AKQuant

pip install akquant

claude mcp add akquant -- uvx --from "akquant[mcp]" akquant-mcp --root /path/to/akquant-demo
codex mcp add akquant -- uvx --from "akquant[mcp]" akquant-mcp --root /path/to/akquant-demo

{
  "mcpServers": {
    "akquant": {
      "command": "uvx",
      "args": ["--from", "akquant[mcp]", "akquant-mcp", "--root", "/path/to/akquant-demo"]
    }
  }
}

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")
# 交易日历: 不传时按工作日推算, 节假日会被当作交易日
trade_dates = ak.tool_trade_date_hist_sina()["trade_date"]


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",
    broker_profile="cn_stock_t1_low_fee",  # A 股佣金/印花税/过户费、T+1、一手 100 股
    trading_calendar=trade_dates,
)

# 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,
)

=== Backtest Result ===
BacktestResult:
                                            Value
name
start_time              2025-02-12 15:00:00+08:00
end_time                2026-02-12 15:00:00+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                     99379.212386
total_pnl                             -620.787614
unrealized_pnl                                0.0
total_return_pct                        -0.620788
annualized_return                       -0.006208
volatility                               0.002456
total_profit                              544.624
total_loss                              -755.5086
total_commission                       409.903014
max_drawdown                             0.006234
max_drawdown_value                     623.409236
max_drawdown_pct                         0.623393
win_rate                                22.580645
loss_rate                               77.419355
winning_trades                               14.0
losing_trades                                48.0
avg_pnl                                 -3.401365
avg_return_pct                          -0.219536
avg_trade_bars                           1.967742
avg_profit                              38.901714
avg_profit_pct                           3.350484
avg_winning_trade_bars                        4.5
avg_loss                               -15.739762
avg_loss_pct                            -1.260791
avg_losing_trade_bars                    1.229167
largest_win                              119.7522
largest_win_pct                         10.156084
largest_win_bars                              7.0
largest_loss                             -70.2532
largest_loss_pct                        -5.399399
largest_loss_bars                             1.0
max_wins                                      2.0
max_losses                                    9.0
sharpe_ratio                            -2.574886
sortino_ratio                            -3.16692
profit_factor                            0.720871
ulcer_index                              0.003099
upi                                     -2.003008
equity_r2                                0.871776
std_error                               69.889352
calmar_ratio                            -0.995821
exposure_time_pct                       48.995984
var_95                                  -0.000262
var_99                                  -0.000623
cvar_95                                 -0.000427
cvar_99                                 -0.000702
sqn                                     -0.800105
kelly_criterion                         -0.087435
max_leverage                             0.014604
min_margin_level                        68.473617

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 = ""

python examples/06_complex_orders.py

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",
)

# 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,
)

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

# 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

@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}},
}