论文标题
加密货币交换中间接内部转换的经验分析
Empirical Analysis of Indirect Internal Conversions in Cryptocurrency Exchanges
论文作者
论文摘要
算法交易在传统金融市场中进行了很好的研究。但是，在集中的加密货币交换中，它受到了较少的关注。商品期货交易委员会（CFTC）将2010美元的$ Flash Crash归因于金融市场历史上最动荡的时期之一，Dow Jones工业平均水平在几分钟内损失了$ 9 \％的价值，它是自动化的订单“欺骗”算法。在本文中，我们使用完整的历史贸易数据集，建立了一组方法，以表征和经验衡量不同的算法交易策略，这是一种大型集中的加密货币交换。我们发现，三角套利的子策略是广泛的，在该套件中，机器人通过中介硬币在两个硬币之间转换，并且与直接的硬币相比获得了有利的汇率。我们衡量该策略的盈利能力，表征其风险，并概述算法交易机器人用来减轻损失的两种策略。我们发现，该策略的汇率为0.144美元\％$，或$ 14.4 $的基点（BPS）比直接汇率更好。 $ 2.71 \％$ $ $ $ $ $ $都可以归因于此策略。
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.
