Papers Algorithmic Trading
“Algorithmic Trading” 태그가 달린 논문 95편 · 필터 해제
AI-Powered Energy Algorithmic Trading: Integrating Hidden Markov Models with Neural Networks
In quantitative finance, machine learning methods are essential for alpha generation. This study introduces a new approach that combines Hidden Markov Models (HMM) and neural networks, integrated with Black-Litterman por…
Algorithmic TradingPortfolio OptimizationReinforcement 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+2A Comprehensive Analysis of Machine Learning Models for Algorithmic Trading of Bitcoin
This study evaluates the performance of 41 machine learning models, including 21 classifiers and 20 regressors, in predicting Bitcoin prices for algorithmic trading. By examining these models under various market conditi…
Algorithmic TradingManagementMacroHFT: Memory Augmented Context-aware Reinforcement Learning On High Frequency Trading
High-frequency trading (HFT) that executes algorithmic trading in short time scales, has recently occupied the majority of cryptocurrency market. Besides traditional quantitative trading methods, reinforcement learning (…
Algorithmic TradingDecision MakingHierarchical Reinforcement Learningreinforcement-learning+3MOT: A Mixture of Actors Reinforcement Learning Method by Optimal Transport for Algorithmic Trading
Algorithmic trading refers to executing buy and sell orders for specific assets based on automatically identified trading opportunities. Strategies based on reinforcement learning (RL) have demonstrated remarkable capabi…
Algorithmic TradingImitation LearningReinforcement Learning (RL)Representation LearningA Deep Reinforcement Learning Approach for Trading Optimization in the Forex Market with Multi-Agent Asynchronous Distribution
In today's forex market traders increasingly turn to algorithmic trading, leveraging computers to seek more profits. Deep learning techniques as cutting-edge advancements in machine learning, capable of identifying patte…
Algorithmic TradingDeep Reinforcement LearningPortfolio Management using Deep Reinforcement Learning
Algorithmic trading or Financial robots have been conquering the stock markets with their ability to fathom complex statistical trading strategies. But with the recent development of deep learning technologies, these str…
Algorithmic TradingDeep Reinforcement LearningManagementreinforcement-learning+1Detecting and Triaging Spoofing using Temporal Convolutional Networks
As algorithmic trading and electronic markets continue to transform the landscape of financial markets, detecting and deterring rogue agents to maintain a fair and efficient marketplace is crucial. The explosion of large…
Algorithmic TradingFinLlama: Financial Sentiment Classification for Algorithmic Trading Applications
There are multiple sources of financial news online which influence market movements and trader's decisions. This highlights the need for accurate sentiment analysis, in addition to having appropriate algorithmic trading…
Algorithmic TradingArticlesClassificationparameter-efficient fine-tuning+2Limit Order Book Simulations: A Review
Limit Order Books (LOBs) serve as a mechanism for buyers and sellers to interact with each other in the financial markets. Modelling and simulating LOBs is quite often necessary for calibrating and fine-tuning the automa…
Algorithmic TradingBlockchain Metrics and Indicators in Cryptocurrency Trading
The objective of this paper is the construction of new indicators that can be useful to operate in the cryptocurrency market. These indicators are based on public data obtained from the blockchain network, specifically f…
Algorithmic TradingESG driven pairs algorithm for sustainable trading: Analysis from the Indian market
This paper proposes an algorithmic trading framework integrating Environmental, Social, and Governance (ESG) ratings with a pairs trading strategy. It addresses the demand for socially responsible investment solutions by…
Algorithmic TradingPredicting risk/reward ratio in financial markets for asset management using machine learning
Financial market forecasting remains a formidable challenge despite the surge in computational capabilities and machine learning advancements. While numerous studies have underscored the precision of computer-generated m…
Algorithmic TradingAsset ManagementManagementAdvancing Algorithmic Trading: A Multi-Technique Enhancement of Deep Q-Network Models
This study enhances a Deep Q-Network (DQN) trading model by incorporating advanced techniques like Prioritized Experience Replay, Regularized Q-Learning, Noisy Networks, Dueling, and Double DQN. Extensive tests on assets…
Algorithmic TradingQ-LearningStock Market Directional Bias Prediction Using ML Algorithms
The stock market has been established since the 13th century, but in the current epoch of time, it is substantially more practicable to anticipate the stock market than it was at any other point in time due to the tools …
Algorithmic TradingPredictionTime SeriesTime Series ForecastingNoxTrader: LSTM-Based Stock Return Momentum Prediction for Quantitative Trading
We introduce NoxTrader, a sophisticated system designed for portfolio construction and trading execution with the primary objective of achieving profitable outcomes in the stock market, specifically aiming to generate mo…
Algorithmic TradingFeature EngineeringTime SeriesTime Series AnalysisEarnHFT: Efficient Hierarchical Reinforcement Learning for High Frequency Trading
High-frequency trading (HFT) uses computer algorithms to make trading decisions in short time scales (e.g., second-level), which is widely used in the Cryptocurrency (Crypto) market (e.g., Bitcoin). Reinforcement learnin…
Algorithmic TradingHierarchical Reinforcement LearningReinforcement LearningSizing Strategies for Algorithmic Trading in Volatile Markets: A Study of Backtesting and Risk Mitigation Analysis
Backtest is a way of financial risk evaluation which helps to analyze how our trading algorithm would work in markets with past time frame. The high volatility situation has always been a critical situation which creates…
Algorithmic TradingINTAGS: Interactive Agent-Guided Simulation
In many applications involving multi-agent system (MAS), it is imperative to test an experimental (Exp) autonomous agent in a high-fidelity simulator prior to its deployment to production, to avoid unexpected losses in t…
Algorithmic TradingCausal InferenceDecision MakingGenerative Adversarial Network+2Commodities Trading through Deep Policy Gradient Methods
Algorithmic trading has gained attention due to its potential for generating superior returns. This paper investigates the effectiveness of deep reinforcement learning (DRL) methods in algorithmic commodities trading. It…
Algorithmic TradingDeep Reinforcement LearningPolicy Gradient MethodsTime Series