PostgreSql 中的“运行产品”聚合/窗口函数？

Question

我正在尝试在 PostgreSql 中标准化日终股票价格。

假设我有一个这样定义的库存表：

create table eod (
  date date not null,
  stock_id int not null,
  split decimal(16,8) not null,
  close decimal(12,6) not null,
  constraint pk_eod primary key (date, stock_id)
);

此表中的数据可能如下所示：

"date","stock_id","eod_split","close"
"2014-06-13",14010920,"1.00000000","182.560000"
"2014-06-13",14010911,"1.00000000","91.280000"
"2014-06-13",14010923,"1.00000000","41.230000"
"2014-06-12",14010911,"1.00000000","92.290000"
"2014-06-12",14010920,"1.00000000","181.220000"
"2014-06-12",14010923,"1.00000000","40.580000"
"2014-06-11",14010920,"1.00000000","182.250000"
"2014-06-11",14010911,"1.00000000","93.860000"
"2014-06-11",14010923,"1.00000000","40.860000"
"2014-06-10",14010911,"1.00000000","94.250000"
"2014-06-10",14010923,"1.00000000","41.110000"
"2014-06-10",14010920,"1.00000000","184.290000"
"2014-06-09",14010920,"1.00000000","186.220000"
"2014-06-09",14010911,"7.00000000","93.700000"
"2014-06-09",14010923,"1.00000000","41.270000"
"2014-06-06",14010923,"1.00000000","41.480000"
"2014-06-06",14010911,"1.00000000","645.570000"
"2014-06-06",14010920,"1.00000000","186.370000"
"2014-06-05",14010920,"1.00000000","185.980000"
"2014-06-05",14010911,"1.00000000","647.350000"
"2014-06-05",14010923,"1.00000000","41.210000"
... 
"2005-03-04",14010920,"1.00000000","92.370000"
"2005-03-04",14010911,"1.00000000","42.810000"
"2005-03-04",14010923,"1.00000000","25.170000"
"2005-03-03",14010923,"1.00000000","25.170000"
"2005-03-03",14010911,"1.00000000","41.790000"
"2005-03-03",14010920,"1.00000000","92.410000"
"2005-03-02",14010920,"1.00000000","92.920000"
"2005-03-02",14010923,"1.00000000","25.260000"
"2005-03-02",14010911,"1.00000000","44.121000"
"2005-03-01",14010920,"1.00000000","93.300000"
"2005-03-01",14010923,"1.00000000","25.280000"
"2005-03-01",14010911,"1.00000000","44.500000"
"2005-02-28",14010923,"1.00000000","25.160000"
"2005-02-28",14010911,"2.00000000","44.860000"
"2005-02-28",14010920,"1.00000000","92.580000"
"2005-02-25",14010923,"1.00000000","25.250000"
"2005-02-25",14010920,"1.00000000","92.800000"
"2005-02-25",14010911,"1.00000000","88.990000"
"2005-02-24",14010923,"1.00000000","25.370000"
"2005-02-24",14010920,"1.00000000","92.640000"
"2005-02-24",14010911,"1.00000000","88.930000"
"2005-02-23",14010923,"1.00000000","25.200000"
"2005-02-23",14010911,"1.00000000","88.230000"
"2005-02-23",14010920,"1.00000000","92.100000"
...
"2003-02-24",14010920,"1.00000000","78.560000"
"2003-02-24",14010911,"1.00000000","14.740000"
"2003-02-24",14010923,"1.00000000","24.070000"
"2003-02-21",14010920,"1.00000000","79.950000"
"2003-02-21",14010923,"1.00000000","24.630000"
"2003-02-21",14010911,"1.00000000","15.000000"
"2003-02-20",14010911,"1.00000000","14.770000"
"2003-02-20",14010920,"1.00000000","79.150000"
"2003-02-20",14010923,"1.00000000","24.140000"
"2003-02-19",14010920,"1.00000000","79.510000"
"2003-02-19",14010911,"1.00000000","14.850000"
"2003-02-19",14010923,"1.00000000","24.530000"
"2003-02-18",14010923,"2.00000000","24.960000"
"2003-02-18",14010911,"1.00000000","15.270000"
"2003-02-18",14010920,"1.00000000","79.330000"
"2003-02-14",14010911,"1.00000000","14.670000"
"2003-02-14",14010920,"1.00000000","77.450000"
"2003-02-14",14010923,"1.00000000","48.300000"
"2003-02-13",14010920,"1.00000000","75.860000"
"2003-02-13",14010911,"1.00000000","14.540000"
"2003-02-13",14010923,"1.00000000","46.990000"

请注意“拆分”列。当记录的拆分值不是 1 时，基本上意味着股票按该因子拆分。IOW，当拆分为 2.0 时，已发行股票的数量翻了一番，但从那时起，每只股票的价值减半。如果股票的价值为每股 100 美元，那么现在它的价值为每股 50 美元。

如果你用原始数字来绘制它，这种事情真的很难看。当公司的整体价值没有显着变化时，就会出现陡峭的悬崖……而当您进行多次拆分时，您最终会得到一个不能正确反映公司趋势的图表，通常是大幅下降。在上面的例子中，如果有 2:1 的分割，你的股票收盘价看起来像 100、100、100、50、50、50。

我想使用此表以合理有效的方式创建“标准化”价格（有相当多的记录要分块）。继续样本，这将显示股票价格在 50, 50, 50, 50, 50, 50。如果有多个拆分，如果我们忽略实际市值变化，数据应该仍然是一致和平滑的。

我的想法是，如果我可以创建拆分值的“运行产品”聚合的 CTE，回到过去，我可以定义每只股票的日期范围以及应用于收盘成本的修正值应该是什么，然后加入返回到 eod 表并在新表中选择每只股票的调整后收盘价。

...问题是，除了一大堆临时表和多步骤流程之外，我无法围绕如何做到这一点。我也不知道有任何内置功能可以使这更容易。

有人可以告诉我如何生成规范化数据吗？

Answer 1

你不需要 CTE。您只需要一个累积产品。Postgres 没有内置的。但是，算术来拯救！

select eod.*,
       exp(sum(ln(eod_split)) over (partition by stock_id order by date)) as cume_split,
       (close *
        exp(sum(ln(eod_split)) over (partition by stock_id order by date))
       ) as normalized_price
from eod;