Intelligent Trading Research Articles

Technical indicator empowered intelligent strategies to predict stock trading signals

Technical analysis is widely employed in stock trading, relying on popular indicators such as MACD, DMI, KST etc. to predict stock trends. Despite their common use, these lagging indicators can occasionally generate misleading signals. In the literature, machine learning researchers developed many intelligent strategies for predicting stock trading signals using these indicators as inputs. However, significant differences exist in how these indicators are applied by technical analysts and machine learning experts. Building on this knowledge, this study developed intelligent stock trading signal prediction strategies using MACD, DMI, and KST indicators, and implemented these strategies with LSTM and GRU networks due to their ability to manage long-term dependencies and maintain context. The proposed intelligent trading strategies were assessed using ARR, SR, and win rate metrics, based on historical trading data from 18 stocks—six each from NEPSE, BSE, and NYSE—leading to four key insights. (1) For predicting stock trading signals, a 5-day lookback period is optimal for intelligent strategies based on MACD and DMI, while a 10-day period is best for the KST-based strategy. (2) Intelligent trading strategies implemented with GRU networks demonstrated superior performance compared to those implemented with LSTM. (3) The intelligent trading strategies based on MACD, DMI, and KST indicators outperform their peer classical stock trading methods. (4) Among the three proposed intelligent strategies, the MACD-based approach is found to be the safest and most effective.

Investment strategies based on investors’ mood: Better for crypto

Objective.Analyze the utility of an algorithmic trading system based on artificial intelligence models that uses Google Trends as predictor of dozens of financial terms, to predict the evolution of S&P 500 index and Bitcoin. Methodology. A trading algorithmic system has been developed that opens a weekly long or short position in S&P 500 and Bitcoin, following the signals issued by an artificial intelligence model that uses Google Tends as predictor for next week market trend. The artificial intelligence models were trained using weekly data from 2013 to 2018 and have been tested in a prospective way from February 2018 to December 2021. Results. Google Trends is a good predictor for global investors’ mood. The artificial intelligence algorithmic trading systems tested in a prospective way has been profitable. Trading strategies based on investors’ mood have been more accurate and profitable for Bitcoin (beating the evolution of the cryptocurrency) than for S&P 500 (not beating the index). Conclusions. This evidence opens a new field for the investigation of trading systems based on big data instead of Chartism. Although there are many trading systems based on Chartism, there are no artificial intelligence trading syste

Retraction Note: An intelligent trading mechanism based on the group trading strategy portfolio to reduce massive loss by the grouping genetic algorithm

Retraction Note: An intelligent trading mechanism based on the group trading strategy portfolio to reduce massive loss by the grouping genetic algorithm

Towards Promoting the Efficiency of Enterprise Investment Management with Artificial Intelligence

With the continuous development and optimization of artificial intelligence, enterprises have begun to pay attention to the use of artificial intelligence for the optimization of work and the improvement of efficiency, and in the management of enterprise investment, a number of companies have begun to use a large number of artificial intelligence technologies in the integration and analysis of data, information processing and analysis, risk management, efficiency improvement, and so on. Enterprises encounter problems in analyzing data and information in investment management, and problems in risk management, all of which affect the improvement of their investment returns. Enterprises face a huge amount of data in the process of investment management, it is difficult to calculate a large number of data models in a relatively short period of time, and it is impossible to analyze and extract more information. In the face of investment risk, the control of risk in the past investment process is more based on experience to judge, there is a great deal of subjective assumptions in risk avoidance. And in the investment process companies also want to increase returns, improve efficiency, avoid risk, and achieve a more automated and intelligent trading process. Enterprises need to use artificial intelligence to technically solve the problems of data aggregation, arithmetic, and analysis; use artificial intelligence to optimize the problem of processing large amounts of information; use artificial intelligence technology to enhance risk management and predictive analysis, strengthen intelligent decision-making and portfolio optimization, and provide insights into big data analysis and market trends. The wide application of artificial intelligence technology in enterprises also has a certain impact on the development of enterprises, and it has a certain impact on the planning, feasibility, whole-process control, investment evaluation, investment report, and investment efficiency of investment in the investment link, which makes it widely concerned in the society and in enterprises.

GAIN PROFITABLE INSIGHTS BY FOCUSING ON MOVING AVERAGES FOR INTELLIGENT TRADING SOLUTIONS

It is more important than ever to find intelligent trading strategies as financial markets get more complex. This paper offers a thorough investigation into the use of moving averages in Python programming for the creation of intelligent trading algorithms. This study explores the complex relationship between data analysis and profitable decision-making, providing a sophisticated viewpoint on the use of moving averages in financial planning. An overview of the Python programming language as a flexible tool for algorithmic trading and quantitative analysis opens the investigation. The discussion then turns to the use of moving averages, a key instrument in technical analysis, in the context of trading strategies. We highlight Python's flexibility by showcasing how moving averages can be easily incorporated into the trading process to improve decision support. To sum up, this paper provides an invaluable resource for traders, scholars, and algorithmic enthusiasts who want to learn more about the relationship between moving averages and Python programming. The framework that is being presented not only broadens the range of tools available for algorithmic trading, but it also emphasises how important data-driven decision-making is in turning information into profits.

A hybrid decision support system for adaptive trading strategies: Combining a rule-based expert system with a deep reinforcement learning strategy

Stock trading strategies pose challenging applications of machine learning for significant commercial yields in the finance industry, drawing the attention of both economists and computer scientists. Until now, many researchers have proposed various methods to implement intelligent trading strategy systems that can support decisions regarding stock trading. Some studies have shown that the problem of trading strategies can be successfully addressed by applying hybrid approaches. Motivated by this, we propose a hybrid decision support system for adaptive trading strategies that combines a rule-based system with deep reinforcement learning to self-improve by learning with human expertise. This study overcomes the limitations of previous hybrid models that mainly have focused on optimizing trading decisions and improving forecasting accuracy. The proposed hybrid model combines decision-making information from a rule-based model to enable the agent of reinforcement learning to capture more trading opportunities. In addition, the investor's available balance states facilitate adaptive learning by interacting with the environment. Moreover, the proposed trading mechanism adjusts the volume size using the policy gradient algorithm's action probabilities, resulting in improved risk-adjusted returns. The proposed hybrid model has the potential to be a reliable trading system in real-world applications through its ability to adapt to different market scenarios, withstand stressful market conditions, reduce transaction costs, scale to various index funds, and extend the proposed hybrid structure. This study highlights the applicability of more advanced machine learning in financial areas, and we also suggest expanding this approach to adaptive decision-making systems in other fields.

Retracted: Machine Learning: The Backbone of Intelligent Trade Credit-Based Systems

Retracted: Machine Learning: The Backbone of Intelligent Trade Credit-Based Systems

Improving CNN-Based Stock Trading by Considering Data Heterogeneity and Burst

In recent years, there have been quite a few attempts to apply intelligent techniques to financial trading, i.e., constructing automatic and intelligent trading framework based on historical stock price. Due to the unpredictable, uncertainty and volatile nature of financial market, researchers have also resorted to deep learning to construct the intelligent trading framework. In this paper, we propose to use CNN as the core functionality of such framework, because it is able to learn the spatial dependency (i.e., between rows and columns) of the input data. However, different with existing deep learning-based trading frameworks, we develop novel normalization process to prepare the stock data. In particular, we first empirically observe that the stock data is intrinsically heterogeneous and bursty, and then validate the heterogeneity and burst nature of stock data from a statistical perspective. Next, we design the data normalization method in a way such that the data heterogeneity is preserved and bursty events are suppressed. We verify out developed CNN-based trading framework plus our new normalization method on 29 stocks. Experiment results show that our approach can outperform other comparing approaches.

Does more intelligent trading strategy win? Interacting trading strategies: an agent-based approach

An artificial financial market is built on top of the Genoa Artificial Stock Market. The market is populated with agents having different trading strategies and they are let to interact with each other. Agents differ in the trading method they use to trade, and they are grouped as noise, technical, statistical analysis, and machine learning traders. The model is validated by the replication of stylized facts in financial asset returns. We were able to replicate the leptokurtic shape of the probability density function, volatility clustering, and the absence of autocorrelation in asset returns. The wealth dynamics for each agent group are analyzed throughout the trading period. Agents with a higher time complexity trading strategy outperform those with a strategy comparing their final wealth.

Statistical arbitrage trading across electricity markets using advantage actor–critic methods

In this paper, risk-constrained arbitrage trading strategies that exploit price differences arising across short-term electricity markets, namely day-ahead (DAM), continuous intraday (CID) and balancing (BAL) markets, are developed and evaluated. To open initial DAM positions, a rule-based trading policy using DAM and CID price forecasts is proposed. DAM prices are predicted using both technical indicator features and data augmentation methods, such as autoencoders and generative adversarial networks. Meanwhile, CID prices are predicted using novel features that are engineered from the limit order book. Using the forecasts, the direction of price movements is correctly predicted the majority of the time. To manage open DAM positions while optimising the risk-reward ratio, deep reinforcement learning agents trained using the advantage actor–critic algorithm (A2C) are employed. Evaluated across Dutch short-term markets, A2C yields profits surpassing those obtained using A3C and other benchmarks. We expect our study to benefit electricity traders and researchers who seek to develop state-of-art intelligent trading strategies.

Innovative Teaching and Learning of Quantum Finance and Intelligent Trading Strategies

Innovative Teaching and Learning of Quantum Finance and Intelligent Trading Strategies

Financial trading decisions based on deep fuzzy self-organizing map

The volatility features of financial data would considerably change in different periods, that is one of the main factors affecting the applications of machine learning in quantitative trading. Therefore, to effectively distinguish fluctuation patterns of financial markets can provide meaningful information for the trading decision. In this article, a novel intelligent trading system based on deep fuzzy self-organizing map (DFSOM) companied with GRU networks is proposed, where DFSOM is utilized for the clustering of financial data to acquire multiple fluctuation patterns in an unsupervised way. Firstly, in order to capture the trend features and evade the effect of high noises in financial data, the images of extended candlestick charts instead of raw data are processed and the obtained features are applied for the following unsupervised learning, where candlestick charts are produced with both price and volume information. Secondly, by using the candlestick features, a two-layer deep fuzzy self-organizing map is constructed to carry out the clustering, where two-layer models carry out the clustering in multiple time scales to improve the processing of time-dependent information. Thirdly, GRU networks are used to implement the prediction task, based on which an intelligent trading model is constructed. The feasibility and effectiveness of the proposed method are verified by using various real financial datasets.

High-frequency direction forecasting and simulation trading of the crude oil futures using Ichimoku KinkoHyo and Fuzzy Rough Set

With the rapid development of the Chinese economy in the last thirty years, crude oil has become an essential commodity and a strategic resource for China. Thus, the topic of crude oil price prediction has drawn significant attention from market participants and researchers. In this study, we propose an intelligent trading decision support system called FRS-NSGA-II-SW, which integrates the Fuzzy Rough Set (FRS), Non-dominated Sorting Genetic Algorithm-II (NSGA-II), and Sliding Window (SW), for high-frequency price direction prediction and simulated trading of the crude oil futures in the Chinese market. Additionally, a Japanese technical indicator Ichimoku KinkoHyo is employed to extract features of high-frequency movement for the proposed model. To assess the performance comprehensively, four evaluation measures, including hit ratio, accumulated return, maximum drawdown, and Sharpe ratio, are employed to investigate the performances of the proposed method and benchmark methods. As a result, the proposed approach produced an average hit ratio of 66.84%, an average accumulated return of 20.39%, an average maximum drawdown of 8.38%, and a Sharpe ratio of 1.22, which outperformed all the benchmark methods. Additionally, Friedman test results indicate that the proposed method significantly outperformed the benchmark methods in terms of prediction accuracy and profit-making ability. Furthermore, experimental results show that integrating the Multi-objective optimization algorithm NSGA-II and the dynamic learning method SW successfully enhanced the performance of the proposed method. The excellent performance of the proposed method demonstrates that it could be applied as an intelligent trading decision support system for market investors and regulators in the Chinese crude oil futures market.

Directional movement index based machine learning strategy for predicting stock trading signals

<p><br />Intelligent stock trading systems are demand of the modern information age. This research paper proposed a directional movement index based machine learning (DMI-ML) strategy for predicting stock trading signals. Performance of the proposed strategy was evaluated in terms of annual rate of return (ARR), Sharpe ratio (SR), and percentage of profitable trades executed by the trading strategy. In addition, performance of the proposed model was evaluated against the strategies viz. traditional DMI, Buy-Hold. From the experimental results, we observed that the proposed strategy outperformed other strategies in terms of all three parameters. On average, the ARR obtained from the DMI-ML strategy was 52.58% higher than the ARR obtained from the Buy-Hold strategy. At the same time, the ARR of the proposed one was found 75.12% higher than the ARR obtained from the traditional DMI strategy. Furthermore, the Sharpe ratio for the DMI-ML strategy was positive for all stocks. On the other side, the percentage of profitable trades executed by the DMI-ML strategy soared in comparison to the percentage of such trades by the traditional DMI. This study also extended analysis of the proposed model with the various intelligent trading strategies proposed by authors in various literatures and concluded that the proposed DMI-ML strategy is the better strategy for stock trading.</p>

A novel ConvLSTM with multifeature fusion for financial intelligent trading

High fluctuation and self-similarity are typical characteristics of financial time series. Furthermore, affected by market environment, such as regular announcement of important economic data, time/date-sensitive fluctuations commonly exist in financial time series. However, the existing learning models were usually lack the consideration of essential characteristics of financial data, where both the fusion learning of multiple temporal features and the necessary attention to time-sensitive fluctuations were ignored. Inspired by this, to represent the temporal characteristics of self-similarity and reveal intrinsic feature details, in this article, time series and its features are converted into visibility graphs using the technique of Gramian Angular Fields, based on which convolutional long short-term memory (ConvLSTM) is applied to implement multifeature fusion learning. Moreover, to capture the time/date-sensitive fluctuation existing in financial time series, a subspace decomposition composed of the fuzzy control mechanism is first introduced into the ConvLSTM model, which considerably improves the prediction performance. On the basis of the proposed learning model, a concise intelligent trading strategy is designed. By using real foreign exchange data, various experiments are implemented to show the effectiveness of the proposed model.

Intelligent Stock Trading Strategy Based on Aroon Indicator

The study of stock trading signal forecasting has piqued the interest of machine learning and technical analysis specialists. One of the popular tools for anticipating buy and sell signals is the Aroon indicator, but it is not utilized by machine learning researchers to predict stock trading signals. This study proposed an intelligent stock trading strategy based on the association between Aroon indicators. The performance of the proposed stock trading strategy was compared to that of a classical Aroon indicator based trading strategy in terms of annual rate of return (ARR), Sharpe ratio (SR), and percentage of gain/loss trades. In terms of all three measures, it was discovered that the intelligent trading strategy outperformed the classical trading method. The intelligent trading method generated 3.91% to 40.07% greater ARR than the classical strategy, and it did so with a positive SR for all 10 stocks studied. In addition, the intelligent approach executed a higher percentage of profitable transactions than the traditional strategy. Thus, it was established that the proposed intelligent trading method is a better and safer trading technique than the classical strategy.

Research on Strategy Discovery and Optimisation of an Intelligent Option Trading System

'Blue Ocean' is a term that refers to an unexploited or uncontested market space. AI, machine learning and Big Data has presented a range of technologies for exploring Blue Ocean market spaces. For example, in the world of financial technology (fintech) introducing new investment strategies could have an impact on investment opportunities. Professor Chien-Feng Huang, Department of Computer Science and Information Engineering, National University of Kaohsiung, Taiwan, conducts novel research to discover and optimise an intelligent option trading system. In one line of research, he is working to solve the problem of combining different strategies to generate generalised models to tackle various financial circumstances while investing. A feasible way to do this, according to the studies of Huang and his team, is to utilise AI-based optimisation methodologies. Through their studies, the researchers are seeking to advance the research and applications of fintech and discover more efficient and effective investment models to create blue ocean strategies within finance. Huang is also working on autonomous self-evolving forecasting models for price movement in high frequency trading (HFT). He and his team have developed novel AI-based models for forecasting price movement in HFT and found that their proposed methods can increase the prediction accuracy for HFT price movement and advance the current state of HFT research. Huang has had encouraging results so far with the stock selection models, pairs-trading models and options-trading models developed and deployed in the market proving viable.

Algorithmic Trading: The Intelligent Trading Systems and Its Impact on Trade Size

Financial markets have come across a phenomenal adoption of advanced and complex technologies in the pursuit of efficient markets. Algorithmic Trading (AT) is one of the prominent moves in this direction and is widely adopted across world markets. The existing literature on AT and its impact on markets is still in the nascent stage primarily due to the inability of most of the markets to directly identify AT. In this study, we directly identify AT and examine its impact on trade sizes which has a key impact on liquidity and price impact of trades. We also use the inverse of Order-to-Trade (1/OTR) ratio as a measure of algorithmic trading efficiency and examine its relationship with size. It is expected that AT has the capability to break large orders into smaller sizes in order to access liquidity and reduce price impact. In this study, we provide empirical evidence for the size effects of AT with direct identification of AT.

Statistical arbitrage trading on the intraday market using the asynchronous advantage actor–critic method

In this paper, we focus on statistical arbitrage trading opportunities involving the continuous exploitation of price differences arising during an intraday trading period with the option of closing positions on the balancing market. We aim to maximise the reward–risk ratio of an autonomous trading strategy. To find an optimal trading policy, we propose utilising the asynchronous advantage actor–critic (A3C) algorithm, a deep reinforcement learning method, with function approximators of two-headed shared deep neural networks. We enforce a risk-constrained trading strategy by limiting the maximum allowed position, and conduct state engineering and selection processes. We introduce a novel reward function and goal-based exploration, i.e. behaviour cloning. Our methodology is evaluated on a case study using the limit order book of the European single intraday coupled market (SIDC) available for the Dutch market area. The majority of hourly products on the test set return a profit. We expect our study to benefit electricity traders, renewable electricity producers and researchers who seek to implement state-of-art intelligent trading strategies.

A Reinforcement Learning approach for the continuous electricity market of Germany: Trading from the perspective of a wind park operator

With the rising extension of renewable energies, the intraday electricity markets have recorded a growing popularity amongst traders as well as electric utilities to cope with the induced volatility of the energy supply. Through their short trading horizon and continuous nature, the intraday markets offer the ability to adjust trading decisions from the day-ahead market or reduce trading risk in a short-term notice. Producers of renewable energies utilize the intraday market to lower their forecast risk, by modifying their provided capacities based on current forecasts. However, the market dynamics are complex due to the fact that the power grids have to remain stable and electricity is only partly storable. Consequently, robust and intelligent trading strategies are required that are capable to operate in the intraday market. In this work, we propose a novel autonomous trading approach based on Deep Reinforcement Learning (DRL) algorithms as a possible solution. For this purpose, we model the intraday trade as a Markov Decision Process (MDP) and employ the Proximal Policy Optimization (PPO) algorithm as our DRL approach. A simulation framework is introduced that enables the trading of the continuous intraday price in a resolution of one minute steps. We test our framework in a case study from the perspective of a wind park operator. We include next to general trade information both price and wind forecasts. On a test scenario of German intraday trading results from 2018, we are able to outperform multiple baselines with at least 45.24% improvement, showing the advantage of the DRL algorithm. However, we also discuss limitations and enhancements of the DRL agent, in order to increase the performance in future works.