Blockchain data analysis lets you see the counterparties

By analyzing the blockchain data set, we will have a better and clearer understanding of cryptocurrencies.

(Image source: pxfuel )

Understanding counterparties is one of the arts of capital market trading. Investor composition for specific assets, such as demographics, trading activity, and even popularity data may be important indicators for predicting asset behavior. In traditional capital markets, investors can only analyze based on data sets related to prices, trading volumes, and orders. In order to obtain other data sets that can provide advantages to their trading strategies, an arms race was launched between different quantitative funds.

• Bitcoin halves: 98% of mining machines will be outdated, and e-waste will exceed 10,000 tons
• In 2020, blockchain technology will land in these six areas!
• Blockchain Security Monthly Report | 11 security incidents occurred in February, with a loss of 48.23 million US dollars

In the cryptocurrency field, in order to understand the behavior of individual investors, blockchain data sets can provide unique information. Today, I want to highlight some interesting blockchain data set analyses that reveal the behavior of cryptocurrency asset owners.

Determining investor ownership of cryptocurrency assets is a tricky job. How do we define ownership: by investment amount? Frequency of transactions? Or something else? Before diving into specific analysis, we should first understand how to define ownership of cryptocurrency assets.

Five basic dimensions of ownership analysis

By analyzing the ownership of cryptocurrency assets, we can infer individual and financial behavior from key elements of the blockchain such as addresses, transactions, and blocks.

There are many characteristics that describe a cryptocurrency asset investor. We often hear words used to describe investors or traders, such as active traders, long-term holders, followers of momentum trends and so on. But when it comes to blockchain datasets, there are 5 basic vectors for us to understand ownership of specific cryptocurrency assets:

1. Profit and Loss: Are Investors Profitable or Lose? How many positions have investors moved?
2. Investor type: Is the investor an individual or an institution? Is it a miner or an exchange?
3. Trading hours: Are investors holding long-term or frequently trading?
4. Position: Are investors holding large or small positions?
5. Population Distribution: Geographic Composition of Investors?

Analysis of cryptocurrency ownership using blockchain datasets

Using the above five basic dimensions, we can draw meaningful ownership analysis results from the blockchain data set. Let's take a look at the results of Bitcoin's analysis.

Profit and loss

1) Are Bitcoin investors making a profit or losing money?

By referring to the current price of Bitcoin and analyzing the in and out of funds, we can understand positions close to the current price. This analysis can be used to infer the potential support and resistance levels of Bitcoin.

3.89 million addresses had inflows of funds, accounting for 49.78%, 3.87 million addresses had outflows of funds, accounting for 49.58%, and approximately 50,000 addresses maintained a balance of funds in and out, accounting for 0.64%

2) Cryptocurrency investors made large transfers

Large transaction analysis can measure large transactions that exist in a given cryptocurrency network. This analysis can be used to predict trading positions for specific cryptocurrency assets.

There were 141.5 thousand transactions in the last 24 hours; the highest number of transactions in seven days: February 19, 2020, 154.1 thousand transactions; the lowest number of transactions: February 22, 2020, 105.1 thousand transactions

Investor type

3) Is a Bitcoin investor an entity or an individual?

Through analysis, we can reveal which addresses in the network belong to the exchange and which addresses belong to the personal wallet. This is an important factor in understanding ownership of a given crypto asset.

transaction hour

4) Bitcoin transfer frequency

By analyzing UTXO, we have classified cryptocurrencies based on the currency days. This analysis helps identify abnormal capital flows.

5) Are Bitcoin investors holding for long periods or trading frequently?

Based on the analysis of trading time, we can position investors as long-term holders, mid-frequency investors or high-frequency traders. This analysis will give you some insight into the time position of individual investors.

There are 18.23 million addresses of long-term holders, accounting for 63.45%, and 2.39 million addresses of high-frequency traders, accounting for 8.3%

Position

6) Are large Bitcoin investors hoarding coins?

In the analysis of investor positions, we distinguish large individual investors from other retail investors. This analysis helps to understand the risks and exposures of a given cryptocurrency asset.

Most retail investors trade, with large investors accounting for 1.4% of total transaction volume

7) The exchange is accumulating Bitcoin

By analyzing the net inflow of funds from the exchange, we can monitor the situation of funds entering and leaving the exchange. This analysis can be used to understand factors such as inventory and risk across exchanges.

Crowd distribution

8) What is the geographical distribution of Bitcoin investors?

Bitcoin investors are mainly located in Asia and Western countries. This analysis helps to understand the geo-risk of a given cryptocurrency asset.

Investors located in Eastern countries accounted for 45.95 and Western countries accounted for 54.05%

The above analysis can help you better understand the ownership composition of crypto assets. The blockchain data set provides a unique source of information. Since other asset classes do not have this type of information, it can help you better understand cryptocurrency assets.

We will continue to update Blocking; if you have any questions or suggestions, please contact us!