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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.hold_days # 是否在建仓期间（前 hold_days 天）\n cash_avg = context.portfolio.portfolio_value / context.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StockRanker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 = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))\n # print('rank order for sell %s' % instruments)\n for instrument in instruments:\n 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# 本代码由可视化策略环境自动生成 2021年7月20日17:38
# 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 m14_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.hold_days = 5

# 回测引擎：每日数据处理函数，每天执行一次
def m14_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.hold_days # 是否在建仓期间（前 hold_days 天）
    cash_avg = context.portfolio.portfolio_value / context.hold_days
    cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)
    cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)
    positions = {e.symbol: p.amount * p.last_sale_price
                 for e, p in context.perf_tracker.position_tracker.positions.items()}

    # 2. 生成卖出订单：hold_days天之后才开始卖出；对持仓的股票，按StockRanker预测的排序末位淘汰
    if not is_staging and cash_for_sell > 0:
        equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}
        instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))
        # print('rank order for sell %s' % instruments)
        for instrument in instruments:
            context.order_target(context.symbol(instrument), 0)
            cash_for_sell -= positions[instrument]
            if cash_for_sell <= 0:
                break

    # 3. 生成买入订单：按StockRanker预测的排序，买入前面的stock_count只股票
    buy_cash_weights = context.stock_weights
    buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
    max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
    for i, instrument in enumerate(buy_instruments):
        cash = cash_for_buy * buy_cash_weights[i]
        if cash > max_cash_per_instrument - positions.get(instrument, 0):
            # 确保股票持仓量不会超过每次股票最大的占用资金量
            cash = max_cash_per_instrument - positions.get(instrument, 0)
        if cash > 0:
            context.order_value(context.symbol(instrument), cash)

# 回测引擎：准备数据，只执行一次
def m14_prepare_bigquant_run(context):
    pass

# 回测引擎：每个单位时间开始前调用一次，即每日开盘前调用一次。
def m14_before_trading_start_bigquant_run(context, data):
    pass


m1 = M.instruments.v2(
    start_date='2010-01-01',
    end_date='2020-12-31',
    market='CN_STOCK_A',
    instrument_list="""002271.SZA
600845.SHA
000636.SZA
000625.SZA
600584.SHA
002167.SZA
600199.SHA
003035.SZA
600745.SHA
603881.SHA""",
    max_count=0
)

m3 = 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/develop/datasource/deprecated/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/develop/bigexpr/usage.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,
    user_functions={}
)

m2 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2021-01-01'),
    end_date=T.live_run_param('trading_date', '2021-07-06'),
    market='CN_STOCK_A',
    instrument_list="""002271.SZA
600845.SHA
000636.SZA
000625.SZA
600584.SHA
002167.SZA
600199.SHA
003035.SZA
600745.SHA
603881.SHA""",
    max_count=0
)

m4 = M.input_features.v1(
    features="""
# #号开始的表示注释，注释需单独一行
# 多个特征，每行一个，可以包含基础特征和衍生特征，特征须为本平台特征
return_5
return_10
return_20
avg_amount_0/avg_amount_5
avg_amount_5/avg_amount_10
rank_avg_amount_0/rank_avg_amount_5
rank_avg_amount_5/rank_avg_amount_10
rank_return_0
rank_return_5
rank_return_10
rank_return_0/rank_return_5
rank_return_5/rank_return_10
mf_net_amount_5
ta_sma_5_0
ta_sma_5_0/ta_sma_10_0>0
ta_sma_10_0/ta_sma_20_0

pe_ttm_0
"""
)

m5 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m4.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m6 = M.derived_feature_extractor.v3(
    input_data=m5.data,
    features=m4.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m7 = M.join.v3(
    data1=m3.data,
    data2=m6.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m10 = M.dropnan.v2(
    input_data=m7.data
)

m8 = M.general_feature_extractor.v7(
    instruments=m2.data,
    features=m4.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m9 = M.derived_feature_extractor.v3(
    input_data=m8.data,
    features=m4.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m11 = M.dropnan.v2(
    input_data=m9.data
)

m15 = M.decision_tree_classifier.v1(
    training_ds=m10.data,
    features=m4.data,
    predict_ds=m11.data,
    criterion='entropy',
    feature_fraction=1,
    max_depth=30,
    min_samples_per_leaf=200,
    key_cols='date,instrument',
    other_train_parameters={}
)

m14 = M.trade.v4(
    instruments=m2.data,
    options_data=m15.predictions,
    start_date='',
    end_date='',
    initialize=m14_initialize_bigquant_run,
    handle_data=m14_handle_data_bigquant_run,
    prepare=m14_prepare_bigquant_run,
    before_trading_start=m14_before_trading_start_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='open',
    order_price_field_sell='close',
    capital_base=100000,
    auto_cancel_non_tradable_orders=True,
    data_frequency='daily',
    price_type='真实价格',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark=''
)

[2021-07-20 17:32:45.073074] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-07-20 17:32:45.119218] INFO: moduleinvoker: instruments.v2 运行完成[0.046128s].

[2021-07-20 17:32:45.131068] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2021-07-20 17:32:57.806842] INFO: 自动标注(股票): 加载历史数据: 21047 行

[2021-07-20 17:32:57.808558] INFO: 自动标注(股票): 开始标注 ..

[2021-07-20 17:32:58.141815] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[13.010764s].

[2021-07-20 17:32:58.153824] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-07-20 17:32:58.203703] INFO: moduleinvoker: instruments.v2 运行完成[0.049852s].

[2021-07-20 17:32:58.218992] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-07-20 17:32:58.274177] INFO: moduleinvoker: input_features.v1 运行完成[0.0552s].

[2021-07-20 17:32:58.294317] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-07-20 17:33:00.671604] INFO: 基础特征抽取: 年份 2009, 特征行数=473

[2021-07-20 17:33:03.452781] INFO: 基础特征抽取: 年份 2010, 特征行数=1896

[2021-07-20 17:33:06.102392] INFO: 基础特征抽取: 年份 2011, 特征行数=1892

[2021-07-20 17:33:09.340905] INFO: 基础特征抽取: 年份 2012, 特征行数=1906

[2021-07-20 17:33:12.841050] INFO: 基础特征抽取: 年份 2013, 特征行数=1831

[2021-07-20 17:33:16.667073] INFO: 基础特征抽取: 年份 2014, 特征行数=1677

[2021-07-20 17:33:19.734389] INFO: 基础特征抽取: 年份 2015, 特征行数=1616

[2021-07-20 17:33:23.342851] INFO: 基础特征抽取: 年份 2016, 特征行数=1690

[2021-07-20 17:33:27.424902] INFO: 基础特征抽取: 年份 2017, 特征行数=2136

[2021-07-20 17:33:31.386232] INFO: 基础特征抽取: 年份 2018, 特征行数=2032

[2021-07-20 17:33:36.304937] INFO: 基础特征抽取: 年份 2019, 特征行数=2195

[2021-07-20 17:33:41.183906] INFO: 基础特征抽取: 年份 2020, 特征行数=2176

[2021-07-20 17:33:41.246363] INFO: 基础特征抽取: 总行数: 21520

[2021-07-20 17:33:41.259821] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[42.965551s].

[2021-07-20 17:33:41.267218] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-07-20 17:33:41.646250] INFO: derived_feature_extractor: 提取完成 avg_amount_0/avg_amount_5, 0.003s

[2021-07-20 17:33:41.650425] INFO: derived_feature_extractor: 提取完成 avg_amount_5/avg_amount_10, 0.002s

[2021-07-20 17:33:41.654450] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_5, 0.002s

[2021-07-20 17:33:41.658039] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_5/rank_avg_amount_10, 0.002s

[2021-07-20 17:33:41.661860] INFO: derived_feature_extractor: 提取完成 rank_return_0/rank_return_5, 0.002s

[2021-07-20 17:33:41.694127] INFO: derived_feature_extractor: 提取完成 rank_return_5/rank_return_10, 0.030s

[2021-07-20 17:33:41.701271] INFO: derived_feature_extractor: 提取完成 ta_sma_5_0/ta_sma_10_0>0, 0.004s

[2021-07-20 17:33:41.706743] INFO: derived_feature_extractor: 提取完成 ta_sma_10_0/ta_sma_20_0, 0.003s

[2021-07-20 17:33:41.750233] INFO: derived_feature_extractor: /y_2009, 473

[2021-07-20 17:33:41.840335] INFO: derived_feature_extractor: /y_2010, 1896

[2021-07-20 17:33:41.910143] INFO: derived_feature_extractor: /y_2011, 1892

[2021-07-20 17:33:41.962707] INFO: derived_feature_extractor: /y_2012, 1906

[2021-07-20 17:33:42.029580] INFO: derived_feature_extractor: /y_2013, 1831

[2021-07-20 17:33:42.135462] INFO: derived_feature_extractor: /y_2014, 1677

[2021-07-20 17:33:42.182986] INFO: derived_feature_extractor: /y_2015, 1616

[2021-07-20 17:33:42.267533] INFO: derived_feature_extractor: /y_2016, 1690

[2021-07-20 17:33:42.355368] INFO: derived_feature_extractor: /y_2017, 2136

[2021-07-20 17:33:42.416942] INFO: derived_feature_extractor: /y_2018, 2032

[2021-07-20 17:33:42.524436] INFO: derived_feature_extractor: /y_2019, 2195

[2021-07-20 17:33:42.572809] INFO: derived_feature_extractor: /y_2020, 2176

[2021-07-20 17:33:42.665045] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[1.397813s].

[2021-07-20 17:33:42.672522] INFO: moduleinvoker: join.v3 开始运行..

[2021-07-20 17:33:43.126784] INFO: join: /y_2009, 行数=0/473, 耗时=0.061908s

[2021-07-20 17:33:43.200752] INFO: join: /y_2010, 行数=1896/1896, 耗时=0.071036s

[2021-07-20 17:33:43.311327] INFO: join: /y_2011, 行数=1891/1892, 耗时=0.105571s

[2021-07-20 17:33:43.427283] INFO: join: /y_2012, 行数=1904/1906, 耗时=0.112341s

[2021-07-20 17:33:43.618581] INFO: join: /y_2013, 行数=1830/1831, 耗时=0.188035s

[2021-07-20 17:33:43.715331] INFO: join: /y_2014, 行数=1675/1677, 耗时=0.093377s

[2021-07-20 17:33:43.818776] INFO: join: /y_2015, 行数=1591/1616, 耗时=0.101044s

[2021-07-20 17:33:43.912939] INFO: join: /y_2016, 行数=1689/1690, 耗时=0.091176s

[2021-07-20 17:33:44.111322] INFO: join: /y_2017, 行数=2117/2136, 耗时=0.196194s

[2021-07-20 17:33:44.210649] INFO: join: /y_2018, 行数=2032/2032, 耗时=0.097162s

[2021-07-20 17:33:44.349877] INFO: join: /y_2019, 行数=2191/2195, 耗时=0.136486s

[2021-07-20 17:33:44.512994] INFO: join: /y_2020, 行数=2126/2176, 耗时=0.159077s

[2021-07-20 17:33:47.285615] INFO: join: 最终行数: 20942

[2021-07-20 17:33:47.315347] INFO: moduleinvoker: join.v3 运行完成[4.642762s].

[2021-07-20 17:33:47.324551] INFO: moduleinvoker: dropnan.v2 开始运行..

[2021-07-20 17:33:47.692170] INFO: dropnan: /y_2009, 0/0

[2021-07-20 17:33:47.754960] INFO: dropnan: /y_2010, 1414/1896

[2021-07-20 17:33:47.842028] INFO: dropnan: /y_2011, 1332/1891

[2021-07-20 17:33:47.907878] INFO: dropnan: /y_2012, 1904/1904

[2021-07-20 17:33:47.968252] INFO: dropnan: /y_2013, 1830/1830

[2021-07-20 17:33:48.105949] INFO: dropnan: /y_2014, 1675/1675

[2021-07-20 17:33:48.186049] INFO: dropnan: /y_2015, 1591/1591

[2021-07-20 17:33:48.266550] INFO: dropnan: /y_2016, 1689/1689

[2021-07-20 17:33:48.337281] INFO: dropnan: /y_2017, 2111/2117

[2021-07-20 17:33:48.405337] INFO: dropnan: /y_2018, 2032/2032

[2021-07-20 17:33:48.498453] INFO: dropnan: /y_2019, 2191/2191

[2021-07-20 17:33:48.567512] INFO: dropnan: /y_2020, 2105/2126

[2021-07-20 17:33:50.103520] INFO: dropnan: 行数: 19874/20942

[2021-07-20 17:33:50.109879] INFO: moduleinvoker: dropnan.v2 运行完成[2.78535s].

[2021-07-20 17:33:50.119084] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-07-20 17:33:54.322381] INFO: 基础特征抽取: 年份 2020, 特征行数=540

[2021-07-20 17:33:57.473133] INFO: 基础特征抽取: 年份 2021, 特征行数=1208

[2021-07-20 17:33:57.498437] INFO: 基础特征抽取: 总行数: 1748

[2021-07-20 17:33:57.507821] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[7.388732s].

[2021-07-20 17:33:57.513747] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-07-20 17:33:57.590218] INFO: derived_feature_extractor: 提取完成 avg_amount_0/avg_amount_5, 0.001s

[2021-07-20 17:33:57.592866] INFO: derived_feature_extractor: 提取完成 avg_amount_5/avg_amount_10, 0.001s

[2021-07-20 17:33:57.598515] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_5, 0.002s

[2021-07-20 17:33:57.602115] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_5/rank_avg_amount_10, 0.001s

[2021-07-20 17:33:57.606176] INFO: derived_feature_extractor: 提取完成 rank_return_0/rank_return_5, 0.001s

[2021-07-20 17:33:57.610033] INFO: derived_feature_extractor: 提取完成 rank_return_5/rank_return_10, 0.002s

[2021-07-20 17:33:57.613927] INFO: derived_feature_extractor: 提取完成 ta_sma_5_0/ta_sma_10_0>0, 0.002s

[2021-07-20 17:33:57.618154] INFO: derived_feature_extractor: 提取完成 ta_sma_10_0/ta_sma_20_0, 0.001s

[2021-07-20 17:33:57.660427] INFO: derived_feature_extractor: /y_2020, 540

[2021-07-20 17:33:57.753907] INFO: derived_feature_extractor: /y_2021, 1208

[2021-07-20 17:33:57.869018] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.355286s].

[2021-07-20 17:33:57.872988] INFO: moduleinvoker: dropnan.v2 开始运行..

[2021-07-20 17:33:57.957368] INFO: dropnan: /y_2020, 536/540

[2021-07-20 17:33:58.023923] INFO: dropnan: /y_2021, 1187/1208

[2021-07-20 17:33:58.084056] INFO: dropnan: 行数: 1723/1748

[2021-07-20 17:33:58.089516] INFO: moduleinvoker: dropnan.v2 运行完成[0.216532s].

[2021-07-20 17:33:58.183358] INFO: moduleinvoker: decision_tree_classifier.v1 开始运行..

[2021-07-20 17:33:59.323782] INFO: moduleinvoker: decision_tree_classifier.v1 运行完成[1.140384s].

[2021-07-20 17:34:01.698201] INFO: moduleinvoker: backtest.v8 开始运行..

[2021-07-20 17:34:01.704978] INFO: backtest: biglearning backtest:V8.5.0

[2021-07-20 17:34:01.707752] INFO: backtest: product_type:stock by specified

[2021-07-20 17:34:02.352520] INFO: moduleinvoker: cached.v2 开始运行..

[2021-07-20 17:34:10.366125] INFO: backtest: 读取股票行情完成:115929

[2021-07-20 17:34:10.739471] INFO: moduleinvoker: cached.v2 运行完成[8.386929s].

[2021-07-20 17:34:11.011854] INFO: algo: TradingAlgorithm V1.8.3

[2021-07-20 17:34:11.239025] INFO: algo: trading transform...

[2021-07-20 17:34:12.606839] INFO: algo: handle_splits get splits [dt:2021-06-24 00:00:00+00:00] [asset:Equity(951 [003035.SZA]), ratio:0.9990448355674744]

[2021-07-20 17:34:12.608957] INFO: Position: position stock handle split[sid:951, orig_amount:300, new_amount:300.0, orig_cost:10.699999809284195, new_cost:10.6898, ratio:0.9990448355674744, last_sale_price:10.460000038146973]

[2021-07-20 17:34:12.610707] INFO: Position: after split: PositionStock(asset:Equity(951 [003035.SZA]), amount:300.0, cost_basis:10.6898, last_sale_price:10.470000267028809)

[2021-07-20 17:34:12.612135] INFO: Position: returning cash: 3.0002

[2021-07-20 17:34:12.628604] INFO: algo: handle_splits get splits [dt:2021-06-25 00:00:00+00:00] [asset:Equity(748 [000636.SZA]), ratio:0.9982443451881409]

[2021-07-20 17:34:12.631660] INFO: Position: position stock handle split[sid:748, orig_amount:700, new_amount:701.0, orig_cost:26.44714491712454, new_cost:26.4007, ratio:0.9982443451881409, last_sale_price:28.43000030517578]

[2021-07-20 17:34:12.633572] INFO: Position: after split: PositionStock(asset:Equity(748 [000636.SZA]), amount:701.0, cost_basis:26.4007, last_sale_price:28.48000144958496)

[2021-07-20 17:34:12.634946] INFO: Position: returning cash: 6.5707

[2021-07-20 17:34:12.705910] INFO: algo: handle_splits get splits [dt:2021-07-01 00:00:00+00:00] [asset:Equity(882 [000625.SZA]), ratio:0.7058599591255188]

[2021-07-20 17:34:12.708542] INFO: Position: position stock handle split[sid:882, orig_amount:1300, new_amount:1841.0, orig_cost:19.88153868455801, new_cost:14.0336, ratio:0.7058599591255188, last_sale_price:18.55000114440918]

[2021-07-20 17:34:12.710042] INFO: Position: after split: PositionStock(asset:Equity(882 [000625.SZA]), amount:1841.0, cost_basis:14.0336, last_sale_price:26.280000686645508)

[2021-07-20 17:34:12.712548] INFO: Position: returning cash: 13.4505

[2021-07-20 17:34:12.758107] INFO: Performance: Simulated 122 trading days out of 122.

[2021-07-20 17:34:12.759578] INFO: Performance: first open: 2021-01-04 09:30:00+00:00

[2021-07-20 17:34:12.760895] INFO: Performance: last close: 2021-07-06 15:00:00+00:00

[2021-07-20 17:34:14.714581] INFO: moduleinvoker: backtest.v8 运行完成[13.016392s].

[2021-07-20 17:34:14.717219] INFO: moduleinvoker: trade.v4 运行完成[15.382425s].

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-f6012d40329846ac914938b61a4f2ea7"}/bigcharts-data-end