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Journal articles on the topic 'Quantitative Trading Strategies'

Author: Grafiati

Published: 7 June 2025

Last updated: 17 July 2025

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1

Liu, Jiaxin. "Research on Quantitative Trading Strategies Based on the Turtle Trading Rule." Highlights in Business, Economics and Management 10 (May 9, 2023): 72–80. http://dx.doi.org/10.54097/hbem.v10i.7933.

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Quantitative investment is a newer investment method than traditional investment methods, with features such as being free from emotions and being able to access and process large amounts of data quickly. Based on this, this paper selects fuel oil, FU2205, as the underlying for the period from May 31, 2021 to November 30, 2021 to investigate the effectiveness of the turtle trading rule in quantitative trading strategies. In this way, the effectiveness of the turtle trading rule is explored. The article first introduces the background and significance of the study, explains the current research

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2

Gao, Junbo. "Applications of machine learning in quantitative trading." Applied and Computational Engineering 82, no. 1 (2024): 124–29. http://dx.doi.org/10.54254/2755-2721/82/20240984.

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Abstract. This paper also addresses quantitative trading where machine learning techniques are applied. Quantitative trading uses historical data and makes future predictions that inform and optimise trading strategies. There are two machine learning techniques used in quantitative trading: supervised and reinforcement learning. In a supervised learning setting, machine learning techniques such as regression analysis, classification models and time series prediction are used to forecast future possible outcomes in the market. Data such as historical prices, volume and financial indicators are

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Liu, Yang, Qi Liu, Hongke Zhao, Zhen Pan, and Chuanren Liu. "Adaptive Quantitative Trading: An Imitative Deep Reinforcement Learning Approach." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (2020): 2128–35. http://dx.doi.org/10.1609/aaai.v34i02.5587.

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In recent years, considerable efforts have been devoted to developing AI techniques for finance research and applications. For instance, AI techniques (e.g., machine learning) can help traders in quantitative trading (QT) by automating two tasks: market condition recognition and trading strategies execution. However, existing methods in QT face challenges such as representing noisy high-frequent financial data and finding the balance between exploration and exploitation of the trading agent with AI techniques. To address the challenges, we propose an adaptive trading model, namely iRDPG, to au

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Shi, Guangde, Jingkai Gao, Ruibin Li, and Jun Shi. "Development of Daily Trading Strategies Based on A Quantitative Trading Decision Model." BCP Business & Management 26 (September 19, 2022): 445–52. http://dx.doi.org/10.54691/bcpbm.v26i.1995.

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Quantitative trading decision models have a key influence on financial investment. Firstly, this study established an LSTM model by using long-term and short-term memory networks and predicted the future prices of gold and bitcoin investment products. Then, according to the time range of gold and bitcoin assets, three types of transactions were determined: cross, non-cross, and inclusion relationship, and the daily trading strategies were determined by the greedy model established by a greedy algorithm. Then, the Sharpe Ratio of the nonparametric method was used to measure the risk of the deve

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Dong, Xinyu, Yining He, and Muyi Lei. "Investment Planning Model Based on Quantitative Trading Strategies." BCP Business & Management 19 (May 31, 2022): 227–35. http://dx.doi.org/10.54691/bcpbm.v19i.808.

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Investment, as a financial activity for the general public, has a wide influence on the development of national enterprises. In the research, the main object is Bitcoin and gold, and the investment prediction of short-term investment is made according to the real financial market, and the investment decision prediction model is established by using XGBoost, BP neural network model, entropy value method, coefficient of variation method, linear programming, and value-at-risk model to solve. The topic provides historical data related to bitcoin and gold. First, for the amount of gold and bitcoin

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ALIEV, Beilak N. "Unification of automated trading systems using the principle of interface segregation." Financial Analytics: Science and Experience 17, no. 3 (2024): 359–66. http://dx.doi.org/10.24891/fa.17.3.359.

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Subject. The article discusses a model for unification of trading strategies architecture based on segregated software interfaces, which will improve the quality of testing of research works aimed at the development of quantitative trading strategies. Objectives. The purpose of the study is to formulate proposals for unification of the architecture of trading strategies for automatic trading systems based on the interface segregation principle. Methods. The study employs the method of empirical observation, analysis of analytical and expert information, practices of software engineering in sys

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7

Xiang, Chengye. "Theory and Practice of Quantitative Investment Strategies." Highlights in Business, Economics and Management 40 (September 1, 2024): 1280–87. http://dx.doi.org/10.54097/q1qy3n32.

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This paper provides an overview of the theory and practice of quantitative investment strategies, beginning with the definition of quantitative investment, its history of development, and its importance in financial markets. It then delves into the theoretical foundations of quantitative investment, including financial market theory, the application of mathematical and statistical methods, and the theory of risk management and portfolio optimisation. Common quantitative investment strategies, such as spread trading, momentum strategy, mean reversion strategy, etc., are then detailed, as well a

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Qin, QiaoXu, GengJian Zhou, and WeiZhou Lin. "Futures Quantitative Trading Strategies Based on Market Capital Flows." Applied Economics and Finance 5, no. 2 (2018): 175. http://dx.doi.org/10.11114/aef.v5i2.3008.

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The purpose of this paper is to establish a futures quantitative trading strategy based on the characteristics of capital flows in the futures market and the factors that influence the Futures rate of return. Firstly, PCA and logistic regression are used as the theoretical basis to analyze the characteristics of future futures with high turnover rate and futures yield in the future, and summarize the characteristics of rotation, continuity and similarity of the capital flow in the futures market. Then combining with the characteristics of the flow of futures funds and the idea of taking profit

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Zhang, Yu, Shulin Xu, and Liwei Hou. "A study of gold and bitcoin trading strategies based on quantitative trading decision models." Highlights in Business, Economics and Management 7 (April 5, 2023): 300–310. http://dx.doi.org/10.54097/hbem.v7i.6962.

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In the computer era, high frequency trading is one of the hottest topics in the financial investment market. In this paper, a quantitative trading decision analysis is conducted for a portfolio consisting of gold and bitcoin, applying gray prediction to get the future asset price, applying time series to predict the risk of purchasing the asset, and combining the two to make a reasonable buying and selling operation. To prove the accuracy of the prediction, the MSE indicator is used to measure the error of the gray prediction model, and the results show that the error is small. Finally this pa

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10

Ryu, Jaepil, and Hyun Joon Shin. "Investment Strategies for KOSPI200 Index Futures Using Negative Correlation of Time-Series." Journal of Derivatives and Quantitative Studies 22, no. 4 (2014): 723–46. http://dx.doi.org/10.1108/jdqs-04-2014-b0006.

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This paper presents 6 time-series that have negative correlation with KOSPI200 Index and a quantitative trading methodology based on stochastic control chart using these time-series. The proposed quantitative trading framework detects trade (long or short) timing by monitoring whether a time-series touches 4 trigger lines, which play a role as control limits in control chart. In other words, a time-series upwardly touches one of trigger line, then the framework take a short position on KOSPI200 Index Futures, while in case of downward touch, it takes a long position. The 6 time-series are deri

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Sun, Wenjie. "Research on Quantitative Investment Strategies Based on Artificial Intelligence." Highlights in Business, Economics and Management 39 (August 8, 2024): 1014–23. http://dx.doi.org/10.54097/pryvrp09.

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Artificial Intelligence (AI) has been developing rapidly in recent years, and the application of AI in various fields has gradually emerged. This paper focuses on exploring the cross-fertilization of AI with the Chinese stock market, and the study adopts 7 types of factors, totaling 29 factor indicators, covering multiple types of value, valuation, leverage, financial quality, growth, technology, and risk factors, etc. Through data preprocessing, feature engineering, and other steps on the factors, and then combined with 8 machine learning algorithms to construct corresponding quantitative tra

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Huang, Linpei, Tengwei Cai, Shuai Feng, and Zhecheng Xie. "Quantitative Trading Strategy Based on Simplified DPG." Highlights in Business, Economics and Management 13 (May 29, 2023): 10–20. http://dx.doi.org/10.54097/hbem.v13i.8616.

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In recent years, investment has become more and more popular, and asset management has also received more and more attention. At the same time, with the development of computer science, more and more machine learning or deep learning algorithms can be used for investment management, such as price forecasting, portfolio and quantitative trading strategies. First of all, in the trading market, most of the ups and downs are cyclical, but they are easily affected by factors such as policies and investment fever, which brings great challenges to the establishment of a reasonable price forecasting m

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Patil, Ganesh. "Prediction and Portfolio Optimization in Quantitative Trading Using Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 1544–53. http://dx.doi.org/10.22214/ijraset.2023.53921.

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Abstract: Quantitative trading is an automated procedure in which trading techniques and judgments are performed using mathematical models. Quantitative trading involves a vast spectrum of computational methods, such as statistics, physics, or machine learning to diagnose, forecast, and benefit big data in finance for acquisition. This work analyses the body components of a quantitative trading technique. Machine learning presents many consequential benefits over conventional algorithmic trading. Machine learning executes numerous trading techniques consistently and acclimates to real-time dem

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Fang, Yujie, Juan Chen, and Zhengxuan Xue. "Research on Quantitative Investment Strategies Based on Deep Learning." Algorithms 12, no. 2 (2019): 35. http://dx.doi.org/10.3390/a12020035.

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This paper takes 50 ETF options in the options market with high transaction complexity as the research goal. The Random Forest (RF) model, the Long Short-Term Memory network (LSTM) model, and the Support Vector Regression (SVR) model are used to predict 50 ETF price. Firstly, the original quantitative investment strategy is taken as the research object, and the 15 min trading frequency, which is more in line with the actual trading situation, is used, and then the Delta hedging concept of the options is introduced to control the risk of the quantitative investment strategy, to achieve the 15 m

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15

Wang, Yifei, Xiaofeng Zhao, Feng Zhang, Siyang Xie, and Zhihan Liu. "Optimal trading strategies based on time series analysis." Advances in Economics and Management Research 7, no. 1 (2023): 730. http://dx.doi.org/10.56028/aemr.7.1.730.2023.

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Quantitative investment has been widely used in the field of foreign finance, especially the rapid development of international investment in the past decade. And financial activity is an important field of national economic activity. The frequency of financial transactions is an important indicator of the complexity of a country's economy, so it is of great significance to study the optimal investment strategy. This article uses daily price streams from past investments in gold, cash, and bitcoin to determine whether traders should buy, hold, or sell assets in their portfolios. The outlier da

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16

Duan, Liran. "Exploring Efficient Quantitative Trading Strategies: A Comprehensive Comparison of Momentum, SMAs and Machine Learning." Advances in Economics, Management and Political Sciences 86, no. 1 (2024): 43–48. http://dx.doi.org/10.54254/2754-1169/86/20240940.

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To provide an objective analysis, this study examines three quantitative trading strategies: Momentum, Moving Average Crossover, and Machine Learning individually but in a common methodological setting. In order to achieve higher returns at lower levels of risk due to the advent of algorithmic trading, such strategies must be explored. The two strategies that we analyze include the Momentum strategy that capitalizes on the persistence in price trends and the Moving Average Crossover strategy that relies on average price movements as trading signals. In addition, in this study, Machine Learning

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He, Yijun, Bo Xu, and Xinpu Su. "High-Frequency Quantitative Trading of Digital Currencies Based on Fusion of Deep Reinforcement Learning Models with Evolutionary Strategies." Journal of Computing and Information Technology 32, no. 1 (2024): 33–45. http://dx.doi.org/10.20532/cit.2024.1005825.

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High-frequency quantitative trading in the emerging digital currency market poses unique challenges due to the lack of established methods for extracting trading information. This paper proposes a deep evolutionary reinforcement learning (DERL) model that combines deep reinforcement learning with evolutionary strategies to address these challenges. Reinforcement learning is applied to data cleaning and factor extraction from a high-frequency, microscopic viewpoint to quantitatively explain the supply and demand imbalance and to create trading strategies. In order to determine whether the algor

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18

Al-Sulaiman, Talal. "Review of Recent Research Directions and Practical Implementation of Low-Frequency Algorithmic Trading." American Journal of Financial Technology and Innovation 2, no. 1 (2024): 1–14. http://dx.doi.org/10.54536/ajfti.v2i1.2354.

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Financial trading has undergone substantial technological evolution, with automation taking center stage, leading to approximately 80% of US market trades being executed by computer systems, predominantly by large financial institutions. The rise of algorithmic trading, poised to engage smaller entities, international markets, and individual traders, drives this article’s exploration of research in this field. Providing a comprehensive overview, it outlines the evolution of trading practices and defines algorithmic trading as a computer-powered tool aiding investment decisions. The article det

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19

Han, Guodong, and Hecheng Li. "An LSTM-based optimization algorithm for enhancing quantitative arbitrage trading." PeerJ Computer Science 10 (July 8, 2024): e2164. http://dx.doi.org/10.7717/peerj-cs.2164.

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Arbitrage trading is a common quantitative trading strategy that leverages the long-term cointegration relationships between multiple related assets to conduct spread trading for profit. Specifically, when the cointegration relationship between two or more related series holds, it utilizes the stability and mean-reverting characteristics of their cointegration relationship for spread trading. However, in real quantitative trading, determining the cointegration relationship based on the Engle-Granger two-step method imposes stringent conditions for the cointegration to hold, which can easily be

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20

Fiemotongha, Joyce Efekpogua, Abbey Ngochindo Igwe, Chikezie Paul Mikki Ewim, and Ekene Cynthia Onukwulu. "Innovative trading strategies for optimizing profitability and reducing risk in global oil and gas markets." Journal of Advance Multidisciplinary Research 2, no. 1 (2023): 48–65. https://doi.org/10.54660/.jamr.2023.2.1.48-65.

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The global oil and gas markets are characterized by extreme price volatility driven by geopolitical events, supply-demand imbalances, and macroeconomic factors. Traditional trading strategies often struggle to maintain profitability while mitigating risks in such unpredictable environments. This study explores the development and implementation of innovative trading strategies that optimize profitability and reduce risk in global oil and gas markets. By leveraging advanced analytics, algorithmic trading, and real-time market intelligence, traders can improve decision-making, enhance risk-adjus

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21

Li, Jiachen, Zhenzhuo Qi, Zhuofeng Li, Guozheng Wang, and Jiayu Peng. "Quantitative trading decision model based on LSTM neural network." Highlights in Business, Economics and Management 5 (February 16, 2023): 583–91. http://dx.doi.org/10.54097/hbem.v5i.5159.

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As trading markets are vulnerable to national policies and economies, some investors choose to leave their assets in the hands of market traders for management. To help market traders find the best portfolio, the best trading strategies were dynamized to obtained by predicting the daily price movements of gold and bitcoin. BP neural network, time series analysis, and LSTM neural network were chosen for modeling quantitative trading decisions and modify the dynamic model of optimal trading. The LSTM neural network was used to indicate the time series because of the best fit of 0.991. And the in

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Li, Yanjin, Xinyu Song, and Xiangjun Yan. "Analysis of Paired Trading Strategies Based on Boll Bands." Advances in Economics, Management and Political Sciences 23, no. 1 (2023): 259–67. http://dx.doi.org/10.54254/2754-1169/23/20230387.

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The development of information technology makes quantitative investment strategy widely used by various investors. Among them, pairing trading strategy has attracted the attention of a large number of investors. In this paper, the validity of paired trading is verified and the validity of Bollinger band signals used for paired trading is tested. To be specific, this paper takes Shanghai and Shenzhen 300 component stocks and China Securities 500 component stocks as data sets. In this paper, the correlation coefficient method is used to match the stocks. This study uses the closing price data of

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23

Liu, Yangxuan, Mingyuan Gao, Yiyang Guo, and Yichen Qiao. "Designing Efficient Pair-Trading Strategies for the Technology Stock Market." Advances in Economics, Management and Political Sciences 106, no. 1 (2024): 359–69. http://dx.doi.org/10.54254/2754-1169/106/20241453.

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Pairs trading is a quantitative trading approach that exploits instances of financial markets exhibiting disequilibrium. Through the identification of a pair of stocks with a his-torical pattern of correlated movement, and under the assumption that their price differentials will return to a central tendency, an investor can seek to gain from this mean-reversion by establishing a long position in the designated pair. Throughout the years, numerous trading frameworks and methodologies have been devised to enhance the efficacy of this strategy. This paper proposes an approach based on cointegrati

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Zhang, Wenbin, and Steven Skiena. "Trading Strategies to Exploit Blog and News Sentiment." Proceedings of the International AAAI Conference on Web and Social Media 4, no. 1 (2010): 375–78. http://dx.doi.org/10.1609/icwsm.v4i1.14075.

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We use quantitative media (blogs, and news as a comparison) data generated by a large-scale natural language processing (NLP) text analysis system to perform a comprehensive and comparative study on how company related news variables anticipates or reflects the company's stock trading volumes and financial returns. Building on our findings, we give a sentiment-based market-neutral trading strategy which gives consistently favorable returns with low volatility over a long period. Our results are significant in confirming the performance of general blog and news sentiment analysis methods over b

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Yang, Liyu, Tongyang Wang, and Ciyu Cai. "Quantitative Factor Exploration Based on Insider Trading Detection." Advances in Economics, Management and Political Sciences 21, no. 1 (2023): 88–100. http://dx.doi.org/10.54254/2754-1169/21/20230238.

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Factor research has always been the focus of financial quantitative forecasting research. In the existing multi-factor strategies, we have noticed that the combination modes of factors are different and arbitrary. We hope to develop a more accurate and effective multi-factor model by selecting the most common and most interpretable multi-factors and combining them with equal weight method and assigned weights developed by Hidden Markov Model after some optimizations applied to selected multi-factors. At the same time, we noticed that in the existing data information, there are reports or infor

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Zhang, Xinchen, Linghao Zhang, Qincheng Zhou, and Xu Jin. "Greedy Strategies with Multiobjective Optimization for Investment Portfolio Problem Modeling." Computational Intelligence and Neuroscience 2022 (May 19, 2022): 1–12. http://dx.doi.org/10.1155/2022/4862772.

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The ultimate purpose of portfolio investment is to reduce investment risk and improve total return on the premise of ensuring reasonable allocation of capital. In this paper, we build a quantitative model to advise on trading based on the price movement of Bitcoin and gold between 2016 and 2021; our goal is to maximize profit while minimizing risk. We mainly use greedy strategies with multiobjective optimization models. For the purpose of obtaining the correct price trend, some popular trend indicator strategies are referred to predict the future price trend in the medium and long term. In add

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Gao, Chenhui. "Risk Hedging Application of Quantitative Trading Risk Assessment Method in Securities Market." Highlights in Business, Economics and Management 42 (November 19, 2024): 177–82. http://dx.doi.org/10.54097/5y86rv58.

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This study focuses on the field of quantitative trading and explores in depth the application of risk assessment methods in securities market risk hedging. Through in-depth analysis of different risk categories in quantitative trading, the transmission mechanism and core influencing factors are clarified, such as market risk, credit risk, operational risk, liquidity risk, etc. Based on capital allocation theory, a dynamic risk parity strategy is created with the goal of balancing asset risk contributions, enhancing the robustness of quantitative strategies, and achieving sustained positive ret

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Li, Fangyi, Zhixing Wang, and Peng Zhou. "Ensemble Investment Strategies Based on Reinforcement Learning." Scientific Programming 2022 (September 8, 2022): 1–9. http://dx.doi.org/10.1155/2022/7648810.

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Due to the rapid development of hardware devices, the analytical processing and algorithmic capabilities of computers are also being enhanced, which makes machine learning play an increasingly important role in the field of quantitative investment. For this reason, the possibility of replacing traditional human traders with automated investment algorithms that have been trained several times has become a hot topic in recent years. The majority of machine algorithms used in today’s stock trading market are supervised learning algorithms, which are still unable to objectively analyse the market

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陆, 羿辰. "The Application of the PBO Algorithm in Quantitative Trading—Taking Gold and Bitcoin Trading Strategies as Examples." Advances in Applied Mathematics 12, no. 04 (2023): 1690–97. http://dx.doi.org/10.12677/aam.2023.124175.

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Liu, Jiaxin. "The Application and Prospects of Deep Learning in the Field of Quantitative Investment." Advances in Economics, Management and Political Sciences 92, no. 1 (2024): 87–93. http://dx.doi.org/10.54254/2754-1169/92/20231328.

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This literature review provides a comprehensive overview of key developments in quantitative trading theory, machine learning-based financial time series forecasting, deep learning-based financial time series forecasting, and modern quantitative investment strategies. It highlights seminal contributions from renowned scholars and researchers in the field. This review first explores the Efficient Market Hypothesis (EMH) proposed by Eugene Fama in 1970 and its empirical validation, which lays the foundation for understanding stock market dynamics. It then focuses on the application of machine le

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Tang, Yi, Xiaoning Wang, and Wenyan Wang. "Securities Quantitative Trading Strategy Based on Deep Learning of Industrial Internet of Things." International Journal of Information Technology and Web Engineering 19, no. 1 (2024): 1–16. http://dx.doi.org/10.4018/ijitwe.347880.

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By combing the shortcomings of the current quantitative securities trading, a new deep reinforcement learning modeling method is proposed to improve the abstraction of state, action and reward function; on the basis of the traditional DQN algorithm, a deep reinforcement learning algorithm model of RB_DRL is proposed. By improving the network structure and connection mode, and redefining the loss function of the network, the improved model performs well in many groups of comparative experiments. A securities quantitative trading system based on deep reinforcement learning is designed, which org

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Chen, Shaozhen, Bangqian Zhang, GengJian Zhou, and Qiaoxu Qin. "Bollinger Bands Trading Strategy Based on Wavelet Analysis." Applied Economics and Finance 5, no. 3 (2018): 49. http://dx.doi.org/10.11114/aef.v5i3.3079.

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With the popularization of the concept of quantitative investment and the introduction of stock index futures in China, the research on the quantitative trading strategies of stock index futures is emerging gradually. This paper takes the CSI 300 stock index futures as the research object and sets up the Bollinger Bands trading strategy to test it, while considering the factors such as returns, retracement and income risk ratio, etc. Furthermore, the paper uses the wavelet noise reduction to process the data of price and the Bollinger Bands trading strategy to test the processed data. Compared

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Li, Yang, Yanchen Zou, and Yanda Qian. "Practice of Python in Programming and Optimization of Quantitative Analysis Model of Fixed Income." Mathematical Modeling and Algorithm Application 3, no. 1 (2024): 23–26. http://dx.doi.org/10.54097/z441c084.

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Python has become an indispensable tool in fixed income trading. This paper introduces the principles and general process of data analysis visualisation by analysing the basic operation and performance of personal investment and finance, combined with data analysis in the era of big data. The library of data analysis tools using Python has become an indispensable tool in fixed income trading. In fixed income trading, it supports fixed income traders to handle tasks such as bond pricing, interest rate modelling, and credit risk analysis Using Python's powerful data processing capabilities, trad

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Li, Wang, Chaozhu Hu, and Youxi Luo. "A Deep Learning Approach with Extensive Sentiment Analysis for Quantitative Investment." Electronics 12, no. 18 (2023): 3960. http://dx.doi.org/10.3390/electronics12183960.

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Recently, deep-learning-based quantitative investment is playing an increasingly important role in the field of finance. However, due to the complexity of the stock market, establishing effective quantitative investment methods is facing challenges from various aspects because of the complexity of the stock market. Existing research has inadequately utilized stock news information, overlooking significant details within news content. By constructing a deep hybrid model for comprehensive analysis of historical trading data and news information, complemented by momentum trading strategies, this

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Leng, Rufeng. "AI-Driven Optimization of Financial Quantitative Trading Algorithms and Enhancement of Market Forecasting Capabilities." Applied and Computational Engineering 100, no. 1 (2024): 1–6. http://dx.doi.org/10.54254/2755-2721/100/20251742.

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Abstract: The application of artificial intelligence (AI) into financial markets has revolutionised quantitative trading and market forecasting by increasing the efficiency of algorithmic trading, improving the accuracy of market predictions and facilitating real-time market decisions. This paper will provide an overview of the application of Al in the financial markets focusing on the use of machine learning (ML), deep learning (DL) and reinforcement learning (RL) in optimizing the trading algorithms, specifically the capability of Al to process very high data points and complex relationships

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Leng, Rufeng. "AI-Driven Optimization of Financial Quantitative Trading Algorithms and Enhancement of Market Forecasting Capabilities." Applied and Computational Engineering 116, no. 1 (2024): 1–6. https://doi.org/10.54254/2755-2721/116/20251742.

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The application of artificial intelligence (AI) into financial markets has revolutionised quantitative trading and market forecasting by increasing the efficiency of algorithmic trading, improving the accuracy of market predictions and facilitating real-time market decisions. This paper will provide an overview of the application of Al in the financial markets focusing on the use of machine learning (ML), deep learning (DL) and reinforcement learning (RL) in optimizing the trading algorithms, specifically the capability of Al to process very high data points and complex relationships that othe

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Wang, Zidu, and Yintian Yang. "Optimization of Quantitative Financial Trading Strategies Based on Machine Learning: Prediction and Decision Models for Stock and Derivatives Markets." Applied and Computational Engineering 115, no. 1 (2024): 147–52. https://doi.org/10.54254/2755-2721/2025.18518.

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Machine learning has become a cornerstone of quantitative finance, which allows for data-driven forecasting, strategy optimization, and trading-by-dot decision making. It analyzes the application of machine learning models such as recurrent neural networks (RNNs), long short term memory (LSTM) networks, and reinforcement learning (RL) in order to generate optimal predictive models and adaptive trading techniques. The research is based on historical stock and derivative market data and follows a rigorous process that includes data preprocessing, feature engineering and model training using rand

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SAYYED, ZAID. "Real Time-Cutting Algorithmic Trading." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35766.

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a. Brief overview of algorithmic trading. Algorithmic trading, also known as algo trading or automated trading, refers to the use of computer algorithms and mathematical models to execute trading orders in financial markets. The primary goal of algorithmic trading is to achieve efficient and optimized execution of trading strategies, leveraging the speed and precision of computer systems. Here is a brief overview of algorithmic trading: 1. **Automation:** Algorithmic trading involves automating the process of buying or selling financial instruments, such as stocks, bonds, currencies, or commod

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Xu, Zishan, Chuanggeng Lin, Zhe Zhuang, and Lidong Wang. "Research on Multistage Dynamic Trading Model Based on Gray Model and Auto-Regressive Integrated Moving Average Model." Discrete Dynamics in Nature and Society 2023 (February 16, 2023): 1–15. http://dx.doi.org/10.1155/2023/1552074.

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Quantitative portfolio investment mainly depends on historical data analysis and market trend prediction to make appropriate decisions, which is an important mean to reduce risks and increase returns. Based on summarizing the existing traditional single forecasting models and multiobjective dynamic programming models, this paper puts forward a new quantitative portfolio model to improve the accuracy of asset price forecasting results and the appropriateness of investment trading strategies, to better realize the maximization of investment returns. This model analyzes and forecasts daily price

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Li, Zhiming, Junzhe Jiang, Yushi Cao, et al. "Logic-Q: Improving Deep Reinforcement Learning-based Quantitative Trading via Program Sketch-based Tuning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 17 (2025): 18584–92. https://doi.org/10.1609/aaai.v39i17.34045.

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Deep reinforcement learning (DRL) has revolutionized quantitative trading (Q-trading) by achieving decent performance without significant human expert knowledge. Despite its achievements, we observe that the current state-of-the-art DRL models are still ineffective in identifying the market trends, causing them to miss good trading opportunity or suffer from large drawdowns when encountering market crashes. To address this limitation, a natural approach is to incorporate human expert knowledge in identifying market trends. Whereas, such knowledge is abstract and hard to be quantified. In order

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Tsai, Yun-Cheng, Jun-Hao Chen, and Jun-Jie Wang. "Predict Forex Trend via Convolutional Neural Networks." Journal of Intelligent Systems 29, no. 1 (2018): 941–58. http://dx.doi.org/10.1515/jisys-2018-0074.

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Abstract Deep learning is an effective approach to solving image recognition problems. People draw intuitive conclusions from trading charts. This study uses the characteristics of deep learning to train computers in imitating this kind of intuition in the context of trading charts. The main goal of our approach is combining the time-series modeling and convolutional neural networks (CNNs) to build a trading model. We propose three steps to build the trading model. First, we preprocess the input data from quantitative data to images. Second, we use a CNN, which is a type of deep learning, to t

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Chu, Jun. "Research on Localization Status and Investment Strategy of Quantitative Fund in China." BCP Business & Management 40 (March 8, 2023): 214–20. http://dx.doi.org/10.54691/bcpbm.v40i.4384.

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In this paper, Quant funds in China based on the market as a starting point for research, at the current mainstream of modern portfolio theory, the factor model and the theoretical basis of dynamic DEA, mainly analyze the modern portfolio theory in the application of funds of funds, not a single security analysis, especially since 2014, when the domestic market emerged a lot of “FOF "and ”FOHF." Most of these portfolio funds do not carry out in-depth analysis of the underlying funds or even understand the trading methods and essence of various strategies, but focus on the combination of variou

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Shen, Zhijie, Zicheng Wei, and Yang Zhang. "A Study of Trade Strategies Based on the Markov Regime Switching Model." Advances in Economics and Management Research 5, no. 1 (2023): 404. http://dx.doi.org/10.56028/aemr.5.1.404.2023.

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Market traders often buy and sell stocks to maximize their total return. For each purchase and sale, there is often a return commission. Having trading technology plays an important role in quantitative trading. In this paper, we first use the XGBoost model to learn the historical price fluctuation data of gold and bitcoin, and the prediction accuracy R2 is between 0.998 and 0.999, which is a good fit. Then LT algorithm and PS algorithm are used to identify the rules, the Markov regime switching model is used to determine the bear and bull market, and finally, the DQN model is used to plan the

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Wang, Jiaheng. "Quantitative trading models based on Sufficient Dimension Reduction and Ensemble Learning." Highlights in Business, Economics and Management 19 (November 2, 2023): 6–16. http://dx.doi.org/10.54097/hbem.v19i.11746.

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With the continuous development of the securities market, constructing quantitative trading strategies with strong generalization ability the adaption to dynamic and changeable market environment is becoming more and more momentous in the field of quantitative finance. Under such circumstances, this paper proposes a stock prediction model based on sufficient dimension reduction theory and ensemble learning, which can be deployed on quantitative trading strategy. The proposed model on the one hand alleviates the curse of dimension by using the sufficient dimension reduction method and maximally

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Zeng, Zhiming, and Weijun Xu. "Online Portfolio Based on Trend Trading Strategy Considering Investor Sentiment Using Text Analysis." International Journal of Fuzzy System Applications 13, no. 1 (2024): 1–20. http://dx.doi.org/10.4018/ijfsa.355246.

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Intelligent online portfolios have become an important research topic in the field of quantitative finance. This paper proposes an online portfolio based on trend trading strategy using fuzzy logic technology analysis method and considering investor sentiment. Firstly, the paper uses SVM classification algorithm to analyze stock comment text data in online forums and constructs investor sentiment indicators. Secondly, the paper transforms the heuristic algorithm of technical trading into corresponding fuzzy IF-THEN rules and combines them into a fuzzy investment decision system. Thirdly, the p

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Wang, Junliang, Jiahao Xu, Qishuo Cheng, and Rathna Kumar. "Research on finance Credit Risk Quantification Model Based on Machine Learning Algorithm." Academic Journal of Science and Technology 10, no. 1 (2024): 290–98. http://dx.doi.org/10.54097/5yzwty57.

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Machine learning is a branch of artificial intelligence (AI) technology that enables systems to learn and make predictions and decisions without the need for explicit programming. Machine learning algorithms learn patterns and relationships from data and are able to gradually improve their accuracy.This paper mainly introduces the application of deep learning in financial academia and the financial industry, with a particular focus on the application of machine learning and deep learning in financial quantitative trading. The paper mentions the complexity and challenges of financial markets an

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Li, Junya. "Comparison of Two Quantitative Strategies: Momentum and Mean-reversion." BCP Business & Management 38 (March 2, 2023): 1326–32. http://dx.doi.org/10.54691/bcpbm.v38i.3890.

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With the development of finance and computers, the field of quantitative finance is gradually gaining attention and research. In this paper, two classic trading strategies are chosen for comparisons (namely momentum strategy and mean reversion strategy) to select the performance of Tesla and Gold in the last decade as the research object. The Tesla and Gold both performances better in momentum strategy than in the mean-reversion strategy. According to the analysis, it demonstrates that volatile stocks are more likely to generate greater profits with a momentum strategy. However, volatile equit

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Ege, Doğan Dursun, Alp Toçoğlu Mansur, and Şatır Emre. "Forecasting Performance of Quantitative Strategies with OpenAI GPT-4." Journal of Intelligent Systems with Applications 7, no. 2 (2024): 24–30. https://doi.org/10.5281/zenodo.14585483.

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The advent of advanced language processing models like OpenAIs GPT-4 presents new opportunities for enhancing financial decision-making. This study aims to explore the potential of GPT-4 in forecasting the performance of quantitative trading strategies, with a focus on the application of specific indicators in a long-short portfolio over a time frame. To achieve this, we employ a novel approach that involves posing targeted questions to GPT regarding the effectiveness of the indicators. The responses of the model are then subjected to a comparison with backtesting results obtained from the cor

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Gong, Lingyue, Weihao Gong, Jiani Luo, and Rongze Yang. "Trend-Enhanced Improved Bollinger Bands Trend-Following High-Frequency Trading Strategy for Futures Market." Highlights in Business, Economics and Management 24 (January 22, 2024): 1702–9. http://dx.doi.org/10.54097/btgvew93.

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The application of digital scientific technologies in the global financial markets has become widespread, which has led to the prevalence of the concept of quantitative trading. Among various quantitative trading strategies, high-frequency trading has gained significant popularity. In this study, we propose an improved Bollinger Bands trend-following high-frequency trading strategy based on trend enhancement. Instead of using the simple moving average (SMA), we replace it with the exponential moving average (EMA). Furthermore, we introduce a 3-times average true range (ATR) limit price rebound

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Azaluddin, Azaluddin, and Jamdia Jamdia. "Marketing Strategy Analysis Using the Quantitative Strategic Planning Matrix (QSPM) to Increase Sales Furniture." Sang Pencerah: Jurnal Ilmiah Universitas Muhammadiyah Buton 8, no. 2 (2022): 566–78. http://dx.doi.org/10.35326/pencerah.v8i2.2236.

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Sarah's furniture trade company is a company that manufactures industrial furniture such as cabinets, chairs, and tables. Sarah's Furniture Trading Business's concern is that the company's revenues from 2019 to 2020 declined owing to internal and external factors. The goal of this research is to discover the marketing strategy employed by the Sarah Furniture Trading Business, as well as how the marketing strategy employs the Quantitative Strategic Planning Matrix approach to enhance sales at the Sarah Furniture Trading Business. Sarah's furniture trade business received a total score of 22.75

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