An investigation into the recurring patterns of forex time series data

Countless theories have been developed by both researchers and financial analyst in an attempt to explain the fluctuation of forex price. By obtaining an intimate understanding of the forex market, traders will hopefully be able to forecast and react to forex price oscillations on-the-fly towards making a profitable investment. In this paper, an investigation into the underlying theory that there exists repeating patterns within the time series data which forms the basis of technical analysis is conducted. The assumption that certain patterns do develop over time and the forex market does not fluctuate in a random manner is used to establish the fact that history repeats itself in forex trading. The patterns and repetitions unveiled within the forex historical data would be an important element for forex forecasting

Tendencias recientes en el pronóstico de series de tiempo financieras usando máquinas de vectores de soporte

Resumen: El pronóstico de las series de tiempo financieras es un área de trabajo intensiva para investigadores y profesionales. En este estudio, analizamos 59 artículos y discutimos sobe el progreso en el análisis de series de tiempo financieras usando máquinas de vectores de soporte. Las principales conclusiones a las que llegamos son: (a) el pronóstico se hace con datos de frecuencia diaria y los estudios con otras frecuencias de tiempo son escasos; (b) la mayoría de los artículos están enfocados en mejorar el proceso de estimación de los parámetros o en el tratamiento previo de las series de tiempo; (c) la mayor parte de los artículos se concentran en el pronóstico de un índice financiero del mercado; (d) los casos experimentales están dispersos, lo que no hace posible comparar entre diferentes estudios.Abstract: Forecasting of financial time series is an intensive working area for researchers and practitioners. In this study, we analyze 59 articles and discuss the progress in financial time series analysis using support vector machines. Our main conclusions are: (a) forecasting is doing in a daily basis and studies in other time scales are scarce; (b) most of works are devoted to improve the parameter estimation process or to preprocessing the time series; (c) most of the work is concerned to forecast market financial index; (d) experimental cases are disperse and it is no possible to compare between different studiesMaestrí

Optimasi Radial Basis Function Neural Network dengan Growing Hierarchial Self Organizing Map pada Data Time Series

Salah satu model JST yang sesuai dengan peramalan data time series, adalah model Radial Basis Function Network (RBFN). Jaringan syaraf tiruan Radial Basis Function merupakan jaringan feed-forward yang memiliki tiga lapisan, yaitu lapisan masukan (input layer), lapisan tersembunyi (hidden layer) dan lapisan keluaran (output layer). Besarnya dimensi input pada jaringan syaraf menyebabkan menurunnya kemampuan komputasi suatu model jaringan. Salah satu cara untuk mengatasi hal tersebut adalah dengan mereduksi dimensi input. Dalam penelitian ini jaringan syaraf tiruan Radial Basis Function dipadukan dengan metode Growing Hierarchical Self Organizing Map (GH-SOM). Penggunaan teknik clustering data pada proses awal, memungkinkan mengurangi dimensi input dengan kehilangan informasi yang minimum. Sehigga dapat mengoptimalkan proses prediksi dengan menggunakan pendekatan RBFN. Prediksi harga saham dengan Optimasi metode Radial Basis Function neural network dengan Menggunakan Growing Hierarchical Self Organizing Map, dengan jumlah vektor data sebanyak 364 dengan SSE sebesar 0,074713 diperoleh akurasi sebesar 94,03

Forex automated trading system based on neural networks

Hlavním cílem této práce je vytvoření forexového automatického obchodního systému s možností přidávat obchodní strategie jako moduly a realizace modulu obchodní strategie založené na neuronových sítích. Implementovaný obchodní systém se skládá z klientské části pro obchodní platformu MetaTrader 4 a ze serverové GUI aplikace. Moduly obchodních strategií jsou realizovány formou dynamických knihoven. Navržená obchodní strategie využívá vícevrstvé neuronové sítě pro predikci směru 45-ti minutového plovoucího průměru zavíracích hodnot ceny v časovém horizontu jedné hodiny. Neuronové sítě byly schopné najít souvislost mezi vstupy a výstupem a predikovat pokles či nárůst s úspěšností vyšší než 50%. Při živém obchodování na demo účtu se pro měnový pár EUR/USD strategie projevila jako zisková, pro měnový pár GBP/USD naopak jako ztrátová. Při testech strategie na historických datech za rok 2014 bylo dosaženo zisku v případě obchodování na měnovém páru EUR/USD ve směru dlouhodobého trendu. Při obchodování proti směru trendu na měnovém páru EUR/USD a ve směru, i proti směru trendu na měnovém páru GBP/USD byla strategie ztrátová.Main goal of this thesis is to create forex automated trading system with possibility to add trading strategies as modules and implementation of trading strategy module based on neural networks. Created trading system is composed of client part for MetaTrader 4 trading platform and server GUI application. Trading strategy modules are implemented as dynamic libraries. Proposed trading strategy uses multilayer neural networks for prediction of direction of 45 minute moving average of close prices in one hour time horizon. Neural networks were able to find relationship between inputs and output and predict drop or growth with success rate higher than 50%. In live demo trading, strategy displayed itself as profitable for currency pair EUR/USD, but it was losing for currency pair GBP/USD. In tests with historical data from year 2014, strategy was profitable for currency pair EUR/USD in case of trading in direction of long-term trend. In case of trading against direction of trend for pair EUR/USD and in case of trading in direction and against direction of trend for pair GBP/USD, strategy was losing.

Avaliação de um algotrading baseado em deep learning para o mercado de capitais utilizando gerenciamento de risco

Financial time series predictions are a challenge due to their nonlinear and chaotic nature. In recent decades, many researchers and investors have studied methods to improve quantitative analysis. In the field of artificial intelligence, sophisticated machine learning techniques, such as deep learning showed better performance. In this work, an automated trading system, an algotrading, to predict future trends of stock index prices Ibovespa is showed and evaluated. Using an LSTM-based (Long Short-Term Memory) agent to learn temporal patterns in the data, the algorithm triggers automatic trades according to the historical data, technical analysis indicators, and risk management. Initially, five different strategies were developed using the LSTM algorithm as a basis, then the model that reported the best performance was selected. During the experimental tests, it was possible to prove that the use of trading strategy and risk management techniques helped to minimize losses and reduce operating costs, which have a direct influence on profitability. Subsequently, the model that obtained the best result, the LSTM-RMODV, underwent several improvements. Among them, the implementation of the Break-even and Trailing Stop techniques, and a series of optimizations for the trading strategy. Then, it was possible to obtain a set of parameters that brought better results to the ATS (Automated Trading System), giving rise to the new model called Algo-LSTM. In the last step, the evaluation of slippage alow to infer that in the long-term the impact of slippage under reasonable market conditions is not significant for the final result. Finally, the results demonstrated that the proposed method, AlgoLSTM, shows better performance when compared with other methods, including the buy-andhold technique. The proposed method also works in bear or bull market conditions, showing a rate over net income based on invested capital of 208.23% in 2019 and 112,81% in 2015. That is, despite the low accuracy, the algorithm is capable of generating consistent profits when all the transaction costs and the income tax over net revenue are considered.Agência 1Previsões de séries temporais financeiras são um desafio devido a sua não linearidade e natureza caótica. Nas últimas décadas, muitos pesquisadores e investidores estudaram métodos para melhorar as análises quantitativas. No campo da inteligência artificial, técnicas sofisticadas de aprendizado de máquina, como a aprendizagem profunda, apresentaram melhor performance. Nesta dissertação, um sistema de negociação automatizado, um algotrading, para prever as tendências futuras dos preços do índice de ações Ibovespa é apresentado e avaliado. Usando um agente baseado em LSTM (Long Short-Term Memory) para aprender padrões temporais dos dados, o algoritmo dispara negociações automáticas de acordo com os dados históricos, indicadores de análise técnica e gerenciamento de risco. Inicialmente, foram desenvolvidas cinco estratégias distintas utilizando o algoritmo LSTM como base, em seguida, foi selecionado o modelo que reportou a melhor performance. Durante os testes experimentais, foi possível demonstrar que a utilização de Trading Strategy e gerenciamento de risco ajudaram a minimizar perdas e reduzir custos operacionais, que possuem influência direta na rentabilidade. Posteriormente, o modelo que obteve melhor resultado, o LSTM-RMODV, foi submetido à diversas melhorias. Entre elas, a implementação das técnicas de Break-even e Trailing Stop e uma série de otimizações do trading strategy. Com isto, foi possível obter um conjunto de parâmetros que trouxe melhores resultados ao ATS (Automated Trading System), dando origem ao novo modelo denominado Algo-LSTM. Em última etapa, a avaliação do slippage permitiu inferir que a longo prazo o impacto do slippage em condições razoáveis de mercado não é significante para o resultado final. Por fim, os resultados demonstraram que o método proposto, o Algo-LSTM, apresenta melhor desempenho quando comparado a outros métodos, incluindo a técnica buy-and-hold. O método proposto também funciona em condições de bear ou bull market, apresentando uma taxa sobre a rentabilidade líquida com base no capital investido de 208,23% em 2019 e 112,81% em 2015. Ou seja, apesar da baixa acurácia, o algoritmo é capaz de gerar retornos consistentes quando considerados todos os custos de transação e imposto de renda devido

Novel neural network models for financial prediction

Financial markets are an important feature of modern economies, where trading decisions can be critical because of their significant impact on social and economic life. Various models and techniques have been applied to describe and predict financial time series in order to develop effective tools in financial prediction. In particular, neural networks have recently gained significant research interest in financial markets as well as in other domains. As financial time series data show a high degree of non-linearity, neural networks represent an attractive approach in this area. This thesis introduces a novel neural network model, the FL-SMIA model, as well as several variations and extensions, namely the FL-SMIA*, D-FL-SMIA, MD-FLSMIA, MD-FL-SMIA-2, M FL-SMIA, and FL-SMIA-RBM. The FL-SMIA model is a model that uses the principles of the Functional Link Neural Network (FLNN) and the Self-organising Multilayer Neural Network using the Immune Algorithm (SMIA). The FL-SMIA model combines the higher-order inputs , i.e. the products of raw input features, with the self-organising hidden layer (SMIA) that dynamically grows and adapts to the input vectors. Based on the promising results of the FL-SMIA network in initial experiments, variations and extensions have been developed using deeper architectures (D FLSMIA), mixed input representations (M-FL-SMIA), a combination of deep and mixed architectures (MD-FL-SMIA), and of the FL-SMIA with the Restricted Boltzmann Machine in the FL-SMIA-RBM. The proposed models have also been compared with other neural network architectures: FLNN, the Multilayer perceptron (MLP), and SMIA. All networks have been evaluated for one day and five days ahead prediction using financial and statistical metrics, focusing on the Relative Profit (RP) and Annualised Volatility (AV). Data-sets of three different types have been used: exchange rates (USD/UKP, USD/EUR, JPY/USD), stock price indices (NASDAQ, DJIA), and commodity prices (OIL and GOLD). In terms of average RP results for the one day ahead prediction, the FL-SMIA was slightly worse than the best model (FLNN) but FL-SMIA model reduced the investment risk by producing the lowest average AV value. We have also observed notable differences between data types. For the five days ahead prediction, the M-FL-SMIA model has the highest average RP and the lowest average AV results. Correlation analysis on the residuals has shown differences in behaviour between FLNN model and FL-SMIA model, encouraging further extensions and variations. Overall, the FL-SMIA model and its extensions will be useful for time series prediction because of their competitive performance and different behaviour to standard neural networks