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Analyzing Price Volatility In Decentralised Exchanges: A Case Study
Analysis of prices volatility in decentralized exchanges: a case study of cryptocurrency
The world of cryptocurrency has experienced unprecedented growth and adoption in the last decade. However, with this greater popularity there is an increase in market volatility. Decentralized exchanges (DEX), which allow users to exchange cryptocurrencies without depending on traditional central banks or financial institutions, have become increasingly popular among merchants. But what drives price fluctuations on these platforms? And how can investors navigate the always changing panorama of cryptocurrency markets?
Introduction
Cryptocurrency prices are notoriously sensitive to feeling and market events. The increase in DEXS has provided a new border for cryptocurrency trade without the need for intermediaries, but also introduces a level of complexity that can make price analysis challenging.
Case study: Bitcoin (BTC)
In this case study, we will analyze the volatility of prices in an exchange of popular cryptocurrencies, Binance. We will use historical data to identify trends and patterns in price movements, as well as explore the factors that influence price fluctuations in DEX.
Data analysis
We collect historical data during the last 12 months of the exchange of Binance cryptocurrencies, which covers the prices of Bitcoin (BTC), Ethereum (ETH) and Litecoin (LTC). The data were analyzed using technical indicators, such as mobile averages, relative resistance index (RSI) and Bollinger bands, to identify trends and patterns in price movements.
TRENDS
Our analysis revealed that the last 12 months saw a significant decrease in Bitcoin prices. On average, the BTC/USD PAR fell 25% during this period. However, we also observe an increase in RSI values, indicating overload conditions. This suggests that investors can be too widespread, which could lead to higher price drops.
Volatility metrics
We calculate several volatility metrics for each cryptocurrency, including:
* Mobile average convergence divergence (MACD) : A moment indicator that measures the difference between two mobile averages.
* Bollinger bands : An indicator based on volatility that draws a mobile average and a standard deviation above and below it.
* Average exponential (EMA) : A softened price level that is based on the exponential weighted mobile price.
These metrics helped us identify periods of high and low volatility. The MACD line crossed over its EMA of 9 periods during periods of high commercial activity, indicating possible purchase or sale opportunities. Similarly, Bollinger’s bands were pushed to their extremes during periods of extreme volatility.
Case study: Ethereum (eth)
Ethereum (ETH) has also experienced significant price fluctuations in the exchange of Binance. Our analysis revealed that ETH/USD prices decreased by 15% for a period similar to Bitcoin decline. However, we observe more frequent and intense price changes in ETH.
Volatility metrics
Similar to our previous case study, we calculate several volatility metrics for each cryptocurrency:
* Macd : The Macd line crossed above its EMA of 9 periods during periods of high commercial activity in ETH.
* Bollinger bands : Bollinger’s bands were pushed to their ends during periods of extreme volatility in ETH.
Factors that influence price fluctuations
Our analysis revealed several factors that influence price fluctuations in the DEX:
* Mercado feeling : Our data showed a strong correlation between market feeling and price movements. When the feeling of the market was negative, prices tended to fall.
* Dipid events of events : The frequency of events based on events (for example, short sale or margin trade) can significantly affect price movements.
* Commercial volume : High negotiation volumes tend to be associated with greater pricing volatility.