Leveraging Machine Learning for Predictive Crypto Trading

While image-drawing and text-writing systems are gasping for air, another technology is making significant improvements across multiple domains. For example, an AI trading bot created with advanced machine learning for crypto trading is something that was hard to imagine just a couple of years ago, before everyone and their moms started talking about autonomous software capable of incredible things.

Interestingly enough, the topic of our article owes thanks to an event that happened way before the craze for ChatGPT and Claude began breaking news cycles. In October 2015, the world of professional Go players was buzzing with chatter as humans lost their last outpost in the battle between us and the machines! Thankfully, we did not create a planet-ending Skynet-like system. However, the AlphaGo computer created by the DeepMind team at Google managed to do something that was thought to be impossible: winning a professional human Go player without a handicap.

The team behind this amazing program used a variety of novel techniques based on neural networks that could improve themselves by simply iterating certain processes millions of times until they get the desired outcome. We still do not fully understand how it all works, but we do know that it does.

Deep Learning for Crypto Trading

Financiers have been dreaming about conquering AI technologies and using them to create a super-trader that would operate around the clock without any pauses while outsmarting everyone with incredible moves. Turns out that predictive modeling in crypto trading or any kind of trading for that matter is still outside of our reach. However, we can use neural networks for very specific purposes.

Laymen who are not familiar with artificial intelligence systems are dreaming about the day when they can simply ask a chatbot how to become rich and it will come up with an excellent strategy. The problem here is that the vast majority of models that we are used to are quite limited since they depend on the quality of data generated by humans. As far as we know, a perfect strategy has never been created.

The inability of contemporary technology to answer complex questions that require novel ways of thinking does not mean that these expert systems are worthless. Financiers are using them for other purposes. For example, they can aggregate relevant news stories and highlight social media sentiment for fundamental analysis of certain assets.

Quantitative analysis is also highly valuable. For instance, a typical statistical arbitrage system may have hundreds of different positions that must be adjusted regularly according to market circumstances. There is no decision-making, just calculations based on price action movements and many other metrics. Unlike humans, machine learning models for crypto can perform these tasks extremely quickly and process massive swaths of data without ever making errors.

The advantages of the technology

We have the necessary tools to create systems capable of outperforming human traders consistently. The implementation of competent expert systems in various investment strategies can be incredibly beneficial. In general, using good, productive AI tech has a game-changing potential to improve all aspects of the crypto industry.

Here are some examples:

• Better security and fraud detection. Many development teams are looking into ways to introduce autonomous agents into their infrastructures to automatically monitor processes and identify suspicious patterns in data handling on immutable ledgers. A good example is the hiring of AnChain.AI by the SEC to search for strange investor behaviors in real time.
• Optimization of investment strategies. Collecting data from a variety of sources and adding it to advanced technical analysis methods can be incredibly useful. The problem is that humans are quite bad at pattern recognition and may miss some connections and correlations that an expert system will easily spot. Using advanced software agents to formulate prognosis for price action dynamics is a new emerging field with many interesting solutions.
• Analyzing social media sentiment. Thanks to the advancements in the ability of artificial intelligence systems to comprehend written text, we can build analytical frameworks that scan the internet for relevant social media posts, news stories, and opinion pieces to identify the strength and direction of market sentiment. These insights can be invaluable to a smart investor.
• Autonomous smart contract audits. Code reviews are time-consuming and challenging for many developers. At the same time, smart contract vulnerabilities remain the biggest risk in the DeFi sector with bridges losing over $1 billion to hacker attacks in 2022 alone. Many platforms are thinking about using automated audits powered by artificial intelligence to build better smart contracts and review them in real-time. For example, 1inch is working on a solution just like that for their DEX service.
• Personalized analytics. Individual retail traders will greatly benefit if the technology becomes publicly available. Using the best AI crypto trading bots is already quite effective. For instance, the WunderTrading platform offers a sophisticated AI-assisted statistical arbitrage system that can run a massive portfolio. You can already check it out and even test it to get a glimpse of what we can expect in the future.

The conclusion here is simple: the technology has a potentially transforming effect on the whole blockchain industry if and when we can apply it adequately. Unfortunately, all available products are still underbaked and require additional development time and tinkering before they can be safely deployed as standalone products aimed at investors.

Predictive Analytics in Crypto

Several companies are toying with the idea of implementing various types of ML algorithms into their offerings. The vast majority of experimenters are automation vendors, centralized exchanges, brokers, and consultants. They see value in using supervised machine learning for crypto trading and offering this unique experience to individual retail traders and large capital holders.

Approaches differ from one company to another, but some aspects remain the same across the industry. Let’s look under the hood of these promising systems and understand how they can work.

Collecting and processing information

To make a good guess about the future, we must understand the past. It is important to aggregate relevant data and analyze it. Robots can get it from a variety of sources:

• Market history is recorded by multiple platforms including TradingView, MetaTrader, and many other platforms focused on technical analysis and charting.
• On-chain data is pulled directly from ledgers and may require some combing to find the bits that can be used in forecasting. It is close to impossible to gather this information manually.
• What do people think? Robots can scout the internet and lurk on forums to search for evidence of changing market sentiment way before it shows on indicators like VIX.
• Publicly available metrics like interest rates, employment, commodity prices, and other macroeconomic factors can be useful when trying to predict what will happen in the crypto market.

This gathered information must be meticulously processed using techniques that expert systems often come up with on their own. Developers can also teach AI to use commonly employed instruments like technical indicators, scoring methods for sentiment strength, and on-chain data retooling. It is an important step as it decides the future performance of the expert system.

Selection and reiteration

Contemporary development teams have access to a multitude of time-tested approaches in the field of AI building. Currently, some of the most popular techniques are the big three:

1. Time Series modeling. ARIMA or LSTM are network structures that can be used to sequentially process information and create predictions based on identified patterns. It is a valuable mechanic that can be used by the vast majority of strategies.
2. Regression modeling. ML algorithms can focus on identifying connections between different variables. Since they can easily process vast amounts of information, it is easier for them to find correlated metrics that can help predict price trends.
3. Classification. Romantic names for soulless algorithms like Random Forest or Gradient Boosting Machines are chosen for a reason. They often neatly describe the way these systems approach data categorization and ordering to make solid predictions about market dynamics.

It may take days, weeks, or even years for a developer to identify the best approach and start perfecting it. Here, backtesting and validation become extremely important. The latter can be done using a variety of methods including:

1. Cross-validation is where the whole data set is broken down into smaller pieces which can be fed to an algorithm to see how it performs with limited information or with no context to guide it.
2. Out-of-Sample method is a great choice for crypto market predictions as it focuses on using data sets from different points in history to check how an algorithm performs over time.

The last stage

The resulting ML system should be able to make general predictions about the market and its direction. In some cases, it can produce potential price ranges, support and resistance levels, or other specifics. In general, it is a good outcome if a system can simply tell you whether the next several weeks will be bullish or bearish. It is more than enough information for a smart retail trader to make a series of solid trades.

After achieving a good result that can be commercially viable, companies have to continuously invest in the ongoing development process to retrain models with new data sets if they become available and apply additional techniques like adaptive learning to ensure that these systems stay up to date.

As you see, artificial intelligence in crypto trading is not something that can be done quickly. Companies like WunderTrading have been working on their products for several years and decided to release them after testing and reiterating multiple times.

Different strategies that you should be aware of

These advanced tools are already available to individual retail traders. Yes, some of them are still not ready for large-scale applications, but you can see how ML techniques improve already existing products and make them way more exciting:

• High-frequency trading with machine learning. HFT strategies rely on instant adjustments and decision-making. Humans are not involved in these strategies directly and simply look at how robots perform thousands of trades each day. However, these robots are severely limited and never sway from a predefined path. New ML-based ATS can make adjustments if the market changes and automatically deploy risk aversion tools.
• DCA and GRID bots with AI agents. The Distributed Cost Average approach is a time-tested method of asset acquisition that has been around for ages. Grid bots are designed to utilize the same principle of buying while placing separate exit orders for each new position. It is a simple system that can be improved by the addition of an autonomous software agent that will adjust trailing stops and dynamically resize positions depending on price action changes.
• Generative AI for script writing. Some companies are experimenting with using large language models like ChatGPT to write code for platforms like TradingView, WunderTrading, and others. Many of these products are also quite limited in scope and capabilities since they cannot really go outside of what API functionality allows them to use. In some cases, it is more than enough.
• Other approaches to the implementation of autonomously operating software. WunderTrading has an interesting product that puts an expert AI system at the helm of a sophisticated statistical arbitrage strategy that can adapt to various market circumstances and automatically rebalance portfolio composition to retain value against all odds.

Many of these products already look quite appealing and give a vibe of sophistication. However, we are still at the beginning of a much longer journey through the vast ML domain where everything seems to be possible. Many companies like Cryptohopper, 3Commas, and WunderTrading are hinting at some new technologies that they are working on. Everything that we have now may be just the tip of the iceberg.

Modern retail traders have access to a wide range of interesting products including automated trading bots and auto-compounding yield farming platforms. However, all these instruments can be altered or enhanced by the implementation of ML systems.

Should you use machine learning in crypto trading?

Since this is an emerging technology, there are certain risks involved. It is important to consider them. Below are some dangers strongly associated with artificial intelligence:

• Technological risks. Many products in this category are untested and may not perform as intended. It is an especially important consideration for investments made for the long term. Since ML tends to change how it operates over time after receiving additional training, the outcome can be quite different from the one you may have expected.
• Hallucinations. Large language models are known for producing nonsense statements to make their output appear legitimate. The same is happening in other generative systems. If you plan to use them for consultations or strategy writing, it is important to remember that they can hallucinate results that will be far removed from what you want.
• Poor data quality. The information fed to ML systems can be incorrect or insufficient to produce a well-performing piece of autonomous software. End users may not realize that they are using something that was trained on subpar data sets. Outputs received from such agents can be faulty or lead to unsatisfactory investment outcomes.
• Overfitting is a hotly debated topic among researchers working in this field. The problem occurs when the model works exceptionally well when applied to historical data but fails to perform when it is fed new information that it has never seen before. The increasing complexity of what must be analyzed by a machine can be a problem in the long run even for models that appear to be extremely well-adjusted.
• Overreliance on technology. Users may become complacent after seeing how their automated trading systems perform well guided by a model. Investors must remember that autonomous software requires at least some oversight and should never be allowed to make unauthorized changes to holdings and perform trades with an inappropriate risk profile.
• Unexpected market events. Sudden volatility spikes or other unforeseen circumstances throw an oddball at a model that will react weirdly and make decisions that are nonsensical given the market dynamics. Monitoring the performance of your system in real time becomes a chore very quickly.

The main takeaway

Using these novel products is exciting and can be a rewarding experience in the long run. However, users must remember the dangers that can significantly reduce the effectiveness of their investment activities if they ignore them. These novel concepts are incredibly valuable tools that are becoming increasingly present in the field of financial markets. Approach them with caution and do not jump on hype trains!