arXiv - QuantFin - Trading and Market Microstructure最新文献_第8页

Optimal Rebalancing in Dynamic AMMs 动态 AMM 中的最优再平衡

arXiv - QuantFin - Trading and Market Microstructure Pub Date : 2024-03-27 DOI: arxiv-2403.18737

Matthew Willetts, Christian Harrington

{"title":"Optimal Rebalancing in Dynamic AMMs","authors":"Matthew Willetts, Christian Harrington","doi":"arxiv-2403.18737","DOIUrl":"https://doi.org/arxiv-2403.18737","url":null,"abstract":"Dynamic AMM pools, as found in Temporal Function Market Making, rebalance\u0000their holdings to a new desired ratio (e.g. moving from being 50-50 between two\u0000assets to being 90-10 in favour of one of them) by introducing an arbitrage\u0000opportunity that disappears when their holdings are in line with their target.\u0000Structuring this arbitrage opportunity reduces to the problem of choosing the\u0000sequence of portfolio weights the pool exposes to the market via its trading\u0000function. Linear interpolation from start weights to end weights has been used\u0000to reduce the cost paid by pools to arbitrageurs to rebalance. Here we obtain\u0000the $textit{optimal}$ interpolation in the limit of small weight changes\u0000(which has the downside of requiring a call to a transcendental function) and\u0000then obtain a cheap-to-compute approximation to that optimal approach that\u0000gives almost the same performance improvement. We then demonstrate this method\u0000on a range of market backtests, including simulating pool performance when\u0000trading fees are present, finding that the new approximately-optimal method of\u0000changing weights gives robust increases in pool performance. For a BTC-ETH-DAI\u0000pool from July 2022 to June 2023, the increases of pool P&L from\u0000approximately-optimal weight changes is $sim25%$ for a range of different\u0000strategies and trading fees.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140311683","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

Revisiting Boehmer et al. (2021): Recent Period, Alternative Method, Different Conclusions 重新审视 Boehmer 等人（2021 年）：新时期、新方法、新结论

arXiv - QuantFin - Trading and Market Microstructure Pub Date : 2024-03-25 DOI: arxiv-2403.17095

David Ardia, Clément Aymard, Tolga Cenesizoglu

引用次数: 0

Anticipatory Gains and Event-Driven Losses in Blockchain-Based Fan Tokens: Evidence from the FIFA World Cup 基于区块链的球迷代币的预期收益和事件驱动损失：来自国际足联世界杯的证据

arXiv - QuantFin - Trading and Market Microstructure Pub Date : 2024-03-23 DOI: arxiv-2403.15810

Aman Saggu, Lennart Ante, Ender Demir

引用次数: 0

FinLlama: Financial Sentiment Classification for Algorithmic Trading Applications FinLlama：算法交易应用中的金融情绪分类

arXiv - QuantFin - Trading and Market Microstructure Pub Date : 2024-03-18 DOI: arxiv-2403.12285

Thanos Konstantinidis, Giorgos Iacovides, Mingxue Xu, Tony G. Constantinides, Danilo Mandic

{"title":"FinLlama: Financial Sentiment Classification for Algorithmic Trading Applications","authors":"Thanos Konstantinidis, Giorgos Iacovides, Mingxue Xu, Tony G. Constantinides, Danilo Mandic","doi":"arxiv-2403.12285","DOIUrl":"https://doi.org/arxiv-2403.12285","url":null,"abstract":"There are multiple sources of financial news online which influence market\u0000movements and trader's decisions. This highlights the need for accurate\u0000sentiment analysis, in addition to having appropriate algorithmic trading\u0000techniques, to arrive at better informed trading decisions. Standard lexicon\u0000based sentiment approaches have demonstrated their power in aiding financial\u0000decisions. However, they are known to suffer from issues related to context\u0000sensitivity and word ordering. Large Language Models (LLMs) can also be used in\u0000this context, but they are not finance-specific and tend to require significant\u0000computational resources. To facilitate a finance specific LLM framework, we\u0000introduce a novel approach based on the Llama 2 7B foundational model, in order\u0000to benefit from its generative nature and comprehensive language manipulation.\u0000This is achieved by fine-tuning the Llama2 7B model on a small portion of\u0000supervised financial sentiment analysis data, so as to jointly handle the\u0000complexities of financial lexicon and context, and further equipping it with a\u0000neural network based decision mechanism. Such a generator-classifier scheme,\u0000referred to as FinLlama, is trained not only to classify the sentiment valence\u0000but also quantify its strength, thus offering traders a nuanced insight into\u0000financial news articles. Complementing this, the implementation of\u0000parameter-efficient fine-tuning through LoRA optimises trainable parameters,\u0000thus minimising computational and memory requirements, without sacrificing\u0000accuracy. Simulation results demonstrate the ability of the proposed FinLlama\u0000to provide a framework for enhanced portfolio management decisions and\u0000increased market returns. These results underpin the ability of FinLlama to\u0000construct high-return portfolios which exhibit enhanced resilience, even during\u0000volatile periods and unpredictable market events.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"122 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140170702","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

Optimal Portfolio Choice with Cross-Impact Propagators 交叉影响传播者的最优投资组合选择

arXiv - QuantFin - Trading and Market Microstructure Pub Date : 2024-03-15 DOI: arxiv-2403.10273

Eduardo Abi Jaber, Eyal Neuman, Sturmius Tuschmann

引用次数: 0

Layer 2 be or Layer not 2 be: Scaling on Uniswap v3 是第 2 层还是非第 2 层：Uniswap v3 上的扩展

arXiv - QuantFin - Trading and Market Microstructure Pub Date : 2024-03-14 DOI: arxiv-2403.09494

Austin Adams

引用次数: 0

Trading Large Orders in the Presence of Multiple High-Frequency Anticipatory Traders 在多个高频预期交易者在场的情况下交易大订单

arXiv - QuantFin - Trading and Market Microstructure Pub Date : 2024-03-13 DOI: arxiv-2403.08202

Ziyi Xu, Xue Cheng

引用次数: 0

Fill Probabilities in a Limit Order Book with State-Dependent Stochastic Order Flows 限价订单簿中的成交概率与状态相关的随机订单流

arXiv - QuantFin - Trading and Market Microstructure Pub Date : 2024-03-05 DOI: arxiv-2403.02572

Felix Lokin, Fenghui Yu

{"title":"Fill Probabilities in a Limit Order Book with State-Dependent Stochastic Order Flows","authors":"Felix Lokin, Fenghui Yu","doi":"arxiv-2403.02572","DOIUrl":"https://doi.org/arxiv-2403.02572","url":null,"abstract":"This paper focuses on computing the fill probabilities for limit orders\u0000positioned at various price levels within the limit order book, which play a\u0000crucial role in optimizing executions. We adopt a generic stochastic model to\u0000capture the dynamics of the order book as a series of queueing systems. This\u0000generic model is state-dependent and also incorporates stylized factors. We\u0000subsequently derive semi-analytical expressions to compute the relevant\u0000probabilities within the context of state-dependent stochastic order flows.\u0000These probabilities cover various scenarios, including the probability of a\u0000change in the mid-price, the fill probabilities of orders posted at the best\u0000quotes, and those posted at a price level deeper than the best quotes in the\u0000book, before the opposite best quote moves. These expressions can be further\u0000generalized to accommodate orders posted even deeper in the order book,\u0000although the associated probabilities are typically very small in such cases.\u0000Lastly, we conduct extensive numerical experiments using real order book data\u0000from the foreign exchange spot market. Our findings suggest that the model is\u0000tractable and possesses the capability to effectively capture the dynamics of\u0000the limit order book. Moreover, the derived formulas and numerical methods\u0000demonstrate reasonably good accuracy in estimating the fill probabilities.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140045531","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

Volatility-based strategy on Chinese equity index ETF options 基于波动率的中国股票指数 ETF 期权策略

arXiv - QuantFin - Trading and Market Microstructure Pub Date : 2024-03-01 DOI: arxiv-2403.00474

Peng Yifeng

{"title":"Volatility-based strategy on Chinese equity index ETF options","authors":"Peng Yifeng","doi":"arxiv-2403.00474","DOIUrl":"https://doi.org/arxiv-2403.00474","url":null,"abstract":"In recent years, there has been quick developments of derivative markets in\u0000China and standardized derivative trading have reached considerable volumes. In\u0000this research, we collect all the daily data of ETF options traded at Shanghai\u0000Stock Exchange and start with a simple short-volatility strategy. The strategy\u0000delivers nice performance before 2018, providing significant excess return over\u0000the buy and hold benchmark. However, after 2018, this strategy starts to\u0000deteriorate and no obvious risk-adjusted return is shown. Based on the\u0000discussion of relationship between the strategy's performance and market's\u0000volatility, we improve the model by adjusting positions and exposure according\u0000to volatility forecasts using methods such as volatility momentum and GARCH.\u0000The new models have improved performance in different ways, where larger upside\u0000capture and smaller drawbacks can be achieved in market fluctuation. This\u0000research has shown potentials of volatility-based trading on Chinese equity\u0000index options, and with further improvement and implementation considerations,\u0000real-world practical trading strategies can be formed.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140026301","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

FinAgent: A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist FinAgent：金融交易多模式基础代理：工具增强型、多样化和通用型

arXiv - QuantFin - Trading and Market Microstructure Pub Date : 2024-02-28 DOI: arxiv-2402.18485

Wentao Zhang, Lingxuan Zhao, Haochong Xia, Shuo Sun, Jiaze Sun, Molei Qin, Xinyi Li, Yuqing Zhao, Yilei Zhao, Xinyu Cai, Longtao Zheng, Xinrun Wang, Bo An

{"title":"FinAgent: A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist","authors":"Wentao Zhang, Lingxuan Zhao, Haochong Xia, Shuo Sun, Jiaze Sun, Molei Qin, Xinyi Li, Yuqing Zhao, Yilei Zhao, Xinyu Cai, Longtao Zheng, Xinrun Wang, Bo An","doi":"arxiv-2402.18485","DOIUrl":"https://doi.org/arxiv-2402.18485","url":null,"abstract":"Financial trading is a crucial component of the markets, informed by a\u0000multimodal information landscape encompassing news, prices, and Kline charts,\u0000and encompasses diverse tasks such as quantitative trading and high-frequency\u0000trading with various assets. While advanced AI techniques like deep learning\u0000and reinforcement learning are extensively utilized in finance, their\u0000application in financial trading tasks often faces challenges due to inadequate\u0000handling of multimodal data and limited generalizability across various tasks.\u0000To address these challenges, we present FinAgent, a multimodal foundational\u0000agent with tool augmentation for financial trading. FinAgent's market\u0000intelligence module processes a diverse range of data-numerical, textual, and\u0000visual-to accurately analyze the financial market. Its unique dual-level\u0000reflection module not only enables rapid adaptation to market dynamics but also\u0000incorporates a diversified memory retrieval system, enhancing the agent's\u0000ability to learn from historical data and improve decision-making processes.\u0000The agent's emphasis on reasoning for actions fosters trust in its financial\u0000decisions. Moreover, FinAgent integrates established trading strategies and\u0000expert insights, ensuring that its trading approaches are both data-driven and\u0000rooted in sound financial principles. With comprehensive experiments on 6\u0000financial datasets, including stocks and Crypto, FinAgent significantly\u0000outperforms 9 state-of-the-art baselines in terms of 6 financial metrics with\u0000over 36% average improvement on profit. Specifically, a 92.27% return (a 84.39%\u0000relative improvement) is achieved on one dataset. Notably, FinAgent is the\u0000first advanced multimodal foundation agent designed for financial trading\u0000tasks.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140009650","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