Here's a little module that will convert all text files in a directory to scid files in the same or another directory. If the constant KEEP_UPDATING is True it will then check the text files every REFRESH seconds and if they've changed it will update the scid files.

Set FILES_IN and FILES_OUT. In windows the format is "C:\\mydir\\mysubdir\\"

Also set Timezone if you wish to change the times in Sierra's scid files (Sierra saves them in the UTC time zone).

Save the code in a file named something like Text2Scid.py and run it using python Text2SCid.py.
Tested with Python 3.7 on Linux & Windows. Have fun:

 

Code

""" Convert's Text files to SCID files for SierraChart It will create a new scid file for every text file it finds format is assumed to be D T O H L C V T with comma separated variables It will continue to update the scid file every REFRESH seconds generally refresh would be set to 1/2 the time period for the text file updates """ import os import struct from time import sleep from datetime import datetime as dt, timedelta as tdelta import subprocess # these are linux format, windows might need to use double backslashes \\ FILES_IN = "/home/john/Python/Trading/textfiles/" FILES_OUT = "/home/john/zRamdisk/SierraData/" TIMEZONE = 0 # scid saved as UTC, use this if the text file is another time zone KEEP_UPDATING = True # set to False to convert and stop; True for live updating REFRESH = 0.25 # refresh every 1/4 second for 1/2 second updating text files TEXT_FILE_SUFFIX = ".txt" # usually .txt or .csv sizeHeader = 0x38 sizeRecord = 0x28 file_header = (b'SCID8\x00\x00\x00\x28\x00\x00\x00\x01\x00\x00\x00\x00\x00' + b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' + b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00' + b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00') def excel_date(datetime, offset=TIMEZONE): """convert date&time with offset in hours to excel format for scid""" delta = datetime - dt(1899, 12, 30) + tdelta(hours=offset) return delta.days + delta.seconds / 86400 def python_date(exceldt, offset=TIMEZONE): """convert date&time with offset in hours to python format""" excel_as_delta = tdelta(days=exceldt//1) + tdelta(seconds=exceldt%1*86400) return dt(1899, 12, 30) + excel_as_delta + tdelta(hours=offset) def write_struct(record, f): if len(record) == 8 and record[0][0].isnumeric(): out = [excel_date(dt.strptime(f'{record[0]} {record[1]}', '%Y-%m-%d %H:%M:%S.%f'))] out.extend([float(s) for s in record[2:]]) # note that SC packs dt, O, H, L, C, T, V not V, T so swap them wt = struct.pack('d4f4I', out[0], out[1], out[2], out[3], out[4], int(out[6]), int(out[5]), 0, 0) f.write(wt) def create_base(f): record = None with open(FILES_OUT + f[: -3] +'scid', "wb") as scidfile: scidfile.write(file_header) with open(FILES_IN + f, "r") as txtfile: for line in txtfile: record = line.split(',') write_struct(record, scidfile) return f'{record[0]} {record[1]}' # will update when date+time changes def update_if_changed(files): for f in files: with open(FILES_IN + f, "r") as txtfile: record = txtfile.readlines()[-1].split(',') last_dt = f'{record[0]} {record[1]}' if files[f] == last_dt: continue files[f] = last_dt with open(FILES_OUT + f[: -3] +'scid', "ab") as scidfile: write_struct(record, scidfile) if __name__ == '__main__': files = sorted(k for k in os.listdir(path=FILES_IN) if k[-4:] == TEXT_FILE_SUFFIX) files = {k: '' for k in files} for f in files: files[f] = create_base(f) print('finished base') if KEEP_UPDATING: # now test each input file each REFRESH interval & update if datetime changed while True: sleep(REFRESH) update_if_changed(files)

hello Kiwi
I saw your code on sierracharts forums (and then here) (the one which reads scid file into pd df) but it keeps crashing for me, and as I am relatively new to programming I could not yet figure out why. May be you have a newer version , you could share?
I am also running sierra charts on linux.

same here. keeps crashing

I figured out a "workaround", probably not as fast as the SCID file. I used a study which is writing the bar+study data to txt file. I can read that into a dataframe and work with that. It takes ~0.01 sec to update and return a dataframe at each update. That is sufficient for now.