In a previous analysis, I looked at transactions on the SmartCash MasterNode Blockchain and compared it to Crave MasterNode Blockchain. I also took some time to explore ZCoin versus SmartCash.
If you missed those posts you can check them out here:
https://steemit.com/utopian-io/@paulag/exploring-smartcash-and-crave-blockchain-data-blockchain-business-intelligence
On a quest to make more MasterNode data comparisons, today we are going to compare Crave with Monoeci.
Both Crave and Monoeci are MasterNode coins. Both coins also have API which will allow me call transactions into PowerBI for analysis.
Monoeci if successful could make an awesome case study for cryptocurrency as it is a local coin for Monaco. You can find out more on their website https://monoeci.io/en/home/
Crave is a payment system that focuses on anonymity and privacy of its users, including features such as the Zerocoin Protocol and LightX instant send. Do check out their website for more information https://www.craveproject.net/
I have selected these two coins to compare because they both have a very comparable market cap and the return on investment for hosting a MasterNode is also very similar.
Aim of Analysis
The aim of this analysis was
• To explore the data presented in the public API for Crave
• See what insights this data can provide
• To explore the data presented in the public API for Monoeci
• See what insights this data can provide
• Compare Crave to Monoeci
Crave Data
As with APIs previously analysed for MasterNode coins, this API returns the last 100 transactions. However, these 100 transactions do not include MasterNode or Staking rewards. This was the first MasterNode APIs that I had looked where MasterNode rewards were not included. It is my opinion, by excluding MN transactions gives a true representation of actual usage on the block.
The last 100 transactions on the blockchain were made up of 539 line items. These 100 transactions moved 2.17 BTC worth of Crave from 218 unique addresses to 90 unique addresses.
These 100 transactions took place over 91K seconds. That equates to 0.0011 transactions per second.
The values shown in light purple relate to data from Coinmarketcap. At the time of analysis, the price of crave was $0.22581 USD.
Information from MasterNodes.online for Crave is shown below
Monoeci Data
The last 100 transactions on the Monoeci blockchain returned by the API also does not contain MasterNode rewards. Delighted as this make the comparison a lot easier.
In total, the 100 transactions took place in 139K seconds, so this works out to be 0.0014 transactions per second.
The last 100 transactions on the blockchain were made up of 1205 line items. These 100 transactions moved 5.6 BTC worth of Monoeci from 466 unique addresses to 336 unique addresses.
The values shown in light purple relate to data from Coinmarketcap. At the time of analysis, the price of Monoeci was $0.92 USD.
Information from MasterNodes.online for Monoeci is shown below:
Comparison
A Crave MasterNode will costs you 23% more than a Monoeci MasterNode, as a Monoeci Node requires 1,000 coins and a Crave node require 5,000 coins. Coin for coin at the time of analysis Crave will cost you 75% less than Monoeci.
There is also 195% more volume traded on exchanges of Crave than Monoeci. However, the Crave supply is 86% greater than that of Monoeci. Of the full supply, 52.48% of Monoeci are locked in. Whereas with Crave 35.5% of coins are locked in.
Has the higher supply and the lower % locked in with Crave led to an increase in trade volume on exchanges due to more liquidity?
Crave processed 0.0011 transactions per second, whereas Monoeci processed 0.0014. This makes the Monoeci blockchain 1.27X more active than Crave for transactions and it also moves 1.69X in BTC equivalent per minute.
Conclusion
MasterNode Coins were tipped as the hot thing for 2018 but wading through the rake of MasterNode coins to find good ones can be a difficult task. As the price of crypto was on a downtrend for so long, now might be a good time to consider an investment in a MasterNode.
Comparing the data from one MasterNode blockchain against another can provide useful insights that can be combine with other available data as I have done in this analysis. In this analysis we looked at 4 data sources, 2 blockchains, coinmarketcap and MasterNodes.online. By combining this data, a more detailed comparison can be made in one place.
I am surprised to see that the Monoeci blockchain is 1.27X more active and Crave but coin for coin, Monoeci is 300% more expensive. To me this would suggest that either Monoeci is overvalued or Crave is undervalued.
The Data and Query
The query was pulled at 29 April 2018 21:30 GMT.
The final M code used in Power BI query editor for Monoeci was:
let
Source = Json.Document(Web.Contents("https://explorer3.Monoeci.cc/ext/getlasttxs/10/100")),
#"Converted to Table" = Record.ToTable(Source),
#"Expanded Value" = Table.ExpandListColumn(#"Converted to Table", "Value"),
#"Expanded Value1" = Table.ExpandRecordColumn(#"Expanded Value", "Value", {"_id", "txid",
"blockhash", "__v", "blockindex", "timestamp", "total", "vout", "vin"}, {"_id", "txid", "blockhash", "__v",
"blockindex", "timestamp", "total", "vout", "vin"}),
#"Expanded vout" = Table.ExpandListColumn(#"Expanded Value1", "vout"),
#"Expanded vout1" = Table.ExpandRecordColumn(#"Expanded vout", "vout", {"addresses",
"amount"}, {"addresses", "amount"}),
#"Expanded addresses" = Table.ExpandListColumn(#"Expanded vout1", "addresses"),
#"Renamed Columns" = Table.RenameColumns(#"Expanded addresses",{{"addresses", "Rec
address"}}),
#"Expanded vin" = Table.ExpandListColumn(#"Renamed Columns", "vin"),
#"Expanded vin1" = Table.ExpandRecordColumn(#"Expanded vin", "vin", {"addresses", "amount"},
{"addresses.1", "amount.1"}),
#"Renamed Columns1" = Table.RenameColumns(#"Expanded vin1",{{"addresses.1", "Sending
address"}, {"amount", "sent amt"}, {"amount.1", "rec amt"}}),
#"Added Custom" = Table.AddColumn(#"Renamed Columns1", "total SMART sent value", each
[sent amt]/100000000),
#"Added Custom1" = Table.AddColumn(#"Added Custom", "total SMART Rec value", each [rec
amt]/100000000),
#"Changed Type" = Table.TransformColumnTypes(#"Added Custom1",{{"total SMART Rec value",
type number}, {"total SMART sent value", type number}, {"rec amt", type number}, {"sent amt", type
number}, {"total", type number}, {"timestamp", type number}, {"blockindex", type number}}),
#"Added Custom2" = Table.AddColumn(#"Changed Type", "coin", each "SMART")
in
#"Added Custom2"
The final M code used in Power BI query editor for Crave was:
let
Source =
Json.Document(Web.Contents("http://explorer.craveproject.net/ext/getlasttxs/10/100")),
#"Converted to Table" = Record.ToTable(Source),
#"Expanded Value" = Table.ExpandListColumn(#"Converted to Table", "Value"),
#"Expanded Value1" = Table.ExpandRecordColumn(#"Expanded Value", "Value", {"_id", "txid",
"blockhash", "__v", "blockindex", "timestamp", "total", "vout", "vin"}, {"_id", "txid", "blockhash", "__v",
"blockindex", "timestamp", "total", "vout", "vin"}),
#"Expanded vout" = Table.ExpandListColumn(#"Expanded Value1", "vout"),
#"Expanded vout1" = Table.ExpandRecordColumn(#"Expanded vout", "vout", {"addresses",
"amount"}, {"addresses", "amount"}),
#"Renamed Columns" = Table.RenameColumns(#"Expanded vout1",{{"addresses", "rec. address"}}),
#"Added Custom2" = Table.AddColumn(#"Renamed Columns", "Coin", each "CRAVE"),
#"Removed Columns" = Table.RemoveColumns(#"Added Custom2",{"vin"}),
#"Added Custom" = Table.AddColumn(#"Removed Columns", "Rec Amt Crave", each
[amount]/100000000),
#"Appended Query" = Table.Combine({#"Added Custom", #"crave (2)"}),
#"Changed Type" = Table.TransformColumnTypes(#"Appended Query",{{"sending amt crave", type
number}, {"sending amount", type number}, {"Rec Amt Crave", type number}, {"amount", type
number}})
in
#"Changed Type"
To get live data from Coinmarketcap, I also connected to the live ticker API for each of the coins and then carried out an append on each of the tables. The final M code used in Power BI query editor was:
let
Source = Json.Document(Web.Contents("https://api.coinmarketcap.com/v1/ticker/crave/")),
#"Converted to Table" = Table.FromList(Source, Splitter.SplitByNothing(), null, null,
ExtraValues.Error),
#"Expanded Column1" = Table.ExpandRecordColumn(#"Converted to Table", "Column1", {"id",
"name", "symbol", "rank", "price_usd", "price_btc", "24h_volume_usd", "market_cap_usd",
"available_supply", "total_supply", "max_supply", "percent_change_1h", "percent_change_24h",
"percent_change_7d", "last_updated"}, {"id", "name", "symbol", "rank", "price_usd", "price_btc",
"24h_volume_usd", "market_cap_usd", "available_supply", "total_supply", "max_supply", "
percent_change_1h", "percent_change_24h", "percent_change_7d", "last_updated"}),
#"Changed Type" = Table.TransformColumnTypes(#"Expanded Column1",{{"rank", type number},
{"price_usd", type number}, {"price_btc", type number}, {"total_supply", type number},
{"24h_volume_usd", type number}, {"market_cap_usd", type number}}),
#"Appended Query" = Table.Combine({#"Changed Type", #"ticker (2)", #"ticker (3)"}),
#"Appended Query1" = Table.Combine({#"Appended Query", #"ticker (4)"})
in
#"Appended Query1"
Once I had gathered all the data, I then carried out many calculations using DAX.
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