Papers Algorithmic Trading
“Algorithmic Trading” 태그가 달린 논문 95편 · 필터 해제
HAELT: A Hybrid Attentive Ensemble Learning Transformer Framework for High-Frequency Stock Price Forecasting
High-frequency stock price prediction is challenging due to non-stationarity, noise, and volatility. To tackle these issues, we propose the Hybrid Attentive Ensemble Learning Transformer (HAELT), a deep learning framewor…
Algorithmic TradingEnsemble LearningStock Price PredictionClusterLOB: Enhancing Trading Strategies by Clustering Orders in Limit Order Books
In the rapidly evolving world of financial markets, understanding the dynamics of limit order book (LOB) is crucial for unraveling market microstructure and participant behavior. We introduce ClusterLOB as a method to cl…
Algorithmic TradingClusteringDeep Learning Models Meet Financial Data Modalities
Algorithmic trading relies on extracting meaningful signals from diverse financial data sources, including candlestick charts, order statistics on put and canceled orders, traded volume data, limit order books, and news …
Algorithmic TradingDeep LearningPortfolio OptimizationAn Advanced Ensemble Deep Learning Framework for Stock Price Prediction Using VAE, Transformer, and LSTM Model
This research proposes a cutting-edge ensemble deep learning framework for stock price prediction by combining three advanced neural network architectures: The particular areas of interest for the research include but ar…
Algorithmic TradingBenchmarkingStock Price PredictionEntropy-Assisted Quality Pattern Identification in Finance
Short-term patterns in financial time series form the cornerstone of many algorithmic trading strategies, yet extracting these patterns reliably from noisy market data remains a formidable challenge. In this paper, we pr…
Algorithmic TradingLarge language models in finance : what is financial sentiment?
Financial sentiment has become a crucial yet complex concept in finance, increasingly used in market forecasting and investment strategies. Despite its growing importance, there remains a need to define and understand wh…
Algorithmic TradingDecision MakingSentiment AnalysisSentiment Classification+2Algorithmic Aspects of Strategic Trading
Algorithmic trading in modern financial markets is widely acknowledged to exhibit strategic, game-theoretic behaviors whose complexity can be difficult to model. A recent series of papers (Chriss, 2024b,c,a, 2025) has ma…
Algorithmic TradingFinBloom: Knowledge Grounding Large Language Model with Real-time Financial Data
Large language models (LLMs) excel at generating human-like responses but often struggle with interactive tasks that require access to real-time information. This limitation poses challenges in finance, where models must…
Algorithmic TradingArticlesLanguage ModelingLanguage Modelling+1A Comprehensive Review: Applicability of Deep Neural Networks in Business Decision Making and Market Prediction Investment
Big data, both in its structured and unstructured formats, have brought in unforeseen challenges in economics and business. How to organize, classify, and then analyze such data to obtain meaningful insights are the ever…
Algorithmic TradingDecision MakingManagementPortfolio OptimizationA Modern Paradigm for Algorithmic Trading
We introduce a novel framework for developing fully-automated trading model algorithms. Unlike the traditional approach, which is grounded in analytical complexity favored by most quantitative analysts, we propose a para…
Algorithmic TradingHidformer: Transformer-Style Neural Network in Stock Price Forecasting
This paper investigates the application of Transformer-based neural networks to stock price forecasting, with a special focus on the intersection of machine learning techniques and financial market analysis. The evolutio…
Algorithmic TradingDecision MakingPredictionStock Price Prediction+4Generalized Mean Absolute Directional Loss as a Solution to Overfitting and High Transaction Costs in Machine Learning Models Used in High-Frequency Algorithmic Investment Strategies
Regardless of the selected asset class and the level of model complexity (Transformer versus LSTM versus Perceptron/RNN), the GMADL loss function produces superior results than standard MSE-type loss functions and has be…
Algorithmic TradingRisk-Adjusted Performance of Random Forest Models in High-Frequency Trading
Because of the theoretical challenges posed by the Efficient Market Hypothesis to technical analysis, the effectiveness of technical indicators in high-frequency trading remains inadequately explored, particularly at the…
Algorithmic TradingFeature EngineeringFeature Importancefeature selection+2Turnover of investment portfolio via covariance matrix of returns
An investment portfolio consists of $n$ algorithmic trading strategies, which generate vectors of positions in trading assets. Sign opposite trades (buy/sell) cross each other as strategies are combined in a portfolio. T…
Algorithmic TradingCalculating Profits and Losses for Algorithmic Trading Strategies: A Short Guide
We present a series of equations that track the total realized and unrealized profits and losses at any time, incorporating the spread. The resulting formalism is ideally suited to evaluate the performance of trading mod…
Algorithmic TradingComposing Ensembles of Instrument-Model Pairs for Optimizing Profitability in Algorithmic Trading
Financial markets are nonlinear with complexity, where different types of assets are traded between buyers and sellers, each having a view to maximize their Return on Investment (ROI). Forecasting market trends is a chal…
Algorithmic TradingEnhancing literature review with LLM and NLP methods. Algorithmic trading case
This study utilizes machine learning algorithms to analyze and organize knowledge in the field of algorithmic trading. By filtering a dataset of 136 million research papers, we identified 14,342 relevant articles publish…
Algorithmic TradingArticlesDimensionality ReductionTrading through Earnings Seasons using Self-Supervised Contrastive Representation Learning
Earnings release is a key economic event in the financial markets and crucial for predicting stock movements. Earnings data gives a glimpse into how a company is doing financially and can hint at where its stock might go…
Algorithmic TradingRepresentation LearningSelf-Supervised LearningAlgorithmic and High-Frequency Trading Problems for Semi-Markov and Hawkes Jump-Diffusion Models
This paper introduces a jump-diffusion pricing model specifically designed for algorithmic trading and high-frequency trading (HFT). The model incorporates independent jump and diffusion processes, providing a more preci…
Algorithmic TradingLSR-IGRU: Stock Trend Prediction Based on Long Short-Term Relationships and Improved GRU
Stock price prediction is a challenging problem in the field of finance and receives widespread attention. In recent years, with the rapid development of technologies such as deep learning and graph neural networks, more…
Algorithmic TradingStock Price PredictionStock Trend Prediction