- MQL5 Wizard: Development of trading robots for MetaTrader 5
- Articles on the development of trading applications
- Introduction
Some people say that AI is not an Ai, and it is logictic script (and it is too far from AI for now for any such as "AI").
- Some discussion about AI (and machine learning in trading) is going on this thread:
Machine learning in trading: theory, models, practice and algo-trading (3753 pages in the thread; about coding, using, and so on, means: professional discussion; because almost all the participants wrote many articles here about machine learning and an AI for example). - The next thread (just a discussion, thing about coding and trading): AI 2023. Meet ChatGPT. (212 pages in the thread)
- and there are some more.
So, it was already disccussed since 2016 (for information), and the discussing/coding/using/etc are continuing every day.
- 2016.05.26
- Alexey Burnakov
- www.mql5.com
Here are some questions to get us started:
Yes, just to start -
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Machine Learning and Neural Network
Neural Network: discussion/development threads
- Machine Learning and Neural Networks - key forum thread
- Better NN EA development thread with indicators, pdf files and so on.
- Better NN EA final thread
- taking NEURAL NETWORKS to the NEXT LEVEL - very interesting thread
- Neural Networks thread (good public discussion)
- How to build a NN-EA in MT4: usefull thread for developers.
- Radial Basis Network (RBN) - As Fit Filter For Price: the thread
Neural Network: Indicators and systems development
- Self-trained MA cross!: development thread for new generation of the indicators
- Levenberg-Marquardt algorithm: development thread
Neural Network: EAs
- CyberiaTrader EA: discussion thread and EAs' thread.
- Self learning expert thread with EAs' files here.
- Artificial Intelligence EAs threads: How to "teach" and to use the AI ("neuron") EA thread and Artificial Intelligence thread
- Forex_NN_Expert EA and indicator thread.
- SpiNNaker - A Neural Network EA thread.
Neural Network: The Books
- What to read and where to learn about Machine Learning (10 free books) - the post.
The article
- Neural Networks Made Easy - MT5
- The Disagreement Problem: Diving Deeper into The Complexity Explainability in AI - MT5
- Quantization in machine learning (Part 1): Theory, sample code, analysis of implementation in CatBoost - MT5
- Quantization in machine learning (Part 2): Data preprocessing, table selection, training CatBoost models - 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
- Neural networks made easy (Part 66): Exploration problems in offline learning - MT5
- Neural networks made easy (Part 67): Using past experience to solve new tasks - MT5
- Neural networks made easy (Part 68): Offline Preference-guided Policy Optimization - 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
- www.mql5.com
- You give the system examples of successful trades and it tries to mimic the "function" that decides on those trades.think of it this way , you provide it with the desired outcome and with everything you knew at that point in time for that outcome before the outcome . It tries to create a giant equation that results in trading or not.It's mathematical programming essentially.
- It will adapt when you provide more examples or as it collects examples live.
- You can't make sure . The latent space the training will create may have some unwanted reactions in it , all within the buy sell close scope of course . In order to anticipate everything you must "browse" the entirety of the latent space . If you could do that however you would also have the processing power available to brute force the solution in the first place.(or hack into satoshi nakamoto's wallet)
4+5 no comment .
The best place to start is python .
There is also pytorch lightning and aws because you will need a lot of horse power.
Some simplifications :
the neural networks have 2 ends , the input side and the output side .
How you train them is you provide a list of observations to the input side and a list or one item that was the outcome to the output side.
It then tries to adjust all connections between the 2 sides so that when you provide observations to the input side it will forecast the list of outcomes to the output side.
for instance .
you give it the list of observations 2 ,2 and the outcome 4
It could be 2*2 or 2+2 it does not know
you then give it another example 3,3 and the outcome 6
it then get's clearer . etc.
But imagine the list of observations in your case would be indicator values BEFORE the trade and on the output side the desired decision (buy , sell , nothing)
It is important for the list of observations provided to have been possible in the past . I've seen a guy trying to guess the outcome of football matches by using the stats of the completed football match. so caution there.
It has to be said however that training a neural network is the equivalent of scoring a 3 point shot from a different stadium.That's where the "horse power" requirements come in .You need to be burning through failed iterations fast
Another aspect is for a given problem we do not know what the equivalency of 2,2=4 and 3,3=6 is for the problems we are trying to solve . In the example we know we are supposed to add and how "fundamentally" different 2,2 and 3,3 are . But in a problem we want to solve it is like sailing in uncharted territory
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