Learning to trade autonomously in stocks and shares: integrating uncertainty into trading strategies

Abstract: Purpose: This paper discusses major stock market trends and provides information on stock market forecasting. Stock market forecasting is essentially an attempt to forecast the future value of the stock market. Doing this manually can be a strenuous task, and thus we need some software and algorithms to make our task easier. This paper also lists a few of those algorithms, formulas, and calculations associated with them. These algorithms and models primarily revolve around the concept of Machine Learning (ML) and Deep Learning. Research Methodology: This study is based on descriptive, quantitative, and cross-sectional research design. We used a multivariate algorithm model and indicators to examine stocks for investing or trading and their efficiency. It concludes with the recommendations for enhancing trading strategies using machine learning algorithms. Results: This study suggests that after comparing and combining the various algorithms using experimental analysis, the random forest algorithm is the most suitable algorithm for forecasting a stock's market prices based on various data points from historical data. Limitations: The applicability of the study was only hampered by unforeseeable tragic events such as economic crisis, market collapse, etc Contribution: Successful stock prediction will be a substantial benefit for stock market institutions and provide real-world answers to the challenges that stock investors face. As a result, gaining significant knowledge on the subject is quite beneficial for us. Keywords: 1. Algorithms 2. Algo-trading 3. Deep learning 4. Machine learning 5. Price prediction 6. Stock market 7. Trading 8. Trends

It has always been a challenge to accurately forecast the behavior of a stock market due to its extremely nonlinear and dynamic nature. Numerous studies have shown that technical indicators describing stocks in conjunction with machine learning models can serve as useful tools for forecasting in the stock market. There are various challenges, and one of them is the choice of the right technical indicators and prediction models. It is believed that there is no optimal set of technical indicators that work well in all market scenarios in a dynamic environment such as the stock market. The statement also applies to different prediction models. There is no definite winner, and different settings can emerge as winners in different market scenarios. On this premise, we propose DSdT: a dynamic scenario‐driven technique for stock price prediction and trading strategy enhancement. The proposed novel technique uses the scenario recognition and integration module to identify and integrate the current market scenario into the forecasting pipeline, resulting in a scenario‐driven stock price prediction. We use a large set of technical indicators and a shallow neural network equipped with a gating mechanism to capture and integrate the current market scenario in the prediction process. Experiments are performed on 11 stocks of the Indian Stock Market. The proposed approach yields mean absolute percentage error (MAPE) of 1.67%compared with 2.4%of its closest nonscenario‐driven counterpart for the next day's stock price prediction task. A trading strategy is also devised using the proposed technique, and the returns are compared with different baselines. Results show that the devised trading strategy yields an approximate average return of 54%compared with 25%of the return obtained by the nearest benchmark.

Introduction. Exchange trading is a fairly widespread type of market process. The emergence and development of exchanges are due to changes in the formation of economic processes between participants in market relations. Complications of trade and economic relations have contributed to the formation of various types of exchanges. The purpose of the research is to develop methodological approaches to assessing the implementation of trading strategies in the commodity exchange market. Results. The essence and structure of a trading strategy are determined. The main advantages and disadvantages of using a trading strategy on the stock market are substantiated. The dependence of trading strategies on the accuracy of quan-titative information is proved on the basis of which they are formed. The main types of technical and fundamental trading strategies are described. Trading strategies based on four levels of management (support strategy, reduction strategy, growth strategy) are considered. The essence of business strategy is defined. Trade strategies are described in the context of the pro-posed classification by areas of the agribusiness entity activity. Exchange trade is defined as a component of relations trans-parency in the market of goods and services of the agrarian exchange market. The financial instruments of the stock market are characterized. Peculiarities of derivatives use in the international practice are given. The role of price is substantiated as the main factor influencing the production of goods and products. The analogy of price formation on the stock and free mar-kets is made. The method of determining the factors influencing the movement of the value of agricultural products is sub-stantiated. The necessity of accurate consideration of the main factors influence in the process of the trading strategy for-mation is proved. The importance of understanding the trade goals for the development and implementation of the strategy is emphasized. Conclusions. The important role of trading strategies in the process of exchange trading is substantiated. The es-sence and features of trade strategies formation are defined. It is established that the use of an effective trading strategy helps to increase the validity level of decisions for traders and investors. Key words: trading strategy, methodical approach, stock market, investor, trader, agricultural products, cost.

Momentum and reversal effects are important phenomena in stock markets. In academia, relevant studies have been conducted for years. Researchers have attempted to analyze these phenomena using statistical methods and to give some plausible explanations. However, those explanations are sometimes unconvincing. Furthermore, it is very difficult to transfer the findings of these studies to real-world investment trading strategies due to the lack of predictive ability. This paper represents the first attempt to adopt machine learning techniques for investigating the momentum and reversal effects occurring in any stock market. In the study, various machine learning techniques, including the Decision Tree (DT), Support Vector Machine (SVM), Multilayer Perceptron Neural Network (MLP), and Long Short-Term Memory Neural Network (LSTM) were explored and compared carefully. Several models built on these machine learning approaches were used to predict the momentum or reversal effect on the stock market of mainland China, thus allowing investors to build corresponding trading strategies. The experimental results demonstrated that these machine learning approaches, especially the SVM, are beneficial for capturing the relevant momentum and reversal effects, and possibly building profitable trading strategies. Moreover, we propose the corresponding trading strategies in terms of market states to acquire the best investment returns.

Promise and Frustration: Machine Learning in Cardiology.

Stock market plays a huge role in the economy of our country. Several attempts have been made to analyse and predict the stock market. While the existing systems try to exploit the patterns of stock prices using historical data, they do not take into the account the poor performance of the system. Moreover, there is no system which provides user specific trading strategies. The proposed solution explores filtration and different trading strategies using RoCE and Fuzzy Logic to solve the problem and predict the portfolio values. It also takes into consideration the sentiment aspect of trading using NLP and combines the two to efficiently to perform trading for even those users who have smattering knowledge about stock market thereby making it suitable for everyone.

Chapter 6 - Convolutional neural networks for fault interpretation – case study examples around the world

The goal of establishing the model in this paper is to find the historical price patterns of gold and bitcoin according to the data provided. The purpose is to maximize returns under various market constraints and avoid loss risk as much as possible. Traders provide the best trading strategy. In this paper, two models are established: model 1: price prediction model based on ARIMA; Model 2: quantitative trading strategy model based on dynamic programming. For Model 1, a classical time series modeling approach based on stock forecasting was used: the ARIMA price forecasting model. The model's validity was demonstrated by analyzing the intrinsic trend of the data movements and verifying the smoothness. Next, historical data were used to fit the parameters of ARIMA, and the forecasting model was determined to be ARIMA (0, 1, 0) by the exhaustive method. Finally, the ARIMA predicts the up and down trends of gold as well as bitcoin, which provides the basis for the trading decision. For Model 2, to better quantify the relationship between investment risk and investment return, the Sharpe ratio is introduced, the Sharpe ratio's value is used as the main parameter of the trading strategy, and the corresponding planning equation is established. Then, based on the up and downtrend of the data predicted by the ARIMA model, the assets are allocated for investment. The model is optimized by a particle swarm algorithm, which accelerates the convergence of the model. Finally, this paper tests the model's accuracy to verify the correctness of the model. By adjusting the commission rate, it is found that the commission rate is negatively correlated with the number of large transactions of gold and bitcoin.

Reimagining multi-criterion decision making by data-driven methods based on machine learning: A literature review

Adaptive stock trading strategies with deep reinforcement learning methods

This study explores the integration of machine learning (ML) techniques with traditional technical indicators to enhance financial stock market trading strategies in the Indian and Malaysian markets. By combining Moving Averages (SMA/EMA), Stochastic Relative Strength Index (Stochastic RSI), and Price-Volume actions (OBV, PVT, A/D Line), the proposed framework aims to improve the predictive accuracy and profitability of trading systems. The research applies supervised learning models, including Support Vector Machines (SVM), Random Forest (RF), and Long Short-Term Memory (LSTM) networks, to classify stock trends and generate trading signals. Empirical analysis based on historical data from the NSE, BSE, and Bursa Malaysia demonstrates that LSTM outperforms other models, achieving the highest accuracy (85.3%) and Sharpe ratio (1.45). The study highlights how the combination of trend-following indicators and ML models effectively minimizes false signals and enhances risk-adjusted returns. Further, comparative Backtesting results show that ML-driven strategies perform better in the Indian market due to higher liquidity and trading volume. The findings contribute to the growing literature on AI-assisted trading strategies and provide actionable insights for traders, analysts, and financial institutions. This research underscores the importance of feature engineering and model customization for adapting trading systems to different emerging market environments.

The purpose of stock market investment is to obtain more profits. In recent years, an increasing number of researchers have tried to implement stock trading based on machine learning. Facing the complex stock market, how to obtain effective information from multisource data and implement dynamic trading strategies is difficult. To solve these problems, this study proposes a new deep reinforcement learning model to implement stock trading, analyzes the stock market through stock data, technical indicators and candlestick charts, and learns dynamic trading strategies. Fusing the features of different data sources extracted by the deep neural network as the state of the stock market, the agent in reinforcement learning makes trading decisions on this basis. Experiments on the Chinese stock market dataset and the S&P 500 stock market dataset show that our trading strategy can obtain higher profits compared with other trading strategies.

Investors are always looking for good stock market trading strategies to maximize their profit. Under the technical school of thought trading rules are developed by studying historical market data to find trends that investors can exploit. These market trends tend to appear when certain features (narrow range, DOJI, etc.) appear in the historical data. Unfortunately, these features often appear only in partial form, which makes trend analysis challenging. In the paper we co-evolve fuzzy trading rules from market trend features. We show how fuzzy membership functions naturally handle partial form features in historical data. The co-evolutionary process is formulated as a zero-sum, competitive game to match how trading strategies are evaluated by brokerage firms. Our experimental results indicate the co-evolutionary process creates trading rule-bases that produce positive returns when evaluated using actual stock market data.

Blockchain and machine learning technology are the most important two technologies that help to develop organizational performance in the marketplace. The purpose of this study is evaluation of the performance management and effect of this technology in the construction industry. In the present time, most of the sector has used machine learning and blockchain technology to grow their business. Among these industries, the construction industry is one of the most important sectors that have increased performance through blockchain and machine learning technology. In the construction industry, supply chain management is the most effective aspect. Machine learning and blockchain technology have helped to improve supply chain management. Moreover, the construction industry has improved its performance management through the implementation of 2.0 technology. This research study has selected the secondary qualitative data collection method to get realistic and genuine data for the research study. The teachers have collected the secondary qualitative data from online articles, books, and journals. In this regard, from the secondary qualitative data, the teachers have conducted the thematic analysis to find the authentic result of this study. In this research study, the researcher has analyzed the importance of blockchain and machine learning to develop performance management in an organization. After all analysis, this research study concludes that machine learning and blockchain technology have affected the construction industry positively to manage the performance.KeywordsData protection2.0 technologyAI technologyLedger technology

The task of forecasting the stock market is not easy because it constitutes a chaotic mechanism which in initial conditions, can arbitrarily change the dynamics. Furthermore, in a competitive environment, as the stock market, the time series' non-linearity is pronounced which immediately impacts the performance of stock price forecasting. The stock price prediction models are divided into two scientific parameters, long-term or short-term. The short-term stock forecast relates to stock or future market forecasts for stock prices and trading strategies for a maximum period of several days between entry and departure. The idea behind these parameters is that the result of the prediction should have a higher accuracy rate than long-term predictions, considering that the stock market is highly competitive. To foresee the short-term, we are planning on incorporating Data Mining algorithms - LSTM (Long Short Term Memory) on the NSE stock market. We forecast prices from various industries based on the accuracy determined using the RMSE of all models. In order to make predictions more precise and appropriate, we have employed historical NSE stock market statistics and used a few Pre-processing processes.