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Algorithmic crypto trading using information-driven bars, triple barrier labeling and deep learning
This paper investigates the optimization of data sampling and target labeling techniques to enhance algorithmic trading strategies in cryptocurrency markets, focusing on Bitcoin (BTC) and Ethereum (ETH). Traditional data sampling methods, such as time bars, often fail to capture the nuances of the continuously active and highly volatile cryptocurrency market and force traders to wait for arbitrary points in time. To address this, we propose an alternative approach using information-driven sampling methods, including the CUSUM filter, range bars, volume bars, and dollar bars, and evaluate their performance using tick-level data from January 2018 to June 2023. Additionally, we introduce the Triple Barrier method for target labeling, which offers a solution tailored for algorithmic trading as opposed to the widely used next-bar prediction. We empirically assess the effectiveness of these data sampling and labeling methods to craft profitable trading strategies. The results demonstrate that the innovative combination of CUSUM-filtered data with Triple Barrier labeling outperforms traditional time bars and next-bar prediction, achieving consistently positive trading performance even after accounting for transaction costs. Moreover, our system enables making trading decisions at any point in time on the basis of market conditions, providing an advantage over traditional methods that rely on fixed time intervals. Furthermore, the paper contributes to the ongoing debate on the applicability of Transformer models to time series classification in the context of algorithmic trading by evaluating various Transformer architectures—including the vanilla Transformer encoder, FEDformer, and Autoformer—alongside other deep learning architectures and classical machine learning models, revealing insights into their relative performance.
Journal Article
Does Algorithmic Trading Reduce Information Acquisition?
I demonstrate an important tension between acquiring information and incorporating it into asset prices. As a salient case, I analyze algorithmic trading (AT), which is typically associated with improved price efficiency. Using a new measure of the information content of prices and a comprehensive panel of 54,879 stock-quarters of Securities and Exchange Commission (SEC) market data, I establish instead that the amount of information in prices decreases by 9% to 13% per standard deviation of AT activity and up to a month before scheduled disclosures. AT thus may reduce price informativeness despite its importance for translating available information into prices.
Journal Article
Trading the measured move : a path to trading success in a world of algos and high-frequency trading
\"A timely guide to profiting in markets dominated by high frequency trading and other computer driven strategiesStrategies employing complex computer algorithms, and often utilizing high frequency trading tactics, have placed individual traders at a significant disadvantage in today's financial markets. It's been estimated that high-frequency traders--one form of computerized trading--accounts for more than half of each day's total equity market trades. In this environment, individual traders need to learn new techniques that can help them navigate modern markets and avoid being whipsawed by larger, institutional players.Trading the Measured Move offers a blueprint for profiting from the price waves created by computer-driven algorithmic and high-frequency trading strategies. The core of author David Halsey's approach is a novel application of Fibonnaci retracements, which he uses to set price targets and low-risk entry points. When properly applied, it allows traders to gauge market sentiment, recognize institutional participation at specific support and resistance levels, and differentiate between short-term and long-term trades at various price points in the market. Provides guidance for individual traders who fear they can't compete in today's high-frequency dominated markets Outlines specific trade set ups, including opening gap strategies, breakouts and failed breakout strategies, range trading strategies, and pivot trading strategies Reveals how to escape institutional strategies designed to profit from slower-moving market participants Engaging and informative, Trading the Measured Move will provide you with a new perspective, and new strategies, to successfully navigate today's computer driven financial markets\"-- Provided by publisher.
Book
Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market
We study the impact of algorithmic trading (AT) in the foreign exchange market using a long time series of high-frequency data that identify computer-generated trading activity. We find that AT causes an improvement in two measures of price efficiency: the frequency of triangular arbitrage opportunities and the autocorrelation of highfrequency returns. We show that the reduction in arbitrage opportunities is associated primarily with computers taking liquidity. This result is consistent with the view that AT improves informational efficiency by speeding up price discovery, but that it may also impose higher adverse selection costs on slower traders. In contrast, the reduction in the autocorrelation of returns owes more to the algorithmic provision of liquidity. We also find evidence consistent with the strategies of algorithmic traders being highly correlated. This correlation, however, does not appear to cause a degradation in market quality, at least not on average.
Journal Article
Algorithmic Trading and Market Quality: International Evidence
We study the effect of algorithmic trading (AT) on market quality between 2001 and 2011 in 42 equity markets around the world. We use an exchange colocation service that increases AT as an exogenous instrument to draw causal inferences about AT on market quality. On average, AT improves liquidity and informational efficiency but increases short-term volatility. Importantly, AT also lowers execution shortfalls for buy-side institutional investors. Our results are surprisingly consistent across markets and thus across a wide range of AT environments. We further document that the beneficial effect of AT is stronger in large stocks than in small stocks.
Journal Article
Systemic failures and organizational risk management in algorithmic trading
This article examines algorithmic trading and some key failures and risks associated with it, including so-called algorithmic ‘flash crashes’. Drawing on documentary sources, 189 interviews with market participants, and fieldwork conducted at an algorithmic trading firm, we argue that automated markets are characterized by tight coupling and complex interactions, which render them prone to large-scale technological accidents, according to Perrow’s normal accident theory. We suggest that the implementation of ideas from research into high-reliability organizations offers a way for trading firms to curb some of the technological risk associated with algorithmic trading. Paradoxically, however, certain systemic conditions in markets can allow individual firms’ high-reliability practices to exacerbate market instability, rather than reduce it. We therefore conclude that in order to make automated markets more stable (and curb the impact of failures), it is important to both widely implement reliability-enhancing practices in trading firms and address the systemic risks that follow from the tight coupling and complex interactions of markets.
Journal Article