If the available history is insufficient, the tester automatically shifts the actual start date forward to the nearest point that meets the requirements.
In such cases, testing begins later than the date specified by the user. The tester log will show a relevant message, for example:
start time changed to 2024.03.15 00:00 to provide data at beginning
class Base { public: void Print(int x) { ::Print("Base int: ", x); } void Print(double y){ ::Print("Base double: ", y); } }; class Derived : public Base { public: void Print(string s){ ::Print("Derived string: ", s); } }; void OnStart() { Derived d; d.Print("text"); // call of Derived::Print(string) d.Print(10); // ATTENTION! Calling Derived::Print(string) since Base::Print is hidden (inaccessible) d.Base::Print(10); // explicit call to hidden parent method }For some time, the MQL5 compiler will issue a warning if a hidden base method is a better match for the call parameters than the available derived method. Example for the above code d.Print(10):
class Base { protected: void Print(int x) { ::Print("Base int: ", x); } void Print(double y){ ::Print("Base double: ", y); } }; class Derived : public Base { public: void Print(string s){ ::Print("Derived string: ", s); } using Base::Print; // return Print overloads from Base }; void OnStart() { Derived d; d.Print("text"); // Derived::Print(string) d.Print(42); // Base::Print(int) d.Print(3.14); // Base::Print(double) }If 'using Base::Print;' is removed, then calls to d.Print(42) and d.Print(3.14) will be unavailable; only Derived::Print(string) will remain.
Published MQL5 Algo Forge User Guide. It highlights all the key benefits of Git without unnecessary complexity:
Cloning Another Developer's Project
Open the project you want to clone on forge.mql5.io and click Fork. Enter a name and description for the fork and save.
In MetaEditor, using the same MQL5 account, run the Refresh command in the Navigator. Your fork will appear in the Shared Projects folder. Download it from Algo Forge using Git Clone. You'll receive not only the project files but also its full commit history and all branches. This means you can continue working on the fork while still having the entire history of the cloned project.
struct A { int y; string func(double x) { return(__FUNCSIG__); } }; struct B : public A { string func(int x) // the method hides A::func { return(__FUNCSIG__); } }; void OnStart(void) { B b; b.func(M_PI); // according to new rules, it is a call to B::func b.A::func(M_PI); // call the hidden method A::func }This change simplifies code readability and eliminates ambiguity that was previously only accompanied by a compiler warning.
input int somename=42; int somename(int x) { return(42); }
int somename(ENUM_TIMEFRAMES TF=PERIOD_CURRENT); int somename(ENUM_TIMEFRAMES TF=0) // error, type mismatch for the default parameter value, despite having the same value { return(42); }
enum A { Value }; enum B { Value // error, name 'Value' is already used in enumeration A }; void OnStart(void) { enum C { Value // OK, 'Value' is not used within the OnStart scope }; }Matching names can be used in different scopes.
template<typename T> void Initializer(matrix<T>& mat,int method=0); matrix<double> A(10,10,Initializer,42); // error, Initializer must be explicitly typed matrix<double> A(10,10,Initializer<double>); // error, missing 'method' parameter (default values no longer supported) matrix<double> A(10,10,Initializer<double>,42); // OK
The latest versions of the MetaTrader 5 mobile app for iOS introduce a range of new features designed to help traders stay abreast of financial markets wherever they are.
Install the latest app version and unlock extended trading capabilities:
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matrix A(2, 3); matrix B(3, 2); matrix C = A @ B; // Result: Matrix C of size [2,2]Matrix multiplication (matrix × vector)
matrix M(2, 3); vector V(3); vector R = M @ V; // Result: Vector R of 2 elementsMatrix multiplication (vector x matrix)
matrix M(2, 3); vector V(1, 2); vector R = V @ M; // Result: Vector R of 3 elementsScalar multiplication (vector × vector)
vector V1(1, 3), V2(1, 3); double r = V1 @ V2; // Result: Scalar
static vector vector::Random( const ulong size, // vector length const double min=0.0, // min value const double max=1.0 // max value ); static matrix matrix::Random( const ulong rows, // number of rows const ulong cols // number of columns const float min=0.0, // min value const float max=1.0 // max value );
The update will be available through the Live Update system.
The latest versions of the MetaTrader 5 mobile app for iOS introduce a range of convenient chart features, along with important stability improvements for a smoother user experience.
Download the latest version of the app and enhance your trading experience:
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This update fixes an error in the calculation of triple swaps in the strategy tester, which occurred under certain combinations of testing conditions. Additionally, a number of minor enhancements and fixes have been implemented to further improve the platform stability.
matrix<complex<T>> matrix<complex<T>>::TransposeConjugate(void) const;The method returns a new conjugate-transposed matrix in which the elements of the original matrix are transposed and converted to their complex conjugates.
int matrix<T>::CompareEqual(const matrix<T>& mat) constThe return values are:
pip install --upgrade MetaTrader5
Function |
Action |
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Singular Value Decomposition, divide-and-conquer algorithm; considered the fastest among other SVD algorithms (lapack function GESDD). |
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Singular Value Decomposition, QR algorithm; considered a classical SVD algorithm (lapack function GESVD). |
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Singular Value Decomposition, QR with pivoting algorithm (lapack function GESVDQ). |
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Singular Value Decomposition, bisection algorithm (lapack function GESVDX). |
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Singular Value Decomposition, Jacobi high-level algorithm (lapack function GEJSV). |
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Singular Value Decomposition, Jacobi low-level algorithm (lapack function GESVJ). The method computes small singular values and their singular vectors with much greater accuracy than other SVD routines in certain cases. |
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Singular Value Decomposition, divide-and-conquer algorithm for bidiagonal matrices (lapack function BDSVDX). |
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Singular Value Decomposition, bisection algorithm for bidiagonal matrices (lapack function BDSVDX). |
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Compute eigenvalues and eigenvectors of a regular square matrix using the classical algorithm (lapack function GEEV). |
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Compute eigenvalues and eigenvectors of a symmetric or Hermitian (complex conjugate) matrix using the divide-and-conquer algorithm (lapack functions SYEVD, HEEVD). |
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A method function for calculating the relative contributions of spectral components based on their eigenvalues |
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A method function for calculating reconstructed and predicted data using spectral components of the input time series. |
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A method function for calculating reconstructed components of the input time series and their contributions. |
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A method function for calculating the reconstructed time series using the first component_count components. |
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The update will be available through the Live Update system.
MetaTrader 5 Android
Update your mobile apps through Google Play, Huawei AppGallery or by downloading the APK file.
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vector<double/complex> operator[](const int i) const; vector<double/complex> operator[](const ulong i) const;They have been replaced by a single method with a constant return value:
const vector<double/complex> operator[](const ulong i) const;This modification will assist in capturing incorrect use of the result in place as in the new Alglib version, the code mat[row][col]=x operates differently from the old version. Previously, this indicated writing to a matrix. Now, the value is written to a temporary object vector<double/complex>, which is immediately destroyed after recording.
bool ArrayToFP16(ushort &dst_array[],const float &src_array[],ENUM_FLOAT16_FORMAT fmt); bool ArrayToFP16(ushort &dst_array[],const double &src_array[],ENUM_FLOAT16_FORMAT fmt); bool ArrayToFP8(uchar &dst_array[],const float &src_array[],ENUM_FLOAT8_FORMAT fmt); bool ArrayToFP8(uchar &dst_array[],const double &src_array[],ENUM_FLOAT8_FORMAT fmt); bool ArrayFromFP16(float &dst_array[],const ushort &src_array[],ENUM_FLOAT16_FORMAT fmt); bool ArrayFromFP16(double &dst_array[],const ushort &src_array[],ENUM_FLOAT16_FORMAT fmt); bool ArrayFromFP8(float &dst_array[],const uchar &src_array[],ENUM_FLOAT8_FORMAT fmt); bool ArrayFromFP8(double &dst_array[],const uchar &src_array[],ENUM_FLOAT8_FORMAT fmt);Since real number formats for 16 and 8 bits may differ, the "fmt" parameter in the conversion functions must indicate which number format needs to be processed. For 16-bit versions, the new enumeration NUM_FLOAT16_FORMAT is used, which currently has the following values:
Improved display of margin requirements in contract specifications. Now, in addition to ratios and initial parameters for calculations, specifications display the final margin values. If the margin amount depends on the position volume, the corresponding levels will be shown in the dialog.
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MQL5
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//+------------------------------------------------------------------+ //| 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(); Print(ma); Print(mb); /* [[(0.1,0.1),(0.2,0.2),(0.3,0.3)] [(0.1,-0.1),(0.2,-0.2),(0.3,-0.3)]] [[(0.1,-0.1),(0.2,-0.2),(0.3,-0.3)] [(0.1,0.1),(0.2,0.2),(0.3,0.3)]] */ ma=mb.Transpose().Conjugate(); Print(ma); /* [[(0.1,0.1),(0.1,-0.1)] [(0.2,0.2),(0.2,-0.2)] [(0.3,0.3),(0.3,-0.3)]] */ }
from sys import argv data_path=argv[0] last_index=data_path.rfind("\\")+1 data_path=data_path[0:last_index] 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) knn.fit(X_train, 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: f.write(onx.SerializeToString())Open the created onnx file in MetaEditor:
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.
//--- declare an array to receive data from the output layer output_probability MyMap output_probability[]; ... //--- model running OnnxRun(model,ONNX_DEBUG_LOGS,float_input,output_label,output_probability);
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