Artificial Intelligence
A deep neural network is a type of artificial neural network that is designed to model complex patterns and relationships in data by mimicking the human brain’s interconnected neurons. It consists of multiple layers of interconnected nodes, known as neurons, organized into an input layer, one or more hidden layers, and an output layer. Each neuron in a layer processes information from the previous layer, applying mathematical operations to produce an output. The depth, or the number of hidden layers, distinguishes deep neural networks from shallow ones. Deep neural networks excel at learning hierarchical representations of data, enabling them to capture intricate features and abstractions. They are widely used in various fields, including image and speech recognition, natural language processing, and more, due to their ability to automatically learn and extract intricate patterns from large datasets.
We have designed a suitable deep neural network for time series data by utilizing currency pair data across various time frames spanning over 20 years. Through meticulous data processing and skillful feature engineering, we trained a model to predict the next market swing. Based on these predictions, we offer appropriate capital management strategies to achieve profitable outcomes from the market.
