Better NN EA development - page 84

[ziko20]

i everyone,i have a strategy that isn't bad in trading,and i want to know how can i add this svm nn to my expert to filtrate the bad signals,any help are really apreciated,thank you

[ziko20]

Any help..................

that's my expert test from 300$ to 7000$ in one month,if i can add a NN to filter signals that's gonna to be more good than that...

[test]

ziko87:
Any help..................

that's my expert test from 300$ to 7000$ in one month,if i can add a NN to filter signals that's gonna to be more good than that...

Why do you expect that NN will improve your EA?

If you did not base the EA on the NN from the start it will be difficult to incorporate NN - and just t start, you will have to decide to use one of the 100s of theoretical models that you will have to translate to MQL then. Already the choice of NN approach will be difficult enough if you do not use some already

[ziko20]

but can we use NN like a filter for signals?

[test]

ziko87:
but can we use NN like a filter for signals?

ziko87

NN is usually used for "prediction". As such ("prediction") I doubt that it can be used as a filter

[Joakin]

Different pack of bpnn bpnn.zip

[mygoodness]

omelette:

I looked in vain for a contact email address - could you post it? As a paid-up owner, it can only help if as many of us as possible complain...

go here

[Sergey Golubev]

Neural Network

Neural Network: discussion/development threads

1. Better NN EA development thread with indicators, pdf files and so on.
2. Better NN EA final thread 
3. taking NEURAL NETWORKS to the NEXT LEVEL - very interesting thread
4. Neural Networks thread (good public discussion)
5. How to build a NN-EA in MT4: usefull thread for developers.
6. Radial Basis Network (RBN) - As Fit Filter For Price: the thread 

Neural Network: Indicators and systems development

1. Self-trained MA cross!: development thread for new generation of the indicators
2. Levenberg-Marquardt algorithm: development thread

Neural Network: EAs

1. CyberiaTrader EA: discussion thread and EAs' thread.
2. Self learning expert thread with EAs' files here.
3. Artificial Intelligence EAs threads: How to "teach" and to use the AI ("neuron") EA thread and Artificial Intelligence  thread
4. Forex_NN_Expert EA and indicator thread.
5. SpiNNaker - A Neural Network EA thread. 

Neural Network: The Books

1. What to read and where to learn about Machine Learning (10 free books) - the post.

The article

• Neural Networks Made Easy - MT5
  
• ALGLIB numerical analysis library in MQL5 - MT5
  
• Data Science and Machine Learning — Neural Network (Part 01): Feed Forward Neural Network demystified - MT5
  
• Data Science and Machine Learning — Neural Network (Part 02): Feed forward NN Architectures Design - MT5
  
• Data Science and Machine Learning (Part 03): Matrix Regressions - MT5
• Data Science and Machine Learning (Part 04): Predicting Current Stock Market Crash - MT5
• Data Science and Machine Learning (Part 05): Decision Trees - MT5
• Data Science and Machine Learning (Part 06): Gradient Descent - MT5
• Data Science and Machine Learning (Part 07): Polynomial Regression - MT5
  
• Data Science and Machine Learning (Part 08): K-Means Clustering in plain MQL5 - MT5
  
• Data Science and Machine Learning (Part 09) : The K-Nearest Neighbors Algorithm (KNN) - MT5
  
• Data Science and Machine Learning (Part 10): Ridge Regression - MT5
  
• Data Science and Machine Learning (Part 11): Naïve Bayes, Probability theory in Trading - MT5
  
• Data Science and Machine Learning (Part 12): Can Self-Training Neural Networks Help You Outsmart the Stock Market? - MT5
  
• Data Science and Machine Learning (Part 13): Improve your financial market analysis with Principal Component Analysis (PCA) - MT5
  
• Data Science and Machine Learning (Part 14): Finding Your Way in the Markets with Kohonen Maps - MT5
  
• Data Science and Machine Learning (Part 15): SVM, A Must-Have Tool in Every Trader's Toolbox - MT5
  
• Data Science and Machine Learning (Part 16): A Refreshing Look at Decision Trees - MT5
  
• Data Science and Machine Learning (Part 17): Money in the Trees? The Art and Science of Random Forests in Forex Trading - MT5
  
• Data Science and Machine Learning (Part 18): The battle of Mastering Market Complexity, Truncated SVD Versus NMF - MT5
• Data Science and Machine Learning (Part 19): Supercharge Your AI models with AdaBoost - MT5
  
• Data Science and Machine Learning (Part 20) : Algorithmic Trading Insights, A Faceoff Between LDA and PCA in MQL5 - MT5
  
• Data Science and Machine Learning(Part 21): Unlocking Neural Networks, Optimization algorithms demystified - MT5
  
• Experiments with neural networks (Part 1): Revisiting geometry - MT5
  
• Experiments with neural networks (Part 2): Smart neural network optimization - MT5
  
• Experiments with neural networks (Part 3): Practical application - MT5
• Experiments with neural networks (Part 4): Templates - MT5
  
• Experiments with neural networks (Part 5): Normalizing inputs for passing to a neural network - MT5
  
• Experiments with neural networks (Part 6): Perceptron as a self-sufficient tool for price forecast - MT5 
• Experiments with neural networks (Part 7): Passing indicators - MT5  
  
• Programming a Deep Neural Network from Scratch using MQL Language  - MT5
  
• Neural networks made easy (Part 2): Network training and testing - MT5
  
• Machine learning in Grid and Martingale trading systems. Would you bet on it? - MT5
  
• Neural networks made easy (Part 3): Convolutional networks - MT5
  
• Neural networks made easy (Part 4): Recurrent networks - MT5 
• Neural networks made easy (Part 5): Multithreaded calculations in OpenCL - MT5 
• Neural networks made easy (Part 6): Experimenting with the neural network learning rate - MT5 
• Neural networks made easy (Part 7): Adaptive optimization methods - MT5 
• Neural networks made easy (Part 8): Attention mechanisms - MT5
• Neural networks made easy (Part 9): Documenting the work - MT5
  
• Neural networks made easy (Part 10): Multi-Head Attention - MT5
  
• Neural networks made easy (Part 11): A take on GPT - MT5
  
• Neural networks made easy (Part 12): Dropout - MT5
  
• Neural networks made easy (Part 13): Batch Normalization - MT5
  
• Neural networks made easy (Part 14): Data clustering - MT5
  
• Neural networks made easy (Part 15): Data clustering using MQL5 - MT5
  
• Neural networks made easy (Part 16): Practical use of clustering - MT5
  
• Neural networks made easy (Part 17): Dimensionality reduction - MT5
  
• Neural networks made easy (Part 18): Association rules - MT5
  
• Neural networks made easy (Part 19): Association rules using MQL5 - MT5
  
• Neural networks made easy (Part 20): Autoencoders - MT5
  
• Neural networks made easy (Part 21): Variational autoencoders (VAE) - MT5
  
• Neural networks made easy (Part 22): Unsupervised learning of recurrent models - MT5
  
• Neural networks made easy (Part 23): Building a tool for Transfer Learning - MT5
  
• Neural networks made easy (Part 24): Improving the tool for Transfer Learning - MT5
  
• Neural networks made easy (Part 25): Practicing Transfer Learning - MT5
  
• Neural networks made easy (Part 26): Reinforcement Learning - MT5
  
• Neural networks made easy (Part 27): Deep Q-Learning (DQN) - MT5
  
• Neural networks made easy (Part 28): Policy gradient algorithm - MT5
  
• Neural networks made easy (Part 29): Advantage Actor-Critic algorithm - MT5
  
• Neural networks made easy (Part 30): Genetic algorithms - MT5
  
• Neural networks made easy (Part 31): Evolutionary algorithms - MT5
  
• Neural networks made easy (Part 32): Distributed Q-Learning - MT5
  
• Neural networks made easy (Part 33): Quantile regression in distributed Q-learning - MT5
  
• Neural networks made easy (Part 34): Fully Parameterized Quantile Function - MT5
• Neural networks made easy (Part 35): Intrinsic Curiosity Module - MT5
  
• Neural networks made easy (Part 36): Relational Reinforcement Learning - MT5
  
• Neural networks made easy (Part 37): Sparse Attention - MT5
  
• Neural networks made easy (Part 38): Self-Supervised Exploration via Disagreement - MT5
  
• Neural networks made easy (Part 39): Go-Explore, a different approach to exploration - MT5
• Neural networks made easy (Part 40): Using Go-Explore on large amounts of data - MT5
  
• Neural networks made easy (Part 41): Hierarchical models - MT5
  
• Neural networks made easy (Part 42): Model procrastination, reasons and solutions - MT5
  
• Neural networks made easy (Part 43): Mastering skills without the reward function - MT5
  
• Neural networks made easy (Part 44): Learning skills with dynamics in mind - MT5 
• Neural networks made easy (Part 45): Training state exploration skills - MT5 
• Neural networks made easy (Part 46): Goal-conditioned reinforcement learning (GCRL)  - MT5 
• Neural networks made easy (Part 47): Continuous action space - MT5 
• Neural networks made easy (Part 48): Methods for reducing overestimation of Q-function values  - MT5 
• Neural networks made easy (Part 49): Soft Actor-Critic - MT5
  
• Neural networks made easy (Part 50): Soft Actor-Critic (model optimization) - MT5
  
• Neural networks made easy (Part 51): Behavior-Guided Actor-Critic (BAC) - MT5
• Neural networks made easy (Part 52): Research with optimism and distribution correction - MT5
• Neural networks made easy (Part 53): Reward decomposition - MT5
• Neural networks made easy (Part 54): Using random encoder for efficient research (RE3) - MT5
• Neural networks made easy (Part 55): Contrastive intrinsic control (CIC) - MT5
  
• Neural networks made easy (Part 56): Using nuclear norm to drive research - MT5
  
• Neural networks made easy (Part 57): Stochastic Marginal Actor-Critic (SMAC) - MT5
• Neural networks made easy (Part 58): Decision Transformer (DT) - MT5
• Neural networks are easy (Part 59): Dichotomy of Control (DoC) - MT5
  
• Neural networks made easy (Part 60): Online Decision Transformer (ODT) - MT5
• Neural networks made easy (Part 61): Optimism issue in offline reinforcement learning - MT5
• Neural networks made easy (Part 62): Using Decision Transformer in hierarchical models - MT5
• Neural networks made easy (Part 63): Unsupervised Pretraining for Decision Transformer (PDT) - MT5
• Neural networks made easy (Part 64): ConserWeightive Behavioral Cloning (CWBC) method - MT5
• Neural networks made easy (Part 65): Distance Weighted Supervised Learning (DWSL) - MT5
  
• Developing a self-adapting algorithm (Part I): Finding a basic pattern  - MT5
  
• Developing a self-adapting algorithm (Part II): Improving efficiency  - MT5
  
• Self-adapting algorithm (Part III): Abandoning optimization - MT5
  
• Deep neural network with Stacked RBM. Self-training, self-control - MT4 
• Practical application of neural networks in trading - MT5
  
• Practical application of neural networks in trading. Python (Part I) - MT5
  
• Practical application of neural networks in trading (Part 2). Computer vision - MT5
  
• Connecting NeuroSolutions Neuronets - MT5
• Using Neural Networks In MetaTrader - MT4
• Price Forecasting Using Neural Networks - MT4
• Recipes for Neuronets - MT4
• Third Generation Neural Networks: Deep Networks - MT5
• Neural Networks Cheap and Cheerful - Link NeuroPro with MetaTrader 5 - MT5
• Creating Neural Network EAs Using MQL5 Wizard and Hlaiman EA Generator - MT5
• Neural network: Self-optimizing Expert Advisor - MT5
• Neural Networks: From Theory to Practice - MT5
• Using MetaTrader 5 Indicators with ENCOG Machine Learning Framework for Timeseries Prediction - MT5
• Using Self-Organizing Feature Maps (Kohonen Maps) in MetaTrader 5 - MT5
• Deep Neural Networks (Part I). Preparing Data - MT5
• Deep Neural Networks (Part II). Working out and selecting predictors - MT5
  
• Mastering Model Interpretation: Gaining Deeper Insight From Your Machine Learning Models - MT5
  

CodeBase

• Next price predictor using Neural Network - indicator for MetaTrader 4
• Easy Neural Network - library for MetaTrader 5
  
• LGLIB - Numerical Analysis Library - library for MetaTrader 4 
  
• ALGLIB - Numerical Analysis Library - library for MetaTrader 5
  
• MTS Neural network plus MACD - expert for MetaTrader 4
• ArtificialIntelligence_Right - expert for MetaTrader 4
• NeuroNirvamanEA - expert for MetaTrader 4
• Create your own neural network predictor easily (example: MA and RSI Predictors) - indicator for MetaTrader 4
• Automated Trading System "Сombo" - expert for MetaTrader 4
• MTC Neural network plus MACD - expert for MetaTrader 5
• Bollinger Band Width calculation with Neural Network using - expert for MetaTrader 5
• PNN Neural Network Class - library for MetaTrader 5
• GRNN Neural Network Class - library for MetaTrader 5
• RBF Neural Network Class - library for MetaTrader 5
• MLP Neural Network Class - library for MetaTrader 5
• Artificial Intelligence - expert for MetaTrader 5

[Sergey Golubev]

There is one thread in Russian forum where the users are talking about it, and it is 2,739 pages on the thread for now (yes, the thread is very popular one).

I am not fully understand about what they are talking about ... something related to this page:
https://cs.stanford.edu/people/karpathy/convnetjs/demo/rldemo.html

..and something about the following ("machine translation" from Russian):

Forum on trading, automated trading systems and testing trading strategies

Machine learning in trading: theory, models, practice and algorithmic trading

mytarmailS , 2022.09.07 07:24

It makes no sense to look at the old model, it does not capture market changes.

I propose to implement as suggested)))

In the sliding window, retrain the model and look at the importance of the features, or simply take some kind of good feature determinant and look at it in the sc. window

Here is an example on a randomly generated sample of 5 features and 1 binary target

for forest and feature selector

X <- matrix (rnorm( 1000 ),ncol = 5 )
Y <- as .factor(sample( 0 : 1 ,nrow(X),replace = T))

head(X)
head(Y)

#install.packages( "randomForest" )
library (randomForest)
rf_imp_mat <- matrix (ncol = ncol(X),nrow = nrow(X))
for (i in 30 :nrow(X)){
  ii <- (i- 29 ):i
  rf <- randomForest(Y[ii]~.,X[ii,],ntree= 100 )
  rf_imp_mat[i,] <- importance(rf)[, 1 ]
}

#install.packages( "FSelectorRcpp" )
library (FSelectorRcpp)
fs_imp_mat <- matrix (ncol = ncol(X),nrow = nrow(X))
for (i in 30 :nrow(X)){
  ii <- (i- 29 ):i
  infg <- information_gain(y = Y[ii],x = as .data.frame(X[ii,]))
  fs_imp_mat[i,] <-  infg$importance
}

par(mfrow=c( 1 , 2 ))
matplot(rf_imp_mat , t= "l" ,lty= 1 ,main= "rf_var_imp" )
matplot(fs_imp_mat , t= "l" ,lty= 1 ,main= "fs_var_imp" )

In R, of course, it is not customary to write in cycles if it is not necessary, but this style confuses beginners, and middle peasants, like me, also confuses ..

but you can write short and so, the code is 3 times less, the result is the same

X <- matrix (rnorm( 1000 ),ncol = 5 )
Y <- as .factor(sample( 0 : 1 ,nrow(X),replace = T))
idx <- embed( 1 :nrow(X),dimension = 30 )[, 30 : 1 ]

library (randomForest)
rf_imp_mat <- t(apply(idx, 1 ,function(i) importance(randomForest(Y[i]~.,X[i,]))[, 1 ]))
library (FSelectorRcpp)
fs_imp_mat <- t(apply(idx,1,function(i) information_gain(y=Y[i],x=as.data.frame(X[i,]))$importance))

par(mfrow=c( 1 , 2 ))
matplot(rf_imp_mat , t= "l" ,lty= 1 ,main= "rf_var_imp" )
matplot(fs_imp_mat , t= "l" ,lty= 1 ,main= "fs_var_imp" )

Also , different feature selectors for every taste, probably 5% of what is in R-ke

[Sergey Golubev]

ONNX and  MQL5 Copilot

The forum

1. MetaEditor, Open AI and ChatGPT - key forum summary thread
2. Practical examples: this page

CodeBase

1. Information about the ONNX model's inputs and outputs - script for MetaTrader 5
2. Get info about inputs and outputs of onnx-model - script for MetaTrader 5

Documentation

1. ONNX models in machine learning (MT5)