When it comes to market analysis, people are pretty firm with the tools, technique, and sentiment they have toward their approach. Our goal is to share three ways to analyze the crypto markets in order to develop good strategies to take on the crypto markets.
There are three basic types of cryptocurrency market analysis:
1. Fundamental
2. Technical Analysis
3. Machine Learning /Sentiment Analysis
Over the years, there’s been a never-ending debate as to which strategy offers the best results in the markets but our belief is that deploying a mix of all three garners better results in your journey as a crypto trader. Think of it like sitting on a three-legged stool if one leg breaks, you come crashing down.
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Fundamental Analysis
A top-down approach to cryptos is usually considered one of the best alternatives. Beginning with Fundamental Analysis, we may consider this as the idea generation phase.
Unlike traditional fiat currencies, cryptocurrencies do not operate as nations which have fundamental data such as GDP, interest rates, unemployment rate, PPI, CPI, and a host of others. Most cryptocurrencies use blockchain technology and, in some cases, DAG (Directed Acyclic Graph) or its variants.
Looking at recent events in cryptocurrency, we’re seeing the development of regulatory adoption, speculative adoption, major improvements in network adoption, and cryptocurrency transactions per day.
Major improvements in adoption include indicators such as Network Security (%), Network Capacity (%), Developer Participation (%), and Popular Interest (%).
Network Security (%)
When it comes to network security, hash rate is arguably one of the most significant indicators for measuring network stability and security. It indicates the compute power required to maintain the platform it operates. High hash rate implies that it would require significant resources to breach the network, which becomes a huge deterrent to hackers and bad actors. Hash rate data can be found on BitInfoCharts.
Network Capacity (%)
Looking at the network capacity, we look at indicators such as unconfirmed transactions, The Bitnodes Project, average mining difficulty per day, hash rate distribution, and so on.
Developer Participation (%)
Many altcoins are open sourced or provide a developer page. Github has a plethora of crypto projects. This is where you can see developer participation through pull requests and commits. This is a clear indication of development and interest in any given project.
Popular Interest (%)
Although popular and public interest across cryptocurrencies can be really difficult to gauge, we often take to Google Trends for keywords such as Bitcoin, altcoins, and blockchain to see variations in the mainstream adoption.
Technical Analysis
Technical analysis is the study of price movements of time series data, which has been a major part of most traders’ tool kit, way before cryptocurrencies existed. It hinges on the notion that history repeats itself. In essence, a trader can look at historical data and decipher current trading states and potential price projections.
Contrary to the efficient market theory, technical analysis believe that all market information is contained in the price of the asset. Price is therefore believed to be the only requirement for successful trading.
Technical analysts search for similar patterns within the historical data of the assets, develop trade ideas with a belief that the price or pattern will act the same way moving forward.
Charts are a primary tool for technical analysis. In the past, analysts would print out the price chart of an asset and plot different technical patterns to project and plan trade setups. Analyses of price patterns often serve as projections to entry or exit points in a trade.
While technical analysis is highly subjective, traditional and crypto traders still look to specific price levels and patterns from historical data with the hope of riding a profit trend.
Machine Learning/Sentiment Analysis
When used correctly, machine learning algorithms can be a powerful tool for solving problems that are easy for humans but difficult for machines. Examples include image and speech recognition. DeepMind, a company owned by Google, recently developed an artificial general intelligence model called AlphaGo, which consistently beats the world best Go player and has since attracted attention in finance and other disciplines.
Deep learning is another exciting development in analysis technology. Made popular by Geoffrey Hinton, deep learning is programmed to recognize abstracts and use representational layers to come to conclusions based on those transitive patterns. This development in technology is being used by hedge fund analytics.
The LSTM (Long Short-Term Memory) of Recurrent Neural Networks are most popular for analyzing FX and cryptocurrency price data.
As far as sentiment analysis, data is gathered from price and influence from the likes of Twitter or social media sentiment. Other options include the use of blockchain explorers, checking for hash rate, and analysis using Python Pandas for causality and correlations.
Conclusion
After reviewing the three major analysis tools and techniques, it seems to make sense that using a combination of all tools makes for an enhanced view of the cryptosphere, which leads toward informed decisions on where and how you want to invest.
Disclaimer
Content provided by CryptoTraderNews is for informational purposes only, and should not be construed as legal, tax, investment, financial, or other advice. All information is of a general nature. As always, there is risk with any investment. In exchange for using our products and services, you agree not to hold CryptoTraderNews Pro, its affiliates, or any third party service provider liable for any possible claim for damages arising from decisions you make based on information made available to you through our services.
