LS T M Neural Network
This software component for MetaTrader 5 is built to enhance the capabilities of your trading environment. This library provides a collection of modular, reusable code. It is utilized by developers to organize common functions, allowing for the integration of complex logic across multiple Expert Advisors, indicators, or scripts without the need for code duplication.
How to Setup and Use LS T M Neural Network
1. Storage: Place library files in the MQL/Libraries directory to ensure they are accessible to your projects.
2. Implementation: Include the library in your code using the #import directive, ensuring you match the exact function names and parameters.
3. Compilation: Ensure the library is present in the directory before you compile your main EA or script, as the compiler links them during this phase.
4. Management: Keep libraries organized in sub-folders if you manage many custom functions to maintain a clean project structure.
Frequently Asked Questions
Q: What is a library file used for? A: Libraries store reusable code modules, allowing you to centralize common logic used by multiple EAs or indicators.
Q: Is a library executable? A: No, libraries are non-executable files containing functions; they must be imported into an EA, indicator, or script to function.
Q: Can I update a library while the platform is running? A: You should compile your EA or script after updating a library to ensure the latest code changes are integrated.
Description & Settings
Attached are the include files for the LSTM. The files included are:
Gates - for the 4 gates used in an LSTMs.
TimeStep - which combines the gates, and in practical usage would represent the time series bars.
LSTMNetwork - implementing the learning algorithms.
Also included is an example LSTMTest script using the Simple RPC indicator, also attached.
To create a new LSTM network, provide the constructor with number of patterns, number of inputs (predictors per timestep) and the number of timesteps, as shown below;
To teach the network, call the Learn function, providing it with the input array, the targets array, the learning error threshold, and the number of learning epochs as below;
After learning, the final error and epochs taken to converge can be acquired from the network as below;
To check a particular pattern against the network, the Calculate function is called, passing the candidate pattern into the function as a parameter as shown;
The Calculate function returns the output. This LSTM has a single output neuron.
If anyone finds bugs or has improvements or any suggestions, please be kind enough to share. Good luck.
