How data analysis is changing trading, and why crypto traders should keep up with the trend.
The importance of data analysis in crypto
Data analysis has grown exponentially in tandem with the digital asset industry, and crypto traders are embracing it as a valid and efficient tool for more effective market predictions. There is a considerable amount of buzz around data analysis for cryptocurrencies, and Algalon is a big advocate for its effectiveness in predictive trading and pattern finding. What are the benefits of data analysis for identifying patterns and what are the most effective methods of data analysis for crypto?
A brief introduction to machine learning
Machine learning is a set of mathematical methods that aim to identify patterns in historical data in order to predict potential outcomes. At the outset, the model is given a training sample of data. Using this sample, the algorithm learns to find events of the desired type.
In every modern system, there are multiple parameters which describe the state of that system. A vehicle, for example, has several sensors which monitor both its internal status and also, the road conditions. This data can all be assembled into one place and then, with the use of complex mathematical methods, the working system can be optimized. This principle has many use-cases across diverse industries, everything from retail to space tech. By using historical data, the future can be predicted, by finding patterns and consistent trends within the data.
As an example, we can collect data on air temperature, pressure, humidity and time of year. We also know which day it rained and which day it did not. From this data, an algorithm can iteratively aim to select a set of identifying rules that predict the chance of rain as accurately as possible. As a result, at the end of the training, we can create an algorithm that can predict with a certain amount of accuracy whether it will rain or not.
In recent years, machine learning has become a popular talking point in many industries. Some refer to one particular event as a watershed moment in machine learning history. Just ten years ago, it was thought that a machine would never be able to beat a human player at Go (a Chinese board game). It was widely presumed that abstract and strategic human thinking could not be replicated by a machine. Four years ago, in 2015, Google’s Deep Mind algorithm, AlphaGo, managed to beat the world’s strongest player 5 points to nil.
Data analysis in traditional asset trading
In traditional trading, there are many ways to use the practice of data analysis: looking for correlations between assets and asset classes, predictive modeling, risk management analysis, and also in finding the optimum portfolio composition. At the moment, exchanges release a significant amount of data for traders to analyze for orders, market depth, trades, etc.
The human brain is unable to simultaneously analyze such a huge inflow of data using statistical modeling. Thanks to big developments in machine learning and exponential growth in overall processing power, it has become possible to write automatic programs and scripts which scrape gigabytes worth of data that can be analyzed in the shortest amount of time. This development helps human traders find more patterns, make more balanced decisions, and grow their profits.
Data analysis for cryptocurrency trade
While patterns in cryptocurrency trade may seem to be irregular and unpredictable, volatile even, there are always patterns to be found and indicators to be taken, when enough of the relevant data is looked at from the right angle. With advanced machine learning, vast amounts of data relating to seemingly arbitrary shifts in crypto price changes can be accumulated at great scale, and then siphoned down into a more succinct and more accessible pattern of findings.
The human mind, in tandem with continuously developing algorithms, can, therefore, analyze the seemingly impossible and find the patterns, no matter how minute. The trader can then decide on the best move/s from a more educated point of view – with a fuller grasp of how all the various pieces of the puzzle slot together.
For the modern trader, and for the modern crypto trader particularly, machine learning and technical analysis must go hand in hand to keep up with the evolution of the technology. Pre-trained algorithms are key to crypto trading success, and those who don’t use them will be left behind.
Algalon’s algorithmic trading approach
Every second at Algalon, we’re scraping data about the latest trades, the depth of the market, and current quotes from crypto exchanges. After we pull this data, a special algorithm constructs more than 100 features from raw data, and these features then reflect different aspects of the market. Some of the parameters are technical analysis parameters – responsible for trends and volatility, other parameters are time-based.
The algorithm (which we have pre-trained using historical data) then makes a statistical decision that concludes the highest mathematical expectation. This algorithmic process includes millions of computational operations per second that a person would not be able to perform manually.
It is, in fact, possible to teach the model to find events of any type: to predict growth, decline, or high volatility. Such mathematical tools allow crypto traders to make more competent and mathematically correct decisions, instead of relying solely on intuition and manual labor.
Machine learning and the future
Every industry that can employ machine learning to maximize its efficiency – from financial to scientific to social sectors – is doing so, and the crypto industry is no different. Data is being pulled from varied sources, ranging from exchange reports, to current market dynamics, to social media analytics. The question is less about whether crypto traders will use machine learning as a data analysis tool for their market predictions and decisions, and more about how they build and teach their algorithms.