交易策略如何调整买卖时间

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(sszy) #1

在进行股票交易时,有时我们希望标的在指定的时间成交,而不是默认的开盘时或收盘时。BigQuant平台的回测模块支持灵活修改,可以将这个问题转化为指定价格成交来解决。本文以AI-可视化策略模板为例,调整买卖时间为:上午9:35买入,下午14:55卖出。

平台回测模块的默认设置为按照开盘价买入、收盘价卖出。但是我们知道实际交易中很难以这两个价格撮合交易,如果想在指定时间交易,如何在回测模块中实现呢?

这个问题可以转化为按照指定时间的价格成交,比如想在早上9:35分买入,也就是以9:35分的这个价格买入。问题解决分为两步:第一,获取预测结果的分钟交易数据,并与预测结果合并;第二,限定价格交易。为了限定价格,此时我们需要在初始化函数中改写 FixedPriceSlippage 类,并在主函数中设置limit_price限价功能。

获取分钟交易数据

Bigquant平台上提供了股票的分钟历史交易数据,储存在‘features_1m_am’ 和 ‘features_1m_pm’这两张表中。'am’表格储存所有的上午数据,以一分钟为界,例如’close_1m_235_0’是距离交易前第235分钟的收盘价,也就是早上9点35分的数据。这两张表格除了有分钟交易价格外,也有分钟的交易量等数据,大家可以自行探索。

需要注意的是,2019年1月2日的预测股票应该以1月3日的价格买入,因此注意做shift(-1)处理。得到最终数据如下图:

merge%E5%90%8E

实际策略中,可以新建一个自定义模块合并分钟数据和预测数据。分钟数据与stockranker的预测结果合并后,共同传入回测模块进行下一步处理。
%E5%90%88%E5%B9%B6

初始化函数修改:FixedPriceSlippage

FixedPriceSlippage顾名思义,即限制滑点。通过在初始化函数中加入以下代码,可以实现在主函数中的order下单函数里加入limit_price功能,也就是限定价格买卖。

# 按指定价格成交
def initialize(context):
    class FixedPriceSlippage(SlippageModel):
        def process_order(self, data, order, bar_volume=0, trigger_check_price=0):
            if order.limit is None:
                price_field = self._price_field_buy if order.amount > 0 else self._price_field_sell
                price = data.current(order.asset, price_field)
            else:
                price = order.limit
            # 返回希望成交的价格和数量
            return (price, order.amount)
    # 如果未限价,设置滑点范围为最低价到最高价,即未限价时按照最低价买入、最高价卖出
    context.fix_slippage = FixedPriceSlippage(price_field_buy='low', price_field_sell='high')
    context.set_slippage(us_equities=context.fix_slippage) # us是universe的简写,如果是期货,需要传入us_future

主函数修改:limit_price

在主函数中需要对下单函数进行修改,限定交易价格为上一步获取的分钟数据。

此外,需要注意回测模块一定要勾选“真实价格”,因为本样例中通过api获取的数据都是真实价格。

样例:修改下单函数:

def handle_data(context, data):
    # 生成限价单,订单的限价为指定的价格,如果是买单,成交价为buy_price,如果是卖单,成交价格为sell_price
    sid = context.symbol('000002.SZA')
    context.order(sid, 200, limit_price=buy_price) # 以指定价格买入
    context.order(sid, -200, limit_price=sell_price) # 以指定价格卖出

结果分析

首先对比修改交易时间前后的交易详情:

修改交易时间前

修改交易时间后

可以发现,交易标的相同,交易价格有微小的差异。

为了探究交易时间不同对收益和风险的影响,本文统一在2019年1月2日至2019年8月23日的区间内进行回测,对比了以下几种交易时间的交易结果:

%E5%AF%B9%E6%AF%94

可以看到,虽然交易价格的差异很微小,但积少成多,即使训练模型和交易逻辑保持一致,最终的交易结果也有着较大的不同。只是从这几次实验来看,交易时间对于收益和风险的影响规律并不明显,但是影响程度还是比较大的:开盘买入收盘卖出,以及接近午盘时买卖可能会有较优的结果。因此,如何合理设置交易时间值得探索。

示例策略

克隆策略

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    In [39]:
    # 本代码由可视化策略环境自动生成 2019年8月28日 17:48
    # 本代码单元只能在可视化模式下编辑。您也可以拷贝代码,粘贴到新建的代码单元或者策略,然后修改。
    
    
    # Python 代码入口函数,input_1/2/3 对应三个输入端,data_1/2/3 对应三个输出端
    def m4_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        pred = input_1.read()
        start_date = pred.date.iloc[0].strftime('%Y-%m-%d') 
        end_time = pred.date.iloc[-1] + datetime.timedelta(days=3) #便于shift操作,并且防止周末的影响
        end_date = end_time.strftime('%Y-%m-%d')
    
        morning_data = DataSource('features_1m_am').read(start_date=start_date, end_date=end_date, fields = ['date','instrument','close_1m_235_0'])
        afternoon_data = DataSource('features_1m_pm').read(start_date=start_date, end_date=end_date, fields = ['date','instrument','close_1m_5_0'])
        morning_data['buy_price'] = morning_data.groupby('instrument',group_keys=False)['close_1m_235_0'].shift(-1)    
        afternoon_data['sell_price'] = afternoon_data.groupby('instrument',group_keys=False)['close_1m_5_0'].shift(-1) #取下一日的数据
    
        
        merge_am = pred.merge(morning_data, how='left')
        merge = merge_am.merge(afternoon_data, how='left')
        data_1 = DataSource.write_df(merge)
        return Outputs(data_1=data_1)
    
    # 后处理函数,可选。输入是主函数的输出,可以在这里对数据做处理,或者返回更友好的outputs数据格式。此函数输出不会被缓存。
    def m4_post_run_bigquant_run(outputs):
        return outputs
    
    # 回测引擎:初始化函数,只执行一次
    def m19_initialize_bigquant_run(context):
        # 加载预测数据
        context.ranker_prediction = context.options['data'].read_df()
    
        # 系统已经设置了默认的交易手续费和滑点,要修改手续费可使用如下函数
        context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
        # 预测数据,通过options传入进来,使用 read_df 函数,加载到内存 (DataFrame)
        # 设置买入的股票数量,这里买入预测股票列表排名靠前的5只
        stock_count = 5
        # 每只的股票的权重,如下的权重分配会使得靠前的股票分配多一点的资金,[0.339160, 0.213986, 0.169580, ..]
        context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])
        # 设置每只股票占用的最大资金比例
        context.max_cash_per_instrument = 0.2
        context.options['hold_days'] = 5
    
        #设置指定价格买卖
        class FixedPriceSlippage(SlippageModel):
            def process_order(self, data, order, bar_volume=0, trigger_check_price=0):
                if order.limit is None:
                    price_field = self._price_field_buy if order.amount > 0 else self._price_field_sell
                    price = data.current(order.asset, price_field)
                else:
                    price = order.limit
                # 返回希望成交的价格和数量
                return (price, order.amount)
        # 如果未进行限价,则使用开盘价买入,收盘价卖出
        context.fix_slippage = FixedPriceSlippage(price_field_buy='low', price_field_sell='high')
        context.set_slippage(us_equities=context.fix_slippage) # us是universe的简写
    # 回测引擎:每日数据处理函数,每天执行一次
    def m19_handle_data_bigquant_run(context, data):
        import datetime
        # 按日期过滤得到今日的预测数据
        current_date = data.current_dt.strftime('%Y-%m-%d')
        ranker_prediction = context.ranker_prediction[
            context.ranker_prediction.date == current_date]
        
    
        # 1. 资金分配
        # 平均持仓时间是hold_days,每日都将买入股票,每日预期使用 1/hold_days 的资金
        # 实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)
        is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)
        cash_avg = context.portfolio.portfolio_value / context.options['hold_days']
        cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)
        cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)
        positions = {e.symbol: p.amount * p.last_sale_price
                     for e, p in context.portfolio.positions.items()}
    
        # 2. 生成卖出订单:hold_days天之后才开始卖出;对持仓的股票,按机器学习算法预测的排序末位淘汰
        if not is_staging and cash_for_sell > 0:
            equities = {e.symbol: e for e, p in context.portfolio.positions.items()}
            instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                    lambda x: x in equities)])))
    
            for instrument in instruments:
                try:
                    sell_price = ranker_prediction[ranker_prediction.instrument==instrument].sell_price.iloc[0]
                    context.order_target(context.symbol(instrument), 0, limit_price=sell_price)
                    cash_for_sell -= positions[instrument]
                except:
                    print('no sale data',current_date)
                    context.order_target(context.symbol(instrument), 0)
                    
                if cash_for_sell <= 0:
                    break
    
        # 3. 生成买入订单:按机器学习算法预测的排序,买入前面的stock_count只股票
        buy_cash_weights = context.stock_weights
        buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
        max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
        for i, instrument in enumerate(buy_instruments):
            cash = cash_for_buy * buy_cash_weights[i]
            if cash > max_cash_per_instrument - positions.get(instrument, 0):
                # 确保股票持仓量不会超过每次股票最大的占用资金量
                cash = max_cash_per_instrument - positions.get(instrument, 0)
            if cash > 0:
                try:
                    buy_price = ranker_prediction[ranker_prediction.instrument==instrument].buy_price.iloc[0]
                    context.order_value(context.symbol(instrument), cash, limit_price=buy_price)
                except:
                    print('no order data',current_date)
                    context.order_value(context.symbol(instrument), cash)
    # 回测引擎:准备数据,只执行一次
    def m19_prepare_bigquant_run(context):
        pass
    
    
    m1 = M.instruments.v2(
        start_date='2016-01-01',
        end_date='2018-12-31',
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m2 = M.advanced_auto_labeler.v2(
        instruments=m1.data,
        label_expr="""# #号开始的表示注释
    # 0. 每行一个,顺序执行,从第二个开始,可以使用label字段
    # 1. 可用数据字段见 https://bigquant.com/docs/develop/datasource/deprecated/history_data.html
    #   添加benchmark_前缀,可使用对应的benchmark数据
    # 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/develop/bigexpr/usage.html>`_
    
    # 计算收益:5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
    shift(close, -5) / shift(open, -1)
    
    # 极值处理:用1%和99%分位的值做clip
    clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))
    
    # 将分数映射到分类,这里使用20个分类
    all_wbins(label, 20)
    
    # 过滤掉一字涨停的情况 (设置label为NaN,在后续处理和训练中会忽略NaN的label)
    where(shift(high, -1) == shift(low, -1), NaN, label)
    """,
        start_date='',
        end_date='',
        benchmark='000300.SHA',
        drop_na_label=True,
        cast_label_int=True
    )
    
    m3 = M.input_features.v1(
        features="""# #号开始的表示注释
    # 多个特征,每行一个,可以包含基础特征和衍生特征
    return_5
    return_10
    return_20
    avg_amount_0/avg_amount_5
    avg_amount_5/avg_amount_20
    rank_avg_amount_0/rank_avg_amount_5
    rank_avg_amount_5/rank_avg_amount_10
    rank_return_0
    rank_return_5
    rank_return_10
    rank_return_0/rank_return_5
    rank_return_5/rank_return_10
    pe_ttm_0
    """
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m16 = M.derived_feature_extractor.v3(
        input_data=m15.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m7 = M.join.v3(
        data1=m2.data,
        data2=m16.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m13 = M.dropnan.v1(
        input_data=m7.data
    )
    
    m6 = M.stock_ranker_train.v5(
        training_ds=m13.data,
        features=m3.data,
        learning_algorithm='排序',
        number_of_leaves=30,
        minimum_docs_per_leaf=1000,
        number_of_trees=20,
        learning_rate=0.1,
        max_bins=1023,
        feature_fraction=1,
        m_lazy_run=False
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2019-01-01'),
        end_date=T.live_run_param('trading_date', '2019-08-23'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m17 = M.general_feature_extractor.v7(
        instruments=m9.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m18 = M.derived_feature_extractor.v3(
        input_data=m17.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m14 = M.dropnan.v1(
        input_data=m18.data
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m6.model,
        data=m14.data,
        m_lazy_run=False
    )
    
    m4 = M.cached.v3(
        input_1=m8.predictions,
        run=m4_run_bigquant_run,
        post_run=m4_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m19 = M.trade.v4(
        instruments=m9.data,
        options_data=m4.data_1,
        start_date='',
        end_date='',
        initialize=m19_initialize_bigquant_run,
        handle_data=m19_handle_data_bigquant_run,
        prepare=m19_prepare_bigquant_run,
        volume_limit=0.025,
        order_price_field_buy='open',
        order_price_field_sell='close',
        capital_base=1000000,
        auto_cancel_non_tradable_orders=True,
        data_frequency='daily',
        price_type='真实价格',
        product_type='股票',
        plot_charts=True,
        backtest_only=False,
        benchmark='000300.SHA'
    )
    
    设置测试数据集,查看训练迭代过程的NDCG
    bigcharts-data-start/{"__id":"bigchart-15dfc7c97c134ad0833794dc994d8e10","__type":"tabs"}/bigcharts-data-end
    • 收益率11.98%
    • 年化收益率19.78%
    • 基准收益率26.91%
    • 阿尔法-0.13
    • 贝塔0.85
    • 夏普比率0.67
    • 胜率0.51
    • 盈亏比1.01
    • 收益波动率28.55%
    • 信息比率-0.06
    • 最大回撤24.75%
    bigcharts-data-start/{"__id":"bigchart-fefea3fe0c4c43f8a34a84bbb0c65774","__type":"tabs"}/bigcharts-data-end

    (iQuant) #4

    (iQuant) #5

    (outside) #6

    期盼已久的功能终于上线了,很实用,可以用来评估策略稳定性和资金容量