# USDT on Tron

This section will show you how you can use a combination of tools available on DashArgos to determine whether transaction activity on a given blockchain are organic, or otherwise.

USDT is a popular US-dollar based stablecoin and is widely used on the Tron blockchain, but it's also used on the Ethereum blockchain.

A good place to start is to see what typical USDT behavior looks like on the various blockchains and compare and contrast the transaction activity.

## 1. USDT Activity on Ethereum

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The following chart shows the Number of Transfers of USDT against time. This isn't the size of the transactions, but rather their number of transfers on any given day, over time.

As you can see from the above chart, the other stablecoins don't really have all that many transactions on the Ethereum blockchain, but USDT and USDC, the two most popular stablecoins, stand out.

The R^2 number is also the coefficient of determination, and measures how well a statistical model predicts an outcome. The lowest possible R^2 number is 0, meaning zero predictive power, and the highest number is 1, meaning a perfect explanation of the data.

Confining our analysis to USDT, as expected, time has no strong predict power for the number of transactions for USDT on the Ethereum blockchain, with USDT having an R^2 number of 0.3115.

And that analysis is consistent with what we experience in real life - while you'd expect seasonal increases in the number of transactions for a currency like the dollar, you wouldn't expect the number of dollar transactions to increase purely as a function of time, nor that such increases be linear.

## 2. USDT on Tron

### 2.1 Overview

Let's take a look now at USDT activity on the Tron blockchain.

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Even if you're not a statistician, you can clearly see that there are some issues with the Number of Transfers for USDT on the Tron blockchain.

With an R^2 number of 0.8707, the above chart for USDT on Tron implies that the number of transfers of USDT on the Tron blockchain has a strong connection with time.

In other words, users are transferring USDT on Tron more frequently, simply as a function of time.

Is that possible?

### 2.2 Size of USDT Transfers on Tron

#### 2.2.1 Order of Magnitude of Transfer and Average Transfer Size

To complete our analysis of USDT on Tron, it's now important to take a look at the size of the USDT transfers being made on Tron.

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As you can see from the chart above, a whopping 16.7% of USDT transfers on Tron are worth less than $1 and the average transfer size was 9 cents.

If a regular bank customer made transactions worth 9 cents to someone every single day at all hours of the day, by the time you've read this sentence, that person's bank account would probably already have been frozen.

And Tron can support a flurry of economically meaningless USDT transactions because of very low or zero transaction fees on the Tron blockchain.

Tron Energy and Bandwidth are used to keep transaction costs either low or free.

#### 2.2.2 Mathematical Proof of Anomalies

Finally, we can apply Benford's law to the USDT transactions on Tron, to show that these aren't naturally-occurring transactions.

Benford's law, or the law of anomalous numbers, or the first-digit law, is an observation that in real-life sets of numerical data, the leading digit is likely to be small.

Benford's law is often used to detect fraud, for instance in accounting reports.

In real-life numbers, the number 1 appears as the leading significant digit about 30% of the time, while 0 appears as the leading significant digit less than 5% of the time.

For any given set of numbers generated by real life, we would expect them to obey Benford's law.

If the USDT transactions on Tron were the result of organic transfers, then we would expect to see that they obey Benford's law, even for transactions worth less than a dollar, which are otherwise economically meaningless.

So let's look at the transactions below $1 for USDT on Tron and see whether they conform to Benford's law.

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So let's line up USDT transactions for less than a dollar and more than a cent on Tron against Benford's law.

First Digit | USDT on Tron ($0.01 < USDT < $1) | Benford's Law |
---|---|---|

1 | 36.1% | 30.1% |

2 | 14.0% | 17.6% |

3 | 7.1% | 12.5% |

4 | 7.0% | 9.7% |

5 | 10.7% | 7.9% |

6 | 11.9% | 6.7% |

7 | 3.5% | 5.8% |

8 | 3.3% | 5.1% |

9 | 6.3% | 4.6% |

Visually the differences between USDT transactions on Tron <= $1 and the normal Benford distribution is quite stark.

And here's a Benford distribution.

Does the USDT transaction behavior on Tron look like something that would occur from organic transactions?

You decide.

## 3. Putting it Altogether

In this example, we worked on USDT on Tron, but you can do the same analysis for any token on any blockchain by following these easy steps.

### 3.1 Building the Transaction Count Look

You can easily build the Transaction Count Look yourself, by following these easy steps. This will give you the absolute number of transfers on any given day.

Go to "Explore" and select the blockchain you care about.

Go to "Block Times" -> "Block Written Date" and click on "Date" and filter on "Date."

Adjust the time filter to the period you care about, and select the token you want covered in the filter and there you have it!

Congratulations! You've just built your first Look that investigates the number of transfers for a token on any given day over a given period of time. It's that simple.

### 3.2 Building the Order of Magnitude of Transfer and Average Transfer Size Look

Now let's build the Order of Magnitude of Transfer and Average Transfer Size Look.

Go to "Explore" and select the blockchain you care about.

Now for the finishing touch, we're going to need to build our own "Custom Fields" which may sound daunting at first, but is really quite simple once you get the hang of it.

Now go ahead and select "% of column" from the "Calculation" window, "Transactions Number of Transfers" from the "Source column" window, "Percent" from the "Format" window, "1" (or any other number of decimals) from the "Decimals" window, and finally name your custom expression whatever you like. For our example, we've named it "Pct of Txns" but you can name it anything you like.

Now hit the "Save" button and voila! You've got your first custom expression.

The same principles of constructing these Looks can be applied and configured to your specific area of interest. Have fun!

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