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    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benckmark_risk=ranker_prediction[ranker_prediction.instrument=='000001.HIX']\n stock_hold_now = [equity.symbol for equity in context.portfolio.positions ]\n # 1. 资金分配\n # 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\n # 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)\n is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)\n cash_avg = context.portfolio.portfolio_value / context.options['hold_days']\n cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)\n cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)\n positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.portfolio.positions.items()}\n if len(benckmark_risk)>0:\n if benckmark_risk.classes_prob_True.iloc[0] < 0.3:\n cash_for_sell*=3\n # 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\n if not is_staging and cash_for_sell > 0:\n equities = {e.symbol: e for e, p in context.portfolio.positions.items()}\n instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities)])))\n\n for instrument in instruments:\n context.order_target(context.symbol(instrument), 0)\n cash_for_sell -= positions[instrument]\n if cash_for_sell <= 0:\n break\n\n # 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\n buy_cash_weights = context.stock_weights\n buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])\n max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument\n for i, instrument in enumerate(buy_instruments):\n cash = cash_for_buy * buy_cash_weights[i]\n if cash > max_cash_per_instrument - positions.get(instrument, 0):\n # 确保股票持仓量不会超过每次股票最大的占用资金量\n cash = max_cash_per_instrument - positions.get(instrument, 0)\n if cash > 0:\n context.order_value(context.symbol(instrument), cash)\n","type":"Literal","bound_global_parameter":null},{"name":"prepare","value":"# 回测引擎:准备数据,只执行一次\ndef bigquant_run(context):\n 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-1)","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-5940"}],"output_ports":[{"name":"data","node_id":"-5940"}],"cacheable":true,"seq_num":15,"comment":"用来检测时间长短带来的收益变化","comment_collapsed":true},{"node_id":"-71","module_id":"BigQuantSpace.use_datasource.use_datasource-v1","parameters":[{"name":"datasource_id","value":"bar1d_index_CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"name":"start_date","value":"2015-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2020-01-01","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-71"},{"name":"features","node_id":"-71"}],"output_ports":[{"name":"data","node_id":"-71"}],"cacheable":true,"seq_num":16,"comment":"","comment_collapsed":true},{"node_id":"-949","module_id":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","parameters":[{"name":"date_col","value":"date","type":"Literal","bound_global_parameter":null},{"name":"instrument_col","value":"instrument","type":"Literal","bound_global_parameter":null},{"name":"drop_na","value":"True","type":"Literal","bound_global_parameter":null},{"name":"remove_extra_columns","value":"False","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"def ts_delay(df,f,d):\n return df[f].shift(d)\ndef ts_std(df,f,d):\n return df[f].rolling(d).std()\ndef ts_mean(df,f,d):\n return df[f].rolling(d).mean()\ndef beta(df,d):\n benchmark = ['000300.SHA'] # 以沪深300为基准计算beta值\n benchmark_df=D.history_data(benchmark,fields=['close'],start_date='2015-01-01',end_date='2020-01-01')\n df[\"close_pct\"]=df['close'].pct_change()\n benchmark_df[\"close_pct\"]=benchmark_df['close'].pct_change()\n return (df['close_pct'].rolling(d).cov(benchmark_df['close_pct']))/benchmark_df['close_pct'].rolling(d).var()\nbigquant_run = {\n 'beta':beta,\n 'ts_delay':ts_delay,\n 'ts_mean':ts_mean,\n 'ts_std':ts_std,\n}","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-949"},{"name":"features","node_id":"-949"}],"output_ports":[{"name":"data","node_id":"-949"}],"cacheable":true,"seq_num":17,"comment":"","comment_collapsed":true},{"node_id":"-2522","module_id":"BigQuantSpace.filter.filter-v3","parameters":[{"name":"expr","value":"instrument=='000001.HIX'","type":"Literal","bound_global_parameter":null},{"name":"output_left_data","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-2522"}],"output_ports":[{"name":"data","node_id":"-2522"},{"name":"left_data","node_id":"-2522"}],"cacheable":true,"seq_num":18,"comment":"","comment_collapsed":true},{"node_id":"-7860","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"volume=volume\n#close 衍生特征\nclose\nma_10=ts_mean('close',10)\nma_20=ts_mean('close',20)\nma_50=ts_mean('close',50)\n#return 衍生特征\nreturn_1=close/ts_delay('close',1)\nreturn_5=close/ts_delay('close',5)\nreturn_10=close/ts_delay('close',10)\nreturn_20=close/ts_delay('close',20)\nreturn_1/return_5\nreturn_5/return_10\nreturn_10/return_20\n# Beta\nbeta_10=beta(10)\nbeta_20=beta(20)\nbeta_50=beta(50)\nlabel = close-ts_delay('close',-10)<0\n","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-7860"}],"output_ports":[{"name":"data","node_id":"-7860"}],"cacheable":true,"seq_num":19,"comment":"","comment_collapsed":true},{"node_id":"-8434","module_id":"BigQuantSpace.use_datasource.use_datasource-v1","parameters":[{"name":"datasource_id","value":"bar1d_index_CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"name":"start_date","value":"2020-11-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2021-12-31","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-8434"},{"name":"features","node_id":"-8434"}],"output_ports":[{"name":"data","node_id":"-8434"}],"cacheable":true,"seq_num":20,"comment":"","comment_collapsed":true},{"node_id":"-8441","module_id":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","parameters":[{"name":"date_col","value":"date","type":"Literal","bound_global_parameter":null},{"name":"instrument_col","value":"instrument","type":"Literal","bound_global_parameter":null},{"name":"drop_na","value":"True","type":"Literal","bound_global_parameter":null},{"name":"remove_extra_columns","value":"True","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"def ts_delay(df,f,d):\n return df[f].shift(d)\ndef ts_std(df,f,d):\n return df[f].rolling(d).std()\ndef ts_mean(df,f,d):\n return df[f].rolling(d).mean()\ndef beta(df,d):\n benchmark = ['000300.SHA'] # 以沪深300为基准计算beta值\n benchmark_df=D.history_data(benchmark,fields=['close'],start_date='2020-11-01',end_date='2021-12-31')\n df[\"close_pct\"]=df['close'].pct_change()\n benchmark_df[\"close_pct\"]=benchmark_df['close'].pct_change()\n return (df['close_pct'].rolling(d).cov(benchmark_df['close_pct']))/benchmark_df['close_pct'].rolling(d).var()\nbigquant_run = {\n 'beta':beta,\n 'ts_delay':ts_delay,\n 'ts_mean':ts_mean,\n 'ts_std':ts_std,\n}","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-8441"},{"name":"features","node_id":"-8441"}],"output_ports":[{"name":"data","node_id":"-8441"}],"cacheable":true,"seq_num":21,"comment":"","comment_collapsed":true},{"node_id":"-8450","module_id":"BigQuantSpace.filter.filter-v3","parameters":[{"name":"expr","value":"instrument=='000001.HIX'","type":"Literal","bound_global_parameter":null},{"name":"output_left_data","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-8450"}],"output_ports":[{"name":"data","node_id":"-8450"},{"name":"left_data","node_id":"-8450"}],"cacheable":true,"seq_num":22,"comment":"","comment_collapsed":true},{"node_id":"-8460","module_id":"BigQuantSpace.dropnan.dropnan-v2","parameters":[],"input_ports":[{"name":"input_data","node_id":"-8460"},{"name":"features","node_id":"-8460"}],"output_ports":[{"name":"data","node_id":"-8460"}],"cacheable":true,"seq_num":23,"comment":"","comment_collapsed":true},{"node_id":"-11127","module_id":"BigQuantSpace.cached.cached-v3","parameters":[{"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=None, input_2=None, input_3=None):\n from sklearn.metrics import balanced_accuracy_score,accuracy_score\n # 示例代码如下。在这里编写您的代码\n if input_1!=None:\n df1 = input_1.read_df()\n print(df1.head())\n print('RandomForest BCA: ',balanced_accuracy_score(df1['label'],df1['pred_label']))\n if input_2!=None:\n df2 = input_2.read_df()\n print(df2.head())\n print('LogisticRegression BCA: ',balanced_accuracy_score(df2['label'],df2['pred_label']))\n if input_3!=None:\n df3 = input_3.read_df()\n print(df3.head())\n print('SVC BCA: ',balanced_accuracy_score(df3['label'],df3['pred_label']))\n return Outputs(data_1=input_1, data_2=None, data_3=None)\n","type":"Literal","bound_global_parameter":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 outputs\n","type":"Literal","bound_global_parameter":null},{"name":"input_ports","value":"","type":"Literal","bound_global_parameter":null},{"name":"params","value":"{}","type":"Literal","bound_global_parameter":null},{"name":"output_ports","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_1","node_id":"-11127"},{"name":"input_2","node_id":"-11127"},{"name":"input_3","node_id":"-11127"}],"output_ports":[{"name":"data_1","node_id":"-11127"},{"name":"data_2","node_id":"-11127"},{"name":"data_3","node_id":"-11127"}],"cacheable":false,"seq_num":24,"comment":"","comment_collapsed":true},{"node_id":"-12766","module_id":"BigQuantSpace.random_forest_classifier.random_forest_classifier-v1","parameters":[{"name":"iterations","value":"1000","type":"Literal","bound_global_parameter":null},{"name":"feature_fraction","value":"1","type":"Literal","bound_global_parameter":null},{"name":"max_depth","value":"30","type":"Literal","bound_global_parameter":null},{"name":"min_samples_per_leaf","value":"10","type":"Literal","bound_global_parameter":null},{"name":"key_cols","value":"date,instrument","type":"Literal","bound_global_parameter":null},{"name":"workers","value":1,"type":"Literal","bound_global_parameter":null},{"name":"random_state","value":0,"type":"Literal","bound_global_parameter":null},{"name":"other_train_parameters","value":"{}","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"training_ds","node_id":"-12766"},{"name":"features","node_id":"-12766"},{"name":"model","node_id":"-12766"},{"name":"predict_ds","node_id":"-12766"}],"output_ports":[{"name":"output_model","node_id":"-12766"},{"name":"predictions","node_id":"-12766"}],"cacheable":true,"seq_num":25,"comment":"","comment_collapsed":true},{"node_id":"-275","module_id":"BigQuantSpace.cached.cached-v3","parameters":[{"name":"run","value":"# 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outputs\n","type":"Literal","bound_global_parameter":null},{"name":"input_ports","value":"","type":"Literal","bound_global_parameter":null},{"name":"params","value":"{}","type":"Literal","bound_global_parameter":null},{"name":"output_ports","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_1","node_id":"-275"},{"name":"input_2","node_id":"-275"},{"name":"input_3","node_id":"-275"}],"output_ports":[{"name":"data_1","node_id":"-275"},{"name":"data_2","node_id":"-275"},{"name":"data_3","node_id":"-275"}],"cacheable":true,"seq_num":26,"comment":"","comment_collapsed":true},{"node_id":"-1661","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"volume\n#close 衍生特征\nclose\nma_10\nma_20\nma_50\n#return 衍生特征\nreturn_1\nreturn_5\nreturn_10\nreturn_20\nreturn_1/return_5\nreturn_5/return_10\nreturn_10/return_20\n# 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    In [2]:
    # 本代码由可视化策略环境自动生成 2022年9月19日 20:05
    # 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。
    
    
    def ts_delay(df,f,d):
        return df[f].shift(d)
    def ts_std(df,f,d):
        return df[f].rolling(d).std()
    def ts_mean(df,f,d):
        return df[f].rolling(d).mean()
    def beta(df,d):
        benchmark = ['000300.SHA']  # 以沪深300为基准计算beta值
        benchmark_df=D.history_data(benchmark,fields=['close'],start_date='2015-01-01',end_date='2020-01-01')
        df["close_pct"]=df['close'].pct_change()
        benchmark_df["close_pct"]=benchmark_df['close'].pct_change()
        return (df['close_pct'].rolling(d).cov(benchmark_df['close_pct']))/benchmark_df['close_pct'].rolling(d).var()
    m17_user_functions_bigquant_run = {
        'beta':beta,
        'ts_delay':ts_delay,
        'ts_mean':ts_mean,
        'ts_std':ts_std,
    }
    def ts_delay(df,f,d):
        return df[f].shift(d)
    def ts_std(df,f,d):
        return df[f].rolling(d).std()
    def ts_mean(df,f,d):
        return df[f].rolling(d).mean()
    def beta(df,d):
        benchmark = ['000300.SHA']  # 以沪深300为基准计算beta值
        benchmark_df=D.history_data(benchmark,fields=['close'],start_date='2020-11-01',end_date='2021-12-31')
        df["close_pct"]=df['close'].pct_change()
        benchmark_df["close_pct"]=benchmark_df['close'].pct_change()
        return (df['close_pct'].rolling(d).cov(benchmark_df['close_pct']))/benchmark_df['close_pct'].rolling(d).var()
    m21_user_functions_bigquant_run = {
        'beta':beta,
        'ts_delay':ts_delay,
        'ts_mean':ts_mean,
        'ts_std':ts_std,
    }
    # 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 m26_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        df_train = input_1.read_df()
        features = input_2.read_pickle()
        df_test = input_3.read_df()
        from sklearn.preprocessing import MinMaxScaler
        scaler=MinMaxScaler()
        df_train[features]=scaler.fit_transform(df_train[features])
        df_test[features]=scaler.transform(df_test[features])
        return Outputs(data_1=DataSource.write_df(df_train), data_2=input_2, data_3=DataSource.write_df(df_test))
    
    # 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 m26_post_run_bigquant_run(outputs):
        return outputs
    
    # 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 m24_run_bigquant_run(input_1=None, input_2=None, input_3=None):
        from sklearn.metrics import balanced_accuracy_score,accuracy_score
        # 示例代码如下。在这里编写您的代码
        if input_1!=None:
            df1 = input_1.read_df()
            print(df1.head())
            print('RandomForest BCA: ',balanced_accuracy_score(df1['label'],df1['pred_label']))
        if input_2!=None:
            df2 = input_2.read_df()
            print(df2.head())
            print('LogisticRegression BCA: ',balanced_accuracy_score(df2['label'],df2['pred_label']))
        if input_3!=None:
            df3 = input_3.read_df()
            print(df3.head())
            print('SVC BCA: ',balanced_accuracy_score(df3['label'],df3['pred_label']))
        return Outputs(data_1=input_1, data_2=None, data_3=None)
    
    # 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 m24_post_run_bigquant_run(outputs):
        return outputs
    
    # 回测引擎:初始化函数,只执行一次
    def m14_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 m14_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')]
        today=data.current_dt.strftime('%Y-%m-%d')
        benckmark_risk=ranker_prediction[ranker_prediction.instrument=='000001.HIX']
        stock_hold_now = [equity.symbol for equity in context.portfolio.positions ]
        # 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()}
        if len(benckmark_risk)>0:
            if benckmark_risk.classes_prob_True.iloc[0] < 0.3:
                cash_for_sell*=3
        # 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 m14_prepare_bigquant_run(context):
        pass
    
    
    m1 = M.instruments.v2(
        start_date='2005-01-01',
        end_date='2016-01-01',
        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.HIX',
        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.input_features.v1(
        features_ds=m3.data,
        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
    
    # 未来2天的 收益
    day_return_2=(shift(close_0, -2)-shift(open_0, -1))/shift(open_0, -1)
    # 未来3天的 收益
    day_return_3=(shift(close_0, -3)-shift(open_0, -1))/shift(open_0, -1)
    # 未来5天的 收益
    day_return_5=(shift(close_0, -5)-shift(open_0, -1))/shift(open_0, -1)"""
    )
    
    m10 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m15.data,
        start_date='',
        end_date='',
        before_start_days=90
    )
    
    m11 = M.derived_feature_extractor.v3(
        input_data=m10.data,
        features=m15.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m5 = M.join.v3(
        data1=m2.data,
        data2=m11.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m8 = M.dropnan.v1(
        input_data=m5.data
    )
    
    m4 = M.stock_ranker_train.v5(
        training_ds=m8.data,
        features=m3.data,
        test_ds=m8.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
    )
    
    m7 = M.instruments.v2(
        start_date='2021-01-01',
        end_date='2021-12-31',
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m12 = M.general_feature_extractor.v7(
        instruments=m7.data,
        features=m15.data,
        start_date='',
        end_date='',
        before_start_days=90
    )
    
    m13 = M.derived_feature_extractor.v3(
        input_data=m12.data,
        features=m15.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m9 = M.dropnan.v1(
        input_data=m13.data
    )
    
    m6 = M.stock_ranker_predict.v5(
        model=m4.model,
        data=m9.data,
        m_lazy_run=False
    )
    
    m16 = M.use_datasource.v1(
        datasource_id='bar1d_index_CN_STOCK_A',
        start_date='2015-01-01',
        end_date='2020-01-01'
    )
    
    m18 = M.filter.v3(
        input_data=m16.data,
        expr='instrument==\'000001.HIX\'',
        output_left_data=False
    )
    
    m19 = M.input_features.v1(
        features="""volume=volume
    #close 衍生特征
    close
    ma_10=ts_mean('close',10)
    ma_20=ts_mean('close',20)
    ma_50=ts_mean('close',50)
    #return 衍生特征
    return_1=close/ts_delay('close',1)
    return_5=close/ts_delay('close',5)
    return_10=close/ts_delay('close',10)
    return_20=close/ts_delay('close',20)
    return_1/return_5
    return_5/return_10
    return_10/return_20
    # Beta
    beta_10=beta(10)
    beta_20=beta(20)
    beta_50=beta(50)
    label = close-ts_delay('close',-10)<0
    """
    )
    
    m17 = M.derived_feature_extractor.v3(
        input_data=m18.data,
        features=m19.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=True,
        remove_extra_columns=False,
        user_functions=m17_user_functions_bigquant_run
    )
    
    m20 = M.use_datasource.v1(
        datasource_id='bar1d_index_CN_STOCK_A',
        start_date='2020-11-01',
        end_date='2021-12-31'
    )
    
    m22 = M.filter.v3(
        input_data=m20.data,
        expr='instrument==\'000001.HIX\'',
        output_left_data=False
    )
    
    m21 = M.derived_feature_extractor.v3(
        input_data=m22.data,
        features=m19.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=True,
        remove_extra_columns=True,
        user_functions=m21_user_functions_bigquant_run
    )
    
    m23 = M.dropnan.v2(
        input_data=m21.data
    )
    
    m27 = M.input_features.v1(
        features="""volume
    #close 衍生特征
    close
    ma_10
    ma_20
    ma_50
    #return 衍生特征
    return_1
    return_5
    return_10
    return_20
    return_1/return_5
    return_5/return_10
    return_10/return_20
    # Beta
    beta_10
    beta_20
    beta_50
    """
    )
    
    m26 = M.cached.v3(
        input_1=m17.data,
        input_2=m27.data,
        input_3=m23.data,
        run=m26_run_bigquant_run,
        post_run=m26_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m25 = M.random_forest_classifier.v1(
        training_ds=m26.data_1,
        features=m26.data_2,
        predict_ds=m26.data_3,
        iterations=1000,
        feature_fraction=1,
        max_depth=30,
        min_samples_per_leaf=10,
        key_cols='date,instrument',
        workers=1,
        random_state=0,
        other_train_parameters={}
    )
    
    m24 = M.cached.v3(
        input_1=m25.predictions,
        run=m24_run_bigquant_run,
        post_run=m24_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports='',
        m_cached=False
    )
    
    m28 = M.concat.v3(
        input_data_1=m6.predictions,
        input_data_2=m24.data_1
    )
    
    m14 = M.trade.v4(
        instruments=m7.data,
        options_data=m28.data,
        start_date='',
        end_date='',
        initialize=m14_initialize_bigquant_run,
        handle_data=m14_handle_data_bigquant_run,
        prepare=m14_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.HIX'
    )
    
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-6a14457a4c8041ac9945ec374ac41af9"}/bigcharts-data-end
        pred_label  classes_prob_False  classes_prob_True       date  instrument  \
    50       False            0.633125           0.366875 2021-01-12  000001.HIX   
    51       False            0.680768           0.319232 2021-01-13  000001.HIX   
    52       False            0.696769           0.303231 2021-01-14  000001.HIX   
    53       False            0.697397           0.302603 2021-01-15  000001.HIX   
    54       False            0.703993           0.296007 2021-01-18  000001.HIX   
    
        label  
    50      0  
    51      0  
    52      0  
    53      0  
    54      0  
    RandomForest BCA:  0.5
    
    • 收益率175.55%
    • 年化收益率186.09%
    • 基准收益率-5.2%
    • 阿尔法1.94
    • 贝塔0.31
    • 夏普比率3.95
    • 胜率0.58
    • 盈亏比1.43
    • 收益波动率26.83%
    • 信息比率0.24
    • 最大回撤10.78%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-9e4d9d0f970d48ee88a89f44e33adb99"}/bigcharts-data-end