Measuring Indirect Coin Conversions and Triangular Arbitrage on Binance

Summary

This study develops methods to identify and measure algorithmic trading activity in Binance’s historical trade data. It focuses on indirect internal conversions: a trader exchanges one coin for another through an intermediary coin and receives a more favorable rate than in a direct conversion. The authors characterize this activity as a sub-strategy of triangular arbitrage and examine its profitability and risks, including two ways bots mitigate losses.

The reported exchange ratio is 0.144%, or 14.4 basis points, better than the direct rate, and the strategy is attributed to 2.71% of all Binance trades. These estimates describe activity on one centralized exchange using historical trades. The excerpt does not provide the observation period, implementation details, or evidence that the measured price advantage remains profitable after fees, execution constraints, and other costs.

Key ideas

• Indirect conversion routes exchanges through an intermediary coin to seek a better rate than a direct trade.
• The study treats this behavior as a sub-strategy of triangular arbitrage.
• The authors use historical Binance trades to measure prevalence, profitability, and risks.
• They report a 0.144% exchange-rate advantage and attribute 2.71% of trades to the strategy.
• The excerpt does not establish whether the measured advantage survives practical trading costs and execution constraints.

Tags

# Empirical Analysis of Indirect Internal Conversions in Cryptocurrency Exchanges


# Empirical Analysis of Indirect Internal Conversions in Cryptocurrency Exchanges









Algorithmic trading is well studied in traditional financial markets. However, it has received less attention in centralized cryptocurrency exchanges. The Commodity Futures Trading Commission (CFTC) attributed the $2010$ flash crash, one of the most turbulent periods in the history of financial markets that saw the Dow Jones Industrial Average lose $9\%$ of its value within minutes, to automated order "spoofing" algorithms. In this paper, we build a set of methodologies to characterize and empirically measure different algorithmic trading strategies in Binance, a large centralized cryptocurrency exchange, using a complete data set of historical trades. We find that a sub-strategy of triangular arbitrage is widespread, where bots convert between two coins through an intermediary coin, and obtain a favorable exchange rate compared to the direct one. We measure the profitability of this strategy, characterize its risks, and outline two strategies that algorithmic trading bots use to mitigate their losses. We find that this strategy yields an exchange ratio that is $0.144\%$, or $14.4$ basis points (bps) better than the direct exchange ratio. $2.71\%$ of all trades on Binance are attributable to this strategy.

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