Self-trained MA cross! - page 8

[Sergey Golubev]

Forum on trading, automated trading systems and testing trading strategies

Levenberg-Marquardt algorithm

Sergey Golubev, 2017.09.19 10:08

New article was published - 

Deep Neural Networks (Part II). Working out and selecting predictors

Contents

• Introduction
• 1. Creating features
• 1.1. Feature transformation
• 1.1.1. Transformation
• 1.1.2. Normalization
• 1.1.3. Discretization
• 1.2. Creating new features
• 2. Choosing predictors
• 2.1. Visual evaluation
• 2.2. Analytical evaluation
• 2.3. Neural network
• Conclusion
• Application

[Sergey Golubev]

 

Forum on trading, automated trading systems and testing trading strategies

...

Sergey Golubev, 2009.10.09 08:43

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
  
• Neural networks made easy (Part 2): Network training and testing - 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  
  
• 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

CodeBase

• Next price predictor using Neural Network - indicator for MetaTrader 4
• Easy Neural Network - library for MetaTrader 5
  
• ALGLIB - Numerical Analysis Library - library for MetaTrader 4
• 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

[Karvin]

This is very interestin, I would like to contribute but I don't see where we are now. Do you get some results?

if you want a self learning EA we need neural networks or similar approach, a synthetic way to achieve smthg is post-maintein the EA and optimize it with a rolling fashion.

Regards

[Sergey Golubev]

Forum on trading, automated trading systems and testing trading strategies

How to start with MetaTrader and forex, the beginning

Sergey Golubev, 2021.02.08 16:30

Developing a self-adapting algorithm (Part I): Finding a basic pattern

Any trading algorithm is generally a tool which may bring profit to an experienced trader or instantly destroy a deposit of an inexperienced one. The issue of creating a profitable and reliable algorithm is that we cannot understand what needs to be done in order to earn money and what methods are used by "successful traders". While HFT, arbitrage, option strategies and calendar spread-based systems boast a solid theoretical basis clearly stating what needs to be done to make profit, the algorithms based on price analysis and fundamental data are much more ambiguous. This area has no full-fledged theoretical basis that would describe pricing making it extremely difficult to create a stable trading algorithm. Trading turns into art here, while science helps systematizing everything.

But is it possible to create a fully automated trading algorithm based only on the analysis of price changes working on any trading instrument without optimization and with no need to manually adjust parameters for each trading instrument separately? Is there an algorithm you can simply apply to a necessary trading instrument chart so that it immediately defines profitable parameters for it? 

[Sergey Golubev]

Forum on trading, automated trading systems and testing trading strategies

How to start with MetaTrader and forex, the beginning

Sergey Golubev, 2021.02.19 18:08

Developing a self-adapting algorithm (Part II): Improving efficiency 

Before reading this article, I recommend that you study the first article "Developing a self-adapting algorithm (Part I): Finding a basic pattern". This is not necessary, since the main point will still be clear, but reading will be more interesting.

In the previous article, I detected a simple pattern and developed a very simple algorithm that exploits it. But the algorithm has no flexible structure, and it makes no sense to expect any outstanding results from it.

We need to greatly improve it so that it becomes more flexible and adjusts its operation parameters depending on the market situation so that it is possible to achieve better results and stability.

[Tobias Johannes Zimmer]

Ahmed Soliman:

Do you think it's achievable idea? let's set on the rounded table!

You would have to find a way to define if you are in a Trend or not and how it is supposed to react to a flat or not, how it calculates StopLoss and TakeProfit based on Market conditions like Trend Volume or Volatility

[Tobias Johannes Zimmer]

Ahmed Soliman:

Do you think it's achievable idea? let's set on the rounded table!

You need to set the algorithm to acknowledge if there is a trend or not, how it calculates SL and TP based on market parameters like Volumes or Volatility and even imply statistic aspects to tell if a pattern was working and if not how it should alternate. Ideas are not the problem here,  programming is.

[Sergey Golubev]

Forum on trading, automated trading systems and testing trading strategies

How to start with MetaTrader and forex, the beginning

Sergey Golubev, 2021.03.12 09:56

Self-adapting algorithm (Part III): Abandoning optimization

Before reading this article, I recommend that you study the second article in the series "Developing a self-adapting algorithm (Part II): Improving efficiency". The methodology applied in the current article differs significantly from everything discussed earlier, but it will be useful to read the previous articles to understand the topic.

[ffoorr]

This strategy, if it would work, would be the same than artificial intelligence.

   Here it is : "non standart automated trading"

It work with accelerator or awesome indicator, instead of an MA, but the principle is the same.

Do a backtest using different data for accelerator (or ma), and then use the best data for forward trading.

The only difference is : instead of being a "self adaptative expert", one's has to make backtest every week (days) to verify that he still has the best value for his EA.

[Muhammad Syamil Bin Abdullah]

ffoorr:

This strategy, if it would work, would be the same than artificial intelligence.

   Here it is : "non standart automated trading"

It work with accelerator or awesome indicator, instead of an MA, but the principle is the same.

Do a backtest using different data for accelerator (or ma), and then use the best data for forward trading.

The only difference is : instead of being a "self adaptative expert", one's has to make backtest every week (days) to verify that he still has the best value for his EA.

Interesting.