Papers Algorithmic Trading
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FinGPT: Democratizing Internet-scale Data for Financial Large Language Models
Large language models (LLMs) have demonstrated remarkable proficiency in understanding and generating human-like texts, which may potentially revolutionize the finance industry. However, existing LLMs often fall short in…
Algorithmic TradingSentiment AnalysisData Cross-Segmentation for Improved Generalization in Reinforcement Learning Based Algorithmic Trading
The use of machine learning in algorithmic trading systems is increasingly common. In a typical set-up, supervised learning is used to predict the future prices of assets, and those predictions drive a simple trading and…
Algorithmic Tradingreinforcement-learningReinforcement Learning (RL)Decomposing cryptocurrency high-frequency price dynamics into recurring and noisy components
This paper investigates the temporal patterns of activity in the cryptocurrency market with a focus on Bitcoin, Ethereum, Dogecoin, and WINkLink from January 2020 to December 2022. Market activity measures - logarithmic …
Algorithmic TradingFinGPT: Open-Source Financial Large Language Models
Large language models (LLMs) have shown the potential of revolutionizing natural language processing tasks in diverse domains, sparking great interest in finance. Accessing high-quality financial data is the first challe…
Algorithmic TradingLanguage ModelingLanguage ModellingLarge Language ModelModel Based Reinforcement Learning with Non-Gaussian Environment Dynamics and its Application to Portfolio Optimization
With the fast development of quantitative portfolio optimization in financial engineering, lots of AI-based algorithmic trading strategies have demonstrated promising results, among which reinforcement learning begins to…
Algorithmic TradingDecision MakingGaussian ProcessesModel-based Reinforcement Learning+3Stock Trading Volume Prediction with Dual-Process Meta-Learning
Volume prediction is one of the fundamental objectives in the Fintech area, which is helpful for many downstream tasks, e.g., algorithmic trading. Previous methods mostly learn a universal model for different stocks. How…
Algorithmic TradingMeta-LearningPredictionAlgorithmic Trading Using Continuous Action Space Deep Reinforcement Learning
Price movement prediction has always been one of the traders' concerns in financial market trading. In order to increase their profit, they can analyze the historical data and predict the price movement. The large size o…
Algorithmic TradingDeep Reinforcement LearningPositionreinforcement-learning+2Model-based gym environments for limit order book trading
Within the mathematical finance literature there is a rich catalogue of mathematical models for studying algorithmic trading problems -- such as market-making and optimal execution -- in limit order books. This paper int…
Algorithmic TradingReinforcement Learning (RL)Deep Reinforcement Learning for Cryptocurrency Trading: Practical Approach to Address Backtest Overfitting
Designing profitable and reliable trading strategies is challenging in the highly volatile cryptocurrency market. Existing works applied deep reinforcement learning methods and optimistically reported increased profits i…
Algorithmic TradingDeep Reinforcement Learningreinforcement-learningReinforcement Learning+1An intelligent algorithmic trading based on a risk-return reinforcement learning algorithm
This scientific paper propose a novel portfolio optimization model using an improved deep reinforcement learning algorithm. The objective function of the optimization model is the weighted sum of the expectation and valu…
Algorithmic TradingDeep Reinforcement LearningPortfolio Optimizationquantile regression+3A Modular Framework for Reinforcement Learning Optimal Execution
In this article, we develop a modular framework for the application of Reinforcement Learning to the problem of Optimal Trade Execution. The framework is designed with flexibility in mind, in order to ease the implementa…
Algorithmic Tradingreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1Learn Continuously, Act Discretely: Hybrid Action-Space Reinforcement Learning For Optimal Execution
Optimal execution is a sequential decision-making problem for cost-saving in algorithmic trading. Studies have found that reinforcement learning (RL) can help decide the order-splitting sizes. However, a problem remains …
Algorithmic Tradingcontinuous-controlContinuous ControlDecision Making+3An Intrinsic Entropy Model for Exchange-Traded Securities
This article introduces an intrinsic entropy model that can be used as an indicator to gauge investor interest in a given exchange-traded security, along with the state of the general market corroborated by individual se…
Algorithmic TradingDecision MakingmodelSolvability of Differential Riccati Equations and Applications to Algorithmic Trading with Signals
We study a differential Riccati equation (DRE) with indefinite matrix coefficients, which arises in a wide class of practical problems. We show that the DRE solves an associated control problem, which is key to provide e…
Algorithmic TradingDeepScalper: A Risk-Aware Reinforcement Learning Framework to Capture Fleeting Intraday Trading Opportunities
Reinforcement learning (RL) techniques have shown great success in many challenging quantitative trading tasks, such as portfolio management and algorithmic trading. Especially, intraday trading is one of the most profit…
Algorithmic TradingDecision MakingDeep Reinforcement LearningManagement+2Intelligent Trading Systems: A Sentiment-Aware Reinforcement Learning Approach
The feasibility of making profitable trades on a single asset on stock exchanges based on patterns identification has long attracted researchers. Reinforcement Learning (RL) and Natural Language Processing have gained no…
Algorithmic TradingGeneral Reinforcement Learningreinforcement-learningReinforcement Learning+4Exploration of Algorithmic Trading Strategies for the Bitcoin Market
Bitcoin is firmly becoming a mainstream asset in our global society. Its highly volatile nature has traders and speculators flooding into the market to take advantage of its significant price swings in the hope of making…
Algorithmic TradingPeriodicity in Cryptocurrency Volatility and Liquidity
We study recurrent patterns in volatility and volume for major cryptocurrencies, Bitcoin and Ether, using data from two centralized exchanges (Coinbase Pro and Binance) and a decentralized exchange (Uniswap V2). We find …
Algorithmic TradingA Wavelet Method for Panel Models with Jump Discontinuities in the Parameters
While a substantial literature on structural break change point analysis exists for univariate time series, research on large panel data models has not been as extensive. In this paper, a novel method for estimating pane…
Algorithmic TradingTime SeriesTime Series AnalysisThe Adaptive Multi-Factor Model and the Financial Market
Modern evolvements of the technologies have been leading to a profound influence on the financial market. The introduction of constituents like Exchange-Traded Funds, and the wide-use of advanced technologies such as alg…
Algorithmic Trading