Research Summary: Towards private on-chain algorithmic trading

@jmcgirk So essentially, there are 2 main axioms of concern. The first point of concern is and I quote from the paper itself;

Blockquote “On-chain trading has the advantage of full transparency favored by DeFI advocates and users, however, transparency kills the competitive advantage a trading algorithm might have over its competitors.”

The second point of concern would be and again I quote;

Blockquote “Running the algorithms off-chain, as crypto-trading bots … may enable privacy. However, without the transparency and integrity properties of on-chain execution, users have to trust the off-chain bots with executing trading algorithms and handling user funds correctly.”

So in light of these 2 axioms of concern, CHAINBOT, the subject of study in this paper provides a 3rd axiomatic perspective of a hybrid solution. By an attempt to integrate both on-chain data aggregation and data collection, then off-chain algorithmic parameterization and outputting of predictor variables back to on-chain execution via zero-knowledges proofs, this paper actually adds another layer of abstraction to the already complicated dichotomy.

In extension, one can then infer the many faults introducing this third axiom can imply. There are 6 assumptions that attribute this work to both being a possible solution and a new problem in its own right. These assumptions are of blockchain security, the validity of the trusted setup, the off-chain bot fund integrity, the DEX integrity, the DEX privacy and lastly, the oracle integrity. In my “humble” opinion, any one of these assumptions can be the downfall of this work in the real world already. Hence I say this, but this work is already a paradigm shift in and of itself.

On top of hard coding a defi arbitrage bot or needing developers to write customized algorithms, CHAINBOT introduces the idea of an integrated ML model admidst the application server side. In the advent of the “AI” revolution, blockchain being able to perform in tandem with AI in a single end to end application is a huge deal. CHAINBOT serves a pioneering anecdote that blockchain is indeed a potentially industry ready technology along side ML.

On another note, CHAINBOT also performs well with the long running gas price issues persistent in blockchain applications.Flash boys, another similar attempt at monetary arbitrage persisted with the same issue of gas price exploitation where stakeholders may as well execute arbitration solely to rack up gas prices for example. I quote;

Blockquote “In our implementation, an average user willing to invest $1000 with CHAINBOT would earn $105, while spending $3 on gas”

I think CHAINBOT is a good start on enabling algorthmic trading involving both ML and blockchain in a single end to end application. If CHAINBOT proves itself faultless, I would think a lot of money would be involved.

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