# Neural Networks for Algorithmic Trading with MQL5 


Examples and source code from the book [Neural Networks for Algorithmic Trading with MQL5](https://www.mql5.com/en/neurobook)  - author [Dmitriy Gizlyk](https://www.mql5.com/en/users/dng/publications)

This repository serves as a practical addition to the book. It contains ready-to-use **scripts** for data preparation and model training, **a neural network library for MQL5**, examples of **CNN / LSTM / Attention / GPT-like architectures**, and **a trading advisor template** for testing models in real market conditions via MetaTrader 5.

![MQL5 Programming for Traders](Book_Algo-MQL5-clear.png)

## Links

- Book (online): https://www.mql5.com/en/neurobook  
- CodeBase publication (archive with examples): https://www.mql5.com/en/code/48097  
- Repository (Algo Forge): https://forge.mql5.io/rosh/NeuroBook  



## Contents

- **MQL5 Neural Network Library** (Core): `Include/realization/`  
  Implementation of networks, layers, and auxiliary components (including OpenCL support and positional encoding).
- **Activation Functions**: `Include/about_ai/activation/` (`activations.mqh`, `activations.py`)
- **Dataset Preparation for Training and Testing**: `Scripts/initial_data/`  
  Script `create_initial_data.mq5` generates file `study_data.csv` and `test_data.csv` in `MQL5/Files`.
- **Models and Tests** (MQL5 + Python):
  - Perceptron and gradient check: `Scripts/perceptron/`
  - Convolutional Neural Networks (CNN): `Scripts/convolution/`
  - Recurrent Networks (LSTM): `Scripts/rnn/`
  - Self-Attention / Multi-Head (examples): `Scripts/attention/`
  - Batch Normalization: `Scripts/batch_norm/`
  - Dropout: `Scripts/dropout/`
  - GPT-like architecture (example): `Scripts/gpt/`
- **OpenCL Acceleration / Performance Testing**:  
  `Scripts/opencl_test.mq5`, `Include/algotrading/mult_vect_ocl.*`
- **Trading Expert Advisor Template** for applying `.net` models: `Experts/ea_template.mq5`
- **Python Template for Experiments**: `Scripts/template.py` (TensorFlow/Keras + MetaTrader5)



## Repository Structure

```text
NeuroBook/
├─ Experts/
│  └─ ea_template.mq5
├─ Include/
│  ├─ about_ai/activation/          # activation functions (MQL5 + Python)
│  ├─ algotrading/                  # OpenCL utilities (multiplication example)
│  └─ realization/                  # neural network and layer implementations in MQL5
├─ Scripts/
│  ├─ initial_data/                 # data preparation (CSV in MQL5/Files)
│  ├─ perceptron/                   # Perceptron + gradient check
│  ├─ convolution/                  # CNN examples + gradient check
│  ├─ rnn/                          # LSTM examples + gradient check
│  ├─ attention/                    # attention / multi-head attention
│  ├─ batch_norm/                   # batch norm
│  ├─ dropout/                      # dropout
│  ├─ gpt/                          # GPT-like example
│  ├─ opencl_test.mq5               # CPU vs OpenCL speed comparison
│  └─ template.py                   # Python training/testing template
├─ KIYA/                            # standalone mini-project/scaffold (mqproj)
└─ NeuroBook.mqproj                 # MetaEditor project
````


## Table of Contents by Book Chapters

Below is a “map” of the chapters and where to find the corresponding examples in this repository.

### Chapter 1 — [Neural Network Building Blocks](https://www.mql5.com/en/neurobook/index/about_ai)

* Activation functions, core AI concepts, and laying the foundation.<br>
  📁 `Include/about_ai/activation/`

### Chapter 2 — [MetaTrader 5 Capabilities for Algorithmic Trading](https://www.mql5.com/en/neurobook/index/algotrading)

* Practical use of MT5 tools, computation acceleration, and more.<br>
  📁 `Scripts/opencl_test.mq5`, `Include/algotrading/`, `Include/realization/opencl.*`  

### Chapter 3 — [Your First MQL5 Model: Data → Model → Test](https://www.mql5.com/en/neurobook/index/realization)

* Dataset preparation, training, and saving the model.<br>
  📁 `Scripts/initial_data/`, `Scripts/perceptron/`

### Chapter 4 — [Core Layer Types: Convolutions and Recurrent Networks](https://www.mql5.com/en/neurobook/index/main_layer_types)

* CNN and LSTM: implementation and testing.<br>
  📁 `Scripts/convolution/`, `Scripts/rnn/`

### Chapter 5 — [Attention Mechanisms](https://www.mql5.com/en/neurobook/index/transformer)

* Self‑Attention and Multi‑Head Self‑Attention, advanced data analysis techniques.<br>
  📁 `Scripts/attention/`, `Include/realization/neuronattention.mqh`, `neuronmhattention.mqh`<br>
  Additionally: a GPT‑like architecture example: 📁 `Scripts/gpt/`, `Include/realization/neurongpt.mqh`

### Chapter 6 — [Improving Convergence: BatchNorm and Dropout](https://www.mql5.com/en/neurobook/index/improvement_realization)

* Practical stabilization and regularization techniques.<br>
  📁 `Scripts/batch_norm/`, `Scripts/dropout/`

### Chapter 7 — [Testing Trading Strategies with Trained Models](https://www.mql5.com/en/neurobook/index/trade_check)

* Using models inside an Expert Advisor and validating them under live-like conditions.<br>
  📁 `Experts/ea_template.mq5`  
  Recommended to test in the [Strategy Tester](https://www.metatrader5.com/en/automated-trading/strategy-tester)



## Quick Start

### 1) Install into MetaTrader 5

1. Open MetaTrader 5 → **File → Open Data Folder**
2. Go to the `MQL5` directory
3. Copy the repository contents (**Experts / Include / Scripts / …**) into your `MQL5/` folder
4. Open MetaEditor (F4) and compile the required files (F7)

> Tip: it’s more convenient to work via `NeuroBook.mqproj` — open the project in MetaEditor and compile directly from the project tree.

### 2) End-to-end example: data → training → Expert Advisor

**Step A. Generate datasets (CSV)**
Run the script:

* `Scripts/initial_data/create_initial_data.mq5`

By default, it will create:

* `MQL5/Files/study_data.csv` — training dataset
* `MQL5/Files/test_data.csv` — test dataset

⚠️ Important:

* before running, make sure the terminal has historical quotes downloaded for the selected period;
* the script uses standard indicators (RSI/MACD) and a ZigZag example.

**Step B. Train the model in MQL5 and save `.net`**
For example, run:

* `Scripts/perceptron/perceptron_test.mq5`

After completion it saves (example):

* `MQL5/Files/Study.net` — the model file
  and an error/loss log (example):
* `MQL5/Files/loss_study.csv`

**Step C. Use the model in an Expert Advisor**
Compile and run:

* `Experts/ea_template.mq5`

Key input parameters in `ea_template`:

* `Model` — model file name (e.g., `Study.net`)
* `Common` — **where to look for the model file**

  * `false`: `MQL5/Files` of the current terminal (training scripts typically save models here)
  * `true`: the terminal’s common folder ([FILE_COMMON](https://www.mql5.com/en/docs/constants/io_constants/fileflags)) if you store models there
* `UseOpenCL` — enable OpenCL (if supported)
* `TimeFrame`, `BarsToPattern`, and trading parameters — adjust to your testing idea

Then test in the Strategy Tester: [https://www.metatrader5.com/en/automated-trading/strategy-tester](https://www.metatrader5.com/en/automated-trading/strategy-tester)



## Python Part (Optional)

This repository includes Python scripts for experimentation and cross-checking approaches, including a template:

* `Scripts/template.py`

It:

* connects to the installed terminal via the `MetaTrader5` module,
* reads `study_data.csv` and `test_data.csv` from `MQL5/Files`,
* trains a model in TensorFlow/Keras,
* saves `model.h5` back into `MQL5/Files`.

Typical dependencies (example):

```bash
pip install MetaTrader5 pandas numpy matplotlib tensorflow
```

> More details on terminal and [environment settings](https://www.metatrader5.com/en/terminal/help/startworking/settings#ea)


## OpenCL

Many examples can be accelerated with OpenCL (the `UseOpenCL` parameter).
To benchmark performance, use `Scripts/opencl_test.mq5` (CPU vs OpenCL comparison).

If OpenCL fails to initialize, check your GPU/CPU drivers and OpenCL support on your system.




## Notes

- All examples are intended for learning and experimentation.  
  Always test thoroughly in the 
  [Strategy Tester](https://www.metatrader5.com/en/automated-trading/strategy-tester) 
  or on a demo account before using real funds.

- The `Scripts/Python` folder contains examples for integrating MetaTrader 5 with Python.  
  Make sure to enable the appropriate [terminal settings](https://www.metatrader5.com/en/terminal/help/startworking/settings#ea):



## License and Usage

All materials are part of the book "MQL5 Programming for Traders" and the MQL5/MetaTrader 5 ecosystem.
Please respect copyright and the MQL5.com/MetaQuotes terms when distributing or reusing code.



## Contributing

* Bugs/improvements: use [**Issues**](https://forge.mql5.io/rosh/NeuroBook/issues).
* Pull Requests: welcome. Submit changes by part for easier review and alignment with the book.