Just a second layer?

Because Matic is just a second-layer project, many people thought that Polygon would die after ETH 2.0 was realized. The second-layer project means that it was intended to employ a new sidechain method to solve some of Ethereum’s main limitations, such as its bandwidth, poor user interface, and lack of community control. It may sound like Matic was created like an add-on to Ethereum and never intended to conquer it, but it is not true. In fact, new coins and projects can be made on Polygon, and it has its gas fees.

Matic is crashing Ethereum.

We will cover the main reasons why I think Matic can someday defeat Ethereum. First of all, it can be surprising, but data shows that Polygon already has more token deployments than Ethereum. That means that more projects create their coins on top of a polygon than ETH. It’s a crazy result for a layer two that supplements Ethereum but surpassed it in new token deployments. Second, if we look at total transactions statistics, we will see that Polygon has almost three times as many transactions as Ethereum. On a daily basis, it has 3.2m transactions against 1.2m (ETH). That shows how much Polygon is used.

We can also look at the contract deployment. On the 3 of January, Polygon had 26.3k contracts against 11.2 on ETH. That also shows how popular Polygon really is. We also should check out gas fees. Polygon’s gas fee is a lot cheaper: $0,002 against $37-$51. With this price, a lot more people can afford to use Polygon. We can see that Ethereum has more gas fees paid. But don’t let that deceive you. It doesn’t mean that Ethereum is won on that one. In fact, on the 31 of December was paid 204k of polygon gas fees. It is a lot. If we look at the graph, we will see that more and more people are paying Poly’s gas fees. All this means that Poly is a lot more scalable and doesn’t have the same kind of congestion problems, its gas fee is very cheap, and many people are using it.

But it’s just hard statistics. What about projects? For example, Uniswap, the biggest AMM out there, is deployed on Polygon. And it’s significant because, in the past, Uniswap was held back by enormous Ethereums gas fees, and now more people can have access to it because of the low gas fees. Not surprising that the number of transactions on Uni exploded afterward. Many projects are moving on Polygon because of its gas fees, and that’s probably why it has more transactions than Ethereum. Polygon also acquired two start-ups, one of which is Ethereum scaling Start-up “Mir”. So that means that Polygon is investing a lot of money to scale itself even more. And the last news that I will share is that Polygon created a $200m fund with Reddit co-finder to back games and social media on the blockchain.

Conclusion:

Today Polygon surpasses Ethereum in many different ways. As we see, Polygon has more transactions, more contracts deployed than Ethereum. Probably the main reason for that is its gas fees that a lot of people and platforms don’t like. Because of that, a lot of platforms are moving on Polygon. In my opinion, this is the main reason why Polygon will take Ethereum’s place in the future. We just need to wait till OpenSea and other NFT platforms start using Polygon. So, I recommend you consider buying $MATIC.

Since time immemorial, we have been following the client-server architecture — which forms the basis for HTTP. In a client-server model, the data is stored in a certain number of servers from which information is accessed according to location-based addressing.

As the internet evolved from web1 to web2, and web2 to web3, the need to transfer huge amounts of data, be it audio or video content became commonplace. But HTTP wasn’t originally designed to facilitate huge magnitudes of data transfer. This led to the adoption of better and more efficient system architecture such as peer-to-peer architecture. Some of the earliest examples of applications built on a peer-to-peer architecture include Napster and Bittorrent. While IPFS was built on a similar architecture, Filecoin is an improvement on IPFS.

Read on as we dive deeper into the underpinnings of IPFS and its drawbacks.

InterPlanetary File System, short for IPFS, is an open-source file-sharing system that has been created by Protocol Labs by leveraging existing innovations in the realm of peer-to-peer systems. What this means is that information and data aren’t stored in a set of storage servers but are distributed across a number of systems (read peers). In essence, IPFS can be called a decentralized storage network. One of the biggest differences between IPFS and HTTP is the fact that the former relies on content-based addressing while the latter relies on location-based addressing.

According to the IPFS documentation, here are the three fundamental principles you should know to understand IPFS:

To learn more about each of these principles that govern the IPFS network, check out the documentation here.

In the previous section, we provided a quick overview of what IPFS is and its underlying principles. Now, we dive a little deeper into the components of IPFS. You can find out how the above mentioned principles tie into the various components in IPFS below:

Sure, IPFS is a decentralized file sharing system that facilitates seamless, secure, and low latency data distribution. Unlike HTTP, it can be used for sharing large files over the internet. But it does come with its own share of limitations.

IPFS, on paper, seems like the best alternative to HTTP for secure, high throughput transactions. But to actualize as a reliable, everyday solution and serve as a database-as-a-service model, it needs to create strong economic incentives. Since the start, IPFS has been built as a community product that will benefit from everyone’s contributions. As a result, no economic incentives were put in place. While lack of economic incentives do not weaken the technology or what IPFS aims to do, they simply make it an impractical solution for long-term use; especially for storing private and enterprise data.

Be it Bittorrent or IPFS– both are perhaps suitable in a voluntary-collaborative space where academic research or sharing a particular type of data (be it music, movies, or books) is the need. This is because IPFS is inherently free and the peers/nodes in the network are not volunteering to store a huge magnitude of private data on their systems for free for a long time. For peers in the IPFS network to facilitate huge storage capacity for a sustained period, an economic incentive model needs to be in place. This isn’t the case with IPFS. Furthermore, storing encrypted data on a peer to peer network such as IPFS means it is hard to de-duplicate or cache efficiently

With IPFS, along with not having an incentive layer, it is close to impossible to verify the integrity of the data that it stores. Peers do not have to submit proofs that they are indeed storing the data or its uptime. Currently, this is being solved by paying centralized gateway providers to pin your data, but this leads to a central point of failure and defeats the purpose of using a decentralized storage provider.

Filecoin is an innovation that sprang from the applications of IPFS. What it does differently is that it introduces an economic model and incentives to IPFS. In simpler terms, Filecoin can be considered an electronic currency or crypto currency, much like Bitcoin. Users can simply provide a portion of their unused storage on their harddrives in exchange for currency.

While Filecoin’s efforts try towards making IPFS a reliable file sharing and storage platform, it is still plagued by some issues. We’ll expand more on Filecoin, what about it works and what doesn’t, in a later blog post. Follow us on our socials and subscribe to our email list on Medium for all our latest blog updates.

An ideal alternative to IPFS would be a file sharing and storage system that is decentralized, highly secure, and has a strong economic incentive model. Some of the known alternatives to IPFS include Filecoin, Storj, and Arweave. As we have already discussed Filecoin above, we will move on to Storj and Arwaeave.

Storj is another decentralized cloud storage network that distributes each file you upload on the network into 80 pieces across its global servers. What this means is that you get to enjoy redundancy against server failures, fast download speeds, and solid security. That being said, Storj, when compared to traditional cloud providers such as Amazon or Azure, does not provide a simple user interface. You would need programmers to configure Storj as it is largely dependent on a command line interface. This becomes a major drawback for Storj as it doesn’t have an intuitive interface that can be used by just about anyone.

Arweave emerged as a protocol that offers permanent storage. It is worth noting that other cloud providers, be it centralized or decentralized, do not offer such permanent storage options. Arweave also charges a fortune for the same, as it currently charges $5 for every GB stored on its network. In comparison, Amazon only charges $0.2 per GB. Although Arweave’s proposition of permanent storage is a novel one, the protocol as a whole, especially due to its steep pricing, is not a practical option for developers and business owners looking for a viable storage solution.

Arcana Network is a decentralized storage network that incentivizes users through the $XAR token, and is optimized for storage of DApps and private data. While services such as Filecoin, Arweave, and IPFS are not a practical solution and strong competition to the traditional cloud storage market, Aracana is.

Arcana offers Social Auth, Access Management, and KMS in an easy to use SDK that takes minutes to integrate for any developer. Check out the testnet on testnet.arcana.network

Arcana Network is a decentralized storage layer for Ethereum, offering storage for DApps built on EVM compatible chains, such as Ethereum, Binance chain, and Polygon (Matic). But Arcana doesn’t stop at storage. To fully realise the privacy and data ownership benefits of decentralized storage, you need a suite of services, which are currently not decentralised. Arcana fixes this with its Privacy Stack. Arcana’s Privacy stack offers Decentralized Storage that is end-to-end encrypted, along with Non-custodial Key Management Services (KMS) and Decentralized Identity and Access Management.

Official Links: Website | Telegram | TG Announcement | Twitter | Medium

In this context, crypto assets can be considered on equal footing with other financial asset classes when designing a portfolio. Here, I apply data science tools to analyse performance and uncover potential relationships between crypto and traditional financial assets. In Part 1, I look at returns, volatility and correlations. In Part 2, I fit some models to the data.

As input for the analysis, I use price data from Yahoo Finance for the period starting Jan 1, 2020 to the time of writing. I have chosen Bitcoin (BTC-USD) and Ethereum’s Ether (ETH-USD) to represent the crypto markets as the biggest crypto assets by capitalisation to date.

I also use selected indices and ETFs characterising equity, fixed income and commodity markets:

This list is not exhaustive and could potentially include many more assets, indices and ETFs. My main motivation was to cover major asset classes and to try to pinpoint specific sectors and assets that may have the strongest relationships with the crypto markets.

I use the Python package pandas, function DataReader which allows extracting data from various Internet sources, including Yahoo Finance. I create a list of tickers corresponding to the required data. Next, I create a dataframe with the daily prices. I use the ‘Adj Close’ column as it is a price adjusted for splits and dividends distribution. To extract daily returns, I calculate the percentage change of the daily prices using the pct_change() method.

Visualisation is a powerful tool in data analysis. It allows one to get the ‘feeling’ of the data, discover if any modifications are needed, and select the best models.

Plotting daily returns results in a very busy plot, so I plot average daily returns for each month.

Here, two periods of extreme returns volatility stand out. In April 2020, violent fluctuations of the oil price coincided with the beginning of the Covid-19 pandemic and OPEC price wars. Price shock was caused by the oversupply and rapidly dropping demand. The price of the US oil futures actually dropped to negative values, the lowest on record.

Second shock begins in February 2022 with the conflict in Ukraine. Visible decoupling of the returns of different asset classes, falling equity and bonds, and rising commodities (with the extreme jump of crude oil in particular) usually associated with the geopolitical uncertainties can be clearly observed.

To smooth the effects of these price shocks, I plot the monthly median returns which are less sensitive to outliers. I also limit the timeline up to February 23, 2022. While the behaviour of crypto and conventional assets at the time of global geopolitical shocks is a highly relevant and interesting topic, here I try to look at the relationships between crypto and traditional assets during the times of perceived geopolitical ‘normality’. Moreover, two different ‘regimes’ might confuse simple linear models trying to work with the time series.

Plotted median returns point at significant volatility of oil prices in 2020, as well as high volatility of returns of Bitcoin and Ethereum, particularly during 2021.

Converting daily return data to monthly data is easily done with the pandas resample() method, specifying required summary function, mean and median in this case.

To plot the data, I use matplotlib.pyplot package and plot() method.

Asset return and volatility (risk) are major factors of the portfolio construction. Most investors want to maximise their returns while limiting the risk.

Daily returns provide more data points for data analysis, however, annualised returns are usually preferred by investors and give a better basis for the comparison of the assets. To annualise daily returns of the assets, I calculate mean returns and multiply them by 253 (average number of trading days a year).

Common measure of return volatility, or risk, is the standard deviation. Standard deviation is calculated as a square root of the variance and has an advantage of having the same measure unit as the mean.

Using standard deviation as a measure of risk, we can calculate risk-adjusted performance measures, for instance Sharpe ratio. Sharpe Ratio is calculated as annualised returns divided by standard deviation. An asset with the higher Sharpe ratio is said to have better risk-adjusted performance. Note that Sharpe ratio penalises both positive and negative deviations while only negative ones are truly undesirable for investors. Still, it is a widely used measure in investment analysis due to its simplicity and intuitive interpretation.

During the analysed period, Bitcoin and Ethereum significantly outperformed conventional assets based on their Sharpe ratio. The best performers among traditional financial assets in the analysis were video games, cybersecurity and tech sector equities, helped by the adoption of the “Stay-at-home” Covid measures.

Calculating mean returns and standard deviations is a straightforward task with corresponding pandas methods. Sharpe ratios can be calculated directly.

Correlations are the foundation of portfolio diversification. In short, the most diversified portfolio is the portfolio of uncorrelated assets. Highly correlated assets tend to move in the same or opposite direction. For instance, if one goes up the other one is also likely to grow, or if one goes up, the other one falls.

Correlation is usually measured with a correlation coefficient which can assume values from -1 to 1. Correlation coefficient values close to 1 point at positively correlated assets (returns tend to move in the same direction), to -1 — negatively correlated (returns tend to move in opposite directions), to 0 — uncorrelated assets.

Convenient way to visualise correlations is the heatmap.

Visual analysis of the correlation heatmap point at some noteworthy observations:

Scatter plots are another common tool to visualise correlations between variables. They are used to detect possible linear relationships. Here, I use Bitcoin as one of the variables in each scatter plot.

The scatter plots point at the existence of a linear relationship between Bitcoin and Ether. Interestingly, the correlation seems to be higher in the area of negative returns, which points towards co-skewness, i.e. higher correlation in tail scenarios than during normal market circumstances.

Further, possible relationships can be suspected between Bitcoin and Hedge Funds, Bitcoin and Video Games, and Bitcoin and Tech. The scatter plot ‘Bitcoin and Bitcoin’ illustrates 2 assets with the correlation coefficient of 1.

Correlation matrix can be calculated in Python using the pandas method corr(). Heatmap can be plotted using the heatmap() function of the seaborn package.

Scatter plots are made using the subplots() function and scatter() method of the matplotlib.pyplot.

Here are some key highlights from the past year:

The Blockchain.com Podcast has hosted a range of esteemed female leaders within and outside of crypto, from developers to NFT artists, entrepreneurs, and lawyers.

Key conversations include:

Earlier this year, Chief Administrative Officer and Chief Legal Officer at Blockchain.com Lindsey Haswell joined forces with a group of nearly 50 female and non-binary leaders including Brit Morin and Jaime Schmidt, to form a brand new decentralized autonomous organization (DAO) called BFF.

BFF aims to educate women and non-binary people in crypto and web3. Other founding members include Mila Kunis, Gwyneth Paltrow, and Randi Zuckerberg.

If you missed it, you can catch up on the first event here:

Blockchain.com was a proud sponsor of the CA Women Lead event in Fall 2021, where we shared crypto and financial literacy knowledge with a group of female leaders across various sectors.

This month, we joined our partners, Unstoppable Domains, in launching Unstoppable Women of Web3, an initiative that aims to provide education and training for the next generation of Web3 talent, particularly women and girls.

And today, Unstoppable Women of Web3 will host a 24-hour Twitter Spaces event where Min Wei, Hannah Lynch, and Lorna Hutchman of Blockchain.com will join other female leaders to educate on web3. You can tune into the session at 12:30 pm PT.

This is by no means an exhaustive list of the incredible work by Blockchain.com colleagues and peers to empower women across the globe to continue to build, invest and influence the world of crypto. We’re just getting started.