arXiv - QuantFin - Trading and Market Microstructure最新文献_第9页

The Random Forest Model for Analyzing and Forecasting the US Stock Market in the Context of Smart Finance 智能金融背景下用于分析和预测美国股市的随机森林模型

arXiv - QuantFin - Trading and Market Microstructure Pub Date : 2024-02-27 DOI: arxiv-2402.17194

Jiajian Zheng, Duan Xin, Qishuo Cheng, Miao Tian, Le Yang

引用次数: 0

Long Short-Term Memory Pattern Recognition in Currency Trading 货币交易中的长短期记忆模式识别

arXiv - QuantFin - Trading and Market Microstructure Pub Date : 2024-02-23 DOI: arxiv-2403.18839

Jai Pal

{"title":"Long Short-Term Memory Pattern Recognition in Currency Trading","authors":"Jai Pal","doi":"arxiv-2403.18839","DOIUrl":"https://doi.org/arxiv-2403.18839","url":null,"abstract":"This study delves into the analysis of financial markets through the lens of\u0000Wyckoff Phases, a framework devised by Richard D. Wyckoff in the early 20th\u0000century. Focusing on the accumulation pattern within the Wyckoff framework, the\u0000research explores the phases of trading range and secondary test, elucidating\u0000their significance in understanding market dynamics and identifying potential\u0000trading opportunities. By dissecting the intricacies of these phases, the study\u0000sheds light on the creation of liquidity through market structure, offering\u0000insights into how traders can leverage this knowledge to anticipate price\u0000movements and make informed decisions. The effective detection and analysis of\u0000Wyckoff patterns necessitate robust computational models capable of processing\u0000complex market data, with spatial data best analyzed using Convolutional Neural\u0000Networks (CNNs) and temporal data through Long Short-Term Memory (LSTM) models.\u0000The creation of training data involves the generation of swing points,\u0000representing significant market movements, and filler points, introducing noise\u0000and enhancing model generalization. Activation functions, such as the sigmoid\u0000function, play a crucial role in determining the output behavior of neural\u0000network models. The results of the study demonstrate the remarkable efficacy of\u0000deep learning models in detecting Wyckoff patterns within financial data,\u0000underscoring their potential for enhancing pattern recognition and analysis in\u0000financial markets. In conclusion, the study highlights the transformative\u0000potential of AI-driven approaches in financial analysis and trading strategies,\u0000with the integration of AI technologies shaping the future of trading and\u0000investment practices.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"82 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140323436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reinforcement Learning for Optimal Execution when Liquidity is Time-Varying 流动性随时间变化时优化执行的强化学习

arXiv - QuantFin - Trading and Market Microstructure Pub Date : 2024-02-19 DOI: arxiv-2402.12049

Andrea Macrì, Fabrizio Lillo

引用次数: 0

Closed-form solutions for generic N-token AMM arbitrage 通用 Noken AMM 套利的闭式解法

arXiv - QuantFin - Trading and Market Microstructure Pub Date : 2024-02-09 DOI: arxiv-2402.06731

Matthew Willetts, Christian Harrington

引用次数: 0

DeepTraderX: Challenging Conventional Trading Strategies with Deep Learning in Multi-Threaded Market Simulations DeepTraderX：在多线程市场模拟中利用深度学习挑战传统交易策略

arXiv - QuantFin - Trading and Market Microstructure Pub Date : 2024-02-06 DOI: arxiv-2403.18831

Armand Mihai Cismaru

{"title":"DeepTraderX: Challenging Conventional Trading Strategies with Deep Learning in Multi-Threaded Market Simulations","authors":"Armand Mihai Cismaru","doi":"arxiv-2403.18831","DOIUrl":"https://doi.org/arxiv-2403.18831","url":null,"abstract":"In this paper, we introduce DeepTraderX (DTX), a simple Deep Learning-based\u0000trader, and present results that demonstrate its performance in a\u0000multi-threaded market simulation. In a total of about 500 simulated market\u0000days, DTX has learned solely by watching the prices that other strategies\u0000produce. By doing this, it has successfully created a mapping from market data\u0000to quotes, either bid or ask orders, to place for an asset. Trained on\u0000historical Level-2 market data, i.e., the Limit Order Book (LOB) for specific\u0000tradable assets, DTX processes the market state $S$ at each timestep $T$ to\u0000determine a price $P$ for market orders. The market data used in both training\u0000and testing was generated from unique market schedules based on real historic\u0000stock market data. DTX was tested extensively against the best strategies in\u0000the literature, with its results validated by statistical analysis. Our\u0000findings underscore DTX's capability to rival, and in many instances, surpass,\u0000the performance of public-domain traders, including those that outclass human\u0000traders, emphasising the efficiency of simple models, as this is required to\u0000succeed in intricate multi-threaded simulations. This highlights the potential\u0000of leveraging \"black-box\" Deep Learning systems to create more efficient\u0000financial markets.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140323284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Perpetual Future Contracts in Centralized and Decentralized Exchanges: Mechanism and Traders' Behavior 集中式和分散式交易所中的永续期货合约：机制与交易者行为

arXiv - QuantFin - Trading and Market Microstructure Pub Date : 2024-02-06 DOI: arxiv-2402.03953

Erdong Chen, Mengzhong Ma, Zixin Nie

{"title":"Perpetual Future Contracts in Centralized and Decentralized Exchanges: Mechanism and Traders' Behavior","authors":"Erdong Chen, Mengzhong Ma, Zixin Nie","doi":"arxiv-2402.03953","DOIUrl":"https://doi.org/arxiv-2402.03953","url":null,"abstract":"This study presents a groundbreaking Systematization of Knowledge (SoK)\u0000initiative, focusing on an in-depth exploration of the dynamics and behavior of\u0000traders on perpetual future contracts across both centralized exchanges (CEXs),\u0000and decentralized exchanges (DEXs). We have refined the existing model for\u0000investigating traders' behavior in reaction to price volatility to create a new\u0000analytical framework specifically for these contract platforms, while also\u0000highlighting the role of blockchain technology in their application. Our\u0000research includes a comparative analysis of historical data from CEXs and a\u0000more extensive examination of complete transactional data on DEXs. On DEX of\u0000Virtual Automated Market Making (VAMM) Model, open interest on short and long\u0000positions exert effect on price volatility in opposite direction, attributable\u0000to VAMM's price formation mechanism. In the DEXs with Oracle Pricing Model, we\u0000observed a distinct asymmetry in trader behavior between buyers and sellers.\u0000Such asymmetry might stem from uninformed traders reacting more strongly to\u0000positive news than to negative, leading to a tendency to accumulate long\u0000positions. This study sheds light on the potential risks and advantages of\u0000using perpetual future contracts within the DeFi space while provides\u0000mathematical basis and empirical insights based on which future theoretical\u0000works can be configurated, offering crucial insights into the rapidly evolving\u0000world of blockchain-based financial instruments.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139760763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning the Market: Sentiment-Based Ensemble Trading Agents 学习市场：基于情绪的集合交易代理

arXiv - QuantFin - Trading and Market Microstructure Pub Date : 2024-02-02 DOI: arxiv-2402.01441

Andrew Ye, James Xu, Yi Wang, Yifan Yu, Daniel Yan, Ryan Chen, Bosheng Dong, Vipin Chaudhary, Shuai Xu

{"title":"Learning the Market: Sentiment-Based Ensemble Trading Agents","authors":"Andrew Ye, James Xu, Yi Wang, Yifan Yu, Daniel Yan, Ryan Chen, Bosheng Dong, Vipin Chaudhary, Shuai Xu","doi":"arxiv-2402.01441","DOIUrl":"https://doi.org/arxiv-2402.01441","url":null,"abstract":"We propose the integration of sentiment analysis and deep-reinforcement\u0000learning ensemble algorithms for stock trading, and design a strategy capable\u0000of dynamically altering its employed agent given concurrent market sentiment.\u0000In particular, we create a simple-yet-effective method for extracting news\u0000sentiment and combine this with general improvements upon existing works,\u0000resulting in automated trading agents that effectively consider both\u0000qualitative market factors and quantitative stock data. We show that our\u0000approach results in a strategy that is profitable, robust, and risk-minimal --\u0000outperforming the traditional ensemble strategy as well as single agent\u0000algorithms and market metrics. Our findings determine that the conventional\u0000practice of switching ensemble agents every fixed-number of months is\u0000sub-optimal, and that a dynamic sentiment-based framework greatly unlocks\u0000additional performance within these agents. Furthermore, as we have designed\u0000our algorithm with simplicity and efficiency in mind, we hypothesize that the\u0000transition of our method from historical evaluation towards real-time trading\u0000with live data should be relatively simple.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139690179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ESG driven pairs algorithm for sustainable trading: Analysis from the Indian market ESG 驱动的可持续交易对算法：印度市场分析

arXiv - QuantFin - Trading and Market Microstructure Pub Date : 2024-01-26 DOI: arxiv-2401.14761

Eeshaan Dutta, Sarthak Diwan, Siddhartha P. Chakrabarty

引用次数: 0

Stylized Facts and Market Microstructure: An In-Depth Exploration of German Bond Futures Market 风格化事实与市场微观结构：德国债券期货市场的深入探索

arXiv - QuantFin - Trading and Market Microstructure Pub Date : 2024-01-19 DOI: arxiv-2401.10722

Hamza Bodor, Laurent Carlier

引用次数: 0

BioFinBERT: Finetuning Large Language Models (LLMs) to Analyze Sentiment of Press Releases and Financial Text Around Inflection Points of Biotech Stocks BioFinBERT：微调大型语言模型 (LLM)，分析生物技术股拐点附近的新闻稿和金融文本情绪

arXiv - QuantFin - Trading and Market Microstructure Pub Date : 2024-01-19 DOI: arxiv-2401.11011

Valentina Aparicio, Daniel Gordon, Sebastian G. Huayamares, Yuhuai Luo

{"title":"BioFinBERT: Finetuning Large Language Models (LLMs) to Analyze Sentiment of Press Releases and Financial Text Around Inflection Points of Biotech Stocks","authors":"Valentina Aparicio, Daniel Gordon, Sebastian G. Huayamares, Yuhuai Luo","doi":"arxiv-2401.11011","DOIUrl":"https://doi.org/arxiv-2401.11011","url":null,"abstract":"Large language models (LLMs) are deep learning algorithms being used to\u0000perform natural language processing tasks in various fields, from social\u0000sciences to finance and biomedical sciences. Developing and training a new LLM\u0000can be very computationally expensive, so it is becoming a common practice to\u0000take existing LLMs and finetune them with carefully curated datasets for\u0000desired applications in different fields. Here, we present BioFinBERT, a\u0000finetuned LLM to perform financial sentiment analysis of public text associated\u0000with stocks of companies in the biotechnology sector. The stocks of biotech\u0000companies developing highly innovative and risky therapeutic drugs tend to\u0000respond very positively or negatively upon a successful or failed clinical\u0000readout or regulatory approval of their drug, respectively. These clinical or\u0000regulatory results are disclosed by the biotech companies via press releases,\u0000which are followed by a significant stock response in many cases. In our\u0000attempt to design a LLM capable of analyzing the sentiment of these press\u0000releases,we first finetuned BioBERT, a biomedical language representation model\u0000designed for biomedical text mining, using financial textual databases. Our\u0000finetuned model, termed BioFinBERT, was then used to perform financial\u0000sentiment analysis of various biotech-related press releases and financial text\u0000around inflection points that significantly affected the price of biotech\u0000stocks.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139559728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0