Backtest
====
定义一个你自己的回测类，继承 :class:`~core.a.A` 类，实现 :func:`~core.a.A.on_day_result()` 和 :func:`~core.a.A.on_day_stock()` 方法，然后调用 start 方法启动

Example
----
.. code-block:: python

    from A import A, Market

    class Demo(A):
        def __init__(self):
            super().__init__()
            # 设置数据路径
            self.set_source_path("/Users/x2h1z/Data/SZ")
            # 配置买入、卖出时间范围
            # 这里设置的是143000 - 150000时间段买入. 第二天的93000 - 100000时间段卖出
            self.init(143000, 30 * 60, 93000, 30 * 60)

            self.factor_result = {}
            self.date = ...
            self.result = []

        def on_day_result(self):
            # 选取排名前五支股票
            a = dict(sorted(self.factor_result.items(), key=lambda x: x[1], reverse=True))
            symbol_codes = list(a.keys())
            # calc 方法会返回一个 generator
            for result in self.calc(self.date, symbol_codes):
                symbol_code = result['symbol']
                pnl = result['pnl']
                rate = result['rate']
                weight = result['weight']
                print(f"Date:{self.date}, SymbolCode:{symbol_code}, Weight:{weight}, Pnl:{pnl}, Rate of return:{rate}%")
                self.result.append({
                    "code": symbol_code,
                    "pnl": pnl,
                    "rate": rate,
                    "f1": self.factor_result[symbol_code]
                })

        def on_day_stock(self, symbol_code: str, date: str, stock_df: pd.DataFrame) -> bool:
            df = stock_df[(stock_df['EndTime'] >= 93000) & (stock_df['EndTime'] <= 143000)]
            # F1 = 阳线数 / 分钟线数
            F1 = len(df[df['OpenPrice'] < df['ClosePrice']]) / len(df)
            self.factor_result[symbol_code] = F1
            return True

        def get_result(self) -> list:
            return self.result

        def main(self, date: str):
            self.date = date
            # 设置市场, 目前仅支持深交所
            self.set_market(Market.SZ)
            # 启动回测
            self.start(date)