July 14, 2021

Octopus: an Intelligent Self-Learning Market Making Algorithm for Illiquid Tokens

Digital assets are the future on finance and when truly liquid will completely reshape our financial markets and how we conduct exchange of assets. The possibilities of tokens, coins and crypto-currencies are near-infinite when everything that represents value receives a digital representation and becomes accessible to all people. Liquidity is the ability to easily buy or sell an asset, without drastically moving the price - liquid assets are less risky and more attractive for investors.

Liquidity was the missing puzzle piece in the digital asset ecosystem until today; flovtec’s market-making algorithms for illiquid tokens solve this challenge. In this Medium post, we explain how flovtec contributes to the growth of the crypto industry with Octopus, our market-making algorithm for illiquid tokens!

Classic or simplistic market-making strategies work well in liquid markets with minor information asymmetry between market participants. They measure the uncertainty of the fair price to inform spread width and skew quotes depending on inventories, recent transactions, alpha metrics, target positions, and views, among other factors. Its goal is to manage inventory risk to generate long-term market-making returns and meet the liquidity needs…

What makes Octopus different?

In illiquid markets, information asymmetry is a significant concern. Market-maker must balance the liquidity needs and protect itself from actions coming from traders in sometimes loosely regulated market environment. As a solution to these challenges, flovtec created Octopus - an intelligent self-learning market-making algorithm designed specifically for illiquid markets and tokens. Octopus makes judgments without relying on other participants' quotes or deals. Octopus learns from every trade interaction with the environment to make informed quoting decisions.

Octopus adaptive learning with Bayesian inference  

Octopus adaptive learning and decision-making model applies Bayesian Inference method and takes into consideration distribution of price probability, distribution of proportion of informed traders and distribution of uninformed traders’ probability to trade. The model continuously updates the price probability after every trade to protect market-maker from the potentially destructive informed flow and foster benefits from the random uninformed flow.

Market-making for Illiquid tokens: Case Study

Octopus has proven its efficiency for illiquid tokens. To share an example, by using Octopus algorithm we helped one of our market-making clients to revive his illiquid token in a short time. Within the first day of market-making the token’s spread was reduced by factor 14 from 6.82% to 0.47%.

The average traded volume of the token pair against USDT continuously increased from $100 to $10’000, significantly boosting the liquidity. The token changed from being seldomly traded in mid-October to being actively traded in mid-December with a 90% uptime (percentage of time quoting bid and ask prices) as greater liquidity enticed more participants to enter the market. As a result, the token has a healthy and developed market, and the community has a liquid token they can trade easily.

flovtec is a Swiss technology company proving market-making solutions to tokens, protocols & digital asset exchanges to create a liquid & efficient market. Reach out to us to make your tokens liquid or to learn more about our market-making algorithms, both for liquid and illiquid tokens!

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