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：
- 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。 - 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。
- 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。 - 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 | English Documentation
安装说明
AKQuant 已发布至 PyPI，无需安装 Rust 环境即可直接使用。
pip install akquant
快速开始
以下是一个简单的策略示例：
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_iso}] Buy 100 at {bar.close:.2f}") # UTC ISO 8601
elif current_pos > 0 and bar.close < bar.open:
self.close_position(symbol=bar.symbol)
print(f"[{bar.timestamp_iso}] Sell 100 at {bar.close:.2f}") # UTC ISO 8601
# 运行回测
result = aq.run_backtest(
data=df,
strategy=MyStrategy,
initial_cash=100000.0,
symbols="sh600000"
)
# 打印回测结果
print("\n=== Backtest Result ===")
print(result)
# 生成最小基准对比报告
benchmark_returns = (
df.set_index("date")["close"].pct_change().fillna(0.0).rename("SIMPLE_BENCH")
)
result.viz.report(
filename="quickstart_report.html",
show=False,
benchmark=benchmark_returns,
)
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。
运行结果示例:
=== 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
closed_trade_count 62.0
execution_count 124.0
open_position_count 0.0
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
复杂订单助手 (OCO / Bracket)
AKQuant 提供了两组复杂订单助手，减少手写订单联动逻辑：
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。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。
from akquant import OrderStatus, Strategy
class BracketHelperStrategy(Strategy):
def __init__(self):
self.entry_order_id = ""
def on_bar(self, bar):
if self.get_position(bar.symbol) > 0 or self.entry_order_id:
return
self.entry_order_id = self.place_bracket(
symbol=bar.symbol,
quantity=100,
stop_trigger_price=bar.close * 0.98,
take_profit_price=bar.close * 1.04,
entry_tag="entry",
stop_tag="stop",
take_profit_tag="take",
)
def on_order(self, order):
if order.id == self.entry_order_id and order.status in (
OrderStatus.Cancelled,
OrderStatus.Rejected,
):
self.entry_order_id = ""
可直接运行完整示例：
python examples/06_complex_orders.py
流式回测 (Streaming)
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：
def on_event(event):
if event["event_type"] == "finished":
payload = event["payload"]
print("status:", payload.get("status"))
print("callback_error_count:", payload.get("callback_error_count"))
result = aq.run_backtest(
data=df,
strategy=MyStrategy,
symbols="sh600000",
on_event=on_event,
show_progress=False,
stream_progress_interval=10,
stream_equity_interval=10,
stream_batch_size=32,
stream_max_buffer=256,
stream_error_mode="continue",
)
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。
关键参数：
stream_progress_interval/stream_equity_interval: 进度与权益事件采样间隔stream_batch_size/stream_max_buffer: 缓冲与批量刷新控制stream_error_mode: 回调异常策略，支持"continue"与"fail_fast"
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。
# 生成交互式 HTML 报告，自动在浏览器中打开
result.viz.report(
show=True,
compact_currency=True, # 金额列按 K/M/B 紧凑显示（默认 True）
)
# 如果你希望金额列保留原始数值精度（不缩写），可关闭：
result.viz.report(
show=False,
filename="report_raw_amount.html",
compact_currency=False,
)
你也可以直接复用结构化分析结果做二次研究：
exposure = result.exposure_df() # 暴露分解（净暴露/总暴露/杠杆）
attr_by_symbol = result.attribution_df(by="symbol")
attr_by_tag = result.attribution_df(by="tag")
capacity = result.capacity_df() # 容量代理（成交率/换手等）
orders_by_strategy = result.orders_by_strategy() # 按策略归属聚合订单
executions_by_strategy = result.executions_by_strategy() # 按策略归属聚合成交
👉 点击查看交互式报表示例 (Interactive Demo)
文档索引
- 📖 核心特性与架构: 了解 AKQuant 的设计理念与性能优势。
- 🛠️ 安装指南: 详细的安装步骤（含源码编译）。
- 🚀 快速入门: 更多示例与基础用法。
- 🤖 机器学习指南: 如何使用内置的 ML 框架进行滚动训练。
- 📚 API 参考: 详细的类与函数文档。
- 💻 贡献指南: 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：
# 1. 使用 uv 环境运行命令
uv sync
# 2. 构建并绑定 Rust 扩展
# 注意：不要使用 `uv run maturin develop`，它会先尝试安装当前项目，
# 对于以 maturin 作为 build backend 的仓库可能卡在 Preparing packages。
uvx maturin develop
# 3. 运行所有测试
uv run pytest
# 4. 运行 Rust 核心测试（自动处理 macOS + uv 环境动态库路径）
./scripts/cargo-test.sh -q
# 5. 仅运行黄金测试
uv run pytest tests/golden/test_golden.py
Citation
Please use this bibtex if you want to cite this repository in your publications:
@misc{akquant,
author = {Albert King and Yaojie Zhang and Chao Liang},
title = {AKQuant},
year = {2026},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/akfamily/akquant}},
}
License
MIT License
1 maintainer
Metadata
1 maintainer
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