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New Concepts in Technical Trading Systems. Trend Research, 1978. ISBN 0-89459-027-8; Ladis Konecny, Stocks and Exchange – the only Book you need, 2013, ISBN 9783848220656, technical analysis = chapter 8. Schabackers, Richard W. Stock Market Theory and Practice, 2011. ISBN 9781258159474
In an example model for the S&P 500 index, [7] Adaptive Modeler demonstrates significant risk-adjusted excess returns after transaction costs. On back-tested historical price data covering a period of 58 years (1950–2008) a compound average annual return of 20.6% was achieved, followed by a compound average annual return of 22.2% over the following 6 year out-of-sample period (2008-2014).
Screen for stocks at one of the best brokers for options trading and look for stocks with average to above-average price gains over time, something above 10 percent. 4. Sell put options to play ...
Software Creator Initial release Software license [a] Open source Platform Written in Interface OpenMP support OpenCL support CUDA support ROCm support [1] Automatic differentiation [2] Has pretrained models Recurrent nets Convolutional nets RBM/DBNs Parallel execution (multi node) Actively developed BigDL: Jason Dai (Intel) 2016 Apache 2.0 ...
These networks are commonly referred to as backpropagation networks. Another form of ANN that is more appropriate for stock prediction is the time recurrent neural network (RNN) or time delay neural network (TDNN). Examples of RNN and TDNN are the Elman, Jordan, and Elman-Jordan networks.
SNNS research neural network simulator. Historically, the most common type of neural network software was intended for researching neural network structures and algorithms. The primary purpose of this type of software is, through simulation, to gain a better understanding of the behavior and the properties of neural network
With the release of version 0.3.0 in April 2016 [4] the use in production and research environments became more widespread. The package was reviewed several months later on the R blog The Beginner Programmer as "R provides a simple and very user friendly package named rnn for working with recurrent neural networks.", [5] which further increased usage.
OpenNN (Open Neural Networks Library) is a software library written in the C++ programming language which implements neural networks, a main area of deep learning research. [1] The library is open-source , licensed under the GNU Lesser General Public License .
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