MetaTrader 5 build 3980: Improvements and fixes

What's new in MetaTrader 5

21 September 2023


  1. New trading report improvements. Fixed the display of the total swaps value and the profit chart by symbols.
  2. Optimized deposit and withdrawal pages. For further details about the new platform integration with payment systems, please read the build 3950 release notes.
  3. Optimized recalculations of financial operations across the entire platform, including the strategy tester. Now profit, margins, and many other parameters are calculated faster.
  4. Updated user interface translations.


  1. Added Conjugate methods for complex, vector<complex> and matrix<complex> types. They implement complex conjugate operations.
    //| Script program start function                                    |
    void OnStart()
       complex a=1+1i;
       complex b=a.Conjugate();
       Print(a, "  ", b);
       (1,1)  (1,-1)
       vectorc va= {0.1+0.1i, 0.2+0.2i, 0.3+0.3i};
       vectorc vb=va.Conjugate();
       Print(va, "  ", vb);
       [(0.1,0.1),(0.2,0.2),(0.3,0.3)]  [(0.1,-0.1),(0.2,-0.2),(0.3,-0.3)]
       matrixc ma(2, 3);
       ma.Row(va, 0);
       ma.Row(vb, 1);
       matrixc mb=ma.Conjugate();
  2. Added handing of ONNX model outputs of the 'Sequence of maps' type.

    For ONNX models that provide Map sequences in the output layer (ONNX_TYPE_SEQUENCE of ONNX_TYPE_MAP), a dynamic or fixed array of structures should be passed as the output parameter. The first two fields of this structure must match the ONNX_TYPE_MAP key and value types and be fixed or dynamic arrays.

    Consider the iris.onnx model created by the following Python script:
    from sys import argv
    from sklearn.datasets import load_iris
    iris_dataset = load_iris()
    from sklearn.model_selection import train_test_split
    X_train, X_test, y_train, y_test = train_test_split(iris_dataset['data'], iris_dataset['target'], random_state=0)
    from sklearn.neighbors import KNeighborsClassifier
    knn = KNeighborsClassifier(n_neighbors=1), y_train)
    #  Convert into ONNX format
    from skl2onnx import convert_sklearn
    from skl2onnx.common.data_types import FloatTensorType
    initial_type = [('float_input', FloatTensorType([None, 4]))]
    onx = convert_sklearn(knn, initial_types=initial_type)
    path = data_path+"iris.onnx"
    with open(path, "wb") as f:
    Open the created onnx file in MetaEditor:

    Viewing the ONNX model in MetaEditor

    The Map sequence is passed as "output_probability". It has a key of INT64 type (which corresponds to long in MQL5) and the float type value. To receive data from this output, declare the following structure:
    struct MyMap
       long              key[];
       float             value[];
    Here we used dynamic arrays with appropriate types. In this case, we can use fixed arrays because the Map for this model always contains 3 key+value pairs.

    Since the Map sequence is returned, an array of such structures should be passed as a parameter for receiving data from output_probability output. This array can be dynamic or fixed, in accordance with the properties of a particular model. Example:
    //--- declare an array to receive data from the output layer output_probability
    MyMap output_probability[];
    //--- model running


  1. Fixed display of output types in the ONNX model viewer.

MetaTrader 5 Web Terminal build 3980

  1. Added Contact Broker section in the web terminal's main menu.
  2. Added error handling for SSL authentications. This authentication type is not supported in the web terminal. One-time passwords can be used instead.
  3. Fixed desktop platform download link in the main menu.
  4. Fixed accounts managing dialog. If the broker does not provide the demo or real account opening option, the relevant menu item will be hidden.