• Poland
  • Great Britain
  • USA
  • Germany
  • France
  • Japan
  • Russia
  • China
  • Netherlands
  • Spain
  • Portugal
  • Italy
  • Czech republic
  • Israel

Artificial Intelligence

Guarantee of precise analysis

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    The mechanism developed by our specialists allows you to analyse every aspect of stock market

    Stock Market Analysis - The missing element in your success

    Stock Market Analysis - The missing element in your success

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  • Highest technology

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    Precise analysis mechanism

    Precise analysis mechanism

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    You will receive a comprehensive and at the same time simple to read report

    Detailed, graphical report

    Detailed, graphical report

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Technology


The integration of intermarket analysis with traditional single-market technical analysis is necessary for profitable trading in the 1990s and beyond. Today’s limited single-market focus must yield to a broader analytic framework that addresses the nonlinear interdependence of today’s financial markets. Neural networks are an excellent tool to implement synergistic analysis. They can be used to synthesize disparate data and find hidden patterns and complex relationships between markets. Neural networks are real, and they do work! In fact, they perform an outstanding job at processing extensive amounts of intermarket data.



Input Data:
Technichal, Fundamental and Intermarket

Backpropagation of Error
INPUTS



HIDDEN



OUTPUT
Output:
Price, Direction or Signal


The back-propagation network is composed of an input layer, one or more hidden layers, and an output layer. The input layer contains a neuron corresponding to each input (independent) variable. The output layer contains a neuron for each (dependent) variable to be predicted. The hidden layer contains neurons that are connected to both the input and output layers. The layers are typically fully connected, with every neuron in one layer connected to each neuron in an adjacent layer. The values associated with each input neuron are fed forward into each neuron in the first hidden layer. They are then multiplied by an appropriate weight, summed, and passed through a transfer function to produce an output. The outputs from the first hidden layer are then fed forward either into the next hidden layer or directly into the output layer in networks that have only one hidden layer. The output layer’s output is the prediction made by the network.