简单的策略示例：

```python
import akquant as aq
import akshare as ak
from akquant import Strategy

# 1. 准备数据
# 使用 akshare 获取 A 股历史数据 (需安装: pip install akshare)
df = ak.stock_zh_a_daily(symbol="sh600000", start_date="20250212", end_date="20260212")


class MyStrategy(Strategy):
    def on_bar(self, bar):
        # 简单策略示例:
        # 当收盘价 > 开盘价 (阳线) -> 买入
        # 当收盘价 < 开盘价 (阴线) -> 卖出

        # 获取当前持仓
        current_pos = self.get_position(bar.symbol)

        if current_pos == 0 and bar.close > bar.open:
            self.buy(symbol=bar.symbol, quantity=100)
            print(f"[{bar.timestamp_str}] Buy 100 at {bar.close:.2f}")

        elif current_pos > 0 and bar.close < bar.open:
            self.close_position(symbol=bar.symbol)
            print(f"[{bar.timestamp_str}] Sell 100 at {bar.close:.2f}")


# 运行回测
result = aq.run_backtest(
    data=df,
    strategy=MyStrategy,
    initial_cash=100000.0,
    symbol="sh600000"
)

# 打印回测结果
print("\n=== Backtest Result ===")
print(result)
```

**运行结果示例:**

```text
=== Backtest Result ===
BacktestResult:
                                            Value
start_time              2025-02-12 00:00:00+08:00
end_time                2026-02-12 00:00:00+08:00
duration                        365 days, 0:00:00
total_bars                                    249
trade_count                                  62.0
initial_market_value                     100000.0
end_market_value                          99804.0
total_pnl                                  -196.0
unrealized_pnl                                0.0
total_return_pct                           -0.196
annualized_return                        -0.00196
volatility                               0.002402
total_profit                                548.0
total_loss                                 -744.0
total_commission                              0.0
max_drawdown                                345.0
max_drawdown_pct                         0.344487
win_rate                                22.580645
loss_rate                               77.419355
winning_trades                               14.0
losing_trades                                48.0
avg_pnl                                  -3.16129
avg_return_pct                          -0.199577
avg_trade_bars                           1.967742
avg_profit                              39.142857
avg_profit_pct                           3.371156
avg_winning_trade_bars                        4.5
avg_loss                                    -15.5
avg_loss_pct                            -1.241041
avg_losing_trade_bars                    1.229167
largest_win                                 120.0
largest_win_pct                         10.178117
largest_win_bars                              7.0
largest_loss                                -70.0
largest_loss_pct                        -5.380477
largest_loss_bars                             1.0
max_wins                                      2.0
max_losses                                    9.0
sharpe_ratio                            -0.816142
sortino_ratio                           -1.066016
profit_factor                            0.736559
ulcer_index                              0.001761
upi                                     -1.113153
equity_r2                                0.399577
std_error                                68.64863
calmar_ratio                            -0.568962
exposure_time_pct                       48.995984
var_95                                   -0.00023
var_99                                   -0.00062
cvar_95                                 -0.000405
cvar_99                                  -0.00069
sqn                                     -0.743693
kelly_criterion                         -0.080763
max_leverage                              0.01458
min_margin_level                        68.587671
```

## 可视化 (Visualization)

AKQuant 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。

```python
# 生成交互式 HTML 报告，自动在浏览器中打开
result.report(show=True)
```


  Strategy Dashboard
  
  👉 点击查看交互式报表示例 (Interactive Demo) (https://akquant.akfamily.xyz/report_demo/)

## 文档索引

*   📖 **[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。
*   🛠️ **[安装指南](docs/zh/installation.md)**: 详细的安装步骤（含源码编译）。
*   🚀 **[快速入门](docs/zh/quickstart.md)**: 更多示例与基础用法。
*   🤖 **[机器学习指南](docs/zh/ml_guide.md)**: 如何使用内置的 ML 框架进行滚动训练。
*   📚 **[API 参考](docs/zh/api.md)**: 详细的类与函数文档。
*   💻 **[贡献指南](CONTRIBUTING.md)**: 如何参与项目开发。

## 🧪 测试与质量保证

AKQuant 采用严格的测试流程以确保回测引擎的准确性：

*   **单元测试**: 覆盖核心 Rust 组件与 Python 接口。
*   **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。

运行测试：
```bash
# 1. 安装开发依赖
pip install -e ".[dev]"

# 2. 运行所有测试
pytest

# 3. 仅运行黄金测试
pytest tests/golden/test_golden.py
```

## 贡献指南

## Citation

Please use this bibtex if you want to cite this repository in your publications:

```bibtex
@misc{akquant,
    author = {Albert King and Yaojie Zhang},
    title = {AKQuant},
    year = {2026},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/akfamily/akquant}},
}
```

## License

MIT License

