Apr 3, 2018
Cryptomon is ready to use platform with real prediction algorithms and custom indicators based on machine learning. First version of Cryptomon was released in June 2017, and since then we built community of experts in machine intelligence around the Cryptomon platform. All these give us a significant competitive advantage over other trading tools and platforms which are still in development and they have using of ML only as a future milestone.
Cryptomon provides a set of predictions and modern indicators for mastering trading strategies. Any crypto trader armed with computational power and data science can make smarter and faster trading decisions and maximize trading profits. So why to use legacy indicator like MA (Moving Average), when we have machine intelligence?
CommunitySharing experience and knowledge transfer are the basis of evolutionary development on any field, especially while developing new prediction algorithms. Members help each other to move to the next level of development. That's why in Cryptomon a lot of attention is paid to the community. All Cryptomon prediction algorithms and machine learning indicators are open sourced on our GitHub page, so everyone can enhance existing or develop new prediction algorithms with our ML stack from https://cryptomon.io/lab. Every enhancement, documented with better prediction performance will be rewarded with CMN tokens (considering use https://gitcoin.co or https://gittoken.org). Detailed contribution reward policy will be released in Q2 2018.
- Trade signals notifications
- Prediction algorithms and indicators
- Machine learning
K-nearest neighborsThe k-nearest neighbors (KNN) algorithm is a non-parametric algorithm that can be used for either classification or regression. Non-parametric means that it makes no assumption about the underlying data or its distribution. It is one of the simplest Machine Learning algorithms, and has applications in a variety of fields, ranging from the healthcare industry, to the finance industry. More info on wikipedia. Source code available on GitHub.
Multilayer perceptron A multilayer perceptron (MLP) is a feedforward artificial neural network model that maps sets of input data onto a set of appropriate outputs. An MLP consists of multiple layers of nodes in a directed graph, with each layer fully connected to the next one. Except for the input nodes, each node is a neuron (or processing element) with a nonlinear activation function. MLP utilizes a supervised learning technique called backpropagation for training the network.MLP is a modification of the standard linear perceptron and can distinguish data that are not linearly separable. More info on wikipedia. Source code available on GitHub.
LSTM A powerful type of neural network designed to handle sequence dependence is called recurrent neural networks. The Long Short-Term Memory network or LSTM network is a type of recurrent neural network used in deep learning because very large architectures can be successfully trained. More info on wikipedia.
WTBS indicatorWhen To Buy and Sell technical indicator is based on CNN - Convolutional Neural Network. NN use a variation of multilayer perceptrons designed to require minimal preprocessing. They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on their shared-weights architecture and translation invariance characteristics. Convolutional networks were inspired by biological processes in which the connectivity pattern between neurons is inspired by the organization of the animal visual cortex. More info on wikipedia. Source code available on GitHub.
Q3 - Q4 2017
Q1 - Q2 2019
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