Crypto Arbitrage Strategies, Execution Risks, and Data Requirements
Summary
The document surveys several cryptocurrency arbitrage approaches: buying and selling across centralized exchanges, comparing decentralized and centralized venues, cycling among assets, trading derivatives against underlying assets, and model driven statistical arbitrage. It explains that these strategies seek to capture price discrepancies across venues or related instruments, and notes that statistical approaches may use frequent, high volume trading.
It identifies execution timing as a central risk because prices can move before both sides of a trade complete. Trading and withdrawal fees can erase apparent spreads, while automated bots may act quickly but require technical oversight and can still lose money. The discussion argues that identifying and testing opportunities requires granular, real time market data across exchanges and blockchains. It offers conceptual descriptions rather than empirical results or a tested strategy, and its closing data vendor promotion is not evidence that arbitrage is reliably profitable.
Key ideas
- Cross exchange arbitrage seeks to exploit price differences for the same asset across venues.
- Decentralized, triangular, cross asset, and statistical arbitrage use different sources of price discrepancy.
- Execution delays can turn an apparent opportunity into a loss as prices move.
- Trading and withdrawal fees must be included when estimating whether a spread is profitable.
- Arbitrage research and testing depend on timely, granular data from relevant markets.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.