tickterial: forex & cfd tick data

Download and cache tick data from Dukascopy Bank SA.

Installation ::Python3

pip install tickterial
tickterial -h to check supported arguments.

Example multi-symbol stream download:

$ tickterial --symbols GBPUSD EURUSD USDJPY XAUUSD --start '2024-04-08 17:00:00' --end '2024-04-10 00:00:00' --progress true

# Output format is: <timestamp, symbol, ask, bid, ask-volume, bid-volume>,[repeated if multiple symbols at same timestamp]
...
timestamp, symbol, ask-price, bid-price, ask-volume, bid-volume
1712696398.152,GBPUSD,1.26785,1.26774,900000,900000
1712696398.192,USDJPY,1.51808,1.51759,1800000,1800000
1712696398.295,XAUUSD,2353.505,2352.835,90,470
1712696398.343,USDJPY,1.51808,1.51755,1800000,1800000
...
#When multiple ticks exist in the same timestamp, the output is concatenated as such:
1712696267.989,EURUSD,1.0857,1.08569,990000,900000,1712696267.989,GBPUSD,1.26778,1.26769,900000,900000
...

Easy, normalized prices, multi-currency, simultaneous and ordered price output!

Why the name?

I thought of tick material as a replacement for tick-data, which has been used way too many times already, both in research and code.

How does it work?

This package downloads tick data on request and caches downloads to disk for subsequent offline calls. Finally, a quick and easy historical data collection for backtesting trading algorithms!
Currency pairs(tested) and more(untested) instruments on this page are supported. Email me for requests.
Note that this code respects their API rate-limits. As such, asyncronous frameworks like aiohttp largely returned errors, before I settled for a syncronous. A work-around is to use a list of proxies for each individual request (planned).

Integrating with your code

from datetime import datetime
from tickterial import Tickloader


def test_download():
	tickloader = Tickloader()
	period = datetime(hour=17, day=8,month=4, year=2024)
	print(period.ctime())
	#
	ticks = list()
	if data := tickloader.download('GBPUSD', period):
		for tick in data:
			ticks.append(tick)
		if ticks:
			print(f'{period} tick count: {len(ticks)}')
			print(ticks[0])
		else:
			print('No data!')

test_download()
print('--end--')

#output:
# {'timestamp': 1689631196.875, 'ask': 1.30788, 'bid': 1.30778, 'ask-vol': 900000, 'bid-vol': 900000}

Notes

Cache is store in UTC. Pass your UTC bias as last parameter to tickloader.download to fix local time offset.
Tick data can only be fetched to the previous hour. Current hour returns 404, thus ignored.
Cache is stored in current working directory(default), path = $(pwd)/.tick-data, or to path specified in --cachedir option, also available while creating Tickterial(cachedir=/your/path) object. Move this directory when migrating to keep your offline tick data.
For multi-symbol downloads, the streams are ordered by timestamp(wow!). You can stream ordered prices for say EURUSD, GBPUSD etc. (Currently, only supported for commandline invokation.)
Keeping track of the number of , should allow easy partitioning of the ticks for backtesting systems. - Voila! My next project.

Support the project

I couldn't afford college. I therefore learn through the web and create amazing software that can impact this world in ways I can. A little help will offload my bills and give me more working time.

You can paypal at me: ngaira14nelson@gmail.com
Contact me for requests, optimizations, pull-requests and every other interesting topic.

TODO

Add support for streaming ordered-ticks to TCP endpoint - planned
Support for pandas dataframes and numpy arrays - differred
Integrate mplfinance for candlestick plotting - differred
Use proxies to perform async downloads - planned
Parse ticks to generate timeframe data - differred
Port to faster languages (C/Rust/GO) - differred

In the spirit of opensource:
	# dev.drui9

drui9/tickterial