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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