How AI and Automation Can Help Crypto Investors Trade Better
Hundreds of new cryptocurrencies have been created and offered to investors through initial coin offerings (ICOs) over the past year. Millions of new users entered the crypto space in 2017 during this ICO boom. More are jumping on the bandwagon this year.
However, despite its rapid rise in popularity, investing in cryptocurrencies isn’t without technical difficulty. Most people who’ve heard of cryptocurrencies – and many who have put money into it – only have a vague understanding of how these work as investment vehicles. Confusion among new investors has been high due to the abundance of coins and their fluctuating valuations.
Cryptocurrencies are highly volatile. It’s has become common to hear stories of investors who entered the crypto market during the boom, only to panic sell when prices suddenly dropped. Even seasoned traders are, at times, influenced by their emotions. It can be hard for traders to ignore the stream of Wall Street bigwigs and government officials expressing their lack of faith in cryptocurrencies when the markets echo their statements.
Navigating crypto investing takes plenty of skill and know-how. Fortunately, there are also some powerful tech-driven tools that could help both fledgling and experienced traders make sense of the wild crypto market. Artificial intelligence (AI) are now finding their way into crypto activities and even creating synergies with blockchain technology to help address these concerns. Ventures like Endor and Signals are all embarking on projects that can potentially impact crypto investing in major ways.
Lower Barriers to Predictions
It is important to understand how AI fits in the context of crypto investing. According to Endor CEO and Co-Founder Dr. Yaniv Altshuler, by using AI, an investor or analyst “can take past-data, let’s say, the price of an asset at each day during the past year, and all of the places and times it was mentioned in social networks and use mathematical tools in order to produce a prediction regarding whatever it is we are interested to predict the asset’s price a month from now.”
Doing such an analysis without AI is tedious and impractical. However, the tools and expertise necessary to analyze such data are only typically available to large enterprises who have the resources to invest. Accurate predictions do not come cheap.
“The quality of the prediction depends on the quantity and quality of the data, the quality and sophistication of the mathematical models used, and with some extent also to the amount of computation power that can be dedicated to solving this problem,” Dr. Altshuler says.
There have already been significant developments in this area. For instance, Endor has successfully worked with several large enterprises in analyzing behavioral patterns in big data. The firm, an MIT spinoff, uses social physics or the use of physics-inspired tools to analyze behavior in human-driven events. It has successfully developed what they call the “Google” of predictive analytics – an AI-driven technology that allows users to simply enter questions and quickly get accurate predictions.
The company is now moving into the crypto space through its Endor.com Protocol – a blockchain protocol that makes predictive analytics accessible to ordinary users. Using a token economy, the protocol encourages participation of data owners and developers who could contribute to improving the system. By tapping the shared expertise and custodianship of crowds, this ecosystem ultimately makes tools and predictions more affordable.
Endor is also able to analyze blockchain data to generate predictions. Behavioral patterns from blockchain transactions could help make sense of the factors that drive the highly-speculative crypto market. Using the protocol, investors will be able to readily spend Endor’s EDR tokens to pay for predictions without having to learn advanced data sciences or invest in big data infrastructure.
Signals is a marketplace for trading strategies. It aims to establish a comprehensive ecosystem that enables crypto traders to make critical decisions informed by hard data. The platform can also be used as a means for data scientists to monetize their insights. By creating and selling indicators and signals crypto traders are able to optimize their profits.
Like Endor’s protocol, Signals’ platform has been developed so that its users do not need to have high levels of technical expertise or extensive programming knowledge. The platform uses AI to combine various trading models in order to make them easier to use. Instead, the algorithms used in market indicators on the Signals platform are assembled and optimized through easily-understandable visual media.
Signals are also developing tools for integrating external data into its network. Signals plan to incorporate platforms that can be used to create prediction markets into its own system. Prediction markets rely on crowd-sourced wisdom to predict the futures of events including financial markets. Having such monetization mechanisms available incentivizes data scientists and savvy investors to share their market predictions with ordinary investors.
Pavel Nemec Signals CEO and Co-Founder says, “There is a large amount of extremely bright people in the data science and crypto trader communities who are not working for Wall Street hedge funds and don’t have access to the necessary infrastructure, resource, and data to train their trading models. We are creating an environment where they will have all of that and more including machine learning-based indicators, data from blockchain-based prediction markets, media monitoring and sentiment analysis, blockchain monitoring to detect the activity of whales, and so on. “
Interoperability and Automation
These prediction and analytical tools help investors make the most informed trading decisions. What could further bolster this, however, are better interoperability among blockchain platforms and automation.
There are now efforts that promote interoperability and data exchange not only across blockchains but with traditional centralized institutions as well. Blockchains currently cannot transact with each other out-of-the-box, limiting trading tokens to crypto exchanges. Because of this, real-world adoption of cryptocurrencies for day-to-day financial transactions has been limited.
Combined with AI-driven services, the interconnectedness of blockchains could allow for the creation of apps that can automatically execute trades based on a multiple of market factors and not just the events concerning one blockchain or cryptocurrency. Investors will be able to take advantage of automation rather than having to constantly monitor all these factors in order to time the market right.
Better Insights and Trading Strategies
Transactional data are also currently limited to each blockchain. While tools such as Endor can be used to analyze blockchain data, insights would only be based on trading and behavioral patterns for one cryptocurrency. Data from cross-chain platforms can reflect how blockchain and cryptocurrencies relate to wider aspects of finance. This has the potential to reveal more complex patterns and yield richer insights when analyzed.
Other forms of interoperability could benefit the space. Data scientists and other experts who have built indicators and trading bots outside of the Signals platform can integrate their creations with application programming interfaces or APIS. This could lead to interesting mashups and diverse functionalities.
Wider interoperability across blockchains eventually creates richer sources of data from which AI can learn and generate insights. This helps traders create more informed and calculated strategies. Used alongside automation and cross-chain platforms, these could also help minimize the impact of volatility and speculation for traders.