Analyzing Price Volatility In Decentralised Exchanges: A Case Study
- 2025-02
- by Cn Vn
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Analysis of price variability in decentralized exchanges: cryptocurrency case study
The world of cryptocurrency has experienced unprecedented growth and adoption over the past decade. However, with this increased popularity, there is an increase in market variability. Decentralized exchanges (DEXS), which allow users to trade cryptocurrencies without relying on traditional central banks or financial institutions, are becoming more and more popular among traders. But what drives price fluctuations on these platforms? And how can investors move around the constantly changing landscape of cryptocurrency markets?
Entry
Cryptocurrency prices are notoriously sensitive to market sentiments and events. The growth of Dexs provided a new limit of cryptocurrency trading without the need for intermediaries, but also introduces a level of complexity that can make price analysis difficult.
Case study: Bitcoin (BTC)
In this case study, we will analyze the variability of the prices of popular cryptocurrency exchange, binance. We will use historical data to identify trends and pricing movements, as well as to examine factors affecting price fluctuations on DEX.
data analysis
We have collected historical data over the last 12 months from the exchange of cryptocurrencies in Binance, including Bitcoin (BTC), Ethereum (ETH) and Litecoin (LTC) prices. The data was analyzed using technical indicators such as movable average, relative force indicator (RSI) and Bollinger bandwidth to identify trends and price movement patterns.
trends
Our analysis has shown that in the last 12 months there has been a significant decrease in bitcoin prices. On average, the BTC/USD pair fell by 25% during this period. However, we have also observed an increase in the value of RSI, which indicates the terms purchased. This suggests that investors may exceed, which can lead to further drops in price.
variability indicators
We have calculated several variability indicators for each cryptocurrency, including:
* Divergence of the movable medium convergence (MacD) : The momentum indicator that measures the difference between two movable average.
* Bollinger bands : indicator based on variability, which performs average movable and standard deviation above and below.
* Movable average lecture (EMA) : Smooth price level based on an interpretative weighted average price.
These indicators helped us identify high and low variability periods. The MacD line exceeded the 9-speed EMA during periods of high trade activity, indicating potential purchase or sale possibilities. Similarly, the Bollinger bands were pushed to their extremes during periods of extreme variability.
Case study: Ethereum (ETH)
Ethereum (ETH) also experienced significant price fluctuations for Binance exchange. Our analysis showed that ETH/USD prices dropped by 15% in a similar period until Bitcoin’s decrease. However, we observed more frequent and intensive price fluctuations in ETH.
variability indicators
As in our previous case study, we have calculated several variability indicators for each cryptocurrency:
* MacD : The MacD line exceeded the 9-speed EMA during periods of high trade activity in ETH.
* Bollinger’s bands : Bollinger’s teams were pushed to their extremes during periods of extreme variability in ETH.
Factors affecting price fluctuations
Our analysis revealed several factors that affect price fluctuations on DEX:
* Market moods : Our data showed a strong correlation between market moods and price movements. When market moods were negative, prices tends to decline.
* Transactions based on events : The frequency of transactions based on events (e.g. short sales or margin trade) can significantly affect price movements.
* Trading volume : High commercial volumes are usually associated with higher price variability.
