- Create a custom symbol with the spec of the target instrument
- Export bars from the source symbol, timeframe by timeframe until you get that :
- The boring part since it's about editing line by line to change the spread to the desired one ...
- Import that folder on any unix shell
xxx@xxx:~/EURUSD$ ls EURUSD_Daily_201201020000_202001310000.csv EURUSD_M10_201705231550_202001310810.csv EURUSD_M4_201901031820_202001310816.csv EURUSD_H1_201201020000_202001310800.csv EURUSD_M1_201910230509_202001310818.csv EURUSD_M5_201809251440_202001310815.csv EURUSD_H12_201201020000_202001310000.csv EURUSD_M12_201611090948_202001310812.csv EURUSD_M6_201806200448_202001310818.csv EURUSD_H2_201201020000_202001310800.csv EURUSD_M15_201601212100_202001310815.csv EURUSD_Monthly_201201010000_202001010000.csv EURUSD_H3_201201020000_202001310600.csv EURUSD_M20_201409151920_202001310800.csv EURUSD_Weekly_201201010000_202001260000.csv EURUSD_H4_201201020000_202001310800.csv EURUSD_M2_201907171810_202001310818.csv fixspread.sh EURUSD_H6_201201020000_202001310600.csv EURUSD_M30_201201022230_202001310800.csv EURUSD_H8_201201020000_202001310800.csv EURUSD_M3_201904110809_202001310818.csv xxx@xxx:~/EURUSD$
- Put the below bash script in the same folder - *quickly done - feel free to improve*
#!/bin/bash
: ${3?"Usage: $0 <fix_spread> <input_folder> <output_folder>"}
#
echo " _ _ __"
echo "| |__ | | ___ _ _ / _|"
echo "| '_ \| |/ _ \| | | | |_"
echo "| |_) | | (_) | |_| | _|"
echo "|_.__/|_|\___/ \__,_|_|"
echo ""
echo "*** Using spread : " $1
echo "*** Input folder : " $2
echo "*** Output folder : " $3
##
overwrite=0
if ([ -d $3 ] && [ "$(ls -A $3)" ]) && ([ $overwrite="0" ])
then
echo "*** Copy already exists."
read -p "Overwrite (y/n)?" choice
case "$choice" in
y|Y ) overwrite=1 && echo "*** Overwriting : " $overwrite;;
n|N ) echo "*** Exiting ..." && exit -1;;
* ) echo "*** Invalid answer. Exiting ..." && exit -1;;
esac
fi
if ([ -d $3 ] && [ ! "$(ls -A $3)" ]) || ([ ! -d $2 ]) || ([ $overwrite="1" ])
then
echo "*** Proceeding on *.csv files ..."
mkdir -p $3
for file in $2/*.csv; do [[ -f "${file}" ]] && awk -v fs=$1 -F "\t" '{print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $7 "\t" $8 "\t" fs}' $file > $3/$file ; done ;
fi
##### - Execute :
xxx@xxx:~/EURUSD$ chmod +x fixspread.sh && ./fixspread.sh 10 ./ output/ _ _ __ | |__ | | ___ _ _ / _| | '_ \| |/ _ \| | | | |_ | |_) | | (_) | |_| | _| |_.__/|_|\___/ \__,_|_| *** Using spread : 10 *** Input folder : ./ *** Output folder : output/ *** Proceeding on *.csv files ... xxx@xxx:~/EURUSD$ ls output/ EURUSD_Daily_201201020000_202001310000.csv EURUSD_H8_201201020000_202001310800.csv EURUSD_M30_201201022230_202001310800.csv EURUSD_H1_201201020000_202001310800.csv EURUSD_M10_201705231550_202001310810.csv EURUSD_M3_201904110809_202001310818.csv EURUSD_H12_201201020000_202001310000.csv EURUSD_M1_201910230509_202001310818.csv EURUSD_M4_201901031820_202001310816.csv EURUSD_H2_201201020000_202001310800.csv EURUSD_M12_201611090948_202001310812.csv EURUSD_M5_201809251440_202001310815.csv EURUSD_H3_201201020000_202001310600.csv EURUSD_M15_201601212100_202001310815.csv EURUSD_M6_201806200448_202001310818.csv EURUSD_H4_201201020000_202001310800.csv EURUSD_M20_201409151920_202001310800.csv EURUSD_Monthly_201201010000_202001010000.csv EURUSD_H6_201201020000_202001310600.csv EURUSD_M2_201907171810_202001310818.csv EURUSD_Weekly_201201010000_202001260000.csv xxx@xxx:~/EURUSD$
- Bring it back to your platform's system & import it file by file, timeframe by timeframe, as bars for the custom symbol previously created
- Enjoy !
UPDATE
It's probably not the way those who integrated python to MQL5 intented to use it, but it did appear to be particularly relevant.
- python3
- metatrader module
- panda module
- python integration enabled in the terminal
pip install Metatrader5 pandas
... you'll find plenty of doc in the forum to set this up ...
python script : import.py
from datetime import datetime from numpy import savetxt import MetaTrader5 as mt5 import pytz import os import shutil import pandas ### settings symbols = ["EURUSD","GBPUSD", "USDJPY"] # case sensitive date_start = [2021, 1, 1] # Year - Month - Day date_end = [2021, 5, 18] # Year - Month - Day spread_mod = 10 delimiter='\t' output_ext=".csv" # display data on the MetaTrader 5 package print("MetaTrader5 package author: ",mt5.__author__) print("MetaTrader5 package version: ",mt5.__version__) # establish connection to MetaTrader 5 terminal if not mt5.initialize(): print("initialize() failed, error code =",mt5.last_error()) quit() # timeframes as a dict timeframe = { "M1" : mt5.TIMEFRAME_M1, "M2" : mt5.TIMEFRAME_M2, "M3" : mt5.TIMEFRAME_M3, "M4" : mt5.TIMEFRAME_M4, "M5" : mt5.TIMEFRAME_M5, "M6" : mt5.TIMEFRAME_M6, "M10" : mt5.TIMEFRAME_M10, "M12" : mt5.TIMEFRAME_M12, "M15" : mt5.TIMEFRAME_M15, "M20" : mt5.TIMEFRAME_M20, "M30" : mt5.TIMEFRAME_M30, "H1" : mt5.TIMEFRAME_H1, "H2" : mt5.TIMEFRAME_H2, "H3" : mt5.TIMEFRAME_H3, "H4" : mt5.TIMEFRAME_H4, "H6" : mt5.TIMEFRAME_H6, "H8" : mt5.TIMEFRAME_H8, "H12" : mt5.TIMEFRAME_H12, "Daily" : mt5.TIMEFRAME_D1, "Weekly" : mt5.TIMEFRAME_W1, "Monthly" : mt5.TIMEFRAME_MN1 } # set time zone to UTC timezone = pytz.timezone("Etc/UTC") utc_from = datetime(date_start[0], date_start[1], date_start[2], tzinfo=timezone) utc_to = datetime(date_end[0], date_end[1], date_end[2], tzinfo=timezone) for pair in symbols: print("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") print("+ SYMBOL : ",pair) print("+ FROM : ",str(date_start[0]) + "-" + str(date_start[1]) + "-" + str(date_start[2])) print("+ TO : ",str(date_end[0]) + "-" + str(date_end[1]) + "-" + str(date_end[2])) print("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") # delete subfolder if already exists shutil.rmtree(pair + "/", True, None) # main loop for i in range(len(timeframe)): try: rates = mt5.copy_rates_range(pair, list(timeframe.values())[i], utc_from, utc_to) if len(rates) > 0: print("+ SYMBOL : ",pair,"--- TIMEFRAME : ",list(timeframe)[i],"--- Bars imported : ",len(rates)) # switch to panda frame rates_frame = pandas.DataFrame(rates) # add column date rates_frame['date']="" # rename columns mapping = {rates_frame.columns[0]:'<TIME>', rates_frame.columns[1]: '<OPEN>', rates_frame.columns[2]:'<HIGH>', rates_frame.columns[3]: '<LOW>',rates_frame.columns[4]:'<CLOSE>', rates_frame.columns[5]: '<TICKVOL>',rates_frame.columns[6]:'<SPREAD>',rates_frame.columns[7]:'<VOL>',rates_frame.columns[8]:'<DATE>'} rates_frame = rates_frame.rename(columns=mapping) # re-arrange columns # .date & time tmp_column = rates_frame.pop('<DATE>') rates_frame.insert(0, '<DATE>', tmp_column) tmp_column = "" date = pandas.to_datetime(rates_frame['<TIME>'], unit='s') rates_frame['<DATE>']=date.dt.strftime('%Y-%m-%d') rates_frame['<TIME>']=date.dt.strftime('%H:%M:%S') # .spread rates_frame['<SPREAD>']=spread_mod tmp_column = rates_frame.pop('<SPREAD>') rates_frame.insert(8, '<SPREAD>', tmp_column) # display frame #print(rates_frame) # re-create folder if do not exist yet to store data files based on symbol name os.makedirs(pair + "/", exist_ok=True); # write csv rates_frame.to_csv(pair + "/" + pair + '_' + str(list(timeframe)[i]) + output_ext, index=False, header=True, sep=delimiter, encoding='utf-8') except TypeError:print("+ SYMBOL : ",pair,"--- TIMEFRAME : ",list(timeframe)[i],"--- No data found. Skipping ...");continue
- Copy/paste & edit the script (symbols, date start - end, spread value)
- Run the script
- In the script folder, you'll find subfolders containing the datas as csv files for each timeframes
- When importing the datas in MT make sur to check the Tick Volume checkbox
... and voila - much more convenient & crossplatform as you can see uh ? ;)
