Pulling data points from multiple webpages

Jimmywinvests · Feb 15, 2016

Hello Exel-sperts (yes i love bad puns),

I have spent weeks reading forums and watching videos only to fail at creating the spreadsheet I set out to; I am hoping that someone on here might be able to help me.
I need to create a spreadsheet which pulls multiple data points (from a few web pages) off yahoo finance. The method however would need to include a macro button which when pressed would refresh the data, as well as work for multiple assessments.

The data I wish to extract (using the company "Telstra" as an example) include the following:

[TABLE="width: 578"]
<tbody>[TR]
[TD]Summary Page[/TD]
[/TR]
[TR]
[TD]https://au.finance.yahoo.com/q?s=TLS.AX[/TD]
[/TR]
[TR]
[TD]Company Name[/TD]
[/TR]
[TR]
[TD]share price[/TD]
[/TR]
[TR]
[TD]Market Cap (mil)[/TD]
[/TR]
[TR]
[TD][/TD]
[/TR]
[TR]
[TD]Balance Sheet Quartly Page[/TD]
[/TR]
[TR]
[TD]https://au.finance.yahoo.com/q/bs?s=TLS.AX[/TD]
[/TR]
[TR]
[TD]Cash and Cash equilavents (of most recent quarter - i.e. left column)[/TD]
[/TR]
[TR]
[TD]Cash from short term investments (of most recent quarter - i.e. left column)[/TD]
[/TR]
[TR]
[TD][/TD]
[/TR]
[TR]
[TD]Cash Flow Annual Page[/TD]
[/TR]
[TR]
[TD]https://au.finance.yahoo.com/q/cf?s=TLS.AX&annual[/TD]
[/TR]
[TR]
[TD]Cash from operating activities (most recent annual figure)[/TD]
[/TR]
[TR]
[TD]Capital expenditures (most recent annual figure)[/TD]
[/TR]
[TR]
[TD][/TD]
[/TR]
[TR]
[TD]Key Statistics Page[/TD]
[/TR]
[TR]
[TD]https://au.finance.yahoo.com/q/ks?s=TLS.AX[/TD]
[/TR]
[TR]
[TD]shares outstanding[/TD]
[/TR]
[TR]
[TD]return on equity[/TD]
[/TR]
[TR]
[TD]revenue growth rate[/TD]
[/TR]
[TR]
[TD]Total debt[/TD]
[/TR]
[TR]
[TD]Operating[/TD]
[/TR]
[TR]
[TD]Forward PE Ratio (optional - include if easy)
[/TD]
[/TR]
</tbody>[/TABLE]

The idea is that this data is scraped off the above websites using the ticker "TLS.AX" as the identifier. Therefore, the headers should populate the first row in the spreadsheet.

The second part to this is that I would need this to work on multiple companies at a time (for example if i listed the circa 2100 companies listed on the ASX in column "A", ideally it would populate all the above listed data for every company; thus allowing the filtering of companies by data point.

Any help is very greatly appreciated. (I am not sure where else to turn to - at this point I even have doubts that excel can fulfill the requirements).

Thank-you to anybody who takes the time to read my problem!

- Jimmy

Jimmywinvests · Feb 29, 2016

Looks like everything is working beautifully! - aside from it randomly stopping at different increments.

Earlier where you said to change the application to from 2 to 3 seconds... Should I do this wherever it occurs throughout the macro?

thanks again for everything. wish I could send you a gift basket

michaelsmith559 · Feb 29, 2016

I'm not sure why it randomly stops unless its just not allowing enough time to download everything or if the webpage is taking a little bit longer to respond. That is beyond me. You could start a new post and ask for any help speeding up the macro and ask why it randomly stops. Other than that I'm really not sure. Glad you like it.
Oh, yes everywhere it says application.wait....2, change the 2 to a 3 and see if that helps.

Mike

Jimmywinvests · Feb 29, 2016

Thank you so very much again! More than helpful. Will start a new thread tomorrow morning on the way to work.

michaelsmith559 · Feb 29, 2016

I also made a sheet to try and predict the probability of a stock being profitable using the historical data. You may find this useful or you may not care for it at all. The one I did, has an accuracy of 83%. I'll post several parts of how to set up the sheet followed by macros, and a function. Compare column G (actual) to column M (predicted).

Excel 2003

A

B

C

D

E

F

G

1

Open

High

Low

Volume

Close

Profit

2

2.45

2.18

335,200

2.3

0

3

2.41

2.35

82,600

2.4

0

4

2.4

2.46

2.31

111,700

2.4

0

5

2.43

2.48

2.4

108,400

2.4

0

6

2.42

2.45

2.41

113,600

2.43

1

7

2.39

2.43

2.35

118,800

2.39

0

8

2.39

2.4

2.3

61,300

2.3

0

9

2.32

2.43

2.3

80,600

2.37

1

10

2.4

2.25

142,600

2.28

0

11

2.24

2.33

2.24

278,100

2.33

1

12

2.05

2.25

2.05

60,700

2.2

1

13

2.01

2.04

1.99

75,900

2.04

1

14

2.01

2.04

1.99

35,000

2.04

1

15

2.02

2.07

2

116,300

2.01

0

16

2.05

2.06

2

60,200

2.03

0

17

2.14

2.15

2.04

137,900

2.1

0

18

2.06

2.11

2.04

39,200

2.11

1

19

2.04

2.05

2.01

49,400

2.05

1

20

2.05

2.08

2.03

45,500

2.06

1

21

2.06

2.08

2.03

65,100

2.08

1

22

2.07

2.02

491,100

2.05

0

23

2.08

2.09

2.02

62,100

2.07

0

24

2.08

2.02

14,000

2.08

0

25

2.08

2.02

35,400

2.08

0

26

2.08

0

2.08

0

27

2.08

2.11

2.05

119,600

2.08

0

28

2.01

2.07

1.98

143,100

2.05

1

29

2.06

2.08

1.95

40,900

1.96

0

30

1.98

2.03

1.98

83,300

2

1

31

1.96

1.98

1.96

15,100

1.98

1

32

1.98

1.93

55,400

1.93

0

33

2

2.15

2

157,300

2.02

1

34

1.98

2

1.98

65,500

2

1

35

1.98

1.97

33,900

1.98

0

36

1.96

1.98

1.94

22,100

1.98

1

37

1.94

1.96

1.9

128,300

1.96

1

38

1.91

1.97

1.89

90,100

1.95

1

39

1.96

1.98

1.95

65,100

1.96

0

40

1.99

1.95

43,600

1.96

0

41

1.97

1.98

1.95

88,800

1.96

0

42

1.93

1.97

1.93

114,200

1.97

1

43

1.93

0

1.93

0

44

1.93

1.88

31,800

1.93

0

45

1.92

1.93

1.9

45,900

1.93

1

46

1.9

1.96

1.87

95,500

1.92

1

47

1.9

0

1.9

0

48

1.9

0

1.9

0

49

1.9

1.86

45,700

1.9

0

50

1.89

1.91

1.88

86,900

1.9

1

51

1.85

1.88

1.85

66,800

1.88

1

52

1.85

88,600

1.85

0

53

1.85

1.88

1.85

72,200

1.85

0

54

1.89

1.83

159,100

1.85

0

55

1.84

1.88

1.84

66,800

1.84

0

56

1.83

1.86

1.8

108,800

1.85

1

57

1.84

1.79

225,500

1.83

0

58

1.8

1.84

1.8

78,100

1.82

1

59

1.78

1.85

1.78

351,100

1.8

1

60

1.71

1.94

1.71

214,600

1.76

1

61

1.62

1.65

1.61

138,000

1.64

1

62

1.59

1.63

1.59

193,000

1.61

1

63

1.62

1.63

1.59

210,500

1.6

0

64

1.65

1.59

330,100

1.63

0

65

1.68

1.59

260,800

1.65

0

66

1.67

1.69

1.63

161,400

1.69

1

67

1.68

1.7

1.65

65,200

1.65

0

Sheet1

Excel 2003

I

J

K

L

M

1

Logit

Exp(Logit)

P(X)

P(Y)

Predict Profit

2

0

3

-4.28724

0.013742745

0.013556442

0.986444

0

4

-0.80379

0.447631231

0.30921634

0.690784

0

5

0.217009

1.242355706

0.554040424

0.44596

1

6

-0.64935

0.52238414

0.343135564

0.343136

0

7

-0.75493

0.470044719

0.319748585

0.680251

0

8

-4.33034

0.01316302

0.012992006

0.987008

0

9

5.420524

225.9974366

0.995594664

0.995595

1

10

-7.23125

0.000723616

0.000723093

0.999277

0

11

4.093263

59.93512371

0.983589104

0.983589

1

12

13.61232

816120.2777

0.999998775

0.999999

1

13

0.378471

1.460050334

0.593504252

0.593504

1

14

0.520235

1.682422343

0.627202628

0.627203

1

15

1.701315

5.481148333

0.845706355

0.154294

1

16

-2.09867

0.12261927

0.109226051

0.890774

0

17

-4.16871

0.015472145

0.015236405

0.984764

0

18

1.851047

6.366482091

0.864249992

0.86425

1

19

-1.41732

0.242361626

0.195081384

0.195081

0

20

0.366336

1.442440124

0.590573382

0.590573

1

21

-0.78437

0.456407546

0.313379004

0.313379

0

22

-4.39709

0.012313078

0.01216331

0.987837

0

23

-2.50065

0.082031391

0.075812395

0.924188

0

24

-3.08006

0.045956553

0.043937344

0.956063

0

25

-3.15423

0.042671096

0.04092479

0.959075

0

26

-1.18792

0.304853733

0.233630579

0.766369

0

27

-0.2859

0.751338646

0.429008203

0.570992

0

28

2.076656

7.977746223

0.88861347

0.888613

1

29

-3.15864

0.042483637

0.04075233

0.959248

0

30

2.547737

12.77815594

0.927421347

0.927421

1

31

0.604502

1.830340489

0.646685618

0.646686

1

32

-2.62253

0.072619161

0.067702651

0.932297

0

33

9.693745

16215.86677

0.999938336

0.999938

1

34

0.371058

1.449267328

0.591714637

0.591715

1

35

-1.31893

0.267420822

0.210996077

0.789004

0

36

-0.0343

0.966284034

0.491426476

0.491426

1

37

-0.95818

0.383589452

0.277242249

0.277242

0

38

2.861389

17.48579206

0.945904401

0.945904

1

39

0.123928

1.131934788

0.530942501

0.469057

1

40

-2.30373

0.099885161

0.090814173

0.909186

0

41

-1.04099

0.353105641

0.260959404

0.739041

0

42

1.84139

6.305294278

0.863112975

0.863113

1

43

-0.74728

0.47365218

0.321413822

0.678586

0

44

-2.39385

0.091277953

0.083643175

0.916357

0

45

-0.74541

0.474538898

0.321821892

0.321822

0

46

2.564779

12.99779091

0.928560156

0.92856

1

47

-0.65915

0.517288883

0.340929726

0.65907

0

48

-0.65915

0.517288883

0.340929726

0.65907

0

49

-2.04663

0.129169696

0.11439352

0.885606

0

50

0.254

1.289171559

0.563160744

0.563161

1

51

1.494566

4.45740354

0.81676268

0.816763

1

52

-0.81937

0.440709057

0.305897332

0.694103

0

53

1.475849

4.374750374

0.813944848

0.186055

1

54

-3.02485

0.048565335

0.046315983

0.953684

0

55

2.270068

9.680056483

0.906367536

0.093632

1

56

0.485937

1.625697676

0.619148842

0.619149

1

57

-2.80085

0.060758643

0.05727848

0.942722

0

58

2.348405

10.46885912

0.912807369

0.912807

1

59

3.699286

40.41843893

0.975856164

0.975856

1

60

16.31605

12189014.95

0.999999918

1

61

1.616161

5.033729329

0.834265022

0.834265

1

62

2.567047

13.02729989

0.928710443

0.92871

1

63

-0.74192

0.476199351

0.322584718

0.677415

0

64

-2.91252

0.054338435

0.051537944

0.948462

0

65

-3.68226

0.025166144

0.024548356

0.975452

0

66

-0.27976

0.755967837

0.430513487

0.430513

1

67

0.331575

1.393160893

0.582142595

0.417857

1

Sheet1

Cell Formulas
Range	Formula
I2	=$O$1+$O$2A2+$O$3B2+$O$4C2+$O$5D2
J2	=EXP(I2)
K2	=J2/(1+J2)
L2	=IF(G2=1,K2,1-K2)

Excel 2003

N

O

P

Q

R

1

Constant

4.922300843

2

A

-108.2769669

3

B

74.61252287

4

C

30.72683636

5

D

-3.46611E-06

6

7

Product

5.2995E-12

8

LL

-25.96340904

9

In Range

Min

Max

10

Open

1.68

Yes

1.59

2.45

11

High

1.7

Yes

1.63

2.48

12

Low

1.65

Yes

1.59

2.41

13

Volume

65200

Yes

0

491100

14

Logit

0.331575189

15

e^Logit

1.393160893

16

17

Probability

58.21%

18

Classify

1

19

Sheet1

Cell Formulas
Range	Formula
O1	=T2
O2	=U2
O3	=V2
O4	=W2
O5	=X2
O7	=PRODUCT(L:L)
O8	=LN(O7)
O14	=O1+O2O10+O3O11+O4O12+O5O13
O15	=EXP(O14)
O17	=O15/(1+O15)
O18	=IF(O17>=T26,1,0)
P10	=IF(AND(O10>=Q10, O10<=R10),"Yes","No")
P11	=IF(AND(O11>=Q11, O11<=R11),"Yes","No")
P12	=IF(AND(O12>=Q12, O12<=R12),"Yes","No")
P13	=IF(AND(O13>=Q13, O13<=R13),"Yes","No")
Q10	=MIN(A:A)
Q11	=MIN(B:B)
Q12	=MIN(C:C)
Q13	=MIN(D:D)
R10	=MAX(A:A)
R11	=MAX(B:B)
R12	=MAX(C:C)
R13	=MAX(D:D)

Excel 2003

S

T

U

V

W

X

1

Beta0

Beta1

Beta2

Beta3

Beta4

2

Coeff

4.922300843

-108.277

74.61252

30.72684

-3.46611E-06

3

SE(Beta)

4.099938642

28.34705

23.5439

18.50453

7.13414E-06

4

z-stat

1.200579148

-3.819691

3.169081

1.660504

-0.485847492

5

p-value

22.99%

0.01%

0.15%

9.68%

62.71%

6

McFaddenR2

42.91%

7

Cox&SnellR2

44.64%

8

Iterations

6

9

LR

39.02240141

10

LR p-value

6.89262E-08

11

xxxxx

12

ActualResponse

13

Prediction

Positive

Negative

14

Positive

25

6

15

Negative

5

30

16

xxxxx

17

Accuracy

83.33%

18

ErrorRate

16.67%

19

HitRate

83.33%

20

TrueNegRate

83.33%

21

FalsePos

16.67%

22

Precision

80.65%

23

NegPredVal

85.71%

24

FalseDiscover

19.35%

25

xxxxx

26

CutOff

43.00%

27

Accuracy

83.33%

Sheet1

Cell Formulas
Range	Formula
T26	=INDEX(S29:S119,MATCH(MAX(T29:T119),T29:T119,0))
T27	=(T14+U15)/(T14+T15+U14+U15)
S1:X25	{=logit(G2:G67,A2:D67,T26,TRUE,TRUE)}
Press CTRL+SHIFT+ENTER to enter array formulas.

Code:

Sub Maximize_CutOff_Value_Logistic_Regression_Example()
Dim i As Single, RowNumber As Integer
RowNumber = 29
Application.ScreenUpdating = False
With ActiveSheet
    For i = 0.1 To 1 Step 0.01
    .Cells(26, 20) = i
    .Cells(RowNumber, 19) = i
    .Cells(RowNumber, 20) = .Cells(27, 20)
    RowNumber = RowNumber + 1
Next i
.Cells(26, 20).Value = "=Index(S29:S119,Match(Max(T29:T119),T29:T119,0))"
End With
Application.ScreenUpdating = True
End Sub

Code:

Sub Predict_Y()
With ActiveSheet
Application.ScreenUpdating = False
lastRow = Range("A" & Rows.Count).End(xlUp).Row
For R = 2 To lastRow
    Range("O10").Value = Range("A" & R).Value
    Range("O11").Value = Range("B" & R).Value
    Range("O12").Value = Range("C" & R).Value
    Range("O13").Value = Range("D" & R).Value
    Range("M" & R).Value = Range("O18").Value
Next R
End With
Application.ScreenUpdating = True
End Sub

Function:

Code:

Option Explicit
Option Base 1

Function Logit(known_y As Range, known_x As Range, Cutoff As Double, Optional Constant As Boolean = True, Optional Stats = False)
    Dim Intercept As Double
    If Constant = True Then
     Intercept = 1
    ElseIf Constant = False Then
     Intercept = 0
    End If

Dim M As Integer:  M = known_x.Columns.Count + Intercept ' number of independent variables
Dim N As Integer:  N = known_x.Rows.Count ' number of observations
Dim IndVar() As Double:  ReDim IndVar(1 To N, 1 To M) As Double
Dim ii As Integer:  ii = 1
Dim jj As Integer:  jj = 1
Dim kk As Integer:  kk = 1
Dim y_bar As Double:  y_bar = 0

For ii = 1 To N
    y_bar = y_bar + known_y(ii) ' calculate the average y
    If Intercept = 1 Then
     IndVar(ii, 1) = 1 ' create intercept
    End If
    For jj = 1 + Intercept To M
     IndVar(ii, jj) = known_x(ii, jj - Intercept) ' load in independent variables
    Next jj
    Next ii
    y_bar = y_bar / N ' average y for calculation of r2

Dim MaxIt As Integer:   MaxIt = 100 ' max number of itertions in Newton Algorithim
Dim cc As Integer:  cc = 1 ' main loop counter
Dim epsilon As Double:  epsilon = 0.000001 ' convergence criteriaq of Newton Algorithim
Dim Err As Double:  Err = 1 ' measure of convergence of Newton Algorithim
Dim y_hats() As Double: ReDim y_hats(1 To N) As Double ' model forecast
Dim Betas() As Double:  ReDim Betas(1 To M) As Double ' estimated betas
Dim z() As Double:  ReDim z(1 To N) As Double
Dim J() As Double:  ReDim J(1 To M) As Double ' Jacobian
Dim H() As Double:  ReDim H(1 To M, 1 To M) As Double ' Hessian
Dim Newt() As Variant:  ReDim Newt(1 To M) As Variant ' Newton Gain
Dim LogLikelihood As Double:    LogLikelihood = 1
Dim LogLikelihoodP As Double:   LogLikelihoodP = 1

Do While cc < MaxIt
 For ii = 1 To N
  For jj = 1 To M
    z(ii) = z(ii) + Betas(jj) * IndVar(ii, jj)
Next jj
y_hats(ii) = 1 / (1 + Exp(-1 * z(ii))) ' model estimate
For jj = 1 To M
 J(jj) = J(jj) + (known_y(ii) - y_hats(ii)) * IndVar(ii, jj)  ' Jacobian
  For kk = 1 To M
   H(jj, kk) = H(jj, kk) - y_hats(ii) * (1 - y_hats(ii)) * IndVar(ii, jj) * IndVar(ii, kk) ' Hessian
Next kk
Next jj
LogLikelihood = LogLikelihood + (known_y(ii) * Log(y_hats(ii)) + (1 - known_y(ii)) * Log(1 - y_hats(ii)))
Next ii

If Abs(LogLikelihood - LogLikelihoodP) < epsilon Then Exit Do ' check if converged, exit if true
LogLikelihoodP = LogLikelihood

Newt = Application.WorksheetFunction.MMult(J, Application.WorksheetFunction.MInverse(H))
For jj = 1 To M
 Betas(jj) = Betas(jj) - Newt(jj)
Next jj

ReDim J(1 To M): ReDim H(1 To M, 1 To M): ReDim z(1 To N) As Double ' clear Jacobian and Hessian matrix
LogLikelihood = 0
cc = cc + 1
Loop

If Stats = False Then ' if stats not selected the output betas and labels
  ReDim Output(1 To 2, 1 To M) As Variant
    For ii = 1 To M
      Output(1, ii) = "Beta" & (ii - 1)
      Output(2, ii) = Betas(ii)
    Next ii
  Logit = Output
  Exit Function
End If

Dim HInv() As Variant: ReDim HInv(1 To M, 1 To M) As Variant
Dim Tstat() As Double: ReDim Tstat(1 To M) As Double
HInv = Application.WorksheetFunction.MInverse(H)
ReDim Output(1 To 25, 1 To M + 1) As Variant
For ii = 1 To M
  Output(1, ii + 1) = "Beta" & (ii - 1) ' label
  Output(2, ii + 1) = Betas(ii) ' betas
  Output(3, ii + 1) = Sqr(-HInv(ii, ii)) ' standard errors
  Output(4, ii + 1) = Output(2, ii + 1) / Output(3, ii + 1)
  Output(5, ii + 1) = (1 - Application.WorksheetFunction.NormSDist(Abs(Output(4, ii + 1)))) * 2 ' p-value
Next ii
  Output(1, 1) = ""
  Output(2, 1) = "Coeff"
  Output(3, 1) = "SE(Beta)"
  Output(4, 1) = "z-stat"
  Output(5, 1) = "p-value"

Dim LogLikelihood0 As Double: LogLikelihood0 = N * (y_bar * Log(y_bar) + (1 - y_bar) * Log(1 - y_bar))
Output(6, 1) = "McFaddenR2":    Output(6, 2) = 1 - LogLikelihood / LogLikelihood0
Output(7, 1) = "Cox&SnellR2":   Output(7, 2) = 1 - Exp(-2 / N * (LogLikelihood - LogLikelihood0))
Output(8, 1) = "Iterations":    Output(8, 2) = cc - 1
Output(9, 1) = "LR":        Output(9, 2) = 2 * (LogLikelihood - LogLikelihood0)
Output(10, 1) = "LR p-value":   Output(10, 2) = Application.WorksheetFunction.ChiDist(Output(9, 2), M - 1) ' p-value for LR

' Contingency table
Dim N_TP As Integer:  Dim N_TN As Integer
Dim N_FP As Integer:  Dim N_FN As Integer

For ii = 1 To N
  If known_y(ii) = 1 And (y_hats(ii) - Cutoff) > 0 Then
    N_TP = N_TP + 1
  ElseIf known_y(ii) = 0 And (y_hats(ii) - Cutoff) <= 0 Then
    N_TN = N_TN + 1
  ElseIf known_y(ii) = 0 And (y_hats(ii) - Cutoff) > 0 Then
    N_FP = N_FP + 1
  ElseIf known_y(ii) = 1 And (y_hats(ii) - Cutoff) <= 0 Then
    N_FN = N_FN + 1
  End If
Next ii

Output(12, 1) = "": Output(12, 2) = "ActualResponse":   Output(13, 1) = "Prediction"
Output(13, 2) = "Positive": Output(13, 3) = "Negative"
Output(14, 1) = "Positive": Output(15, 1) = "Negative"
Output(14, 2) = N_TP:       Output(14, 3) = N_FP
Output(15, 2) = N_FN:       Output(15, 3) = N_TN

Output(17, 1) = "Accuracy": Output(17, 2) = (N_TP + N_TN) / (N_TP + N_TN + N_FP + N_FN)
Output(18, 1) = "ErrorRate":    Output(18, 2) = 1 - Output(17, 2)
Output(19, 1) = "HitRate":  Output(19, 2) = N_TP / (N_TP + N_FN)
Output(20, 1) = "TrueNegRate":  Output(20, 2) = N_TN / (N_TN + N_FP)
Output(21, 1) = "FalsePos": Output(21, 2) = 1 - Output(20, 2)

Output(22, 1) = "Precision":        If N_TP + N_FP = 0 Then Output(22, 2) = "Error" Else Output(22, 2) = N_TP / (N_TP + N_FP)
Output(23, 1) = "NegPredVal":       If N_TN + N_FP = 0 Then Output(23, 2) = "Error" Else Output(23, 2) = N_TN / (N_TN + N_FN)
Output(24, 1) = "FalseDiscover":    If N_FP + N_TP = 0 Then Output(24, 2) = "Error" Else Output(24, 2) = N_FP / (N_FP + N_TP)

For ii = 1 To M + 1
  Output(11, ii) = "xxxxx": Output(16, ii) = "xxxxx":   Output(25, ii) = "xxxxx"
Next ii
For ii = 3 To M + 1
    Output(6, ii) = "": Output(7, ii) = "": Output(8, ii) = ""
    Output(9, ii) = "": Output(10, ii) = "":    Output(12, ii) = ""
    Output(17, ii) = "":    Output(18, ii) = "":    Output(19, ii) = ""
    Output(20, ii) = "":    Output(21, ii) = "":    Output(22, ii) = ""
    Output(23, ii) = "":    Output(24, ii) = "":
Next ii
For ii = 4 To M + 1
    Output(13, ii) = "":    Output(14, ii) = "":    Output(15, ii) = ""
Next ii
Logit = Output

End Function

Hope you like.

Mike

michaelsmith559 · Feb 29, 2016

I was not sure if you wanted to add a "probability of profit" column to the final summary sheet. Also, do not put too much "stock", no pun intended, into the above logistic regression. Let me know what you think.

michaelsmith559 · Feb 29, 2016

Sorry for the above post #34 & 35 being posted here. It was meant to be a private message.

Jimmywinvests · Feb 29, 2016

That looks very interesting! I cannot wait to run it after work today.

Jimmywinvests · Feb 29, 2016

I'm thinking this might work well as a second spreadsheet. Additionally I think I will have a go at copy pasting the current price sections of the macro into its own spreadsheet (as this value changes much more frequently and it seems near impossible to run the whole macro everyday!

michaelsmith559 · Feb 29, 2016

Hope you like it. Glad to help.

Mike

Pulling data points from multiple webpages

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Pulling data points from multiple webpages

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