INTRODUCTION
The objective of this analysis is look into the Steemit transfer activity to evaluate the behavior of Steemit based on user's transfer activities and transactions for 2017. It will look into the steem block chain database to evaluate user's transfer activities based on:
- Full and monthly sum and averages of SBD and STEEM transfers and distribution; and
- monthly sum average of SBD and STEEM as to different transaction type such as transfer to vesting, delegation vesting shares, transfer to and from savings;.
OUTLINE OF ANALYSIS
This analysis covers the following sections:
I. Scope of Analysis
II. Data presentation and analysis
- SBD and STEEM transfer transactions
- Transfer to vesting and Delegation vesting shares
- Transfer to and from Savings
III. Summary of Analysis
IV. Tools and Codes
I. SCOPE OF ANALYSIS
The analysis performed using the data extracted from @arcange 's SteemSQL database. There are three sets of data is used:
- A Steemit accounts transfer activities for the 12 month time frame ( January to December 2017) which involves the number of transfer done, average and sum amount of transfer in STEEM and SBD;and,
- A monthly statistics that involve the TxTransfer section of the Steem blockchain database to acquire data specific to the transaction type and amount type involve in the transfer.
The data extracted is taken as the sum and average of the number of transfer and, average and sum of amount transferred for the subjected timestamp. This is done to assess the behavior and performance of Steemit account's transfer activities. The data extracted and subjected to analysis is presented using graphs and tables, as applicable. In addition, a data interpretation and summary of result is presented.
II. DATA PRESENTATION AND ANALYSIS
The following data tables and graphs presented below are based on the data extracted from SteemSQL as of March 15, 2018 ( 10 AM UCT ).
1. SBD and STEEM Transfer Transactions
In this section, we will look into the SBD and STEEM transfer involve in each account transaction for 2017. From the extracted data, we come up with a total of 3,415,831 transfer activities on Steemit for 2017.
Graph 1.1: Amount of SBD and STEEM transfer transaction shares
In graph 1.1, it shows the amount of SBD and STEEM shares which is involve in the 2017 Steemit account's transfer. A total of 3,415,831 transfers was done in 2017 which allocates 2,224,410 SBD and 1,188,572 STEEM transfer transaction. A 2,849 transfer transactions represents the sum of the cancel transfer to and from savings transactions where no amount of SBD and STEEM was indicated. Cancel transfer to and from savings is a unique transaction type in the Steemit blockchain which only indicates that there was a canceled transfer involving funds to and from savings has happened in the Steemit blockchain. This data does not show or indicate the number or amount in SBD or STEEM hence there was no actual transfer had happened. However, this data was take into account in this analysis to present the actual counts of this transaction type ( cancel ttransfer to and from savings) equal to 2,849 which reveals the complete set of unique transfer transactions in 2017.
Table 1.1: SBD and STEEM monthly transfer transaction
Graph 1.2: SBD and STEEM monthly transfer transaction
From the graph 1.2, we can foresee a positive increasing trends of the number of transfers done per month on 2017. We can draw from table 1.1 an average close to 185,368 SBD and 99,048 STEEM transfers transactions done in 2017. A 407,322 increase from the SBD transfer transaction from May to August 2017. In addition, A decrease in transfers can be observe from August to December amounting to - 105,700 SBD. Overall, we can see a positive and increasing trend in the number of transaction as 2017 ends. In terms of the STEEM transfers, the graph indicates a mirror positive trends as to the SBD transfers.
2. Transfer to vesting and Delegation Vesting Shares
In this section, we will analyze the transfer to vesting and delegation shares transaction done by each steemit accounts in 2017. By the way, Transfer to vesting involves transfer or conversion of STEEM to STEEM POWER while Delegation Vesting involves transaction pertaining to delegation of STEEM POWER.
Table 1.2: Monthly transfer to vesting and delegation vesting transaction
Graph 1.3: Monthly transfer to vesting and delegation vesting transaction
From table 1.2, A 6,894,439.506 STEEM on average is transfer to vesting; and a 25,112,412.92 STEEM POWER is delegated in 2017. We can see from graph 1.3 that the vesting delegation is steadily fluctuating which highlight a peak value in March 2017 equal to 41,868,888.77 STEEM POWER. At 2017 year end, it closed to 33,554,456.37 STEEM POWER delegated. In terms of the vesting transfers, we can see a normal up and down trends in the data for 2017 highlighted by an average amount of transfer equal to 6,894,439.506 STEEM.
3. Transfers to and from savings
In 2017, a total of 342,156.28 SBD and 27,765,063.34 STEEM was transferred on savings. At the same time, A total of 441,536.764 SBD and 27,255,239.43 STEEM is withdrawn from savings.
Table 1.3: Transfer to savings
Table 1.4: Transfer from savings
Graph 1.4: Amount in SBD transfer from and to Savings
In graph 1.4, we have drawn close to 28,513.02 SBD and 36,794.73 SBD is transferred to and from savings respectively on average. A highest transferred SBD sum to savings was on September 2017 which is equal to 40,670.491 SBD. On the other hand, we can drawn the highest withdrawn SBD from savings on March 2017, amounting to 80, 780.212. In 2017, there is a higher amount of SBD withdrawn as compared to the SBD being deposited to savings based on the average value from the SBD sum from the tables.
Graph 1.5: Amount in STEEM transfer from and to Savings
A total of 27,765,063.34 STEEM and 27,255,239.43 STEEM was transferred to and from savings respectively in 2017. On average, 2313755.278 STEEM and 2,271,269.953 STEEM was transferred to and from savings in 2017. This is highlighted by the highest deposit to savings amounting to 21, 648,136.791 STEEM in March 2017; and a highest withdrawn from savings is equal to 3,309,097.822 STEEM in June 2017.
Overall, the transfer to and from savings data for both SBD and STEEM shows a healthy and good balance in the amount coming in and out in the savings of each Steemit's accounts. This is supported by a low differential between the averages of transfer to and from savings even with the influx in deposits and withdraws in March and September 2017, respectively.
III. SUMMARY OF ANALYSIS
In 2017, A total of 3,415,831 transfers was done in 2017 which allocates 2,224,410 SBD and 1,188,572 STEEM transfer transaction. We can draw a positive increasing trends of the number of transfer activity done per month where December 2017 reaches 456,790 transfer transactions done. This is a positive indicator that there would be a foreseen increase of this transaction in 2018. In terms of transfer to vesting and vesting delegations, a 6,894,439.506 STEEM on average is transfer to vesting; and a 25,112,412.92 STEEM POWER is delegated in 2017. The vesting and delegation transactions have a steady normal up and down trends in the data for 2017which highlighted by an average amount of transfer equal to 6,894,439.506 STEEM. Furthermore, a total of 342,156.28 SBD and 27,765,063.34 STEEM was transferred on savings. At the same time, A total of 441,536.764 SBD and 27,255,239.43 STEEM is withdrawn from savings. This was the highlights of the transfer to and from saving transaction 2017. We e have drawn a close to 28,513.02 SBD and 36,794.73 SBD is transferred to and from savings respectively on average. At the same time, on average, 2313755.278 STEEM and 2,271,269.953 STEEM was transferred to and from savings in 2017.
IV. TOOLS AND CODES
I extracted the data from SteemSQL by importing it on Microsoft Excel using SQL Queries. I used Microsoft Excel to visualize and create a graph for the data. The data is extracted by using COUNT() and SUM() function from TxTransfers. For the monthly transfer data, I run different queries using the different conditions to extract specific set of data in accordance to different transaction type as to transfer, transfer_to_vesting , and delegation_vesting _shares. I also categorize resul per transaction type as to amount_symbol e.g. SBD and STEEM, as applicable. The variables being treated are authors, no. of ID/accounts, amount transfer, and transaction type. The full SQL script is shown below.
| SQL query for Full Data for TxTransfers |
|---|
SELECT * FROM TxTransfers WHERE timestamp >= '2017/01/01' AND timestamp <'2018/01/01' |
| SQL query for SBD and STEEM transfer shares. |
|---|
To get each data for SBD and STEEM, you need to change the |
SELECT COUNT(ID) AS Account, SUM(amount) AS Amount_Transfer FROM TxTransfers WHERE amount_symbol='' AND timestamp >= '2017/01/01' AND timestamp <'2018/01/01' |
| SQL query for monthly transfer to vesting and delegation |
|---|
To get each data for specific transaction type, you need to change the |
SELECT MONTH(timestamp) AS Month, COUNT(ID) AS Account, SUM(amount) AS Amount_Transfer FROM TxTransfers WHERE type='' AND timestamp >= '2017/01/01' AND timestamp < '2018/01/01' GROUP BY month(timestamp) |
| SQL query for monthly transfer to and from savings |
|---|
To get each data for specific transaction type, you need to change the |
SELECT MONTH(timestamp) AS Month, COUNT(ID) AS Account, SUM(amount) AS Amount_Transfer FROM TxTransfers WHERE type='' AND amount_symbol='' AND timestamp >= '2017/01/01' AND timestamp < '2018/01/01' GROUP BY month(timestamp) |
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Thank you, @crokkon!
Hey @crokkon, I just gave you a tip for your hard work on moderation. Upvote this comment to support the utopian moderators and increase your future rewards!
Hello @juecoree,
There is a major error in the WHERE clauses of all the queries you show in your posts:
WHERE ... timestamp >= '2017/01/01' AND timestamp >='2018/01/01'The first filter (
timestamp >='2017/01/01') is useless as it will be excluded by the second one.The second filter should be
timestamp <'2018/01/01'.I am afraid all the data you presented in your post are invalid!
@arcange thanks for pointing one this errors. I made a typo in the write up. I have actually run
WHERE timestamp >='2017/01/01' AND timestamp < '2018/01/01'. I also update the post so that to correct misinformation in my sql queries.Very nice the steem....
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