Financial market analysis is tough, high frequency trading requires huge computing resources as well as solid strategies. Even then, success is not guaranteed. For fiat currency the data and assumptions that many trading analysts draw from goes back decades. By contrast some of the worlds most popular cryptocurrencies have histories barely spanning 3 years!
Strategies for high frequency traders vary, but the general idea is to calculate if current prices and signals might indicate some future movement and calculate whether that indication means Buy, Sell or Hold! The timescales are short, so mining the probabilities and getting answers quickly is the the name of the game. It is here that Machine Learning – specifically “Quantum Machine Learning” can bring new opportunity for traders to gain competitive advantage.
In 2019 Toridion carried out in-depth forex analytics using our TQNN networks to see how the technology could 1) eek out common signals 2) extract learned signals in realtime. The results were encouraging to say the least. More recently we engaged with a well known cryptocurrency trading provider to explore further how our technologies could help algorithmic trading engines make faster, more accurate decisions. The concluding finding was that QML could integrate well with automated trading algorithmic approaches and potentially deliver advantages in both architecture and performance versus other analytical approaches in use.
With an average result time in the sub 0.2 second range for data sets with billions of points, it is clear that TQNN's sheer speed has huge potential to power future automated trading algorithms to new levels of performance.
Toridion remain committed to the fintech sector and will continue to develop our technology roadmap to give financial traders the tools they need to succeed.
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Toridion Finance News Update
25/09/2020: We have recently released two papers outlining results of using Toridion TQNN quantum inspired machine learning to predict Ethereum cryptocurrency. CHeckout the finance page for full details