关于pandas里字符串省略号 怎么显示完整


(mefan) #1
pandas.set_option('display.max_colwidth',500)
pandas.set_option('display.width',500)
pandas.set_option('max_colwidth', 2000)
pandas.set_option('max_columns', 2000)
pandas.set_option('max_rows', 2000)
pandas.set_option('max_colwidth', 2000)
pandas.set_option('line_width', 2000)




gaindf= m6.feature_gains.read_df()
for gain in (gaindf[gaindf.gain>0]['feature'].astype(str).values):
    print(gain)

出现省略号,怎么显示完整?

(fs_current_liabilities_0+fs_non_current_liabilities_0)/(fs_current...
(fs_current_liabilities_0+fs_non_current_liabilities_0)/ fs_common_...
fs_operating_revenue_ttm_0/(fs_current_assets_0+fs_non_current_asse...

(iQuant) #2

您好,收到您的反馈,已将问题提交给策略工程师,稍后他将为您解答。


(polll) #3

pd.set_option(‘display.max_rows’, None) # 设置显示最大行
pd.set_option(‘display.max_columns’, None) # 设置显示最大列,None为显示所有列


(mefan) #5

还是一样

克隆策略

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#号开始的表示注释\n# 0. 每行一个,顺序执行,从第二个开始,可以使用label字段\n# 1. 可用数据字段见 https://bigquant.com/docs/data_history_data.html\n# 添加benchmark_前缀,可使用对应的benchmark数据\n# 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/big_expr.html>`_\n\n# 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)\nshift(close, -5) / shift(open, -1)\n\n# 极值处理:用1%和99%分位的值做clip\nclip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))\n\n# 将分数映射到分类,这里使用20个分类\nall_wbins(label, 20)\n\n# 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)\nwhere(shift(high, -1) == shift(low, -1), NaN, 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回测引擎:每日数据处理函数,每天执行一次\ndef bigquant_run(context, data):\n # 按日期过滤得到今日的预测数据\n ranker_prediction = context.ranker_prediction[\n context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]\n\n # 1. 资金分配\n # 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\n # 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)\n is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)\n cash_avg = context.portfolio.portfolio_value / context.options['hold_days']\n cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)\n cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)\n positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.perf_tracker.position_tracker.positions.items()}\n\n # 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\n if not is_staging and cash_for_sell > 0:\n equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}\n instruments = 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    In [8]:
    # 本代码由可视化策略环境自动生成 2019年1月9日 10:45
    # 本代码单元只能在可视化模式下编辑。您也可以拷贝代码,粘贴到新建的代码单元或者策略,然后修改。
    
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m19_handle_data_bigquant_run(context, data):
        # 按日期过滤得到今日的预测数据
        ranker_prediction = context.ranker_prediction[
            context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]
    
        # 1. 资金分配
        # 平均持仓时间是hold_days,每日都将买入股票,每日预期使用 1/hold_days 的资金
        # 实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)
        is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)
        cash_avg = context.portfolio.portfolio_value / context.options['hold_days']
        cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)
        cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)
        positions = {e.symbol: p.amount * p.last_sale_price
                     for e, p in context.perf_tracker.position_tracker.positions.items()}
    
        # 2. 生成卖出订单:hold_days天之后才开始卖出;对持仓的股票,按机器学习算法预测的排序末位淘汰
        if not is_staging and cash_for_sell > 0:
            equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}
            instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                    lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))
            # print('rank order for sell %s' % instruments)
            for instrument in instruments:
                context.order_target(context.symbol(instrument), 0)
                cash_for_sell -= positions[instrument]
                if cash_for_sell <= 0:
                    break
    
        # 3. 生成买入订单:按机器学习算法预测的排序,买入前面的stock_count只股票
        buy_cash_weights = context.stock_weights
        buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
        max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
        for i, instrument in enumerate(buy_instruments):
            cash = cash_for_buy * buy_cash_weights[i]
            if cash > max_cash_per_instrument - positions.get(instrument, 0):
                # 确保股票持仓量不会超过每次股票最大的占用资金量
                cash = max_cash_per_instrument - positions.get(instrument, 0)
            if cash > 0:
                context.order_value(context.symbol(instrument), cash)
    
    # 回测引擎:准备数据,只执行一次
    def m19_prepare_bigquant_run(context):
        pass
    
    # 回测引擎:初始化函数,只执行一次
    def m19_initialize_bigquant_run(context):
        # 加载预测数据
        context.ranker_prediction = context.options['data'].read_df()
    
        # 系统已经设置了默认的交易手续费和滑点,要修改手续费可使用如下函数
        context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
        # 预测数据,通过options传入进来,使用 read_df 函数,加载到内存 (DataFrame)
        # 设置买入的股票数量,这里买入预测股票列表排名靠前的5只
        stock_count = 5
        # 每只的股票的权重,如下的权重分配会使得靠前的股票分配多一点的资金,[0.339160, 0.213986, 0.169580, ..]
        context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])
        # 设置每只股票占用的最大资金比例
        context.max_cash_per_instrument = 0.2
        context.options['hold_days'] = 5
    
    
    m1 = M.instruments.v2(
        start_date='2018-01-01',
        end_date='2018-02-01',
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m2 = M.advanced_auto_labeler.v2(
        instruments=m1.data,
        label_expr="""# #号开始的表示注释
    # 0. 每行一个,顺序执行,从第二个开始,可以使用label字段
    # 1. 可用数据字段见 https://bigquant.com/docs/data_history_data.html
    #   添加benchmark_前缀,可使用对应的benchmark数据
    # 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/big_expr.html>`_
    
    # 计算收益:5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
    shift(close, -5) / shift(open, -1)
    
    # 极值处理:用1%和99%分位的值做clip
    clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))
    
    # 将分数映射到分类,这里使用20个分类
    all_wbins(label, 20)
    
    # 过滤掉一字涨停的情况 (设置label为NaN,在后续处理和训练中会忽略NaN的label)
    where(shift(high, -1) == shift(low, -1), NaN, label)
    """,
        start_date='',
        end_date='',
        benchmark='000300.SHA',
        drop_na_label=True,
        cast_label_int=True
    )
    
    m3 = M.input_features.v1(
        features="""
    fs_operating_revenue_ttm_0/(fs_current_assets_0+fs_non_current_assets_0)
    fs_current_assets_0/fs_current_liabilities_0
    fs_roe_0
    fs_roe_ttm_0
    fs_roa_0
    (fs_current_liabilities_0+fs_non_current_liabilities_0)/(fs_current_assets_0+fs_non_current_assets_0)
    (fs_current_liabilities_0+fs_non_current_liabilities_0)/ fs_common_equity_0
    
    (high_0-low_0+high_1-low_1+high_2-low_2+high_3-low_3+high_4-low_4)/5
    (volume_0+volume_1+volume_2+volume_3+volume_4+volume_5+volume_6+volume_7+volume_8+volume_9+volume_10+volume_11+volume_12+volume_13+volume_14+volume_15+volume_16+volume_17+volume_18+volume_19)/20
    (volume_0+volume_1+volume_2+volume_3+volume_4+volume_5+volume_6+volume_7+volume_8+volume_9)/10
    (volume_0+volume_1+volume_2+volume_3+volume_4)/5
    avg_amount_40
    avg_amount_20
    #抄来的end"""
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m16 = M.derived_feature_extractor.v3(
        input_data=m15.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m7 = M.join.v3(
        data1=m2.data,
        data2=m16.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m13 = M.dropnan.v1(
        input_data=m7.data
    )
    
    m6 = M.stock_ranker_train.v5(
        training_ds=m13.data,
        features=m3.data,
        learning_algorithm='排序',
        number_of_leaves=30,
        minimum_docs_per_leaf=1000,
        number_of_trees=20,
        learning_rate=0.1,
        max_bins=1023,
        feature_fraction=1,
        m_lazy_run=False
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2018-02-01'),
        end_date=T.live_run_param('trading_date', '2018-03-01'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m17 = M.general_feature_extractor.v7(
        instruments=m9.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m18 = M.derived_feature_extractor.v3(
        input_data=m17.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m14 = M.dropnan.v1(
        input_data=m18.data
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m6.model,
        data=m14.data,
        m_lazy_run=False
    )
    
    m19 = M.trade.v4(
        instruments=m9.data,
        options_data=m8.predictions,
        start_date='',
        end_date='',
        handle_data=m19_handle_data_bigquant_run,
        prepare=m19_prepare_bigquant_run,
        initialize=m19_initialize_bigquant_run,
        volume_limit=0.025,
        order_price_field_buy='open',
        order_price_field_sell='close',
        capital_base=1000000,
        auto_cancel_non_tradable_orders=True,
        data_frequency='daily',
        price_type='后复权',
        product_type='股票',
        plot_charts=True,
        backtest_only=False,
        benchmark=''
    )
    
    [2019-01-09 10:44:24.635343] INFO: bigquant: instruments.v2 开始运行..
    [2019-01-09 10:44:24.639713] INFO: bigquant: 命中缓存
    [2019-01-09 10:44:24.640513] INFO: bigquant: instruments.v2 运行完成[0.005211s].
    [2019-01-09 10:44:24.642578] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
    [2019-01-09 10:44:24.645965] INFO: bigquant: 命中缓存
    [2019-01-09 10:44:24.646771] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.004215s].
    [2019-01-09 10:44:24.648690] INFO: bigquant: input_features.v1 开始运行..
    [2019-01-09 10:44:24.661926] INFO: bigquant: input_features.v1 运行完成[0.013223s].
    [2019-01-09 10:44:24.667002] INFO: bigquant: general_feature_extractor.v7 开始运行..
    [2019-01-09 10:44:47.739303] INFO: 基础特征抽取: 年份 2018, 特征行数=75116
    [2019-01-09 10:44:47.746089] INFO: 基础特征抽取: 总行数: 75116
    [2019-01-09 10:44:47.748737] INFO: bigquant: general_feature_extractor.v7 运行完成[23.081716s].
    [2019-01-09 10:44:47.751216] INFO: bigquant: derived_feature_extractor.v3 开始运行..
    [2019-01-09 10:44:47.844649] INFO: derived_feature_extractor: 提取完成 fs_operating_revenue_ttm_0/(fs_current_assets_0+fs_non_current_assets_0), 0.002s
    [2019-01-09 10:44:47.847245] INFO: derived_feature_extractor: 提取完成 fs_current_assets_0/fs_current_liabilities_0, 0.001s
    [2019-01-09 10:44:47.849687] INFO: derived_feature_extractor: 提取完成 (fs_current_liabilities_0+fs_non_current_liabilities_0)/(fs_current_assets_0+fs_non_current_assets_0), 0.002s
    [2019-01-09 10:44:47.852131] INFO: derived_feature_extractor: 提取完成 (fs_current_liabilities_0+fs_non_current_liabilities_0)/ fs_common_equity_0, 0.002s
    [2019-01-09 10:44:47.855543] INFO: derived_feature_extractor: 提取完成 (high_0-low_0+high_1-low_1+high_2-low_2+high_3-low_3+high_4-low_4)/5, 0.002s
    [2019-01-09 10:44:47.859267] INFO: derived_feature_extractor: 提取完成 (volume_0+volume_1+volume_2+volume_3+volume_4+volume_5+volume_6+volume_7+volume_8+volume_9+volume_10+volume_11+volume_12+volume_13+volume_14+volume_15+volume_16+volume_17+volume_18+volume_19)/20, 0.003s
    [2019-01-09 10:44:47.861925] INFO: derived_feature_extractor: 提取完成 (volume_0+volume_1+volume_2+volume_3+volume_4+volume_5+volume_6+volume_7+volume_8+volume_9)/10, 0.002s
    [2019-01-09 10:44:47.864583] INFO: derived_feature_extractor: 提取完成 (volume_0+volume_1+volume_2+volume_3+volume_4)/5, 0.002s
    [2019-01-09 10:44:47.954069] INFO: derived_feature_extractor: /y_2018, 75116
    [2019-01-09 10:44:48.609536] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.858339s].
    [2019-01-09 10:44:48.612349] INFO: bigquant: join.v3 开始运行..
    [2019-01-09 10:44:49.018445] INFO: join: /y_2018, 行数=58210/75116, 耗时=0.266654s
    [2019-01-09 10:44:49.045655] INFO: join: 最终行数: 58210
    [2019-01-09 10:44:49.047637] INFO: bigquant: join.v3 运行完成[0.435278s].
    [2019-01-09 10:44:49.050342] INFO: bigquant: dropnan.v1 开始运行..
    [2019-01-09 10:44:49.396701] INFO: dropnan: /y_2018, 53522/58210
    [2019-01-09 10:44:49.406397] INFO: dropnan: 行数: 53522/58210
    [2019-01-09 10:44:49.409783] INFO: bigquant: dropnan.v1 运行完成[0.359436s].
    [2019-01-09 10:44:49.412338] INFO: bigquant: stock_ranker_train.v5 开始运行..
    [2019-01-09 10:44:49.527035] INFO: StockRanker: 特征预处理 ..
    [2019-01-09 10:44:49.604959] INFO: StockRanker: prepare data: training ..
    [2019-01-09 10:44:52.508837] INFO: StockRanker训练: 885c01b8 准备训练: 53522 行数
    [2019-01-09 10:44:52.540600] INFO: StockRanker训练: 正在训练 ..
    [2019-01-09 10:45:23.197782] INFO: bigquant: stock_ranker_train.v5 运行完成[33.785433s].
    
    In [9]:
    pd.set_option('display.max_rows', None) # 设置显示最大行
    pd.set_option('display.max_columns', None) # 设置显示最大列,None为显示所有列
    gaindf= m6.feature_gains.read_df()
    for gain in (gaindf[gaindf.gain>0]['feature'].astype(str).values):
        print(gain)
    
    fs_roe_ttm_0
    (volume_0+volume_1+volume_2+volume_3+volume_4+volume_5+volume_6+vol...
    (high_0-low_0+high_1-low_1+high_2-low_2+high_3-low_3+high_4-low_4)/5
    fs_roa_0
    (fs_current_liabilities_0+fs_non_current_liabilities_0)/ fs_common_...
    avg_amount_40
    fs_roe_0
    avg_amount_20
    fs_current_assets_0/fs_current_liabilities_0
    fs_operating_revenue_ttm_0/(fs_current_assets_0+fs_non_current_asse...
    (volume_0+volume_1+volume_2+volume_3+volume_4+volume_5+volume_6+vol...
    (fs_current_liabilities_0+fs_non_current_liabilities_0)/(fs_current...
    (volume_0+volume_1+volume_2+volume_3+volume_4)/5
    
    In [5]:
    m20.result.best_params_
    
    Out[5]:
    {'m6.number_of_trees': 20}
    In [6]:
    m20.result.best_score_
    
    Out[6]:
    -0.4944559465026433

    (iQuant) #6

    这个不是pandas显示的问题,是在模型里做了最长截断显示。我们来优化,显示完整因子字符串。


    (mefan) #7

    ??tj了?


    (达达) #8

    您好,这个feature_gains返回的就是截断的字符串,末尾…省略号,不是设置问题,已经反馈开发处理


    (iQuant) #9

    感谢。显示的问题我们会优化下,你可以参考如下方式解决你的这个具体问题。试试看呢?

    target_feature = m3.data.read_pickle() 
    tmp = m6.feature_gains.read_df()
    print('初始数据:',tmp,'\n')
    feature_importance = tmp['feature']
    gain = list(tmp['gain'])
    def change(x):
        for i in target_feature:
            if i[:67] == x[:67]:
                x = i
        return x
    assert len(feature_importance) ==len(gain)
    new = [{change(feature_importance[i]):gain[i]} for i in range(len(feature_importance))]
    print('完整的因子及其得分','\n',new)