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In the screenshot, you will see the daily prices of Nasdaq. Each candle has a High, Low, Open and Close price. I have drawn a regression line with a 2 standard deviation channel on either side. How would I go about determining the following odds:

Price reversing back to the mean from the top of the channel Price reversing back to the mean from the bottom of the channel

Here is the relevant data in CSV format:

Date,open,high,low,close
2020-04-22,8638.04,8791.67,8584.55,8606.75
2020-04-23,8606.75,8786.69,8503.14,8773.93
2020-04-26,8773.93,8904.59,8736.59,8824.04
2020-04-27,8824.04,8953.96,8662.47,8707.38
2020-04-28,8707.38,9128.59,8707.38,9128.17
2020-04-29,9128.17,9154.75,8866.86,8879.61
2020-04-30,8879.61,8888.91,8682.93,8727.01
2020-05-03,8727.01,8838.2,8568.02,8820.48
2020-05-04,8820.48,9030.75,8810.19,8945.63
2020-05-05,8945.63,9068.2,8890.4,8957.83
2020-05-06,8957.83,9137.67,8942.08,9129.77
2020-05-07,9129.77,9246.73,9115.09,9225.91
2020-05-10,9225.91,9346.54,9127.77,9289.57
2020-05-11,9289.57,9354.5,9048.46,9050.96
2020-05-12,9050.96,9212.14,8886.47,9022.94
2020-05-13,9022.94,9113.27,8856.21,9097.25
2020-05-14,9097.25,9156.71,8933.83,9103.21
2020-05-17,9103.21,9369.66,9103.21,9324.09
2020-05-18,9324.09,9424.65,9291.39,9305.1
2020-05-19,9305.1,9502.52,9280.79,9499.07
2020-05-20,9499.07,9515.22,9355.88,9363.9
2020-05-21,9363.9,9422.59,9246.58,9410.25
2020-05-24,9410.25,9541.35,9395.05,9534.01
2020-05-25,9534.01,9608.78,9376.65,9416.38
2020-05-26,9416.38,9512.9,9177.67,9446.79
2020-05-27,9446.79,9569.72,9324.69,9462.54
2020-05-28,9462.54,9586.35,9376.22,9580.78
2020-05-31,9580.78,9609.06,9454.77,9596.79
2020-06-01,9596.79,9673.83,9509.08,9661.21
2020-06-02,9661.21,9730.72,9638.96,9692.7
2020-06-03,9692.7,9744.5,9574.55,9645.42
2020-06-04,9645.42,9847.5,9603.96,9810.66
2020-06-07,9810.66,9902.49,9748.95,9884.84
2020-06-08,9884.84,10006.7,9813.53,9963.26
2020-06-09,9963.26,10157.12,9960.27,10088.48
2020-06-10,10088.48,10108.41,9585.13,9621.5
2020-06-11,9621.5,9849.63,9495.38,9646
2020-06-14,9646,9816,9381.75,9811.77
2020-06-15,9811.77,10014,9797.8,9969.67
2020-06-16,9969.67,10059.42,9926.92,9998.25
2020-06-17,9998.25,10041.38,9879.25,10003.09
2020-06-18,10003.09,10125.67,9929.69,9932.92
2020-06-21,9932.92,10147.67,9856.86,10134.79
2020-06-22,10134.79,10309.42,9985.74,10190.49
2020-06-23,10190.49,10255.2,9941.56,10029.03
2020-06-24,10029.03,10120.51,9899.8,10109.29
2020-06-25,10109.29,10134.14,9837.73,9880.49
2020-06-28,9880.49,10010.65,9743.03,9999.65
2020-06-29,9999.65,10184.18,9953.2,10151.46
2020-06-30,10151.46,10321.62,10088.89,10268.39
2020-07-01,10268.39,10433.51,10259.41,10360.82
2020-07-02,10360.82,10400.9,10320.4,10338.29
2020-07-05,10338.29,10626.21,10338.29,10610.1
2020-07-06,10610.1,10706.55,10517.89,10538.77
2020-07-07,10538.77,10685.05,10517.73,10683.92
2020-07-08,10683.92,10786.46,10572.29,10735.73
2020-07-09,10735.73,10854.72,10637.74,10850.22
2020-07-12,10850.22,11070.48,10574.11,10610.96
2020-07-13,10610.96,10705.54,10371.63,10656
2020-07-14,10656,10778.78,10563.94,10701.21
2020-07-15,10701.21,10710.39,10488.15,10546.97
2020-07-16,10546.97,10681.67,10535.74,10636.56
2020-07-19,10636.56,10972.65,10558.93,10959.53

Many thanks.

enter image description here

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    $\begingroup$ if you want help from stats community you need to explain and clearly define your terms: price reversal, mean, channel etc. these are very TA domain specific terms. alternatively, you can go to quant.stackexchange.com site. you may not like what they'll tell you though. TA is nonsense, in my opinion, but it can be great to establish this on your own by actually back testing different TA techniques. $\endgroup$
    – Aksakal
    Commented Jul 20, 2020 at 20:56
  • $\begingroup$ Why linear regression and not logistic regression? $\endgroup$
    – Tim
    Commented Jul 20, 2020 at 21:19
  • $\begingroup$ I don't know @Tim. Why would I use one over the other? I'm here to learn. $\endgroup$
    – Grantx
    Commented Jul 20, 2020 at 22:15
  • $\begingroup$ @Grantx linear regression can predict anything between $-\infty$ to $\infty$ so it is a pretty poor choice if you need to predict probabilities. Logistic regression predict values between 0 and 1, so probabilities. $\endgroup$
    – Tim
    Commented Jul 20, 2020 at 22:36
  • $\begingroup$ Thank you @Tim. You should make that the answer and I will give you the credit. $\endgroup$
    – Grantx
    Commented Jul 21, 2020 at 9:01

2 Answers 2

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Linear regression is a model that can predict values in unrestricted domain, from $-\infty$ to $\infty$. If you want a regression model to predict probabilities, then the to-go model is logistic regression that restricts the outputs to $[0, 1]$ range.

For more details see other questions tagged as , for example the What is the difference between linear regression and logistic regression? thread.

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See Dr Abdul Rahim Wong's paper on Calculation of price graphs reversal point, from average regression lines method. Link https://data.mendeley.com/datasets/ytz7pyg8y9/1

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    $\begingroup$ While this link may answer the question, it is better to include the essential parts of the answer here and provide the link for reference. Link-only answers can become invalid if the linked page changes. - From Review $\endgroup$
    – User1865345
    Commented Oct 9, 2023 at 12:39

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