Forex historical data in Python

Do you only need historical currency values?

Try using a module with a class (from a module ). I am using python 3, but I doubt this is too much. forex_python

datetime

datetime

These exchange rates are 3 hours (CET) data from the European Central Bank since 1999.

>>> from datetime import datetime
>>> from forex_python.converter import get_rate

>>> t = datetime(2001, 10, 18)  # the 18th of October, 2001
>>> get_rate("USD", "GBP", t)
0.69233
>>> get_rate("GBP", "USD", t)
1.4444
>>> 1 / 1.4444   # check
0.6923289947382997 

>>> t = datetime(2006, 6, 26)  # June 26th, 2006
>>> get_rate("GBP", "USD", t)
1.8202

      

        
        
        
      

    

So
on 18/10/01, 1 USD = 0.69 GBP,
26 June 2006, 1 GBP == 1.82 USD.

FXCM recently released an official python wrapper for forexconnect.

There is a support forum: http://www.fxcodebase.com/code/viewforum.php?f=51&sid=e2b414c06f9714c605f117f74d689a9b

Here is a code snippet from the article on getting history:

from forexconnect import fxcorepy, ForexConnect
    with ForexConnect() as fx:
        try:
            fx.login("user_id", "password", "fxcorporate.com/Hosts.jsp",
                     "Demo", session_status_callback=session_status_changed)

            history = fx.get_history("EUR/USD", "H1",
                                    datetime.datetime.strptime("MM.DD.YYYY HH:MM:SS", '%m.%d.%Y %H:%M:%S').replace(tzinfo=datetime.timezone.utc),
                                    datetime.datetime.strptime("MM.DD.YYYY HH:MM:SS", '%m.%d.%Y %H:%M:%S').replace(tzinfo=datetime.timezone.utc))

      

        
        
        
      

    

There is free historical data based on pepper ticks in monthly CSV format starting in 2009 (visit: https://www.truefx.com/?page=downloads ) for most popular pairs. I wrote Python code using selenium to download all CSV files (the script will download all CSV files to the forex name folder):

import datetime, time, os
from dateutil.relativedelta import relativedelta

from selenium import webdriver

tmp_dir = os.path.join(os.getcwd(), 'forex')
if not os.path.isdir(tmp_dir): os.makedirs(tmp_dir)
options = webdriver.ChromeOptions();
options.add_argument("--window-size=1300,900")
options.add_experimental_option("prefs", {
    "download.default_directory": tmp_dir,
    "download.prompt_for_download": False,
    "download.directory_upgrade": True,
    "safebrowsing.enabled": False,
    "safebrowsing.disable_download_protection": True
})
options.add_argument("--disable-gpu")
options.add_argument("--disable-extensions")
options.add_argument('--disable-logging')
options.add_argument('--ignore-certificate-errors')
options.add_argument('--ignore-certificate-errors-spki-list')
options.add_argument('--no-sandbox')
browser = webdriver.Chrome(options=options)

pairs = ['AUDJPY', 'AUDNZD', 'AUDUSD', 'CADJPY', 'CHFJPY', 'EURCHF', 'EURGBP', 'EURJPY', 'EURUSD', 'GBPJPY', 'GBPUSD', 'NZDUSD', 'USDCAD', 'USDCHF', 'USDJPY']
for pair in pairs:
    curr_date = datetime.datetime(2015, 1, 1)
    while curr_date + relativedelta(months=1) < datetime.datetime.now():
        file_name = '{p}-{ym2}.zip'.format(p=pair, ym2=curr_date.strftime('%Y-%m'))
        url = 'http://www.truefx.com/dev/data/{y}/{ym1}/{p}-{ym2}.zip'.format(
            y=curr_date.strftime('%Y'),
            ym1=curr_date.strftime('%B').upper()+'-'+curr_date.strftime('%Y') if curr_date <= datetime.datetime(2017, 3, 1) else curr_date.strftime('%Y-%m'),
            p=pair,
            ym2=curr_date.strftime('%Y-%m')
        )
        file_found = False
        for root, dirs, files in os.walk(tmp_dir):
            for file in files:
                if file_name in file: file_found = True 
        if not file_found:
            time.sleep(5)
            browser.get (url)
            file_downloaded = False
            while not file_downloaded:
                time.sleep(1)
                for root, dirs, files in os.walk(tmp_dir):
                    for file in files:
                        if file_name in file and not '.crdownload' in file: file_downloaded = True
            print(file_name, 'downloaded from', url)
        curr_date = curr_date + relativedelta(months=1)
print('completed')

      

        
        
        
      

    

source: https://gist.github.com/mamedshahmaliyev/bca9242b7ea6a13b3f76dee7a5aa111a

You could use fxcmpy. http://fxcmpy.tpq.io/

here's a quick example:

import matplotlib.pyplot as plt
import datetime as dt
import fxcmpy

con = fxcmpy.fxcmpy(config_file='fxcm.cfg') 
# must optain API Token, see link for details.

start = dt.datetime(2018, 7, 6,8,0,0)
end = dt.datetime(2018, 7, 7,18,0,0)

c = con.get_candles('XAU/USD', period='m1', columns=['bidclose','tickqty'], start=start, end=end )

# Basic plotting of close and volumne data

fig, ax = plt.subplots(figsize=(11,8))
ax.plot(c.index,c['bidclose'], lw=1, color='B',label="Close")
ax2= ax.twinx()
ax2.plot(c.index,c['tickqty'], lw=1, color='G',label="Volume")
plot.show()