如何过滤出波动幅度相对基准指数的波幅小于5%的个股

新手专区
标签: #<Tag:0x00007fcf6719a8f0>

(luckychan) #1

如想选出三个月个股日均波动幅度相对于基准指数(深证指数399001)的日均波动幅度不大于5倍的个股作为交易标的,应该怎样代码实现。请大神指教,谢谢!


(达达) #2

可以参考这篇帖子
按您的想法做了一个可视化的策略

克隆策略

    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    In [91]:
    # 本代码由可视化策略环境自动生成 2018年7月7日 17:14
    # 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='2014-12-01',
        end_date='2015-01-01',
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m2 = M.advanced_auto_labeler.v2(
        instruments=m1.data,
        label_expr="""# #号开始的表示注释
    # 0. 每行一个,顺序执行,从第二个开始,可以使用label字段
    # 1. 可用数据字段见 https://bigquant.com/docs/data_history_data.html
    #   添加benchmark_前缀,可使用对应的benchmark数据
    # 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/big_expr.html>`_
    
    # 计算收益:5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
    shift(close, -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
    )
    
    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"""
    )
    
    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
    #这里因为计算振幅需要用到个股的最高价和最低价,因此自定义函数中必须含high_0和low_0作为输入
    high_0
    low_0
    self_diy(high_0,low_0)"""
    )
    
    m4 = M.general_feature_extractor.v6(
        instruments=m1.data,
        features=m15.data,
        start_date='',
        end_date='',
        before_start_days=300
    )
    
    def self_diy(df,high_0,low_0): 
        df=m4.data.read_df()
        #获取个股的每日波幅
        df['bofu_stock']=df.groupby('instrument').apply(lambda df:df['high_0']/df['low_0']-1).reset_index(drop=True)
        #获取个股90日波幅均值
        df['bofu_avg_stock']=df.groupby('instrument').apply(lambda x:pd.rolling_mean(x['bofu_stock'],90)).reset_index(drop=True)
    
        #获取起始时间
        start=min(df.date).strftime('%Y-%m-%d')
        end =max(df.date).strftime('%Y-%m-%d')
        #获取指数历史数据
        df_szzs = D.history_data('399001.SZA',
                                   start_date=start,  # 多取200天的数据
                                    end_date=end,fields=['date', 'high','low'])
        #获取指数的每日波幅
        df_szzs['bofu_szzs']=df_szzs['high']/df_szzs['low']-1
        #获取指数90日波幅均值
        df_szzs['bofu_avg_szzs']=pd.rolling_mean(df_szzs['bofu_szzs'],90)
    
        #与个股的合并
        df = df_szzs[['date','bofu_avg_szzs']].merge(df, on='date', how='left')
    
        #通过high_0列计算相对波幅比值,并返回给self_diy(high_0,low_0)指标
        df['high_0']=df['bofu_avg_stock']/df['bofu_avg_szzs']
        return df['high_0']
                    
    m5_user_functions_bigquant_run = {
        'self_diy': self_diy
    }
    
    m5 = M.derived_feature_extractor.v2(
        input_data=m4.data,
        features=m15.data,
        date_col='date',
        instrument_col='instrument',
        user_functions=m5_user_functions_bigquant_run
    )
    
    # 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 m16_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()
        df1=df[df['self_diy(high_0,low_0)']<=5]
        data_1 = DataSource.write_df(df1)
        return Outputs(data_1=data_1, data_2=None, data_3=None)
    
    m16 = M.cached.v3(
        input_1=m5.data,
        run=m16_run_bigquant_run
    )
    
    m7 = M.join.v3(
        data1=m2.data,
        data2=m16.data_1,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m13 = M.dropnan.v1(
        input_data=m7.data
    )
    
    m6 = M.stock_ranker_train.v5(
        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,
        m_lazy_run=False
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2015-11-01'),
        end_date=T.live_run_param('trading_date', '2016-03-01'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m10 = M.general_feature_extractor.v6(
        instruments=m9.data,
        features=m15.data,
        start_date='',
        end_date='',
        before_start_days=300
    )
    
    def self_diy(df,high_0,low_0): 
        df=m10.data.read_df()
        #获取个股的每日波幅
        df['bofu_stock']=df.groupby('instrument').apply(lambda df:df['high_0']/df['low_0']-1).reset_index(drop=True)
        #获取个股90日波幅均值
        df['bofu_avg_stock']=df.groupby('instrument').apply(lambda x:pd.rolling_mean(x['bofu_stock'],90)).reset_index(drop=True)
    
        #获取起始时间
        start=min(df.date).strftime('%Y-%m-%d')
        end =max(df.date).strftime('%Y-%m-%d')
        #获取指数历史数据
        df_szzs = D.history_data('399001.SZA',
                                   start_date=start,  # 多取200天的数据
                                    end_date=end,fields=['date', 'high','low'])
        #获取指数的每日波幅
        df_szzs['bofu_szzs']=df_szzs['high']/df_szzs['low']-1
        #获取指数90日波幅均值
        df_szzs['bofu_avg_szzs']=pd.rolling_mean(df_szzs['bofu_szzs'],90)
    
        #与个股的合并
        df = df_szzs[['date','bofu_avg_szzs']].merge(df, on='date', how='left')
    
        #通过high_0列计算相对波幅比值,并返回给self_diy(high_0,low_0)指标
        df['high_0']=df['bofu_avg_stock']/df['bofu_avg_szzs']
        return df['high_0']
                    
    m11_user_functions_bigquant_run = {
        'self_diy': self_diy
    }
    
    m11 = M.derived_feature_extractor.v2(
        input_data=m10.data,
        features=m15.data,
        date_col='date',
        instrument_col='instrument',
        user_functions=m11_user_functions_bigquant_run
    )
    
    # Python 代码入口函数,input_1/2/3 对应三个输入端,data_1/2/3 对应三个输出端
    def m17_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        df = input_1.read_df()
        df1=df[df['self_diy(high_0,low_0)']<=5]
        data_1 = DataSource.write_df(df1)
        return Outputs(data_1=data_1, data_2=None, data_3=None)
    m17 = M.cached.v3(
        input_1=m11.data,
        run=m17_run_bigquant_run
    )
    
    m14 = M.dropnan.v1(
        input_data=m17.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):
        # 按日期过滤得到今日的预测数据
        ranker_prediction = context.ranker_prediction[
            context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]
    
        # 1. 资金分配
        # 平均持仓时间是hold_days,每日都将买入股票,每日预期使用 1/hold_days 的资金
        # 实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)
        is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)
        cash_avg = context.portfolio.portfolio_value / 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.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 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='close',
        capital_base=1000000,
        benchmark='000300.SHA',
        auto_cancel_non_tradable_orders=True,
        data_frequency='daily',
        price_type='后复权',
        plot_charts=True,
        backtest_only=False,
        amount_integer=False
    )
    
    [2018-07-07 17:15:24.873903] INFO: bigquant: instruments.v2 开始运行..
    [2018-07-07 17:15:24.876553] INFO: bigquant: 命中缓存
    [2018-07-07 17:15:24.877367] INFO: bigquant: instruments.v2 运行完成[0.003481s].
    [2018-07-07 17:15:24.884161] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
    [2018-07-07 17:15:24.886181] INFO: bigquant: 命中缓存
    [2018-07-07 17:15:24.886892] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.002737s].
    [2018-07-07 17:15:24.890755] INFO: bigquant: input_features.v1 开始运行..
    [2018-07-07 17:15:24.894327] INFO: bigquant: input_features.v1 运行完成[0.003565s].
    [2018-07-07 17:15:24.897819] INFO: bigquant: input_features.v1 开始运行..
    [2018-07-07 17:15:24.901792] INFO: bigquant: input_features.v1 运行完成[0.003969s].
    [2018-07-07 17:15:24.909078] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2018-07-07 17:15:24.910816] INFO: bigquant: 命中缓存
    [2018-07-07 17:15:24.911723] INFO: bigquant: general_feature_extractor.v6 运行完成[0.002659s].
    [2018-07-07 17:15:24.919645] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2018-07-07 17:15:25.065154] INFO: derived_feature_extractor: 提取完成 return_5/return_10, 0.002s
    [2018-07-07 17:15:32.659931] INFO: derived_feature_extractor: 提取完成 self_diy(high_0,low_0), 7.593s
    [2018-07-07 17:15:32.796588] INFO: derived_feature_extractor: /y_2014, 520463
    [2018-07-07 17:15:33.035673] INFO: bigquant: derived_feature_extractor.v2 运行完成[8.115992s].
    [2018-07-07 17:15:33.044406] INFO: bigquant: cached.v3 开始运行..
    [2018-07-07 17:15:33.273765] INFO: bigquant: cached.v3 运行完成[0.229343s].
    [2018-07-07 17:15:33.282221] INFO: bigquant: join.v3 开始运行..
    [2018-07-07 17:15:33.417226] INFO: join: /data, 行数=11678/144820, 耗时=0.102194s
    [2018-07-07 17:15:33.429071] INFO: join: 最终行数: 11678
    [2018-07-07 17:15:33.430668] INFO: bigquant: join.v3 运行完成[0.148484s].
    [2018-07-07 17:15:33.438314] INFO: bigquant: dropnan.v1 开始运行..
    [2018-07-07 17:15:33.487088] INFO: dropnan: /data, 11640/11678
    [2018-07-07 17:15:33.492614] INFO: dropnan: 行数: 11640/11678
    [2018-07-07 17:15:33.495200] INFO: bigquant: dropnan.v1 运行完成[0.056899s].
    [2018-07-07 17:15:33.505277] INFO: bigquant: stock_ranker_train.v5 开始运行..
    [2018-07-07 17:15:33.545082] INFO: df2bin: prepare bins ..
    [2018-07-07 17:15:33.549872] INFO: df2bin: prepare data: training ..
    [2018-07-07 17:15:33.555900] INFO: df2bin: sort ..
    [2018-07-07 17:15:34.117636] INFO: stock_ranker_train: 4d4b399c 准备训练: 11640 行数
    [2018-07-07 17:15:36.352757] INFO: bigquant: stock_ranker_train.v5 运行完成[2.847469s].
    [2018-07-07 17:15:36.358699] INFO: bigquant: instruments.v2 开始运行..
    [2018-07-07 17:15:36.361307] INFO: bigquant: 命中缓存
    [2018-07-07 17:15:36.362346] INFO: bigquant: instruments.v2 运行完成[0.00365s].
    [2018-07-07 17:15:36.371114] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2018-07-07 17:15:36.373566] INFO: bigquant: 命中缓存
    [2018-07-07 17:15:36.374579] INFO: bigquant: general_feature_extractor.v6 运行完成[0.003473s].
    [2018-07-07 17:15:36.383329] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2018-07-07 17:15:36.582043] INFO: derived_feature_extractor: 提取完成 return_5/return_10, 0.002s
    [2018-07-07 17:15:45.481891] INFO: derived_feature_extractor: 提取完成 self_diy(high_0,low_0), 8.899s
    [2018-07-07 17:15:45.625369] INFO: derived_feature_extractor: /y_2015, 569523
    [2018-07-07 17:15:45.927810] INFO: derived_feature_extractor: /y_2016, 94453
    [2018-07-07 17:15:45.989916] INFO: bigquant: derived_feature_extractor.v2 运行完成[9.606575s].
    [2018-07-07 17:15:45.999444] INFO: bigquant: cached.v3 开始运行..
    [2018-07-07 17:15:46.390144] INFO: bigquant: cached.v3 运行完成[0.39071s].
    [2018-07-07 17:15:46.397620] INFO: bigquant: dropnan.v1 开始运行..
    [2018-07-07 17:15:46.623859] INFO: dropnan: /data, 285436/287117
    [2018-07-07 17:15:46.632003] INFO: dropnan: 行数: 285436/287117
    [2018-07-07 17:15:46.643410] INFO: bigquant: dropnan.v1 运行完成[0.245788s].
    [2018-07-07 17:15:46.653045] INFO: bigquant: stock_ranker_predict.v5 开始运行..
    [2018-07-07 17:15:46.806421] INFO: df2bin: prepare data: prediction ..
    [2018-07-07 17:15:49.281197] INFO: stock_ranker_predict: 准备预测: 285436 行
    [2018-07-07 17:15:51.642343] INFO: bigquant: stock_ranker_predict.v5 运行完成[4.989306s].
    [2018-07-07 17:15:51.670801] INFO: bigquant: backtest.v7 开始运行..
    [2018-07-07 17:15:51.779023] INFO: algo: set price type:backward_adjusted
    [2018-07-07 17:16:10.629115] INFO: Performance: Simulated 81 trading days out of 81.
    [2018-07-07 17:16:10.630168] INFO: Performance: first open: 2015-11-02 01:30:00+00:00
    [2018-07-07 17:16:10.630995] INFO: Performance: last close: 2016-03-01 07:00:00+00:00
    
    • 收益率-25.39%
    • 年化收益率-59.8%
    • 基准收益率-17.07%
    • 阿尔法-0.08
    • 贝塔1.16
    • 夏普比率-1.23
    • 胜率0.488
    • 盈亏比0.843
    • 收益波动率51.05%
    • 信息比率-0.53
    • 最大回撤39.96%
    [2018-07-07 17:16:11.588842] INFO: bigquant: backtest.v7 运行完成[19.918041s].
    

    (luckychan) #3

    谢谢达达,辛苦了。


    (luckychan) #4
    instruments = ['000001.SZA','600000.SHA']   
    df = D.history_data(instruments, start_date='2018-7-3', end_date='2018-07-13',
                        fields=['close']) #获取历史数据
    df['ma2_stock']=df.groupby('instrument').apply(lambda x:pd.rolling_mean(x['close'],2)).reset_index(drop=True)
    df
    
    date	instrument	close	ma2_stock
    0	2018-07-03	000001.SZA	921.697327	NaN
    1	2018-07-03	600000.SHA	114.083572	918.508057
    2	2018-07-04	000001.SZA	915.318787	914.787231
    3	2018-07-04	600000.SHA	113.595520	917.444946
    4	2018-07-05	000001.SZA	914.255676	940.301361
    5	2018-07-05	600000.SHA	112.985443	957.310791
    6	2018-07-06	000001.SZA	920.634216	944.022186
    7	2018-07-06	600000.SHA	114.327606	946.355072
    8	2018-07-09	000001.SZA	959.968506	959.318848
    9	2018-07-09	600000.SHA	117.133942	NaN
    10	2018-07-10	000001.SZA	954.653076	113.839546
    11	2018-07-10	600000.SHA	116.767899	113.290482
    12	2018-07-11	000001.SZA	933.391296	113.656525
    13	2018-07-11	600000.SHA	114.449623	115.730774
    14	2018-07-12	000001.SZA	959.318848	116.950920
    15	2018-07-12	600000.SHA	116.767899	115.608761
    16	2018-07-13	000001.SZA	959.318848	115.608761
    17	2018-07-13	600000.SHA	117.014496	116.891197
    

    上面样例中使用的 groupby函数出来的结果有些问题,上面做了个测试,计算出的均线没有放在对应的位置,而是按顺序填充,请教这个函数的正确用法,或者还有其它方法可以达到需要的结果。谢谢


    (达达) #5
    instruments = ['000001.SZA','600000.SHA']
    df = D.history_data(instruments, start_date='2018-7-3', end_date='2018-07-13',
    fields=['close']) #获取历史数据
    df['ma2_stock']=df.groupby('instrument')['close'].apply(lambda x:pd.rolling_mean(x,2))
    df
    

    (luckychan) #6

    这个结果就对了,谢谢达达。