An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. This is true of ANNs as well.
Why do you use neural networks for trading
Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an "expert" in the category of information it has been given to analyze. This expert can then be used to provide projections given new situations of interest and answer "what if" questions.
Quantitative and qualitative methodologies for forecasting help managers to develop business goals and objectives. Business forecasts can be based on historical data patterns that are used to predict future market behavior. The time series method of forecasting is one data analysis tool that measures historical data points -- for instance, using line charts -- to forecast future conditions and events. The goal of the time series method is to identify meaningful characteristics in the data that can be used in making statements about future outcomes.
To generate the deep and invariant features for one-step-ahead forex price prediction, Genesis ANNNB presents a deep learning framework for financial time series using a deep learning-based forecasting scheme that integrates the architecture of stacked autoencoders and long-short-term memory. The framework involves three stages:
- data preprocessing using the wavelet transform, which is applied to decompose the forex price time series to eliminate noise;
- application of the stacked autoencoders, which has a deep architecture trained in an unsupervised manner; and
- the use of long-short-term memory with delays to generate the one-step-ahead output.
Expert Advisor recommended configuration
- Recommended forex Pairs: EURUSD
- Time-frame: H1 Chart
- Minimum deposit: $100
- Recommended spread but not limited: 1 pip
- Lot - Contract size
- Expert Comment - Here you put a comment to identify the trades made by the EA
- Trade on Monday
- Trade on Tuesday
- Trade on Wednesday
- Trade on Thursday
- Trade on Friday
- Using Trade Hour
- Start Hour
- End Hour