Library "WIPNNetwork" this is a work in progress (WIP) and prone to have some errors, so use at your own risk... let me know if you find any issues.. Method for a generalized Neural Network. network(x) Generalized Neural Network Method. Parameters: x : TODO: add parameter x description here Returns: TODO: add what function returns
Library "FunctionNNLayer" Generalized Neural Network Layer method. function(inputs, weights, n_nodes, activation_function, bias, alpha, scale) Generalized Layer. Parameters: inputs : float array, input values. weights : float array, weight values. n_nodes : int, number of nodes in layer. activation_function : string, default='sigmoid',...
Library "FunctionNNPerceptron" Perceptron Function for Neural networks. function(inputs, weights, bias, activation_function, alpha, scale) generalized perceptron node for Neural Networks. Parameters: inputs : float array, the inputs of the perceptron. weights : float array, the weights for inputs. bias : float, default=1.0, the default bias...
Library "MLActivationFunctions" Activation functions for Neural networks. binary_step(value) Basic threshold output classifier to activate/deactivate neuron. Parameters: value : float, value to process. Returns: float linear(value) Input is the same as output. Parameters: value : float, value to process. Returns: float sigmoid(value) ...
Library "MLLossFunctions" Methods for Loss functions. mse(expects, predicts) Mean Squared Error (MSE) " MSE = 1/N * sum ((y - y')^2) ". Parameters: expects : float array, expected values. predicts : float array, prediction values. Returns: float binary_cross_entropy(expects, predicts) Binary Cross-Entropy Loss (log). Parameters: ...
Hey Traders, Tonight I figured I'd release a special indicator that I've had in the works for years and finally was able to piece it together using pine. It's an extremely accurate take on the RSI. I plan to continue to refine the indicator and add more features, but as it is this is still one you can make a lot of money with. (((((Please note: all circles and...
Greeting Traders! I am back with another ML strategy. :D I kept my word with combining my machine learning algorithms from Python and integrating them into Tradingview. Thanks to Tradingview's new release of Pinescript v5 it is now possible. This strategy respects the Sortino Ratio and was created using 2 years of data for 50 different cryptocurrencies. That is a...
Daily trend indicator based on financial astrology cycles detected with advanced machine learning techniques for some of the most important market indexes: DJI, UK100, SPX, IBC, IXIC, NI225, BANKNIFTY, NIFTY and GLD fund (not index) for Gold predictions. The daily price trend is forecasted through planets cycles (angular aspects, speed phases, declination zone),...
This daily trend indicator is based on financial astrology cycles detected with advanced machine learning techniques for the crypto-currencies research portfolio: ADA, BAT, BNB, BTC, DASH, EOS, ETC, ETH, LINK, LTC, XLM, XMR, XRP, ZEC and ZRX. The daily price trend is forecasted through this planets cycles (angular aspects, speed, declination), fast ones are based...
This is an experimental strategy that uses a Volume-weighted MA (VWMA) crossing together with Machine Learning kNN filter that uses ADX and MFI to predict, whether the signal is useful. k-nearest neighbours (kNN) is one of the simplest Machine Learning classification algorithms: it puts input parameters in a multidimensional space, and then when a new set of...
Astrology machine learning cycles indicator signals with technical MAs indicators strategy, based on signals index of Github project github.com
kNN-based Strategy (FX and Crypto) Description: This update to the popular kNN-based strategy features: improvements in the business logic, an adjustible k value for the kNN model, one more feature (MOM), a streamlined signal filter and some other minor fixes. Now this script works in all timeframes ! I intentionally decided to...
LVQ-based Strategy (FX and Crypto) Description: Learning Vector Quantization (LVQ) can be understood as a special case of an artificial neural network, more precisely, it applies a winner-take-all learning-based approach. It is based on prototype supervised learning classification task and trains its weights through a competitive learning...
Multi-timeframe Strategy based on Logistic Regression algorithm Description: This strategy uses a classic machine learning algorithm that came from statistics - Logistic Regression (LR). The first and most important thing about logistic regression is that it is not a 'Regression' but a 'Classification' algorithm. The name itself is somewhat misleading....
Perceptron-based strategy Description: The Learning Perceptron is the simplest possible artificial neural network (ANN), consisting of just a single neuron and capable of learning a certain class of binary classification problems. The idea behind ANNs is that by selecting good values for the weight parameters (and the bias), the ANN can model the relationships...
This is a multi-timeframe version of the kNN-based strategy.
kNN-based Strategy (FX and Crypto) Description: This strategy uses a classic machine learning algorithm - k Nearest Neighbours (kNN) - to let you find a prediction for the next (tomorrow's, next month's, etc.) market move. Being an unsupervised machine learning algorithm, kNN is one of the most simple learning algorithms. To do a prediction of the next market...
Hello Traders/Programmers, For long time I thought that if it's possible to make a script that has own memory and criterias in Pine. it would learn and find patterns as images according to given criterias. after we have arrays of strings, lines, labels I tried and made this experimental script. The script works only for Long positions. Now lets look at how it...