反转因子的精细结构-结合其他因子使用

策略分享
反转因子
研报复现
因子组合
标签: #<Tag:0x00007fb3e71e70b0> #<Tag:0x00007fb3e71e6ef8> #<Tag:0x00007fb3e71e6c50> #<Tag:0x00007fb3e71e69f8>

(iQuant) #1

反转因子的精细结构-研报复现 中,我们实现了反转因子,并结合StockRanker实现了一个简单的策略。本文展示如何再结合更多因子,一起训练和构建策略。

  • m11:输入我们要引入的其他因子
  • m5:抽取基础因子
  • m11:抽取衍生因子
  • m12:合并反转因子数据和其他因子数据

查看模型特征权重

帮助我们更好的理解特征贡献度。数值越大的,表示因子/特征越重要。从这里可以看到,反转因子似乎并没有研报现实的重要。

image

完整策略

代码如下,仅供参考,未做任何效果优化,欢迎改进实验

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 2019年3月5日 00:22
    # 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'
    )
    
    m11 = M.input_features.v1(
        features='return_5'
    )
    
    m5 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m11.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m10 = M.derived_feature_extractor.v3(
        input_data=m5.data,
        features=m11.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m12 = M.join.v3(
        data1=m4.data_1,
        data2=m10.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m25 = M.general_feature_extractor.v7(
        instruments=m9.data,
        features=m11.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m26 = M.derived_feature_extractor.v3(
        input_data=m25.data,
        features=m11.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m27 = M.join.v3(
        data1=m24.data_1,
        data2=m26.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m20 = M.input_features.v1(
        features_ds=m11.data,
        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=m27.data,
        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=m12.data,
        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-6.47%
    • 年化收益率-5.82%
    • 基准收益率-6.97%
    • 阿尔法0.0
    • 贝塔0.79
    • 夏普比率-0.2
    • 胜率0.5
    • 盈亏比1.02
    • 收益波动率27.05%
    • 信息比率0.01
    • 最大回撤35.36%