克隆策略

基于持仓的标签示例：

根据以下要求进行基金筛选，筛选逻辑作为“消费”标签保存

1、过往4个报告期的平均规模 > 10亿；

2、过往4个报告期的股票市值占净资产的比例 > 50%；

3、最新报告期在消费类行业上的标准化配置比例大于40%，其中消费类行业指申万一级行业分类中的食品饮料、家用电器、休闲服务和农林牧渔。标准化配置指：a、全持仓维度下，在消费类行业上的配置市值和股票配置市值的比值；b、季报前十大维度下，在消费类行业上的配置市值和前十大股票配置市值的比值

4、过往8个报告期，有5期以上在消费类行业上的标准化配置比例大于40%

基于业绩的标签示例：

以消费标签为范围，以T为时间周期参数计算标签内基金在T日内的相对排名序列，选取相对排名在a%分位至b%分位的基金

回测示例：

以以上两个标签为选基标准，等权构建（或以相对排名加权）构建基金组合，进行回测。

    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回测引擎：初始化函数，只执行一次\ndef bigquant_run(context):\n df = context.options['data'].read()\n context.indicator_data = df[['date','instrument']].groupby('date').apply(lambda x:x.instrument.to_list())\n\n context.set_commission(PerOrder(buy_cost=0.0001, sell_cost=0.0001, min_cost=5))\n\n print('====', context.indicator_data)\n ","type":"Literal","bound_global_parameter":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 \n # 当前的日期\n date = data.current_dt.strftime('%Y-%m-%d')\n \n try:\n cur_data = context.indicator_data[date.replace('-', '')]\n except:\n return \n # 根据日期获取调仓需要买入的股票的列表\n stock_to_buy = list(cur_data)\n # 通过positions对象，使用列表生成式的方法获取目前持仓的股票列表\n stock_hold_now = [equity.symbol for equity in context.portfolio.positions]\n # 继续持有的股票：调仓时，如果买入的股票已经存在于目前的持仓里，那么应继续持有\n no_need_to_sell = [i for i in stock_hold_now if i in stock_to_buy]\n # 需要卖出的股票\n stock_to_sell = [i for i in stock_hold_now if i not in no_need_to_sell]\n \n # 卖出\n for stock in 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    In [2]:
    # 本代码由可视化策略环境自动生成 2021年11月26日 10:51
# 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。


# 回测引擎：初始化函数，只执行一次
def m21_initialize_bigquant_run(context):
    df = context.options['data'].read()
    context.indicator_data = df[['date','instrument']].groupby('date').apply(lambda x:x.instrument.to_list())

    context.set_commission(PerOrder(buy_cost=0.0001, sell_cost=0.0001, min_cost=5))

    print('====', context.indicator_data)
    
# 回测引擎：每日数据处理函数，每天执行一次
def m21_handle_data_bigquant_run(context, data):
     
    # 当前的日期
    date = data.current_dt.strftime('%Y-%m-%d')
    
    try:
        cur_data = context.indicator_data[date.replace('-', '')]
    except:
        return 
    # 根据日期获取调仓需要买入的股票的列表
    stock_to_buy = list(cur_data)
    # 通过positions对象，使用列表生成式的方法获取目前持仓的股票列表
    stock_hold_now = [equity.symbol for equity in context.portfolio.positions]
    # 继续持有的股票：调仓时，如果买入的股票已经存在于目前的持仓里，那么应继续持有
    no_need_to_sell = [i for i in stock_hold_now if i in stock_to_buy]
    # 需要卖出的股票
    stock_to_sell = [i for i in stock_hold_now if i not in no_need_to_sell]
  
    # 卖出
    for stock in stock_to_sell:
        # 如果该股票停牌，则没法成交。因此需要用can_trade方法检查下该股票的状态
        # 如果返回真值，则可以正常下单，否则会出错
        # 因为stock是字符串格式，我们用symbol方法将其转化成平台可以接受的形式：Equity格式

        if data.can_trade(context.symbol(stock)):
            try:
                context.order_target_percent(context.symbol(stock), 0)
            except ValueError as e:
                print('*****', date, e)
    # 如果当天没有买入的股票，就返回
    if len(stock_to_buy) == 0:
        return

    # 等权重买入 
    weight =  1 / len(stock_to_buy)
    
    # 买入
    for stock in stock_to_buy:
        if data.can_trade(context.symbol(stock)):
            # 下单使得某只股票的持仓权重达到weight，因为
            # weight大于0,因此是等权重买入
            try:
                context.order_target_percent(context.symbol(stock), weight)
            except ValueError as e:
                print('*****',  date, e)
  
# 回测引擎：准备数据，只执行一次
def m21_prepare_bigquant_run(context):
    pass


m1 = M.use_datasource.v1(
    datasource_id='nav_CN_FUND',
    start_date='2015-01-01',
    end_date=''
)

m10 = M.trade_data_generation.v1(
    input=m1.data,
    category='CN_FUND'
)

m2 = M.input_features.v1(
    features="""mom = nav_acc / nav_acc.shift(60) -1 
rk = rank(mom)"""
)

m4 = M.derived_feature_extractor.v3(
    input_data=m1.data,
    features=m2.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m7 = M.resample.v1(
    data1=m4.data,
    group='instrument',
    sessions='Q'
)

m3 = M.use_datasource.v1(
    datasource_id='share_CN_FUND',
    start_date='2015-01-01',
    end_date=''
)

m9 = M.resample.v1(
    data1=m3.data,
    group='instrument',
    sessions='Q'
)

m5 = M.use_datasource.v1(
    datasource_id='portfolio_CN_FUND',
    start_date='2015-01-01',
    end_date=''
)

m8 = M.add_fund_industry_pos_factor.v2(
    input_1=m5.data
)

m6 = M.joinx.v2(
    data1=m9.data,
    data2=m7.data,
    data3=m8.data_1,
    on='date,instrument',
    how='inner',
    sort=False
)

m24 = M.input_features.v1(
    features="""size= share*nav
stock_mkv_div_total_asset =  stock_mkv_sum / size 
top10_stock_mkv_div_total_asset = top10_stock_mkv_sum / size 

comsumption_stock_mkv  = 农林牧渔 + 家用电器 + 食品饮料 + 休闲服务 + 化工
comsumption_stock_div_stock_mkv = comsumption_stock_mkv / stock_mkv_sum

c0 = mean(size, 4) > 1000000000
c1 = ts_min(stock_mkv_div_total_asset, 4) > 0.5
c2 = comsumption_stock_div_stock_mkv > 0.4
c3 = sum(np.where(comsumption_stock_div_stock_mkv>0.4, 1, 0), 8) >=5

 """
)

m25 = M.derived_feature_extractor.v3(
    input_data=m6.data,
    features=m24.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m26 = M.filter.v3(
    input_data=m25.data,
    expr='c0&c1&c2&c3&(rk>0.01)&(rk<0.99)',
    output_left_data=False
)

m21 = M.trade.v4(
    instruments=m10.instrument_list,
    options_data=m26.data,
    history_ds=m10.history_data,
    trading_calendar=m10.calendar,
    start_date='2017-04-17',
    end_date='',
    initialize=m21_initialize_bigquant_run,
    handle_data=m21_handle_data_bigquant_run,
    prepare=m21_prepare_bigquant_run,
    volume_limit=0,
    order_price_field_buy='open',
    order_price_field_sell='open',
    capital_base=1000000,
    auto_cancel_non_tradable_orders=True,
    data_frequency='daily',
    price_type='真实价格',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark='000300.SHA'
)

    ==== date
2016-12-31                                         [159928.ZOF]
2017-03-31                              [110011.OF, 159928.ZOF]
2017-06-30                                          [110011.OF]
2017-09-30                              [110011.OF, 161725.ZOF]
2017-12-31                 [159928.ZOF, 161725.ZOF, 162605.ZOF]
2018-03-31                              [110011.OF, 161725.ZOF]
2018-06-30    [110011.OF, 159928.ZOF, 160222.ZOF, 161725.ZOF...
2018-09-30    [001076.OF, 002851.OF, 110011.OF, 159928.ZOF, ...
2018-12-31    [001076.OF, 159928.ZOF, 161725.ZOF, 162605.ZOF...
2019-03-31    [001076.OF, 110011.OF, 160222.ZOF, 161725.ZOF,...
2019-06-30    [001076.OF, 001927.OF, 110011.OF, 159928.ZOF, ...
2019-09-30    [001076.OF, 001373.OF, 001927.OF, 004986.OF, 1...
2019-12-31    [001044.OF, 005450.OF, 159928.ZOF, 160222.ZOF,...
2020-03-31    [000603.OF, 001044.OF, 001076.OF, 001373.OF, 0...
2020-06-30    [000974.OF, 001076.OF, 001373.OF, 005450.OF, 1...
2020-09-30    [000628.OF, 000974.OF, 001044.OF, 001076.OF, 0...
2020-12-31    [001044.OF, 001076.OF, 001349.OF, 001373.OF, 0...
2021-03-31    [000529.OF, 000692.OF, 001076.OF, 001373.OF, 0...
2021-06-30    [000529.OF, 001076.OF, 001349.OF, 001631.OF, 0...
2021-09-30     [159905.ZOF, 159928.ZOF, 512690.HOF, 515650.HOF]
dtype: object
    • 收益率137.25%
    • 年化收益率21.41%
    • 基准收益率40.92%
    • 阿尔法0.13
    • 贝塔0.98
    • 夏普比率0.84
    • 胜率0.61
    • 盈亏比2.45
    • 收益波动率22.81%
    • 信息比率0.06
    • 最大回撤29.07%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-aea61ee2f5ca4087a5f65e5de916f90c"}/bigcharts-data-end

    从交易详情里可看到选出的基金示例有：

    2020-12-18 159928.ZOF 消费ETF

    2020-12-18 160222.ZOF 食品LOF

    2020-12-18 162605.ZOF

    2020-12-18 005450.OF 华夏稳胜混合

    2020-12-18 519915.OF 富国消费主题混合

    2020-12-18 001076.OF

    2020-12-18 001373.OF

    2020-12-18 110011.OF 易方达优质精选混合

    2020-12-18 159905.ZOF

    2020-12-18 160910.ZOF

    2020-12-18 161725.ZOF

    2020-12-18 000974.OF 安信消费医药股票

    2020-12-18 001044.OF

    2020-12-18 001632.OF 天弘中证食品饮料

    2020-12-18 002621.OF

    2020-12-18 003634.OF 嘉实农业产业股票