获取到分钟数据,用分钟数据预测当日走势报错“'NoneType' object is not iterable”

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标签: #<Tag:0x00007f04438c8770>

(developer) #1

获取到一分钟数据,因子也要自己去计算吗?还是说因子自动使用我计算出来的数据,比如close_5就代表的是前五分钟的收盘价

克隆策略

    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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, input_2, input_3,before_days):\n # 示例代码如下。在这里编写您的代码\n start_date=input_1.read_pickle()['start_date']\n end_date=input_1.read_pickle()['end_date']\n ins=input_1.read_pickle()['instruments'][0]\n df = DataSource('bar1m_'+ins).read(start_date=start_date,end_date=end_date).set_index('date')\n df['adjust_factor']=1.0\n \n def resample(df,period):\n # https://pandas-docs.github.io/pandas-docs-travis/timeseries.html#offset-aliases\n Xmin_df=pd.DataFrame()\n Xmin_df['open'] = df['open'].resample(period, how='first')\n Xmin_df['high'] = df['high'].resample(period, how='max')\n Xmin_df['low'] = df['low'].resample(period, how='min')\n Xmin_df['close'] = df['close'].resample(period, how='last')\n Xmin_df['volume'] = df['volume'].resample(period, how='sum')\n Xmin_df['amount'] = df['amount'].resample(period, how='sum')\n Xmin_df.dropna(inplace=True)\n return Xmin_df\n df = df.groupby('instrument').apply(lambda 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回测引擎:初始化函数,只执行一次\ndef bigquant_run(context):\n # 加载预测数据\n context.ranker_prediction = context.options['data'].read_df()\n\n # 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\n context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))\n # 预测数据,通过options传入进来,使用 read_df 函数,加载到内存 (DataFrame)\n # 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\n stock_count = 5\n # 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, ..]\n context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])\n # 设置每只股票占用的最大资金比例\n context.max_cash_per_instrument = 0.2\n context.hold_days = 5\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"handle_data","Value":"# 回测引擎:每日数据处理函数,每天执行一次\ndef bigquant_run(context, data):\n # 按日期过滤得到今日的预测数据\n ranker_prediction = context.ranker_prediction[\n context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]\n\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.hold_days # 是否在建仓期间(前 hold_days 天)\n cash_avg = context.portfolio.portfolio_value / context.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.perf_tracker.position_tracker.positions.items()}\n\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StockRanker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.perf_tracker.position_tracker.positions.items()}\n instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))\n # print('rank order for sell %s' % instruments)\n for instrument in instruments:\n 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    In [71]:
    # 本代码由可视化策略环境自动生成 2020年3月27日 12:34
    # 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 m5_run_bigquant_run(input_1, input_2, input_3,before_days):
        # 示例代码如下。在这里编写您的代码
        start_date=input_1.read_pickle()['start_date']
        end_date=input_1.read_pickle()['end_date']
        ins=input_1.read_pickle()['instruments'][0]
        df = DataSource('bar1m_'+ins).read(start_date=start_date,end_date=end_date).set_index('date')
        df['adjust_factor']=1.0
        
        def resample(df,period):
            # https://pandas-docs.github.io/pandas-docs-travis/timeseries.html#offset-aliases
            Xmin_df=pd.DataFrame()
            Xmin_df['open'] = df['open'].resample(period, how='first')
            Xmin_df['high'] = df['high'].resample(period, how='max')
            Xmin_df['low'] = df['low'].resample(period, how='min')
            Xmin_df['close'] = df['close'].resample(period, how='last')
            Xmin_df['volume'] = df['volume'].resample(period, how='sum')
            Xmin_df['amount'] = df['amount'].resample(period, how='sum')
            Xmin_df.dropna(inplace=True)
            return Xmin_df
        df = df.groupby('instrument').apply(lambda x:resample(x, '5min')).reset_index()
        data_1 = DataSource.write_df(df)
        return Outputs(data_1=data_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 m5_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 m6_run_bigquant_run(input_1, input_2, input_3,before_days):
        # 示例代码如下。在这里编写您的代码
        start_date=input_1.read_pickle()['start_date']
        end_date=input_1.read_pickle()['end_date']
        ins=input_1.read_pickle()['instruments'][0]
        df = DataSource('bar1m_'+ins).read(start_date=start_date,end_date=end_date).set_index('date')
        df['adjust_factor']=1.0
        
        def resample(df,period):
            # https://pandas-docs.github.io/pandas-docs-travis/timeseries.html#offset-aliases
            Xmin_df=pd.DataFrame()
            Xmin_df['open'] = df['open'].resample(period, how='first')
            Xmin_df['high'] = df['high'].resample(period, how='max')
            Xmin_df['low'] = df['low'].resample(period, how='min')
            Xmin_df['close'] = df['close'].resample(period, how='last')
            Xmin_df['volume'] = df['volume'].resample(period, how='sum')
            Xmin_df['amount'] = df['amount'].resample(period, how='sum')
            Xmin_df.dropna(inplace=True)
            return Xmin_df
        df = df.groupby('instrument').apply(lambda x:resample(x, '5min')).reset_index()
        data_1 = DataSource.write_df(df)
        return Outputs(data_1=data_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 m6_post_run_bigquant_run(outputs):
        return outputs
    
    # 回测引擎:初始化函数,只执行一次
    def m10_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.hold_days = 5
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m10_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.hold_days # 是否在建仓期间(前 hold_days 天)
        cash_avg = context.portfolio.portfolio_value / context.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.perf_tracker.position_tracker.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.perf_tracker.position_tracker.positions.items()}
            instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                    lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))
            # print('rank order for sell %s' % instruments)
            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)
    
    # 回测引擎:准备数据,只执行一次
    def m10_prepare_bigquant_run(context):
        pass
    
    # 回测引擎:每个单位时间开始前调用一次,即每日开盘前调用一次。
    def m10_before_trading_start_bigquant_run(context, data):
        pass
    
    
    m1 = M.instruments.v2(
        start_date='2010-01-01 09:01:00',
        end_date='2015-01-01 15:00:00',
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m5 = M.cached.v3(
        input_1=m1.data,
        run=m5_run_bigquant_run,
        post_run=m5_post_run_bigquant_run,
        input_ports='',
        params='{\'before_days\':1}',
        output_ports='',
        m_cached=False
    )
    
    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,
        user_functions={}
    )
    
    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
    buy_condition=where(ta_rsi(close,14)>60,1,0)
    sell_condition=where(ta_rsi(close,14)<40,1,0)"""
    )
    
    m11 = M.general_feature_extractor.v7(
        instruments=m5.data_2,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m16 = M.derived_feature_extractor.v3(
        input_data=m5.data_1,
        features=m11.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
    )
    
    m4 = M.stock_ranker_train.v6(
        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,
        data_row_fraction=1,
        ndcg_discount_base=1,
        m_lazy_run=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
    )
    
    m6 = M.cached.v3(
        input_1=m9.data,
        run=m6_run_bigquant_run,
        post_run=m6_post_run_bigquant_run,
        input_ports='',
        params='{\'before_days\':1}',
        output_ports='',
        m_cached=False
    )
    
    m17 = M.general_feature_extractor.v7(
        instruments=m6.data_1,
        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=m4.model,
        data=m14.data,
        m_lazy_run=False
    )
    
    m10 = M.trade.v4(
        instruments=m8.predictions,
        options_data=m9.data,
        start_date='',
        end_date='',
        initialize=m10_initialize_bigquant_run,
        handle_data=m10_handle_data_bigquant_run,
        prepare=m10_prepare_bigquant_run,
        before_trading_start=m10_before_trading_start_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=''
    )
    

    基础特征抽取(general_feature_extractor)使用错误,你可以:

    1.一键查看文档

    2.一键搜索答案

    ---------------------------------------------------------------------------
    TypeError                                 Traceback (most recent call last)
    <ipython-input-71-ef54bb5bdb9b> in <module>()
         90     start_date='',
         91     end_date='',
    ---> 92     before_start_days=0
         93 )
         94 
    
    TypeError: 'NoneType' object is not iterable

    (达达) #2

    想法不错,就是可能会计算量很大,我这里限定到10只股票给你做了一个案例case,你可以试试。如果有跑出好结果希望能分享分享~注意分钟的成交率设置可以尽量大点保证下单量能尽快成交,默认的2.5%设置可能会导致要花很多K线时间成交以及无法完全日内成交。
    另外就是性能可能随着你回测时间拉长和股票数量增加会降低,毕竟分钟的数据量太多了。
    如果放开股票数量可以自己把代码列表模块的属性参数最大数量修改为0。

    克隆策略

      {"Description":"实验创建于2017/8/26","Summary":"","Graph":{"EdgesInternal":[{"DestinationInputPortId":"-1483:input_1","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"DestinationInputPortId":"-238:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-222:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-591:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-4478:options_data","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:predictions"},{"DestinationInputPortId":"-3488:input_1","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-4478:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:data","SourceOutputPortId":"-86:data"},{"DestinationInputPortId":"-11965:data2","SourceOutputPortId":"-222:data"},{"DestinationInputPortId":"-86:input_data","SourceOutputPortId":"-238:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:model","SourceOutputPortId":"-591:model"},{"DestinationInputPortId":"-222:input_data","SourceOutputPortId":"-1483:data_1"},{"DestinationInputPortId":"-226:input_data","SourceOutputPortId":"-1483:data_1"},{"DestinationInputPortId":"-238:input_data","SourceOutputPortId":"-3488:data_1"},{"DestinationInputPortId":"-591:training_ds","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-84:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-84:input_data","SourceOutputPortId":"-11965:data"},{"DestinationInputPortId":"-11965:data1","SourceOutputPortId":"-226:data"}],"ModuleNodes":[{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2010-01-01 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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, input_2, input_3,before_days):\n # 示例代码如下。在这里编写您的代码\n start_date=input_1.read_pickle()['start_date']\n end_date=input_1.read_pickle()['end_date']\n ins=input_1.read_pickle()['instruments']\n df_all = pd.DataFrame()\n for k in ins:\n df = DataSource('bar1m_'+k).read(start_date=start_date,end_date=end_date).set_index('date')\n df['adjust_factor']=1.0\n df_all = pd.concat([df_all,df])\n def resample(df,period):\n # https://pandas-docs.github.io/pandas-docs-travis/timeseries.html#offset-aliases\n Xmin_df=pd.DataFrame()\n Xmin_df['open'] = df['open'].resample(period, how='first')\n Xmin_df['high'] = df['high'].resample(period, how='max')\n Xmin_df['low'] = df['low'].resample(period, how='min')\n Xmin_df['close'] = df['close'].resample(period, how='last')\n Xmin_df['volume'] = df['volume'].resample(period, how='sum')\n Xmin_df['amount'] = df['amount'].resample(period, how='sum')\n Xmin_df.dropna(inplace=True)\n return Xmin_df\n df_all = df_all.groupby('instrument').apply(lambda x:resample(x, '5min')).reset_index()\n data_1 = DataSource.write_df(df_all)\n return Outputs(data_1=data_1, data_2=None, data_3=None)\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 outputs\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"input_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"params","Value":"{'before_days':1}","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"output_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-1483"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-1483"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-1483"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-1483","OutputType":null},{"Name":"data_2","NodeId":"-1483","OutputType":null},{"Name":"data_3","NodeId":"-1483","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":5,"IsPartOfPartialRun":null,"Comment":"获取分钟线","CommentCollapsed":true},{"Id":"-3488","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, input_2, input_3,before_days):\n # 示例代码如下。在这里编写您的代码\n start_date=input_1.read_pickle()['start_date']\n end_date=input_1.read_pickle()['end_date']\n ins=input_1.read_pickle()['instruments']\n df_all = pd.DataFrame()\n for k in ins:\n df = DataSource('bar1m_'+k).read(start_date=start_date,end_date=end_date).set_index('date')\n df['adjust_factor']=1.0\n df_all = pd.concat([df_all,df])\n def resample(df,period):\n # https://pandas-docs.github.io/pandas-docs-travis/timeseries.html#offset-aliases\n Xmin_df=pd.DataFrame()\n Xmin_df['open'] = df['open'].resample(period, how='first')\n Xmin_df['high'] = df['high'].resample(period, how='max')\n Xmin_df['low'] = df['low'].resample(period, how='min')\n Xmin_df['close'] = df['close'].resample(period, how='last')\n Xmin_df['volume'] = df['volume'].resample(period, how='sum')\n Xmin_df['amount'] = df['amount'].resample(period, how='sum')\n Xmin_df.dropna(inplace=True)\n return Xmin_df\n df_all = df_all.groupby('instrument').apply(lambda x:resample(x, '5min')).reset_index()\n data_1 = DataSource.write_df(df_all)\n return Outputs(data_1=data_1, data_2=None, data_3=None)\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 outputs\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"input_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"params","Value":"{'before_days':1}","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"output_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-3488"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-3488"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-3488"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-3488","OutputType":null},{"Name":"data_2","NodeId":"-3488","OutputType":null},{"Name":"data_3","NodeId":"-3488","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":6,"IsPartOfPartialRun":null,"Comment":"获取分钟线","CommentCollapsed":true},{"Id":"-4478","ModuleId":"BigQuantSpace.trade.trade-v4","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"initialize","Value":"# 回测引擎:初始化函数,只执行一次\ndef bigquant_run(context):\n # 加载预测数据\n context.ranker_prediction = context.options['data'].read_df()\n\n # 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\n context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))\n # 预测数据,通过options传入进来,使用 read_df 函数,加载到内存 (DataFrame)\n # 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\n stock_count = 5\n # 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, ..]\n context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])\n # 设置每只股票占用的最大资金比例\n context.max_cash_per_instrument = 0.2\n context.hold_days = 5\n context.buy_flag = 0\n context.sell_flag = 1","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"handle_data","Value":"# 回测引擎:每日数据处理函数,每天执行一次\ndef bigquant_run(context, data):\n # 每天只在固定时间买入轮仓\n if data.current_dt.strftime('%H:%M:%S')=='09:40:00':\n context.buy_flag = 1\n else:\n context.buy_flag = 0\n\n # 每天只在固定时间卖出轮仓\n if data.current_dt.strftime('%H:%M:%S')=='14:50:00':\n context.sell_flag = 1\n else:\n context.sell_flag = 0\n \n if context.buy_flag==0 and context.sell_flag==0:\n return\n \n # 按日期过滤得到今日的预测数据\n ranker_prediction = context.ranker_prediction[\n context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d %H:%M:%S')]\n\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.hold_days # 是否在建仓期间(前 hold_days 天)\n cash_avg = context.portfolio.portfolio_value / context.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.perf_tracker.position_tracker.positions.items()}\n\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StockRanker预测的排序末位淘汰\n if context.sell_flag>0:\n if not is_staging and cash_for_sell > 0:\n equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}\n instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))\n # print('rank order for sell %s' % instruments)\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StockRanker预测的排序,买入前面的stock_count只股票\n if context.buy_flag>0:\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","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"prepare","Value":"# 回测引擎:准备数据,只执行一次\ndef bigquant_run(context):\n pass\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_trading_start","Value":"# 回测引擎:每个单位时间开始前调用一次,即每日开盘前调用一次。\ndef bigquant_run(context, data):\n 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      In [47]:
      # 本代码由可视化策略环境自动生成 2020年3月27日 15:37
      # 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 m5_run_bigquant_run(input_1, input_2, input_3,before_days):
          # 示例代码如下。在这里编写您的代码
          start_date=input_1.read_pickle()['start_date']
          end_date=input_1.read_pickle()['end_date']
          ins=input_1.read_pickle()['instruments']
          df_all = pd.DataFrame()
          for k in ins:
              df = DataSource('bar1m_'+k).read(start_date=start_date,end_date=end_date).set_index('date')
              df['adjust_factor']=1.0
              df_all = pd.concat([df_all,df])
          def resample(df,period):
              # https://pandas-docs.github.io/pandas-docs-travis/timeseries.html#offset-aliases
              Xmin_df=pd.DataFrame()
              Xmin_df['open'] = df['open'].resample(period, how='first')
              Xmin_df['high'] = df['high'].resample(period, how='max')
              Xmin_df['low'] = df['low'].resample(period, how='min')
              Xmin_df['close'] = df['close'].resample(period, how='last')
              Xmin_df['volume'] = df['volume'].resample(period, how='sum')
              Xmin_df['amount'] = df['amount'].resample(period, how='sum')
              Xmin_df.dropna(inplace=True)
              return Xmin_df
          df_all = df_all.groupby('instrument').apply(lambda x:resample(x, '5min')).reset_index()
          data_1 = DataSource.write_df(df_all)
          return Outputs(data_1=data_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 m5_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 m6_run_bigquant_run(input_1, input_2, input_3,before_days):
          # 示例代码如下。在这里编写您的代码
          start_date=input_1.read_pickle()['start_date']
          end_date=input_1.read_pickle()['end_date']
          ins=input_1.read_pickle()['instruments']
          df_all = pd.DataFrame()
          for k in ins:
              df = DataSource('bar1m_'+k).read(start_date=start_date,end_date=end_date).set_index('date')
              df['adjust_factor']=1.0
              df_all = pd.concat([df_all,df])
          def resample(df,period):
              # https://pandas-docs.github.io/pandas-docs-travis/timeseries.html#offset-aliases
              Xmin_df=pd.DataFrame()
              Xmin_df['open'] = df['open'].resample(period, how='first')
              Xmin_df['high'] = df['high'].resample(period, how='max')
              Xmin_df['low'] = df['low'].resample(period, how='min')
              Xmin_df['close'] = df['close'].resample(period, how='last')
              Xmin_df['volume'] = df['volume'].resample(period, how='sum')
              Xmin_df['amount'] = df['amount'].resample(period, how='sum')
              Xmin_df.dropna(inplace=True)
              return Xmin_df
          df_all = df_all.groupby('instrument').apply(lambda x:resample(x, '5min')).reset_index()
          data_1 = DataSource.write_df(df_all)
          return Outputs(data_1=data_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 m6_post_run_bigquant_run(outputs):
          return outputs
      
      # 回测引擎:初始化函数,只执行一次
      def m10_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.hold_days = 5
          context.buy_flag = 0
          context.sell_flag = 1
      # 回测引擎:每日数据处理函数,每天执行一次
      def m10_handle_data_bigquant_run(context, data):
          # 每天只在固定时间买入轮仓
          if data.current_dt.strftime('%H:%M:%S')=='09:40:00':
              context.buy_flag = 1
          else:
              context.buy_flag = 0
      
          # 每天只在固定时间卖出轮仓
          if data.current_dt.strftime('%H:%M:%S')=='14:50:00':
              context.sell_flag = 1
          else:
              context.sell_flag = 0
         
          if context.buy_flag==0 and context.sell_flag==0:
              return
          
          # 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 %H:%M:%S')]
      
          # 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.hold_days # 是否在建仓期间(前 hold_days 天)
          cash_avg = context.portfolio.portfolio_value / context.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.perf_tracker.position_tracker.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 context.sell_flag>0:
              if not is_staging and cash_for_sell > 0:
                  equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}
                  instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                          lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))
                  # print('rank order for sell %s' % instruments)
                  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
          if context.buy_flag>0:
              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 m10_prepare_bigquant_run(context):
          pass
      
      # 回测引擎:每个单位时间开始前调用一次,即每日开盘前调用一次。
      def m10_before_trading_start_bigquant_run(context, data):
          pass
      
      
      m1 = M.instruments.v2(
          start_date='2010-01-01 09:01:00',
          end_date='2015-01-01 15:00:00',
          market='CN_STOCK_A',
          instrument_list='',
          max_count=10
      )
      
      m5 = M.cached.v3(
          input_1=m1.data,
          run=m5_run_bigquant_run,
          post_run=m5_post_run_bigquant_run,
          input_ports='',
          params='{\'before_days\':1}',
          output_ports=''
      )
      
      m11 = M.auto_labeler_on_datasource.v1(
          input_data=m5.data_1,
          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
      # 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)
      """,
          drop_na_label=True,
          cast_label_int=True,
          date_col='date',
          instrument_col='instrument',
          user_functions={}
      )
      
      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
      ta_rsi(close,14)/60
      ta_rsi(close,14)/40"""
      )
      
      m16 = M.derived_feature_extractor.v3(
          input_data=m5.data_1,
          features=m3.data,
          date_col='date',
          instrument_col='instrument',
          drop_na=False,
          remove_extra_columns=False
      )
      
      m7 = M.join.v3(
          data1=m11.data,
          data2=m16.data,
          on='date,instrument',
          how='inner',
          sort=False
      )
      
      m13 = M.dropnan.v1(
          input_data=m7.data
      )
      
      m4 = M.stock_ranker_train.v6(
          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,
          data_row_fraction=1,
          ndcg_discount_base=1,
          m_lazy_run=False
      )
      
      m9 = M.instruments.v2(
          start_date=T.live_run_param('trading_date', '2015-01-02 09:00:00'),
          end_date=T.live_run_param('trading_date', '2015-03-01 15:00:00'),
          market='CN_STOCK_A',
          instrument_list='',
          max_count=10
      )
      
      m6 = M.cached.v3(
          input_1=m9.data,
          run=m6_run_bigquant_run,
          post_run=m6_post_run_bigquant_run,
          input_ports='',
          params='{\'before_days\':1}',
          output_ports=''
      )
      
      m18 = M.derived_feature_extractor.v3(
          input_data=m6.data_1,
          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=m4.model,
          data=m14.data,
          m_lazy_run=False
      )
      
      m10 = M.trade.v4(
          instruments=m9.data,
          options_data=m8.predictions,
          start_date='',
          end_date='',
          initialize=m10_initialize_bigquant_run,
          handle_data=m10_handle_data_bigquant_run,
          prepare=m10_prepare_bigquant_run,
          before_trading_start=m10_before_trading_start_bigquant_run,
          volume_limit=0.025,
          order_price_field_buy='open',
          order_price_field_sell='close',
          capital_base=1000001,
          auto_cancel_non_tradable_orders=True,
          data_frequency='minute',
          price_type='真实价格',
          product_type='股票',
          plot_charts=True,
          backtest_only=False,
          benchmark=''
      )
      
      设置测试数据集,查看训练迭代过程的NDCG
      bigcharts-data-start/{"__type":"tabs","__id":"bigchart-7d78b98952bc4bacbac5303f450381eb"}/bigcharts-data-end
      • 收益率3.74%
      • 年化收益率30.22%
      • 基准收益率1.11%
      • 阿尔法0.21
      • 贝塔0.41
      • 夏普比率1.51
      • 胜率0.61
      • 盈亏比1.16
      • 收益波动率16.39%
      • 信息比率0.04
      • 最大回撤3.66%
      bigcharts-data-start/{"__type":"tabs","__id":"bigchart-c05ad2d1e0b34027856d3e29f3401212"}/bigcharts-data-end

      (developer) #3

      谢谢老师,我还有几个问题:
      1.用分钟的数据预测当日股票走势应该如何标注呢,上面那种标注方式等于用分钟走势预测后面几分钟的数据
      2.股票是T+1这个逻辑我怎么实现呢
      3.标注的股票是根据日线的,但是我抽取的是分钟的因子,这俩个数据如何关联起来


      (达达) #4

      我现在给你的是分钟的标注,你可以拉长周期标注,或者按日给每分钟的数据标注为同一个值,主要看你思路。控制好列名,可以在date基础上做一列day,最后用day列来合并因子和标注。


      (developer) #5

      我思考一下 感谢老师指导


      (biggerer) #6

      https://i.bigquant.com/user/albertech/lab/share/%E5%88%86%E9%92%9F%E7%AD%96%E7%95%A5%E7%A0%94%E7%A9%B6.ipynb

      老师帮看一下,为什么修改了一下就跑不通了呢?


      (albertech) #7

      老师请指导一下,如何拉长周期标注?
      是否能给个例子演示一下?比如30分钟周期的策略。

      谢谢!