Digitex Tokenomics: The Use of DGTX in the DFE Network
Executive Summary
This report has analysed the Digitex Futures Exchange with respect to the Equation of Exchange in order to try and produce a price prediction model for the value of the DGTX token based on its use case. This report contains a top level view of the mathematics which govern token price, the biggest drivers of the token price will be adoption of the Digitex Futures Exchange and the liquidity demand of holders. The report also presents a case for implementing a dynamic tick value in order to facilitate growth of the Exchange. A more in depth analysis of the mathematics can be found in the valuation report.
The growing use of DGTX in the DFE Network will restrict supply and result in an appreciating price of the DGTX token. The top four competitors currently have an open interest valuation of just over $1,000,000,000. With Digitex converting 10% of this, current investors stand to potentially make enormous ROI.
Disclosure
I am not a financial advisor.
This is not financial advice.
Do your own research.
Be accountable for your own investments.
DGTX Brief Overview
The aim of this is to consider the use case of the DGTX token and create a speculative model for the ‘tokenomics’ of DGTX. It will try to maintain focus on the utilitarian value of the token, disregarding factors such as speculative hype, fomo and fud. Outside of the crypto wild west, economics is a science which focuses on the scientific study of the ownership, use and exchange of scarce resources. We will consider DGTX as a scarce resource focusing on how exchange will affect its fair market price.
DGTX was created as the medium of exchange for the Digitex Futures Exchange (DFE) network. This is to say that the use case of DGTX is to facilitate commission free cryptographic (monetary) exchange in various markets. The process for taking part in this exchange looks as follows.
The above diagram highlights the following transactions and uses of the DGTX coin.
(i) Entering and Exiting from DGTX via BTC and ETH.
(ii) Storing and staking DGTX on price appreciation/speculation.
(iii) Using DGTX in the DFE markets.
What this tells us is that the total circulating supply of DGTX is going to be distributed amongst active DFE users, stakers, holders and individuals entering/exiting from their DGTX positions. To date it is not possible to accurately predict what percentage ratio this may be split into, however, we will use this information to develop an estimate for the relationships of variables which will have a direct impact on the growth of the DGTX token.
Equation of Exchange — Background
Regarding traditional monetary economics the equation of exchange can be used to show the relationship between the price of an asset and the consumption of goods in an ecosystem. As such, we will apply this to the DGTX utility token within the DFE platform. The equation of exchange began as follows:
Where
M = Monetary Supply, or average currency units in circulation in a year
V = Velocity of money, or the average number of times a currency unit changes hands per year
P = the average price level of goods during the year
T = an index of the real value of aggregate transactions
M.V can be interpreted as the average currency units in circulation in a given time frame, multiplied by the average number of times each currency unit changes hands in that same time.
P.T can be interpreted as the average price level of goods during the given timeframe multiplied by the real value of purchases during that time. This amounts to the total money spent on purchases in that period of time.
Considering this within an economy, we see that the equation of exchange dictates that the total amount of money changing hands in an economy will always equal the total monetary value of the goods and services in that economy. Today, economists restate the equation as:
Where
Q = an index of real expenditure
P.Q = nominal GDP
This redefines the equation such that total nominal expenditure is always equal to total nominal income, relating the changes in monetary supply to changes in overall level of prices. By rearranging this we can use the equation of exchange to derive the total demand for money in an economy with financial markets in equilibrium as:
This tells us that the demand for money is proportional to both the nominal income and the inverse of the velocity of money. The inverse of the velocity of money represents the demand to hold cash balances, so this equation tells us that the demand for money in an economy is made up of two key components:
(i) The demand for use in transactions, (P.Q)
(ii) The liquidity demand, (1/V)
Regarding Digitex, these represent the use of the DGTX token on the platform and the demand of consumers and investors to hold this token.
Equation of Exchange — DGTX Tokenomics
Although tokenomics can be highly subject to hype, manipulation and emotional reactions, we will address the equation of exchange simply as a means of understanding the potential of the DGTX token should it adhere to this projection. Now we must consider the DFE network and use case of DGTX to best develop the variables for this relationship.
M
The first variable to consider is the Monetary Supply, M. This is the average currency units in circulation in a year. As this is not a traditional use case we will consider this in terms of available units of DGTX in circulation at any given time. Currently 1,000,000,000DGTX exist and at the time of writing, 786,260,027DGTX are currently in circulation. The market makers hold 100 million, the treasury sale releases 100million on a pre-set schedule ending June 2021 and the rest are held by the team. This means that 78.6% of tokens are currently in circulation which will increase to 90% by June 2021 assuming no token issuance occurs. As such we will stay within this timeline for our evaluation and take this increase in total circulatory supply into account.
The equation of exchange ‘relates the changes in monetary supply to changes in price levels’ and as such, we must consider how the monetary supply will be affected by usage. Digging into the DGTX network outlined in diagram 1 above, we see that access to the circulating supply will be restricted by the following actions:
(i) Holders
(ii) Stakers
(iii) Traders
A speculative investor will buy and hold their tokens until a price point is met which they are happy to capitalise on said tokens, removing these tokens from the available circulating supply at that point in time. A staker will lock their tokens into a contract for DAO returns, rendering these tokens inaccessible at that point in time. A trader will open positions within the DFE, as such, will further restrict the available circulating supply at that moment in time. What this means is the available circulating supply will vary dynamically based on these factors. Thus:
These values will be difficult to quantify, so for simplicity, if we consider the Monetary Supply to be the available supply, we can estimate this by considering the total DGTX tokens that are available to change hands within the given time frame for entrance/exit purposes into and out of the holder, staker and trader categories.
Traders
The traders Coefficient is the ratio of trading account balances currently in open positions to total trading account balance. That is to say, a trader’s entire balance may not be held in open interest, but a portion held in trading balance to place more trades.
Stakers
There is currently no staking available and as such the value of this is equal to zero.
Holders
Quantifying the number of DGTX held by speculators is a difficult thing to do. If we consider the current state of the DGTX market, by nature, the vast majority of wallets are speculative holders. As such we will model the price prediction for a variety of holder percentages.
This leaves us with the following equation for M:
V
The next variable to consider is the velocity of the DGTX token. This is simply how fast it changes hands during a given period. This variable is inversely proportional to the token demand and as such a reduced velocity will drive up demand and result in an increased price. As mentioned above, this represents an individuals’ desire to hold the token. Holders restrict supply by reducing velocity which results in an increased demand:supply ratio. This variable can be obtained by finding the volume of DGTX transactions within the DGTX/ETH and DGTX/BTC markets and dividing it by the total supply of DGTX. It is important that this considers the same time frame as other variables.
P
The third variable to consider is the average price level of goods during a given period, P. Regarding DGTX, the goods are trades within the DFE network using the DGTX token. After all, a trade is simply a transaction between two individual parties, like exchanging cash for goods and services. When one gets, the other gives and vice versa. But how do we quantify this for the DGTX token? Perhaps margin.
The Digitex Knowledge Base defines margin as ‘the amount required in order to enter a position’. Margin is calculated as follows:
The first assumption here is that the tick value is fixed, and the team has no plans on changing it. This report outlines the need to have a dynamically adjusted tick value based on both the DGTX and the commodity price.
From the margin equation we can see that leverage plays a key role in the margin contribution of your trade. The higher your leverage, the lower your margin for the same contract position. (We are not going to delve into liquidation as it is not relevant at this time). The key point here is that the average leverage will be needed in order to find the average margin.
The Price of BTC will also have an effect such that as BTC gains value, more DGTX is required to open a single contract on the exchange. Therefore, an averaged BTC price across our time frame will also be required. At this point it is worth noting the correlation between BTC and Minimum Contract Value requirements. An increase in the value of BTC -vs- USD will result in an increased minimum contract value in order to open a trade on the DFE. Examples below outline this relationship.
Analysing the above we see that:
1. As BTC increases in value it corresponds to an increasing minimum contract value (if we assume a constant DGTX value).
2. As BTC increases in value we see that the Margin requirement per contract increases (if we assume a constant DGTX value).
3. As BTC increases in value we see the number of contracts required to fulfil the desired trade position decrease (if we assume a constant DGTX value).
For a constant trading position, points 2 and 3 effectively cancel each other out. However, we see that for a constant DGTX valuation and a constant tick value, the minimum entrance to trade (1 contract) will increase with BTC price. In reality, the DGTX price will not be fixed at $0.10, so let’s consider DGTX growth for the same variables. Example:
Now we see that for a growing DGTX value, the minimum entrance to trade continues to grow. In essence, the tick value will need to be a function of a dynamic DGTX price in order to keep the market accessible to smaller bankroll traders. The below chart shows the contract price for growth of both BTC and DGTX vs USD.
Therefore, our first assumption of a constant tick value of 0.1DGTX needs to be addressed and it is now assumed that it will be a function of DGTX price to maintain a constant contract value in USD terms. An alternative is decimal contracts for smaller income traders, but, assuming the team want whole integer number of contracts, I would propose a dynamic tick value. See DGTX valuation report for update on how this can be achieved.
Now, addressing the equation of exchange, we can consider P to be the average margin value of trades for a given time period.
Q
The final variable is the index of expenditure, Q. This is simply how many transactions are processed by the network in the given period. This is DGTX — DGTX transactions that take place in the DFE in the form of contracts. The reason for this is because it is the use case of the DGTX token. As such the index of expenditure is the total trades in these markets that take place within the investigated time frame.
DGTX Price
In order to project a fair market price of the DGTX coin we now revisit the equation of exchange and consider the units of each variable.
M = The total amount of available DGTX in circulation in a year.
V = Velocity of DGTX token versus ETH & BTC in a year.
P = The average margin of trade per year.
Q = The total number of transactions processed by the network in a year.
Plugging in our previously defined relationships:
This equation essential acts as a seesaw, where the balancing variable will be the token price of DGTX. This report will not cover the mathematical conversion from the equation of exchange to DGTX token valuation as it is covered in the DGTX valuation report. However, what we will discuss is the key variables of this formula.
The two biggest drivers for growth of the DGTX token price will simply be the adoption of the exchange and the liquidity demand. The adoption of the exchange can be monitored by tracking the Open Interest on the ladder. The amount of open interest tells us how many contracts are live within the DFE at any given time and as such, the demand for use. Below is a plot of DGTX price versus DFE adoption by analysing an increasing average daily open interest.
This is the first of many plots to highlight the potential growth of the DGTX coin due to growing use within the DFE. This plot highlights open interests from 1,000,000 to 10,000,000 and maintains a conservative diminishing estimate on total number of holders. The plot uses conservative values for different variables and represents a growth in open interests from 0–10% of the total open interest on BTC markets held by Digitex’ top four futures exchange competitors.
There are a multitude of variables which are open to interpretation and assumption at this point in time, of which many are within the scope of future work. Further analysis and price prediction models will come over the next few week. We will systematically address and analyse these variables to draw up a prediction for multiple adoption scenarios.
Summary
The digitex futures exchange has been analysed with respect to the equation of exchange in order to try and produce a price prediction model for the value of the DGTX coin. A top level view of the mathematics shows us that the biggest driver of the token price will be its use in the Digitex Futures Exchange and overall market adoption of the Digitex Futures Exchange. This tells us that as use increases, supply reduces and the price must rise in order to balance this.
We see that early investors have the potential to gain an incomprehensible ROI when mainnet launches and the exchange starts to convert users. An adoption case has been charted to convey the potential of this token should it achieve a conversion equating to 10% of it’s top four competitors current daily open interest.
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