QGA SVM Forex aims to implement a Quantum Genetic Algorithm and Support Vector Machine for feature selection and price classification. The system utilizes a ZeroMQ bridge to connect Python 3.8 with the trading terminal. The included expert advisors focus on calculating Quantum Price Levels using wavefunctions and the Quartic Schrodinger Equation. It is primarily a research-oriented project, as several core machine learning components are listed as development goals in the documentation.
Quantum Genetic Algorithm for parameter selection
ZeroMQ bridge between Python and MetaTrader
Support Vector Machine classification model
Quantum Price Level calculation via Schrodinger Equation
1Download the sourceUse "Download source (.zip)" above, or clone TerenceLiu98/QGA-SVM-Forex from GitHub, and unzip it.
2Open the data folderIn MetaTrader choose File, then Open Data Folder, and go to MQL4/Experts or MQL5/Experts.
3Copy the filesCopy the .mq4 / .mq5 file into Experts, and any .mqh files into the Include folder the code expects.
4Compile in MetaEditorOpen the file in MetaEditor and press Compile. Fix any missing includes the compiler reports.
5Test before you tradeRun it in the Strategy Tester, then attach it to a demo chart and allow algorithmic trading.
Third-party code. ForexR did not write, test or endorse this EA, and the summary above was generated automatically from its README and code. ForexR lists only repositories with readable source code and never hosts files. Read the code, backtest it and use a demo account first; automated trading can lose money quickly.
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