反转因子的精细结构-研报复现

策略分享
反转因子
研报复现
标签: #<Tag:0x00007fb00e02e638> #<Tag:0x00007fb00e02e4d0> #<Tag:0x00007fb00e02e390>

(iQuant) #1

本文实现了研报 反转因子的精细结构.pdf (939.2 KB) 中提到的反转因子。

反转因子计算方法

反转因子的W式切割经过长期反复的摸索,我们找到了一个反转因子的有效切割方案,简称W式切割。具体操作步骤如下:

  1. 在每个月底,对于股票s,回溯其过去N个交易日的数据(为方便处理,N取偶数);
  2. 对于股票s,逐日计算平均单笔成交金额D(D=当日成交金额/当日成交笔数),将N个交易日按D值从大到小排序,前N/2个交易日称为高D组,后N/2个交易日称为低D组;
  3. 对于股票s,将高D组交易日的涨跌幅加总[1],得到因子M_high;将低D组交易日的涨跌幅加总,得到因子M_low;
  4. 对于所有股票,分别按照上述流程计算因子值。

W式切割的核心步骤是,按照“单笔成交金额”对交易日进行排序分组[2]。我们以20日收益率因子为例(即N取20),来说明W式切割方案的出色效果。样本空间为全部A股(剔除ST和上市未满60日的股票),回测时段仍为2010年至2018年。统计结果如图表4所示,结论是:M_high因子是非常强的反转因子(rankIC均值为-0.082),而M_low因子是较弱的动量因子(rankIC均值为0.018)[3]。

反转因子抽取和计算实现

  • m3,反转因子需要用到的两个因子
  • m15,抽取需要用到的基础因子(amount_0, deal_number_0, high_0, low_0)
  • m16,计算衍生因子
  • m4,在自定义模块中,计算 M_high, M_low,代码如下。第一个版本用的是pandas dataframe的rolling来实现;第二个版本优化了代码,去掉了rolling,使用中位数等方式,速度提升了~50倍。论用好pandas的重要性。
  • m21,计算 M=M_high-M_low

def bigquant_run(input_1, N=20):
    assert N % 2 == 0

    import pandas as pd
    import numpy as np
    N2 = N // 2
    df = input_1.read()

    def process_instrument(x):
        x['Median'] = x['amount_0/deal_number_0'].rolling(N).median()
        high = None
        low = None
        for i in range(N):
            y = x['amount_0/deal_number_0'].shift(i) > x['Median']
            h = y.astype(np.int32) * x['high_0/low_0'].shift(i)
            if high is None:
                high = h
            else:
                high += h
            l = (~y).astype(np.int32) * x['high_0/low_0'].shift(i)
            if low is None:
                low = l
            else:
                low += l
        x['M_high'] = high
        x['M_low'] = low
        return x

    g = df[['amount_0/deal_number_0', 'high_0/low_0']].groupby(df['instrument'], as_index=False)
    df2 = g.apply(process_instrument)
    # 并行计算版
    # df2 = pd.concat(T.parallel_map(process_instrument, [{'x': x[1]} for x in g]))
    df['M_high'], df['M_low'] = df2['M_high'], df2['M_low']

    data_1 = DataSource.write_df(df[['date', 'instrument', 'M_high', 'M_low']])

    return Outputs(data_1=data_1)

完整策略代码

这里使用AI可视化模版策略

  • 将反转因子作为唯一的特征
  • 使用StockRanker算法
  • 在2015~2017的数据上训练模型
  • 预测2018~2019年
  • 使用默认目标,未来5日收益
  • 其他参数也都使用默认的

结果:多头策略,有15.49%的相对收益。

StockRanker可以最大限度的挖掘因子的alpha。一般一个长期有效的因子,通过StockRanker优化,都会有比较显著的收益。从结果看,这个因子似乎并没有研报里显示的那样长期效果。

改进建议:

你可以克隆策略,做更多尝试

克隆策略

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5\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_trading_start","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"volume_limit","Value":0.025,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"order_price_field_buy","Value":"open","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"order_price_field_sell","Value":"close","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"capital_base","Value":1000000,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"auto_cancel_non_tradable_orders","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"data_frequency","Value":"daily","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"price_type","Value":"后复权","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"product_type","Value":"股票","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"plot_charts","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"backtest_only","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"benchmark","Value":"000300.SHA","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-250"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"options_data","NodeId":"-250"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"history_ds","NodeId":"-250"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"benchmark_ds","NodeId":"-250"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"trading_calendar","NodeId":"-250"}],"OutputPortsInternal":[{"Name":"raw_perf","NodeId":"-250","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":19,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-227","ModuleId":"BigQuantSpace.cached.cached-v3","ModuleParameters":[{"Name":"run","Value":"def bigquant_run(input_1, N=20):\n assert N % 2 == 0\n\n import pandas as pd\n import numpy as np\n N2 = N // 2\n df = input_1.read()\n\n def process_instrument(x):\n x['Median'] = x['amount_0/deal_number_0'].rolling(N).median()\n high = None\n low = None\n for i in range(N):\n y = x['amount_0/deal_number_0'].shift(i) > x['Median']\n h = y.astype(np.int32) * x['high_0/low_0'].shift(i)\n if high is None:\n high = h\n else:\n high += h\n l = (~y).astype(np.int32) * x['high_0/low_0'].shift(i)\n if low is None:\n low = l\n else:\n low += l\n x['M_high'] = high\n x['M_low'] = low\n return x\n\n g = df[['amount_0/deal_number_0', 'high_0/low_0']].groupby(df['instrument'], as_index=False)\n df2 = g.apply(process_instrument)\n # 并行计算版\n # df2 = pd.concat(T.parallel_map(process_instrument, [{'x': x[1]} for x in g]))\n df['M_high'], df['M_low'] = df2['M_high'], df2['M_low']\n\n data_1 = DataSource.write_df(df[['date', 'instrument', 'M_high', 'M_low']])\n\n return 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    In [7]:
    # 本代码由可视化策略环境自动生成 2019年3月4日 23:49
    # 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 m4_run_bigquant_run(input_1, N=20):
        assert N % 2 == 0
    
        import pandas as pd
        import numpy as np
        N2 = N // 2
        df = input_1.read()
    
        def process_instrument(x):
            x['Median'] = x['amount_0/deal_number_0'].rolling(N).median()
            high = None
            low = None
            for i in range(N):
                y = x['amount_0/deal_number_0'].shift(i) > x['Median']
                h = y.astype(np.int32) * x['high_0/low_0'].shift(i)
                if high is None:
                    high = h
                else:
                    high += h
                l = (~y).astype(np.int32) * x['high_0/low_0'].shift(i)
                if low is None:
                    low = l
                else:
                    low += l
            x['M_high'] = high
            x['M_low'] = low
            return x
    
        g = df[['amount_0/deal_number_0', 'high_0/low_0']].groupby(df['instrument'], as_index=False)
        df2 = g.apply(process_instrument)
        # 并行计算版
        # df2 = pd.concat(T.parallel_map(process_instrument, [{'x': x[1]} for x in g]))
        df['M_high'], df['M_low'] = df2['M_high'], df2['M_low']
    
        data_1 = DataSource.write_df(df[['date', 'instrument', 'M_high', 'M_low']])
    
        return Outputs(data_1=data_1)
    
    # 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
    
    def m24_run_bigquant_run(input_1, N=20):
        assert N % 2 == 0
    
        import pandas as pd
        import numpy as np
        N2 = N // 2
        df = input_1.read()
    
        def process_instrument(x):
            x['Median'] = x['amount_0/deal_number_0'].rolling(N).median()
            high = None
            low = None
            for i in range(N):
                y = x['amount_0/deal_number_0'].shift(i) > x['Median']
                h = y.astype(np.int32) * x['high_0/low_0'].shift(i)
                if high is None:
                    high = h
                else:
                    high += h
                l = (~y).astype(np.int32) * x['high_0/low_0'].shift(i)
                if low is None:
                    low = l
                else:
                    low += l
            x['M_high'] = high
            x['M_low'] = low
            return x
    
        g = df[['amount_0/deal_number_0', 'high_0/low_0']].groupby(df['instrument'], as_index=False)
        df2 = g.apply(process_instrument)
        # 并行计算版
        # df2 = pd.concat(T.parallel_map(process_instrument, [{'x': x[1]} for x in g]))
        df['M_high'], df['M_low'] = df2['M_high'], df2['M_low']
    
        data_1 = DataSource.write_df(df[['date', 'instrument', 'M_high', 'M_low']])
    
        return Outputs(data_1=data_1)
    
    # 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:每日数据处理函数,每天执行一次
    def m19_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.portfolio.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.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 m19_prepare_bigquant_run(context):
        pass
    
    # 回测引擎:初始化函数,只执行一次
    def m19_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
    
    
    m1 = M.instruments.v2(
        start_date='2015-01-01',
        end_date='2017-12-31',
        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.SHA',
        drop_na_label=True,
        cast_label_int=True
    )
    
    m3 = M.input_features.v1(
        features="""amount_0/deal_number_0
    high_0/low_0"""
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m16 = M.derived_feature_extractor.v3(
        input_data=m15.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m4 = M.cached.v3(
        input_1=m16.data,
        run=m4_run_bigquant_run,
        post_run=m4_post_run_bigquant_run,
        input_ports='input_1',
        params="""{
        'N': 20
    }""",
        output_ports='data_1'
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2018-01-01'),
        end_date=T.live_run_param('trading_date', '2019-03-01'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m22 = M.general_feature_extractor.v7(
        instruments=m9.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=40
    )
    
    m23 = M.derived_feature_extractor.v3(
        input_data=m22.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m24 = M.cached.v3(
        input_1=m23.data,
        run=m24_run_bigquant_run,
        post_run=m24_post_run_bigquant_run,
        input_ports='input_1',
        params="""{
        'N': 20
    }""",
        output_ports='data_1'
    )
    
    m20 = M.input_features.v1(
        features="""# 再抽取一些其他训练特征一起训练
    M_high-M_low"""
    )
    
    m28 = M.derived_feature_extractor.v3(
        input_data=m24.data_1,
        features=m20.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m14 = M.dropnan.v1(
        input_data=m28.data
    )
    
    m21 = M.derived_feature_extractor.v3(
        input_data=m4.data_1,
        features=m20.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m7 = M.join.v3(
        data1=m2.data,
        data2=m21.data,
        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=m20.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
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m6.model,
        data=m14.data,
        m_lazy_run=False
    )
    
    m19 = M.trade.v4(
        instruments=m9.data,
        options_data=m8.predictions,
        start_date='',
        end_date='',
        handle_data=m19_handle_data_bigquant_run,
        prepare=m19_prepare_bigquant_run,
        initialize=m19_initialize_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='后复权',
        product_type='股票',
        plot_charts=True,
        backtest_only=False,
        benchmark='000300.SHA'
    )
    
    • 收益率8.06%
    • 年化收益率7.2%
    • 基准收益率-6.97%
    • 阿尔法0.14
    • 贝塔0.89
    • 夏普比率0.28
    • 胜率0.52
    • 盈亏比1.02
    • 收益波动率28.62%
    • 信息比率0.05
    • 最大回撤33.74%

    反转因子的精细结构-结合其他因子使用
    (tkyz) #2

    👍👍👍,试试看!


    (wujunjun) #3

    小Q,这些证券公司的研究报告在哪能找到啊,特别是关于量化这方面的


    (iQuant) #4

    您好,BigQuant和一些券商有着合作关系,可以获取一些内部研报,我们也会分享给大家,可以多多关注社区动态,您也可以在网上搜一下获取研报的一些终端,这里就不做推荐了哈,挺多的。


    (wujunjun) #5

    自定义模块当中的代码:
    g = df[[‘amount_0/deal_number_0’, ‘high_0/low_0’]].groupby(df[‘instrument’], as_index=False)
    这个为什么要用groupby函数呢?这个返回出来的是一个对象,压根没法查看。不用这个函数有影响么?


    (达达) #6

    这个数据处理是分成了两步,先按照股票代码分组,然后将每个分组的数据通过apply计算。
    您也可以写成一行 groupby('instrument).apply()这种形式


    (think) #7

    学好pandas,生活质量会提高


    (hugo) #8

    你好 那个pdf链接貌似打不开


    (wujunjun) #9

    之前是能打开的,现在打不开了


    (iQuant) #11

    已经可以打开了哈


    (iQuant) #12

    已经可以打开了哈


    (liu) #13

    看不懂。。。。


    (liu) #14

    请问可不可以把因子构造过程说下呢?看不懂<呜呜呜呜>


    (iQuant) #15

    您好,可以参考一下文档板块:https://bigquant.com/docs/develop/datasource/deprecated/features.html