PostgreSQL: How to Predict Stock Price Using Univariate Linear Regression

By digoal

Background

This article gives a simple example of univariate linear regression using PostgreSQL. The data used here is the closing price of the stock.

If you want to perform multivariate analysis, you need to add the dimensions to calculate the expected value manually. For example, you can perform regression calculations on the market index, turnover, lowest, highest price, and price range.

Examples are as follows:

Enter the test in the actual order — for example, the test time and the ID range from small to large. Here, the end goal is to use the previous value to predict the next value.

The first record is the oldest value, and the last record is the latest value.

create table test(id int, cnt numeric);

The following is Alibaba's closing price data netizens provided.

Format: date, close, volume, open, high, low

vi ali.txt  
2015/3/3,81.58,39173430,82.95,83.25,80.03  
2015/3/2,84,11175870,85,85.02,83.75  
2015/2/27,85.12,8362081,85.95,86.56,85  
2015/2/26,85.37,8758019,86.92,87.16,85.2  
2015/2/25,86.19,13592080,84.38,86.83,84.36  
2015/2/24,84.69,15817560,85.53,85.53,83.88  
2015/2/23,85.47,9352529,86.51,86.68,85.25  
2015/2/20,86.64,7805009,87.25,87.29,86.38  
2015/2/19,86.89,7568275,86.81,87.87,86.71  
2015/2/18,86.74,7415961,87.1,87.43,86.5  
2015/2/17,86.85,15133130,88.78,88.99,86.7  
2015/2/13,89.05,14587390,88.2,89.3,87.65  
2015/2/12,87.1,15143910,85.6,88.3,85.55  
2015/2/11,86,12281340,87.58,87.7,85.82  
2015/2/10,87.26,12026260,87.01,87.47,86.52  
2015/2/9,86,12088070,85.83,86.75,85.47  
2015/2/6,85.68,17597490,87.11,87.4,85.42  
2015/2/5,87,28867570,89.58,89.84,86.1  
2015/2/4,90,14599400,90.99,91.88,89.48  
2015/2/3,90.61,13475970,91.65,91.65,89.9  
2015/2/2,90.13,18845140,91.13,91.66,88.61  
2015/1/30,89.08,36747370,89.6,92,88.11  
2015/1/29,89.81,76430640,90.53,90.74,87.36  
2015/1/28,98.45,42012700,100.3,101.49,97.79  
2015/1/27,102.94,15591560,102.89,103.57,100.58  
2015/1/26,103.99,10656340,104.4,105.13,103.33  
2015/1/23,103.11,9860504,104.02,105.2,103.02  
2015/1/22,104,11400180,104.6,104.92,103.1  
2015/1/21,103.29,15141470,100.75,103.86,100.32  
2015/1/20,100.04,12049470,98.3,100.21,97.59  
2015/1/16,96.89,13313120,96.09,97.8,95.52  
2015/1/15,96.31,18182200,99.67,100.14,96.02  
2015/1/14,99.58,17785990,99.28,100.18,98.06  
2015/1/13,100.77,11275790,102.59,102.85,100.01  
2015/1/12,101.62,7946740,103.2,103.36,101.21  
2015/1/9,103.02,10193410,105.24,105.3,102.89  
2015/1/8,105.03,12904640,102.95,105.3399,102.68  
2015/1/7,102.13,11000010,104.59,104.74,102.03  
2015/1/6,103.32,15662710,101.25,103.85,100.11  
2015/1/5,101,18313750,102.76,103.02,99.9  
2015/1/2,103.6,12286400,104.24,104.72,102.52  
2014/12/31,103.94,10254350,106.46,106.47,103.69  
2014/12/30,105.75,10185890,105.64,106.71,105.13  
2014/12/29,105.98,8059191,105.95,107.66,105.64  
2014/12/26,105.95,6523956,105.99,106.94,105.5  
2014/12/24,105.95,5870358,105.68,107.21,105.6  
2014/12/23,105.52,19077270,108.3,108.47,103.88  
2014/12/22,108.77,12985130,110.63,110.98,108.53  
2014/12/19,110.65,14785340,109.93,110.65,108.04  
2014/12/18,109.25,22749890,110.58,111.2,108.26  
2014/12/17,109.02,17242940,107.11,109.19,106.28  
2014/12/16,105.77,21655360,103.75,107.68,103.7  
2014/12/15,104.7,16476750,106.39,107.77,103.7  
2014/12/12,105.11,14504050,104.7,107.45,104.179  
2014/12/11,104.97,15658440,104.44,106.84,104.29  
2014/12/10,103.88,18398440,107.09,107.38,103.51  
2014/12/9,107.48,24805130,102.27,107.95,101.2  
2014/12/8,105.07,19166550,105.97,107.4,104.2101  
2014/12/5,107.9,12125090,109.6,110.35,107.76  
2014/12/4,109.17,10790400,110.1,110.5,108.91  
2014/12/3,110.64,16164860,110.4,111.68,108.798

Import the data into the database:

create table tmp(c1 text,c2 numeric,c3 numeric,c4 numeric,c5 numeric,c6 numeric);  
copy tmp from '/home/postgres/ali.txt' with (format csv);

Change the order:

insert into test select row_number() over(order by ctid desc), c2 from tmp;

Checksum function:

CREATE OR REPLACE FUNCTION public.check_predict(  
IN ov integer,   --  校验哪条记录  
OUT rv numeric,  --  真实值  
OUT pv numeric,  --  预测值  
OUT dev numeric, --  误差  
OUT zv numeric,  --  自变量  
OUT v_slope numeric, --  斜率  
OUT v_inter numeric, --  截距  
OUT v_r2  numeric  --  相关性  
)  
 RETURNS record  
 LANGUAGE plpgsql  
AS $function$  
declare   
  r2_1 numeric := 0; -- 相关性  
  r2_2 numeric := 0; -- 最大相关性  
  var int;           --  样本起点  
  inter_1 numeric;  --  截距  
  slope_1 numeric;  --  斜率  
  inter_2 numeric;  --  最大相关性截距  
  slope_2 numeric;  --  最大相关性斜率  
  realv numeric;    --  真实数据  
  predicv numeric;  --  预测数据  
  offset_var int := ov;   -- 倒数第? 个值的预测值, 不停迭代, 最后计算所有的实际值和预测值的corr, 选择最佳相关?  
  v_cnt int8 := 0;  
  v_lmt int := 90;  --  使用的最大样本集, 影响预测准确度  
  v_min int := 5;   --  使用的最小样本数, 影响预测准确度  
  zcnt numeric := 0;    --  自变量  
begin  
  select count(*) into v_cnt from test;  
  
  --  自变量 t1  
  --  因变量 t2  
  
  --  筛选最大相关度的样本数, 并记录下储斜率, 截距.  
  for i in 0..v_lmt   
  loop  
    with t1 as (select row_number() over(order by id desc) as rn,* from (select id,lag(cnt,1) over(order by id) as zbl_cnt,cnt from test order by id desc offset offset_var limit v_lmt+v_min) t  
                where t.zbl_cnt is not null  
           )  
      select regr_intercept(t1.cnt,t1.zbl_cnt), regr_slope(t1.cnt,t1.zbl_cnt), regr_r2(t1.cnt,t1.zbl_cnt)   
        into inter_1,slope_1,r2_1  
      from t1  
      where t1.rn<=i+v_min;  
  
    if r2_1>r2_2 then   
      inter_2 := inter_1;  
      slope_2 := slope_1;  
      r2_2 := r2_1;  
      var := i+v_min;  
    end if;  
  end loop;  
  
  raise notice 'offset:%, 最大相关度样本数:%, 截距%, 斜率%, 相关性%', offset_var, var, round(inter_2,4), round(slope_2,4), round(r2_2,4);  
  select slope_2*cnt+inter_2, cnt into predicv,zcnt from test order by id desc offset offset_var limit 1;  
  
  --  offset_var=0的自变量, 表示最近一个值, 所以真实值还不存在, 那么realv=NULL  
  if  offset_var=0 then  
    realv := NULL;  
  else  
    select cnt into realv from test order by id desc offset offset_var-1 limit 1;  
  end if;  
  
  raise notice '自变量%, 真实数据%, 预测数据%, 本次预测偏差%%%', zcnt, realv, round(predicv,2), abs(1-round(predicv/realv,4))*100;  
  
  rv := realv;  
  pv := round(predicv,2);  
  dev := abs(1-round(predicv/realv,4));  
  zv  := zcnt;  
  v_slope := slope_2;  
  v_inter := inter_2;  
  v_r2 := r2_2;  
  
  return;  
end;  
$function$;

If the prediction result deviates a lot, look at the deviation data displayed in the dev column.

If your data contains data on today's closing price, the first line is the forecast closing price for tomorrow.

postgres=# select (check_predict(i)).* from generate_series(0,50) t(i);  
   rv   |   pv   |  dev   |   zv   |      v_slope       |      v_inter       |        v_r2          
--------+--------+--------+--------+--------------------+--------------------+--------------------  
        |  81.50 |        |  81.58 |  0.930196213911424 |   5.61421408650552 |  0.461071168376043  
  81.58 |  84.90 | 0.0407 |     84 |  0.560745221248429 |   37.8015394128117 |   0.23571694348864  
     84 |  85.84 | 0.0219 |  85.12 |  0.416036492272315 |    50.428211921701 |  0.160707728559778  
  85.12 |  86.06 | 0.0110 |  85.37 |   0.31332110091768 |   59.3087633551948 | 0.0916611831863153  
  85.37 |  86.40 | 0.0121 |  86.19 |  0.223065327998904 |   67.1752208792373 | 0.0892441401437571  
  86.19 |  85.51 | 0.0078 |  84.69 |  0.509756185614852 |    42.343562005006 |  0.338718526733452  
  84.69 |  85.97 | 0.0151 |  85.47 |   0.60492702580558 |   34.2628366224153 |  0.408610576552954  
  85.47 |  86.86 | 0.0163 |  86.64 |  0.641095276611868 |   31.3167837973076 |  0.422562923551744  
  86.64 |  87.06 | 0.0049 |  86.89 |    0.6396258966744 |   31.4865883689919 |  0.442022365274712  
  86.89 |  87.31 | 0.0048 |  86.74 |  0.314176461801723 |   60.0558838893752 |  0.345972028763493  
  86.74 |  87.07 | 0.0038 |  86.85 |  0.556953610508981 |   38.6967242480077 |  0.709038352162874  
  86.85 |  88.54 | 0.0194 |  89.05 |   0.71646536311035 |   24.7355217235248 |  0.806474667992637  
  89.05 |  86.82 | 0.0250 |   87.1 |  0.828893551212771 |   14.6233989516049 |  0.843205122436764  
   87.1 |  85.63 | 0.0169 |     86 |  0.876681721784925 |   10.2362647858593 |  0.868757340937373  
     86 |  86.79 | 0.0092 |  87.26 |  0.907607969680494 |   7.59324024469571 |  0.872354617595825  
  87.26 |  85.35 | 0.0219 |     86 |  0.963046324742614 |   2.52920153372814 |  0.854052902081886  
     86 |  85.04 | 0.0111 |  85.68 |  0.984857752501153 |  0.658717160003272 |  0.818153667442131  
  85.68 |  86.65 | 0.0113 |     87 |  0.968673034113725 |    2.3751701572406 |  0.782575372168168  
     87 |  90.00 | 0.0345 |     90 |  0.910991684444145 |   8.01456054235934 |  0.742602997511869  
     90 |  90.69 | 0.0076 |  90.61 |  0.897821640533335 |   9.33440206466203 |  0.713122343798605  
  90.61 |  90.13 | 0.0053 |  90.13 |  0.909958318693295 |   8.11836657246885 |  0.676080976394243  
  90.13 |  88.54 | 0.0177 |  89.08 |  0.959429536023691 |   3.07240205281187 |  0.608007093767562  
  89.08 |  89.76 | 0.0076 |  89.81 |   0.91029848144619 |   8.00500306348446 |  0.404654669040077  
  89.81 |  99.27 | 0.1054 |  98.45 |  0.657207082561843 |   34.5727385355619 |  0.393214748683796  
  98.45 | 102.53 | 0.0415 | 102.94 |  0.704409588700779 |   30.0196224514047 |  0.502462865561183  
 102.94 | 103.45 | 0.0049 | 103.99 |  0.703297557503838 |   30.3100075437914 |   0.46367875870034  
 103.99 | 102.47 | 0.0147 | 103.11 |   0.62837962126522 |   37.6738507164757 |  0.403487822887036  
 103.11 | 103.10 | 0.0001 |    104 |  0.643348564991283 |   36.1926878205066 |  0.412471976813411  
    104 | 102.47 | 0.0147 | 103.29 |  0.609715827989882 |   39.4938840550046 |  0.434204693264911  
 103.29 | 100.19 | 0.0300 | 100.04 |  0.714689142171646 |   28.6965783703781 |  0.579882468389968  
 100.04 |  96.87 | 0.0317 |  96.89 |  0.891706172449747 |   10.4761802869589 |  0.673676693300631  
  96.89 |  95.65 | 0.0128 |  96.31 |  0.996480964507578 | -0.324693104249417 |  0.614510491219348  
  96.31 |  99.98 | 0.0381 |  99.58 |  0.805484441868333 |   19.7717089277751 |  0.512838450280928  
  99.58 | 101.72 | 0.0215 | 100.77 |  0.580782945386486 |   43.1942330487114 |  0.411626746536197  
 100.77 | 102.31 | 0.0153 | 101.62 |   0.60960923807431 |   40.3630398853694 |  0.534574960836081  
 101.62 | 103.28 | 0.0163 | 103.02 |  0.715901485259652 |   29.5241464649391 |  0.578753862361246  
 103.02 | 104.93 | 0.0185 | 105.03 |  0.755513345311285 |   25.5752266330287 |  0.637919333414959  
 105.03 | 102.17 | 0.0272 | 102.13 |   0.85397742981656 |   14.9562551932185 |  0.706505426646503  
 102.13 | 103.39 | 0.0123 | 103.32 |  0.816331012523094 |   19.0456461040167 |  0.678069331200632  
 103.32 |  97.81 | 0.0533 |    101 |   1.59944623063686 |  -63.7322919498157 |  0.754678672545085  
    101 | 103.53 | 0.0251 |  103.6 |  0.863779780883968 |   14.0441886225418 |  0.661109144730258  
  103.6 | 103.94 | 0.0033 | 103.94 |  0.838387460079692 |   16.7970520107185 |  0.577059699997509  
 103.94 | 105.92 | 0.0191 | 105.75 | -0.133229605047718 |   120.010159783732 |  0.804055392782976  
 105.75 | 105.93 | 0.0017 | 105.98 | -0.145112549418295 |   121.311379359051 |  0.969009451994014  
 105.98 | 105.95 | 0.0003 | 105.95 |                  0 |   105.949999994131 |                  1  
 105.95 | 105.89 | 0.0005 | 105.95 |  -0.13230769230761 |   119.911107692316 |   0.99999999999768  
 105.95 |  99.90 | 0.0571 | 105.52 |   1.72872340425854 |  -82.5132446808904 |   1.00000000000077  
 105.52 | 111.29 | 0.0547 | 108.77 |  -1.34285714286032 |   257.357142857695 |   1.00000000000725  
 108.77 | 119.17 | 0.0956 | 110.65 |   6.08695652160159 |  -554.349999972815 |   1.00000000000081  
 110.65 | 109.27 | 0.0125 | 109.25 | 0.0707692307696992 |    101.53473846147 |  0.999999999967684  
 109.25 | 118.89 | 0.0883 | 109.02 |   3.03738317759831 |  -212.244018694157 |   1.00000000001163  
(51 rows)

The prediction results for the stock price of Guizhou Moutai: The prediction deviation is still relatively ideal. (Maybe external factors greatly influence some stocks, while others are relatively small because this article does not introduce the multivariate analysis.)

postgres=# select (check_predict(i)).* from generate_series(0,50) t(i);  
   rv   |   pv   |  dev   |   zv   |      v_slope      |      v_inter      |       v_r2          
--------+--------+--------+--------+-------------------+-------------------+-------------------  
        | 155.68 |        | 154.75 | 0.749327368763041 |  39.7193487454018 | 0.443299946747894  
 154.75 | 156.33 | 0.0102 | 155.00 | 0.658983381690956 |  54.1888692212598 |  0.31605703462515  
 155.00 | 158.61 | 0.0233 | 157.81 | 0.505691375378367 |  78.8096738403756 | 0.240429553874766  
 157.81 | 159.35 | 0.0097 | 159.02 | 0.471362121189687 |  84.3914502631931 |  0.23632158869318  
 159.02 | 159.89 | 0.0055 | 159.87 | 0.502631474677655 |  79.5350129389031 | 0.262084379639135  
 159.87 | 160.46 | 0.0037 | 161.00 | 0.486478989379233 |  82.1377272523488 |  0.29236983925612  
 161.00 | 161.73 | 0.0045 | 162.74 | 0.592859008065614 |  65.2441140525351 |  0.38051931752417  
 162.74 | 161.22 | 0.0094 | 162.33 | 0.522125410976863 |  76.4612098979163 | 0.450724600316921  
 162.33 | 161.07 | 0.0078 | 161.50 | 0.670999115633002 |  52.7013374696206 | 0.535249445391989  
 161.50 | 160.77 | 0.0045 | 160.92 |  0.72782591656929 |  43.6501486130006 | 0.574758659704234  
 160.92 | 156.66 | 0.0264 | 156.49 | 0.885275616598876 |  18.1277330183775 | 0.655113491671201  
 156.49 | 157.82 | 0.0085 | 156.70 | 0.757494701869893 |    39.12388483696 | 0.495319263616962  
 156.70 | 161.18 | 0.0286 | 160.71 | 0.725555251667536 |  44.5773888438119 | 0.584398418586498  
 160.71 | 160.13 | 0.0036 | 159.25 | 0.718340613774721 |  45.7320036637828 | 0.626975960572309  
 159.25 | 158.92 | 0.0021 | 158.02 | 0.776715149169011 |  36.1819821709587 | 0.676670415905896  
 158.02 | 159.06 | 0.0066 | 158.47 | 0.812642712296025 |  30.2825991865648 | 0.679920408166994  
 158.47 | 160.77 | 0.0145 | 160.50 | 0.824536957368669 |  28.4277502764865 | 0.708045226118134  
 160.50 | 161.95 | 0.0090 | 161.90 | 0.843561194042496 |  25.3766280000652 | 0.722573245190375  
 161.90 | 161.51 | 0.0024 | 161.49 | 0.847987413256958 |  24.5663395825434 | 0.721633674185219  
 161.49 | 163.60 | 0.0131 | 163.87 | 0.870581601947346 |  20.9406294921026 | 0.725206713695765  
 163.87 | 163.51 | 0.0022 | 163.70 | 0.833749346623708 |  27.0240967784583 | 0.670383837164832  
 163.70 | 168.62 | 0.0301 | 167.65 |  1.05254359115864 | -7.83507005049674 | 0.735574587910647  
 167.65 | 165.25 | 0.0143 | 165.70 | 0.883481284376093 |  18.8595907684336 | 0.566222610315986  
 165.70 | 163.61 | 0.0126 | 164.70 | 0.760665237060595 |  38.3314388491422 | 0.437037290387292  
 164.70 | 161.46 | 0.0197 | 162.58 |  0.63926420779486 |  57.5312362089702 | 0.386820980329109  
 162.58 | 158.80 | 0.0232 | 158.81 | 0.717636038072266 |  44.8353807921515 | 0.569066830110592  
 158.81 | 158.34 | 0.0030 | 158.20 | 0.761022139545313 |  37.9453143278904 | 0.597076723252649  
 158.20 | 156.01 | 0.0138 | 156.00 | 0.890859569629593 |  17.0349264413836 | 0.660262576219616  
 156.00 | 155.91 | 0.0006 | 156.00 | 0.928603251444626 |   11.051262047026 | 0.569697453056979  
 156.00 | 158.28 | 0.0146 | 157.72 | 0.723658951735034 |  44.1435343246399 |  0.44078135697188  
 157.72 | 159.30 | 0.0100 | 158.50 | 0.593435970103012 |  65.2440679649604 | 0.394699579585899  
 158.50 | 159.91 | 0.0089 | 159.27 | 0.632807705774723 |  59.1184436985211 | 0.387382904311161  
 159.27 | 160.63 | 0.0085 | 160.00 | 0.604419927649386 |  63.9233718732132 | 0.330175855885006  
 160.00 | 162.13 | 0.0133 | 162.00 | 0.479607769753778 |  84.4368575468047 | 0.257868311166715  
 162.00 | 159.99 | 0.0124 | 158.74 | 0.624745284091551 |  60.8132213491609 | 0.426844197393141  
 158.74 | 160.57 | 0.0115 | 159.75 |   0.6106793906001 |  63.0161876923878 | 0.337283942825567  
 159.75 | 161.95 | 0.0138 | 162.00 | 0.453875422780405 |  88.4214116897916 | 0.235385451808691  
 162.00 | 162.13 | 0.0008 | 162.20 | 0.514218487026421 |  78.7230165782795 | 0.291003288858034  
 162.20 | 162.09 | 0.0007 | 162.02 | 0.556324729871488 |  71.9532675373276 | 0.326799171637252  
 162.02 | 162.16 | 0.0009 | 162.04 | 0.579222185293716 |  68.3028288619664 | 0.344557168377077  
 162.04 | 162.16 | 0.0007 | 161.81 | 0.556599549002475 |  72.0969606318282 | 0.307924224254188  
 161.81 | 162.66 | 0.0052 | 162.48 | 0.500693143269678 |   81.305630489949 | 0.298678253677398  
 162.48 | 162.22 | 0.0016 | 161.96 | 0.568957052943135 |  70.0759551450734 | 0.346462148633283  
 161.96 | 162.89 | 0.0057 | 163.10 |  0.39281014679101 |  98.8215502236369 | 0.340803249529129  
 163.10 | 164.15 | 0.0065 | 164.80 | 0.640630057919025 |  58.5763291969098 | 0.536313525971144  
 164.80 | 163.63 | 0.0071 | 164.00 |   0.7153108267714 |  46.3161409479079 | 0.611327694763799  
 164.00 | 162.73 | 0.0078 | 163.00 | 0.671618352789207 |  53.2545511273858 | 0.679718050633678  
 163.00 | 160.66 | 0.0144 | 160.17 | 0.646714009825995 |  57.0713176290233 | 0.804063835532055  
 160.17 | 159.11 | 0.0066 | 158.29 | 0.769358075679891 |  37.3298385738639 | 0.848976826898375  
 158.29 | 161.29 | 0.0190 | 160.74 | 0.845694258048787 |  25.3560723887127 | 0.887140705169265  
 160.74 | 165.13 | 0.0273 | 164.30 | 0.928055617122675 |  12.6470087942632 | 0.910309053795291  
(51 rows)

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PostgreSQL: How to Predict Stock Price Using Univariate Linear Regression

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