股票代码过滤-支持股票类别、上市板、行业和ST过滤

股票代码过滤
标签: #<Tag:0x00007f20cc0969a8>

(iQuant) #1

在策略开发过程中,经常需要对股票代码做过滤。A股股票过滤 提供了常用对股票过滤能力。

模块功能

A股股票过滤 支持股票类别、上市板、行业和ST过滤。

模块原理

此模块对输入数据需要date和instrument列。

对于给定对数据,模块会读取对应时间段的股票类别、上市板、行业和ST状态数据(按需读取,只在读取过滤用到的数据,以达到最好的性能),并用过滤条件过滤给定数据集上的股票。

输出为满足条件的股票。如果需要同时输出不满足条件的股票,可以勾选 输出剩余数据,并在第一个输出端获得此数据。

如何使用

  1. 新建策略:通过模版新建一个 可视化AI策略
  2. 平台现在支持非常多的模块,我们可以使用搜索功能。在左侧模块链接搜索关键词 过滤 找到模块 A股股票过滤
    image
  3. 如下图,在训练数据和预测数据的 基础特征抽取 后分布添加 A股股票过滤
  4. 配置 A股股票过滤,如上图,这里我们只过滤到ST股票,在ST配置里,去掉 全部,只选择 正常

示例代码

克隆策略

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    In [2]:
    # 本代码由可视化策略环境自动生成 2019年1月9日 17:29
    # 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: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.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 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='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.general_feature_extractor.v7(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m20 = M.chinaa_stock_filter.v1(
        input_data=m15.data,
        index_constituent_cond=['全部'],
        board_cond=['全部'],
        industry_cond=['全部'],
        st_cond=['正常'],
        output_left_data=False
    )
    
    m16 = M.derived_feature_extractor.v3(
        input_data=m20.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=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
    )
    
    m17 = M.general_feature_extractor.v7(
        instruments=m9.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m21 = M.chinaa_stock_filter.v1(
        input_data=m17.data,
        index_constituent_cond=['全部'],
        board_cond=['全部'],
        industry_cond=['全部'],
        st_cond=['正常'],
        output_left_data=False
    )
    
    m18 = M.derived_feature_extractor.v3(
        input_data=m21.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m14 = M.dropnan.v1(
        input_data=m18.data
    )
    
    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'
    )
    
    [2019-01-09 17:16:09.652099] INFO: bigquant: instruments.v2 开始运行..
    [2019-01-09 17:16:09.656827] INFO: bigquant: 命中缓存
    [2019-01-09 17:16:09.657738] INFO: bigquant: instruments.v2 运行完成[0.005671s].
    [2019-01-09 17:16:09.661869] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
    [2019-01-09 17:16:09.666961] INFO: bigquant: 命中缓存
    [2019-01-09 17:16:09.668113] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.00626s].
    [2019-01-09 17:16:09.670218] INFO: bigquant: input_features.v1 开始运行..
    [2019-01-09 17:16:09.674236] INFO: bigquant: 命中缓存
    [2019-01-09 17:16:09.675031] INFO: bigquant: input_features.v1 运行完成[0.004841s].
    [2019-01-09 17:16:09.682499] INFO: bigquant: general_feature_extractor.v7 开始运行..
    [2019-01-09 17:16:09.687098] INFO: bigquant: 命中缓存
    [2019-01-09 17:16:09.688091] INFO: bigquant: general_feature_extractor.v7 运行完成[0.005602s].
    [2019-01-09 17:16:09.690644] INFO: bigquant: chinaa_stock_filter.v1 开始运行..
    [2019-01-09 17:16:11.538926] INFO: A股股票过滤: 过滤 /y_2010, 401771/0/431567
    [2019-01-09 17:16:14.056753] INFO: A股股票过滤: 过滤 /y_2011, 482231/0/511455
    [2019-01-09 17:16:20.544777] INFO: A股股票过滤: 过滤 /y_2012, 541178/0/565675
    [2019-01-09 17:16:28.513650] INFO: A股股票过滤: 过滤 /y_2013, 550859/0/564168
    [2019-01-09 17:16:33.701876] INFO: A股股票过滤: 过滤 /y_2014, 561221/0/569948
    [2019-01-09 17:16:33.704846] INFO: A股股票过滤: 过滤完成, 2537260 + 0
    [2019-01-09 17:16:33.720200] INFO: bigquant: chinaa_stock_filter.v1 运行完成[24.029502s].
    [2019-01-09 17:16:33.724668] INFO: bigquant: derived_feature_extractor.v3 开始运行..
    [2019-01-09 17:16:35.082934] INFO: derived_feature_extractor: 提取完成 avg_amount_0/avg_amount_5, 0.007s
    [2019-01-09 17:16:35.090694] INFO: derived_feature_extractor: 提取完成 avg_amount_5/avg_amount_20, 0.006s
    [2019-01-09 17:16:35.098248] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_5, 0.006s
    [2019-01-09 17:16:35.107277] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_5/rank_avg_amount_10, 0.008s
    [2019-01-09 17:16:35.113661] INFO: derived_feature_extractor: 提取完成 rank_return_0/rank_return_5, 0.005s
    [2019-01-09 17:16:35.120753] INFO: derived_feature_extractor: 提取完成 rank_return_5/rank_return_10, 0.006s
    [2019-01-09 17:16:36.049721] INFO: derived_feature_extractor: /y_2010, 401771
    [2019-01-09 17:16:36.538701] INFO: derived_feature_extractor: /y_2011, 482231
    [2019-01-09 17:16:37.167070] INFO: derived_feature_extractor: /y_2012, 541178
    [2019-01-09 17:16:37.816764] INFO: derived_feature_extractor: /y_2013, 550859
    [2019-01-09 17:16:38.397026] INFO: derived_feature_extractor: /y_2014, 561221
    [2019-01-09 17:16:39.089729] INFO: bigquant: derived_feature_extractor.v3 运行完成[5.365008s].
    [2019-01-09 17:16:39.116519] INFO: bigquant: join.v3 开始运行..
    [2019-01-09 17:16:42.574727] INFO: join: /y_2010, 行数=401478/401771, 耗时=1.796538s
    [2019-01-09 17:16:44.521961] INFO: join: /y_2011, 行数=481982/482231, 耗时=1.931225s
    [2019-01-09 17:16:46.563948] INFO: join: /y_2012, 行数=540654/541178, 耗时=2.022222s
    [2019-01-09 17:16:48.797575] INFO: join: /y_2013, 行数=550031/550859, 耗时=2.210053s
    [2019-01-09 17:16:50.879912] INFO: join: /y_2014, 行数=546785/561221, 耗时=2.056064s
    [2019-01-09 17:16:51.030295] INFO: join: 最终行数: 2520930
    [2019-01-09 17:16:51.032371] INFO: bigquant: join.v3 运行完成[11.915875s].
    [2019-01-09 17:16:51.035816] INFO: bigquant: dropnan.v1 开始运行..
    [2019-01-09 17:16:51.619037] INFO: dropnan: /y_2010, 394189/401478
    [2019-01-09 17:16:52.234827] INFO: dropnan: /y_2011, 475786/481982
    [2019-01-09 17:16:52.951183] INFO: dropnan: /y_2012, 537181/540654
    [2019-01-09 17:16:53.729616] INFO: dropnan: /y_2013, 550001/550031
    [2019-01-09 17:16:54.620753] INFO: dropnan: /y_2014, 545002/546785
    [2019-01-09 17:16:54.678118] INFO: dropnan: 行数: 2502159/2520930
    [2019-01-09 17:16:54.703499] INFO: bigquant: dropnan.v1 运行完成[3.667666s].
    [2019-01-09 17:16:54.708614] INFO: bigquant: stock_ranker_train.v5 开始运行..
    [2019-01-09 17:16:57.400607] INFO: StockRanker: 特征预处理 ..
    [2019-01-09 17:17:01.151637] INFO: StockRanker: prepare data: training ..
    [2019-01-09 17:17:06.445527] INFO: StockRanker: sort ..
    [2019-01-09 17:17:40.782249] INFO: StockRanker训练: 4e8776ca 准备训练: 2502159 行数
    [2019-01-09 17:17:40.905835] INFO: StockRanker训练: 正在训练 ..
    [2019-01-09 17:20:43.246057] INFO: bigquant: stock_ranker_train.v5 运行完成[228.537412s].
    [2019-01-09 17:20:43.249221] INFO: bigquant: instruments.v2 开始运行..
    [2019-01-09 17:20:43.255058] INFO: bigquant: 命中缓存
    [2019-01-09 17:20:43.256564] INFO: bigquant: instruments.v2 运行完成[0.007347s].
    [2019-01-09 17:20:43.263466] INFO: bigquant: general_feature_extractor.v7 开始运行..
    [2019-01-09 17:20:43.269724] INFO: bigquant: 命中缓存
    [2019-01-09 17:20:43.270668] INFO: bigquant: general_feature_extractor.v7 运行完成[0.007202s].
    [2019-01-09 17:20:43.273655] INFO: bigquant: chinaa_stock_filter.v1 开始运行..
    [2019-01-09 17:20:43.279034] INFO: bigquant: 命中缓存
    [2019-01-09 17:20:43.280150] INFO: bigquant: chinaa_stock_filter.v1 运行完成[0.006474s].
    [2019-01-09 17:20:43.283073] INFO: bigquant: derived_feature_extractor.v3 开始运行..
    [2019-01-09 17:20:44.404295] INFO: derived_feature_extractor: 提取完成 avg_amount_0/avg_amount_5, 0.004s
    [2019-01-09 17:20:44.409530] INFO: derived_feature_extractor: 提取完成 avg_amount_5/avg_amount_20, 0.004s
    [2019-01-09 17:20:44.418690] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_5, 0.008s
    [2019-01-09 17:20:44.423586] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_5/rank_avg_amount_10, 0.004s
    [2019-01-09 17:20:44.428446] INFO: derived_feature_extractor: 提取完成 rank_return_0/rank_return_5, 0.003s
    [2019-01-09 17:20:44.432838] INFO: derived_feature_extractor: 提取完成 rank_return_5/rank_return_10, 0.003s
    [2019-01-09 17:20:44.904857] INFO: derived_feature_extractor: /y_2015, 561179
    [2019-01-09 17:20:45.530804] INFO: derived_feature_extractor: /y_2016, 630635
    [2019-01-09 17:20:46.169600] INFO: bigquant: derived_feature_extractor.v3 运行完成[2.886495s].
    [2019-01-09 17:20:46.172962] INFO: bigquant: dropnan.v1 开始运行..
    [2019-01-09 17:20:46.890694] INFO: dropnan: /y_2015, 556627/561179
    [2019-01-09 17:20:47.756763] INFO: dropnan: /y_2016, 626001/630635
    [2019-01-09 17:20:47.778141] INFO: dropnan: 行数: 1182628/1191814
    [2019-01-09 17:20:47.811145] INFO: bigquant: dropnan.v1 运行完成[1.638139s].
    [2019-01-09 17:20:47.815902] INFO: bigquant: stock_ranker_predict.v5 开始运行..
    [2019-01-09 17:20:48.830383] INFO: StockRanker: prepare data: prediction ..
    [2019-01-09 17:21:05.650062] INFO: stock_ranker_predict: 准备预测: 1182628 行
    [2019-01-09 17:21:05.652579] INFO: stock_ranker_predict: 正在预测 ..
    [2019-01-09 17:21:36.248859] INFO: bigquant: stock_ranker_predict.v5 运行完成[48.432916s].
    [2019-01-09 17:21:36.370868] INFO: bigquant: backtest.v8 开始运行..
    [2019-01-09 17:21:36.380073] INFO: bigquant: biglearning backtest:V8.1.6
    [2019-01-09 17:21:36.382294] INFO: bigquant: product_type:stock by specified
    [2019-01-09 17:22:02.096610] INFO: bigquant: 读取股票行情完成:1990277
    [2019-01-09 17:22:27.430477] INFO: algo: TradingAlgorithm V1.4.2
    [2019-01-09 17:22:37.942247] INFO: algo: trading transform...
    [2019-01-09 17:22:49.786020] INFO: Performance: Simulated 488 trading days out of 488.
    [2019-01-09 17:22:49.787049] INFO: Performance: first open: 2015-01-05 09:30:00+00:00
    [2019-01-09 17:22:49.788028] INFO: Performance: last close: 2016-12-30 15:00:00+00:00
    
    • 收益率230.87%
    • 年化收益率85.5%
    • 基准收益率-6.33%
    • 阿尔法0.7
    • 贝塔1.01
    • 夏普比率1.53
    • 胜率0.61
    • 盈亏比0.89
    • 收益波动率45.24%
    • 信息比率0.14
    • 最大回撤44.62%
    [2019-01-09 17:22:53.006520] INFO: bigquant: backtest.v8 运行完成[76.635628s].
    

    我如何在策略中过滤掉ST股票呢???
    我如何在策略中过滤掉ST股票呢???