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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.
Développement d'indicateurs de réseaux neuronaux
Bonjour !
J'essaie de créer des indicateurs de réseaux neuronaux pour Metatrader4, et j'aimerais avoir quelques suggestions, surtout en ce qui concerne les entrées et sorties du réseau, et peut-être la structure ou le type de réseau que vous considérez comme le meilleur pour cette application.
Pour autant que je sache, les meilleures sorties pour la prévision des séries financières sont la prévision de la fourchette de prix, la prévision des sommets ou des creux, et ce genre de choses. Prévoir directement le prix (ouverture, fermeture) ne donne pas de bons résultats pour de nombreuses raisons, par exemple un petit décalage dans le temps entre l'heure d'ouverture et l'heure de fermeture pourrait changer considérablement leurs valeurs.
Si quelqu'un a une suggestion, je serai heureux de l'écouter et de l'essayer.
A propos, je ne suis pas un expert en programmation de réseaux neuronaux, j'ai juste une bonne idée générale sur le sujet =P.
Merci d'avance,
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 met en œuvre des méthodes d'exploration de données sous la forme d'un ensemble de fonctions. Celles-ci peuvent être intégrées dans d'autres outils logiciels en utilisant une interface de programmation d'applications (API) pour l'interaction entre l'outil logiciel et les tâches d'analyse prédictive. À cet égard, il n'existe pas d'interface utilisateur graphique, mais certaines fonctions peuvent permettre l'intégration d'outils de visualisation spécifiques.
Le principal avantage d'OpenNN est sa haute performance. Cette bibliothèque se démarque en termes de vitesse d'exécution et d'allocation de mémoire. Elle est constamment optimisée et parallélisée afin de maximiser son efficacité.
Réseau neuronal
Réseau neuronal : fils de discussion/développement
Réseau neuronal : Développement d'indicateurs et de systèmes
Réseau neuronal : EAs
Réseau neuronal : Les livres