Design and Performance Study of Improved Fuzzy System with Genetic Algorithm

Technical trading relies heavily on analysis, most of which is statistical in nature. When the data to be modeled is nonlinear, imprecise, or complicated, fuzzy inference systems (FISs) are used in conjunction with computational, mathematical, and statistical modeling methodologies to simulate technical trading. Fuzzy logic may be modeled using linear, nonlinear, geometric, dynamic, and integer programming. These techniques, when combined with fuzzy logic, help the decision-maker arrive at a better solution while still facing some degree of ambiguity or uncertainty. The moving average method is a useful metric that may give trade recommendations to aid investors further. While trading signals inform investors of when to purchase and sell, a simple moving average provides no such information. In this research, we suggest a fuzzy moving average approach in which the intensity of trading signals, measured in terms of trading volume, is determined by using the fuzzy logic rule. In this research, we propose using fuzzy logic technical trading rules, which are more resistant to decision-making mistakes, to mitigate the trading uncertainty inherent in the conventional technical indicators method

Combining Enterprise Knowledge Graph and News Sentiment Analysis for Stock Price Prediction

Many state of the art methods analyze sentiments in news to predict stock price. When predicting stock price movement, the correlation between stocks is a factor that can’t be ignored because correlated stocks could cause co-movement. Traditional methods of measuring the correlation between stocks are mostly based on the similarity between corresponding stock price data, while ignoring the business relationships between companies, such as shareholding, cooperation and supply-customer relationships. To solve this problem, this paper proposes a new method to calculate the correlation by using the enterprise knowledge graph embedding that systematically considers various types of relationships between listed stocks. Further, we employ Gated Recurrent Unit (GRU) model to combine the correlated stocks’ news sentiment, the focal stock’s news sentiment and the focal stock’s quantitative features to predict the focal stock’s price movement. Results show that our method has an improvement of 8.1% compared with the traditional method

Techniques for Stock Market Prediction: A Review

Stock market forecasting has long been viewed as a vital real-life topic in economics world. There are many challenges in stock market prediction systems such as the Efficient Market Hypothesis (EMH), Nonlinearity, complex, diverse datasets, and parameter optimization. A stock's value on the stock market fluctuates due to many factors like previous trends of the stock, the current news, twitter feeds, any online customer feedbacks etc. In this paper, the literature is critically analysed on approaches used for stock market prediction in terms of stock datasets, features used, evaluation metrics used, statistical, machine learning and deep learning techniques along with the directions for the future. The focus of this review is on trend and value prediction for stocks. Overall, 68 research papers have been considered for review from years 1998-2023. From the review, Indian stock market datasets are found to be most frequently used datasets. Evaluation metrics used commonly are accuracy and Mean Absolute Percentage Error. ARIMA is reported as the most used frequently statistical technique for stick market prediction. Long-Short Term Memory and Support Vector Machine are the commonly used algorithms in stock market prediction. The advantages and disadvantages of frequently used evaluation metrics, machine learning, deep learning and statistical approaches are also included in this survey

Генеративно-змагальна нейронна мережа для прогнозування фондового ринку

Магістерська дисертація: 106 с., 22 табл., 19 рис. 2 дод., 37 джерел. Об’єкт дослідження – рух цін акцій Goldman Saches. Мета роботи – реалізувати генеративно-змагальну нейронну мережу для прогнозування фондового ринку. Дослідити вплив значень параметрів моделі на точність моделі. Порівняти результати прогнозу методу з іншими методами прогнозу. Методи дослідження – реалізувати генеративно-змагальну нейронну мережу і дослідити її роботу. У роботі було запропоновано просту у використанні модель прогнозування фондовго ринку. Було використано навчання з підкріпленням при оптимізації гіперпараметрів, адже фондові ринки весь час змінюються. Навіть якщо вдається навчити GAN та LSTM створювати надзвичайно точні результати, результати можуть бути дійсними лише протягом певного періоду. Це означає, що потрібно постійно оптимізувати весь процес. Заснована на мережі глибокого навчання, ця модель досягає здатності прогнозування, що перевершує інші базові методи, за допомогою змагальних тренувань, мінімізації втрат прогнозування напрямку та втрати прогнозованих помилок. У майбутньому ми спробуємо створити середовище RL для тестування торгових алгоритмів, які вирішують, коли і як торгувати. Вихід з GAN буде одним із параметрів у середовищі.Master thesis: 106 p., 22 tables, 19 figures. 2 items, 37 sources. The object of the study is the movement of Goldman Saches shares. The purpose of the work is to implement a hybrid generative-competitive neural network for stock market forecasting. Investigate the influence of model parameter values on model accuracy. Compare the results of the forecast method with other forecast methods. Research methods - to implement a generative-competitive neural network and explore its work. In paper, an easy-to-use stock market forecasting model was proposed. Reinforced training was used to optimize hyperparameters, as stock markets are constantly changing. Even if you can teach GAN and LSTM to create extremely accurate results, the results can only be valid for a certain period of time. This means that you need to constantly optimize the whole process. Based on a network of deep learning, this model achieves the ability to predict, which surpasses other basic methods, through competitive training, minimizing the loss of directional prediction and loss of predicted errors. Moreover, the effects of model update cycles on predictability are analyzed, and experimental results show that a smaller model update cycle can obtain better prediction results. In the future, we will try to create an RL environment for testing trading algorithms that decide when and how to trade. Exiting the GAN will be one of the parameters in the environment

Previsão e geração de sinais EMG de Parkison com redes neurais

Orientador: Esther Luna ColombiniDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: A doença de Parkinson é uma desordem neurodegenerativa que afeta aproximadamente 2% da população mundial acima de 60 anos (7-10 milhões de pessoas), e é caracterizada por sintomas como tremor em repouso e em movimento, que podem causar graves restrições na vida dos pacientes e também estão associados a sintomas não motores como dificuldade para dormir, depressão e fadiga. Apenas no Brasil, existem mais de 200.000 pessoas diagnosticadas com doença de Parkinson, número que pode duplicar até 2030 devido ao envelhecimento da população brasileira. Neste contexto, o desenvolvimento de novos tratamentos e formas de assistência que possam melhorar a qualidade de vida e a autonomia de pacientes é extremamente importante. Neste trabalho, são propostas novas técnicas baseadas em Redes Neurais para a previsão e geração de sinais de eletromiografia (EMG) do tremor em pacientes, para o suporte ao desenvolvimento de novos dispositivos e técnicas para assistência a pacientes. Primeiro, comparamos diferentes modelos de Redes Neuras, utilizando perceptron multicamadas (MLP) e redes neurais recorrentes (RNN) para a previsão dos sinais EMG de doença de Parkinson, antecipando os padrões de tremor em repouso. Os resultados experimentais indicam que os modelos propostos adaptam-se aos padrões específicos de cada paciente, gerando previsões acuradas para os sinais puros ou envelopes EMG. Segundo, são propostos duas novas técnicas para aumento de dados baseadas em redes adversárias generativas convolucionais profundas (DCGANs) e transferência de estilo (ST) para aumentar sinais EMG, cujos resultados mostram que os modelos propostos conseguem adaptar-se aos diferentes formatos, frequências e amplitudes de tremor, simulando os padrões específicos de cada paciente e estendendo as bases de dados existentes para diferentes protocolos de movimento. Ambos resultados sugerem que o emprego de redes neurais na geração e previsão de sinais biológicos complexos como sinais EMG pode ser bem-sucedido, permitindo o uso de tais modelos para a extensão dos dados de pacientes e para geração de sinais de tremor que auxiliem no desenvolvimento e validação de novas técnicas de supressão de tremor em pacientesAbstract: Parkinson¿s Disease (PD) is a neurodegenerative disorder that affects approximately 2% of the world¿s population over 60 years old (7-10 million people). It is characterized by symptoms like resting and action tremors, which cause severe impairments to the patient¿s life and may also cause non-motor symptoms such as difficulty to sleep, depression, and fatigue. Only in Brazil, there are more than 200,000 people with PD, a number that might double by 2030 due to the aging of the population. In such a context, developing new treatments and assistance techniques that can improve PD patient¿s life quality and autonomy are extremely important. In this work, we propose novel methods based on Neural Networks (NN) for predicting and generating patient-specific PD electromyography (EMG) tremor signals, to support the development of new assisting devices and techniques. First, we compare different NN models, using the multi-layer perceptron (MLPs) and recurrent neural network (RNN) for predicting PD EMG signals, to anticipate resting tremor patterns. The experimental results indicate that the proposed models can adapt to the patient¿s specific tremor patterns and provide reasonable predictions for both EMG envelopes and EMG raw signals. Next, we propose two new data augmentation approaches based on Deep Convolutional Generative Adversarial Networks (DCGANs) and Style Transfer (ST) for augmenting EMG signals. Results show that the proposed models can adapt to different shapes, frequencies, and amplitudes of tremor, simulating each patient¿s specific tremor patterns and extending them to different sets of movement protocols. All results suggest that Neural Networks can successfully be used for predicting and generating complex biological signals like EMG, allowing these models to be used for extending patients¿ datasets and generating tremor signals. These new data could help to validate treatment approaches on different movement scenarios, contributing to the development of new techniques for tremor suppression on patientsMestradoCiência da ComputaçãoMestre em Ciência da Computaçã

Machine learning in stock indices trading and pairs trading

This thesis focuses on two fields of machine learning in quantitative trading. The first field uses machine learning to forecast financial time series (Chapters 2 and 3), and then builds a simple trading strategy based on the forecast results. The second (Chapter 4) applies machine learning to optimize decision-making for pairs trading. In Chapter 2, a hybrid Support Vector Machine (SVM) model is proposed and applied to the task of forecasting the daily returns of five popular stock indices in the world, including the S&P500, NKY, CAC, FTSE100 and DAX. The trading application covers the 1997 Asian financial crisis and 2007-2008 global financial crisis. The originality of this work is that the Binary Gravity Search Algorithm (BGSA) is utilized, in order to optimize the parameters and inputs of SVM. The results show that the forecasts made by this model are significantly better than the Random Walk (RW), SVM, best predictors and Buy-and-Hold. The average accuracy of BGSA-SVM for five stock indices is 52.6%-53.1%. The performance of the BGSA-SVM model is not affected by the market crisis, which shows the robustness of this model. In general, this study proves that a profitable trading strategy based on BGSA-SVM prediction can be realized in a real stock market. Chapter 3 focuses on the application of Artificial Neural Networks (ANNs) in forecasting stock indices. It applies the Multi-layer Perceptron (MLP), Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM) neural network to the task of forecasting and trading FTSE100 and INDU indices. The forecasting accuracy and trading performances of MLP, CNN and LSTM are compared under the binary classifications architecture and eight classifications architecture. Then, Chapter 3 combines the forecasts of three ANNs (MLP, CNN and LSTM) by Simple Average, Granger-Ramanathan’s Regression Approach (GRR) and the Least Absolute Shrinkage and Selection Operator (LASSO). Finally, this chapter uses different leverage ratios in trading according to the different daily forecasting probability to improve the trading performance. In Chapter 3, the statistical and trading performances are estimated throughout the period 2000-2018. LSTM slightly outperforms MLP and CNN in terms of average accuracy and average annualized returns. The combination methods do not present improved empirical evidence. Trading using different leverage ratios improves the annualized average return, while the volatility increases. Chapter 4 uses five pairs trading strategies to conduct in-sample training and backtesting on 35 commodities in the major commodity markets from 1980 to 2018. The Distance Method (DIM) and the Co-integration Approach (CA) are used for pairs formation. The Simple Thresholds (ST) strategy, Genetic Algorithm (GA) and Deep Reinforcement Learning (DRL) are employed to determine trading actions. Traditional DIM-ST, CA-ST and CA-DIM-ST are used as benchmark models. The GA is used to optimize the trading thresholds in ST strategy, which is called the CA-GA-ST strategy. Chapter 4 proposes a novel DRL structure for determining trading actions, which replaces the ST decision method. This novel DRL structure is then combined with CA and called the CA-DRL trading strategy. The average annualized returns of the traditional DIM-ST, CA-ST and CA-DIM-ST methods are close to zero. CA-GA-ST uses GA to optimize searches for thresholds. GA selects a smaller range of thresholds, which improves the in-sample performance. However, the average out-of-sample performance only improves slightly, with an average annual return of 1.84% but an increased risk. CA-DRL strategy uses CA to select pairs and then employs DRL to trade the pairs, providing a satisfactory trading performance: the average annualized return reaches 12.49%; the Sharpe Ratio reaches 1.853. Thus, the CA-DRL trading strategy is significantly superior to traditional methods and to CA-GA-ST