抛砖引玉——概念板块选股策略

策略分享
标签: #<Tag:0x00007f25a0580ad0>

(胖大帅) #1

突然有个想法,是否可以考虑对概念板块进行特征排序呢,然后根据选择的板块进行选股。
这里用板块的资金流比例和成交额中位数作为特征进行板块的训练排名,尝试用板块所有个股的5日收益率中位数作为训练标签。交易逻辑考虑了止损,买入逻辑是先选出当日排名最优板块,然后选择其中资金流入最大的5只股票,卖出逻辑与模板的保持一致。
感觉可视化可以做的策略很多,这里抛砖引玉,提供个思路。

克隆策略

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outputs\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"input_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"params","Value":"{}","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"output_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-213"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-213"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-213"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-213","OutputType":null},{"Name":"data_2","NodeId":"-213","OutputType":null},{"Name":"data_3","NodeId":"-213","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":4,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-221","ModuleId":"BigQuantSpace.input_features.input_features-v1","ModuleParameters":[{"Name":"features","Value":"\n# #号开始的表示注释\n# 多个特征,每行一个,可以包含基础特征和衍生特征\nsum((mf_net_pct_xl_0+mf_net_pct_l_0-mf_net_pct_s_0)*turn_0,5)\nsum(amount_0,5)","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features_ds","NodeId":"-221"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-221","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":5,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-236","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):\n # 示例代码如下。在这里编写您的代码\n df=input_1.read_df()\n fac_list=input_2.read_pickle()+['label']\n df['concept']=df['concept'].apply(lambda x:x.split(';'))\n #计算概念板块因子的均值,同时构建instrument为板块名称,fac_list为因子列(计算板块因子均值为例),date为索引的DataFrame\n dfr=df['concept'].apply(lambda x:pd.Series(x)).stack().reset_index(level=1, drop=True).to_frame('concept')\n columns_=[k for k in df.columns.tolist() if k!='concept']\n df1=df[columns_].join(dfr,how='left')\n concept=df1.drop_duplicates().sort_values(by=['date','instrument']).reset_index(drop=True)\n df_final=concept.groupby(['date','concept'])[fac_list].median().reset_index()\n df_final=df_final.rename(columns={'concept':'instrument'})\n data_1 = DataSource.write_df(df_final)\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 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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):\n # 示例代码如下。在这里编写您的代码\n df=input_1.read_df()\n fac_list=input_2.read_pickle()\n df['concept']=df['concept'].apply(lambda x:x.split(';'))\n #计算概念板块因子的均值,同时构建instrument为板块名称,fac_list为因子列(计算板块因子均值为例),date为索引的DataFrame\n dfr=df['concept'].apply(lambda x:pd.Series(x)).stack().reset_index(level=1, drop=True).to_frame('concept')\n columns_=[k for k in df.columns.tolist() if k!='concept']\n df1=df[columns_].join(dfr,how='left')\n concept=df1.drop_duplicates().sort_values(by=['date','instrument']).reset_index(drop=True)\n df_final=concept.groupby(['date','concept'])[fac_list].median().reset_index()\n df_final=df_final.rename(columns={'concept':'instrument'})\n data_1 = DataSource.write_df(df_final)\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":"{}","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"output_ports","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-138"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-138"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_3","NodeId":"-138"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-138","OutputType":null},{"Name":"data_2","NodeId":"-138","OutputType":null},{"Name":"data_3","NodeId":"-138","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":10,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-150","ModuleId":"BigQuantSpace.cached.cached-v3","ModuleParameters":[{"Name":"run","Value":"# 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    In [211]:
    # 本代码由可视化策略环境自动生成 2018年8月28日 20:52
    # 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。
    
    
    m1 = M.instruments.v2(
        start_date='2017-01-01',
        end_date='2018-05-01',
        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, -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
    )
    
    # Python 代码入口函数,input_1/2/3 对应三个输入端,data_1/2/3 对应三个输出端
    def m4_run_bigquant_run(input_1, input_2, input_3):
        instrument=input_1.read_pickle()['instruments']
        start=input_1.read_pickle()['start_date']
        end=input_1.read_pickle()['end_date']
        df = D.history_data(instrument, start, end, ['concept']).dropna()
        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 m4_post_run_bigquant_run(outputs):
        return outputs
    
    m4 = M.cached.v3(
        input_1=m1.data,
        run=m4_run_bigquant_run,
        post_run=m4_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2018-05-01'),
        end_date=T.live_run_param('trading_date', '2018-08-20'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    # 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 m19_run_bigquant_run(input_1, input_2, input_3):
        instrument=input_1.read_pickle()['instruments']
        start=input_1.read_pickle()['start_date']
        end=input_1.read_pickle()['end_date']
        df = D.history_data(instrument, start, end, ['concept']).dropna()
        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 m19_post_run_bigquant_run(outputs):
        return outputs
    
    m19 = M.cached.v3(
        input_1=m9.data,
        run=m19_run_bigquant_run,
        post_run=m19_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m5 = 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
    sum((mf_net_pct_xl_0+mf_net_pct_l_0-mf_net_pct_s_0)*turn_0,5)
    sum(amount_0,5)"""
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m5.data,
        start_date='',
        end_date='',
        before_start_days=90
    )
    
    m16 = M.derived_feature_extractor.v3(
        input_data=m15.data,
        features=m5.data,
        date_col='date',
        instrument_col='instrument'
    )
    
    m7 = M.join.v3(
        data1=m4.data_1,
        data2=m16.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m3 = M.join.v3(
        data1=m2.data,
        data2=m7.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m17 = M.general_feature_extractor.v7(
        instruments=m9.data,
        features=m5.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m18 = M.derived_feature_extractor.v3(
        input_data=m17.data,
        features=m5.data,
        date_col='date',
        instrument_col='instrument'
    )
    
    m20 = M.join.v3(
        data1=m18.data,
        data2=m19.data_1,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    # 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 m11_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=input_1.read_df()
        fac_list=input_2.read_pickle()+['label']
        df['concept']=df['concept'].apply(lambda x:x.split(';'))
        #计算概念板块因子的均值,同时构建instrument为板块名称,fac_list为因子列(计算板块因子均值为例),date为索引的DataFrame
        dfr=df['concept'].apply(lambda x:pd.Series(x)).stack().reset_index(level=1, drop=True).to_frame('concept')
        columns_=[k for k in df.columns.tolist() if k!='concept']
        df1=df[columns_].join(dfr,how='left')
        concept=df1.drop_duplicates().sort_values(by=['date','instrument']).reset_index(drop=True)
        df_final=concept.groupby(['date','concept'])[fac_list].median().reset_index()
        df_final=df_final.rename(columns={'concept':'instrument'})
        data_1 = DataSource.write_df(df_final)
        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 m11_post_run_bigquant_run(outputs):
        return outputs
    
    m11 = M.cached.v3(
        input_1=m3.data,
        input_2=m5.data,
        run=m11_run_bigquant_run,
        post_run=m11_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m13 = M.dropnan.v1(
        input_data=m11.data_1
    )
    
    m6 = M.stock_ranker_train.v5(
        training_ds=m13.data,
        features=m5.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
    )
    
    # 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 m10_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=input_1.read_df()
        fac_list=input_2.read_pickle()
        df['concept']=df['concept'].apply(lambda x:x.split(';'))
        #计算概念板块因子的均值,同时构建instrument为板块名称,fac_list为因子列(计算板块因子均值为例),date为索引的DataFrame
        dfr=df['concept'].apply(lambda x:pd.Series(x)).stack().reset_index(level=1, drop=True).to_frame('concept')
        columns_=[k for k in df.columns.tolist() if k!='concept']
        df1=df[columns_].join(dfr,how='left')
        concept=df1.drop_duplicates().sort_values(by=['date','instrument']).reset_index(drop=True)
        df_final=concept.groupby(['date','concept'])[fac_list].median().reset_index()
        df_final=df_final.rename(columns={'concept':'instrument'})
        data_1 = DataSource.write_df(df_final)
        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 m10_post_run_bigquant_run(outputs):
        return outputs
    
    m10 = M.cached.v3(
        input_1=m20.data,
        input_2=m5.data,
        run=m10_run_bigquant_run,
        post_run=m10_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m14 = M.dropnan.v1(
        input_data=m10.data_1
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m6.model,
        data=m14.data,
        m_lazy_run=False
    )
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m12_handle_data_bigquant_run(context, data):
        today=data.current_dt.strftime('%Y-%m-%d')
        # 按日期过滤得到今日的预测数据
        ranker_prediction = context.ranker_prediction[
            context.ranker_prediction.date == today]
     #------------------------------------------止损模块START--------------------------------------------
        positions = {e.symbol: p.cost_basis  for e, p in context.portfolio.positions.items()}
        # 新建当日止损股票列表是为了handle_data 策略逻辑部分不再对该股票进行判断
        current_stoploss_stock = [] 
        if len(positions) > 0:
            for i in positions.keys():
                stock_cost = positions[i] 
                stock_market_price = data.current(context.symbol(i), 'price') 
                # 亏5%就止损
                if (stock_market_price - stock_cost) / stock_cost <= -0.05:   
                    context.order_target_percent(context.symbol(i),0)     
                    current_stoploss_stock.append(i)
                    #print('日期:',date,'股票:',i,'出现止损状况')
        #-------------------------------------------止损模块END---------------------------------------------
        try:
            concept_today=ranker_prediction.instrument.values.tolist()[0]
            df = D.history_data(D.instruments(), today,today, ['concept','mf_net_amount_main']).dropna()
            df_filter=df[df['concept'].apply(lambda x:concept_today in x.split(';') if x else False)]
            df_filter=df_filter.sort_values(by=['mf_net_amount_main'],ascending=False)
        except:
            return
        # 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.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 = positions.keys()
            # 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(df_filter.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 m12_prepare_bigquant_run(context):
        pass
    
    # 回测引擎:初始化函数,只执行一次
    def m12_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
        
    m12 = M.trade.v3(
        instruments=m9.data,
        options_data=m8.predictions,
        start_date='',
        end_date='',
        handle_data=m12_handle_data_bigquant_run,
        prepare=m12_prepare_bigquant_run,
        initialize=m12_initialize_bigquant_run,
        volume_limit=0.025,
        order_price_field_buy='open',
        order_price_field_sell='open',
        capital_base=1000001,
        benchmark='000016.SHA',
        auto_cancel_non_tradable_orders=True,
        data_frequency='daily',
        price_type='后复权',
        plot_charts=True,
        backtest_only=False,
        amount_integer=False
    )
    
    [2018-08-28 20:40:50.052946] INFO: bigquant: instruments.v2 开始运行..
    [2018-08-28 20:40:50.057591] INFO: bigquant: 命中缓存
    [2018-08-28 20:40:50.058880] INFO: bigquant: instruments.v2 运行完成[0.005977s].
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    [2018-08-28 20:40:50.064725] INFO: bigquant: 命中缓存
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    [2018-08-28 20:40:50.069244] INFO: bigquant: cached.v3 开始运行..
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    [2018-08-28 20:40:50.180170] INFO: bigquant: dropnan.v1 运行完成[0.003908s].
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    [2018-08-28 20:40:50.225670] INFO: bigquant: stock_ranker_train.v5 运行完成[0.006393s].
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    [2018-08-28 20:40:50.242778] INFO: bigquant: dropnan.v1 运行完成[0.003752s].
    [2018-08-28 20:40:50.319784] INFO: bigquant: stock_ranker_predict.v5 开始运行..
    [2018-08-28 20:40:50.325083] INFO: bigquant: 命中缓存
    [2018-08-28 20:40:50.326045] INFO: bigquant: stock_ranker_predict.v5 运行完成[0.006282s].
    [2018-08-28 20:40:50.522990] INFO: bigquant: backtest.v7 开始运行..
    [2018-08-28 20:40:50.525510] INFO: bigquant: biglearning backtest:V7.1.2
    [2018-08-28 20:40:59.671946] INFO: algo: TradingAlgorithm V1.2.5
    [2018-08-28 20:42:32.394095] INFO: Performance: Simulated 78 trading days out of 78.
    [2018-08-28 20:42:32.395699] INFO: Performance: first open: 2018-05-02 09:30:00+00:00
    [2018-08-28 20:42:32.397157] INFO: Performance: last close: 2018-08-20 15:00:00+00:00
    
    • 收益率-10.11%
    • 年化收益率-29.13%
    • 基准收益率-9.21%
    • 阿尔法-0.14
    • 贝塔0.68
    • 夏普比率-2.03
    • 胜率0.41
    • 盈亏比0.94
    • 收益波动率17.67%
    • 信息比率-0.02
    • 最大回撤15.37%
    [2018-08-28 20:42:39.849226] INFO: bigquant: backtest.v7 运行完成[109.326206s].
    

    (xuan) #2

    应该可以更好把,谢谢