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Using MetaTrader 5 Indicators with ENCOG Machine Learning Framework for Timeseries Prediction. Introduction This article will introduce MetaTrader 5 to ENCOG - advanced neural network and machine learning framework developed by Heaton Research.

Using MetaTrader 5 Indicators with ENCOG Machine Learning Framework for Timeseries Prediction

There are previously described methods I know of that enable MetaTrader to use machine learning techniques: FANN, NeuroSolutions, Matlab and NeuroShell. I hope that ENCOG will be a complementary solution since it is a robust and well designed code. Why I chose ENCOG? There are a few reasons. ENCOG is used in two other commercial trading software packages.

As you can see this is quite a long feature list. This introductory article focuses on feed forward Neural Network architecture with Resilient Propagation (RPROP) training. The knowledge that enabled me to write this article is based on tutorials available on Heaton Research website and very recent articles on predicition of financial timeseries in NinjaTrader. BussetiOsbandWong-DeepLearningForTimeSeriesModeling. Using deep learning for time series prediction. Neurolab - Simple and powerfull neural network library for python. Feed-forward neural network for python (ffnet) — ffnet 0.7.1 documentation. PyBrain. Videos This video presentation was shown at the ICML Workshop for Open Source ML Software on June 25, 2010.

PyBrain

It explains some of the features and algorithms of PyBrain and gives tutorials on how to install and use PyBrain for different tasks. This video shows some of the learning features in PyBrain in action. Algorithms We implemented many useful standard and advanced algorithms in PyBrain, and in some cases created interfaces to existing libraries (e.g. Cuda-convnet - High-performance C++/CUDA implementation of convolutional neural networks. Note July 18, 2014: I've released an update to cuda-convnet, called cuda-convnet2.

cuda-convnet - High-performance C++/CUDA implementation of convolutional neural networks

The two main new features are faster training on Kepler-generation GPUs and support for multi-GPU training. This is a fast C++/CUDA implementation of convolutional (or more generally, feed-forward) neural networks. It can model arbitrary layer connectivity and network depth. Any directed acyclic graph of layers will do. Fermi-generation GPU (GTX 4xx, GTX 5xx, or Tesla equivalent) required. Documentation Compiling -- how to check out and compile this code. Fast results 11% error on CIFAR-10 in 75 minutes, with image translations and horizontal reflections (def, params). 13% error on CIFAR-10 in 25 minutes, with image translations and horizontal reflections (def, params). Filters learned by this net: Neural networks [3.9] : Conditional random fields - factor graph. Machine learning - Pattern recognition in time series. Welcome — Pylearn2 dev documentation. Warning This project does not have any current developer.

Welcome — Pylearn2 dev documentation

We will continue to review pull requests and merge them when appropriate, but do not expect new development unless someone decides to work on it. There are other machine learning frameworks built on top of Theano that could interest you, such as: Blocks, Keras and Lasagne. Don’t expect a clean road without bumps! If you find a bug please write to pylearn-dev@googlegroups.com. Pylearn2 is a machine learning library. Researchers add features as they need them. There is no PyPI download yet, so Pylearn2 cannot be installed using e.g. pip. Git clone To make Pylearn2 available in your Python installation, run the following command in the top-level pylearn2 directory (which should have been created by the previous command): You may need to use sudo to invoke this command with administrator privileges.

Python setup.py develop --user. Pattern Recognition and Machine Learning Christophe M Bishop. Christopher M. Bishop. This leading textbook provides a comprehensive introduction to the fields of pattern recognition and machine learning.

Christopher M. Bishop

It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. This is the first machine learning textbook to include a comprehensive coverage of recent developments such as probabilistic graphical models and deterministic inference methods, and to emphasize a modern Bayesian perspective. It is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.

This hard cover book has 738 pages in full colour, and there are 431 graded exercises (with solutions available below). To view inside this book go to Amazon. Available from Downloads Contents list and sample chapter (Chapter 8: Graphical Models) in PDF format. Support for course tutors. PDF: The Elements of Statistical Learning / Data Mining, Inference, and Prediction.