MetaTrader 5 Python package

The announcement -

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New MetaTrader 5 Platform beta build 2245: DirectX functions for 3D visualization in MQL5 and symbol settings in Strategy Tester

MQL5: Fixed time operations in the MetaTrader module for Python integration. Now all output data use the time of the trading server to which the terminal is connected.

MetaEditor: Added ability to compile C/C++ and Python projects directly from MetaEditor. Now, multi-lingual projects can be managed using the built-in editor.

It is not related to Python package but it is very interesting -

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Statistical Distributions in MQL5 - taking the best of R and making it faster

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Contents

1. Functions for calculating the statistical characteristics of array elements
   
   • 1.1. MathMean()
   • 1.2. MathVariance()
   • 1.3. MathSkewness()
   • 1.4. MathKurtosis()
   • 1.5. MathMoments()
   • 1.6. MathMedian()
   • 1.7. MathStandardDeviation(
     
   • 1.8. MathAverageDeviation()
   
   
2. Functions for working with statistical distributions
   2.1. Normal distribution
   
   • 2.1.1. MathProbabilityDensityNormal()
   • 2.1.2. MathCumulativeDistributionNormal()
   • 2.1.3. MathQuantileNormal()
   • 2.1.4. MathRandomNormal()
   • 2.1.5. MathMomentsNormal()
   
   2.2. Log-normal distribution
   
   • 2.2.1. MathProbabilityDensityLognormal()
   • 2.2.2. MathCumulativeDistributionLognormal()
   • 2.2.3. MathQuantileLognormal()
   • 2.2.4. MathRandomLognormal()
   • 2.2.5. MathMomentsLognormal()
   
   2.3. Beta distribution
   
   • 2.3.1. MathProbabilityDensityBeta()
   • 2.3.2. MathCumulativeDistributionBeta()
   • 2.3.3. MathQuantileBeta()
   • 2.3.4. MathRandomBeta()
   • 2.3.5. MathMomentsBeta()
   
   2.4. Noncentral beta distribution
   
   • 2.4.1. MathProbabilityDensityNoncentralBeta()
   • 2.4.2. MathCumulativeDistributionNoncentralBeta()
   • 2.4.3. MathQuantileNoncentralBeta()
   • 2.4.4. MathRandomNoncentralBeta()
   • 2.4.5. MathMomentsNoncentralBeta()
   
   2.5. Gamma distribution
   
   • 2.5.1. MathProbabilityDensityGamma()
   • 2.5.2. MathCumulativeDistributionGamma()
   • 2.5.3. MathQuantileGamma()
   • 2.5.4. MathRandomGamma()
   • 2.5.5. MathMomentsGamma()
   
   2.6. Chi-squared distribution
   
   • 2.6.1. MathProbabilityDensityChiSquare()
   • 2.6.2. MathCumulativeDistributionChiSquare()
   • 2.6.3. MathQuantileChiSquare()
   • 2.6.4. MathRandomChiSquare()
   • 2.6.5. MathMomentsChiSquare()
   
   2.7. Noncentral chi-squared distribution
   
   • 2.7.1. MathProbabilityDensityNoncentralChiSquare()
   • 2.7.2. MathCumulativeDistributionNoncentralChiSquare()
   • 2.7.3. MathQuantileNoncentralChiSquare()
   • 2.7.4. MathRandomNoncentralChiSquare()
   • 2.7.5. MathMomentsNoncentralChiSquare()
   
   2.8. Exponential distribution
   
   • 2.8.1. MathProbabilityDensityExponential()
   • 2.8.2. MathCumulativeDistributionExponential()
   • 2.8.3. MathQuantileExponential()
   • 2.8.4. MathRandomExponential()
   • 2.8.5. MathMomentsExponential()
   
   2.9. F-distribution
   
   • 2.9.1. MathProbabilityDensityF()
   • 2.9.2. MathCumulativeDistributionF()
   • 2.9.3. MathQuantileF()
   • 2.9.4. MathRandomF()
   • 2.9.5. MathMomentsF()
   
   2.10. Noncentral F-distribution
   
   • 2.10.1. MathProbabilityDensityNoncentralF()
   • 2.10.2. MathCumulativeDistributionNoncentralF()
   • 2.10.3. MathQuantileNoncentralF()
   • 2.10.4. MathRandomNoncentralF()
   • 2.10.5. MathMomentsNoncentralF()
   
   2.11. t-distribution
   
   • 2.11.1. MathProbabilityDensityT()
   • 2.11.2. MathCumulativeDistributionT()
   • 2.11.3. MathQuantileT()
   • 2.11.4. MathRandomT()
   • 2.11.5. MathMomentsT()
   
   2.12 Noncentral t-distribution
   
   • 2.12.1. MathProbabilityDensityNoncentralT()
   • 2.12.2. MathCumulativeDistributionNoncentralT()
   • 2.12.3. MathQuantileNoncentralT()
   • 2.12.4. MathRandomNoncentralT()
   • 2.12.5. MathMomentsNoncentralT()
   
   2.13. Logistic distribution
   
   • 2.13.1. MathProbabilityDensityLogistic()
   • 2.13.2. MathCumulativeDistributionLogistic()
   • 2.13.3. MathQuantileLogistic()
   • 2.13.4. MathRandomLogistic()
   • 2.13.5. MathMomentsLogistic()
   
   2.14. Cauchy distribution
   
   • 2.14.1. MathProbabilityDensityCauchy()
   • 2.14.2. MathCumulativeDistributionCauchy()
   • 2.14.3. MathQuantileCauchy()
   • 2.14.4. MathRandomCauchy()
   • 2.14.5. MathMomentsCauchy()
   
   2.15. Uniform distribution
   
   • 2.15.1. MathProbabilityDensityUniform()
   • 2.15.2. MathCumulativeDistributionUniform()
   • 2.15.3. MathQuantileUniform()
   • 2.15.4. MathRandomUniform()
   • 2.15.5. MathMomentsUniform()
   
   2.16. Weibull distribution
   
   • 2.16.1. MathProbabilityDensityWeibull()
   • 2.16.2. MathCumulativeDistributionWeibull()
   • 2.16.3. MathQuantileWeibull()
   • 2.16.4. MathRandomWeibull()
   • 2.16.5. MathMomentsWeibull()
   
   
   2.17. Binomial distribution
   
   
   • 2.17.1. MathProbabilityDensityBinomial()
   • 2.17.2. MathCumulativeDistributionBinomial()
   • 2.17.3. MathQuantileBinomial()
   • 2.17.4. MathRandomBinomial()
   • 2.17.5. MathMomentsBinomial()
   
   2.18. Negative binomial distribution
   
   • 2.18.1. MathProbabilityDensityNegativeBinomial()
   • 2.18.2. MathCumulativeDistributionNegativeBinomial()
   • 2.18.3. MathQuantileNegativeBinomial()
   • 2.18.4. MathRandomNegativeBinomial()
   • 2.18.5. MathMomentsNegativeBinomial()
   
   2.19. Geometric distribution
   
   • 2.19.1. MathProbabilityDensityGeometric()
   • 2.19.2. MathCumulativeDistributionGeometric()
   • 2.19.3. MathQuantileGeometric()
   • 2.19.4. MathRandomGeometric()
   • 2.19.5. MathMomentsGeometric(
     
   
   2.20. Hypergeometric distribution
   
   • 2.20.1. MathProbabilityDensityHypergeometric()
   • 2.20.2. MathCumulativeDistributionHypergeometric()
   • 2.20.3. MathQuantileHypergeometric()
   • 2.20.4. MathRandomHypergeometric()
   • 2.20.5. MathMomentsHypergeometric()
   
   2.21. Poisson distribution
   
   • 2.21.1. MathProbabilityDensityPoisson()
   • 2.21.2. MathCumulativeDistributionPoisson()
   • 2.21.3. MathQuantilePoisson()
   • 2.21.4. MathRandomPoisson()
   • 2.21.5. MathMomentsPoisson()
   
   
3. Table of correspondence to the statistical functions in R
4. An example of using the functions
   
   
5. Comparison of calculation speed
   
   
6. Detected calculation errors in R
   
   
7. References

The announcement -

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new beta of the terminal and a new full-time python library - read more here

New version of MetaTrader 5 for Python 5.0.18 and beta version of MetaTrader 5 build 2319
with new functions and examples.

• beta MT5 is downloaded via Help -> Check beta version
• python library:
  
  
  Code:

  pip install --upgrade metatrader5

more info on post #52 on mql5 forum.

New MetaTrader 5 Platform Build 2340: Managing account settings in the Tester and expanded integration with Python

The new MetaTrader 5 version will be available through the LiveUpdate system.

The announcement -

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• MetaEditor: Added new functionality for working with SQLite databases
• MetaEditor: Expanded support for multi-lingual projects. This update provides wider possibilities for working with Python scripts
• MQL5: Completely revised Python integration. The update involves many new functions and new command naming
• MQL5: Significantly accelerated re-launch of MQL5 programs and re-creation of custom indicators from MQL5 programs. In some cases, operations are performed 100 times faster
• MQL5: Added functions for working with databases
• MQL5: Added FileSelectDialog function, which calls the system dialog creating/opening a file or a folder
• MQL5: New DEAL_FEE value in the ENUM_DEAL_PROPERTY_DOUBLE enumeration. It is used for a deal fee. The value indicates a separate commission type charged by the broker
• Tester: Added functionality to specify custom trading account settings during strategy testing, such as trading limitations, margin settings and commission. The new functions provide extended capabilities for modeling various trading conditions
• Updated documentation
• read more here

Contents

• Modern algorithmic trading in MetaTrader 5
• Functions for working with databases
• Simple query
• Debugging SQL queries in MetaEditor
• Auto reading of query results into the structure using DatabaseReadBind()
• Accelerating transactions by wrapping them into DatabaseTransactionBegin()/DatabaseTransactionCommit()
• Handling trading history deals
• Portfolio analysis by strategies
• Analyzing deals by symbols
• Analyzing deals by entry hours
• Convenient data output to the EA log in DatabasePrint()
• Data import/export
• Saving optimization results to the database
• Optimizing query execution using indices
• Integrating database handling into MetaEditor

MQL5 is a perfect solution for algorithmic trading since it is as close to C++ as possible in terms of both syntax and computation speed. The MetaTrader 5 platform offers its users the modern specialized language for developing trading robots and custom indicators allowing them to go beyond simple trading tasks and create analytical systems of any complexity.

In addition to asynchronous trading functions and math libraries, traders also have access to the network functions, importing data to Python, parallel computing in OpenCL, native support for .NET libraries with "smart" function import, integration with MS Visual Studio and data visualization using DirectX. These indispensable tools in the arsenal of modern algorithmic trading currently allow users to solve a variety of tasks without leaving the MetaTrader 5 trading platform.

more...

Originally Posted by mql5

New MetaTrader 5 Platform Build 2340: Managing account settings in the Tester and expanded integration with Python

The new MetaTrader 5 version will be available through the LiveUpdate system.

The announcement -

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MetaTrader 5 Platform Build 2340 -

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The Main Study

• MetaTrader module for integration with Python - documentation
• Python Releases for Windows - website
• MetaTrader5 : Python Package - website

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The Article

• MetaTrader 5 and Python integration: receiving and sending data - the article
• Practical application of neural networks in trading. Python (Part I) - the article
• Probability theory and mathematical statistics with examples (part I): Fundamentals and elementary theory - the article
• Gradient Boosting (CatBoost) in the development of trading systems. A naive approach - the article
• SQLite: Native handling of SQL databases in MQL5 - the article
• Exploring Seasonal Patterns of Financial Time Series with Boxplot - the article
• Deep Neural Networks (Part VII). Ensemble of neural networks: stacking - the article
• Deep Neural Networks (Part VI). Ensemble of neural network classifiers: bagging - the article
• Deep Neural Networks (Part IV). Creating, training and testing a model of neural network - the article
• Deep Neural Networks (Part III). Sample selection and dimensionality reduction - the article
• Practical application of neural networks in trading - the article
• Practical application of neural networks in trading. Python (Part I) - the article
• Practical application of neural networks in trading (Part 2). Computer vision - the article

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Metatrader 5 Help

• MetaTrader 5 build 2360: Extension of SQLite integration
• MetaTrader 5 build 2340 simplifies working with SQLite and Python — algorithmic trading increased accessibility
• MetaTrader 5 Build 2340: Managing account settings in the Tester and expanded integration with Python
• Integration with Python and support for Market and Signals services in Wine (Linux/macOS) in MetaTrader 5 build 2085
• MetaTrader 5 platform build 2085: Integration with Python and Strategy Tester improvements

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The CodeBase

• Optimal F for MQL5 (per Ralph Vince) - library for MetaTrader 5
• ZeroMQ asynchronous communication - library for MetaTrader 4

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The Forum

• New MetaTrader 5 platform build 2085: Integration with Python and Strategy Tester improvements
• MetaTrader 5 Python User Group - how to use Python in Metatrader - key thread (in Russian)
• Terminal: Added new API enabling request of MetaTrader 5 terminal data through applications using the Python language - MetaQuotes Announcement
• Machine Learning in MT4 - Python and R - small thread
• Interaction between mql4 and Python - the thread

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The Blogs
Something to Read - Python for Finance: Analyze Big Financial Data - the blog post (the book)
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more to follow ..

New MetaTrader 5 Platform Build 2450: "Subscriptions" service, UI improvements and revised features in MetaEditor

more..

• Practical application of neural networks in trading - the article
• Practical application of neural networks in trading. Python (Part I) - the article
• Practical application of neural networks in trading (Part 2). Computer vision - the article