MetaTrader 5 Python package
- MetaTrader 5 Python User Group - the summary
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The Main Study
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The Article
Metatrader 5 Help
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The CodeBase
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The Forum
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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 ..
MetaTrader 5 platform build 2055: Integration with Python, C++ like scope
MetaTrader 5 platform beta build 2055: Integration with Python, C++ like scope and Strategy Tester improvements
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MetaTrader 5 Client Terminal build 2055
- Terminal: Added new API enabling request of MetaTrader 5 terminal data through applications using the Python language.
Python is a modern high-level programming language for developing scripts and applications. It contains multiple libraries for machine learning, process automation, as well as data analysis and visualization.
MetaTrader package for Python is designed for efficient and fast obtaining of exchange data via interprocessor communication directly from the MetaTrader 5 terminal. The data received this way can be further used for statistical calculations and machine learning.
Connection
- Download the latest Python version at https://www.python.org/downloads/windows
- During Python installation, check "Add Python X.X to PATH%" to enable launch of Python scripts from the command line.
- Install the MetaTrader 5 module from the command line
pip install MetaTrader5
- Add matplotlib and pytz packages
pip install matplotlib
pip install pytz
Functions
- MT5Initialize establishes connection with the MetaTrader 5 terminal
- MT5Shutdown closes the previously established connection to the MetaTrader 5 terminal
- MT5TerminalInfo gets status and parameters of the connected MetaTrader 5 terminal
- MT5Version returns the MetaTrader 5 terminal version
- MT5WaitForTerminal waits till the MetaTrader 5 terminal connects to the trade server
- MT5CopyRatesFrom gets bars from the MetaTrader 5 terminal starting from the specified date
- MT5CopyRatesFromPos gets bars from the MetaTrader 5 terminal starting from the specified index
- MT5CopyRatesRange gets bars in the specified date range from the MetaTrader 5 terminal
- MT5CopyTicksFrom gets ticks from the MetaTrader 5 terminal starting from the specified date
- MT5CopyTicksRange gets ticks for the specified date range from the MetaTrader 5 terminal
New MetaTrader 5 platform build 2085
New MetaTrader 5 platform build 2085: Integration with Python and Strategy Tester improvements
Quote:
Terminal: Added new API which enables request of MetaTrader 5 terminal data through applications using Python language.
Python is a modern high-level programming language for developing scripts and applications. It contains multiple libraries for machine learning, process automation, as well as data analysis and visualization.
MetaTrader package for Python is designed for efficient and fast obtaining of exchange data via interprocessor communication, directly from the MetaTrader 5 terminal. The data received via this pathway can be further used for statistical calculations and machine learning.
Connection
- Download the latest Python version at https://www.python.org/downloads/windows
- During Python installation, check "Add Python X.X to PATH%" to enable the launch of Python scripts from the command line.
- Install the MetaTrader 5 module from the command line
pip install MetaTrader5
- Add matplotlib and pytz packages
pip install matplotlib
pip install pytz
Functions
- MT5Initialize establishes connection with the MetaTrader 5 terminal
- MT5Shutdown closes the previously established connection to the MetaTrader 5 terminal
- MT5TerminalInfo receives status and parameters of the connected MetaTrader 5 terminal
- MT5Version returns the MetaTrader 5 terminal version
- MT5WaitForTerminal waits till the MetaTrader 5 terminal connects to the trade server
- MT5CopyRatesFrom receives bars from the MetaTrader 5 terminal starting from the specified date
- MT5CopyRatesFromPos receives bars from the MetaTrader 5 terminal starting from the specified index
- MT5CopyRatesRange receives bars in the specified date range from the MetaTrader 5 terminal
- MT5CopyTicksFrom receives ticks from the MetaTrader 5 terminal starting from the specified date
- MT5CopyTicksRange receives ticks for the specified date range from the MetaTrader 5 terminal
MetaTrader 5.0.7 for Python
Released an updated version of MetaTrader 5.0.7 for Python.
You can upgrade as follows:
Code:
pip install --upgrade matplotlib
pip install --upgrade MetaTrader5
Changes/fixes on the updated version -
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Transition from true / false results to detailed numeric codes.
Improved standby modes for data availability from the terminal.
MetaTrader module for Python integration
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
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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.
New version of MT5 for Python 5.0.18 and beta version of MetaTrader 5 build 2319
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.
1 Attachment(s)
expanded integration with Python
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.
Attachment 38429
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
1 Attachment(s)
SQLite: Native handling of SQL databases in MQL5
Contents
Attachment 38452
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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...
New MetaTrader 5 Platform Build 2450
New MetaTrader 5 Platform Build 2450: "Subscriptions" service, UI improvements and revised features in MetaEditor
more..
1 Attachment(s)
Understand and efficiently use OpenCL API by recreating built-in support as DLL on Linux (Part 1): Motivation and validation
Attachment 46955
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OpenCL (Open Computing Language) is framework that allows users to write programs to execute across CPU (Central Processing Unit), GPU (Graphics Processing Unit), or dedicated accelerator device with benefit that it can speed up heavy computation required as per problem domain.
MetaTrader 5 supports OpenCL version 1.2. It has
several built-in functions that users can take benefit in using of out-of-box.
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Everything you need to learn about the MQL5 program structure
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Every software in any programming language has a structure, after understanding this structure we can create or develop our software smoothly. MQL5 language's programs are the same as any other programming language has their structure and it is supposed to be understood by the developer to achieve the objectives of his project smoothly and effectively. In this article, we will provide information in this context to try to deliver its content easily as possible.
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Algorithmic Trading With MetaTrader 5 And R For Beginners
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For community members transitioning from R, irrespective of their background in Academia or Scientific Computation, the MetaQuotes community welcomes you with open arms. Despite the advancements in Python, and the exclusive integration of Python as the only other fully supported language within the MetaTrader terminal, individuals proficient in R need not perceive their programming skills as obsolete. This article challenges any notion suggesting obsolescence by illustrating that, with the application of creativity and a little ingenuity, it remains entirely feasible to construct a comprehensive algorithmic trading advisor using R and MetaTrader 5.
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Deep Learning Forecast and ordering with Python and MetaTrader5 python package and ONNX model file
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Deep learning is a subfield of machine learning that focuses on artificial neural networks, inspired by the structure and function of the human brain. It involves training models to perform tasks without explicit programming but by learning patterns and representations from data. Deep learning has gained significant attention due to its ability to automatically learn hierarchical features and representations, making it effective in various domains such as image and speech recognition, natural language processing, and more.
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Deep Learning GRU model with Python to ONNX with EA, and GRU vs LSTM models
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This is the continuation of
Deep Learning Forecast and Order Placement using Python, the MetaTrader5 Python package and an ONNX model file, but you continue this one without the previous one. All will be explained. Everything we will use is included in this article. In this section, we will guide you through the entire process, culminating in the creation of an Expert Advisor (EA) for trading and subsequent testing.
Machine learning is a subset of artificial intelligence (AI) that focuses on developing algorithms and statistical models that enable computers to perform tasks without being explicitly programmed. The primary goal of machine learning is to enable computers to learn from data and improve their performance over time.
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Cross-validation and basics of causal inference in CatBoost models, export to ONNX format
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In the previous articles, I have described various ways to use machine learning algorithms to create trading systems. Some turned out to be quite successful, others (mostly from early publications) were greatly overtrained. Thus, the sequence of my articles reflects the evolution of understanding: what machine learning is actually capable of. We are, of course, talking about the classification of time series.
The current article is a development of the previous topic and the next step towards creating a self-training algorithm that is able to look for patterns in data while minimizing overfitting. After all, we want to get a real effect from the use of machine learning, so that it not only generalizes training examples, but also determines the presence of cause-and-effect relationships in them.
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Seasonality Filtering and time period for Deep Learning ONNX models with python for EA
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When I read the article:
Benefiting from Forex market seasonality, I thought of this to make this what I think is an interesting article. I could start comparing an EA with and without seasonality's and with to see if it can benefit.
I all ready new that markets depend on seasonality since I read that the Mark Zuckerberg got the money for Facebook, from an investor that got his money investing the one that they gave him in the Bar Mitzvah in stocks of petrol when the climate in the Caribbean there were going to have hurricanes. He first studied the climate and forecasted that there would be a bad weather during a period of time.
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Python, ONNX and MetaTrader 5: Creating a RandomForest model with RobustScaler and PolynomialFeatures data preprocessing
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Random Forest is widely used in a variety of fields, and its flexibility makes it suitable for both classification and regression problems. In a classification task, the model decides which of the predefined classes the current state belongs to. For example, in the financial market, this could mean a decision to buy (class 1) or sell (class 0) an asset based on a variety of indicators.
However, in this article, we will focus on regression problems. Regression in machine learning is an attempt to predict the future numerical values of a time series based on its past values. Instead of classification, where we assign objects to certain classes, in regression we aim to predict specific numbers. This could be, for example, forecasting stock prices, predicting temperature or any other numerical variable.
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Automated Parameter Optimization for Trading Strategies Using Python and MQL5
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Self-optimization algorithms for trading strategies include parameter optimization, evolutionary algorithms, heuristic methods, gradient-based techniques, machine learning, and simulation-based optimization. Each has unique pros and cons, tailored for different trading needs and market conditions.
Python programs are an excellent tool to try ideas, create graphics quickly and confirm theoretical statements with historical trading data. Python allows to develop and adjust models agilely, which facilitates experimentation with different strategies and parameters. Its ability to generate detailed graphs and visualizations helps interpret the results more intuitively. In addition, the possibility of integrating historical data allows verifying how strategies would have worked in past scenarios, providing practical validation to the theories raised. This combination of speed, flexibility and analytical capacity makes Python an invaluable tool for any trader that seeks to optimize their strategies and better understand financial markets.
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Build Self Optimizing Expert Advisors With MQL5 And Python
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This article demonstrates how we can intelligently achieve our goal by using a transition matrix to model market behavior and determine whether to employ trend-following or mean-reverting strategies. We start by developing a high-level understanding of transition matrices. Then, we explore how these mathematical tools can be used to create intelligent trading algorithms with enhanced decision-making abilities.
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Data Science and ML (Part 29): Essential Tips for Selecting the Best Forex Data for AI Training Purposes
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With all the trading data and information such as indicators (there are more than 36 built-in indicators in MetaTrader 5), symbol pairs (there are more than 100 symbols) that can also be used as data for correlation strategies, there are also news which are valuable data for traders, etc. The point I'm trying to raise is that there is abundant information for traders to use in manual trading or when trying to build Artificial Intelligence models to help us make smart trading decisions in our trading robots.
Out of all the information we have at hand, there has to be some bad information (that is just common sense). Not all indicators, data, strategy, etc. are useful for a particular trading symbol, strategy, or situation. How do we determine the right information for trading and machine learning models for maximum efficiency and profitability? This is where feature selection comes into play.
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Self Optimizing Expert Advisor with MQL5 And Python (Part III): Cracking The Boom 1000 Algorithm
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We will analyze all of Deriv’s synthetic markets individually, starting with their best known synthetic market, the Boom 1000. The Boom 1000 is notorious for its volatile and unpredictable behavior. The market is characterized by slow, short and equally sized bear candles that are randomly followed by violent, skyscraper sized bull candles. The bull candles are especially challenging to mitigate because the ticks associated with the candle normally aren’t sent to the client terminal, meaning that all stop losses are breached with guaranteed slippage every time.
Therefore, most successful traders have created strategies loosely based on only taking buy opportunities when trading the Boom 1000. Recall that the Boom 1000 could fall for 20 mins on the M1 chart, and retrace that entire movement in 1 candle! Therefore, given its overpowered bullish nature, successful traders look to use this to their advantage by attributing more weight to buy setups on the Boom 1000, than they would to a sell setup.
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Applying Localized Feature Selection in Python and MQL5
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In this article, we explore a feature selection algorithm introduced in the paper
'Local Feature Selection for Data Classification' by Narges Armanfard, James P. Reilly, and Majid Komeili. This method aims to identify predictive features that are often overlooked by traditional selection techniques due to their limited global utility. We will begin with a general overview of the algorithm, followed by its implementation in Python to create classifier models suitable for export to MetaTrader 5.
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Self Optimizing Expert Advisor With MQL5 And Python (Part IV): Stacking Models
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In this series of articles, we will discuss different ways of building trading applications capable of dynamically adjusting themselves to evolving market conditions. There are potentially infinite ways we can approach this problem but, it is unlikely that all possible solutions will be valid. Therefore, our goal today is to demonstrate and empirically analyze the merits and shortcomings of different possible solutions, to help you improve your trading strategies.
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Self Optimizing Expert Advisor With MQL5 And Python (Part V): Deep Markov Models
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In our previous discussion on Markov Chains, linked
here, we demonstrated how to use a transition matrix to understand the probabilistic behavior of the market. Our transition matrix summarized a lot of information for us. It not only guided us on when to buy and sell, it also informed us whether our market had strong trends or was mostly mean reverting. In today's discussion, we shall change our definition of the system state from the moving averages we used in our first discussion to the Relative Strength Indicator (RSI) indicator instead.
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Self Optimizing Expert Advisor With MQL5 And Python (Part VI): Taking Advantage of Deep Double Descent
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Overfitting in machine learning can take on many different forms. Most commonly, it happens when an AI model learns too much of the noise in the data, and fails to make any useful generalizations. This leads to dismal performance when we assess the model on data it has not seen before. There are many techniques that have been developed to mitigate overfitting, but such methods can often prove challenging to implement, especially when you are just getting started on your journey. However, a recent paper, published by a group of diligent Harvard Alumni, suggests that on certain tasks, overfitting may be a problem of the past. This article will walk you through the research paper, and demonstrate how you can build world-class AI models, inline with the world's leading research.
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Multiple Symbol Analysis With Python And MQL5 (Part II): Principal Components Analysis For Portfolio Optimization
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For members of our community looking to sell Expert Advisors, this article will demonstrate how you can create a seamless experience for your end users. Our trading application will flexible and robust at the same time. I will show you how to create trading applications that will allow your clients to easily switch between high, medium and low-risk trading modes. While the PCA algorithm will take care of the heavy lifting for your end users in the background.
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From Python to MQL5: A Journey into Quantum-Inspired Trading Systems
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This article explores the application of quantum-inspired concepts in trading systems, bridging theoretical quantum computing with practical implementation in MQL5. We’ll introduce essential quantum principles and guide you from Python prototyping to MQL5 integration, with real-world performance data.
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Developing a trading robot in Python (Part 3): Implementing a model-based trading algorithm
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In the
first article of the series, we loaded the dataset, placed labels, enriched the dataset and also performed dataset labeling. The
second article was devoted to the creation and training of the model, as well as implementation of cross-validation and bagging.
Now that our model is trained and tested, it is time to start real trading using the MetaTrader 5 library for Python. This powerful library allows us to automate trading directly through Python using the functions and classes provided by the MetaTrader 5 platform.
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