Machine learning and AI are ideally suited for cryptocurrency investing

Just as institutional investors were quick to recognize the benefits of automated trading solutions, cryptocurrency investors will be the ultimate beneficiaries of the introduction of these services to the blockchain ecosystem.

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The cryptocurrency market has been one of the most exciting to watch over the last year, considering its blistering returns and daily swings which can inspire panic in even the most seasoned traders.  Apart from knocking established financial markets down a peg, their rapid growth saw cryptocurrency instruments outpace gains in more traditional asset classes, garnering a cult-like following and adoration from users.  Although some non-believers are eager to label to bitcoin and its contemporaries as frauds, or inside a terminal bubble, the incredible momentum of the industry accompanied by the large numbers of individuals flocking to open e-wallets and exchange accounts is impressive. 

Despite the tremendous growth witnessed in the space, the one area that many of these newly minted traders struggle is in regulating their emotions.  As one of the primary forces that hamstrings most individuals, psychology plays a crucial role in modern investing environments, especially during the decision-making process.  If anything, our own psychology is our biggest constraint in the equation, forcing errors and bad decisions often at the expense of more effective strategies.  However, blockchain-based developments and distributed processing power mean that traditionally unavailable tools for resisting this paradigm are slowly making their way into the mainstream.

Distancing emotion from the investing equation

Simply put, human beings are not as disciplined as machines when it comes to decision-making, and rightfully so.  Many of our decisions are the result of personal biases and emotion, with our predisposition towards rationalization filling in the blanks and disqualifying our objectivity.  In the event of cognitive dissonance, the results are dramatically worse, especially when combined with investing.  One of the reasons automated trading strategies have risen to such prominence so quickly is that emotions are a limiting factor for humans that machines do not encounter. 

Automated trading technologies are categorically designed to remove all thinking and guesswork from the equation, boiling calculated risk down to its simplest elements.  The best way to think about it is the binary decision-making inherent in if-then functions. For emotionless machines, pre-programmed functions eschew emotions in trading decisions, leading to better control of risk-reward if properly designed with predetermined entry and exit conditions for trades.  If something doesn’t work, it can be reprogrammed.  On the other hand, reprogramming humans is no easy feat.

A treasure trove of data abounds

Financial institutions and investment funds have long been at the vanguard of new technologies, especially artificial intelligence, deploying them to gain both a qualitative and quantitative edge when it comes to market-making, hedging, and generating returns.  Considering the immense amount of data created by global financial markets daily, technology has long been a tool for helping these firms sift through the enormous volume of information to glean valuable insights.  Furthermore, electronic communication networks (ECNs) opened the door to the proliferation of automated (black box) trading strategies that benefited from speedier execution. 

This eventually resulted in the explosion of high-frequency trading, whereby complex algorithms make instantaneous buy or sell decisions based on predefined rules.  Because they are readily able to decipher information quicker than the human brain and thereby expedite the decision-making process, they have been very successful systems for the entities that have fully exploited their potential.  In effect, this advent created a two-tiered marketplace and a massive asymmetry, putting greater distance between retail investors and their more sophisticated peers.  However, thanks to blockchain and its greater focus on transparently sharing data, that is all about to change.

Leveling the playing field

One of the best attributes of blockchain-based cryptocurrencies is the unparalleled level of transparency they provide to all market participants, a feature that even today’s centralized exchanges cannot match. For cryptocurrencies, a market which is still largely uninhabited by institutional forces thanks to its decentralized nature and uneven regulatory oversight, it is the perfect place for more widespread introduction of strategies employing machine learning and artificial intelligence.  For traders, the benefit is obvious.  Instead of relying solely on emotion alone to make investing decisions, tools are now available that distribute the powerful attributes of machine learning and AI to traders of all shapes and sizes. 

One such company working to bring these types of solutions to investors is Signals, a startup devoting its efforts to aggregate disparate forms of information into a single platform that is simple enough for individuals with no background in programming, AI, or machine learning.  By combing the internet for information on sentiment or enabling traders to build their own mechanized systems for making decisions, Signals harnesses the power of blockchain to inform users about potential opportunities and help them overcome ingrained biases characteristic of human decision-making.  By deploying the distributed processing power of blockchain, Signals effectively delivers supercomputer processing capabilities to its users, enabling the interpretation of large datasets.

“With the advent of powerful computational technology, the financial sector and trading industry has been transformed through the replacement of traditional auction-to-computer transactions in the early 70’s, with algorithmic trading systems. Machines take emotions out of trading and make it a pure numbers game, cutting through the noise of trading signals and processing huge data sets that a normal human never could,” reads the company’s white paper.

Furthermore, by incorporating deep machine learning techniques, actionable strategies can be uncovered thanks to pattern identification alongside the analysis of multiple variables simultaneously.  Users with winning strategies can even monetize their successful strategies thanks to the tokenized ecosystem built-into the platform.

Another interesting example of the intersection of AI and crypto investing comes from the Daneel Assistant Company, which deploys IBM’s Watson to help investors make more-informed decisions through data intelligence.  Known for its prowess related to interpreting language and emotions, Watson has already been applied across multiple fields, making it a natural fit for the cryptocurrency market.  Apart from combing the web for insights, Daneel provides a natural language processing tool which enables investors to formulate queries which are responded to in-kind with an applicable answer from the system.  The easiest way to think about the solution is a personal assistant that does all the research and legwork to advise on potential trading strategies by deriving insights from sentiment, emotion, data, and language.

Adapting and evolving in tandem

Machine learning and artificial intelligence have a natural application when trading cryptocurrencies considering the implications of its decentralized architecture, disparate infrastructure, and numerous sources of data.  Just as institutional investors were quick to recognize the benefits of automated trading solutions, cryptocurrency investors will be the ultimate beneficiaries of the introduction of these services to the blockchain ecosystem.  By leveling the playing field through AI and machine learning techniques, the application of these technologies through new financial services empower all investors to overcome the most significant factor limiting their investment returns: themselves.

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