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.
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