I'm trying to find the historical data for 3 month or 6 month lows.

Thinkorswim has it (NYLO3M) and barcharts.com has it (M3LN) but they don't match...and thinkorswim only goes back to 2012.

Does anyone know of another source where I could get this data?

  • 1
    $\begingroup$ Are you looking just for the number of stocks making new lows, or also the names of these stocks? $\endgroup$
    – nbbo2
    Commented May 21, 2021 at 0:12
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    $\begingroup$ Just the number of stocks that are making new 3 month lows (and/or 6 month lows). I've been able to easily find 12 month lows...but not for the other intervals @noob2 $\endgroup$
    – user749798
    Commented May 21, 2021 at 15:11

1 Answer 1


I suggest you to :

  1. Visit : yahoo finance Nasdaq page
  2. Click on "Historical Data"
  3. Enter your date range
  4. Click on "download"
  5. You get a .csv file with all your data (including the lows)
  6. If you do not know how to clean a csv file, I suggest you click on the first column which includes your data, then "Data" ribbon, then "Text To column" and you're there !

I hope it helps !

  • $\begingroup$ Thanks, but this won't help with finding the number of stocks that make 3 month new lows. This just downloads stock prices. $\endgroup$
    – user749798
    Commented May 21, 2021 at 15:12
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    $\begingroup$ Maybe I am missing something but once you have daily data, it should not be too difficult to do it without a datasource? pandas.pydata.org/docs/reference/api/… pulls the lowest value within any rolling window - you could filter for current value and if it matches, you know it is low in that period. or something like '''x = np.random.random(50) df = pd.DataFrame({'random': x}) minimum = df['random'].rolling(window = 4).min() == df['random'] mins = df[minimum]''' as a dummy example in Python $\endgroup$
    – AKdemy
    Commented May 21, 2021 at 20:24
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    $\begingroup$ Note to anyone doing this with yahoo finance - this will cause survivorship bias because yahoo finance doesn't track delisted stocks. $\endgroup$ Commented Oct 13, 2022 at 20:15

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