The crypto-currency market returned retail investors’ appetite to trading, who were disappointed with too slow growth of traditional financial tools. The lack of high barriers to entry, the real roller coaster with daily fluctuations in dozens of percent in prices for cryptocurrencies, exciting minds of prospects of profitability and equally sharp falls, cutting investment portfolios in several times. It is because of its spontaneity and an unpredictability at first glance the crypto-currency market is the best suited to become the basis for creating trade solutions based on artificial intelligence.
Volatility is both a friend and an enemy of the participants in the crypto-currency market. It is volatility that attracts non-professional traders, who are fascinated by the prospects of high profitability, and on the other hand it does not allow the flow of institutional money to enter the crypto-currencies.
Forecasts of when the volatility of the currency decreases and helps them to become a more stable means of payment, vary from “never” to “in 10 years”. The first steps in this direction, according to some analysts, have already been made. Since early 2018, bitcoin volatility has fallen to its lowest level ever, said Bill Baruch, broker and president of Blue Line Futures.
There is another technology that can help the cryptomarket overcome excessive volatility and at the same time increase its liquidity, and it has already gained popularity in the traditional market. It is the artificial intelligence.
To understand how this can help cryptomarket overcome volatility, let us turn to the history of how AI has conquered traditional trading desks. The first to come to success were solutions for high-frequency HFT-trade, which help to buy and sell securities in a fraction of a second.
Robots Conquer Wall Street
A pioneer in the market of high-frequency algorithms was Steve Swanson, a graduate of the Faculty of Applied Mathematics of the College of Charleston. Together with the teacher from the College Jim Hox and the teacher of finance at the University of Rutgers David Whitcomb, he created the first computer codes for working in the stock market, which has been used prognostic formulas that could predict stock prices for a period of 30 to 60 seconds. Everything has changed in 1989, which was considered the official date of the birth of high-frequency trade in the US: the US Securities and Exchange Commission (SEC) allowed the usage of electronic solutions in the trading. Swanson, Hawks and Whitcomb has founded the trading company Automated Trading Desk, which in 2007 was acquired by the Citigroup banking group.
Already by 2010, algorithmic and high-frequency trade accounted for 60% to 70% of US exchange trades, by 2014 the share rose to 75%, and by 2017, according to JPMorgan, traditional traders accounted for no more than 10% of trading volume.
Together with the development of technologies, solutions based on artificial intelligence, which manage assets more effectively than people, began to appear in the market. According to the statistics of the research company Prequin, by 2015 9% of hedge funds (1360), under management of which were about $ 197 billion of assets, used computer models to perform trading operations.
In January 2016, the Hong Kong company Aidiya by entrepreneur Ben Goirtsel launched a hedge fund that carried out all exchange transactions with American securities based on artificial intelligence without human intervention.
“If we all die, he will still continue to trade,” Goirzel remarked ironically, describing his project. Aidiya Hedge Fund used several AI-based solutions, including one based on probabilistic logic. Every day, the fund’s system analyzed a huge body of data, from market prices and trading volumes to macroeconomic data and corporate financial reports, and issued its own market forecasts on their basis. The results of this approach were staggering: according to Herzell, on the first day the fund received a profit of 2%.
In October 2017 on Wall Street, the first traded fund (ETF), fully managed by artificial intelligence – AI Powered Equity ETF, appeared. For the first week, it went up by 1%, thus outstripping the S & P 500 index, and by August 2018 its shares went up by 20%. ETF operates on the basis of IBM Watson, a supercomputer that daily analyzes the news background of 6,000 American companies, from financial statements to social network records. At the same time, Watson is constantly learning – every decision it makes is analyzed, and if the transaction is unprofitable, then the next time the algorithm will act differently to get the desired result.
But the arrival of machines on the Wall Street led to another result – markets have become less volatile, because they were dominated by robots, which left no room for emotional and irrational solutions. This is confirmed by research data of scientists from the University of Erlangen-Nuremberg, who developed algorithms that use historical data of the markets.
Reproducing market development, they found a quantitative algorithm that allowed to achieve 73% return on investment annually from 1992 to 2015, taking into account transaction costs. And although during the financial crises in 2000 and 2008 the algorithm showed an incredible profitability (545% and 681%, respectively), in general, scientists found that the profit from AI algorithms decreased after 2001. They came to the conclusion that this is due to the dominance of robots in the auction, when one algorithm competes in profitability not with the person, but with another algorithm.
It is this property of algorithms and AI solutions that can change the character of the crypto-currency market.
Artificial Intelligence Will Change The Cryptomarket
High emotionality of the participants of the cryptomarket has already become an object of study among developers trying to create solutions based on artificial intelligence to maximize the profit of traders. One of the first steps in this area was the creation of a bot that uses a neural network to predict the rates of cryptocurrencies.
The NeuroBot constantly analyzes prices on a set of crypto-instruments, applies patterns from traditional technical analysis, including Fibonacci lines and Elliott waves, and considers signal indicators. According to the creators of the bot, it allows you to make forecasts for 24, 48 hours and a week with an accuracy of 70% to 90%.
NeuroBot offers a paid subscription service. The cost varies from $ 3 to $ 10 per month, depending on the trading pair and the crypto currency. Payment is accepted in the ETH at the project address. But according to users’ reviews, NeuroBot’s forecasts are too unstable: literally in half an hour the forecast for 48 hours changes from growth to fall. This is because the bot is too susceptible to fluctuations in the crypto market, which leads to the issuance of contradictory forecasts.
A more customized AI solution for cryptotraders was created by the new Bistox crypto-exchange, based on the NEM blockchain. The platform offers the users a unique service: the AI-assistant D.A.N.N.I. (Decentralized Artificial Neural Network Integration). D.A.N.N.I. analyzes the strategies of professional crypto traders, historical courses move, and based on these data provides forecasts and advices to non-professional players in the market. In addition, it analyzes the mood in the market through news feeds. This is a personal assistant-robot, which helps traders to make correct trading decisions and improve the degree of risk management. AI is not exposed to emotions and fear of lost profits, is able to learn a large amount of data and help traders make the right decisions.
As far as the solutions for trading in crypto-currencies based on machine learning and artificial intelligence emerge on the market, the crypto market will grow, stabilize and leave the high-volatility model behind, as has happened with traditional financial assets. But if in the traditional market solutions based on AI and machine learning are destined for large funds and are not available for direct use by the general public, then in the case of crypto-currencies similar applications can be used by all market participants.