There are no two ways about it: the turn of the decade has brought about massive changes in the way we view money and technology. It was in the year 2020 when the value of Bitcoin and other cryptocurrencies skyrocketed to unfathomable heights. While it has returned to baseline in the months following that, who’s to say that the ball won’t start rolling again?
The growth of digital currencies as a whole is by no means ending. Especially not with the more recent surge of machine learning and artificial intelligence. With more eyes on artificial intelligence, people across all industries are looking for ways to integrate this shiny new tool into various aspects of their lives—and crypto enthusiasts are no different.
From price forecasting to automated trading, there are a plethora of ways machine learning can be seamlessly integrated to benefit the average crypto trader. Let’s look at this phenomenon in more depth and see how people use cryptocurrency.
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What is Machine Learning?
Machine learning is a type of artificial intelligence (AI) that focuses on the development of algorithms and statistical models. The data produced by these models allows programs to learn and make decisions or projections based on historic data. These decisions aren’t explicitly programmed, but rather a result of autonomous and pre-programmed continuous learning.
In simpler words, machine learning allows computers to learn and improve from experience without human intervention. As mentioned earlier, machine learning has a wide array of applications. For one, it can help software developers provide suggestive text on social media platforms. Another way it helps is it provides a framework for the operations of new innovative technology, such as self-driving cars.
Crypto is naturally the next major step. With the complexity and relatively recent emergence of cryptocurrency, machine learning is a very potent tool that has all the resources to aid crypto traders and developers in making the most out of this digital currency.
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5 Ways Machine Learning Supports Cryptocurrency
Here are some ways machine learning can help people deal with cryptocurrency.
1. Forecasting Price Movements
Employing AI and machine learning in the financial scene is nothing new. It’s been around for more than five years, and the scene is still vibrant as ever. There are exchange-traded funds such as the ARK Autonomous Technology & Robotics ETF that employ AI tools run by financial analysts that perform relatively well, being in the green after five years (2018-2023).
That said, there are also moments wherein the heavy reliance on AI tools can cause your portfolio to bleed red. The AI-Powered Equity ETF is one example of a fund that is currently underperforming. With that said, it’s important to use AI crypto analysis tools sparingly and carefully. You wouldn’t want to put all your chips in an algorithmic basket as AI is unable to fully grasp the complexity of human behaviour and market movements.
You’ll have to review the technicalities of it and observe graph movements yourself in a proper Bitcoin exchange.
That’s not to say machine learning applications are entirely useless though. AI helps a lot in providing some historic analysis that you can certainly derive ideas from. Use AI as a clarification tool and always consider the human element before pushing through with a trade.
2. Automated Trading
Machine learning uses data pulled from past data sets to find the optimal time to trade a stock or cryptocurrency for profit. The most common example of this comes in the form of crypto bots. These bots operate 24/7 and can execute trades when pre-determined parameters have been met.
The parameters are often set by humans and can come in a wide range of risk strategies. Some bots are fairly low-risk (and low-return). Other bots have been programmed to be more aggressive in their strategy. For busy crypto investors and occasional traders, relying on these bots can be a good way of getting a favourable return on investment. They also operate more quickly than manual traders, which can help put you at an advantage in times of surging market movement.
That said, trading solely through this method is not foolproof. Cryptocurrency, and a whole lot more investment vehicles, have a low signal-to-noise ratio. This means that they would need multiple variables to occur to have a deep, long-term impact on its overall stock price. Furthermore, many predictive models at this time aren’t fully capable of analysing the broader financial market. This can lead to lapses in judgment, which is a risk that some may not be able to take.
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3. Fraud Detection
With the complicated nature of cryptocurrency, there are bound to be scammers and fraudulent actors that prey on unsuspecting individuals. In fact, there are multiple crypto scams that have collectively contributed to a loss of $200 million in just the second quarter of 2023 alone.
Machine learning algorithms, however, can make it easier to detect fraudulent activity. In particular, they can analyse transaction patterns, user behaviours, and other anomalies to potentially flag fraud attempts and stop them before they occur. Furthermore, machine learning programs can also help secure smart contracts, or blockchain-run contracts that require pre-determined conditions to be met to be fully executed. Machine learning also helps protect the system from errors, bugs, and additional vulnerabilities.
By leveraging this technology, cryptocurrency platforms and exchanges can implement robust security measures to protect their users from falling victim to fraudulent schemes, contributing to a safer and more trustworthy crypto ecosystem.
4. Sentiment Analysis
While graphs and technical figures contribute a great deal to the projected value of a cryptocurrency, sentiment analysis plays an equally important role in understanding the market dynamics. This type of analysis utilizes machine learning algorithms to analyze human conversations surrounding a specific topic—or cryptocurrency, in this case.
Cryptocurrencies that are prone to facing extreme ends of language (good or bad) tend to be more volatile in price than digital currency with lower sentiment. This is because the presence of highly emotive language can indicate a higher inclination towards emotion-based decision-making as opposed to critical analysis. Using sentiment analysis, therefore, is a great way for opportunists to determine cryptocurrencies that may be inflated in value and make investment decisions accordingly.
5. Mining Development
Blockchain mining is a peer-to-peer process that involves a computer continuously solving a mathematical equation to earn a reward. It’s also used to mint and verify new tokens in a blockchain network. Some machine learning applications can aid in boosting the efficiency of mining development, particularly in the software side of the equation. AI can help speed up inefficient resource mining and optimize computational algorithms to help miners get more bang for their buck.