arXiv - QuantFin - Computational Finance最新文献_第5页

New intelligent empowerment for digital transformation 数字化转型的全新智能赋能

arXiv - QuantFin - Computational Finance Pub Date : 2024-06-26 DOI: arxiv-2406.18440

Peng Yifeng, Gao Chen

引用次数: 0

$text{Alpha}^2$: Discovering Logical Formulaic Alphas using Deep Reinforcement Learning $text{Alpha}^2$：使用深度强化学习发现逻辑公式字母

arXiv - QuantFin - Computational Finance Pub Date : 2024-06-24 DOI: arxiv-2406.16505

Feng Xu, Yan Yin, Xinyu Zhang, Tianyuan Liu, Shengyi Jiang, Zongzhang Zhang

{"title":"$text{Alpha}^2$: Discovering Logical Formulaic Alphas using Deep Reinforcement Learning","authors":"Feng Xu, Yan Yin, Xinyu Zhang, Tianyuan Liu, Shengyi Jiang, Zongzhang Zhang","doi":"arxiv-2406.16505","DOIUrl":"https://doi.org/arxiv-2406.16505","url":null,"abstract":"Alphas are pivotal in providing signals for quantitative trading. The\u0000industry highly values the discovery of formulaic alphas for their\u0000interpretability and ease of analysis, compared with the expressive yet\u0000overfitting-prone black-box alphas. In this work, we focus on discovering\u0000formulaic alphas. Prior studies on automatically generating a collection of\u0000formulaic alphas were mostly based on genetic programming (GP), which is known\u0000to suffer from the problems of being sensitive to the initial population,\u0000converting to local optima, and slow computation speed. Recent efforts\u0000employing deep reinforcement learning (DRL) for alpha discovery have not fully\u0000addressed key practical considerations such as alpha correlations and validity,\u0000which are crucial for their effectiveness. In this work, we propose a novel\u0000framework for alpha discovery using DRL by formulating the alpha discovery\u0000process as program construction. Our agent, $text{Alpha}^2$, assembles an\u0000alpha program optimized for an evaluation metric. A search algorithm guided by\u0000DRL navigates through the search space based on value estimates for potential\u0000alpha outcomes. The evaluation metric encourages both the performance and the\u0000diversity of alphas for a better final trading strategy. Our formulation of\u0000searching alphas also brings the advantage of pre-calculation dimensional\u0000analysis, ensuring the logical soundness of alphas, and pruning the vast search\u0000space to a large extent. Empirical experiments on real-world stock markets\u0000demonstrates $text{Alpha}^2$'s capability to identify a diverse set of logical\u0000and effective alphas, which significantly improves the performance of the final\u0000trading strategy. The code of our method is available at\u0000https://github.com/x35f/alpha2.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141520788","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

Profit Maximization In Arbitrage Loops 套利循环中的利润最大化

arXiv - QuantFin - Computational Finance Pub Date : 2024-06-24 DOI: arxiv-2406.16600

Yu Zhang, Zichen Li, Tao Yan, Qianyu Liu, Nicolo Vallarano, Claudio Tessone

{"title":"Profit Maximization In Arbitrage Loops","authors":"Yu Zhang, Zichen Li, Tao Yan, Qianyu Liu, Nicolo Vallarano, Claudio Tessone","doi":"arxiv-2406.16600","DOIUrl":"https://doi.org/arxiv-2406.16600","url":null,"abstract":"Cyclic arbitrage chances exist abundantly among decentralized exchanges\u0000(DEXs), like Uniswap V2. For an arbitrage cycle (loop), researchers or\u0000practitioners usually choose a specific token, such as Ether as input, and\u0000optimize their input amount to get the net maximal amount of the specific token\u0000as arbitrage profit. By considering the tokens' prices from CEXs in this paper,\u0000the new arbitrage profit, called monetized arbitrage profit, will be quantified\u0000as the product of the net number of a specific token we got from the arbitrage\u0000loop and its corresponding price in CEXs. Based on this concept, we put forward\u0000three different strategies to maximize the monetized arbitrage profit for each\u0000arbitrage loop. The first strategy is called the MaxPrice strategy. Under this\u0000strategy, arbitrageurs start arbitrage only from the token with the highest CEX\u0000price. The second strategy is called the MaxMax strategy. Under this strategy,\u0000we calculate the monetized arbitrage profit for each token as input in turn in\u0000the arbitrage loop. Then, we pick up the most maximal monetized arbitrage\u0000profit among them as the monetized arbitrage profit of the MaxMax strategy. The\u0000third one is called the Convex Optimization strategy. By mapping the MaxMax\u0000strategy to a convex optimization problem, we proved that the Convex\u0000Optimization strategy could get more profit in theory than the MaxMax strategy,\u0000which is proved again in a given example. We also proved that if no arbitrage\u0000profit exists according to the MaxMax strategy, then the Convex Optimization\u0000strategy can not detect any arbitrage profit, either. However, the empirical\u0000data analysis denotes that the profitability of the Convex Optimization\u0000strategy is almost equal to that of the MaxMax strategy, and the MaxPrice\u0000strategy is not reliable in getting the maximal monetized arbitrage profit\u0000compared to the MaxMax strategy.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141501639","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

An Improved Algorithm to Identify More Arbitrage Opportunities on Decentralized Exchanges 在去中心化交易所上识别更多套利机会的改进算法

arXiv - QuantFin - Computational Finance Pub Date : 2024-06-24 DOI: arxiv-2406.16573

Yu Zhang, Tao Yan, Jianhong Lin, Benjamin Kraner, Claudio Tessone

{"title":"An Improved Algorithm to Identify More Arbitrage Opportunities on Decentralized Exchanges","authors":"Yu Zhang, Tao Yan, Jianhong Lin, Benjamin Kraner, Claudio Tessone","doi":"arxiv-2406.16573","DOIUrl":"https://doi.org/arxiv-2406.16573","url":null,"abstract":"In decentralized exchanges (DEXs), the arbitrage paths exist abundantly in\u0000the form of both arbitrage loops (e.g. the arbitrage path starts from token A\u0000and back to token A again in the end, A, B,..., A) and non-loops (e.g. the\u0000arbitrage path starts from token A and stops at a different token N, A, B,...,\u0000N). The Moore-Bellman-Ford algorithm, often coupled with the ``walk to the\u0000root\" technique, is commonly employed for detecting arbitrage loops in the\u0000token graph of decentralized exchanges (DEXs) such as Uniswap. However, a\u0000limitation of this algorithm is its ability to recognize only a limited number\u0000of arbitrage loops in each run. Additionally, it cannot specify the starting\u0000token of the detected arbitrage loops, further constraining its effectiveness\u0000in certain scenarios. Another limitation of this algorithm is its incapacity to\u0000detect non-loop arbitrage paths between any specified pairs of tokens. In this\u0000paper, we develop a new method to solve these problems by combining the line\u0000graph and a modified Moore-Bellman-Ford algorithm (MMBF). This method can help\u0000to find more arbitrage loops by detecting at least one arbitrage loop starting\u0000from any specified tokens in the DEXs and can detect the non-loop arbitrage\u0000paths between any pair of tokens. Then, we applied our algorithm to Uniswap V2\u0000and found more arbitrage loops and non-loops indeed compared with applying the\u0000Moore-Bellman-Ford (MBF) combined algorithm. The found arbitrage profit by our\u0000method in some arbitrage paths can be even as high as one million dollars, far\u0000larger than that found by the MBF combined algorithm. Finally, we statistically\u0000compare the distribution of arbitrage path lengths and the arbitrage profit\u0000detected by both our method and the MBF combined algorithm, and depict how\u0000potential arbitrage opportunities change with time by our method.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141501640","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

What Teaches Robots to Walk, Teaches Them to Trade too -- Regime Adaptive Execution using Informed Data and LLMs 教机器人走路，也教它们做交易--利用知情数据和 LLM 进行制度自适应执行

arXiv - QuantFin - Computational Finance Pub Date : 2024-06-20 DOI: arxiv-2406.15508

Raeid Saqur

{"title":"What Teaches Robots to Walk, Teaches Them to Trade too -- Regime Adaptive Execution using Informed Data and LLMs","authors":"Raeid Saqur","doi":"arxiv-2406.15508","DOIUrl":"https://doi.org/arxiv-2406.15508","url":null,"abstract":"Machine learning techniques applied to the problem of financial market\u0000forecasting struggle with dynamic regime switching, or underlying correlation\u0000and covariance shifts in true (hidden) market variables. Drawing inspiration\u0000from the success of reinforcement learning in robotics, particularly in agile\u0000locomotion adaptation of quadruped robots to unseen terrains, we introduce an\u0000innovative approach that leverages world knowledge of pretrained LLMs (aka.\u0000'privileged information' in robotics) and dynamically adapts them using\u0000intrinsic, natural market rewards using LLM alignment technique we dub as\u0000\"Reinforcement Learning from Market Feedback\" (**RLMF**). Strong empirical\u0000results demonstrate the efficacy of our method in adapting to regime shifts in\u0000financial markets, a challenge that has long plagued predictive models in this\u0000domain. The proposed algorithmic framework outperforms best-performing SOTA LLM\u0000models on the existing (FLARE) benchmark stock-movement (SM) tasks by more than\u000015% improved accuracy. On the recently proposed NIFTY SM task, our adaptive\u0000policy outperforms the SOTA best performing trillion parameter models like\u0000GPT-4. The paper details the dual-phase, teacher-student architecture and\u0000implementation of our model, the empirical results obtained, and an analysis of\u0000the role of language embeddings in terms of Information Gain.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"2012 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141520789","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

Integral Betti signature confirms the hyperbolic geometry of brain, climate, and financial networks 贝蒂积分特征证实了大脑、气候和金融网络的双曲几何学

arXiv - QuantFin - Computational Finance Pub Date : 2024-06-19 DOI: arxiv-2406.15505

Luigi Caputi, Anna Pidnebesna, Jaroslav Hlinka

{"title":"Integral Betti signature confirms the hyperbolic geometry of brain, climate, and financial networks","authors":"Luigi Caputi, Anna Pidnebesna, Jaroslav Hlinka","doi":"arxiv-2406.15505","DOIUrl":"https://doi.org/arxiv-2406.15505","url":null,"abstract":"This paper extends the possibility to examine the underlying curvature of\u0000data through the lens of topology by using the Betti curves, tools of\u0000Persistent Homology, as key topological descriptors, building on the clique\u0000topology approach. It was previously shown that Betti curves distinguish random\u0000from Euclidean geometric matrices - i.e. distance matrices of points randomly\u0000distributed in a cube with Euclidean distance. In line with previous\u0000experiments, we consider their low-dimensional approximations named integral\u0000Betti values, or signatures that effectively distinguish not only Euclidean,\u0000but also spherical and hyperbolic geometric matrices, both from purely random\u0000matrices as well as among themselves. To prove this, we analyse the behaviour\u0000of Betti curves for various geometric matrices -- i.e. distance matrices of\u0000points randomly distributed on manifolds of constant sectional curvature,\u0000considering the classical models of curvature 0, 1, -1, given by the Euclidean\u0000space, the sphere, and the hyperbolic space. We further investigate the\u0000dependence of integral Betti signatures on factors including the sample size\u0000and dimension. This is important for assessment of real-world connectivity\u0000matrices, as we show that the standard approach to network construction gives\u0000rise to (spurious) spherical geometry, with topology dependent on sample\u0000dimensions. Finally, we use the manifolds of constant curvature as comparison\u0000models to infer curvature underlying real-world datasets coming from\u0000neuroscience, finance and climate. Their associated topological features\u0000exhibit a hyperbolic character: the integral Betti signatures associated to\u0000these datasets sit in between Euclidean and hyperbolic (of small curvature).\u0000The potential confounding ``hyperbologenic effect'' of intrinsic low-rank\u0000modular structures is also evaluated through simulations.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141520790","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

Death, Taxes, and Inequality. Can a Minimal Model Explain Real Economic Inequality? 死亡、税收与不平等。最小模型能否解释实际经济不平等？

arXiv - QuantFin - Computational Finance Pub Date : 2024-06-19 DOI: arxiv-2406.13789

John C. Stevenson

引用次数: 0

Reinforcement Learning for Corporate Bond Trading: A Sell Side Perspective 公司债券交易的强化学习：卖方视角

arXiv - QuantFin - Computational Finance Pub Date : 2024-06-18 DOI: arxiv-2406.12983

Samuel Atkins, Ali Fathi, Sammy Assefa

{"title":"Reinforcement Learning for Corporate Bond Trading: A Sell Side Perspective","authors":"Samuel Atkins, Ali Fathi, Sammy Assefa","doi":"arxiv-2406.12983","DOIUrl":"https://doi.org/arxiv-2406.12983","url":null,"abstract":"A corporate bond trader in a typical sell side institution such as a bank\u0000provides liquidity to the market participants by buying/selling securities and\u0000maintaining an inventory. Upon receiving a request for a buy/sell price quote\u0000(RFQ), the trader provides a quote by adding a spread over a textit{prevalent\u0000market price}. For illiquid bonds, the market price is harder to observe, and\u0000traders often resort to available benchmark bond prices (such as MarketAxess,\u0000Bloomberg, etc.). In cite{Bergault2023ModelingLI}, the concept of textit{Fair\u0000Transfer Price} for an illiquid corporate bond was introduced which is derived\u0000from an infinite horizon stochastic optimal control problem (for maximizing the\u0000trader's expected P&L, regularized by the quadratic variation). In this paper,\u0000we consider the same optimization objective, however, we approach the\u0000estimation of an optimal bid-ask spread quoting strategy in a data driven\u0000manner and show that it can be learned using Reinforcement Learning.\u0000Furthermore, we perform extensive outcome analysis to examine the\u0000reasonableness of the trained agent's behavior.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141501641","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

Operator Deep Smoothing for Implied Volatility 操作员深度平滑隐含波动率

arXiv - QuantFin - Computational Finance Pub Date : 2024-06-17 DOI: arxiv-2406.11520

Lukas Gonon, Antoine Jacquier, Ruben Wiedemann

{"title":"Operator Deep Smoothing for Implied Volatility","authors":"Lukas Gonon, Antoine Jacquier, Ruben Wiedemann","doi":"arxiv-2406.11520","DOIUrl":"https://doi.org/arxiv-2406.11520","url":null,"abstract":"We devise a novel method for implied volatility smoothing based on neural\u0000operators. The goal of implied volatility smoothing is to construct a smooth\u0000surface that links the collection of prices observed at a specific instant on a\u0000given option market. Such price data arises highly dynamically in ever-changing\u0000spatial configurations, which poses a major limitation to foundational machine\u0000learning approaches using classical neural networks. While large models in\u0000language and image processing deliver breakthrough results on vast corpora of\u0000raw data, in financial engineering the generalization from big historical\u0000datasets has been hindered by the need for considerable data pre-processing. In\u0000particular, implied volatility smoothing has remained an instance-by-instance,\u0000hands-on process both for neural network-based and traditional parametric\u0000strategies. Our general operator deep smoothing approach, instead, directly\u0000maps observed data to smoothed surfaces. We adapt the graph neural operator\u0000architecture to do so with high accuracy on ten years of raw intraday S&P 500\u0000options data, using a single set of weights. The trained operator adheres to\u0000critical no-arbitrage constraints and is robust with respect to subsampling of\u0000inputs (occurring in practice in the context of outlier removal). We provide\u0000extensive historical benchmarks and showcase the generalization capability of\u0000our approach in a comparison with SVI, an industry standard parametrization for\u0000implied volatility. The operator deep smoothing approach thus opens up the use\u0000of neural networks on large historical datasets in financial engineering.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141528880","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

DeepUnifiedMom: Unified Time-series Momentum Portfolio Construction via Multi-Task Learning with Multi-Gate Mixture of Experts DeepUnifiedMom：通过多任务学习与多门专家混合构建统一的时间序列动量投资组合

arXiv - QuantFin - Computational Finance Pub Date : 2024-06-13 DOI: arxiv-2406.08742

Joel Ong, Dorien Herremans

{"title":"DeepUnifiedMom: Unified Time-series Momentum Portfolio Construction via Multi-Task Learning with Multi-Gate Mixture of Experts","authors":"Joel Ong, Dorien Herremans","doi":"arxiv-2406.08742","DOIUrl":"https://doi.org/arxiv-2406.08742","url":null,"abstract":"This paper introduces DeepUnifiedMom, a deep learning framework that enhances\u0000portfolio management through a multi-task learning approach and a multi-gate\u0000mixture of experts. The essence of DeepUnifiedMom lies in its ability to create\u0000unified momentum portfolios that incorporate the dynamics of time series\u0000momentum across a spectrum of time frames, a feature often missing in\u0000traditional momentum strategies. Our comprehensive backtesting, encompassing\u0000diverse asset classes such as equity indexes, fixed income, foreign exchange,\u0000and commodities, demonstrates that DeepUnifiedMom consistently outperforms\u0000benchmark models, even after factoring in transaction costs. This superior\u0000performance underscores DeepUnifiedMom's capability to capture the full\u0000spectrum of momentum opportunities within financial markets. The findings\u0000highlight DeepUnifiedMom as an effective tool for practitioners looking to\u0000exploit the entire range of momentum opportunities. It offers a compelling\u0000solution for improving risk-adjusted returns and is a valuable strategy for\u0000navigating the complexities of portfolio management.","PeriodicalId":501294,"journal":{"name":"arXiv - QuantFin - Computational Finance","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141520792","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