tickterial 1.1.2

Download tick data from Dukascopy Bank SA to local cache, and simulate multi-symbol time-ordered price streams to stdout.

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: [e-mail]
• 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