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Predicting the direction and movement of stock index prices is difficult, often leading to excessive trading, transaction costs, and missed opportunities. Often traders need a systematic method to not only spot trading opportunities, but to also provide a consistent approach, thereby minimizing trading errors and costs. While mechanical trading systems exist, they are usually designed for a specific stock, stock index, or other financial asset, and are often highly dependent on preselected inputs and model parameters that are expected to continue providing trading information well after the initial training or back-tested model development period. The following research leads to a detailed trading model that provides a more effective and intelligent way for recognizing trading signals and assisting investors with trading decisions by utilizing a system that adapts both the inputs and the prediction model based on the desired output. To illustrate the adaptive approach, multiple inputs and modeling techniques are utilized, including neural networks, particle swarm optimization, and denoising. Simulations with stock indexes illustrate how traders can generate higher returns using the developed adaptive decision support system model. The benefits of adding adaptive and intelligent decision making to forecasts are also discussed.
The Foreign Exchange Market is the biggest and one of the most liquid markets in the world. This market has always been one of the most challenging markets as far as short term prediction is concerned. Due to the chaotic, noisy, and non-stationary nature of the data, the majority of the research has been focused on daily, weekly, or even monthly prediction. The literature review revealed that there is a gap for intra-day market prediction. Identifying this gap, this paper introduces a prediction and decision making model based on Artificial Neural Networks (ANN) and Genetic Algorithms. The dataset utilized for this research comprises of 70 weeks of past currency rates of the 3 most traded currency pairs: GBP\USD, EUR\GBP, and EUR\USD. The initial statistical tests confirmed with a significance of more than 95% that the daily FOREX currency rates time series are not randomly distributed. Another important result is that the proposed model achieved 72.5% prediction accuracy. Furthermore, implementing the optimal trading strategy, this model produced 23.3% Annualized Net Return. (c) 2013 Elsevier Ltd. All rights reserved.
Desarrollo de indicadores de redes neuronales
¡Hola!
Estoy tratando de hacer algunos indicadores de red neuronal para metatrader4, y me gustaría tener algunas sugerencias, sobre todo en relación con las entradas y salidas de la red, y tal vez la estructura o el tipo de red que usted considera el mejor para esta aplicación.
Por lo que sé las mejores salidas para la previsión de series financieras, son la previsión de rangos de precios, previsión de máximos o mínimos, y ese tipo de cosas. Pronosticar directamente el precio (apertura, cierre) no da buenos resultados debido a numerosas razones, por ejemplo un pequeño cambio en el tiempo entre la hora de apertura y la hora de cierre podría cambiar sus valores considerablemente.
Si alguien tiene una sugerencia estaré encantado de escucharla y probarla.
Por cierto, no soy un experto programador de redes neuronales, sólo tengo una buena idea general sobre el tema =P.
Gracias de antemano,
JCC
The Foreign Exchange Market is the biggest and one of the most liquid markets in the world. This market has always been one of the most challenging markets as far as short term prediction is concerned. Due to the chaotic, noisy, and non-stationary nature of the data, the majority of the research has been focused on daily, weekly, or even monthly prediction. The literature review revealed that there is a gap for intra-day market prediction. Identifying this gap, this paper introduces a prediction and decision making model based on Artificial Neural Networks (ANN) and Genetic Algorithms. The dataset utilized for this research comprises of 70 weeks of past currency rates of the 3 most traded currency pairs: GBP\USD, EUR\GBP, and EUR\USD. The initial statistical tests confirmed with a significance of more than 95% that the daily FOREX currency rates time series are not randomly distributed. Another important result is that the proposed model achieved 72.5% prediction accuracy. Furthermore, implementing the optimal trading strategy, this model produced 23.3% Annualized Net Return.
OpenNN (Open Neural Networks Library) is a software library written in the C++ programming language which implements neural networks, a main area of deep learning research..
OpenNN implementa los métodos de minería de datos como un conjunto de funciones. Éstas pueden integrarse en otras herramientas de software mediante una interfaz de programación de aplicaciones (API) para la interacción entre la herramienta de software y las tareas de análisis predictivo. En este sentido, se echa en falta una interfaz gráfica de usuario, pero algunas funciones pueden soportar la integración de herramientas de visualización específicas.
La principal ventaja de OpenNN es su alto rendimiento. Esta biblioteca destaca en términos de velocidad de ejecución y asignación de memoria. Se optimiza y paraleliza constantemente para maximizar su eficacia.
Red neuronal
Red neuronal: hilos de discusión/desarrollo
Red Neural: Indicadores y desarrollo de sistemas
Red neuronal: EAs
Red Neural: Los libros