--- title: "MetaTrader 5 build 3390: Float in OpenCL and in mathematical functions, Activation and Loss methods for machine learning - Release Notes" description: "Updated fundamental database for trading instruments. The number of data aggregators available for exchange instruments has been... - Release Notes" image: "https://www.metatrader5.com/i/logo_metatrader5.png" url: "https://www.metatrader5.com/en/releasenotes/terminal/2247" --- # MetaTrader 5 build 3390: Float in OpenCL and in mathematical functions, Activation and Loss methods for machine learning ## Updated fundamental database for trading instruments. The number of data aggregators available for exchange instruments has been expanded to 15. Users will be able to access information on even more tickers via the most popular economic aggregators 4 August 2022 ### Terminal 1. Added automatic opening of a tutorial during the first connection to a trading account. This will assist beginners in learning trading basics and in exploring platform features. The tutorial is divided into several sections, each of which provides brief information on a specific topic. The topic completion progress is shown with a blue line. ![Added automatic opening of a tutorial during the first connection to a trading account. ](https://www.metatrader5.com/c/17/0/achievements__1.png) 2. Fixed 'Close profitable'/'Close losing' [bulk operations](https://www.metatrader5.com/en/terminal/help/trading/performing_deals#bulk_positions). Previously, the platform used opposite positions if they existed. For example, if you had two losing Buy positions for EURUSD and one profitable Sell position for EURUSD, all three positions would be closed during the 'Close losing' bulk operation. Buy and Sell would be closed by a 'Close by' operation, while the remaining Buy would be closed by a normal operation. Now the commands operate properly: they only close the selected positions, either profitable or losing. 3. Fixed display of negative historical prices. Such prices will appear correctly for all timeframes. 4. Optimized and significantly reduced system resource consumption by the terminal. 5. Updated fundamental database for trading instruments. The number of data aggregators available for exchange instruments has been expanded to 15. Users will be able to access information on even more tickers via the most popular economic aggregators. ![Updated fundamental database for trading instruments. ](https://www.metatrader5.com/c/17/0/fundamental.png) About 7,000 securities and more than 2,000 ETFs are listed on the global exchange market. Furthermore, exchanges provide futures and other derivatives. The MetaTrader 5 platform offers access to a huge database of exchange instruments. To access the relevant fundamental data, users can switch to the selected aggregator's website in one click directly from the Market Watch. For convenience, the platform includes a selection of information sources for each financial instrument. 6. Fixed Stop Loss and Take Profit indication in the new order placing window. For FIFO accounts, stop levels will be automatically set in accordance with the stop levels of existing open positions for the same instrument. This procedure is required to comply with the FIFO rule. ### MQL5 1. [Mathematical functions](https://www.mql5.com/en/docs/math) can now be applied to matrices and vectors. We continue expanding algorithmic trading and machine learning capabilities in the MetaTrader 5 platform. Previously, we have added new data types: [matrices and vectors](https://www.mql5.com/en/articles/9805), which eliminate the need to use arrays for data processing. More than 70 methods have been added to MQL5 for operations with these data types. The new methods enable linear algebra and statistics calculations in a single operation. Multiplication, transformation and systems of equations can be implemented easily, without extra coding. The latest update includes mathematical functions.Mathematical functions were originally designed to perform relevant operations on scalar values. From this build and onward, most of the functions can be applied to matrices and vectors. These include MathAbs, MathArccos, MathArcsin, MathArctan, MathCeil, MathCos, MathExp, MathFloor, MathLog, MathLog10, MathMod, MathPow, MathRound, MathSin, MathSqrt, MathTan, MathExpm1, MathLog1p, MathArccosh, MathArcsinh, MathArctanh, MathCosh, MathSinh, and MathTanh. Such operations imply element-wise handling of matrices and vectors. Example: ``` //--- matrix a= {{1, 4}, {9, 16}}; Print("matrix a=\n",a); a=MathSqrt(a); Print("MatrSqrt(a)=\n",a); /* matrix a= [[1,4] [9,16]] MatrSqrt(a)= [[1,2] [3,4]] */ ``` For [MathMod](https://www.mql5.com/en/docs/math/mathmin) and [MathPow](https://www.mql5.com/en/docs/math/mathpow), the second element can be either a scalar or a matrix/vector of the appropriate size. The following example shows how to calculate the standard deviation by applying math functions to a vector. ``` //+------------------------------------------------------------------+ //| Script program start function | //+------------------------------------------------------------------+ void OnStart() { //--- Use the initializing function to populate the vector vector r(10, ArrayRandom); // Array of random numbers from 0 to 1 //--- Calculate the average value double avr=r.Mean(); // Array mean value vector d=r-avr; // Calculate an array of deviations from the mean Print("avr(r)=", avr); Print("r=", r); Print("d=", d); vector s2=MathPow(d, 2); // Array of squared deviations double sum=s2.Sum(); // Sum of squared deviations //--- Calculate standard deviation in two ways double std=MathSqrt(sum/r.Size()); Print(" std(r)=", std); Print("r.Std()=", r.Std()); } /* avr(r)=0.5300302133243813 r=[0.8346201971495713,0.8031556138798182,0.6696676534318063,0.05386516922513505,0.5491195410016175,0.8224433118686484,... d=[0.30458998382519,0.2731254005554369,0.1396374401074251,-0.4761650440992462,0.01908932767723626,0.2924130985442671, ... std(r)=0.2838269732183663 r.Std()=0.2838269732183663 */ //+------------------------------------------------------------------+ //| Fills the vector with random values | //+------------------------------------------------------------------+ void ArrayRandom(vector& v) { for(ulong i=0; i, matrix, vector, vector instead of the corresponding matrix, matrixf, vector and vectorf types. 3. Improved mathematical functions for operations with the [float](https://www.mql5.com/en/docs/basis/types/double) type. The newly implemented possibility to apply mathematical functions to 'float' matrix and vectors has enabled an improvement in mathematical functions applied to 'float' scalars. Previously, these function parameters were unconditionally cast to the 'double' type, then the corresponding implementation of the mathematical function was called, and the result was cast back to the 'float' type. Now the operations are implemented without extra type casting. The following example shows the difference in the mathematical sine calculations: ``` //+------------------------------------------------------------------+ //| Script program start function | //+------------------------------------------------------------------+ void OnStart() { //--- Array of random numbers from 0 to 1 vector d(10, ArrayRandom); for(ulong i=0; i