Why Machine Learning Can’t Always Crack the Code: The Limitations of Machine Learning for Crypto Trading
Cryptocurrency trading has been on the rise, and with it, the use of machine learning algorithms to make profitable trades. It’s easy to be seduced by the buzz and hype. However, while machine learning can be incredibly powerful, there are important limitations to using it for cryptocurrency trading.
Limitations of Machine Learning for Crypto Trading
Complete and Accurate Data Sets
One of the most significant limitations of machine learning is that it requires a lot of data to make accurate predictions. For cryptocurrency trading, this can be difficult since there is no centralized exchange, and the data can be incomplete or inaccurate. Additionally, historical data may not be as relevant in the world of cryptocurrencies since the market is incredibly volatile, and the price can be affected by factors that are difficult to predict.
See the Crypto-ML data set.
Another limitation of machine learning is that it can be biased. Machine learning algorithms are only as good as the data they are trained on. If the data is biased or incomplete, the algorithm will produce biased results. This can be a significant issue in cryptocurrency trading since the data is often incomplete or difficult to verify.
The Human Matters
Machine learning and AI are buzzwords. The tools to employ them are becoming more and more accessible. But that doesn’t mean just anyone can create an effective machine learning model.
Building an effective Bitcoin machine learning model requires a high level of expertise and experience in data science, statistics, programming, and the specific domain of the problem being addressed. An expert is required to design, develop, and implement the appropriate model to analyze and understand complex data sets. The expert is also responsible for selecting the right algorithms, validating the model, and fine-tuning the model to achieve accurate results.
A machine learning model needs to be trained, which requires a great deal of data preparation, cleansing, and transformation. An expert in machine learning can navigate the complexities of the data and preprocess it appropriately to ensure the model’s optimal performance. They can also make informed decisions on the variables to use in the model, including feature selection and engineering, which requires a thorough understanding of the problem domain.
So don’t trust a service just because it says “machine learning.” It may be hype, poorly designed, and completely ineffective.
Crypto Market Randomness
Inherent randomness is another challenge when it comes to using machine learning for cryptocurrency trading. The crypto market is incredibly volatile and can be influenced by external factors like news and government regulations. This randomness can make it difficult for machine learning algorithms to make accurate predictions, leading to significant losses.
According to a recent study, most machine learning algorithms for cryptocurrency trading are not profitable. The study found that out of 1,000 machine learning algorithms, only 10% were profitable, and those that were profitable had a low return on investment.
Furthermore, even successful machine learning models require constant monitoring and tweaking. The market can change quickly, and algorithms must adapt to stay profitable. Without constant adjustments, a profitable model can quickly become unprofitable.
Machine Learning is a Tool, Not a Silver Bullet
Finally, while machine learning can be powerful, it is not a silver bullet. It is only one tool in a trader’s toolbox, and it must be used in combination with other strategies to be effective. A successful trader must have a deep understanding of the market and use multiple strategies to make profitable trades.
Limitations of Machine Learning for Crypto Trading
While machine learning can be a powerful tool for cryptocurrency trading, it is not without its limitations. The data can be incomplete, biased, or difficult to verify, and the market can be volatile and influenced by external factors. Even successful machine learning models require constant monitoring and tweaking, and they must be used in combination with other strategies to be effective.
Therefore, traders should approach machine learning with caution and always use it as one tool in their trading toolbox.
How Crypto-ML uses Machine Learning and AI
Crypto-ML has been publicly providing machine learning and AI tools for crypto traders and investors since February of 2017. We post our results publicly, partially to favor honesty but also to hold ourselves accountable and strive for better and better results. You can’t obfuscate or hide from the reality of hard numbers showing you predicted versus actual.
While we started as a trading platform that included auto trading, we now focus on long-term investing. When investing, the data sets are much broader and richer. We can look at big economic factors. We can look at patterns that affect all financial markets. We apply timeless principles of investing during panic and taking profits during greed.
On our site, you’ll find:
These come together as a suite of tools to supplement your decision-making process, help you identify patterns in the crypto markets, and hopefully make you a better long-term investor.
Crypto-ML provides machine learning for crypto traders and investors. Gain crystal-clear signals and deep market insights. Add predictive capabilities to your toolbox. Learn more and join for free.