大盘风控功能¶

In [8]:

# 本代码由可视化策略环境自动生成 2021年12月24日 18:49
# 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：初始化函数，只执行一次
def m7_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 = 0.1
    context.options['hold_days'] = 1
 
    from zipline.finance.slippage import SlippageModel
    class FixedPriceSlippage(SlippageModel):
        def process_order(self, data, order, bar_volume=0, trigger_check_price=0):
            if order.limit is None:
                price_field = self._price_field_buy if order.amount > 0 else self._price_field_sell
                price = data.current(order.asset, price_field)
            else:
                price = data.current(order.asset, self._price_field_buy)
            # 返回希望成交的价格和数量
            return (price, order.amount)
    # 设置price_field,默认是开盘买入，收盘卖出
    context.fix_slippage = FixedPriceSlippage(price_field_buy='open', price_field_sell='close')
    context.set_slippage(us_equities=context.fix_slippage)

# 回测引擎：每日数据处理函数，每天执行一次
def m7_handle_data_bigquant_run(context, data):
    #获取当日日期
    today = data.current_dt.strftime('%Y-%m-%d')
    stock_hold_now = [equity.symbol for equity in context.portfolio.positions ]
    #大盘风控模块，读取风控数据    
    benckmark_risk=context.benckmark_risk[today]
    context.symbol
    #当risk为1时，市场有风险，全部平仓，不再执行其它操作
    if benckmark_risk > 0:
        for instrument in stock_hold_now:
            context.order_target(symbol(instrument), 0)
        print(today,'大盘风控止损触发,全仓卖出')
        return

    #------------------------------------------止损模块START--------------------------------------------
    equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}
    
    # 新建当日止损股票列表是为了handle_data 策略逻辑部分不再对该股票进行判断
    stoploss_stock = [] 
    if len(equities) > 0:
        for i in equities.keys():
            stock_market_price = data.current(context.symbol(i), 'price')  # 最新市场价格
            last_sale_date = equities[i].last_sale_date   # 上次交易日期
            delta_days = data.current_dt - last_sale_date  
            hold_days = delta_days.days # 持仓天数
            # 建仓以来的最高价
            highest_price_since_buy = data.history(context.symbol(i), 'high', hold_days, '1d').max()
            # 确定止损位置
            stoploss_line = highest_price_since_buy - highest_price_since_buy * 0.025
            #record('止损位置', stoploss_line)
            # 如果价格下穿止损位置
            if stock_market_price < stoploss_line:
                context.order_target_percent(context.symbol(i), 0)     
                stoploss_stock.append(i)
        if len(stoploss_stock)>0:
            print('日期：', today, '股票：', stoploss_stock, '出现跟踪止损状况')
    #-------------------------------------------止损模块END---------------------------------------------    
   
    # 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 m7_prepare_bigquant_run(context):
    #在数据准备函数中一次性计算每日的大盘风控条件相比于在handle中每日计算风控条件可以提高回测速度
    # 多取50天的数据便于计算均值(保证回测的第一天均值不为Nan值)，其中context.start_date和context.end_date是回测指定的起始时间和终止时间
    start_date= (pd.to_datetime(context.start_date) - datetime.timedelta(days=50)).strftime('%Y-%m-%d') 
    df=DataSource('bar1d_index_CN_STOCK_A').read(start_date=start_date,end_date=context.end_date,fields=['close'])
    benckmark_data=df[df.instrument=='000001.HIX']
    #计算上证指数5日涨幅
    benckmark_data['ret5']=benckmark_data['close']/benckmark_data['close'].shift(5)-1
    #计算大盘风控条件，如果5日涨幅小于-4%则设置风险状态risk为1，否则为0
    benckmark_data['risk'] = np.where(benckmark_data['ret5']<-0.05,1,0)
    #修改日期格式为字符串(便于在handle中使用字符串日期索引来查看每日的风险状态)
    benckmark_data['date']=benckmark_data['date'].apply(lambda x:x.strftime('%Y-%m-%d'))
    #设置日期为索引
    benckmark_data.set_index('date',inplace=True)
    #把风控序列输出给全局变量context.benckmark_risk
    context.benckmark_risk=benckmark_data['risk']
    
    # 获取股票名称 用于过滤st和退市股
    context.name_df = DataSource('instruments_CN_STOCK_A').read()
    # 获取涨跌停状态
    context.price_limit_status = DataSource('stock_status_CN_STOCK_A').read(fields=['price_limit_status'])

def m7_before_trading_start_bigquant_run(context, data):
    df_price_limit_status=context.price_limit_status.set_index('date')
    today=data.current_dt.strftime('%Y-%m-%d')
    # 得到当前未完成订单
    for orders in get_open_orders().values():
        # 循环，撤销订单
        for _order in orders:
            ins=str(_order.sid.symbol)
            if data.can_trade(_order.sid):
                #判断一下如果当日涨停，则取消卖单
                if  df_price_limit_status[df_price_limit_status.instrument==ins].price_limit_status.loc[today]>2 and _order.amount<0:
                    #cancel_order(_order)
                    print(today,'尾盘涨停',ins) 

g = T.Graph({

    'm1': 'M.instruments.v2',
    'm1.start_date': '2021-01-01',
    'm1.end_date': '2021-12-22',
    'm1.market': 'CN_STOCK_A',
    'm1.instrument_list': '',
    'm1.max_count': 0,

    'm2': 'M.advanced_auto_labeler.v2',
    'm2.instruments': T.Graph.OutputPort('m1.data'),
    'm2.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)
1*((shift(close, -3) / shift(open, -1))-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)
where(shift(high, -1) == shift(open, -1), NaN, label)
where(shift(low, -1) == shift(close, -1), NaN, label)
""",
    'm2.start_date': '',
    'm2.end_date': '',
    'm2.benchmark': '000300.SHA',
    'm2.drop_na_label': True,
    'm2.cast_label_int': True,

    'm3': 'M.input_features.v1',
    'm3.features': """rank(correlation((high_0+low_0+close_0+open_0)/4,volume_0,4))*rank(correlation(rank(low_0),rank(mean(volume_0,50)),12))
ta_trix_14_0
mean(turn_0*(return_0-1),15)
mean(mf_net_pct_m_0,30)
(mean(close_0,3)+mean(close_0,6)+mean(close_0,12)+mean(close_0,24))/(4*close_0)
rank(correlation((high_0+low_0+close_0+open_0)/4,volume_0,4))*rank(correlation(rank(low_0),rank(mean(volume_0,50)),12))
-1*rank(covariance(rank(close_0),rank(volume_0),5))
cond3=price_limit_status_0==2
cond2=shift(ta_macd_dea(close_0),-1)<ta_macd_dea(close_0)
cond4=ta_macd_dea(close_0)<ta_macd_dif(close_0)
""",

    'm15': 'M.general_feature_extractor.v7',
    'm15.instruments': T.Graph.OutputPort('m1.data'),
    'm15.features': T.Graph.OutputPort('m3.data'),
    'm15.start_date': '',
    'm15.end_date': '',
    'm15.before_start_days': 400,

    'm16': 'M.derived_feature_extractor.v3',
    'm16.input_data': T.Graph.OutputPort('m15.data'),
    'm16.features': T.Graph.OutputPort('m3.data'),
    'm16.date_col': 'date',
    'm16.instrument_col': 'instrument',
    'm16.drop_na': False,
    'm16.remove_extra_columns': False,

    'm12': 'M.join.v3',
    'm12.data1': T.Graph.OutputPort('m2.data'),
    'm12.data2': T.Graph.OutputPort('m16.data'),
    'm12.on': 'date,instrument',
    'm12.how': 'inner',
    'm12.sort': False,

    'm14': 'M.filter.v3',
    'm14.input_data': T.Graph.OutputPort('m12.data'),
    'm14.expr': 'cond3&cond2&cond4',
    'm14.output_left_data': False,

    'm5': 'M.dropnan.v2',
    'm5.input_data': T.Graph.OutputPort('m14.data'),

    'm4': 'M.features_short.v1',
    'm4.input_1': T.Graph.OutputPort('m3.data'),

    'm9': 'M.instruments.v2',
    'm9.start_date': '2021-01-01',
    'm9.end_date': '2021-12-22',
    'm9.market': 'CN_STOCK_A',
    'm9.instrument_list': '',
    'm9.max_count': 0,

    'm17': 'M.general_feature_extractor.v7',
    'm17.instruments': T.Graph.OutputPort('m9.data'),
    'm17.features': T.Graph.OutputPort('m3.data'),
    'm17.start_date': '',
    'm17.end_date': '',
    'm17.before_start_days': 400,

    'm18': 'M.derived_feature_extractor.v3',
    'm18.input_data': T.Graph.OutputPort('m17.data'),
    'm18.features': T.Graph.OutputPort('m3.data'),
    'm18.date_col': 'date',
    'm18.instrument_col': 'instrument',
    'm18.drop_na': False,
    'm18.remove_extra_columns': False,

    'm11': 'M.chinaa_stock_filter.v1',
    'm11.input_data': T.Graph.OutputPort('m18.data'),
    'm11.index_constituent_cond': ['全部'],
    'm11.board_cond': ['上证主板', '深证主板', '创业板'],
    'm11.industry_cond': ['全部'],
    'm11.st_cond': ['正常'],
    'm11.delist_cond': ['非退市'],
    'm11.output_left_data': False,

    'm19': 'M.filter.v3',
    'm19.input_data': T.Graph.OutputPort('m11.data'),
    'm19.expr': 'cond3&cond2&cond4',
    'm19.output_left_data': False,

    'm10': 'M.dropnan.v2',
    'm10.input_data': T.Graph.OutputPort('m19.data'),

    'm6': 'M.xgboost.v1',
    'm6.training_ds': T.Graph.OutputPort('m5.data'),
    'm6.features': T.Graph.OutputPort('m4.data_1'),
    'm6.predict_ds': T.Graph.OutputPort('m10.data'),
    'm6.num_boost_round': 108,
    'm6.objective': '排序(pairwise)',
    'm6.booster': 'gbtree',
    'm6.max_depth': 30,
    'm6.key_cols': 'date,instrument',
    'm6.group_col': 'date',
    'm6.nthread': 3,
    'm6.n_gpus': -1,
    'm6.other_train_parameters': {},

    'm7': 'M.trade.v4',
    'm7.instruments': T.Graph.OutputPort('m9.data'),
    'm7.options_data': T.Graph.OutputPort('m6.predictions'),
    'm7.start_date': '',
    'm7.end_date': '',
    'm7.initialize': m7_initialize_bigquant_run,
    'm7.handle_data': m7_handle_data_bigquant_run,
    'm7.prepare': m7_prepare_bigquant_run,
    'm7.before_trading_start': m7_before_trading_start_bigquant_run,
    'm7.volume_limit': 1,
    'm7.order_price_field_buy': 'open',
    'm7.order_price_field_sell': 'close',
    'm7.capital_base': 100000,
    'm7.auto_cancel_non_tradable_orders': True,
    'm7.data_frequency': 'daily',
    'm7.price_type': '真实价格',
    'm7.product_type': '股票',
    'm7.plot_charts': True,
    'm7.backtest_only': False,
    'm7.benchmark': '',
})

# g.run({})


def m8_run_bigquant_run(
    bq_graph,
    inputs,
    trading_days_market='CN', # 使用那个市场的交易日历, TODO
    train_instruments_mid='m1', # 训练数据 证券代码列表 模块id
    test_instruments_mid='m9', # 测试数据 证券代码列表 模块id
    predict_mid='m6', # 预测 模块id
    trade_mid='m7', # 回测 模块id
    start_date='2021-01-01', # 数据开始日期
    end_date=T.live_run_param('trading_date', '2021-12-23'), # 数据结束日期
    train_update_days=1, # 更新周期，按交易日计算，每多少天更新一次
    train_update_days_for_live=None, #模拟实盘模式下的更新周期，按交易日计算，每多少天更新一次。如果需要在模拟实盘阶段使用不同的模型更新周期，可以设置这个参数
    train_data_min_days=220, # 最小数据天数，按交易日计算，所以第一个滚动的结束日期是 从开始日期到开始日期+最小数据天数
    train_data_max_days=1000, # 最大数据天数，按交易日计算，0，表示没有限制，否则每一个滚动的开始日期=max(此滚动的结束日期-最大数据天数, 开始日期
    rolling_count_for_live=0, #实盘模式下滚动次数，模拟实盘模式下，取最后多少次滚动。一般在模拟实盘模式下，只用到最后一次滚动训练的模型，这里可以设置为1；如果你的滚动训练数据时间段很短，以至于期间可能没有训练数据，这里可以设置大一点。0表示没有限制
):
    def merge_datasources(input_1):
        df_list = [ds[0].read_df().set_index('date').loc[ds[1]:].reset_index() for ds in input_1]
        df = pd.concat(df_list)
        instrument_data = {
            'start_date': df['date'].min().strftime('%Y-%m-%d'),
            'end_date': df['date'].max().strftime('%Y-%m-%d'),
            'instruments': list(set(df['instrument'])),
        }
        return Outputs(data=DataSource.write_df(df), instrument_data=DataSource.write_pickle(instrument_data))

    def gen_rolling_dates(trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live):
        # 是否实盘模式
        tdays = list(D.trading_days(market=trading_days_market, start_date=start_date, end_date=end_date)['date'])
        is_live_run = T.live_run_param('trading_date', None) is not None

        if is_live_run and train_update_days_for_live:
            train_update_days = train_update_days_for_live

        rollings = []
        train_end_date = train_data_min_days
        while train_end_date < len(tdays):
            if train_data_max_days is not None and train_data_max_days > 0:
                train_start_date = max(train_end_date - train_data_max_days, 0)
            else:
                train_start_date = 0
            rollings.append({
                'train_start_date': tdays[train_start_date].strftime('%Y-%m-%d'),
                'train_end_date': tdays[train_end_date - 1].strftime('%Y-%m-%d'),
                'test_start_date': tdays[train_end_date].strftime('%Y-%m-%d'),
                'test_end_date': tdays[min(train_end_date + train_update_days, len(tdays)) - 1].strftime('%Y-%m-%d'),
            })
            train_end_date += train_update_days

        if not rollings:
            raise Exception('没有滚动需要执行，请检查配置')

        if is_live_run and rolling_count_for_live:
            rollings = rollings[-rolling_count_for_live:]

        return rollings

    g = bq_graph

    rolling_dates = gen_rolling_dates(
        trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live)

    # 训练和预测
    results = []
    for rolling in rolling_dates:
        parameters = {}
        # 先禁用回测
        parameters[trade_mid + '.__enabled__'] = False
        parameters[train_instruments_mid + '.start_date'] = rolling['train_start_date']
        parameters[train_instruments_mid + '.end_date'] = rolling['train_end_date']
        parameters[test_instruments_mid + '.start_date'] = rolling['test_start_date']
        parameters[test_instruments_mid + '.end_date'] = rolling['test_end_date']
        # print('------ rolling_train:', parameters)
        results.append(g.run(parameters))

    # 合并预测结果并回测
    mx = M.cached.v3(run=merge_datasources, input_1=[[result[predict_mid].predictions, result[test_instruments_mid].data.read_pickle()['start_date']] for result in results])
    parameters = {}
    parameters['*.__enabled__'] = False
    parameters[trade_mid + '.__enabled__'] = True
    parameters[trade_mid + '.instruments'] = mx.instrument_data
    parameters[trade_mid + '.options_data'] = mx.data

    trade = g.run(parameters)

    return {'rollings': results, 'trade': trade}


m8 = M.hyper_rolling_train.v1(
    run=m8_run_bigquant_run,
    run_now=True,
    bq_graph=g
)

[2021-12-24 14:24:32.588682] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-12-24 14:24:32.683608] INFO: moduleinvoker: instruments.v2 运行完成[0.094927s].

[2021-12-24 14:24:32.688296] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-12-24 14:24:32.693827] INFO: moduleinvoker: 命中缓存

[2021-12-24 14:24:32.695278] INFO: moduleinvoker: input_features.v1 运行完成[0.006986s].

[2021-12-24 14:24:32.701027] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-12-24 14:24:32.795545] INFO: moduleinvoker: instruments.v2 运行完成[0.094507s].

[2021-12-24 14:24:32.805352] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2021-12-24 14:24:33.560003] INFO: 自动标注(股票): 加载历史数据: 954590 行

[2021-12-24 14:24:33.561757] INFO: 自动标注(股票): 开始标注 ..

[2021-12-24 14:24:35.321750] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[2.516395s].

[2021-12-24 14:24:35.334363] INFO: moduleinvoker: features_short.v1 开始运行..

[2021-12-24 14:24:35.341177] INFO: moduleinvoker: 命中缓存

[2021-12-24 14:24:35.342615] INFO: moduleinvoker: features_short.v1 运行完成[0.008269s].

[2021-12-24 14:24:35.356264] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-12-24 14:24:38.829628] INFO: 基础特征抽取: 年份 2019, 特征行数=81785

[2021-12-24 14:24:46.380933] INFO: 基础特征抽取: 年份 2020, 特征行数=944770

[2021-12-24 14:24:54.846195] INFO: 基础特征抽取: 年份 2021, 特征行数=954590

[2021-12-24 14:24:54.956586] INFO: 基础特征抽取: 总行数: 1981145

[2021-12-24 14:24:54.968932] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[19.612657s].

[2021-12-24 14:24:55.001333] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-12-24 14:24:59.583931] INFO: 基础特征抽取: 年份 2020, 特征行数=195010

[2021-12-24 14:25:06.355285] INFO: 基础特征抽取: 年份 2021, 特征行数=958407

[2021-12-24 14:25:06.447993] INFO: 基础特征抽取: 总行数: 1153417

[2021-12-24 14:25:06.455372] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[11.454057s].

[2021-12-24 14:25:06.464299] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-12-24 14:26:14.929009] INFO: derived_feature_extractor: 提取完成 rank(correlation((high_0+low_0+close_0+open_0)/4,volume_0,4))*rank(correlation(rank(low_0),rank(mean(volume_0,50)),12)), 64.754s

[2021-12-24 14:26:16.262255] INFO: derived_feature_extractor: 提取完成 mean(turn_0*(return_0-1),15), 1.332s

[2021-12-24 14:26:17.605056] INFO: derived_feature_extractor: 提取完成 mean(mf_net_pct_m_0,30), 1.341s

[2021-12-24 14:26:22.839449] INFO: derived_feature_extractor: 提取完成 (mean(close_0,3)+mean(close_0,6)+mean(close_0,12)+mean(close_0,24))/(4*close_0), 5.233s

[2021-12-24 14:26:43.972710] INFO: derived_feature_extractor: 提取完成 -1*rank(covariance(rank(close_0),rank(volume_0),5)), 21.132s

[2021-12-24 14:26:43.977973] INFO: derived_feature_extractor: 提取完成 cond3=price_limit_status_0==2, 0.004s

[2021-12-24 14:26:54.626474] INFO: derived_feature_extractor: 提取完成 cond2=shift(ta_macd_dea(close_0),-1)[2021-12-24 14:27:04.788663] INFO: derived_feature_extractor: 提取完成 cond4=ta_macd_dea(close_0)[2021-12-24 14:27:05.217198] INFO: derived_feature_extractor: /y_2019, 81785
[2021-12-24 14:27:06.847405] INFO: derived_feature_extractor: /y_2020, 944770
[2021-12-24 14:27:08.927998] INFO: derived_feature_extractor: /y_2021, 954590
[2021-12-24 14:27:09.633290] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[123.168984s].
[2021-12-24 14:27:09.642292] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-12-24 14:28:09.635775] INFO: derived_feature_extractor: 提取完成 rank(correlation((high_0+low_0+close_0+open_0)/4,volume_0,4))*rank(correlation(rank(low_0),rank(mean(volume_0,50)),12)), 58.009s
[2021-12-24 14:28:10.421985] INFO: derived_feature_extractor: 提取完成 mean(turn_0*(return_0-1),15), 0.785s
[2021-12-24 14:28:11.195125] INFO: derived_feature_extractor: 提取完成 mean(mf_net_pct_m_0,30), 0.772s
[2021-12-24 14:28:14.345159] INFO: derived_feature_extractor: 提取完成 (mean(close_0,3)+mean(close_0,6)+mean(close_0,12)+mean(close_0,24))/(4*close_0), 3.149s
[2021-12-24 14:28:34.313121] INFO: derived_feature_extractor: 提取完成 -1*rank(covariance(rank(close_0),rank(volume_0),5)), 19.966s
[2021-12-24 14:28:34.316545] INFO: derived_feature_extractor: 提取完成 cond3=price_limit_status_0==2, 0.002s
[2021-12-24 14:28:43.558828] INFO: derived_feature_extractor: 提取完成 cond2=shift(ta_macd_dea(close_0),-1)[2021-12-24 14:28:52.372092] INFO: derived_feature_extractor: 提取完成 cond4=ta_macd_dea(close_0)[2021-12-24 14:28:52.862843] INFO: derived_feature_extractor: /y_2020, 195010
[2021-12-24 14:28:54.478758] INFO: derived_feature_extractor: /y_2021, 958407
[2021-12-24 14:28:55.137623] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[105.495325s].
[2021-12-24 14:28:55.147123] INFO: moduleinvoker: join.v3 开始运行..
[2021-12-24 14:28:56.906687] INFO: join: /y_2019, 行数=0/81785, 耗时=0.404665s
[2021-12-24 14:28:59.024506] INFO: join: /y_2020, 行数=0/944770, 耗时=2.115831s
[2021-12-24 14:29:01.872724] INFO: join: /y_2021, 行数=838599/954590, 耗时=2.8421s
[2021-12-24 14:29:01.956397] INFO: join: 最终行数: 838599
[2021-12-24 14:29:01.971223] INFO: moduleinvoker: join.v3 运行完成[6.8241s].
[2021-12-24 14:29:01.980306] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..
[2021-12-24 14:29:02.984076] INFO: A股股票过滤: 过滤 /y_2020, 176052/0/195010
[2021-12-24 14:29:06.470159] INFO: A股股票过滤: 过滤 /y_2021, 851591/0/958407
[2021-12-24 14:29:06.481860] INFO: A股股票过滤: 过滤完成, 1027643 + 0
[2021-12-24 14:29:06.528079] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[4.547769s].
[2021-12-24 14:29:06.536792] INFO: moduleinvoker: filter.v3 开始运行..
[2021-12-24 14:29:06.556663] INFO: filter: 使用表达式 cond3&cond2&cond4 过滤
[2021-12-24 14:29:06.625507] INFO: filter: 过滤 /y_2019, 0/0/0
[2021-12-24 14:29:06.650601] INFO: filter: 过滤 /y_2020, 0/0/0
[2021-12-24 14:29:07.352720] INFO: filter: 过滤 /y_2021, 28046/0/838599
[2021-12-24 14:29:07.386114] INFO: moduleinvoker: filter.v3 运行完成[0.849312s].
[2021-12-24 14:29:07.394377] INFO: moduleinvoker: filter.v3 开始运行..
[2021-12-24 14:29:07.414425] INFO: filter: 使用表达式 cond3&cond2&cond4 过滤
[2021-12-24 14:29:07.607178] INFO: filter: 过滤 /y_2020, 1285/0/176052
[2021-12-24 14:29:08.033432] INFO: filter: 过滤 /y_2021, 32556/0/851591
[2021-12-24 14:29:08.056310] INFO: moduleinvoker: filter.v3 运行完成[0.661923s].
[2021-12-24 14:29:08.064923] INFO: moduleinvoker: dropnan.v2 开始运行..
[2021-12-24 14:29:08.198186] INFO: dropnan: /y_2021, 27734/28046
[2021-12-24 14:29:08.249349] INFO: dropnan: 行数: 27734/28046
[2021-12-24 14:29:08.254032] INFO: moduleinvoker: dropnan.v2 运行完成[0.189112s].
[2021-12-24 14:29:08.262429] INFO: moduleinvoker: dropnan.v2 开始运行..
[2021-12-24 14:29:08.343778] INFO: dropnan: /y_2020, 0/1285
[2021-12-24 14:29:08.412047] INFO: dropnan: /y_2021, 30397/32556
[2021-12-24 14:29:08.472309] INFO: dropnan: 行数: 30397/33841
[2021-12-24 14:29:08.476725] INFO: moduleinvoker: dropnan.v2 运行完成[0.214299s].
[2021-12-24 14:29:08.483757] INFO: moduleinvoker: xgboost.v1 开始运行..
[2021-12-24 14:29:26.310711] INFO: moduleinvoker: xgboost.v1 运行完成[17.826936s].
[2021-12-24 14:29:26.318379] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-12-24 14:29:26.450429] INFO: moduleinvoker: instruments.v2 运行完成[0.132033s].
[2021-12-24 14:29:26.458090] INFO: moduleinvoker: input_features.v1 开始运行..
[2021-12-24 14:29:26.467124] INFO: moduleinvoker: 命中缓存
[2021-12-24 14:29:26.469536] INFO: moduleinvoker: input_features.v1 运行完成[0.011468s].
[2021-12-24 14:29:26.476418] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-12-24 14:29:26.570422] INFO: moduleinvoker: instruments.v2 运行完成[0.094s].
[2021-12-24 14:29:26.580302] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..
[2021-12-24 14:29:27.471870] INFO: 自动标注(股票): 加载历史数据: 959220 行
[2021-12-24 14:29:27.473573] INFO: 自动标注(股票): 开始标注 ..
[2021-12-24 14:29:30.184422] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[3.604124s].
[2021-12-24 14:29:30.196918] INFO: moduleinvoker: features_short.v1 开始运行..
[2021-12-24 14:29:30.205488] INFO: moduleinvoker: 命中缓存
[2021-12-24 14:29:30.207164] INFO: moduleinvoker: features_short.v1 运行完成[0.010267s].
[2021-12-24 14:29:30.224266] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2021-12-24 14:29:36.467475] INFO: 基础特征抽取: 年份 2019, 特征行数=81785
[2021-12-24 14:29:43.183158] INFO: 基础特征抽取: 年份 2020, 特征行数=944770
[2021-12-24 14:29:50.268394] INFO: 基础特征抽取: 年份 2021, 特征行数=959220
[2021-12-24 14:29:50.360344] INFO: 基础特征抽取: 总行数: 1985775
[2021-12-24 14:29:50.369116] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[20.144867s].
[2021-12-24 14:29:50.384482] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2021-12-24 14:29:55.410703] INFO: 基础特征抽取: 年份 2020, 特征行数=190983
[2021-12-24 14:30:02.040122] INFO: 基础特征抽取: 年份 2021, 特征行数=963039
[2021-12-24 14:30:02.105740] INFO: 基础特征抽取: 总行数: 1154022
[2021-12-24 14:30:02.115125] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[11.730659s].
[2021-12-24 14:30:02.123672] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-12-24 14:31:14.047607] INFO: derived_feature_extractor: 提取完成 rank(correlation((high_0+low_0+close_0+open_0)/4,volume_0,4))*rank(correlation(rank(low_0),rank(mean(volume_0,50)),12)), 68.135s
[2021-12-24 14:31:15.577233] INFO: derived_feature_extractor: 提取完成 mean(turn_0*(return_0-1),15), 1.528s
[2021-12-24 14:31:17.025492] INFO: derived_feature_extractor: 提取完成 mean(mf_net_pct_m_0,30), 1.447s
[2021-12-24 14:31:22.903890] INFO: derived_feature_extractor: 提取完成 (mean(close_0,3)+mean(close_0,6)+mean(close_0,12)+mean(close_0,24))/(4*close_0), 5.877s
[2021-12-24 14:31:45.785248] INFO: derived_feature_extractor: 提取完成 -1*rank(covariance(rank(close_0),rank(volume_0),5)), 22.879s
[2021-12-24 14:31:45.790988] INFO: derived_feature_extractor: 提取完成 cond3=price_limit_status_0==2, 0.004s
[2021-12-24 14:31:56.074057] INFO: derived_feature_extractor: 提取完成 cond2=shift(ta_macd_dea(close_0),-1)[2021-12-24 14:32:07.350402] INFO: derived_feature_extractor: 提取完成 cond4=ta_macd_dea(close_0)[2021-12-24 14:32:07.836644] INFO: derived_feature_extractor: /y_2019, 81785
[2021-12-24 14:32:09.750988] INFO: derived_feature_extractor: /y_2020, 944770
[2021-12-24 14:32:11.850164] INFO: derived_feature_extractor: /y_2021, 959220
[2021-12-24 14:32:12.500666] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[130.376989s].
[2021-12-24 14:32:12.510480] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-12-24 14:33:16.959626] INFO: derived_feature_extractor: 提取完成 rank(correlation((high_0+low_0+close_0+open_0)/4,volume_0,4))*rank(correlation(rank(low_0),rank(mean(volume_0,50)),12)), 62.087s
[2021-12-24 14:33:17.769893] INFO: derived_feature_extractor: 提取完成 mean(turn_0*(return_0-1),15), 0.809s
[2021-12-24 14:33:18.534529] INFO: derived_feature_extractor: 提取完成 mean(mf_net_pct_m_0,30), 0.763s
[2021-12-24 14:33:21.656519] INFO: derived_feature_extractor: 提取完成 (mean(close_0,3)+mean(close_0,6)+mean(close_0,12)+mean(close_0,24))/(4*close_0), 3.121s
[2021-12-24 14:33:40.743851] INFO: derived_feature_extractor: 提取完成 -1*rank(covariance(rank(close_0),rank(volume_0),5)), 19.086s
[2021-12-24 14:33:40.747262] INFO: derived_feature_extractor: 提取完成 cond3=price_limit_status_0==2, 0.002s
[2021-12-24 14:33:49.734928] INFO: derived_feature_extractor: 提取完成 cond2=shift(ta_macd_dea(close_0),-1)[2021-12-24 14:33:59.044773] INFO: derived_feature_extractor: 提取完成 cond4=ta_macd_dea(close_0)[2021-12-24 14:33:59.600456] INFO: derived_feature_extractor: /y_2020, 190983
[2021-12-24 14:34:01.292445] INFO: derived_feature_extractor: /y_2021, 963039
[2021-12-24 14:34:02.116046] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[109.605569s].
[2021-12-24 14:34:02.124964] INFO: moduleinvoker: join.v3 开始运行..
[2021-12-24 14:34:05.019899] INFO: join: /y_2019, 行数=0/81785, 耗时=0.422098s
[2021-12-24 14:34:07.161143] INFO: join: /y_2020, 行数=0/944770, 耗时=2.139389s
[2021-12-24 14:34:09.924509] INFO: join: /y_2021, 行数=842889/959220, 耗时=2.758857s
[2021-12-24 14:34:10.000703] INFO: join: 最终行数: 842889
[2021-12-24 14:34:10.162165] INFO: moduleinvoker: join.v3 运行完成[8.037196s].
[2021-12-24 14:34:10.171367] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..
[2021-12-24 14:34:22.904846] INFO: A股股票过滤: 过滤 /y_2020, 172408/0/190983
[2021-12-24 14:34:29.461575] INFO: A股股票过滤: 过滤 /y_2021, 855600/0/963039
[2021-12-24 14:34:29.466405] INFO: A股股票过滤: 过滤完成, 1028008 + 0
[2021-12-24 14:34:29.512571] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[19.341198s].
[2021-12-24 14:34:29.521792] INFO: moduleinvoker: filter.v3 开始运行..
[2021-12-24 14:34:29.539089] INFO: filter: 使用表达式 cond3&cond2&cond4 过滤
[2021-12-24 14:34:29.622506] INFO: filter: 过滤 /y_2019, 0/0/0
[2021-12-24 14:34:29.652740] INFO: filter: 过滤 /y_2020, 0/0/0
[2021-12-24 14:34:30.293048] INFO: filter: 过滤 /y_2021, 28282/0/842889
[2021-12-24 14:34:30.317174] INFO: moduleinvoker: filter.v3 运行完成[0.795385s].
[2021-12-24 14:34:30.325137] INFO: moduleinvoker: filter.v3 开始运行..
[2021-12-24 14:34:30.341235] INFO: filter: 使用表达式 cond3&cond2&cond4 过滤
[2021-12-24 14:34:30.632799] INFO: filter: 过滤 /y_2020, 1142/0/172408
[2021-12-24 14:34:31.054108] INFO: filter: 过滤 /y_2021, 32758/0/855600
[2021-12-24 14:34:31.115054] INFO: moduleinvoker: filter.v3 运行完成[0.7899s].
[2021-12-24 14:34:31.123943] INFO: moduleinvoker: dropnan.v2 开始运行..
[2021-12-24 14:34:31.312193] INFO: dropnan: /y_2021, 27967/28282
[2021-12-24 14:34:31.405843] INFO: dropnan: 行数: 27967/28282
[2021-12-24 14:34:31.412682] INFO: moduleinvoker: dropnan.v2 运行完成[0.288736s].
[2021-12-24 14:34:31.423028] INFO: moduleinvoker: dropnan.v2 开始运行..
[2021-12-24 14:34:31.556759] INFO: dropnan: /y_2020, 0/1142
[2021-12-24 14:34:31.633277] INFO: dropnan: /y_2021, 30574/32758
[2021-12-24 14:34:31.694301] INFO: dropnan: 行数: 30574/33900
[2021-12-24 14:34:31.699544] INFO: moduleinvoker: dropnan.v2 运行完成[0.276506s].
[2021-12-24 14:34:31.707846] INFO: moduleinvoker: xgboost.v1 开始运行..
[2021-12-24 14:34:47.657758] INFO: moduleinvoker: xgboost.v1 运行完成[15.949917s].
[2021-12-24 14:34:47.664742] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-12-24 14:34:47.787230] INFO: moduleinvoker: instruments.v2 运行完成[0.122489s].
[2021-12-24 14:34:47.791868] INFO: moduleinvoker: input_features.v1 开始运行..
[2021-12-24 14:34:47.816151] INFO: moduleinvoker: 命中缓存
[2021-12-24 14:34:47.818101] INFO: moduleinvoker: input_features.v1 运行完成[0.026246s].
[2021-12-24 14:34:47.823022] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-12-24 14:34:47.927289] INFO: moduleinvoker: instruments.v2 运行完成[0.104255s].
[2021-12-24 14:34:47.936040] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..
[2021-12-24 14:34:48.655598] INFO: 自动标注(股票): 加载历史数据: 963852 行
[2021-12-24 14:34:48.657159] INFO: 自动标注(股票): 开始标注 ..
[2021-12-24 14:34:50.379379] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[2.443332s].
[2021-12-24 14:34:50.403527] INFO: moduleinvoker: features_short.v1 开始运行..
[2021-12-24 14:34:50.411361] INFO: moduleinvoker: 命中缓存
[2021-12-24 14:34:50.412989] INFO: moduleinvoker: features_short.v1 运行完成[0.009483s].
[2021-12-24 14:34:50.437677] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2021-12-24 14:34:53.400447] INFO: 基础特征抽取: 年份 2019, 特征行数=81785
[2021-12-24 14:34:59.492308] INFO: 基础特征抽取: 年份 2020, 特征行数=944770
[2021-12-24 14:35:05.270636] INFO: 基础特征抽取: 年份 2021, 特征行数=963852
[2021-12-24 14:35:05.410146] INFO: 基础特征抽取: 总行数: 1990407
[2021-12-24 14:35:05.418099] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[14.980439s].
[2021-12-24 14:35:05.434619] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2021-12-24 14:35:08.865464] INFO: 基础特征抽取: 年份 2020, 特征行数=186956
[2021-12-24 14:35:14.549715] INFO: 基础特征抽取: 年份 2021, 特征行数=967671
[2021-12-24 14:35:14.624118] INFO: 基础特征抽取: 总行数: 1154627
[2021-12-24 14:35:14.632317] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[9.197722s].
[2021-12-24 14:35:14.640072] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-12-24 14:36:44.210709] INFO: derived_feature_extractor: 提取完成 rank(correlation((high_0+low_0+close_0+open_0)/4,volume_0,4))*rank(correlation(rank(low_0),rank(mean(volume_0,50)),12)), 86.137s
[2021-12-24 14:37:01.641324] INFO: derived_feature_extractor: 提取完成 mean(turn_0*(return_0-1),15), 17.429s
[2021-12-24 14:37:05.294475] INFO: derived_feature_extractor: 提取完成 mean(mf_net_pct_m_0,30), 3.652s
[2021-12-24 14:37:10.610749] INFO: derived_feature_extractor: 提取完成 (mean(close_0,3)+mean(close_0,6)+mean(close_0,12)+mean(close_0,24))/(4*close_0), 5.315s
[2021-12-24 14:37:31.855231] INFO: derived_feature_extractor: 提取完成 -1*rank(covariance(rank(close_0),rank(volume_0),5)), 21.243s
[2021-12-24 14:37:31.860382] INFO: derived_feature_extractor: 提取完成 cond3=price_limit_status_0==2, 0.004s
[2021-12-24 14:37:41.837799] INFO: derived_feature_extractor: 提取完成 cond2=shift(ta_macd_dea(close_0),-1)[2021-12-24 14:37:51.831504] INFO: derived_feature_extractor: 提取完成 cond4=ta_macd_dea(close_0)[2021-12-24 14:37:52.276007] INFO: derived_feature_extractor: /y_2019, 81785
[2021-12-24 14:37:53.859910] INFO: derived_feature_extractor: /y_2020, 944770
[2021-12-24 14:37:56.073593] INFO: derived_feature_extractor: /y_2021, 963852
[2021-12-24 14:37:56.796553] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[162.156469s].
[2021-12-24 14:37:56.806542] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-12-24 14:39:15.018543] INFO: derived_feature_extractor: 提取完成 rank(correlation((high_0+low_0+close_0+open_0)/4,volume_0,4))*rank(correlation(rank(low_0),rank(mean(volume_0,50)),12)), 75.488s
[2021-12-24 14:39:22.304459] INFO: derived_feature_extractor: 提取完成 mean(turn_0*(return_0-1),15), 7.284s
[2021-12-24 14:39:25.969206] INFO: derived_feature_extractor: 提取完成 mean(mf_net_pct_m_0,30), 3.663s
[2021-12-24 14:39:37.085268] INFO: derived_feature_extractor: 提取完成 (mean(close_0,3)+mean(close_0,6)+mean(close_0,12)+mean(close_0,24))/(4*close_0), 11.114s
[2021-12-24 14:39:57.651022] INFO: derived_feature_extractor: 提取完成 -1*rank(covariance(rank(close_0),rank(volume_0),5)), 20.564s
[2021-12-24 14:39:57.654267] INFO: derived_feature_extractor: 提取完成 cond3=price_limit_status_0==2, 0.002s
[2021-12-24 14:40:27.605045] INFO: derived_feature_extractor: 提取完成 cond2=shift(ta_macd_dea(close_0),-1)[2021-12-24 14:40:56.428043] INFO: derived_feature_extractor: 提取完成 cond4=ta_macd_dea(close_0)[2021-12-24 14:40:56.955996] INFO: derived_feature_extractor: /y_2020, 186956
[2021-12-24 14:41:10.170086] INFO: derived_feature_extractor: /y_2021, 967671
[2021-12-24 14:41:12.201957] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[195.395414s].
[2021-12-24 14:41:12.246361] INFO: moduleinvoker: join.v3 开始运行..
[2021-12-24 14:41:14.070623] INFO: join: /y_2019, 行数=0/81785, 耗时=0.420415s
[2021-12-24 14:41:16.252827] INFO: join: /y_2020, 行数=0/944770, 耗时=2.179974s
[2021-12-24 14:41:19.217510] INFO: join: /y_2021, 行数=847292/963852, 耗时=2.960065s
[2021-12-24 14:41:19.388369] INFO: join: 最终行数: 847292
[2021-12-24 14:41:19.400796] INFO: moduleinvoker: join.v3 运行完成[7.154435s].
[2021-12-24 14:41:19.409481] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..
[2021-12-24 14:41:20.432443] INFO: A股股票过滤: 过滤 /y_2020, 168765/0/186956
[2021-12-24 14:41:23.894510] INFO: A股股票过滤: 过滤 /y_2021, 859610/0/967671
[2021-12-24 14:41:23.898543] INFO: A股股票过滤: 过滤完成, 1028375 + 0
[2021-12-24 14:41:23.926975] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[4.517488s].
[2021-12-24 14:41:23.936421] INFO: moduleinvoker: filter.v3 开始运行..
[2021-12-24 14:41:23.950528] INFO: filter: 使用表达式 cond3&cond2&cond4 过滤
[2021-12-24 14:41:24.048137] INFO: filter: 过滤 /y_2019, 0/0/0
[2021-12-24 14:41:24.085538] INFO: filter: 过滤 /y_2020, 0/0/0
[2021-12-24 14:41:24.933014] INFO: filter: 过滤 /y_2021, 28458/0/847292
[2021-12-24 14:41:24.975798] INFO: moduleinvoker: filter.v3 运行完成[1.039372s].
[2021-12-24 14:41:24.985011] INFO: moduleinvoker: filter.v3 开始运行..
[2021-12-24 14:41:25.019507] INFO: filter: 使用表达式 cond3&cond2&cond4 过滤
[2021-12-24 14:41:25.171605] INFO: filter: 过滤 /y_2020, 1055/0/168765
[2021-12-24 14:41:25.537905] INFO: filter: 过滤 /y_2021, 32917/0/859610
[2021-12-24 14:41:25.591707] INFO: moduleinvoker: filter.v3 运行完成[0.606691s].
[2021-12-24 14:41:25.601063] INFO: moduleinvoker: dropnan.v2 开始运行..
[2021-12-24 14:41:26.031416] INFO: dropnan: /y_2021, 28140/28458
[2021-12-24 14:41:26.111496] INFO: dropnan: 行数: 28140/28458
[2021-12-24 14:41:26.116792] INFO: moduleinvoker: dropnan.v2 运行完成[0.515724s].
[2021-12-24 14:41:26.125565] INFO: moduleinvoker: dropnan.v2 开始运行..
[2021-12-24 14:41:26.195788] INFO: dropnan: /y_2020, 0/1055
[2021-12-24 14:41:26.300638] INFO: dropnan: /y_2021, 30600/32917
[2021-12-24 14:41:26.363642] INFO: dropnan: 行数: 30600/33972
[2021-12-24 14:41:26.397342] INFO: moduleinvoker: dropnan.v2 运行完成[0.271766s].
[2021-12-24 14:41:26.405473] INFO: moduleinvoker: xgboost.v1 开始运行..
[2021-12-24 14:41:43.035660] INFO: moduleinvoker: xgboost.v1 运行完成[16.63019s].
[2021-12-24 14:41:43.041727] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-12-24 14:41:43.117792] INFO: moduleinvoker: instruments.v2 运行完成[0.076066s].
[2021-12-24 14:41:43.123330] INFO: moduleinvoker: input_features.v1 开始运行..
[2021-12-24 14:41:43.133914] INFO: moduleinvoker: 命中缓存
[2021-12-24 14:41:43.135365] INFO: moduleinvoker: input_features.v1 运行完成[0.012042s].
[2021-12-24 14:41:43.140281] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-12-24 14:41:43.210487] INFO: moduleinvoker: instruments.v2 运行完成[0.0702s].
[2021-12-24 14:41:43.246548] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..
[2021-12-24 14:41:43.967311] INFO: 自动标注(股票): 加载历史数据: 968484 行
[2021-12-24 14:41:43.968887] INFO: 自动标注(股票): 开始标注 ..
[2021-12-24 14:41:45.802791] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[2.556238s].
[2021-12-24 14:41:45.824635] INFO: moduleinvoker: features_short.v1 开始运行..
[2021-12-24 14:41:45.831248] INFO: moduleinvoker: 命中缓存
[2021-12-24 14:41:45.832768] INFO: moduleinvoker: features_short.v1 运行完成[0.008154s].
[2021-12-24 14:41:45.850772] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2021-12-24 14:41:48.819726] INFO: 基础特征抽取: 年份 2019, 特征行数=81785
[2021-12-24 14:41:54.195893] INFO: 基础特征抽取: 年份 2020, 特征行数=944770
[2021-12-24 14:41:59.871523] INFO: 基础特征抽取: 年份 2021, 特征行数=968484
[2021-12-24 14:41:59.964467] INFO: 基础特征抽取: 总行数: 1995039
[2021-12-24 14:41:59.971631] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[14.120873s].
[2021-12-24 14:41:59.987146] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2021-12-24 14:42:02.948547] INFO: 基础特征抽取: 年份 2020, 特征行数=178891
[2021-12-24 14:42:08.337448] INFO: 基础特征抽取: 年份 2021, 特征行数=972308
[2021-12-24 14:42:08.410432] INFO: 基础特征抽取: 总行数: 1151199
[2021-12-24 14:42:08.418373] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[8.431248s].
[2021-12-24 14:42:08.426275] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-12-24 14:43:16.398627] INFO: derived_feature_extractor: 提取完成 rank(correlation((high_0+low_0+close_0+open_0)/4,volume_0,4))*rank(correlation(rank(low_0),rank(mean(volume_0,50)),12)), 64.368s
[2021-12-24 14:43:17.820664] INFO: derived_feature_extractor: 提取完成 mean(turn_0*(return_0-1),15), 1.421s
[2021-12-24 14:43:19.194612] INFO: derived_feature_extractor: 提取完成 mean(mf_net_pct_m_0,30), 1.372s
[2021-12-24 14:43:24.616914] INFO: derived_feature_extractor: 提取完成 (mean(close_0,3)+mean(close_0,6)+mean(close_0,12)+mean(close_0,24))/(4*close_0), 5.421s
[2021-12-24 14:43:45.270242] INFO: derived_feature_extractor: 提取完成 -1*rank(covariance(rank(close_0),rank(volume_0),5)), 20.652s
[2021-12-24 14:43:45.274887] INFO: derived_feature_extractor: 提取完成 cond3=price_limit_status_0==2, 0.003s
[2021-12-24 14:43:55.164269] INFO: derived_feature_extractor: 提取完成 cond2=shift(ta_macd_dea(close_0),-1)[2021-12-24 14:44:05.003668] INFO: derived_feature_extractor: 提取完成 cond4=ta_macd_dea(close_0)[2021-12-24 14:44:05.438052] INFO: derived_feature_extractor: /y_2019, 81785
[2021-12-24 14:44:06.924295] INFO: derived_feature_extractor: /y_2020, 944770
[2021-12-24 14:44:08.907740] INFO: derived_feature_extractor: /y_2021, 968484
[2021-12-24 14:44:09.598356] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[121.172076s].
[2021-12-24 14:44:09.605981] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-12-24 14:45:11.729574] INFO: derived_feature_extractor: 提取完成 rank(correlation((high_0+low_0+close_0+open_0)/4,volume_0,4))*rank(correlation(rank(low_0),rank(mean(volume_0,50)),12)), 60.212s
[2021-12-24 14:45:12.490930] INFO: derived_feature_extractor: 提取完成 mean(turn_0*(return_0-1),15), 0.760s
[2021-12-24 14:45:13.253244] INFO: derived_feature_extractor: 提取完成 mean(mf_net_pct_m_0,30), 0.761s
[2021-12-24 14:45:16.308458] INFO: derived_feature_extractor: 提取完成 (mean(close_0,3)+mean(close_0,6)+mean(close_0,12)+mean(close_0,24))/(4*close_0), 3.054s
[2021-12-24 14:45:35.288641] INFO: derived_feature_extractor: 提取完成 -1*rank(covariance(rank(close_0),rank(volume_0),5)), 18.979s
[2021-12-24 14:45:35.292236] INFO: derived_feature_extractor: 提取完成 cond3=price_limit_status_0==2, 0.002s
[2021-12-24 14:45:55.225867] INFO: derived_feature_extractor: 提取完成 cond2=shift(ta_macd_dea(close_0),-1)[2021-12-24 14:46:20.657049] INFO: derived_feature_extractor: 提取完成 cond4=ta_macd_dea(close_0)[2021-12-24 14:46:24.312302] INFO: derived_feature_extractor: /y_2020, 178891
[2021-12-24 14:46:27.552248] INFO: derived_feature_extractor: /y_2021, 972308
[2021-12-24 14:46:33.836347] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[144.230359s].
[2021-12-24 14:46:33.877090] INFO: moduleinvoker: join.v3 开始运行..
[2021-12-24 14:46:35.831862] INFO: join: /y_2019, 行数=0/81785, 耗时=0.452399s
[2021-12-24 14:46:50.728380] INFO: join: /y_2020, 行数=0/944770, 耗时=14.894686s
[2021-12-24 14:47:57.484843] INFO: join: /y_2021, 行数=851705/968484, 耗时=66.746564s
[2021-12-24 14:47:57.602024] INFO: join: 最终行数: 851705
[2021-12-24 14:47:57.614425] INFO: moduleinvoker: join.v3 运行完成[83.737336s].
[2021-12-24 14:47:57.658885] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..
[2021-12-24 14:47:58.661223] INFO: A股股票过滤: 过滤 /y_2020, 161469/0/178891
[2021-12-24 14:48:44.715872] INFO: A股股票过滤: 过滤 /y_2021, 863623/0/972308
[2021-12-24 14:48:44.721851] INFO: A股股票过滤: 过滤完成, 1025092 + 0
[2021-12-24 14:48:44.771334] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[47.112437s].
[2021-12-24 14:48:44.822243] INFO: moduleinvoker: filter.v3 开始运行..
[2021-12-24 14:48:44.837166] INFO: filter: 使用表达式 cond3&cond2&cond4 过滤
[2021-12-24 14:48:44.947226] INFO: filter: 过滤 /y_2019, 0/0/0
[2021-12-24 14:48:44.982105] INFO: filter: 过滤 /y_2020, 0/0/0
[2021-12-24 14:49:06.724978] INFO: filter: 过滤 /y_2021, 28527/0/851705
[2021-12-24 14:49:06.760158] INFO: moduleinvoker: filter.v3 运行完成[21.937921s].
[2021-12-24 14:49:06.789683] INFO: moduleinvoker: filter.v3 开始运行..
[2021-12-24 14:49:06.846104] INFO: filter: 使用表达式 cond3&cond2&cond4 过滤
[2021-12-24 14:49:07.008859] INFO: filter: 过滤 /y_2020, 917/0/161469
[2021-12-24 14:49:19.838857] INFO: filter: 过滤 /y_2021, 33303/0/863623
[2021-12-24 14:49:19.881726] INFO: moduleinvoker: filter.v3 运行完成[13.092039s].
[2021-12-24 14:49:19.894577] INFO: moduleinvoker: dropnan.v2 开始运行..
[2021-12-24 14:49:20.016505] INFO: dropnan: /y_2021, 28208/28527
[2021-12-24 14:49:20.073972] INFO: dropnan: 行数: 28208/28527
[2021-12-24 14:49:20.078949] INFO: moduleinvoker: dropnan.v2 运行完成[0.18438s].
[2021-12-24 14:49:20.087487] INFO: moduleinvoker: dropnan.v2 开始运行..
[2021-12-24 14:49:20.157535] INFO: dropnan: /y_2020, 0/917
[2021-12-24 14:49:20.238015] INFO: dropnan: /y_2021, 30453/33303
[2021-12-24 14:49:20.282207] INFO: dropnan: 行数: 30453/34220
[2021-12-24 14:49:20.287589] INFO: moduleinvoker: dropnan.v2 运行完成[0.20008s].
[2021-12-24 14:49:20.296963] INFO: moduleinvoker: xgboost.v1 开始运行..
[2021-12-24 14:49:39.143519] INFO: moduleinvoker: xgboost.v1 运行完成[18.846557s].
[2021-12-24 14:49:39.151942] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-12-24 14:49:39.239411] INFO: moduleinvoker: instruments.v2 运行完成[0.087464s].
[2021-12-24 14:49:39.245880] INFO: moduleinvoker: input_features.v1 开始运行..
[2021-12-24 14:49:39.254104] INFO: moduleinvoker: 命中缓存
[2021-12-24 14:49:39.256101] INFO: moduleinvoker: input_features.v1 运行完成[0.010238s].
[2021-12-24 14:49:39.262717] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-12-24 14:49:39.337123] INFO: moduleinvoker: instruments.v2 运行完成[0.074389s].
[2021-12-24 14:49:39.349849] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..
[2021-12-24 14:49:48.385235] INFO: 自动标注(股票): 加载历史数据: 973121 行
[2021-12-24 14:49:48.386771] INFO: 自动标注(股票): 开始标注 ..
[2021-12-24 14:49:50.110975] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[10.761142s].
[2021-12-24 14:49:50.150764] INFO: moduleinvoker: features_short.v1 开始运行..
[2021-12-24 14:49:50.158433] INFO: moduleinvoker: 命中缓存
[2021-12-24 14:49:50.160189] INFO: moduleinvoker: features_short.v1 运行完成[0.009449s].
[2021-12-24 14:49:50.176288] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2021-12-24 14:49:55.249757] INFO: 基础特征抽取: 年份 2019, 特征行数=81785
[2021-12-24 14:50:01.261944] INFO: 基础特征抽取: 年份 2020, 特征行数=944770
[2021-12-24 14:50:07.566112] INFO: 基础特征抽取: 年份 2021, 特征行数=973121
[2021-12-24 14:50:07.701864] INFO: 基础特征抽取: 总行数: 1999676
[2021-12-24 14:50:07.710197] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[17.533929s].
[2021-12-24 14:50:07.724728] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2021-12-24 14:50:11.330051] INFO: 基础特征抽取: 年份 2020, 特征行数=178891
[2021-12-24 14:50:18.496186] INFO: 基础特征抽取: 年份 2021, 特征行数=976947
[2021-12-24 14:50:18.559153] INFO: 基础特征抽取: 总行数: 1155838
[2021-12-24 14:50:18.570192] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[10.84546s].
[2021-12-24 14:50:18.582327] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-12-24 14:51:24.832793] INFO: derived_feature_extractor: 提取完成 rank(correlation((high_0+low_0+close_0+open_0)/4,volume_0,4))*rank(correlation(rank(low_0),rank(mean(volume_0,50)),12)), 62.650s
[2021-12-24 14:51:26.168330] INFO: derived_feature_extractor: 提取完成 mean(turn_0*(return_0-1),15), 1.334s
[2021-12-24 14:51:27.505851] INFO: derived_feature_extractor: 提取完成 mean(mf_net_pct_m_0,30), 1.336s
[2021-12-24 14:51:32.884019] INFO: derived_feature_extractor: 提取完成 (mean(close_0,3)+mean(close_0,6)+mean(close_0,12)+mean(close_0,24))/(4*close_0), 5.377s
[2021-12-24 14:51:54.420545] INFO: derived_feature_extractor: 提取完成 -1*rank(covariance(rank(close_0),rank(volume_0),5)), 21.535s
[2021-12-24 14:51:54.428450] INFO: derived_feature_extractor: 提取完成 cond3=price_limit_status_0==2, 0.006s
[2021-12-24 14:52:04.237931] INFO: derived_feature_extractor: 提取完成 cond2=shift(ta_macd_dea(close_0),-1)[2021-12-24 14:52:13.966509] INFO: derived_feature_extractor: 提取完成 cond4=ta_macd_dea(close_0)[2021-12-24 14:52:14.445620] INFO: derived_feature_extractor: /y_2019, 81785
[2021-12-24 14:52:16.040052] INFO: derived_feature_extractor: /y_2020, 944770
[2021-12-24 14:52:17.983997] INFO: derived_feature_extractor: /y_2021, 973121
[2021-12-24 14:52:18.608518] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[120.026242s].
[2021-12-24 14:52:18.615568] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-12-24 14:53:18.390483] INFO: derived_feature_extractor: 提取完成 rank(correlation((high_0+low_0+close_0+open_0)/4,volume_0,4))*rank(correlation(rank(low_0),rank(mean(volume_0,50)),12)), 57.928s
[2021-12-24 14:53:19.190578] INFO: derived_feature_extractor: 提取完成 mean(turn_0*(return_0-1),15), 0.798s
[2021-12-24 14:53:19.965293] INFO: derived_feature_extractor: 提取完成 mean(mf_net_pct_m_0,30), 0.773s
[2021-12-24 14:53:23.103384] INFO: derived_feature_extractor: 提取完成 (mean(close_0,3)+mean(close_0,6)+mean(close_0,12)+mean(close_0,24))/(4*close_0), 3.137s
[2021-12-24 14:53:42.064877] INFO: derived_feature_extractor: 提取完成 -1*rank(covariance(rank(close_0),rank(volume_0),5)), 18.960s
[2021-12-24 14:53:42.067972] INFO: derived_feature_extractor: 提取完成 cond3=price_limit_status_0==2, 0.002s
[2021-12-24 14:53:51.105256] INFO: derived_feature_extractor: 提取完成 cond2=shift(ta_macd_dea(close_0),-1)[2021-12-24 14:54:00.151261] INFO: derived_feature_extractor: 提取完成 cond4=ta_macd_dea(close_0)[2021-12-24 14:54:00.617198] INFO: derived_feature_extractor: /y_2020, 178891
[2021-12-24 14:54:02.181363] INFO: derived_feature_extractor: /y_2021, 976947
[2021-12-24 14:54:02.797276] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[104.181647s].
[2021-12-24 14:54:02.806350] INFO: moduleinvoker: join.v3 开始运行..
[2021-12-24 14:54:04.613045] INFO: join: /y_2019, 行数=0/81785, 耗时=0.452274s
[2021-12-24 14:54:06.806969] INFO: join: /y_2020, 行数=0/944770, 耗时=2.191542s
[2021-12-24 14:54:09.795404] INFO: join: /y_2021, 行数=855506/973121, 耗时=2.983765s
[2021-12-24 14:54:09.876251] INFO: join: 最终行数: 855506
[2021-12-24 14:54:09.889398] INFO: moduleinvoker: join.v3 运行完成[7.083048s].
[2021-12-24 14:54:09.898310] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..
[2021-12-24 14:54:10.924661] INFO: A股股票过滤: 过滤 /y_2020, 161469/0/178891
[2021-12-24 14:54:14.866338] INFO: A股股票过滤: 过滤 /y_2021, 867638/0/976947
[2021-12-24 14:54:14.870491] INFO: A股股票过滤: 过滤完成, 1029107 + 0
[2021-12-24 14:54:14.901042] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[5.002724s].
[2021-12-24 14:54:14.909519] INFO: moduleinvoker: filter.v3 开始运行..
[2021-12-24 14:54:14.922802] INFO: filter: 使用表达式 cond3&cond2&cond4 过滤
[2021-12-24 14:54:14.993113] INFO: filter: 过滤 /y_2019, 0/0/0
[2021-12-24 14:54:15.029368] INFO: filter: 过滤 /y_2020, 0/0/0
[2021-12-24 14:54:15.694151] INFO: filter: 过滤 /y_2021, 28723/0/855506
[2021-12-24 14:54:15.718415] INFO: moduleinvoker: filter.v3 运行完成[0.808892s].
[2021-12-24 14:54:15.726314] INFO: moduleinvoker: filter.v3 开始运行..
[2021-12-24 14:54:15.740942] INFO: filter: 使用表达式 cond3&cond2&cond4 过滤
[2021-12-24 14:54:15.960827] INFO: filter: 过滤 /y_2020, 917/0/161469
[2021-12-24 14:54:16.410927] INFO: filter: 过滤 /y_2021, 33709/0/867638
[2021-12-24 14:54:16.443715] INFO: moduleinvoker: filter.v3 运行完成[0.717386s].
[2021-12-24 14:54:16.460554] INFO: moduleinvoker: dropnan.v2 开始运行..
[2021-12-24 14:54:16.602650] INFO: dropnan: /y_2021, 28400/28723
[2021-12-24 14:54:16.692990] INFO: dropnan: 行数: 28400/28723
[2021-12-24 14:54:16.698523] INFO: moduleinvoker: dropnan.v2 运行完成[0.237929s].
[2021-12-24 14:54:16.710043] INFO: moduleinvoker: dropnan.v2 开始运行..
[2021-12-24 14:54:16.778390] INFO: dropnan: /y_2020, 0/917
[2021-12-24 14:54:16.883724] INFO: dropnan: /y_2021, 30856/33709
[2021-12-24 14:54:16.926167] INFO: dropnan: 行数: 30856/34626
[2021-12-24 14:54:16.931502] INFO: moduleinvoker: dropnan.v2 运行完成[0.221463s].
[2021-12-24 14:54:16.944587] INFO: moduleinvoker: xgboost.v1 开始运行..
[2021-12-24 14:54:37.260130] INFO: moduleinvoker: xgboost.v1 运行完成[20.315538s].
[2021-12-24 14:54:37.282976] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-12-24 14:54:37.453304] INFO: moduleinvoker: instruments.v2 运行完成[0.170332s].
[2021-12-24 14:54:37.459298] INFO: moduleinvoker: input_features.v1 开始运行..
[2021-12-24 14:54:37.468688] INFO: moduleinvoker: 命中缓存
[2021-12-24 14:54:37.470944] INFO: moduleinvoker: input_features.v1 运行完成[0.011662s].
[2021-12-24 14:54:37.477344] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-12-24 14:54:37.602645] INFO: moduleinvoker: instruments.v2 运行完成[0.125305s].
[2021-12-24 14:54:37.613878] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..
[2021-12-24 14:54:38.516889] INFO: 自动标注(股票): 加载历史数据: 977760 行
[2021-12-24 14:54:38.518587] INFO: 自动标注(股票): 开始标注 ..
[2021-12-24 14:54:44.248891] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[6.635021s].
[2021-12-24 14:54:44.274821] INFO: moduleinvoker: features_short.v1 开始运行..
[2021-12-24 14:54:44.282861] INFO: moduleinvoker: 命中缓存
[2021-12-24 14:54:44.284436] INFO: moduleinvoker: features_short.v1 运行完成[0.00965s].
[2021-12-24 14:54:44.302625] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2021-12-24 14:54:50.434501] INFO: 基础特征抽取: 年份 2019, 特征行数=81785
[2021-12-24 14:55:19.865202] INFO: 基础特征抽取: 年份 2020, 特征行数=944770
[2021-12-24 14:55:36.741558] INFO: 基础特征抽取: 年份 2021, 特征行数=977760
[2021-12-24 14:55:36.830527] INFO: 基础特征抽取: 总行数: 2004315
[2021-12-24 14:55:36.838605] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[52.535985s].
[2021-12-24 14:55:36.874584] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2021-12-24 14:55:41.668430] INFO: 基础特征抽取: 年份 2020, 特征行数=174858
[2021-12-24 14:56:04.659643] INFO: 基础特征抽取: 年份 2021, 特征行数=981587
[2021-12-24 14:56:04.759627] INFO: 基础特征抽取: 总行数: 1156445
[2021-12-24 14:56:04.769461] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[27.894875s].
[2021-12-24 14:56:04.810756] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-12-24 14:58:44.825460] INFO: derived_feature_extractor: 提取完成 rank(correlation((high_0+low_0+close_0+open_0)/4,volume_0,4))*rank(correlation(rank(low_0),rank(mean(volume_0,50)),12)), 112.124s
[2021-12-24 14:58:46.348458] INFO: derived_feature_extractor: 提取完成 mean(turn_0*(return_0-1),15), 1.521s
[2021-12-24 14:58:47.900275] INFO: derived_feature_extractor: 提取完成 mean(mf_net_pct_m_0,30), 1.550s
[2021-12-24 14:58:54.122416] INFO: derived_feature_extractor: 提取完成 (mean(close_0,3)+mean(close_0,6)+mean(close_0,12)+mean(close_0,24))/(4*close_0), 6.220s
[2021-12-24 14:59:27.131135] INFO: derived_feature_extractor: 提取完成 -1*rank(covariance(rank(close_0),rank(volume_0),5)), 33.007s
[2021-12-24 14:59:27.140163] INFO: derived_feature_extractor: 提取完成 cond3=price_limit_status_0==2, 0.007s
[2021-12-24 14:59:56.156013] INFO: derived_feature_extractor: 提取完成 cond2=shift(ta_macd_dea(close_0),-1)[2021-12-24 15:00:12.623470] INFO: derived_feature_extractor: 提取完成 cond4=ta_macd_dea(close_0)[2021-12-24 15:00:13.199642] INFO: derived_feature_extractor: /y_2019, 81785
[2021-12-24 15:00:15.243488] INFO: derived_feature_extractor: /y_2020, 944770
[2021-12-24 15:00:17.615515] INFO: derived_feature_extractor: /y_2021, 977760
[2021-12-24 15:00:18.646409] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[253.835665s].
[2021-12-24 15:00:18.659648] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-12-24 15:01:24.528044] INFO: derived_feature_extractor: 提取完成 rank(correlation((high_0+low_0+close_0+open_0)/4,volume_0,4))*rank(correlation(rank(low_0),rank(mean(volume_0,50)),12)), 63.464s
[2021-12-24 15:01:25.338689] INFO: derived_feature_extractor: 提取完成 mean(turn_0*(return_0-1),15), 0.809s
[2021-12-24 15:01:26.142130] INFO: derived_feature_extractor: 提取完成 mean(mf_net_pct_m_0,30), 0.802s
[2021-12-24 15:01:29.438234] INFO: derived_feature_extractor: 提取完成 (mean(close_0,3)+mean(close_0,6)+mean(close_0,12)+mean(close_0,24))/(4*close_0), 3.295s
[2021-12-24 15:01:49.276119] INFO: derived_feature_extractor: 提取完成 -1*rank(covariance(rank(close_0),rank(volume_0),5)), 19.836s
[2021-12-24 15:01:49.279274] INFO: derived_feature_extractor: 提取完成 cond3=price_limit_status_0==2, 0.002s
[2021-12-24 15:01:58.195794] INFO: derived_feature_extractor: 提取完成 cond2=shift(ta_macd_dea(close_0),-1)[2021-12-24 15:02:06.855672] INFO: derived_feature_extractor: 提取完成 cond4=ta_macd_dea(close_0)[2021-12-24 15:02:07.571969] INFO: derived_feature_extractor: /y_2020, 174858
[2021-12-24 15:02:09.496439] INFO: derived_feature_extractor: /y_2021, 981587
[2021-12-24 15:02:10.435858] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[111.776217s].
[2021-12-24 15:02:10.446152] INFO: moduleinvoker: join.v3 开始运行..
[2021-12-24 15:02:12.687246] INFO: join: /y_2019, 行数=0/81785, 耗时=0.42821s
[2021-12-24 15:02:14.925495] INFO: join: /y_2020, 行数=0/944770, 耗时=2.236215s
[2021-12-24 15:02:18.096471] INFO: join: /y_2021, 行数=859844/977760, 耗时=3.164316s
[2021-12-24 15:02:18.237031] INFO: join: 最终行数: 859844
[2021-12-24 15:02:18.254413] INFO: moduleinvoker: join.v3 运行完成[7.80825s].
[2021-12-24 15:02:18.265044] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..
[2021-12-24 15:02:19.491425] INFO: A股股票过滤: 过滤 /y_2020, 157820/0/174858
[2021-12-24 15:02:23.200112] INFO: A股股票过滤: 过滤 /y_2021, 871654/0/981587
[2021-12-24 15:02:23.204042] INFO: A股股票过滤: 过滤完成, 1029474 + 0
[2021-12-24 15:02:23.226646] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[4.961605s].
[2021-12-24 15:02:23.234885] INFO: moduleinvoker: filter.v3 开始运行..
[2021-12-24 15:02:23.271299] INFO: filter: 使用表达式 cond3&cond2&cond4 过滤
[2021-12-24 15:02:23.365382] INFO: filter: 过滤 /y_2019, 0/0/0
[2021-12-24 15:02:23.392452] INFO: filter: 过滤 /y_2020, 0/0/0
[2021-12-24 15:02:24.235606] INFO: filter: 过滤 /y_2021, 28911/0/859844
[2021-12-24 15:02:24.258865] INFO: moduleinvoker: filter.v3 运行完成[1.023978s].
[2021-12-24 15:02:24.266374] INFO: moduleinvoker: filter.v3 开始运行..
[2021-12-24 15:02:24.278974] INFO: filter: 使用表达式 cond3&cond2&cond4 过滤
[2021-12-24 15:02:24.418330] INFO: filter: 过滤 /y_2020, 872/0/157820
[2021-12-24 15:02:24.761365] INFO: filter: 过滤 /y_2021, 33894/0/871654
[2021-12-24 15:02:24.787449] INFO: moduleinvoker: filter.v3 运行完成[0.521064s].
[2021-12-24 15:02:24.795466] INFO: moduleinvoker: dropnan.v2 开始运行..
[2021-12-24 15:02:24.981320] INFO: dropnan: /y_2021, 28581/28911
[2021-12-24 15:02:25.054317] INFO: dropnan: 行数: 28581/28911
[2021-12-24 15:02:25.059095] INFO: moduleinvoker: dropnan.v2 运行完成[0.263617s].
[2021-12-24 15:02:25.066717] INFO: moduleinvoker: dropnan.v2 开始运行..
[2021-12-24 15:02:25.130196] INFO: dropnan: /y_2020, 0/872
[2021-12-24 15:02:25.197776] INFO: dropnan: /y_2021, 30840/33894
[2021-12-24 15:02:25.232870] INFO: dropnan: 行数: 30840/34766
[2021-12-24 15:02:25.237286] INFO: moduleinvoker: dropnan.v2 运行完成[0.170575s].
[2021-12-24 15:02:25.244382] INFO: moduleinvoker: xgboost.v1 开始运行..
[2021-12-24 15:02:42.387470] INFO: moduleinvoker: xgboost.v1 运行完成[17.143085s].
[2021-12-24 15:02:42.394122] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-12-24 15:02:42.718998] INFO: moduleinvoker: instruments.v2 运行完成[0.324882s].
[2021-12-24 15:02:42.723514] INFO: moduleinvoker: input_features.v1 开始运行..
[2021-12-24 15:02:42.730778] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:02:42.732282] INFO: moduleinvoker: input_features.v1 运行完成[0.008774s].
[2021-12-24 15:02:42.736842] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-12-24 15:02:42.891347] INFO: moduleinvoker: instruments.v2 运行完成[0.154494s].
[2021-12-24 15:02:42.900121] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..
[2021-12-24 15:02:43.570579] INFO: 自动标注(股票): 加载历史数据: 982400 行
[2021-12-24 15:02:43.572098] INFO: 自动标注(股票): 开始标注 ..
[2021-12-24 15:02:45.693572] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[2.79345s].
[2021-12-24 15:02:45.722796] INFO: moduleinvoker: features_short.v1 开始运行..
[2021-12-24 15:02:45.730054] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:02:45.731395] INFO: moduleinvoker: features_short.v1 运行完成[0.008623s].
[2021-12-24 15:02:45.745654] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2021-12-24 15:02:48.822315] INFO: 基础特征抽取: 年份 2019, 特征行数=81785
[2021-12-24 15:02:55.954708] INFO: 基础特征抽取: 年份 2020, 特征行数=944770
[2021-12-24 15:03:04.527909] INFO: 基础特征抽取: 年份 2021, 特征行数=982400
[2021-12-24 15:03:04.943538] INFO: 基础特征抽取: 总行数: 2008955
[2021-12-24 15:03:04.951154] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[19.205501s].
[2021-12-24 15:03:04.964747] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2021-12-24 15:03:08.743029] INFO: 基础特征抽取: 年份 2020, 特征行数=170825
[2021-12-24 15:03:16.388095] INFO: 基础特征抽取: 年份 2021, 特征行数=986229
[2021-12-24 15:03:16.481405] INFO: 基础特征抽取: 总行数: 1157054
[2021-12-24 15:03:16.489251] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[11.524506s].
[2021-12-24 15:03:16.496519] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-12-24 15:04:24.350432] INFO: derived_feature_extractor: 提取完成 rank(correlation((high_0+low_0+close_0+open_0)/4,volume_0,4))*rank(correlation(rank(low_0),rank(mean(volume_0,50)),12)), 63.793s
[2021-12-24 15:04:25.684645] INFO: derived_feature_extractor: 提取完成 mean(turn_0*(return_0-1),15), 1.333s
[2021-12-24 15:04:27.034461] INFO: derived_feature_extractor: 提取完成 mean(mf_net_pct_m_0,30), 1.348s
[2021-12-24 15:04:32.361393] INFO: derived_feature_extractor: 提取完成 (mean(close_0,3)+mean(close_0,6)+mean(close_0,12)+mean(close_0,24))/(4*close_0), 5.325s
[2021-12-24 15:04:53.564653] INFO: derived_feature_extractor: 提取完成 -1*rank(covariance(rank(close_0),rank(volume_0),5)), 21.202s
[2021-12-24 15:04:53.569228] INFO: derived_feature_extractor: 提取完成 cond3=price_limit_status_0==2, 0.003s
[2021-12-24 15:05:03.667275] INFO: derived_feature_extractor: 提取完成 cond2=shift(ta_macd_dea(close_0),-1)[2021-12-24 15:05:13.581253] INFO: derived_feature_extractor: 提取完成 cond4=ta_macd_dea(close_0)[2021-12-24 15:05:14.098538] INFO: derived_feature_extractor: /y_2019, 81785
[2021-12-24 15:05:15.866874] INFO: derived_feature_extractor: /y_2020, 944770
[2021-12-24 15:05:17.991470] INFO: derived_feature_extractor: /y_2021, 982400
[2021-12-24 15:05:18.807015] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[122.310493s].
[2021-12-24 15:05:18.814743] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-12-24 15:06:21.271267] INFO: derived_feature_extractor: 提取完成 rank(correlation((high_0+low_0+close_0+open_0)/4,volume_0,4))*rank(correlation(rank(low_0),rank(mean(volume_0,50)),12)), 60.422s
[2021-12-24 15:06:22.201251] INFO: derived_feature_extractor: 提取完成 mean(turn_0*(return_0-1),15), 0.928s
[2021-12-24 15:06:23.066455] INFO: derived_feature_extractor: 提取完成 mean(mf_net_pct_m_0,30), 0.864s
[2021-12-24 15:06:26.488474] INFO: derived_feature_extractor: 提取完成 (mean(close_0,3)+mean(close_0,6)+mean(close_0,12)+mean(close_0,24))/(4*close_0), 3.421s
[2021-12-24 15:06:46.157447] INFO: derived_feature_extractor: 提取完成 -1*rank(covariance(rank(close_0),rank(volume_0),5)), 19.667s
[2021-12-24 15:06:46.160784] INFO: derived_feature_extractor: 提取完成 cond3=price_limit_status_0==2, 0.002s
[2021-12-24 15:06:56.035980] INFO: derived_feature_extractor: 提取完成 cond2=shift(ta_macd_dea(close_0),-1)[2021-12-24 15:07:05.153068] INFO: derived_feature_extractor: 提取完成 cond4=ta_macd_dea(close_0)[2021-12-24 15:07:05.606600] INFO: derived_feature_extractor: /y_2020, 170825
[2021-12-24 15:07:07.255844] INFO: derived_feature_extractor: /y_2021, 986229
[2021-12-24 15:07:07.836186] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[109.021439s].
[2021-12-24 15:07:07.844920] INFO: moduleinvoker: join.v3 开始运行..
[2021-12-24 15:07:10.225408] INFO: join: /y_2019, 行数=0/81785, 耗时=0.75335s
[2021-12-24 15:07:12.642933] INFO: join: /y_2020, 行数=0/944770, 耗时=2.415569s
[2021-12-24 15:07:16.079899] INFO: join: /y_2021, 行数=863593/982400, 耗时=3.4306s
[2021-12-24 15:07:16.185736] INFO: join: 最终行数: 863593
[2021-12-24 15:07:16.200193] INFO: moduleinvoker: join.v3 运行完成[8.355268s].
[2021-12-24 15:07:16.208840] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..
[2021-12-24 15:07:17.122308] INFO: A股股票过滤: 过滤 /y_2020, 154173/0/170825
[2021-12-24 15:07:20.778132] INFO: A股股票过滤: 过滤 /y_2021, 875671/0/986229
[2021-12-24 15:07:20.783126] INFO: A股股票过滤: 过滤完成, 1029844 + 0
[2021-12-24 15:07:20.808478] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[4.599651s].
[2021-12-24 15:07:20.817514] INFO: moduleinvoker: filter.v3 开始运行..
[2021-12-24 15:07:20.830559] INFO: filter: 使用表达式 cond3&cond2&cond4 过滤
[2021-12-24 15:07:20.956307] INFO: filter: 过滤 /y_2019, 0/0/0
[2021-12-24 15:07:20.986153] INFO: filter: 过滤 /y_2020, 0/0/0
[2021-12-24 15:07:21.808350] INFO: filter: 过滤 /y_2021, 29228/0/863593
[2021-12-24 15:07:21.839593] INFO: moduleinvoker: filter.v3 运行完成[1.02208s].
[2021-12-24 15:07:21.848294] INFO: moduleinvoker: filter.v3 开始运行..
[2021-12-24 15:07:21.865551] INFO: filter: 使用表达式 cond3&cond2&cond4 过滤
[2021-12-24 15:07:22.082362] INFO: filter: 过滤 /y_2020, 854/0/154173
[2021-12-24 15:07:22.597263] INFO: filter: 过滤 /y_2021, 34038/0/875671
[2021-12-24 15:07:22.621416] INFO: moduleinvoker: filter.v3 运行完成[0.773119s].
[2021-12-24 15:07:22.630375] INFO: moduleinvoker: dropnan.v2 开始运行..
[2021-12-24 15:07:22.763128] INFO: dropnan: /y_2021, 28893/29228
[2021-12-24 15:07:22.804230] INFO: dropnan: 行数: 28893/29228
[2021-12-24 15:07:22.809135] INFO: moduleinvoker: dropnan.v2 运行完成[0.178767s].
[2021-12-24 15:07:22.817609] INFO: moduleinvoker: dropnan.v2 开始运行..
[2021-12-24 15:07:22.907391] INFO: dropnan: /y_2020, 0/854
[2021-12-24 15:07:23.060112] INFO: dropnan: /y_2021, 30741/34038
[2021-12-24 15:07:23.159786] INFO: dropnan: 行数: 30741/34892
[2021-12-24 15:07:23.164441] INFO: moduleinvoker: dropnan.v2 运行完成[0.346831s].
[2021-12-24 15:07:23.171741] INFO: moduleinvoker: xgboost.v1 开始运行..
[2021-12-24 15:07:40.990369] INFO: moduleinvoker: xgboost.v1 运行完成[17.818628s].
[2021-12-24 15:07:40.996636] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-12-24 15:07:41.110303] INFO: moduleinvoker: instruments.v2 运行完成[0.113643s].
[2021-12-24 15:07:41.115759] INFO: moduleinvoker: input_features.v1 开始运行..
[2021-12-24 15:07:41.122422] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:07:41.124608] INFO: moduleinvoker: input_features.v1 运行完成[0.008854s].
[2021-12-24 15:07:41.130688] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-12-24 15:07:41.209183] INFO: moduleinvoker: instruments.v2 运行完成[0.078478s].
[2021-12-24 15:07:41.219104] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..
[2021-12-24 15:07:41.891110] INFO: 自动标注(股票): 加载历史数据: 987042 行
[2021-12-24 15:07:41.892730] INFO: 自动标注(股票): 开始标注 ..
[2021-12-24 15:07:43.598110] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[2.379008s].
[2021-12-24 15:07:43.613739] INFO: moduleinvoker: features_short.v1 开始运行..
[2021-12-24 15:07:43.621035] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:07:43.622301] INFO: moduleinvoker: features_short.v1 运行完成[0.008566s].
[2021-12-24 15:07:43.635989] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2021-12-24 15:07:47.009030] INFO: 基础特征抽取: 年份 2019, 特征行数=81785
[2021-12-24 15:07:53.587160] INFO: 基础特征抽取: 年份 2020, 特征行数=944770
[2021-12-24 15:08:00.368907] INFO: 基础特征抽取: 年份 2021, 特征行数=987042
[2021-12-24 15:08:00.473306] INFO: 基础特征抽取: 总行数: 2013597
[2021-12-24 15:08:00.488708] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[16.85271s].
[2021-12-24 15:08:00.505362] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2021-12-24 15:08:03.886945] INFO: 基础特征抽取: 年份 2020, 特征行数=166791
[2021-12-24 15:08:10.375050] INFO: 基础特征抽取: 年份 2021, 特征行数=990874
[2021-12-24 15:08:10.445890] INFO: 基础特征抽取: 总行数: 1157665
[2021-12-24 15:08:10.454421] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[9.949072s].
[2021-12-24 15:08:10.461802] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-12-24 15:09:19.392565] INFO: derived_feature_extractor: 提取完成 rank(correlation((high_0+low_0+close_0+open_0)/4,volume_0,4))*rank(correlation(rank(low_0),rank(mean(volume_0,50)),12)), 65.154s
[2021-12-24 15:09:20.760219] INFO: derived_feature_extractor: 提取完成 mean(turn_0*(return_0-1),15), 1.366s
[2021-12-24 15:09:22.149935] INFO: derived_feature_extractor: 提取完成 mean(mf_net_pct_m_0,30), 1.388s
[2021-12-24 15:09:27.514606] INFO: derived_feature_extractor: 提取完成 (mean(close_0,3)+mean(close_0,6)+mean(close_0,12)+mean(close_0,24))/(4*close_0), 5.363s
[2021-12-24 15:09:49.642763] INFO: derived_feature_extractor: 提取完成 -1*rank(covariance(rank(close_0),rank(volume_0),5)), 22.127s
[2021-12-24 15:09:49.646740] INFO: derived_feature_extractor: 提取完成 cond3=price_limit_status_0==2, 0.002s
[2021-12-24 15:10:00.554518] INFO: derived_feature_extractor: 提取完成 cond2=shift(ta_macd_dea(close_0),-1)[2021-12-24 15:10:11.638896] INFO: derived_feature_extractor: 提取完成 cond4=ta_macd_dea(close_0)[2021-12-24 15:10:12.114706] INFO: derived_feature_extractor: /y_2019, 81785
[2021-12-24 15:10:13.877986] INFO: derived_feature_extractor: /y_2020, 944770
[2021-12-24 15:10:16.119333] INFO: derived_feature_extractor: /y_2021, 987042
[2021-12-24 15:10:16.980539] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[126.518734s].
[2021-12-24 15:10:16.988273] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-12-24 15:11:19.750545] INFO: derived_feature_extractor: 提取完成 rank(correlation((high_0+low_0+close_0+open_0)/4,volume_0,4))*rank(correlation(rank(low_0),rank(mean(volume_0,50)),12)), 60.710s
[2021-12-24 15:11:20.525526] INFO: derived_feature_extractor: 提取完成 mean(turn_0*(return_0-1),15), 0.773s
[2021-12-24 15:11:21.300922] INFO: derived_feature_extractor: 提取完成 mean(mf_net_pct_m_0,30), 0.774s
[2021-12-24 15:11:24.385735] INFO: derived_feature_extractor: 提取完成 (mean(close_0,3)+mean(close_0,6)+mean(close_0,12)+mean(close_0,24))/(4*close_0), 3.083s
[2021-12-24 15:11:43.351942] INFO: derived_feature_extractor: 提取完成 -1*rank(covariance(rank(close_0),rank(volume_0),5)), 18.964s
[2021-12-24 15:11:43.355314] INFO: derived_feature_extractor: 提取完成 cond3=price_limit_status_0==2, 0.002s
[2021-12-24 15:11:52.291741] INFO: derived_feature_extractor: 提取完成 cond2=shift(ta_macd_dea(close_0),-1)[2021-12-24 15:12:01.280397] INFO: derived_feature_extractor: 提取完成 cond4=ta_macd_dea(close_0)[2021-12-24 15:12:01.739330] INFO: derived_feature_extractor: /y_2020, 166791
[2021-12-24 15:12:03.590243] INFO: derived_feature_extractor: /y_2021, 990874
[2021-12-24 15:12:04.339889] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[107.351605s].
[2021-12-24 15:12:04.348468] INFO: moduleinvoker: join.v3 开始运行..
[2021-12-24 15:12:06.230930] INFO: join: /y_2019, 行数=0/81785, 耗时=0.423815s
[2021-12-24 15:12:08.410577] INFO: join: /y_2020, 行数=0/944770, 耗时=2.17771s
[2021-12-24 15:12:11.449487] INFO: join: /y_2021, 行数=867831/987042, 耗时=3.033086s
[2021-12-24 15:12:11.546490] INFO: join: 最终行数: 867831
[2021-12-24 15:12:11.557568] INFO: moduleinvoker: join.v3 运行完成[7.209098s].
[2021-12-24 15:12:11.565424] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..
[2021-12-24 15:12:12.389188] INFO: A股股票过滤: 过滤 /y_2020, 150526/0/166791
[2021-12-24 15:12:15.903887] INFO: A股股票过滤: 过滤 /y_2021, 879689/0/990874
[2021-12-24 15:12:15.908149] INFO: A股股票过滤: 过滤完成, 1030215 + 0
[2021-12-24 15:12:15.932178] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[4.366746s].
[2021-12-24 15:12:15.941000] INFO: moduleinvoker: filter.v3 开始运行..
[2021-12-24 15:12:15.954284] INFO: filter: 使用表达式 cond3&cond2&cond4 过滤
[2021-12-24 15:12:16.028255] INFO: filter: 过滤 /y_2019, 0/0/0
[2021-12-24 15:12:16.059472] INFO: filter: 过滤 /y_2020, 0/0/0
[2021-12-24 15:12:16.759673] INFO: filter: 过滤 /y_2021, 29652/0/867831
[2021-12-24 15:12:16.786879] INFO: moduleinvoker: filter.v3 运行完成[0.845877s].
[2021-12-24 15:12:16.795034] INFO: moduleinvoker: filter.v3 开始运行..
[2021-12-24 15:12:16.809120] INFO: filter: 使用表达式 cond3&cond2&cond4 过滤
[2021-12-24 15:12:16.950543] INFO: filter: 过滤 /y_2020, 745/0/150526
[2021-12-24 15:12:17.282123] INFO: filter: 过滤 /y_2021, 34159/0/879689
[2021-12-24 15:12:17.309206] INFO: moduleinvoker: filter.v3 运行完成[0.514165s].
[2021-12-24 15:12:17.318686] INFO: moduleinvoker: dropnan.v2 开始运行..
[2021-12-24 15:12:17.446295] INFO: dropnan: /y_2021, 29313/29652
[2021-12-24 15:12:17.488419] INFO: dropnan: 行数: 29313/29652
[2021-12-24 15:12:17.493697] INFO: moduleinvoker: dropnan.v2 运行完成[0.175014s].
[2021-12-24 15:12:17.502013] INFO: moduleinvoker: dropnan.v2 开始运行..
[2021-12-24 15:12:17.567053] INFO: dropnan: /y_2020, 0/745
[2021-12-24 15:12:17.633967] INFO: dropnan: /y_2021, 30566/34159
[2021-12-24 15:12:17.709070] INFO: dropnan: 行数: 30566/34904
[2021-12-24 15:12:17.713972] INFO: moduleinvoker: dropnan.v2 运行完成[0.211961s].
[2021-12-24 15:12:17.721052] INFO: moduleinvoker: xgboost.v1 开始运行..
[2021-12-24 15:12:34.832666] INFO: moduleinvoker: xgboost.v1 运行完成[17.111609s].
[2021-12-24 15:12:34.840145] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-12-24 15:12:34.938334] INFO: moduleinvoker: instruments.v2 运行完成[0.098185s].
[2021-12-24 15:12:34.943248] INFO: moduleinvoker: input_features.v1 开始运行..
[2021-12-24 15:12:34.949460] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:12:34.951137] INFO: moduleinvoker: input_features.v1 运行完成[0.007902s].
[2021-12-24 15:12:34.956340] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-12-24 15:12:35.061253] INFO: moduleinvoker: instruments.v2 运行完成[0.104901s].
[2021-12-24 15:12:35.070917] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..
[2021-12-24 15:12:35.760299] INFO: 自动标注(股票): 加载历史数据: 991687 行
[2021-12-24 15:12:35.761876] INFO: 自动标注(股票): 开始标注 ..
[2021-12-24 15:12:37.490245] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[2.419325s].
[2021-12-24 15:12:37.503341] INFO: moduleinvoker: features_short.v1 开始运行..
[2021-12-24 15:12:37.510209] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:12:37.511575] INFO: moduleinvoker: features_short.v1 运行完成[0.008256s].
[2021-12-24 15:12:37.524270] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2021-12-24 15:12:40.281912] INFO: 基础特征抽取: 年份 2019, 特征行数=81785
[2021-12-24 15:12:46.123986] INFO: 基础特征抽取: 年份 2020, 特征行数=944770
[2021-12-24 15:12:52.308023] INFO: 基础特征抽取: 年份 2021, 特征行数=991687
[2021-12-24 15:12:52.387198] INFO: 基础特征抽取: 总行数: 2018242
[2021-12-24 15:12:52.395194] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[14.870915s].
[2021-12-24 15:12:52.408523] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2021-12-24 15:12:55.650719] INFO: 基础特征抽取: 年份 2020, 特征行数=158716
[2021-12-24 15:13:02.382454] INFO: 基础特征抽取: 年份 2021, 特征行数=995519
[2021-12-24 15:13:02.464080] INFO: 基础特征抽取: 总行数: 1154235
[2021-12-24 15:13:02.472985] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[10.064472s].
[2021-12-24 15:13:02.480618] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-12-24 15:14:10.139381] INFO: derived_feature_extractor: 提取完成 rank(correlation((high_0+low_0+close_0+open_0)/4,volume_0,4))*rank(correlation(rank(low_0),rank(mean(volume_0,50)),12)), 64.247s
[2021-12-24 15:14:11.473863] INFO: derived_feature_extractor: 提取完成 mean(turn_0*(return_0-1),15), 1.333s
[2021-12-24 15:14:12.795484] INFO: derived_feature_extractor: 提取完成 mean(mf_net_pct_m_0,30), 1.320s
[2021-12-24 15:14:18.169896] INFO: derived_feature_extractor: 提取完成 (mean(close_0,3)+mean(close_0,6)+mean(close_0,12)+mean(close_0,24))/(4*close_0), 5.373s
[2021-12-24 15:14:39.168281] INFO: derived_feature_extractor: 提取完成 -1*rank(covariance(rank(close_0),rank(volume_0),5)), 20.997s
[2021-12-24 15:14:39.172059] INFO: derived_feature_extractor: 提取完成 cond3=price_limit_status_0==2, 0.002s
[2021-12-24 15:14:49.557484] INFO: derived_feature_extractor: 提取完成 cond2=shift(ta_macd_dea(close_0),-1)[2021-12-24 15:14:59.844080] INFO: derived_feature_extractor: 提取完成 cond4=ta_macd_dea(close_0)[2021-12-24 15:15:00.263404] INFO: derived_feature_extractor: /y_2019, 81785
[2021-12-24 15:15:01.747667] INFO: derived_feature_extractor: /y_2020, 944770
[2021-12-24 15:15:03.786553] INFO: derived_feature_extractor: /y_2021, 991687
[2021-12-24 15:15:04.412096] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[121.931474s].
[2021-12-24 15:15:04.419765] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-12-24 15:16:06.370117] INFO: derived_feature_extractor: 提取完成 rank(correlation((high_0+low_0+close_0+open_0)/4,volume_0,4))*rank(correlation(rank(low_0),rank(mean(volume_0,50)),12)), 59.964s
[2021-12-24 15:16:07.216427] INFO: derived_feature_extractor: 提取完成 mean(turn_0*(return_0-1),15), 0.845s
[2021-12-24 15:16:08.029428] INFO: derived_feature_extractor: 提取完成 mean(mf_net_pct_m_0,30), 0.811s
[2021-12-24 15:16:11.266300] INFO: derived_feature_extractor: 提取完成 (mean(close_0,3)+mean(close_0,6)+mean(close_0,12)+mean(close_0,24))/(4*close_0), 3.235s
[2021-12-24 15:16:31.377074] INFO: derived_feature_extractor: 提取完成 -1*rank(covariance(rank(close_0),rank(volume_0),5)), 20.109s
[2021-12-24 15:16:31.380368] INFO: derived_feature_extractor: 提取完成 cond3=price_limit_status_0==2, 0.002s
[2021-12-24 15:16:40.154389] INFO: derived_feature_extractor: 提取完成 cond2=shift(ta_macd_dea(close_0),-1)[2021-12-24 15:16:49.543033] INFO: derived_feature_extractor: 提取完成 cond4=ta_macd_dea(close_0)[2021-12-24 15:16:49.986738] INFO: derived_feature_extractor: /y_2020, 158716
[2021-12-24 15:16:51.625750] INFO: derived_feature_extractor: /y_2021, 995519
[2021-12-24 15:16:52.339352] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[107.919569s].
[2021-12-24 15:16:52.347086] INFO: moduleinvoker: join.v3 开始运行..
[2021-12-24 15:16:54.310953] INFO: join: /y_2019, 行数=0/81785, 耗时=0.43229s
[2021-12-24 15:16:56.418459] INFO: join: /y_2020, 行数=0/944770, 耗时=2.105699s
[2021-12-24 15:16:59.386315] INFO: join: /y_2021, 行数=872144/991687, 耗时=2.961848s
[2021-12-24 15:16:59.459706] INFO: join: 最终行数: 872144
[2021-12-24 15:16:59.470516] INFO: moduleinvoker: join.v3 运行完成[7.123426s].
[2021-12-24 15:16:59.478023] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..
[2021-12-24 15:17:00.247762] INFO: A股股票过滤: 过滤 /y_2020, 143225/0/158716
[2021-12-24 15:17:03.630379] INFO: A股股票过滤: 过滤 /y_2021, 883707/0/995519
[2021-12-24 15:17:03.634595] INFO: A股股票过滤: 过滤完成, 1026932 + 0
[2021-12-24 15:17:03.664363] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[4.186329s].
[2021-12-24 15:17:03.675034] INFO: moduleinvoker: filter.v3 开始运行..
[2021-12-24 15:17:03.713273] INFO: filter: 使用表达式 cond3&cond2&cond4 过滤
[2021-12-24 15:17:03.764484] INFO: filter: 过滤 /y_2019, 0/0/0
[2021-12-24 15:17:03.791800] INFO: filter: 过滤 /y_2020, 0/0/0
[2021-12-24 15:17:04.447468] INFO: filter: 过滤 /y_2021, 29827/0/872144
[2021-12-24 15:17:04.471367] INFO: moduleinvoker: filter.v3 运行完成[0.796317s].
[2021-12-24 15:17:04.480617] INFO: moduleinvoker: filter.v3 开始运行..
[2021-12-24 15:17:04.495056] INFO: filter: 使用表达式 cond3&cond2&cond4 过滤
[2021-12-24 15:17:04.638585] INFO: filter: 过滤 /y_2020, 408/0/143225
[2021-12-24 15:17:05.011648] INFO: filter: 过滤 /y_2021, 34245/0/883707
[2021-12-24 15:17:05.045959] INFO: moduleinvoker: filter.v3 运行完成[0.565333s].
[2021-12-24 15:17:05.053760] INFO: moduleinvoker: dropnan.v2 开始运行..
[2021-12-24 15:17:05.179660] INFO: dropnan: /y_2021, 29486/29827
[2021-12-24 15:17:05.222100] INFO: dropnan: 行数: 29486/29827
[2021-12-24 15:17:05.226457] INFO: moduleinvoker: dropnan.v2 运行完成[0.172694s].
[2021-12-24 15:17:05.234795] INFO: moduleinvoker: dropnan.v2 开始运行..
[2021-12-24 15:17:05.336606] INFO: dropnan: /y_2020, 0/408
[2021-12-24 15:17:05.426025] INFO: dropnan: /y_2021, 30422/34245
[2021-12-24 15:17:05.459471] INFO: dropnan: 行数: 30422/34653
[2021-12-24 15:17:05.463379] INFO: moduleinvoker: dropnan.v2 运行完成[0.228592s].
[2021-12-24 15:17:05.487382] INFO: moduleinvoker: xgboost.v1 开始运行..
[2021-12-24 15:17:21.719265] INFO: moduleinvoker: xgboost.v1 运行完成[16.231885s].
[2021-12-24 15:17:21.725946] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-12-24 15:17:21.809647] INFO: moduleinvoker: instruments.v2 运行完成[0.083697s].
[2021-12-24 15:17:21.814135] INFO: moduleinvoker: input_features.v1 开始运行..
[2021-12-24 15:17:21.820970] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:17:21.822646] INFO: moduleinvoker: input_features.v1 运行完成[0.008522s].
[2021-12-24 15:17:21.827956] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-12-24 15:17:21.889361] INFO: moduleinvoker: instruments.v2 运行完成[0.061395s].
[2021-12-24 15:17:21.898249] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..
[2021-12-24 15:17:22.581143] INFO: 自动标注(股票): 加载历史数据: 996332 行
[2021-12-24 15:17:22.582637] INFO: 自动标注(股票): 开始标注 ..
[2021-12-24 15:17:24.242674] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[2.344447s].
[2021-12-24 15:17:24.254480] INFO: moduleinvoker: features_short.v1 开始运行..
[2021-12-24 15:17:24.261276] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:17:24.263195] INFO: moduleinvoker: features_short.v1 运行完成[0.008732s].
[2021-12-24 15:17:24.278798] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2021-12-24 15:17:26.652556] INFO: 基础特征抽取: 年份 2019, 特征行数=81785
[2021-12-24 15:17:32.335580] INFO: 基础特征抽取: 年份 2020, 特征行数=944770
[2021-12-24 15:17:38.207425] INFO: 基础特征抽取: 年份 2021, 特征行数=996332
[2021-12-24 15:17:38.296177] INFO: 基础特征抽取: 总行数: 2022887
[2021-12-24 15:17:38.327325] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[14.048512s].
[2021-12-24 15:17:38.340314] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2021-12-24 15:17:41.254158] INFO: 基础特征抽取: 年份 2020, 特征行数=158716
[2021-12-24 15:17:46.973744] INFO: 基础特征抽取: 年份 2021, 特征行数=1000164
[2021-12-24 15:17:47.038104] INFO: 基础特征抽取: 总行数: 1158880
[2021-12-24 15:17:47.046031] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[8.705721s].
[2021-12-24 15:17:47.053567] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-12-24 15:18:53.171140] INFO: derived_feature_extractor: 提取完成 rank(correlation((high_0+low_0+close_0+open_0)/4,volume_0,4))*rank(correlation(rank(low_0),rank(mean(volume_0,50)),12)), 62.668s
[2021-12-24 15:18:54.556775] INFO: derived_feature_extractor: 提取完成 mean(turn_0*(return_0-1),15), 1.384s
[2021-12-24 15:18:55.940928] INFO: derived_feature_extractor: 提取完成 mean(mf_net_pct_m_0,30), 1.383s
[2021-12-24 15:19:01.498668] INFO: derived_feature_extractor: 提取完成 (mean(close_0,3)+mean(close_0,6)+mean(close_0,12)+mean(close_0,24))/(4*close_0), 5.556s
[2021-12-24 15:19:22.759730] INFO: derived_feature_extractor: 提取完成 -1*rank(covariance(rank(close_0),rank(volume_0),5)), 21.259s
[2021-12-24 15:19:22.763104] INFO: derived_feature_extractor: 提取完成 cond3=price_limit_status_0==2, 0.002s
[2021-12-24 15:19:32.573750] INFO: derived_feature_extractor: 提取完成 cond2=shift(ta_macd_dea(close_0),-1)[2021-12-24 15:19:42.834400] INFO: derived_feature_extractor: 提取完成 cond4=ta_macd_dea(close_0)[2021-12-24 15:19:43.286354] INFO: derived_feature_extractor: /y_2019, 81785
[2021-12-24 15:19:44.862676] INFO: derived_feature_extractor: /y_2020, 944770
[2021-12-24 15:19:46.938315] INFO: derived_feature_extractor: /y_2021, 996332
[2021-12-24 15:19:47.710187] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[120.656614s].
[2021-12-24 15:19:47.718297] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-12-24 15:20:47.726371] INFO: derived_feature_extractor: 提取完成 rank(correlation((high_0+low_0+close_0+open_0)/4,volume_0,4))*rank(correlation(rank(low_0),rank(mean(volume_0,50)),12)), 58.089s
[2021-12-24 15:20:48.508073] INFO: derived_feature_extractor: 提取完成 mean(turn_0*(return_0-1),15), 0.780s
[2021-12-24 15:20:49.281117] INFO: derived_feature_extractor: 提取完成 mean(mf_net_pct_m_0,30), 0.771s
[2021-12-24 15:20:52.341639] INFO: derived_feature_extractor: 提取完成 (mean(close_0,3)+mean(close_0,6)+mean(close_0,12)+mean(close_0,24))/(4*close_0), 3.059s
[2021-12-24 15:21:11.211793] INFO: derived_feature_extractor: 提取完成 -1*rank(covariance(rank(close_0),rank(volume_0),5)), 18.868s
[2021-12-24 15:21:11.215007] INFO: derived_feature_extractor: 提取完成 cond3=price_limit_status_0==2, 0.002s
[2021-12-24 15:21:20.179063] INFO: derived_feature_extractor: 提取完成 cond2=shift(ta_macd_dea(close_0),-1)[2021-12-24 15:21:28.546928] INFO: derived_feature_extractor: 提取完成 cond4=ta_macd_dea(close_0)[2021-12-24 15:21:28.995227] INFO: derived_feature_extractor: /y_2020, 158716
[2021-12-24 15:21:30.653010] INFO: derived_feature_extractor: /y_2021, 1000164
[2021-12-24 15:21:31.244708] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[103.526419s].
[2021-12-24 15:21:31.253491] INFO: moduleinvoker: join.v3 开始运行..
[2021-12-24 15:21:33.210042] INFO: join: /y_2019, 行数=0/81785, 耗时=0.459287s
[2021-12-24 15:21:35.412105] INFO: join: /y_2020, 行数=0/944770, 耗时=2.200198s
[2021-12-24 15:21:38.512462] INFO: join: /y_2021, 行数=876468/996332, 耗时=3.094543s
[2021-12-24 15:21:38.593678] INFO: join: 最终行数: 876468
[2021-12-24 15:21:38.607968] INFO: moduleinvoker: join.v3 运行完成[7.354399s].
[2021-12-24 15:21:38.624183] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..
[2021-12-24 15:21:39.429539] INFO: A股股票过滤: 过滤 /y_2020, 143225/0/158716
[2021-12-24 15:21:42.942768] INFO: A股股票过滤: 过滤 /y_2021, 887725/0/1000164
[2021-12-24 15:21:42.947019] INFO: A股股票过滤: 过滤完成, 1030950 + 0
[2021-12-24 15:21:42.976323] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[4.352138s].
[2021-12-24 15:21:43.008121] INFO: moduleinvoker: filter.v3 开始运行..
[2021-12-24 15:21:43.022186] INFO: filter: 使用表达式 cond3&cond2&cond4 过滤
[2021-12-24 15:21:43.103412] INFO: filter: 过滤 /y_2019, 0/0/0
[2021-12-24 15:21:43.134344] INFO: filter: 过滤 /y_2020, 0/0/0
[2021-12-24 15:21:43.847570] INFO: filter: 过滤 /y_2021, 29959/0/876468
[2021-12-24 15:21:43.871495] INFO: moduleinvoker: filter.v3 运行完成[0.86337s].
[2021-12-24 15:21:43.886457] INFO: moduleinvoker: filter.v3 开始运行..
[2021-12-24 15:21:43.905842] INFO: filter: 使用表达式 cond3&cond2&cond4 过滤
[2021-12-24 15:21:44.109609] INFO: filter: 过滤 /y_2020, 408/0/143225
[2021-12-24 15:21:44.579527] INFO: filter: 过滤 /y_2021, 34339/0/887725
[2021-12-24 15:21:44.608974] INFO: moduleinvoker: filter.v3 运行完成[0.722511s].
[2021-12-24 15:21:44.617728] INFO: moduleinvoker: dropnan.v2 开始运行..
[2021-12-24 15:21:44.733503] INFO: dropnan: /y_2021, 29617/29959
[2021-12-24 15:21:44.803820] INFO: dropnan: 行数: 29617/29959
[2021-12-24 15:21:44.808633] INFO: moduleinvoker: dropnan.v2 运行完成[0.190902s].
[2021-12-24 15:21:44.817020] INFO: moduleinvoker: dropnan.v2 开始运行..
[2021-12-24 15:21:44.924855] INFO: dropnan: /y_2020, 0/408
[2021-12-24 15:21:45.002132] INFO: dropnan: /y_2021, 30515/34339
[2021-12-24 15:21:45.045102] INFO: dropnan: 行数: 30515/34747
[2021-12-24 15:21:45.049677] INFO: moduleinvoker: dropnan.v2 运行完成[0.23266s].
[2021-12-24 15:21:45.056363] INFO: moduleinvoker: xgboost.v1 开始运行..
[2021-12-24 15:22:02.820006] INFO: moduleinvoker: xgboost.v1 运行完成[17.763643s].
[2021-12-24 15:22:02.830375] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-12-24 15:22:02.847513] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:02.849132] INFO: moduleinvoker: instruments.v2 运行完成[0.018755s].
[2021-12-24 15:22:02.853516] INFO: moduleinvoker: input_features.v1 开始运行..
[2021-12-24 15:22:02.862277] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:02.864028] INFO: moduleinvoker: input_features.v1 运行完成[0.010516s].
[2021-12-24 15:22:02.893455] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-12-24 15:22:02.909518] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:02.911119] INFO: moduleinvoker: instruments.v2 运行完成[0.017685s].
[2021-12-24 15:22:02.918793] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..
[2021-12-24 15:22:02.926456] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:02.927661] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.00887s].
[2021-12-24 15:22:02.939605] INFO: moduleinvoker: features_short.v1 开始运行..
[2021-12-24 15:22:02.945224] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:02.946350] INFO: moduleinvoker: features_short.v1 运行完成[0.00675s].
[2021-12-24 15:22:02.959381] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2021-12-24 15:22:02.967130] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:02.968894] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.009528s].
[2021-12-24 15:22:02.980350] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2021-12-24 15:22:02.994298] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:02.995781] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.01544s].
[2021-12-24 15:22:03.003060] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-12-24 15:22:03.012336] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:03.013719] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.010663s].
[2021-12-24 15:22:03.020967] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-12-24 15:22:03.031685] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:03.033007] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.012042s].
[2021-12-24 15:22:03.041767] INFO: moduleinvoker: join.v3 开始运行..
[2021-12-24 15:22:03.057273] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:03.058863] INFO: moduleinvoker: join.v3 运行完成[0.017092s].
[2021-12-24 15:22:03.067071] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..
[2021-12-24 15:22:03.087760] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:03.089298] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[0.022239s].
[2021-12-24 15:22:03.097164] INFO: moduleinvoker: filter.v3 开始运行..
[2021-12-24 15:22:03.105297] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:03.106690] INFO: moduleinvoker: filter.v3 运行完成[0.009531s].
[2021-12-24 15:22:03.114362] INFO: moduleinvoker: filter.v3 开始运行..
[2021-12-24 15:22:03.128584] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:03.129769] INFO: moduleinvoker: filter.v3 运行完成[0.015408s].
[2021-12-24 15:22:03.137407] INFO: moduleinvoker: dropnan.v2 开始运行..
[2021-12-24 15:22:03.143110] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:03.144233] INFO: moduleinvoker: dropnan.v2 运行完成[0.006828s].
[2021-12-24 15:22:03.151671] INFO: moduleinvoker: dropnan.v2 开始运行..
[2021-12-24 15:22:03.159984] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:03.161464] INFO: moduleinvoker: dropnan.v2 运行完成[0.009801s].
[2021-12-24 15:22:03.168197] INFO: moduleinvoker: xgboost.v1 开始运行..
[2021-12-24 15:22:03.179358] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:03.180787] INFO: moduleinvoker: xgboost.v1 运行完成[0.012593s].
[2021-12-24 15:22:03.186427] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-12-24 15:22:03.194555] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:03.196061] INFO: moduleinvoker: instruments.v2 运行完成[0.009639s].
[2021-12-24 15:22:03.199815] INFO: moduleinvoker: input_features.v1 开始运行..
[2021-12-24 15:22:03.206133] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:03.207429] INFO: moduleinvoker: input_features.v1 运行完成[0.007617s].
[2021-12-24 15:22:03.212065] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-12-24 15:22:03.218164] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:03.219326] INFO: moduleinvoker: instruments.v2 运行完成[0.007262s].
[2021-12-24 15:22:03.228184] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..
[2021-12-24 15:22:03.236979] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:03.238156] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.009972s].
[2021-12-24 15:22:03.248950] INFO: moduleinvoker: features_short.v1 开始运行..
[2021-12-24 15:22:03.254820] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:03.256283] INFO: moduleinvoker: features_short.v1 运行完成[0.007355s].
[2021-12-24 15:22:03.275205] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2021-12-24 15:22:03.299023] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:03.300505] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.025325s].
[2021-12-24 15:22:03.312016] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2021-12-24 15:22:03.319685] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:03.320861] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.008853s].
[2021-12-24 15:22:03.326635] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-12-24 15:22:03.334822] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:03.336079] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.009442s].
[2021-12-24 15:22:03.341939] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-12-24 15:22:03.350580] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:03.351981] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.010046s].
[2021-12-24 15:22:03.359203] INFO: moduleinvoker: join.v3 开始运行..
[2021-12-24 15:22:03.367829] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:03.369105] INFO: moduleinvoker: join.v3 运行完成[0.009904s].
[2021-12-24 15:22:03.377632] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..
[2021-12-24 15:22:03.386088] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:03.387397] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[0.009768s].
[2021-12-24 15:22:03.394713] INFO: moduleinvoker: filter.v3 开始运行..
[2021-12-24 15:22:03.404978] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:03.406181] INFO: moduleinvoker: filter.v3 运行完成[0.011467s].
[2021-12-24 15:22:03.414098] INFO: moduleinvoker: filter.v3 开始运行..
[2021-12-24 15:22:03.421508] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:03.422641] INFO: moduleinvoker: filter.v3 运行完成[0.008543s].
[2021-12-24 15:22:03.430123] INFO: moduleinvoker: dropnan.v2 开始运行..
[2021-12-24 15:22:03.438982] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:03.440592] INFO: moduleinvoker: dropnan.v2 运行完成[0.010466s].
[2021-12-24 15:22:03.448541] INFO: moduleinvoker: dropnan.v2 开始运行..
[2021-12-24 15:22:03.455712] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:03.457003] INFO: moduleinvoker: dropnan.v2 运行完成[0.008462s].
[2021-12-24 15:22:03.463398] INFO: moduleinvoker: xgboost.v1 开始运行..
[2021-12-24 15:22:03.474672] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:03.476018] INFO: moduleinvoker: xgboost.v1 运行完成[0.012622s].
[2021-12-24 15:22:03.481025] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-12-24 15:22:03.490743] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:03.492007] INFO: moduleinvoker: instruments.v2 运行完成[0.010981s].
[2021-12-24 15:22:03.495429] INFO: moduleinvoker: input_features.v1 开始运行..
[2021-12-24 15:22:03.500846] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:03.502011] INFO: moduleinvoker: input_features.v1 运行完成[0.006585s].
[2021-12-24 15:22:03.506256] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-12-24 15:22:03.514327] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:03.515594] INFO: moduleinvoker: instruments.v2 运行完成[0.009337s].
[2021-12-24 15:22:03.522843] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..
[2021-12-24 15:22:03.531647] INFO: moduleinvoker: 命中缓存
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[2021-12-24 15:22:04.824916] INFO: moduleinvoker: join.v3 运行完成[0.009493s].
[2021-12-24 15:22:04.834593] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..
[2021-12-24 15:22:04.843589] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:04.844667] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[0.010074s].
[2021-12-24 15:22:04.852067] INFO: moduleinvoker: filter.v3 开始运行..
[2021-12-24 15:22:04.864467] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:04.865640] INFO: moduleinvoker: filter.v3 运行完成[0.013572s].
[2021-12-24 15:22:04.872827] INFO: moduleinvoker: filter.v3 开始运行..
[2021-12-24 15:22:04.883149] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:04.884291] INFO: moduleinvoker: filter.v3 运行完成[0.011476s].
[2021-12-24 15:22:04.890875] INFO: moduleinvoker: dropnan.v2 开始运行..
[2021-12-24 15:22:04.905557] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:04.906657] INFO: moduleinvoker: dropnan.v2 运行完成[0.015782s].
[2021-12-24 15:22:04.914333] INFO: moduleinvoker: dropnan.v2 开始运行..
[2021-12-24 15:22:04.923042] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:04.924406] INFO: moduleinvoker: dropnan.v2 运行完成[0.01007s].
[2021-12-24 15:22:04.930311] INFO: moduleinvoker: xgboost.v1 开始运行..
[2021-12-24 15:22:04.954847] INFO: moduleinvoker: 命中缓存
[2021-12-24 15:22:04.956069] INFO: moduleinvoker: xgboost.v1 运行完成[0.025762s].
[2021-12-24 15:22:05.157247] INFO: moduleinvoker: cached.v3 开始运行..
[2021-12-24 15:22:06.210785] ERROR: moduleinvoker: module name: cached, module version: v3, trackeback: ValueError: NaTType does not support strftime
[2021-12-24 15:22:06.215557] ERROR: moduleinvoker: module name: hyper_rolling_train, module version: v1, trackeback: ValueError: NaTType does not support strftime

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-8-a6a3ddd00fab> in <module>
    395 
    396 
--> 397 m8 = M.hyper_rolling_train.v1(
    398     run=m8_run_bigquant_run,
    399     run_now=True,

<ipython-input-8-a6a3ddd00fab> in m8_run_bigquant_run(bq_graph, inputs, trading_days_market, train_instruments_mid, test_instruments_mid, predict_mid, trade_mid, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live)
    383 
    384     # 合并预测结果并回测
--> 385     mx = M.cached.v3(run=merge_datasources, input_1=[[result[predict_mid].predictions, result[test_instruments_mid].data.read_pickle()['start_date']] for result in results])
    386     parameters = {}
    387     parameters['*.__enabled__'] = False

<ipython-input-8-a6a3ddd00fab> in merge_datasources(input_1)
    327         df = pd.concat(df_list)
    328         instrument_data = {
--> 329             'start_date': df['date'].min().strftime('%Y-%m-%d'),
    330             'end_date': df['date'].max().strftime('%Y-%m-%d'),
    331             'instruments': list(set(df['instrument'])),

ValueError: NaTType does not support strftime