Benefits of Crypto Price Analysis Using Log Returns Methodology
The analyzed data also helps measure market sentiment towards individual crypto assets and collective projects. The information from good analysis guides traders to invest in assets with positive market sentiment and expected share increase in the future.
Crypto market analysts insist on using log returns instead of price series analysis when evaluating market conditions. Analysis of log returns involves evaluating the price changes over a specific period to identify if it indicates a positive or negative trend. The evaluation process involves SciPy tools to publicly provide better optics of the publicly available crypto trading data.
Python Crypto Analysis
The execution of Python crypto analysis strategies relies on applying scientific and numerical tools for Python (SciPy). The SciPy tools comprise two sets of libraries that perform particular functions of the cryptanalysis process. Data analysis and manipulation tools help break down different data elements based on user-defined metrics. The other set comprises data visualization tools to present the analyzed log-returns data in an understandable format for traders.
There are benefits of using SciPy tools to analyze crypto price changes based on their log returns instead of the price series.
Return Series Creates Normalized Metrics for Comparison
Analysis of crypto-asset prices based on their raw price data provides confusing optics compared to other asset trends. Crypto price series analysis restricts the analysts to only study the trends in a unique asset since there is a disparity with the prices of other projects. For example, the market value of BTC and that of ETH vary, so it is impossible to find metrics to compare them using the raw data.
However, log returns create normalized data sets across the various crypto projects irrespective of their varying individual prices. These data sets define the trends of an individual asset over a period making it possible to create comparisons between two or more price trends.
Return Series are Easy to Manipulate for Summary Purposes
The reliance upon raw data on crypto price variations for analysis leads to fewer useful outcomes for a summary. The data needs manipulation to create log returns that offer a smaller workload to work with when making summaries. The log returns to present an easy data set for manipulating different metrics to get the desired optics.
The SciPy tools manipulate the log returns to produce outcomes that summarize the condition of the market regarding the individual as well as collective cryptocurrency projects. The outcomes give traders a view of the short-term and long-term price trends without emphasizing the actual price values of the assets. The visualization tools employ structures such as charts and histograms that provide clear summaries for decision-making.
The normalized data comparisons and manipulated summaries help to describe different crypto market phenomena. The following are some of the aspects one learns of the market trends using information from crypto log return analysis with SciPy tools:
Moving Averages
Log-return analytics help establish the long-term trends in the crypto market by overlooking the fluctuations on the micro-level. The moving average uses the arithmetic means of the price data sets to smooth out short-term price changes to look at the macro-level trends. The data is necessary for traders to make the right decisions in markets experiencing growth or recession and consolidate markets.
Price Volatility
Log returns help to analyze the movement of cryptocurrency prices for a period to establish the standard deviation of the price of an asset. The projects that show considerable deviation have high volatility, while those with low deviation are less volatile.
The volatility level of a crypto asset is important in determining its reaction to different market conditions. Different projects react differently to market-influencing elements such as conflicts and financial bubbles, which affect market sentiment and thus interest from traders. An asset's reaction to market conditions allows traders to determine where to invest.
Correlation on Returns
Normalizing crypto data sets using log returns for analysis allows analysts to study price correlations across multiple projects. Data visualization tools allow for visual comparison of trends in price changes of different cryptocurrency assets irrespective of individual value. Therefore, it is easy to understand price correlations between selected assets by studying the correlation coefficients shown by the SciPy tools.
The correlation index shows how changes in one asset's price influenced that of another asset during the same period for which the returns are under analysis. Correlation outcomes include +1, which indicates a strong positive, -1 for strong negative, and 0 for zero price association between the comparable assets.
Conclusion
The fluctuations in crypto prices are of interest to many traders who intend to buy or sell particular crypto assets to meet their trading goals. Therefore, it is important to note the benefits they enjoy by relying on data analysis carried out using SciPy tools to summarize the log returns.