arXiv research

Study shows HFT benefits large traders under certain conditions.

New measures detect HFT activity, revealing its impact on stock prices.

DEX allows HFTs to quickly acquire speed, improving price discovery without sacrificing liquidity.

High-speed computerized trading, often called "high-frequency trading" (HFT), has increased dramatically in financial markets over the last decade. In the US and Europe, it now accounts for nearly one-half of all trades. Although evidence suggests that HFT contributes to the efficiency of markets, there are concerns it…

Study on HFTs' interactions with a large trader using mean field game theory.

Nearly one-half of all trades in financial markets are executed by high-speed, autonomous computer programs -- a type of trading often called high-frequency trading (HFT). Although evidence suggests that HFT increases the efficiency of markets, it is unclear how or why it produces this outcome. Here we create a simple …

JaxMARL-HFT accelerates MARL for HFT with 240x speedup.

Modeling HFT interactions reveals market instability.

We study Nash equilibria for inventory-averse high-frequency traders (HFTs), who trade to exploit information about future price changes. For discrete trading rounds, the HFTs' optimal trading strategies and their equilibrium price impact are described by a system of nonlinear equations; explicit solutions obtain aroun…

Study shows HFT improves market liquidity indicators.

Recent technological development has enabled researchers to study social phenomena scientifically in detail and financial markets has particularly attracted physicists since the Brownian motion has played the key role as in physics. In our previous report (arXiv:1703.06739; to appear in Phys. Rev. Lett.), we have prese…

This paper explores how RL enhances HFT strategies in volatile markets.

Classifies two-dimensional extended homotopy field theories with aspherical targets.

A microscopic model is established for financial Brownian motion from the direct observation of the dynamics of high-frequency traders (HFTs) in a foreign exchange market. Furthermore, a theoretical framework parallel to molecular kinetic theory is developed for the systematic description of the financial market from m…

We define polynomial tangle invariants $\nabla_T^s$ via Kauffman states and Alexander codes and investigate some of their properties. In particular, we prove symmetry relations for $\nabla_T^s$ of 4-ended tangles and deduce that the multivariable Alexander polynomial is invariant under Conway mutation. The invariants $…

MacroHFT uses memory and context-aware reinforcement learning to improve HFT performance.

High-frequency traders can act as either small informed traders or round-trippers, affecting price discovery and liquidity.

We model the behavior of three agent classes acting dynamically in a limit order book of a financial asset. Namely, we consider market makers (MM), high-frequency trading (HFT) firms, and institutional brokers (IB). Given a prior dynamic of the order book, similar to the one considered in the Queue-Reactive models [14,…

EarnHFT tackles HFT challenges with hierarchical RL, significantly outperforming existing methods.

SNNs enhance high-frequency price spike forecasting in HFT environments.

The purpose of this thesis is to define a "local" version of Ozsváth and Szabó's Heegaard Floer homology $\operatorname{\widehat{HFL}}$ for links in the 3-dimensional sphere, i.e. a Heegaard Floer homology $\operatorname{\widehat{HFT}}$ for tangles in the closed 3-ball. After studying basic properties of $\operatorname…

Investigates market dynamics with informed traders and high-frequency traders.

This paper deals with a stochastic order-driven market model with waiting costs, for order books with heterogenous traders. Offer and demand of liquidity drives price formation and traders anticipate future evolutions of the order book. The natural framework we use is mean field game theory, a class of stochastic diffe…

Study automates feature selection and clustering for HFT stock price forecasting.

Paper develops models for better HFT and algorithmic trading.

Financial trading is at the forefront of time-series analysis, and has grown hand-in-hand with it. The advent of electronic trading has allowed complex machine learning solutions to enter the field of financial trading. Financial markets have both long term and short term signals and thus a good predictive model in fin…

T-KAN improves HFT LOB forecasting with learnable splines.

ALPE improves mid-price forecasting in HFT with real-time data.

Stablecoins are unstable, but some are more stable than others.

Research optimizes C++ patterns for HFT, reducing latency and improving profitability.

This paper examines transitions in sniping behavior among algorithmic traders, finding new profitable strategies.

Optimizes real-time data processing in HFT algorithms using machine learning.

FlowHFT learns adaptive trading strategies from multiple models for diverse market conditions.

Corrects gaps in a method for optimizing high-frequency trading strategies.

Short-term trend-following has stopped delivering profits since 2009, especially on smaller market ticks.

Nowadays, with the availability of massive amount of trade data collected, the dynamics of the financial markets pose both a challenge and an opportunity for high frequency traders. In order to take advantage of the rapid, subtle movement of assets in High Frequency Trading (HFT), an automatic algorithm to analyze and …

New method characterizes thin links via Conway spheres and tangle decompositions.

High Frequency Trading (HFT) represents an ever growing proportion of all financial transactions as most markets have now switched to electronic order book systems. The main goal of the paper is to propose continuous time equations which generalize the self-financing relationships of frictionless markets to electronic …

Financial time-series forecasting has long been a challenging problem because of the inherently noisy and stochastic nature of the market. In the High-Frequency Trading (HFT), forecasting for trading purposes is even a more challenging task since an automated inference system is required to be both accurate and fast. I…

Study finds a phase transition in flash crashes involving large and liquid stocks.

The study examines how backrun auctions can protect traders from price manipulation.

This study examines lead-lag relationships in Chinese futures markets using high-frequency data.

RL agents optimize order execution in a realistic market simulation.

In this paper we introduce a completely continuous and time-variate model of the evolution of market limit orders based on the existence, uniqueness, and regularity of the solutions to a type of stochastic partial differential equations obtained in Zheng and Sowers (2012). In contrary to several models proposed and res…

Enhanced deep learning model predicts stock price movement using LOB data.

Proposes a graph neural network for futures price prediction.

We present a large-scale study of commonality in liquidity and resilience across assets in an ultra high-frequency (millisecond-timestamped) Limit Order Book (LOB) dataset from a pan-European electronic equity trading facility. We first show that extant work in quantifying liquidity commonality through the degree of ex…