QKA — 快量化

Quant Kit for A-shares

简洁易用的 A 股量化回测框架。

安装

包安装

pip install qka

需要 Python 3.10+。

AI 技能安装

为 Claude Code、Cursor 等 AI 编程工具安装 QKA 技能：

npx skills add zsrl/qka

安装后，AI 助手即可自动加载 QKA 框架的 API 文档，生成符合规范的量化策略代码。

快速上手

数据

from qka import Data

data = Data(
    symbols=['000001.SZ', '600000.SH'],
    indicators={
        'sma_5':  ('ta.trend.sma_indicator', 'close', 5),
        'rsi_14': ('ta.momentum.rsi', 'close', 14),
    },
)
df = data.get()  # 返回宽表 DataFrame，列名 {symbol}|{factor}

策略

from qka import Strategy

class MyStrategy(Strategy):
    def __init__(self):
        super().__init__()
        self.lookback = 20  # 自定义参数

    def on_bar(self, date):
        close = self.get('close')            # 当前横截面
        hist = self.history('close', 20)     # 历史窗口
        # 交易逻辑：self.broker.buy / self.broker.sell
        # 仓位计算：self.sizing.percent / self.sizing.fixed_shares

回测

from qka import Backtest

strategy = MyStrategy()
bt = Backtest(data, strategy)
bt.run(cash=200000, start_date='2024-01-01', benchmark='000300.SH')
print(bt.metrics['total_return_pct'])   # 总收益率
print(bt.metrics['sharpe_ratio'])        # 夏普比率

核心能力

数据获取 — 写个股票代码就能取到本地，自动缓存、自动补齐最新数据，重复使用秒读
多数据源 — 内置 tickflow、baostock、tdx 三个行情源，改一个 source= 即可切换，各源缓存分目录存放、互不干扰
技术指标 — 均线、MACD、RSI、ATR 等 ta 库全部指标，外加 α/β、夏普、最大回撤等内置指标，声明一下即可，策略里不用自己 rolling
估值字段 — 换手率、涨跌幅、停牌与 ST 标记、市盈率、市净率等，需要哪些就取哪些
模拟行情 — 一条 Simulate 就能造出单边上涨、单边下跌、来回震荡的走势，不必等真实行情，用来验证策略是不是真在按逻辑交易
多标的回测 — 一次传一篮子股票，self.get() 取当日横截面，self.history() 取历史窗口
仓位管理 — 按资金比例、按金额、按股数、按 ATR 风险四种算法
交易成本 — 佣金、印花税、滑点均已内置，也可自行调整
绩效指标 — 总收益、年化、夏普、最大回撤、Calmar、胜率、盈亏比等 13 项，跑完即有
回测报告 — 净值曲线、回撤曲线、月度收益热力图、逐笔交易明细，一键生成 HTML，浏览器直接打开
基准对比 — 传入基准指数即可对比收益、计算 β / α

文档

框架 API 完整文档见 skills/qka/SKILL.md——所有类的方法签名、参数、返回值和约束都在里面。

完整示例

from qka import Data, Strategy, Backtest

data = Data(
    symbols=['000001.SZ'],
    indicators={
        'sma_5':  ('ta.trend.sma_indicator', 'close', 5),
        'sma_20': ('ta.trend.sma_indicator', 'close', 20),
    },
)

class MaCross(Strategy):
    def __init__(self):
        super().__init__()
        self.pct = 0.2

    def on_bar(self, date):
        close = self.get('close')
        fast = self.get('sma_5')
        slow = self.get('sma_20')
        for sym in close.index:
            price = float(close[sym])
            if price <= 0:
                continue
            if fast[sym] > slow[sym]:
                size = self.sizing.percent(self.pct, price)
                if size > 0:
                    self.broker.buy(sym, price, size)
            else:
                pos = self.broker.positions.get(sym, {}).get('size', 0)
                if pos > 0:
                    self.broker.sell(sym, price, pos)

strategy = MaCross()
bt = Backtest(data, strategy)
bt.run(cash=200000, start_date='2024-01-01')
print(bt.metrics['total_return_pct'])

许可证

MIT

致谢

tickflow — 免费 A 股 / ETF / 可转债行情（默认数据源）
baostock — 免费 A 股数据（估值等扩展字段）
通达信 — 本机「TQ 版」客户端的行情（source='tdx'，仅 Windows）
ta — 技术指标库

⚠️ 量化交易存在风险，请充分了解后再使用本框架。

Metadata

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