Compile and analyze stock data
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My attempt to model stock...

Python 3.5+

Notes about updates at the bottom... also shout out to helpful Redditor u/siem


If you are looking at this, chances are you were on Reddit. This is currently documented very very poorly. You have been warned. Hopefully, you just want to pull stock histories yourself, and if so, just scroll down and follow the steps. If you really want to know what else it does (i.e. retrieve live stock prices, dimensionality reduction, transformation, all that ML stuff) You will probably find much better resources else where. But, of course, you are more than welcome to look through the code. For now, I'm a bit too busy and streassed out by work to document anything thoroughly, so... have fun!


  • Pandas
  • Numpy
  • PyTorch
  • Scikit-learn
  • Seaborn (if you decided to play around with the other stuff)

Only Pandas is really involved in scraping data, but since I import these at the top, you might want to have these so you don't get errors

Pull stock histories

1. Update AMEX, NYSE, and NASDAQ listing files.

  • This is not a mission critical step, but it's a good idea to do to prevent getting too excited about a symbol that's already delisted or have repeated rows of the same data because it's delisted (the latter can be solved by more careful programming though).
  • Register and download AMEX.txt, NYSE.txt, and NASDAQ.txt from (click the little down arrow icon next to "DOWNLOAD SYMBOL LIST")
  • Replace the current AMEX.txt, NYSE.txt, and NASDAQ.txt files under redtide/data/ folder with the new files

2. Compile listing symbols.

ONLY if you updated the listing files in step 1, if not, skip this step

  • Delete all_symbols.txt and excluded_symbols.txt under the redtide/data/ folder
  • Navigate to the redtide/src/ folder and run the following command in a terminal.
$ python3 -v -c
  • This will check each symbol in the 3 exchange listing files to see if their data can be pulled from Yahoo Finance. If yes, they are compiled to all_symbols.txt. If not, they are compiled to excluded_symbols.txt
  • When it's done, you'll see the new all_symbols.txt and excluded_symbols.txt under the redtide/data/ folder

3. Pull data.

  • Navigate to redtide/src/ folder, and run:
$ python3 -v -d
  • This will take a long time. How long depends on the number of processors you have and your internet connection speed.
  • When this is done, you will see a full_history/ folder under redtide/data/ that contains all the goodies (i.e. AMD.csv).

Other stuff

If you want to know some other options:

$ python3 -h


  • u/siem found a problem with how Macs handles forking. His full comment:

"I had problems running the code at first on my Mac ("python +[__NSPlaceholderDate initialize] may have been in progress in another thread when fork() was called.") - supposedly Apple has changed their fork implementation to disallow forking with active threads. When I ran this before running the code I didn't get errors:


  • u/siem also found that after 2000-3000 pulls, connection to Yahoo Finance could fail. Possibly due to a tempoary IP ban. Suggested that there be a 1 sec delay between each request. While I was working on this, I found realized that my code to retry connection was implemented incorrectly, so I changed that part completely. While fixing everything, I realized that I cannot pause threads independently with time.sleep(1) when I'm using multiprocessing's, so... I'm open to suggestions, because right now, I just have a max attempt of 20 and hoping that one of those would return something (fortunately this works for me... but definitely not the best solution)
  • New: If connection fails or you get banned temporary, it will try to fetch for the failed ones again (maximum of 5 passes). If after 5 passes , there are still failed symbols left, they will be written to a failed_symbs-<5 random characters>.txt file. And you'll see a suggestion to run something like the following to retry.
$ python3 -v -d --file failed_symbs-abe93.txt


I don't respond very quickly, but I always respond: