arXiv - QuantFin - Computational Finance最新文献_第10页

Limit Order Book Simulations: A Review 限价订单簿模拟：回顾

arXiv - QuantFin - Computational Finance Pub Date : 2024-02-27 DOI: arxiv-2402.17359

Konark Jain, Nick Firoozye, Jonathan Kochems, Philip Treleaven

引用次数: 0

Time series generation for option pricing on quantum computers using tensor network 利用张量网络在量子计算机上生成用于期权定价的时间序列

arXiv - QuantFin - Computational Finance Pub Date : 2024-02-27 DOI: arxiv-2402.17148

Nozomu Kobayashi, Yoshiyuki Suimon, Koichi Miyamoto

引用次数: 0

Optimizing Portfolio Management and Risk Assessment in Digital Assets Using Deep Learning for Predictive Analysis 利用深度学习进行预测分析，优化数字资产的投资组合管理和风险评估

arXiv - QuantFin - Computational Finance Pub Date : 2024-02-25 DOI: arxiv-2402.15994

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

{"title":"Optimizing Portfolio Management and Risk Assessment in Digital Assets Using Deep Learning for Predictive Analysis","authors":"Qishuo Cheng, Le Yang, Jiajian Zheng, Miao Tian, Duan Xin","doi":"arxiv-2402.15994","DOIUrl":"https://doi.org/arxiv-2402.15994","url":null,"abstract":"Portfolio management issues have been extensively studied in the field of\u0000artificial intelligence in recent years, but existing deep learning-based\u0000quantitative trading methods have some areas where they could be improved.\u0000First of all, the prediction mode of stocks is singular; often, only one\u0000trading expert is trained by a model, and the trading decision is solely based\u0000on the prediction results of the model. Secondly, the data source used by the\u0000model is relatively simple, and only considers the data of the stock itself,\u0000ignoring the impact of the whole market risk on the stock. In this paper, the\u0000DQN algorithm is introduced into asset management portfolios in a novel and\u0000straightforward way, and the performance greatly exceeds the benchmark, which\u0000fully proves the effectiveness of the DRL algorithm in portfolio management.\u0000This also inspires us to consider the complexity of financial problems, and the\u0000use of algorithms should be fully combined with the problems to adapt. Finally,\u0000in this paper, the strategy is implemented by selecting the assets and actions\u0000with the largest Q value. Since different assets are trained separately as\u0000environments, there may be a phenomenon of Q value drift among different assets\u0000(different assets have different Q value distribution areas), which may easily\u0000lead to incorrect asset selection. Consider adding constraints so that the Q\u0000values of different assets share a Q value distribution to improve results.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"52 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139977398","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

A Note on Optimal Liquidation with Linear Price Impact 关于线性价格影响下的最优清算的说明

arXiv - QuantFin - Computational Finance Pub Date : 2024-02-21 DOI: arxiv-2402.14100

Yan Dolinsky, Doron Greenstein

引用次数: 0

Deep Hedging with Market Impact 影响市场的深度套期保值

arXiv - QuantFin - Computational Finance Pub Date : 2024-02-20 DOI: arxiv-2402.13326

Andrei Neagu, Frédéric Godin, Clarence Simard, Leila Kosseim

{"title":"Deep Hedging with Market Impact","authors":"Andrei Neagu, Frédéric Godin, Clarence Simard, Leila Kosseim","doi":"arxiv-2402.13326","DOIUrl":"https://doi.org/arxiv-2402.13326","url":null,"abstract":"Dynamic hedging is the practice of periodically transacting financial\u0000instruments to offset the risk caused by an investment or a liability. Dynamic\u0000hedging optimization can be framed as a sequential decision problem; thus,\u0000Reinforcement Learning (RL) models were recently proposed to tackle this task.\u0000However, existing RL works for hedging do not consider market impact caused by\u0000the finite liquidity of traded instruments. Integrating such feature can be\u0000crucial to achieve optimal performance when hedging options on stocks with\u0000limited liquidity. In this paper, we propose a novel general market impact\u0000dynamic hedging model based on Deep Reinforcement Learning (DRL) that considers\u0000several realistic features such as convex market impacts, and impact\u0000persistence through time. The optimal policy obtained from the DRL model is\u0000analysed using several option hedging simulations and compared to commonly used\u0000procedures such as delta hedging. Results show our DRL model behaves better in\u0000contexts of low liquidity by, among others: 1) learning the extent to which\u0000portfolio rebalancing actions should be dampened or delayed to avoid high\u0000costs, 2) factoring in the impact of features not considered by conventional\u0000approaches, such as previous hedging errors through the portfolio value, and\u0000the underlying asset's drift (i.e. the magnitude of its expected return).","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"282 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139925390","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

Modelling crypto markets by multi-agent reinforcement learning 通过多代理强化学习为加密货币市场建模

arXiv - QuantFin - Computational Finance Pub Date : 2024-02-16 DOI: arxiv-2402.10803

Johann Lussange, Stefano Vrizzi, Stefano Palminteri, Boris Gutkin

{"title":"Modelling crypto markets by multi-agent reinforcement learning","authors":"Johann Lussange, Stefano Vrizzi, Stefano Palminteri, Boris Gutkin","doi":"arxiv-2402.10803","DOIUrl":"https://doi.org/arxiv-2402.10803","url":null,"abstract":"Building on a previous foundation work (Lussange et al. 2020), this study\u0000introduces a multi-agent reinforcement learning (MARL) model simulating crypto\u0000markets, which is calibrated to the Binance's daily closing prices of $153$\u0000cryptocurrencies that were continuously traded between 2018 and 2022. Unlike\u0000previous agent-based models (ABM) or multi-agent systems (MAS) which relied on\u0000zero-intelligence agents or single autonomous agent methodologies, our approach\u0000relies on endowing agents with reinforcement learning (RL) techniques in order\u0000to model crypto markets. This integration is designed to emulate, with a\u0000bottom-up approach to complexity inference, both individual and collective\u0000agents, ensuring robustness in the recent volatile conditions of such markets\u0000and during the COVID-19 era. A key feature of our model also lies in the fact\u0000that its autonomous agents perform asset price valuation based on two sources\u0000of information: the market prices themselves, and the approximation of the\u0000crypto assets fundamental values beyond what those market prices are. Our MAS\u0000calibration against real market data allows for an accurate emulation of crypto\u0000markets microstructure and probing key market behaviors, in both the bearish\u0000and bullish regimes of that particular time period.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139903705","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

Alpha-GPT 2.0: Human-in-the-Loop AI for Quantitative Investment Alpha-GPT 2.0：用于量化投资的环形人工智能

arXiv - QuantFin - Computational Finance Pub Date : 2024-02-15 DOI: arxiv-2402.09746

Hang Yuan, Saizhuo Wang, Jian Guo

引用次数: 0

RiskMiner: Discovering Formulaic Alphas via Risk Seeking Monte Carlo Tree Search RiskMiner：通过风险寻求蒙特卡洛树搜索发现公式字母表

arXiv - QuantFin - Computational Finance Pub Date : 2024-02-11 DOI: arxiv-2402.07080

Tao Ren, Ruihan Zhou, Jinyang Jiang, Jiafeng Liang, Qinghao Wang, Yijie Peng

{"title":"RiskMiner: Discovering Formulaic Alphas via Risk Seeking Monte Carlo Tree Search","authors":"Tao Ren, Ruihan Zhou, Jinyang Jiang, Jiafeng Liang, Qinghao Wang, Yijie Peng","doi":"arxiv-2402.07080","DOIUrl":"https://doi.org/arxiv-2402.07080","url":null,"abstract":"The formulaic alphas are mathematical formulas that transform raw stock data\u0000into indicated signals. In the industry, a collection of formulaic alphas is\u0000combined to enhance modeling accuracy. Existing alpha mining only employs the\u0000neural network agent, unable to utilize the structural information of the\u0000solution space. Moreover, they didn't consider the correlation between alphas\u0000in the collection, which limits the synergistic performance. To address these\u0000problems, we propose a novel alpha mining framework, which formulates the alpha\u0000mining problems as a reward-dense Markov Decision Process (MDP) and solves the\u0000MDP by the risk-seeking Monte Carlo Tree Search (MCTS). The MCTS-based agent\u0000fully exploits the structural information of discrete solution space and the\u0000risk-seeking policy explicitly optimizes the best-case performance rather than\u0000average outcomes. Comprehensive experiments are conducted to demonstrate the\u0000efficiency of our framework. Our method outperforms all state-of-the-art\u0000benchmarks on two real-world stock sets under various metrics. Backtest\u0000experiments show that our alphas achieve the most profitable results under a\u0000realistic trading setting.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139753426","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

A monotone piecewise constant control integration approach for the two-factor uncertain volatility model 双因素不确定波动模型的单调片断常数控制积分法

arXiv - QuantFin - Computational Finance Pub Date : 2024-02-09 DOI: arxiv-2402.06840

Duy-Minh Dang, Hao Zhou

{"title":"A monotone piecewise constant control integration approach for the two-factor uncertain volatility model","authors":"Duy-Minh Dang, Hao Zhou","doi":"arxiv-2402.06840","DOIUrl":"https://doi.org/arxiv-2402.06840","url":null,"abstract":"Prices of option contracts on two assets within uncertain volatility models\u0000for worst and best-case scenarios satisfy a two-dimensional\u0000Hamilton-Jacobi-Bellman (HJB) partial differential equation (PDE) with cross\u0000derivatives terms. Traditional methods mainly involve finite differences and\u0000policy iteration. This \"discretize, then optimize\" paradigm requires complex\u0000rotations of computational stencils for monotonicity. This paper presents a novel and more streamlined \"decompose and integrate,\u0000then optimize\" approach to tackle the aforementioned HJB PDE. Within each\u0000timestep, our strategy employs a piecewise constant control, breaking down the\u0000HJB PDE into independent linear two-dimensional PDEs. Using known closed-form\u0000expressions for the Fourier transforms of the Green's functions associated with\u0000these PDEs, we determine an explicit formula for these functions. Since the\u0000Green's functions are non-negative, the solutions to the PDEs, cast as\u0000two-dimensional convolution integrals, can be conveniently approximated using a\u0000monotone integration method. Such integration methods, including a composite\u0000quadrature rule, are generally available in popular programming languages. To\u0000further enhance efficiency, we propose an implementation of this monotone\u0000integration scheme via Fast Fourier Transforms, exploiting the Toeplitz matrix\u0000structure. Optimal control is subsequently obtained by efficiently synthesizing\u0000the solutions of the individual PDEs. The proposed monotone piecewise constant control method is demonstrated to be\u0000both $ell_{infty} $-stable and consistent in the viscosity sense, ensuring\u0000its convergence to the viscosity solution of the HJB equation. Numerical\u0000results show remarkable agreement with benchmark solutions obtained by\u0000unconditionally monotone finite differences, tree methods, and Monte Carlo\u0000simulation, underscoring the robustness and effectiveness of our method.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139753437","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

QuantAgent: Seeking Holy Grail in Trading by Self-Improving Large Language Model QuantAgent：通过自我完善大型语言模型寻找交易圣杯

arXiv - QuantFin - Computational Finance Pub Date : 2024-02-06 DOI: arxiv-2402.03755

Saizhuo Wang, Hang Yuan, Lionel M. Ni, Jian Guo

引用次数: 0