Analysis of frequent trading effects of various machine learning models

• URL: http://arxiv.org/abs/2311.10719v1
• Date: Thu, 14 Sep 2023 05:17:09 GMT
• Title: Analysis of frequent trading effects of various machine learning models
• Authors: Jiahao Chen, Xiaofei Li
• Abstract summary: The proposed algorithm employs neural network predictions to generate trading signals and execute buy and sell operations. By harnessing the power of neural networks, the algorithm enhances the accuracy and reliability of the trading strategy.
• Score: 8.975239844705415
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, high-frequency trading has emerged as a crucial strategy in stock trading. This study aims to develop an advanced high-frequency trading algorithm and compare the performance of three different mathematical models: the combination of the cross-entropy loss function and the quasi-Newton algorithm, the FCNN model, and the vector machine. The proposed algorithm employs neural network predictions to generate trading signals and execute buy and sell operations based on specific conditions. By harnessing the power of neural networks, the algorithm enhances the accuracy and reliability of the trading strategy. To assess the effectiveness of the algorithm, the study evaluates the performance of the three mathematical models. The combination of the cross-entropy loss function and the quasi-Newton algorithm is a widely utilized logistic regression approach. The FCNN model, on the other hand, is a deep learning algorithm that can extract and classify features from stock data. Meanwhile, the vector machine is a supervised learning algorithm recognized for achieving improved classification results by mapping data into high-dimensional spaces. By comparing the performance of these three models, the study aims to determine the most effective approach for high-frequency trading. This research makes a valuable contribution by introducing a novel methodology for high-frequency trading, thereby providing investors with a more accurate and reliable stock trading strategy.

Related papers

• AI-Powered Energy Algorithmic Trading: Integrating Hidden Markov Models with Neural Networks [0.0]
  This study introduces a new approach that combines Hidden Markov Models (HMM) and neural networks, integrated with Black-Litterman portfolio optimization. During the COVID period ( 2019-2022), this dual-model approach achieved a 83% return with a Sharpe ratio of 0.77.
  arXiv  Detail & Related papers  (2024-07-29T10:26:52Z)
• Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market [0.0]
  The study seeks to develop an effective strategy based on the novel framework of statistical arbitrage based on graph clustering algorithms. The study seeks to provide an integrated approach to optimal signal detection and risk management.
  arXiv  Detail & Related papers  (2024-06-15T17:25:32Z)
• Cryptocurrency Portfolio Optimization by Neural Networks [81.20955733184398]
  This paper proposes an effective algorithm based on neural networks to take advantage of these investment products. A deep neural network, which outputs the allocation weight of each asset at a time interval, is trained to maximize the Sharpe ratio. A novel loss term is proposed to regulate the network's bias towards a specific asset, thus enforcing the network to learn an allocation strategy that is close to a minimum variance strategy.
  arXiv  Detail & Related papers  (2023-10-02T12:33:28Z)
• Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL [106.82295532402335]
  Existing reinforcement learning algorithms suffer from computational intractability, strong statistical assumptions, and suboptimal sample complexity. We provide the first computationally efficient algorithm that attains rate-optimal sample complexity with respect to the desired accuracy level. Our algorithm, MusIK, combines systematic exploration with representation learning based on multi-step inverse kinematics.
  arXiv  Detail & Related papers  (2023-04-12T14:51:47Z)
• Improved Algorithms for Neural Active Learning [74.89097665112621]
  We improve the theoretical and empirical performance of neural-network(NN)-based active learning algorithms for the non-parametric streaming setting. We introduce two regret metrics by minimizing the population loss that are more suitable in active learning than the one used in state-of-the-art (SOTA) related work.
  arXiv  Detail & Related papers  (2022-10-02T05:03:38Z)
• Compatible deep neural network framework with financial time series data, including data preprocessor, neural network model and trading strategy [2.347843817145202]
  This research introduces a new deep neural network architecture and a novel idea of how to prepare financial data before feeding them to the model. Three different datasets are used to evaluate this method, where results indicate that this framework can provide us with profitable and robust predictions.
  arXiv  Detail & Related papers  (2022-05-11T20:44:08Z)
• Scalable computation of prediction intervals for neural networks via matrix sketching [79.44177623781043]
  Existing algorithms for uncertainty estimation require modifying the model architecture and training procedure. This work proposes a new algorithm that can be applied to a given trained neural network and produces approximate prediction intervals.
  arXiv  Detail & Related papers  (2022-05-06T13:18:31Z)
• Gone Fishing: Neural Active Learning with Fisher Embeddings [55.08537975896764]
  There is an increasing need for active learning algorithms that are compatible with deep neural networks. This article introduces BAIT, a practical representation of tractable, and high-performing active learning algorithm for neural networks.
  arXiv  Detail & Related papers  (2021-06-17T17:26:31Z)
• MCTG:Multi-frequency continuous-share trading algorithm with GARCH based on deep reinforcement learning [5.1727003187913665]
  We propose an algorithm called the Multi-frequency Continuous-share Trading algorithm with GARCH (MCTG) to solve the problems above. The latter with a continuous action space of the reinforcement learning algorithm is used to solve the problem of trading stock shares. Experiments in different industries of Chinese stock market show our method achieves more extra profit comparing with basic DRL methods and bench model.
  arXiv  Detail & Related papers  (2021-05-08T08:00:56Z)
• Model-Based Deep Learning [155.063817656602]
  Signal processing, communications, and control have traditionally relied on classical statistical modeling techniques. Deep neural networks (DNNs) use generic architectures which learn to operate from data, and demonstrate excellent performance. We are interested in hybrid techniques that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches.
  arXiv  Detail & Related papers  (2020-12-15T16:29:49Z)
• An Application of Deep Reinforcement Learning to Algorithmic Trading [4.523089386111081]
  This scientific research paper presents an innovative approach based on deep reinforcement learning (DRL) to solve the algorithmic trading problem. It proposes a novel DRL trading strategy so as to maximise the resulting Sharpe ratio performance indicator on a broad range of stock markets. The training of the resulting reinforcement learning (RL) agent is entirely based on the generation of artificial trajectories from a limited set of stock market historical data.
  arXiv  Detail & Related papers  (2020-04-07T14:57:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.