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NeuralCode Neural Networks Trading 1.0

Neural Networks Trading System in Microsoft Excel
Last updated: January 5, 2009
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NeuralCode Neural Networks Trading 1.0 Summary:

Publisher: ConnectCode Pte Ltd
Rating:
Download NeuralCode Neural Networks Trading 1.0Download Now
  • Windows - 535KB
  • Licence: Commercial, $129.00 to buy
  • Date Added: October 23, 2008
  • Release status: New Release

NeuralCode Neural Networks Trading 1.0 Description:

NeuralCode is an industrial grade Artificial Neural Networks implementation for financial prediction. The software can take data like the Opening price, High, Low, Volume and other technical indicators for predicting or uncovering trends and patterns. The software is implemented entirely without external object libraries and thus will not require data to be passed out for processing. This results in extremely efficient and optimized processing.

Software OS: Windows2000,WinXP,Windows2003,Windows Vista Home Basic...
Setup Filename: NeuralCode.exe
System Requirements: Excel 2002/2003/XP/2007

NeuralCode Neural Networks Trading 1.0 Tags:

neural networks trading,neural networks predictor,neural networks forecaster

NeuralCode Neural Networks Trading 1.0 Full Description:

NeuralCode is an industrial grade Artificial Neural Networks implementation for financial prediction. The software can take data like the Opening price, High, Low, Volume and other technical indicators for predicting or uncovering trends and patterns. It is designed to utilize Supervised Learning with Multi-Layer Perceptrons and Optimized Back Propagation for complex learning.


In mathematical theory, under suitable conditions, the iterative procedure of adjusting the weights causes the networks to converge to a set of correct weights that can be useful for predicting complex (non linearly separable problems) trends and patterns. Thus NeuralCode is a very useful tool to be used together with financial Technical Indicators like moving average, true range, relative strength index , moving average convergence and divergence.

The software is implemented entirely without external object libraries and thus will not require data to be passed out for processing. This results in extremely efficient and optimized processing. The development of the software is focused on accuracy and thus will ensure the global minimum is found instead of just a local minimum. After training, the prediction is almost instantaneous.


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