循环训练加过滤后无数据


(runningpig) #1

再次请教一个问题。现在在循环训练中加入过滤约束条件以后,如果将m9中的开始结束时间设置得很接近(或者实盘模式下),会有“no data left after dropnan”报错,看上去像是过滤后无数据了。还请指点一下问题出在哪?

克隆策略

    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    In [15]:
    # 本代码由可视化策略环境自动生成 2018年3月13日 18:34
    # 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。
    
    
    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
    std(volume_0,10)
    std(volume_0,20)"""
    )
    
    m18 = M.input_features.v1(
        features_ds=m3.data,
        features="""st_status_0
    list_days_0
    list_board_0
    in_csi300_0"""
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2018-02-11'),
        end_date=T.live_run_param('trading_date', '2018-03-12'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m10 = M.general_feature_extractor.v6(
        instruments=m9.data,
        features=m18.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m11 = M.derived_feature_extractor.v2(
        input_data=m10.data,
        features=m18.data,
        date_col='date',
        instrument_col='instrument'
    )
    
    m20 = M.filter.v3(
        input_data=m11.data,
        expr='st_status_0==0 & list_days_0>120 & in_csi300_0==1',
        output_left_data=False
    )
    
    m14 = M.dropnan.v1(
        input_data=m20.data
    )
    
    m15 = M.rolling_conf.v1(
        start_date='2010-01-01',
        end_date=T.live_run_param('trading_date', '2017-01-01'),
        rolling_update_days=365,
        rolling_min_days=730,
        rolling_max_days=0,
        rolling_count_for_live=1
    )
    
    m1 = M.instruments.v2(
        rolling_conf=m15.data,
        start_date='',
        end_date='',
        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
    )
    
    m4 = M.general_feature_extractor.v6(
        instruments=m1.data,
        features=m18.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m5 = M.derived_feature_extractor.v2(
        input_data=m4.data,
        features=m18.data,
        date_col='date',
        instrument_col='instrument'
    )
    
    m19 = M.filter.v3(
        input_data=m5.data,
        expr='st_status_0==0 & list_days_0>120 & in_csi300_0==1',
        output_left_data=False
    )
    
    m7 = M.join.v3(
        data1=m2.data,
        data2=m19.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=True
    )
    
    m16 = M.rolling_run.v1(
        run=m6.m_lazy_run,
        input_list=m15.data,
        param_name='rolling_input'
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m16.data,
        data=m14.data,
        m_lazy_run=True
    )
    
    m17 = M.rolling_run_predict.v1(
        predict=m8.m_lazy_run,
        model_param_name='model',
        data_param_name='data'
    )
    
    # 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 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=m17.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=100000,
        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
    )
    
    [2018-03-13 18:31:58.678970] INFO: bigquant: input_features.v1 开始运行..
    [2018-03-13 18:31:58.683625] INFO: bigquant: 命中缓存
    [2018-03-13 18:31:58.684986] INFO: bigquant: input_features.v1 运行完成[0.006054s].
    [2018-03-13 18:31:58.692899] INFO: bigquant: input_features.v1 开始运行..
    [2018-03-13 18:31:58.697656] INFO: bigquant: 命中缓存
    [2018-03-13 18:31:58.710781] INFO: bigquant: input_features.v1 运行完成[0.017886s].
    [2018-03-13 18:31:58.719570] INFO: bigquant: instruments.v2 开始运行..
    [2018-03-13 18:31:58.777900] INFO: bigquant: instruments.v2 运行完成[0.058312s].
    [2018-03-13 18:31:58.807268] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2018-03-13 18:32:31.133823] INFO: 基础特征抽取: 年份 2017, 特征行数=674465
    [2018-03-13 18:32:39.067123] INFO: 基础特征抽取: 年份 2018, 特征行数=145523
    [2018-03-13 18:32:39.088018] INFO: 基础特征抽取: 总行数: 819988
    [2018-03-13 18:32:39.097268] INFO: bigquant: general_feature_extractor.v6 运行完成[40.290011s].
    [2018-03-13 18:32:39.111740] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2018-03-13 18:32:44.418773] INFO: derived_feature_extractor: 提取完成 std(volume_0,10), 4.950s
    [2018-03-13 18:32:49.087612] INFO: derived_feature_extractor: 提取完成 std(volume_0,20), 4.661s
    [2018-03-13 18:32:49.378127] INFO: derived_feature_extractor: /y_2017, 674465
    [2018-03-13 18:32:50.006513] INFO: derived_feature_extractor: /y_2018, 145523
    [2018-03-13 18:32:50.177187] INFO: bigquant: derived_feature_extractor.v2 运行完成[11.06535s].
    [2018-03-13 18:32:50.198044] INFO: bigquant: filter.v3 开始运行..
    [2018-03-13 18:32:50.206442] INFO: filter: 使用表达式 st_status_0==0 & list_days_0>120 & in_csi300_0==1 过滤
    [2018-03-13 18:32:50.701802] INFO: filter: 过滤 /y_2017, 62407/674465
    [2018-03-13 18:32:51.055740] INFO: filter: 过滤 /y_2018, 12899/145523
    [2018-03-13 18:32:51.107163] INFO: bigquant: filter.v3 运行完成[0.909087s].
    [2018-03-13 18:32:51.142916] INFO: bigquant: dropnan.v1 开始运行..
    [2018-03-13 18:32:51.377627] INFO: dropnan: /y_2017, 56765/62407
    [2018-03-13 18:32:51.466101] INFO: dropnan: /y_2018, 12899/12899
    [2018-03-13 18:32:51.479430] INFO: dropnan: 行数: 69664/75306
    [2018-03-13 18:32:51.486684] INFO: bigquant: dropnan.v1 运行完成[0.343776s].
    [2018-03-13 18:32:51.579733] INFO: 滚动运行配置: 生成了 6 次滚动,第一次 {'start_date': '2010-01-01', 'end_date': '2011-12-31'},最后一次 {'start_date': '2010-01-01', 'end_date': '2016-12-29'}
    [2018-03-13 18:32:51.617445] INFO: bigquant: instruments.v2 开始运行..
    [2018-03-13 18:32:51.623693] INFO: bigquant: 命中缓存
    [2018-03-13 18:32:51.625899] INFO: bigquant: instruments.v2 运行完成[0.00863s].
    [2018-03-13 18:32:51.646487] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
    [2018-03-13 18:32:51.659221] INFO: bigquant: 命中缓存
    [2018-03-13 18:32:51.667226] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.020719s].
    [2018-03-13 18:32:51.693635] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2018-03-13 18:33:21.376007] INFO: 基础特征抽取: 年份 2010, 特征行数=431567
    [2018-03-13 18:33:59.169946] INFO: 基础特征抽取: 年份 2011, 特征行数=511455
    
    ---------------------------------------------------------------------------
    KeyboardInterrupt                         Traceback (most recent call last)
    <ipython-input-15-f97c892acc75> in <module>()
        101     start_date='',
        102     end_date='',
    --> 103     before_start_days=0
        104 )
        105 
    
    KeyboardInterrupt: 


    (iQuant) #2

    可以看看是不是这个原因:模拟交易中出现there is no data after dropnan 错误


    (runningpig) #3

    确实如此,多谢解答