【干货】在大量测试因子做策略的时候,如何节约运行时间?答案在这里

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(polll) #1

相信大家都会遇到这样的场景,在训练模型时,偶尔会新增或者删除1、2个因子,或者更改1个因子,但如果每次直接改”输入特征列表”的话,每次都需要重新抽因子,这块会花费大量时间。本例子就是希望提供一个思路和demo解决此类问题,不得不说,用起来真的很爽,节省了不少时间。

克隆策略

内容介绍

场景

当训练模型的因子数达到数十个甚至数百个后,如果稍微更换或者增删一个因子的话,抽取因子的模块会重新运行花费太多时间,本demo基于此场景。

模块介绍

m3模块是输入特征列表模块,旨在输入基本的因子,可以是基础因子,也可以是衍生因子(表达式构建的因子),该模块的因子在第一次确定后,后面不会进行调整,没有调整修改的话,运行时就能击中缓存,以便节省时间。

m28因子是增加因子需要输入的因子列表,新加因子或者替换某个因子都可以在该模块输入。

m20模块的目的在于解决更换因子的场景,比如将 ta_ma(close_0,5)调整为ta_ma(close_0,4)。那么只需在m28模块里新增输入调整后的因子 ta_ma(close_0,4),并在m20里记录下被调整的因子 ta_ma(close_0,5)。

m6模块是将因子进行合并,并去掉不需要的因子,整理出训练模型时的实际因子列表。

目的

期初的因子抽取都通过m3模块抽取,第一次运行后,后面的运行都会命中缓存,以便节约运行时间。

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    In [ ]:
    # 本代码由可视化策略环境自动生成 2020年12月15日 11:16
    # 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 m6_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        
        f1 = input_1.read()
        f2 = input_2.read()
        f3 = input_3.read()
        features = [i for i in f1+f2 if i not in f3]
        data_1 = DataSource.write_pickle(features)
        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 m6_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
    
    # 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):
        # 按日期过滤得到今日的预测数据
        ranker_prediction = context.ranker_prediction[
            context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]
    
        # 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:
                context.order_target(context.symbol(instrument), 0)
                cash_for_sell -= positions[instrument]
                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:
                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='2010-01-01',
        end_date='2015-01-01',
        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="""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
    correlation(close_0, open_0, 10)
    mean(close_0, 2)
    mean(close_0, 3)
    mean(close_0, 4)
    mean(close_0, 5)
    mean(close_0, 6)
    mean(close_0, 7)
    mean(close_0, 8)
    mean(close_0, 9)
    mean(close_0, 10)
    mean(close_0, 11)
    mean(close_0, 12)
    mean(close_0, 13)
    mean(close_0, 14)
    mean(close_0, 15)
    mean(close_0, 16)
    mean(close_0, 17)
    mean(close_0, 18)
    mean(close_0, 19)
    mean(close_0, 20)
    mean(close_0, 21)
    mean(close_0, 22)
    mean(close_0, 23)
    mean(close_0, 24)
    mean(close_0, 25)
    mean(close_0, 26)
    mean(close_0, 27)
    mean(close_0, 28)
    mean(close_0, 29)
    mean(close_0, 30)
    mean(close_0, 31)
    mean(close_0, 32)
    mean(close_0, 33)
    mean(close_0, 34)
    mean(close_0, 35)
    mean(close_0, 36)
    mean(close_0, 37)
    mean(close_0, 38)
    mean(close_0, 39)
    mean(close_0, 40)
    """
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=90
    )
    
    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
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2015-01-01'),
        end_date=T.live_run_param('trading_date', '2017-01-01'),
        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=90
    )
    
    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
    )
    
    m20 = M.input_features.v1(
        features='ta_ma(close_0,5)'
    )
    
    m28 = M.input_features.v1(
        features="""# 新加因子
    ta_ma(close_0,5)
    ta_ma(close_0,7)
    ta_sma(open_0,10)
    market_cap_float_0
    pb_lf_0
     
    # 替换因子
    ta_ma(close_0,4) """
    )
    
    m23 = M.general_feature_extractor.v7(
        instruments=m9.data,
        features=m28.data,
        start_date='',
        end_date='',
        before_start_days=90
    )
    
    m24 = M.derived_feature_extractor.v3(
        input_data=m23.data,
        features=m28.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m25 = M.join.v3(
        data1=m18.data,
        data2=m24.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m10 = M.dropnan.v2(
        input_data=m25.data
    )
    
    m27 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m28.data,
        start_date='',
        end_date='',
        before_start_days=90
    )
    
    m12 = M.derived_feature_extractor.v3(
        input_data=m27.data,
        features=m28.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m29 = M.join.v3(
        data1=m16.data,
        data2=m12.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m7 = M.join.v3(
        data1=m2.data,
        data2=m29.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m5 = M.dropnan.v2(
        input_data=m7.data
    )
    
    m6 = M.cached.v3(
        input_1=m3.data,
        input_2=m28.data,
        input_3=m20.data,
        run=m6_run_bigquant_run,
        post_run=m6_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m4 = M.stock_ranker_train.v6(
        training_ds=m5.data,
        features=m6.data_1,
        learning_algorithm='排序',
        number_of_leaves=5,
        minimum_docs_per_leaf=1000,
        number_of_trees=20,
        learning_rate=0.1,
        max_bins=1023,
        feature_fraction=1,
        data_row_fraction=1,
        ndcg_discount_base=1,
        m_lazy_run=False
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m4.model,
        data=m10.data,
        m_lazy_run=False
    )
    
    m19 = M.trade.v4(
        instruments=m9.data,
        options_data=m8.predictions,
        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'
    )