MetaTrader 5 Python package

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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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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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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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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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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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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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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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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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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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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