Sale!

Artificial Neural Network (ANN)

$7,49$499,00

ANN algorithms work really well but in mysterious ways, it’s like a black box on a trained set of data. Parameters can’t be predicted but we have made an easy optimization tool that quickly tests a lot of different ANN prediction numbers and show you something profitable for your markets.

Donate and gain Access to all Tradingview scripts.

$7,49 / month
$19,39 / 3 months
$38,79 / 6 months
Original price was: $560,00.Current price is: $499,00. / 1111 months

ANN was trained on big market caps like BTC , ETH, XRP, but works much better on indices and stocks. Please use optimizer to find something profitable for your market, not all of them will work, but it sure makes some interesting setups for trading.

ANN works really good but in mysterious ways, it’s like a black box on a trained set of data. Parameters can’t be predicted but I made an easy optimization tool that quickly tests a lot of different ANN prediction numbers.

https://www.tradingview.com/script/JlT33BaL-Artificial-Neural-Network-Starbots/
https://www.tradingview.com/script/YtXTJ7EG-Artificial-Neural-Network-Optimizer-Starbots/

Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains.[1][2]

An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron receives signals then processes them and can signal neurons connected to it. The “signal” at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called edges. Neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold.

Typically, neurons are aggregated into layers. Different layers may perform different transformations on their inputs. Signals travel from the first layer (the input layer), to the last layer (the output layer), possibly after traversing the layers multiple times.

A network is typically called a deep neural network if it has at least 2 hidden layers.[3]

Training

Neural networks are typically trained through empirical risk minimization. This method is based on the idea of optimizing the network’s parameters to minimize the difference, or empirical risk, between the predicted output and the actual target values in a given dataset.[4] Gradient based methods such as backpropagation are usually used to estimate the parameters of the network.[4] During the training phase, ANNs learn from labeled training data by iteratively updating their parameters to minimize a defined loss function.[5] This method allows the network to generalize to unseen data.

 

By using Star Bots Trading Algorithms, you agree with the terms of EULA and consent to be bound by them. You use the Trading Algorithm at your behalf and at your own risk. Star Bots is not responsibile or liable for your’s direct or indirect losses, damages, costs and expenses. You are not allowed to edit, copy, reproduce, distribute, resell, use the Trading Algorithm for signalling services, copy-trading or otherwise use the Trading Algorithm for your own or other’s interests or for any commercial purpose. If such or any other breach of EULA occur, you’re contract will be terminated immediately and you are obligated to stop all access and use of this Strategy.

You may also like…

Scroll to Top