Incentive Analysis: Arbitrage & Market Maker Keepers in MakerDAO

Eden Dhaliwal
Apr 18 · 5 min read

by Eden Dhaliwal & Geoff Lefevre

We’re excited about what’s happening in the crypto space right now. Yes, the crypto winter still has a long thaw ahead, but you can really see the community is working hard, learning from past lessons and applying more rigour to developing viable cryptonetworks. There is a next generation of decentralized n-sided networks appearing with greater sophistication due to the integration of more participants, better validated mechanisms and a greater understanding of game theoretic blindspots. With more roles and greater complexity, comes with it the challenge of sufficiently incentivizing actors in the network while optimizing towards economic alignment. And while we all know rewards should be higher in the early going (in order to effectively motivate new actors and overcome switching costs), knowing what it takes to attract new actors in quantitative terms helps in developing a computational model for a network.

To examine what it takes to encourage new agents on a network, we decided to create a simple business case around running a single Arbitrage and Market Maker Keepers agent on MakerDao. We took the view of an independent actor rather than a commercial, scale-minded operator. We estimated fixed and variable costs to be the following:

Fixed Costs:

Hardware = $1000

Development Time = $5000 (25hrs @$200/hr)

Electricity = $300 (Single desktop: 1yr 24/7)

Variable Costs:

Exchange Fees = .02%

Gas Price = $.0003

Methodology

First, we took random samples on Market Orders from Oasis. Using the same time window, we then used Dai/Eth prices to determine the mean and standard deviation of key metrics used in the model to simulate asset prices and market buy/sell orders at each time step. Further research was conducted to identify wallet addresses managed by Keepers, upon which we took more random samples and examined the data to estimate the average number of transactions a Keeper executes in a day.

To model both Market Maker and Arbitrage Keeper opportunities, we used Markov Decision Process (MDP) where at each time step, the Keeper decided to execute a trade based on set requirements and constraints.

The mean output values from the simulations were used to produce a 12 month Net Present Value model for each Keeper. All prices are in Dai.


Arbitrage Keeper Analysis

Which provided us the following outputs:

And the resulting 12 month NPV for Arbitrage Keeper:


Market Maker Keeper

Which provided us the following outputs:

And the resulting 12 month NPV for Market Making Keeper:


Future Extensions

Data Collection

Optimisation of Keeper strategies

Multi-Agent Dynamics

Gas Arbitrage


Findings

Keeper Operations

Average % Profitable Transactions (Market Maker only) = 0.21

Average # Profitable Transactions / Day (Market Maker only) = 18.38

Arbitrage Keeper Economics (all prices in Dai)

Average Revenue / Transaction = 1.29

Average Profit / Transaction = 0.77

Market Maker Keeper Economics (all prices in Dai)

Average Revenue / Transaction = 19.41

Average Profit / Transaction = 19.38


Conclusion

Again, this is only a simulation and just a static snapshot, but it does provide important insights in terms of what kind of switching/opportunity costs need to be overcome if you are looking to acquire agents that can perform arbitrage and market making activities in your network. Performing this type (comparative) analysis will increasingly become an important part of Token Ecosystem Creation in a world where new network opportunities are proliferating.

Outlier Ventures

Outlier Ventures invest and partner with tokenised communities that are creating the new decentralised economy

Eden Dhaliwal

Written by

Partner & Head of Cryptoeconomics at Outlier Ventures

Outlier Ventures

Outlier Ventures invest and partner with tokenised communities that are creating the new decentralised economy