Results 1 to 10 of about 4,165 (288)
Social signals and algorithmic trading of Bitcoin [PDF]
Royal Society Open Science, 2015 The availability of data on digital traces is growing to unprecedented sizes, but inferring actionable knowledge from large-scale data is far from being trivial. This is especially important for computational finance, where digital traces of human behaviour offer a great potential to drive trading strategies.
Garcia David, Schweitzer Frank
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ITM Web of Conferences, 2021 Algorithmic trading uses algorithms that follow a trend and defined set of instructions to perform a trade. The trade can generate revenue at an inhuman and enhanced speed and frequency. The characterized sets of trading guidelines that are passed on to the program are reliant upon timing, value, amount, or any mathematical model. Aside from profitable
Vanita Mane +3 more
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Algorithmic trading in turbulent markets [PDF]
Pacific-Basin Finance Journal, 2020 Does Algorithmic Trading (AT) exacerbate price swings in turbulent markets? We find that stocks with high AT experience less price drops (surges) on days when the market declines (increases) for more than 2%. This result is consistent with the view that AT minimizes price pressures and mitigates transitory pricing errors. Further analyses show that the
Zhou, Hao, Kalev, Petko S., Frino, Alex
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Detection of algorithmic trading [PDF]
Physica A: Statistical Mechanics and its Applications, 2017 We develop a new approach to reflect the behavior of algorithmic traders. Specifically, we provide an analytical and tractable way to infer patterns of quote volatility and price momentum consistent with different types of strategies employed by algorithmic traders, and we propose two ratios to quantify these patterns.
Dimitar Bogoev, Arzé Karam
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Machine learning for algorithmic trading [PDF]
E3S Web of Conferences, 2020 The purpose of the study is to confirm the feasibility of using machine learning methods to predict the behavior of the foreign exchange market. The article examines the theoretical and practical aspects of the implementation of artificial neural networks in the process of Internet trading.
I Razveeva +2 more
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Short Sales and Trade Classification Algorithms [PDF]
Journal of Financial Markets, 2008 This paper demonstrates that the Lee-Ready and other commonly used trade classification algorithms classify short sales as buyer-initiated significantly more than 50% of the time. This result is due in part to regulations which require short sales be executed on an uptick or zero-uptick. In addition, while the literature considers immediacy premiums in
Paul Asquith +2 more
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The Complexity of Cryptocurrencies Algorithmic Trading
Mathematics, 2022 In this research, we provided an answer to a very important trading question, what is the optimal number of technical tools in order to achieve the best trading results for both swing trade that uses daily bars and intraday trade that uses minutes bars?
Gil Cohen, Mahmoud Qadan
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ALGORITHMIC TRADING WITH LEARNING [PDF]
International Journal of Theoretical and Applied Finance, 2013 We propose a model where an algorithmic trader takes a view on the distribution of prices at a future date and then decides how to trade in the direction of their predictions using the optimal mix of market and limit orders. As time goes by, the trader learns from changes in prices and updates their predictions to tweak their strategy.
Cartea, A, Jaimungal, S, Kinzebulatov, D
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Algorithmic aspects of bandwidth trading [PDF]
ACM Transactions on Algorithms, 2003 We study algorithmic problems that are motivated by bandwidth trading in next-generation networks. Typically, bandwidth trading involves sellers (e.g., network operators) interested in selling bandwidth pipes that offer to buyers a guaranteed level of service for a specified time interval.
Julia Chuzhoy +3 more
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Algorithmic Trading and Information [PDF]
SSRN Electronic Journal, 2011 We examine algorithmic trades (AT) and their role in the price discovery process in the 30 DAX stocks on the Deutsche Boerse. AT liquidity demand represents 52% of volume and AT supplies liquidity on 50% of volume. AT act strategically by monitoring the market for liquidity and deviations of price from fundamental value. AT consume liquidity when it is
Terrence Hendershott, Ryan Riordan
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