Try Quests to earn SOLAlgorand Price Correlation
In this analysis, the price action correlation of both ALGO and SOL coins has been evaluated against the price action of other large-cap assets such as BTC and ETH. In addition, the effects of price on network activity and vice-versa have also been investigated.
Algorand and Solana as mid-cap and large-cap blockchains, respectively, have had two of the vivid ecosystems in the crypto space. The year 2022 started with a downtrend and continued further down to mark a new bear market in the crypto space alongside all the other major industries. As the global economy has continued to slow down, the price of many crypto assets, especially the altcoins, hit harder compared to risk-off assets such as Bitcoin. In this analysis, the correlation between the price of ALGO and SOL has been compared to BTC and ETH. The network activity of these two blockchains and its effects on the price of action of their native tokens has also been evaluated since the start of the year.
The price data of ALGO was selected from the prices_swap table of the algorand schema while the price of BTC, ETH, and SOL were collected from the fact_hourly_token_prices table of the core schema of the ethereum database. the token addresses of these three coins were used to get a daily average of their price. The year-to-date (YTD) was considered as the time frequency of this analysis.
>Wrapped BTC Token Address: 0x2260fac5e5542a773aa44fbcfedf7c193bc2c599 > >Wrapped ETH Token Address: 0xc02aaa39b223fe8d0a0e5c4f27ead9083c756cc2 > >Wrapped SOL Token Address: 0xd31a59c85ae9d8edefec411d448f90841571b89c > >ALGO Asset ID: 0
Since the price range of these coins has been different from each other, two different methods were taken into account to normalize the data. First, the price on the last day of the measured period was selected as the anchor point and the prices of other days were subtracted and then divided by the price of the last day. In the second approach, the minimum and maximum prices of each coin were selected during the measured period and the price of each day was normalized using these two extremes.
>Method 1: normalized price = (price - current_date_price) / current_date_price * 100 > >Method 2: normalized price = (price - min_price) / (max_price - min_price) * 100
For the analysis of network activity, the data in the transactions, transfers, and swaps tables of the algorand schema, and the data in the fact_transactions, fact_transfers, and fact_swaps of the core schema of the solana database were compared with their respective coin prices.
>Note 1: There were some inconsistencies in the data, especially in January on Algorand which were ignored during the analysis. > >Note 2:The analysis of the transactions table of Solana put a massive amount of computation load on the velocity which resulted in the table being removed from the final query.
The daily price of BTC, ETH, SOL, and ALGO over the U.S. Dollar has been plotted in the next two charts using two different methods of normalization. Overall, it can be seen that the crypto market moves in a direction where Bitcoin moves. In other words, the whole crypto market is heavily correlated to the price action of Bitcoin. It is worth mentioning that the price movements of Bitcoin are more subtle compared to other assets which are more volatile. That is why Bitcoin is considered a risk-off asset compared to altcoins which are considered risk-on assets.
The analysis of ALGO price action vs the daily number of transactions and network users has demonstrated that the network performance has been in line with the price movement. As the price has continued to drop over the past few months, the number of transactions and the number of users have also decreased. There have been periods of high volatility in price, especially when a drastic downward movement happened, which resulted in a temporary increase in user activity. The downward movements of late February and the May crash were influenced by Terra's two examples.
To dive deeper into the network usage of both Algorand and Solana, their native token transfers have been analyzed and plotted in the next two charts vs their subsequent price action since the beginning of 2022. There has been an inverse relationship between the price and the volume of the transfer which indicates that as the price has continued to drop to lower levels, the volatility and the transfer volume of native tokens have increased. This can be interpreted in both bearish and bullish ways. When the price goes down, the holders face a sense of fear, uncertainty, and doubt (FUD) which might result in them panic selling their assets. On the other hand, others with cash at hand might find the price dips interesting and want to acquire tokens at those low prices. In any case, the volatility results in a higher transfer volume over time.
The changes in swap behavior of Algorand and Solana users have been plotted vs their token's prices in the following chart. Interestingly, both the number of swaps and the number of swappers on Algorand have demonstrated an opposite behavior compared to Solana. For Algorand, the trend has been similar to the price action of ALGO while on Solana, the trend of swaps has been contrary to the price of SOL. The data indicates that on Algorand, users have lost their interest in the network with the decrease in the price of ALGO. However, the Solana users have become more active with the drops in the prices.
Overall, the price action analysis of Algorand and Solana has demonstrated that on a macro level, the altcoins, regardless of their market cap, are heavily correlated with the price action of Bitcoin and the whole crypto market movements. Although there has been a period of high volatility in both networks opposite of the market direction, all of them were short-lived and could be considered reactionary measures. With the drops in prices, the holders act to preserve their holdings, and the new users tend to enter the ecosystem at a discount in price. In all cases, these types of actions are results of the drop in price not the cause of it.