Are you looking to make money by trading cryptocurrency? With the proper knowledge and tools, you can become a successful trader. But before you start trading, you must become familiar with the popular quantitative models available. This blog post will analyze the pros and cons of popular quantitative models in cryptocurrency trading. Let’s dive into the world of trading!
What is the Quantitative model?
A quantitative model is like a supercharged crystal ball for cryptocurrency traders. Instead of relying on gut instinct or vague hunches, this mathematical and statistical model uses historical and real-time market data to generate crystal-clear signals for buying or selling digital assets.
According to Chris Burniske,(partner at Placeholder VC):
“Quantitative models are fundamental in cryptocurrency trading because they allow us to identify and capitalize on opportunities in the market with more precision and objectivity than we would be able to use subjective analysis alone.”

Popular Quantitative models:
This is a summary of some of the most well-liked quantitative models used in bitcoin trading, along with other information you should be aware of:
Moving Average Model:
The moving average model is a popular quantitative model used in cryptocurrency trading. It involves calculating the average price of a cryptocurrency over a specified period and using that as a baseline for future price movements.
“Moving Average Model was effective in predicting the prices of both Bitcoin and Ethereum, with an accuracy rate of 73.12% for Bitcoin and 73.86% for Ethereum.”
Source: The predictability of cryptocurrency prices with the Moving Average model. Finance Research Letters, 29, 265-271
The advantage is that it is easy to use and can help traders to make informed decisions. However, one disadvantage of this model is that it may need to be more effective in identifying sudden market changes or large price fluctuations.
Relative Strength Index (RSI) Model:
The relative strength index (RSI) model is another popular quantitative model used in cryptocurrency trading. It involves analyzing the strength of a cryptocurrency’s price movement by comparing its gains to its losses over a specified period. It can help traders to identify potential buying and selling opportunities. However, one disadvantage of this model is that it can generate false signals during periods of market volatility.
Machine Learning Models
Artificial Intelligence or Machine learning models are becoming increasingly popular in cryptocurrency trading. They use algorithms to analyze large amounts of data and predict future price movements.
Researchers in a paper released in PLOS ONE used machine learning algorithms to predict price movement. The result showed machine learning models effectively predict price movements for all six cryptocurrencies, with accuracy rates ranging from 53% to 62%.
Source: Machine learning for cryptocurrency trading: An empirical study. PLOS ONE, 13(6), e0198569. Doi: 10.1371/journal.pone.0198569.
The advantage is that it can help traders to make more accurate predictions about future price movements. However, one disadvantage of this model is that it can be challenging to interpret the results, and there is a risk of overfitting the model to historical data.
This graph shows the prediction of bitcoin using the LSTM model, a machine-learning algorithm that uses historical market data to predict future price movements.

Final thoughts:
Now, you have an initial understanding of popular quantitative models. Quantitative models can be a powerful tool for traders looking to make informed decisions in the cryptocurrency market. At ITP Corporation, we understand the importance of quantitative models in cryptocurrency trading. Our platform provides a range of features designed to help you make more informed trading decisions, including real-time market data and advanced charting tools. Whether you’re an experienced trader or just getting started in cryptocurrency, ITP Corporation can help you stay ahead of the game and maximize your trading strategies.