在AI策略中对股票特征做过滤,以过滤ST股票为例

可视化
st股票
数据过滤
标签: #<Tag:0x00007fb3e06af748> #<Tag:0x00007fb3e06af608> #<Tag:0x00007fb3e06af4a0>

(iQuant) #1

很多用户提到如何剔除掉ST股票。在平台上执行此操作是很容易的且直观。如下展示了如何不需要写代码就可以剔除ST股票,同时也可以用这种方式对股票做其他过滤。

使用模板新建一个策略:策略 > 新建 > 可视化策略 - AI选股策略

  1. 抽取ST数据:为了剔除ST股票,我们需要提取ST特征值,通过文档查到相关特征是 st_status_0。如下添加模块(m15)和连线。

  2. 数据过滤:在训练数据和预测数据上添加 数据过滤模块(m16, m17) ,设置过滤条件 st_status_0==0

就这么简单。运行策略查看效果吧。

示例策略代码

克隆策略

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    In [3]:
    # 本代码由可视化策略环境自动生成 2017年10月21日 00:13
    # 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='2010-01-01',
        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
    return_20
    avg_amount_0/avg_amount_5
    avg_amount_5/avg_amount_20
    rank_avg_amount_0/rank_avg_amount_5
    rank_avg_amount_5/rank_avg_amount_10
    rank_return_0
    rank_return_5
    rank_return_10
    rank_return_0/rank_return_5
    rank_return_5/rank_return_10
    pe_ttm_0
    """
    )
    
    m15 = M.input_features.v1(
        features_ds=m3.data,
        features='st_status_0'
    )
    
    m4 = M.general_feature_extractor.v6(
        instruments=m1.data,
        features=m15.data,
        start_date='',
        end_date=''
    )
    
    m5 = M.derived_feature_extractor.v2(
        input_data=m4.data,
        features=m15.data,
        date_col='date',
        instrument_col='instrument'
    )
    
    m16 = M.filter.v3(
        input_data=m5.data,
        expr='st_status_0==0',
        output_left_data=False
    )
    
    m7 = M.join.v3(
        data1=m2.data,
        data2=m16.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=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', '2015-01-01'),
        end_date=T.live_run_param('trading_date', '2017-01-01'),
        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=''
    )
    
    m11 = M.derived_feature_extractor.v2(
        input_data=m10.data,
        features=m15.data,
        date_col='date',
        instrument_col='instrument'
    )
    
    m17 = M.filter.v3(
        input_data=m11.data,
        expr='st_status_0==0',
        output_left_data=False
    )
    
    m14 = M.dropnan.v1(
        input_data=m17.data
    )
    
    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StockRanker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StockRanker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',
        plot_charts=True,
        backtest_only=False
    )
    
    [2017-10-21 00:08:46.896142] INFO: bigquant: instruments.v2 开始运行..
    [2017-10-21 00:08:46.898791] INFO: bigquant: 命中缓存
    [2017-10-21 00:08:46.899579] INFO: bigquant: instruments.v2 运行完成[0.003456s].
    [2017-10-21 00:08:46.909981] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
    [2017-10-21 00:08:46.912098] INFO: bigquant: 命中缓存
    [2017-10-21 00:08:46.912947] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.002967s].
    [2017-10-21 00:08:46.916101] INFO: bigquant: input_features.v1 开始运行..
    [2017-10-21 00:08:46.918736] INFO: bigquant: 命中缓存
    [2017-10-21 00:08:46.919543] INFO: bigquant: input_features.v1 运行完成[0.00344s].
    [2017-10-21 00:08:46.922725] INFO: bigquant: input_features.v1 开始运行..
    [2017-10-21 00:08:46.924541] INFO: bigquant: 命中缓存
    [2017-10-21 00:08:46.925320] INFO: bigquant: input_features.v1 运行完成[0.002594s].
    [2017-10-21 00:08:46.930585] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2017-10-21 00:08:46.932496] INFO: bigquant: 命中缓存
    [2017-10-21 00:08:46.933328] INFO: bigquant: general_feature_extractor.v6 运行完成[0.00274s].
    [2017-10-21 00:08:46.938882] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2017-10-21 00:08:46.940877] INFO: bigquant: 命中缓存
    [2017-10-21 00:08:46.941711] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.002826s].
    [2017-10-21 00:08:46.946814] INFO: bigquant: filter.v3 开始运行..
    [2017-10-21 00:08:46.948692] INFO: bigquant: 命中缓存
    [2017-10-21 00:08:46.949414] INFO: bigquant: filter.v3 运行完成[0.002598s].
    [2017-10-21 00:08:46.957828] INFO: bigquant: join.v3 开始运行..
    [2017-10-21 00:08:55.179831] INFO: join: /y_2010, 行数=401478/401771, 耗时=4.93799s
    [2017-10-21 00:09:01.997586] INFO: join: /y_2011, 行数=481982/482231, 耗时=6.80545s
    [2017-10-21 00:09:05.890332] INFO: join: /y_2012, 行数=540654/541178, 耗时=3.877193s
    [2017-10-21 00:09:09.192708] INFO: join: /y_2013, 行数=550031/550859, 耗时=3.28128s
    [2017-10-21 00:09:12.422191] INFO: join: /y_2014, 行数=546785/561221, 耗时=3.210005s
    [2017-10-21 00:09:12.531316] INFO: join: 最终行数: 2520930
    [2017-10-21 00:09:12.533160] INFO: bigquant: join.v3 运行完成[25.575322s].
    [2017-10-21 00:09:12.541507] INFO: bigquant: dropnan.v1 开始运行..
    [2017-10-21 00:09:13.136183] INFO: dropnan: /y_2010, 394189/401478
    [2017-10-21 00:09:13.738506] INFO: dropnan: /y_2011, 475786/481982
    [2017-10-21 00:09:14.434177] INFO: dropnan: /y_2012, 537181/540654
    [2017-10-21 00:09:15.177418] INFO: dropnan: /y_2013, 550001/550031
    [2017-10-21 00:09:15.867179] INFO: dropnan: /y_2014, 545002/546785
    [2017-10-21 00:09:15.899358] INFO: dropnan: 行数: 2502159/2520930
    [2017-10-21 00:09:15.918645] INFO: bigquant: dropnan.v1 运行完成[3.377088s].
    [2017-10-21 00:09:15.933248] INFO: bigquant: stock_ranker_train.v5 开始运行..
    [2017-10-21 00:09:20.282839] INFO: df2bin: prepare bins ..
    [2017-10-21 00:09:23.352237] INFO: df2bin: prepare data: training ..
    [2017-10-21 00:09:29.825082] INFO: df2bin: sort ..
    [2017-10-21 00:10:00.033130] INFO: stock_ranker_train: 05363ba2 准备训练: 2502159 行数
    [2017-10-21 00:11:46.836468] INFO: bigquant: stock_ranker_train.v5 运行完成[150.903062s].
    [2017-10-21 00:11:46.842046] INFO: bigquant: instruments.v2 开始运行..
    [2017-10-21 00:11:46.844575] INFO: bigquant: 命中缓存
    [2017-10-21 00:11:46.845421] INFO: bigquant: instruments.v2 运行完成[0.003381s].
    [2017-10-21 00:11:46.851834] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2017-10-21 00:12:00.130559] INFO: 基础特征抽取: 年份 2015, 特征行数=569698
    [2017-10-21 00:12:14.408736] INFO: 基础特征抽取: 年份 2016, 特征行数=641546
    [2017-10-21 00:12:21.501530] INFO: 基础特征抽取: 年份 2017, 特征行数=0
    [2017-10-21 00:12:21.539364] INFO: 基础特征抽取: 总行数: 1211244
    [2017-10-21 00:12:21.541712] INFO: bigquant: general_feature_extractor.v6 运行完成[34.689856s].
    [2017-10-21 00:12:21.549316] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2017-10-21 00:12:22.604585] INFO: derived_feature_extractor: 提取完成 avg_amount_0/avg_amount_5, 0.004s
    [2017-10-21 00:12:22.608600] INFO: derived_feature_extractor: 提取完成 avg_amount_5/avg_amount_20, 0.003s
    [2017-10-21 00:12:22.613167] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_5, 0.003s
    [2017-10-21 00:12:22.616783] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_5/rank_avg_amount_10, 0.003s
    [2017-10-21 00:12:22.620190] INFO: derived_feature_extractor: 提取完成 rank_return_0/rank_return_5, 0.002s
    [2017-10-21 00:12:22.623654] INFO: derived_feature_extractor: 提取完成 rank_return_5/rank_return_10, 0.003s
    [2017-10-21 00:12:23.957521] INFO: derived_feature_extractor: /y_2015, 569698
    [2017-10-21 00:12:25.167801] INFO: derived_feature_extractor: /y_2016, 641546
    [2017-10-21 00:12:27.064141] INFO: bigquant: derived_feature_extractor.v2 运行完成[5.514794s].
    [2017-10-21 00:12:27.070533] INFO: bigquant: filter.v3 开始运行..
    [2017-10-21 00:12:27.074305] INFO: filter: 使用表达式 st_status_0==0 过滤
    [2017-10-21 00:12:28.000592] INFO: filter: 过滤 /y_2015, 561179/569698
    [2017-10-21 00:12:29.102841] INFO: filter: 过滤 /y_2016, 630635/641546
    [2017-10-21 00:12:29.137459] INFO: bigquant: filter.v3 运行完成[2.066902s].
    [2017-10-21 00:12:29.143549] INFO: bigquant: dropnan.v1 开始运行..
    [2017-10-21 00:12:30.341900] INFO: dropnan: /y_2015, 556627/561179
    [2017-10-21 00:12:32.162949] INFO: dropnan: /y_2016, 626001/630635
    [2017-10-21 00:12:32.181629] INFO: dropnan: 行数: 1182628/1191814
    [2017-10-21 00:12:32.208288] INFO: bigquant: dropnan.v1 运行完成[3.0647s].
    [2017-10-21 00:12:32.220527] INFO: bigquant: stock_ranker_predict.v5 开始运行..
    [2017-10-21 00:12:34.548268] INFO: df2bin: prepare data: prediction ..
    [2017-10-21 00:12:55.990003] INFO: stock_ranker_predict: 准备预测: 1182628 行
    [2017-10-21 00:13:04.556810] INFO: bigquant: stock_ranker_predict.v5 运行完成[32.336271s].
    [2017-10-21 00:13:04.611354] INFO: bigquant: backtest.v7 开始运行..
    [2017-10-21 00:13:37.475245] INFO: Performance: Simulated 488 trading days out of 488.
    [2017-10-21 00:13:37.476509] INFO: Performance: first open: 2015-01-05 14:30:00+00:00
    [2017-10-21 00:13:37.477434] INFO: Performance: last close: 2016-12-30 20:00:00+00:00
    
    • 收益率218.52%
    • 年化收益率81.89%
    • 基准收益率-6.33%
    • 阿尔法0.85
    • 贝塔1.01
    • 夏普比率1.75
    • 收益波动率44.94%
    • 信息比率2.72
    • 最大回撤44.84%
    [2017-10-21 00:13:39.603561] INFO: bigquant: backtest.v7 运行完成[34.992177s].
    

    拜托大神贴一个在回测主函数里添加选股--例如包含简单的交易策略,买入卖出点,非ST等--的例子吧。
    如何在可视化策略生成器中过滤ST股
    stockranker 模型组合的条件表达式
    关于证券列表
    策略研究常用功能
    如何避免选到 退字股? 过滤ST股票对其不起作用
    如何过滤60天内没有涨停的股票
    BigQuant平台高效使用指南
    请问如何在策略中过滤创业版?
    (tifariti) #2

    把特征st_status_0直接加到m3模块中的pe_ttm_0后面不行吗?@iQuant


    (小Q) #3

    可以的,直接加入即可,而且不需要逗号、顿号做分割,如下:


    (luckychan) #4

    请问为什么将上面M3,M16,M17的st_status_0更换成list_board_0,list_board_0==3(目的过滤创业板股票),但出现下面的错误:
    UnboundLocalError Traceback (most recent call last)
    in ()
    100 max_bins=1023,
    101 feature_fraction=1,
    –> 102 m_lazy_run=False
    103 )
    104

    UnboundLocalError: local variable ‘curr_cuts’ referenced before assignment

    请指教。


    (iQuant) #6

    如果想要过滤掉创业板的股票,通过 数据过滤模块 的list_board_0 ==3没有问题啊,您再试试! 如果还遇到问题,建议您直接把代码分享到社区,这样方便debug.


    (siyishenqing) #7

    我据此写了一个过滤总市值大于30.8亿的策略,可是最终的每日持仓中依然有像贵州茅台这样的大盘股,请问问题出在哪?代码如下
    m15 = M.input_features.v1(
    features_ds=m3.data,
    features=‘market_cap_0’
    )

    m4 = M.general_feature_extractor.v6(
    instruments=m1.data,
    features=m15.data,
    start_date=’’,
    end_date=’’
    )

    m5 = M.derived_feature_extractor.v2(
    input_data=m4.data,
    features=m15.data,
    date_col=‘date’,
    instrument_col=‘instrument’
    )

    m16 = M.filter.v3(
    input_data=m5.data,
    expr=‘market_cap_0>3080000000’,
    output_left_data=False
    )
    运行日志中也显示已经使用了过滤器,只是结果有问题,还请告知一下如果要最终的策略里去掉相关的股票如何实现(好像去掉st股,最终的策略每日持仓中也会存在st股?)


    (达达) #8

    感觉这个问题可能要看一下在最开始的因子列表中是否包含了市值这项,因为程序的逻辑是按照因子抽取得到对应的列数据形成dataframe,过滤器是将最后dataframe按你选的条件过滤,如果dataframe没有这个列那可能是没法执行的 您可以试试


    (达达) #9

    如果您希望最后输入stockranker算法训练的因子中不包含这个多余的市值因子,那您可以在因子列表和stockranker之间的连线中加一个自定义模块,因子列表模块的数据您可以执行m3.data.read_pickle()查看 其实就是个列表 自定义模块中只要加入一个删除语句把这个市值因子去掉后再传给stockranker应该就可以了


    (sensezeng) #10

    我有一个疑问。
    如果直接使用“证券代码列表”初步筛选股票,如去除ST,去除亏损等。这样就不用把ST属性,净利润等因子加入特征列表。而且提升效率。为什么不这样设计?


    (达达) #11

    您的想法很对,对于简单的过滤而言是等效的,简单的可以直接在证券代码列表后使用。最后的过滤其实是方便复杂衍生因子和自定义因子过滤用的,而且如果ST和净利润这些因子加入特征列表仅仅是为了过滤的话其实是给模型训练引入了多余的训练因子,还需要剔除掉这些专门用来做数据过滤的因子才能保证训练是有效的。比如行业市值过滤时,行业属性和市值都是辅助计算的变量,我们只希望用来清洗数据而不希望它们作为特征因子输入到模型中。可以参看:
    https://community.bigquant.com/t/AI可视化模板的细化/8617/1


    (tankle) #12

    我也遇到了同样的问题,请问解决了嘛


    (luckychan) #13

    应该写成这样才对吧:
    expr=‘market_cap_0<3080000000’,滤出结果为小于30.8亿市值的股票。


    (shenhaiyangthu) #14

    我试了下 发现报错:

    During handling of the above exception, another exception occurred:

    UndefinedVariableError Traceback (most recent call last)
    UndefinedVariableError: name ‘st_status’ is not defined

    用st_status_0 也不行


    (iQuant) #15

    收到您的提问,已提交给策略工程师,会尽快为您回复。


    (达达) #16

    st_status_0您加入因子了么?需要有这个因子才能过滤的


    (shenhaiyangthu) #17

    在特征列表里加上?


    (达达) #18

    您看看例子,有两个特征列表的,连在一起 第二个特征列表里填的st_status_0
    image


    (shenhaiyangthu) #19

    的确 把上面的给漏了 非常感谢!