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    {"Description":"实验创建于2017/8/26","Summary":"","Graph":{"EdgesInternal":[{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"DestinationInputPortId":"-107:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"DestinationInputPortId":"-779:data1","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:data"},{"DestinationInputPortId":"-3661:features_ds","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-1038:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-819:input_data","SourceOutputPortId":"-107:data"},{"DestinationInputPortId":"-1038:training_ds","SourceOutputPortId":"-648:data"},{"DestinationInputPortId":"-161:instruments","SourceOutputPortId":"-152:data"},{"DestinationInputPortId":"-142:instruments","SourceOutputPortId":"-152:data"},{"DestinationInputPortId":"-837:input_data","SourceOutputPortId":"-161:data"},{"DestinationInputPortId":"-1871:input_ds","SourceOutputPortId":"-187:data"},{"DestinationInputPortId":"-1038:predict_ds","SourceOutputPortId":"-187:data"},{"DestinationInputPortId":"-2908:input_1","SourceOutputPortId":"-779:data"},{"DestinationInputPortId":"-779:data2","SourceOutputPortId":"-819:data"},{"DestinationInputPortId":"-2911:input_1","SourceOutputPortId":"-837:data"},{"DestinationInputPortId":"-2982:input_data","SourceOutputPortId":"-2908:data_1"},{"DestinationInputPortId":"-1288:input_data","SourceOutputPortId":"-2911:data_1"},{"DestinationInputPortId":"-161:features","SourceOutputPortId":"-1206:data"},{"DestinationInputPortId":"-837:features","SourceOutputPortId":"-1206:data"},{"DestinationInputPortId":"-1877:data2","SourceOutputPortId":"-1871:data"},{"DestinationInputPortId":"-344:input_ds","SourceOutputPortId":"-1877:data"},{"DestinationInputPortId":"-107:features","SourceOutputPortId":"-3661:data"},{"DestinationInputPortId":"-819:features","SourceOutputPortId":"-3661:data"},{"DestinationInputPortId":"-1206:features_ds","SourceOutputPortId":"-3661:data"},{"DestinationInputPortId":"-142:options_data","SourceOutputPortId":"-175:data_1"},{"DestinationInputPortId":"-175:input_1","SourceOutputPortId":"-344:sorted_data"},{"DestinationInputPortId":"-1877:data1","SourceOutputPortId":"-1038:predictions"},{"DestinationInputPortId":"-223:input_data","SourceOutputPortId":"-2982:data"},{"DestinationInputPortId":"-172:input_data","SourceOutputPortId":"-1288:data"},{"DestinationInputPortId":"-648:input_data","SourceOutputPortId":"-223:data"},{"DestinationInputPortId":"-187:input_data","SourceOutputPortId":"-172:data"}],"ModuleNodes":[{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2016-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2019-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"market","Value":"CN_STOCK_A","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_list","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"max_count","Value":"0","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"rolling_conf","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":1,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15","ModuleId":"BigQuantSpace.advanced_auto_labeler.advanced_auto_labeler-v2","ModuleParameters":[{"Name":"label_expr","Value":"# 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#号开始的表示注释,注释需单独一行\n# 多个特征,每行一个,可以包含基础特征和衍生特征,特征须为本平台特征\n#f15 = mean(close_1, 5)\n#f05 = mean(close_0, 5)\n#f110 = mean(close_1, 10)\n#f010 = mean(close_0, 10)\nclose_0","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features_ds","NodeId":"-3661"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-3661","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":20,"IsPartOfPartialRun":null,"Comment":"过滤条件所需的特征","CommentCollapsed":false},{"Id":"-175","ModuleId":"BigQuantSpace.cached.cached-v3","ModuleParameters":[{"Name":"run","Value":"# 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\ndef bigquant_run(input_1):\n # 示例代码如下。在这里编写您的代码\n df = input_1.read_df()\n df = pd.DataFrame(df)\n df = df.groupby(['date','score'], as_index = True, sort = False).apply(lambda x: x.sort_values('ranker', ascending = False))\n df = df.reset_index(drop=True)\n data_1 = DataSource.write_df(df)\n \n return Outputs(data_1=data_1)\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"post_run","Value":"# 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。\ndef bigquant_run(outputs):\n return 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    In [2]:
    # 本代码由可视化策略环境自动生成 2020年11月21日 16:38
    # 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 m17_run_bigquant_run(input_1):
        # 示例代码如下。在这里编写您的代码
        df = input_1.read_df()
        df = pd.DataFrame(df)
        df = df.groupby(['date','score'], as_index = True, sort = False).apply(lambda x: x.sort_values('ranker', ascending = False))
        df = df.reset_index(drop=True)
        data_1 = DataSource.write_df(df)
       
        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 m17_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 = 160
        # 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 = 0.00625
        context.options['hold_days'] = 1
    def m19_handle_data_bigquant_run(context, data):
        #------------------------START:加入下面if的两行代码到之前到主函数的最前部分-------------------
        # 相隔几天(以3天举例)运行一下handle_data函数
        if context.trading_day_index % 22 != 0:
            return 
        #------------------------END:加上这两句代码在主函数就能实现隔几天运行---------------------
    
         # 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/2# / 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StockRanker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StockRanker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)
    
    m1 = M.instruments.v2(
        start_date='2016-01-01',
        end_date='2019-01-01',
        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/data_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/big_expr.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, -23)/shift(close, -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='000905.HIX',
        drop_na_label=True,
        cast_label_int=True
    )
    
    m3 = M.input_features.v1(
        features="""Alpha1 = rank_return_20
    Alpha2 = swing_volatility_60_0
    Alpha3 = return_5
    Alpha4 = ta_rsi_14_0
    #Alpha5 = ta_cci_28_0
    #Alpha6 = ta_aroon_up_28_0
    Alpha7 = std(turn_0, 10)
    #Alpha8 = std(avg_turn_5, 5)
    Alpha9 = std(mean(deal_number_0, 10),10)
    Alpha10 = std(mean(volume_0, 60), 60)
    Alpha11 = mean(mf_net_amount_main_0, 60)
    Alpha12 = mean(mf_net_amount_l_0, 90)
    Alpha13 = mean(mf_net_pct_s_0, 90)
    Alpha14 = std(avg_turn_30, 180)
    Alpha15 = std(mean(turn_0, 120), 120)
    Alpha16 = std(mean(deal_number_0, 90), 90)
    Alpha17 = std(mean(deal_number_0, 180), 180)
    Alpha18 = std(mean(volume_0, 180), 180)
    Alpha19 = std(mean(volume_0, 90), 90)
    #Alpha20 = fs_eps_0
    #Alpha21 = fs_roe_0
    #Alpha22 = fs_roe_ttm_0
    #Alpha23 = fs_roa_0
    #Alpha24 = sh_holder_avg_pct_0
    #Alpha25 = ta_bbands_lowerband_28_0
    #Alpha26 = ta_wma_20_0"""
    )
    
    m20 = M.input_features.v1(
        features_ds=m3.data,
        features="""
    # #号开始的表示注释,注释需单独一行
    # 多个特征,每行一个,可以包含基础特征和衍生特征,特征须为本平台特征
    #f15 = mean(close_1, 5)
    #f05 = mean(close_0, 5)
    #f110 = mean(close_1, 10)
    #f010 = mean(close_0, 10)
    close_0"""
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m20.data,
        start_date='',
        end_date='',
        before_start_days=600
    )
    
    m24 = M.derived_feature_extractor.v3(
        input_data=m15.data,
        features=m20.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False,
        user_functions={}
    )
    
    m10 = M.join.v3(
        data1=m2.data,
        data2=m24.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m6 = M.filtet_st_stock.v7(
        input_1=m10.data
    )
    
    m4 = M.filter.v3(
        input_data=m6.data_1,
        expr='close_0 > 0',
        output_left_data=False
    )
    
    m9 = M.chinaa_stock_filter.v1(
        input_data=m4.data,
        index_constituent_cond=['中证800'],
        board_cond=['全部'],
        industry_cond=['全部'],
        st_cond=['全部'],
        delist_cond=['全部'],
        output_left_data=False
    )
    
    m5 = M.dropnan.v2(
        input_data=m9.data
    )
    
    m12 = M.input_features.v1(
        features_ds=m20.data,
        features="""#每档排序指标,默认从大到小排序,若想从小到大排序,在前面加负号-
    ranker = close_0/mean(close_0, 5)
    
    #过滤条件
    #f15 = mean(close_1, 5)
    #f05 = mean(close_0, 5)
    #f110 = mean(close_1, 10)
    #f010 = mean(close_0, 10)
    
    
    """
    )
    
    m16 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2019-01-01'),
        end_date=T.live_run_param('trading_date', '2020-11-20'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m18 = M.general_feature_extractor.v7(
        instruments=m16.data,
        features=m12.data,
        start_date='',
        end_date='',
        before_start_days=600
    )
    
    m26 = M.derived_feature_extractor.v3(
        input_data=m18.data,
        features=m12.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False,
        user_functions={}
    )
    
    m7 = M.filtet_st_stock.v7(
        input_1=m26.data
    )
    
    m8 = M.filter.v3(
        input_data=m7.data_1,
        expr='close_0 > 0',
        output_left_data=False
    )
    
    m11 = M.chinaa_stock_filter.v1(
        input_data=m8.data,
        index_constituent_cond=['中证800'],
        board_cond=['全部'],
        industry_cond=['全部'],
        st_cond=['全部'],
        delist_cond=['全部'],
        output_left_data=False
    )
    
    m22 = M.dropnan.v2(
        input_data=m11.data
    )
    
    m13 = M.select_columns.v3(
        input_ds=m22.data,
        columns='date,instrument,ranker',
        reverse_select=False
    )
    
    m23 = M.stock_ranker.v2(
        training_ds=m5.data,
        features=m3.data,
        predict_ds=m22.data,
        learning_algorithm='排序',
        number_of_leaves=40,
        minimum_docs_per_leaf=2000,
        number_of_trees=30,
        learning_rate=0.1,
        max_bins=1023,
        feature_fraction=1,
        data_row_fraction=1,
        ndcg_discount_base=1,
        slim_data=True
    )
    
    m14 = M.join.v3(
        data1=m23.predictions,
        data2=m13.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m21 = M.sort.v4(
        input_ds=m14.data,
        sort_by='position',
        group_by='date',
        keep_columns='--',
        ascending=True
    )
    
    m17 = M.cached.v3(
        input_1=m21.sorted_data,
        run=m17_run_bigquant_run,
        post_run=m17_post_run_bigquant_run,
        input_ports='input_1',
        params='{}',
        output_ports=''
    )
    
    m19 = M.trade.v4(
        instruments=m16.data,
        options_data=m17.data_1,
        start_date='',
        end_date='',
        initialize=m19_initialize_bigquant_run,
        handle_data=m19_handle_data_bigquant_run,
        volume_limit=0,
        order_price_field_buy='close',
        order_price_field_sell='close',
        capital_base=200000000,
        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='000905.HIX'
    )
    
    列: ['date', 'instrument', 'ranker']
    /data: 376417
    
    设置评估测试数据集,查看训练曲线
    [视频教程]StockRanker训练曲线
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-de2e5205580e4c3b9ac9a50520c2f088"}/bigcharts-data-end
    设置评估测试数据集,查看训练曲线
    [视频教程]StockRanker训练曲线
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-6f8618e52f8543c6930cdca58522c429"}/bigcharts-data-end
    • 收益率35.12%
    • 年化收益率18.01%
    • 基准收益率54.1%
    • 阿尔法0.06
    • 贝塔0.34
    • 夏普比率1.52
    • 胜率0.55
    • 盈亏比1.54
    • 收益波动率9.24%
    • 信息比率-0.04
    • 最大回撤6.64%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-d3fca2d04f1b49a7a646a65f20765af4"}/bigcharts-data-end
    In [3]:
    #m4.predictions.read_all_df().to_csv('3.csv')
    
    In [4]:
    #m21.sorted_data.read_all_df()
    
    In [5]:
    #m4.predictions.read_df().to_csv('1.csv')
    #dt.to_csv('3.csv')