为什么中性化处理后依然小市值暴露这么高

新手专区
标签: #<Tag:0x00007fc828e6ebd0>

(naclo) #1

请教大家一个问题,为什么我按照社区的帖子,做了市值中性化处理后,小市值因子的暴露依然很大,几乎没有实质性的下降?感谢!

克隆策略

    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    In [5]:
    # 本代码由可视化策略环境自动生成 2020年1月9日 13:54
    # 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 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 = 2
        # 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, ..]
        #[0.3,0.3,0.3]#
        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 = 0.8
        context.options['hold_days'] = 3
    
    # 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):
        #获取当日日期
        today = data.current_dt.strftime('%Y-%m-%d')
        stock_hold_now = [equity.symbol for equity in context.portfolio.positions ]
        #大盘风控模块,读取风控数据    
        benckmark_risk=context.benckmark_risk[today]
        context.symbol
        #当risk为1时,市场有风险,全部平仓,不再执行其它操作
        if benckmark_risk > 0:
            for instrument in stock_hold_now:
                context.order_target(symbol(instrument), 0)
            print(today,'大盘风控止损触发,全仓卖出')
            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')]
    
        # 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):
        #在数据准备函数中一次性计算每日的大盘风控条件相比于在handle中每日计算风控条件可以提高回测速度
        # 多取50天的数据便于计算均值(保证回测的第一天均值不为Nan值),其中context.start_date和context.end_date是回测指定的起始时间和终止时间
        start_date= (pd.to_datetime(context.start_date) - datetime.timedelta(days=50)).strftime('%Y-%m-%d') 
        df=DataSource('bar1d_index_CN_STOCK_A').read(start_date=start_date,end_date=context.end_date,fields=['close'])
    
        #这里以上证指数000001.HIX为例
        benckmark_data=df[df.instrument=='000001.HIX']
        #计算上证指数5日涨幅
        benckmark_data['ret5']=benckmark_data['close']/benckmark_data['close'].shift(5)-1
        #计算大盘风控条件,如果5日涨幅小于-4%则设置风险状态risk为1,否则为0
        benckmark_data['risk'] = np.where(benckmark_data['ret5']<-0.04,1,0)
        #修改日期格式为字符串(便于在handle中使用字符串日期索引来查看每日的风险状态)
        benckmark_data['date']=benckmark_data['date'].apply(lambda x:x.strftime('%Y-%m-%d'))
        #设置日期为索引
        benckmark_data.set_index('date',inplace=True)
        #把风控序列输出给全局变量context.benckmark_risk
        context.benckmark_risk=benckmark_data['risk']
    
    m1 = M.instruments.v2(
        start_date='2010-01-01',
        end_date='2017-01-01',
        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/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, -3) / 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
    
    rank(pb_lf_0)
    rank(std(volume_0,5))/rank(std(volume_0,28))
    rank_beta_csi500_5_0
    rank(turn_5)
    rank_beta_industry_5_0
    correlation(close_0,log1p(volume_0),5)
    rank(return_120)
    rank(return_10/std(close_0,10))
    rank(ta_macd_macd_12_26_9_0)
    rank(mean(high_0/low_0-1,5))/rank(mean(high_0/low_0-1,28))"""
    )
    
    m10 = M.input_features.v1(
        features_ds=m3.data,
        features="""
    # #号开始的表示注释,注释需单独一行
    # 多个特征,每行一个,可以包含基础特征和衍生特征,特征须为本平台特征
    industry_sw_level1_0
    market_cap_float_0"""
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m10.data,
        start_date='',
        end_date=''
    )
    
    m16 = M.derived_feature_extractor.v3(
        input_data=m15.data,
        features=m10.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=True,
        remove_extra_columns=False
    )
    
    m12 = M.neutralize.v13(
        input_1=m16.data,
        input_2=m3.data,
        market_value_key=True,
        industry_output_key=True,
        market_col_name='market_cap_float_0',
        industry_sw_col_name='industry_sw_level1_0',
        columns_input=''
    )
    
    m7 = M.join.v3(
        data1=m2.data,
        data2=m12.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m8 = M.dropnan.v1(
        input_data=m7.data
    )
    
    m13 = M.filter_stockcode.v2(
        input_1=m8.data,
        start='688'
    )
    
    m4 = M.chinaa_stock_filter.v1(
        input_data=m13.data_1,
        index_constituent_cond=['全部'],
        board_cond=['全部'],
        industry_cond=['全部'],
        st_cond=['正常'],
        output_left_data=False
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2017-01-01'),
        end_date=T.live_run_param('trading_date', '2017-12-31'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m17 = M.general_feature_extractor.v7(
        instruments=m9.data,
        features=m10.data,
        start_date='',
        end_date='',
        before_start_days=400
    )
    
    m18 = M.derived_feature_extractor.v3(
        input_data=m17.data,
        features=m10.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=True,
        remove_extra_columns=False
    )
    
    m20 = M.neutralize.v13(
        input_1=m18.data,
        input_2=m3.data,
        market_value_key=True,
        industry_output_key=True,
        market_col_name='market_cap_float_0',
        industry_sw_col_name='industry_sw_level1_0',
        columns_input=''
    )
    
    m11 = M.dropnan.v1(
        input_data=m20.data
    )
    
    m14 = M.filter_stockcode.v2(
        input_1=m11.data,
        start='688'
    )
    
    m5 = M.chinaa_stock_filter.v1(
        input_data=m14.data_1,
        index_constituent_cond=['全部'],
        board_cond=['全部'],
        industry_cond=['全部'],
        st_cond=['正常'],
        output_left_data=False
    )
    
    m6 = M.stock_ranker.v2(
        training_ds=m4.data,
        features=m3.data,
        predict_ds=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,
        slim_data=True
    )
    
    m19 = M.trade.v4(
        instruments=m9.data,
        options_data=m6.predictions,
        start_date='',
        end_date='',
        initialize=m19_initialize_bigquant_run,
        handle_data=m19_handle_data_bigquant_run,
        prepare=m19_prepare_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='000300.SHA'
    )
    
    设置测试数据集,查看训练迭代过程的NDCG
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-f069609148d846de8b617e447da1a4a2"}/bigcharts-data-end
    • 收益率-13.62%
    • 年化收益率-14.03%
    • 基准收益率21.78%
    • 阿尔法-0.25
    • 贝塔0.55
    • 夏普比率-0.66
    • 胜率0.52
    • 盈亏比0.91
    • 收益波动率23.21%
    • 信息比率-0.09
    • 最大回撤31.06%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-9f29810988654018bcdbe8d42e1abef2"}/bigcharts-data-end
    In [6]:
    m19.pyfolio_full_tear_sheet()
    
    Entire data start date: 2017-01-03
    Entire data end date: 2017-12-29
    
    
    Backtest Months: 11
    
    Performance statistics Backtest
    cum_returns_final -0.14
    annual_return -0.14
    annual_volatility 0.23
    sharpe_ratio -0.53
    calmar_ratio -0.45
    stability_of_timeseries 0.39
    max_drawdown -0.31
    omega_ratio 0.91
    sortino_ratio -0.67
    skew -1.08
    kurtosis 3.04
    tail_ratio 0.86
    common_sense_ratio 0.74
    information_ratio -0.09
    alpha -0.22
    beta 0.46
    Worst Drawdown Periods net drawdown in % peak date valley date recovery date duration
    0 31.06 2017-01-04 2017-07-17 NaT NaN
    1 0.00 2017-01-03 2017-01-03 2017-01-03 1
    2 0.00 2017-01-03 2017-01-03 2017-01-03 1
    3 0.00 2017-01-03 2017-01-03 2017-01-03 1
    4 0.00 2017-01-03 2017-01-03 2017-01-03 1
    [-0.03  -0.066]
    
    <Figure size 1400x7200 with 12 Axes>
    Top 10 long positions of all time max
    Equity(2122 [300475.SZA]) 47.55%
    Equity(2477 [600071.SHA]) 43.06%
    Equity(2751 [002412.SZA]) 33.43%
    Equity(1358 [603958.SHA]) 33.16%
    Equity(1229 [600070.SHA]) 31.93%
    Equity(51 [300462.SZA]) 31.81%
    Equity(1923 [002845.SZA]) 31.67%
    Equity(1795 [300505.SZA]) 31.65%
    Equity(585 [300108.SZA]) 31.63%
    Equity(1806 [002164.SZA]) 31.57%
    <Figure size 1400x3000 with 5 Axes>
    <Figure size 1400x1800 with 3 Axes>
    In [7]:
    m19.risk_analyze()
    

    (达达) #2

    因子市值中性化处理后,不代表训练的模型选股没有市值偏向性。如果你市值中性化之后的因子线性分组,你可以发现每组里有大市值股票也有小市值股票。但是非线性模型可能会有偏向性的暴露。