Autonio Whitepaper

Abstract

According to a recent JP Morgan report 90% of all Wall Street trading volume is attributed to
automated HFT and Index Funds.1 Advanced learning algorithms have largely been the domain
of trading banks and hedge funds that use AI as part of a larger strategy that employs a variety of
trading tactics. Their types of models react to split second changes in relationships between
different markets. The spoils go to the banks with the fastest models and the fastest execution
that can take advantage of short term transient market inefficiencies. Because the markets traded
are mature, the movements are usually minute, with huge transactions that drive correspondingly
high profits.

On the other hand, cryptocurrencies are the brave new world of trading. The markets are
relatively young and still very inefficient. It is not unusual to see bitcoins move 10% in a single
day. In addition, due to size and potential regulatory conflicts, the cryptocurrency markets are
largely shunned by established trading houses leaving the field open for dynamic young
companies to take advantage of the opportunities in the markets. With market capitalization ballooning (cryptocurrency
market capitalization has grown from $8.8bn to $80.6bn in the past year with no signs of a slowdown), the time is right
for a dispassionate, selflearning trading engine to take advantage of an environment where less professional and less
disciplined traders create many profit opportunities. Cryptocurrencies are obviously a natural fit for
automated trading; the market volatility can be hugely profitable using AI-based trading and is
rapidly gaining popularity. Automated trading is mainly achieved using scripts that need some
coding experience and are devoid of GUI, or through one of about half a dozen commercial
products requiring expensive memberships. Crypto trading may not be easy and sure-shot thing
for everyone.

And, therefore we introduce AUTONIO

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Autonio Whitepaper

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