如何过滤60天内没有涨停的股票

新手专区
标签: #<Tag:0x00007f7286386508>

(www232000) #1

没有涨停的股票是没有活力的,请大神帮忙, 如何在可视化策略里过滤60天内没有涨停的股票


(luckychan) #2

那过滤出60天内有涨停的股票同理,可以参考这个在AI策略中对股票特征做过滤,以过滤ST股票为例


(www232000) #3

谢谢回复,我是新手,还是不知道60天内没有涨停的表达式


(iQuant) #4

https://community.bigquant.com/t/282个因子快速定义的秘密:表达式引擎/1936
可以参考此文,您要的因子可以表达为where(ts_max(price_limit_status_0,2)>60,1,0)


(www232000) #5

非常感谢


(iQuant) #6

给您一个例子参考

克隆策略

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    In [1]:
    # 本代码由可视化策略环境自动生成 2018年5月3日 09:48
    # 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='2014-01-01',
        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
    """
    )
    
    m15 = M.input_features.v1(
        features_ds=m3.data,
        features='where(ts_max(price_limit_status_0,60)>2,1,0)'
    )
    
    m4 = M.general_feature_extractor.v6(
        instruments=m1.data,
        features=m15.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m5 = M.derived_feature_extractor.v2(
        input_data=m4.data,
        features=m15.data,
        date_col='date',
        instrument_col='instrument',
        m_cached=False
    )
    
    # Python 代码入口函数,input_1/2/3 对应三个输入端,data_1/2/3 对应三个输出端
    def m16_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        df = input_1.read_df()
        df.rename(columns={'where(ts_max(price_limit_status_0,60)>2,1,0)':'self_func'}, inplace = True)
        data_1 = DataSource.write_df(df)
        return Outputs(data_1=data_1, data_2=None, data_3=None)
    
    m16 = M.cached.v3(
        input_1=m5.data,
        run=m16_run_bigquant_run
    )
    
    m17 = M.filter.v3(
        input_data=m16.data_1,
        expr='self_func>0',
        output_left_data=False
    )
    
    m7 = M.join.v3(
        data1=m2.data,
        data2=m17.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='',
        before_start_days=0
    )
    
    m11 = M.derived_feature_extractor.v2(
        input_data=m10.data,
        features=m15.data,
        date_col='date',
        instrument_col='instrument'
    )
    
    # Python 代码入口函数,input_1/2/3 对应三个输入端,data_1/2/3 对应三个输出端
    def m19_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        df = input_1.read_df()
        df.rename(columns={'where(ts_max(price_limit_status_0,60)>2,1,0)':'self_func'}, inplace = True)
        data_1 = DataSource.write_df(df)
        return Outputs(data_1=data_1, data_2=None, data_3=None)
    
    m19 = M.cached.v3(
        input_1=m11.data,
        run=m19_run_bigquant_run
    )
    
    m18 = M.filter.v3(
        input_data=m19.data_1,
        expr='self_func>0',
        output_left_data=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
    )
    
    # 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',
        price_type='后复权',
        plot_charts=True,
        backtest_only=False,
        amount_integer=False
    )
    
    [2018-05-03 09:46:11.453021] INFO: bigquant: instruments.v2 开始运行..
    [2018-05-03 09:46:11.470213] INFO: bigquant: 命中缓存
    [2018-05-03 09:46:11.471547] INFO: bigquant: instruments.v2 运行完成[0.018551s].
    [2018-05-03 09:46:11.540951] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
    [2018-05-03 09:46:11.548286] INFO: bigquant: 命中缓存
    [2018-05-03 09:46:11.549563] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.008633s].
    [2018-05-03 09:46:11.558899] INFO: bigquant: input_features.v1 开始运行..
    [2018-05-03 09:46:11.625605] INFO: bigquant: 命中缓存
    [2018-05-03 09:46:11.626658] INFO: bigquant: input_features.v1 运行完成[0.06778s].
    [2018-05-03 09:46:11.630919] INFO: bigquant: input_features.v1 开始运行..
    [2018-05-03 09:46:11.633237] INFO: bigquant: 命中缓存
    [2018-05-03 09:46:11.633949] INFO: bigquant: input_features.v1 运行完成[0.00303s].
    [2018-05-03 09:46:11.693715] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2018-05-03 09:46:11.699252] INFO: bigquant: 命中缓存
    [2018-05-03 09:46:11.700433] INFO: bigquant: general_feature_extractor.v6 运行完成[0.006728s].
    [2018-05-03 09:46:11.713109] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2018-05-03 09:46:13.910282] INFO: derived_feature_extractor: 提取完成 where(ts_max(price_limit_status_0,60)>2,1,0), 1.732s
    [2018-05-03 09:46:14.264229] INFO: derived_feature_extractor: /y_2014, 569948
    [2018-05-03 09:46:14.653426] INFO: bigquant: derived_feature_extractor.v2 运行完成[2.940249s].
    [2018-05-03 09:46:14.673531] INFO: bigquant: cached.v3 开始运行..
    [2018-05-03 09:46:15.346392] INFO: bigquant: cached.v3 运行完成[0.67284s].
    [2018-05-03 09:46:15.362981] INFO: bigquant: filter.v3 开始运行..
    [2018-05-03 09:46:15.367155] INFO: filter: 使用表达式 self_func>0 过滤
    [2018-05-03 09:46:15.674663] INFO: filter: 过滤 /data, 141274/569948
    [2018-05-03 09:46:15.688404] INFO: bigquant: filter.v3 运行完成[0.325375s].
    [2018-05-03 09:46:15.705698] INFO: bigquant: join.v3 开始运行..
    [2018-05-03 09:46:16.765904] INFO: join: /data, 行数=135366/141274, 耗时=0.599806s
    [2018-05-03 09:46:16.789665] INFO: join: 最终行数: 135366
    [2018-05-03 09:46:16.791593] INFO: bigquant: join.v3 运行完成[1.08593s].
    [2018-05-03 09:46:16.803704] INFO: bigquant: dropnan.v1 开始运行..
    [2018-05-03 09:46:16.947248] INFO: dropnan: /data, 135366/135366
    [2018-05-03 09:46:16.954999] INFO: dropnan: 行数: 135366/135366
    [2018-05-03 09:46:16.964821] INFO: bigquant: dropnan.v1 运行完成[0.161108s].
    [2018-05-03 09:46:16.980443] INFO: bigquant: stock_ranker_train.v5 开始运行..
    [2018-05-03 09:46:17.142847] INFO: df2bin: prepare bins ..
    [2018-05-03 09:46:17.159820] INFO: df2bin: prepare data: training ..
    [2018-05-03 09:46:17.178224] INFO: df2bin: sort ..
    [2018-05-03 09:46:18.648035] INFO: stock_ranker_train: c51dde7e 准备训练: 135366 行数
    [2018-05-03 09:46:27.736767] INFO: bigquant: stock_ranker_train.v5 运行完成[10.756324s].
    [2018-05-03 09:46:27.746648] INFO: bigquant: instruments.v2 开始运行..
    [2018-05-03 09:46:27.754651] INFO: bigquant: 命中缓存
    [2018-05-03 09:46:27.755599] INFO: bigquant: instruments.v2 运行完成[0.008997s].
    [2018-05-03 09:46:27.764974] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2018-05-03 09:46:27.767510] INFO: bigquant: 命中缓存
    [2018-05-03 09:46:27.768364] INFO: bigquant: general_feature_extractor.v6 运行完成[0.003379s].
    [2018-05-03 09:46:27.774322] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2018-05-03 09:46:27.776601] INFO: bigquant: 命中缓存
    [2018-05-03 09:46:27.777339] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.003018s].
    [2018-05-03 09:46:27.784586] INFO: bigquant: cached.v3 开始运行..
    [2018-05-03 09:46:29.732446] INFO: bigquant: cached.v3 运行完成[1.947826s].
    [2018-05-03 09:46:29.740097] INFO: bigquant: filter.v3 开始运行..
    [2018-05-03 09:46:29.745948] INFO: filter: 使用表达式 self_func>0 过滤
    [2018-05-03 09:46:30.824942] INFO: filter: 过滤 /data, 629411/1211244
    [2018-05-03 09:46:30.852217] INFO: bigquant: filter.v3 运行完成[1.112107s].
    [2018-05-03 09:46:30.860015] INFO: bigquant: dropnan.v1 开始运行..
    [2018-05-03 09:46:31.796263] INFO: dropnan: /data, 629411/629411
    [2018-05-03 09:46:31.814620] INFO: dropnan: 行数: 629411/629411
    [2018-05-03 09:46:31.850372] INFO: bigquant: dropnan.v1 运行完成[0.990323s].
    [2018-05-03 09:46:31.869063] INFO: bigquant: stock_ranker_predict.v5 开始运行..
    [2018-05-03 09:46:32.188125] INFO: df2bin: prepare data: prediction ..
    [2018-05-03 09:46:37.656794] INFO: stock_ranker_predict: 准备预测: 629411 行
    [2018-05-03 09:46:42.140627] INFO: bigquant: stock_ranker_predict.v5 运行完成[10.27152s].
    [2018-05-03 09:46:42.204044] INFO: bigquant: backtest.v7 开始运行..
    [2018-05-03 09:46:42.314140] INFO: algo: set price type:backward_adjusted
    [2018-05-03 09:47:27.129788] INFO: Performance: Simulated 488 trading days out of 488.
    [2018-05-03 09:47:27.131163] INFO: Performance: first open: 2015-01-05 01:30:00+00:00
    [2018-05-03 09:47:27.132096] INFO: Performance: last close: 2016-12-30 07:00:00+00:00
    
    • 收益率-0.06%
    • 年化收益率-0.03%
    • 基准收益率-6.33%
    • 阿尔法0.03
    • 贝塔1.02
    • 夏普比率-0.08
    • 胜率0.552
    • 盈亏比0.867
    • 收益波动率41.23%
    • 信息比率0.13
    • 最大回撤58.57%
    [2018-05-03 09:47:31.173802] INFO: bigquant: backtest.v7 运行完成[48.969771s].
    


    (luckychan) #7

    原来是要用一个自定义模块代码处理,解决我的问题了,赞!

    建议小Q在思路上发多些示例代码给我们学习。


    (iQuant) #8

    https://community.bigquant.com/t/可视化模板自定义指标过滤的实现/9092