The Most Active and Friendliest
Affiliate Marketing Community Online!

“Active  “Clickaine”/

Ask Me Anything Free campaign and conversion analysis.


Well-Known Member
What the title says^

I have programmed an analysis program that can make a Linear Regression trends out of any x, y set of data

  • impressions, ( campaign or siteid, date) x, y set of data
  • ctr (to landing page), date) x, y set of data
  • most other x, y set of data ---numbers are numbers
all data must be in a text file made with notepad, or exported in csv (from an advertising or network source), or from exel exported in csv.
fields (columns) separated by a ,

not sure what the minimum time should be but 60 - 90 days should be ok.

my objective it to test the accuracy we can make 2 projections
  1. the first half of the data
  2. the whole length of the data and you can see the trends.
This is a limited offer maybe I can do this 5 or 10 times to get a sample of its accuracy for CPA type stuff --networks are encouraged to participate
No disclosure of the location or exact nature is needed just two sets of numbers any x, y set of data you want me to test.


the x=value y=value i can convert (top right of chart)

mouse moved --you will have to mark off where after it is charted

this is an explanation (study course) describing liner regression principles.

I worked up some different y data; FINRA short sales, Last Close and Daily trading volume --same program different data feed sources

this could be clicks, LP CTR and s2s sales conversions with some tweaks
Transportation / Freight & Logistics Svcs. / Marine Freight & Logistics
Last edited:
This looks fun! I've played around with regression in the past.

Did you look to other regression models than linear? The last close data from your first post doesn't look linear.

For me to understand what you are trying to do, could you tell a bit more about the meaning behind the values. Its not clear what you are trying to predict exactly, i.e. what are the values of last close (what does it mean if a data point has a last close of 130), volume etc. Similarly, you are making predictions based on time, is this relative to another point in time or just a date?
unix time is the x value =the date you do know what UNIX Time means? And Scientific notation right?

and the y values (vertical) is the price per share of GOGL a global bulk dry shipping business; grain, and other dry materials. Just an example. that is a linear regression chart. the scale is the price not 0 ..100

It's done with Python Linear Regression - statsmodels 0.14.0

Do you have at least 10,000 points of data? and some specific query to chart?

GOGL is 396,730,455 shares for the year data ; the contents of each trade is not known to me e.g.; 100 or 1000 share trades each.

in progres SQL: note the shorter time period NOT 1 yr

CREATE TEMP TABLE temp_symbol_data AS
SELECT last_day, lastsale
FROM finance.nasdaqdaily
WHERE symbol = 'GOGL'
AND last_day BETWEEN '2023-08-01'::date
AND '2023-11-10'::date;
symbol |       slope        |     intercept      |      r_squared
GOGL   | 2816955.6883357265 | 1673835202.3837886 | 0.12641333051528245
(1 row)

Now what are you talking about? Your link is also Python Numpy
The last close data from your first post doesn't look linear.

#!/usr/bin/env python

import csv
import matplotlib.pyplot as plt
import numpy as np
from scipy.optimize import curve_fit
import datetime
from scipy.stats import linregress

while True:
Last edited:
Thank for the explanation, I understand the examples much better now. I used regression for prediction mostly, so I thought polynomial or sinusoidal would fit the data of the very first graph better. But from an analytical point of view it makes more sense.
unix time is the x value =the date you do know what UNIX Time means? And Scientific notation right?
Yeah, should have looked that up.

You made me think this is actually a nice idea to have, so you can see how you are doing over time (visitors, commission, etc). So I asked my good friend chatGPT how to do this in Google Sheets. Turns out Google sheets has a trend line feature that does lineair (and polynomial and other) regression. Links are below if anyone wants to use it. :)

Google Sheet: Linear Regression

It's not as cool as doing it in python but I think it is easier for non programmers to use. Now I'm just thinking what kind of other data would be nice to use this for.
Last edited:
OK, that has been around for years in excel and I had been using Gnumeric scientific spreadsheets to do regression; linear fit, polynomial and exponential --manually for years.

That spreadsheet process takes time. I generate these graphs direct from csv data in about 2 to 3 seconds --but that is not the point.
Do you have at least 10,000 points of data? and some specific query to chart?
^^^ that is the point. I want datasets to prove, disprove, or hypothetically project trends on this "so called CPA data" with some degree of truth.
Thank for the visibility bump anyway ;)

People ask about clicks rates CTR on creatives changing, conversions changing, etc all the time --trending this data may show something. Certainly more that pulling 'pat' answers out of the air. Or, what some hype blog or YouTube video says on the internet.

It could be this or that or maybe this --show me the data ...
Not done yet ;)

Built a *Bloomberg Terminal*

Takes a minute or two to get all of the data.

The 4 x 4 charts to the right about 8- 12 seconds from local databases.
FINRA (top right) takes the time --it's a download each time.

I am trending --that is the red line ;)

Now, if anyone had real web stats LOL anonymized is fine,


below is a mock up testing portfolio --see if this will really work :D