Papers PAIR TRADING
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Dynamic Multi-Pair Trading Strategy in Cryptocurrency Markets with Deep Reinforcement Learning
This study aims to determine whether the application of Deep Reinforcement Learning (DRL) as a specialized execution overlay can enhance pair trading in highly volatile cryptocurrency markets. Although classical implemen…
Reinforcement LearningPAIR TRADINGMoira: Language-driven Hierarchical Reinforcement Learning for Pair Trading
Many sequential decision-making problems exhibit hierarchical structure, where high-level semantic choices constrain downstream actions and feedback is delayed and ambiguous. Learning in such settings is challenging due …
Hierarchical Reinforcement LearningPAIR TRADINGDeep reinforcement learning for optimal trading with partial information
Reinforcement Learning (RL) applied to financial problems has been the subject of a lively area of research. The use of RL for optimal trading strategies that exploit latent information in the market is, to the best of o…
Reinforcement LearningPAIR TRADINGSignature Decomposition Method Applying to Pair Trading
Quantitative trading strategies based on medium- and high-frequency data have long been of significant interest in the futures market. The advancement of statistical arbitrage and deep learning techniques has improved th…
PAIR TRADINGReinforcement Learning Pair Trading: A Dynamic Scaling approach
Cryptocurrency is a cryptography-based digital asset with extremely volatile prices. Around USD 70 billion worth of cryptocurrency is traded daily on exchanges. Trading cryptocurrency is difficult due to the inherent vol…
Algorithmic TradingDecision MakingPAIR TRADINGreinforcement-learning+2Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market
The study seeks to develop an effective strategy based on the novel framework of statistical arbitrage based on graph clustering algorithms. Amalgamation of quantitative and machine learning methods, including the Kelly …
Graph ClusteringManagementPAIR TRADINGMTRGL:Effective Temporal Correlation Discerning through Multi-modal Temporal Relational Graph Learning
In this study, we explore the synergy of deep learning and financial market applications, focusing on pair trading. This market-neutral strategy is integral to quantitative finance and is apt for advanced deep-learning t…
Deep LearningGraph LearningGraph Neural NetworkLink Prediction+2Linear and nonlinear causality in financial markets
Identifying and quantifying co-dependence between financial instruments is a key challenge for researchers and practitioners in the financial industry. Linear measures such as the Pearson correlation are still widely use…
Causal InferenceManagementPAIR TRADINGOptimal pair trading: consumption-investment problem
We expose a simple solution of the consumption-investment problem pair trading. The proof is based on the remark that the HJB equation can be reduced to a linear parabolic equation solvable explicitly.
PAIR TRADINGSignature Trading: A Path-Dependent Extension of the Mean-Variance Framework with Exogenous Signals
In this article we introduce a portfolio optimisation framework, in which the use of rough path signatures (Lyons, 1998) provides a novel method of incorporating path-dependencies in the joint signal-asset dynamics, natu…
PAIR TRADINGMastering Pair Trading with Risk-Aware Recurrent Reinforcement Learning
Although pair trading is the simplest hedging strategy for an investor to eliminate market risk, it is still a great challenge for reinforcement learning (RL) methods to perform pair trading as human expertise. It requir…
PAIR TRADINGreinforcement-learningReinforcement LearningReinforcement Learning (RL)Select and Trade: Towards Unified Pair Trading with Hierarchical Reinforcement Learning
Pair trading is one of the most effective statistical arbitrage strategies which seeks a neutral profit by hedging a pair of selected assets. Existing methods generally decompose the task into two separate steps: pair se…
Hierarchical Reinforcement LearningPAIR TRADINGreinforcement-learningReinforcement Learning (RL)Designing Efficient Pair-Trading Strategies Using Cointegration for the Indian Stock Market
A pair-trading strategy is an approach that utilizes the fluctuations between prices of a pair of stocks in a short-term time frame, while in the long-term the pair may exhibit a strong association and co-movement patter…
PAIR TRADINGMultivariate Pair Trading by Volatility & Model Adaption Trade-off
Pair trading is one of the most discussed topics among financial researches. Despite a growing base of work, portfolio management for multivariate time series is rarely discussed. On the other hand, most researches focus…
ManagementPAIR TRADINGTime SeriesTime Series AnalysisOn the Profitability of Optimal Mean Reversion Trading Strategies
We study the profitability of optimal mean reversion trading strategies in the US equity market. Different from regular pair trading practice, we apply maximum likelihood method to construct the optimal static pairs trad…
PAIR TRADINGMean-Reversion and Optimization
The purpose of these notes is to provide a systematic quantitative framework - in what is intended to be a "pedagogical" fashion - for discussing mean-reversion and optimization. We start with pair trading and add comple…
PAIR TRADINGregression