Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Private On-Chain Algorithmic Trading   While quantitative automation related to trading crypto-assets such as ERC-20
tokens has become relatively commonplace, with services such as 3Commas and
Shrimpy offering user-friendly web-driven services for even the average crypto
trader, we have not yet seen the emergence of on-chain trading as a phenomenon.
We hypothesize that just like decentralized exchanges (DEXes) that by now are
by some measures more popular than traditional exchanges, process in the space
of decentralized finance (DeFi) may enable attractive online trading automation
options. In this paper we present ChainBot, an approach for creating
algorithmic trading bots with the help of blockchain technology. We show how to
partition the computation into on- and off-chain components in a way that
provides a measure of end-to-end integrity, while preserving the algorithmic
"secret sauce". Our system is enabled with a careful use of algorithm
partitioning, zero-knowledge proofs and smart contracts. We also show that with
layer-2 (L2) technologies, trades can be kept private, which means that
algorithmic parameters are difficult to recover by a chain observer. Our
approach offers more transparent access to liquidity and better
censorship-resistance compared to traditional off-chain trading approaches. We
develop a sample ChainBot and train it on historical data, resulting in returns
that are up to 2.4x the buy-and-hold strategy, which we use as our baseline.
Our measurements show that across 1000 runs, the end-to-end average execution
time for our system is 48.4 seconds. We demonstrate that the frequency of
trading does not significantly affect the rate of return and Sharpe ratio,
which indicates that we do not have to trade at every block, thereby
significantly saving in terms of gas fees. In our implementation, a user who
invests \$1,000 would earn \$105, and spend \$3 on gas; assuming a user pool of
1,000 subscribers.

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The categories of this paper are: math-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: cmp-lg
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The categories of this paper are: bayes-an
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The categories of this paper are: stat
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The categories of this paper are: nlin
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The categories of this paper are: chao-dyn
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The categories of this paper are: dg-ga
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The categories of this paper are: solv-int
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The categories of this paper are: econ
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The categories of this paper are: hep-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: quant-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: alg-geom