by Analytics Insight


June 22, 2021

With so many advancements in technology and analytical tools, it is becoming difficult for traders to keep pace. Machine learning is one of the most discussed topics. If you want to know where these two fields intersect, let’s first clarify the meaning of each of the terms.

How does Forex trading work?

Foreign exchange, or Forex, is the process of converting from one currency to another. The value of each specific currency is determined by market factors such as trade, investment, tourism, and geopolitical risk.

Forex is usually traded in specific amounts called lots, which is basically the number of currency units you are going to buy or sell. The standard lot size is 100,000 currency units.

There are three main methods of trading Forex that are commonly used by traders depending on their goals:

  • The Spot Market – This is the primary Forex market, where currency pairs are switched in real time and exchange rates are set based on supply and demand. This trade is a “direct swap” of two currencies, has the shortest timeframe, involves cash rather than a contract, and the agreed transaction does not include interest. One of the most popular forms of Forex trading is spot trading.
  • The Futures Market – In this type, instead of immediately entering into a transaction, Forextraders can enter into (private) contracts with another trader to lock in an exchange rate with a certain volume of currencies at a future date regardless of market rates. then.
  • The Futures Market – Likewise, traders can choose to buy or sell a fixed amount of a currency at a certain exchange rate on a future date. Unlike the futures market, this is done on the stock exchange rather than privately, where traders enter into a legally binding contract.

What is machine learning?

Machine learning (ML) is the study of computer algorithms that automatically improve over time through experience and use of data. It is considered a branch of artificial intelligence. As new technologies have made trading faster and easier, ML is becoming more and more important in the world of Forex trading.

In order to implement Machine Learning in Forex trading, one must first create algorithms. These algorithms examine the data to spot trends and predict future events.

Algorithmic tools used in Forex

In Forex trading, a wide range of algorithmic tools based on machine learning are applied, including:

SVM

SVM or Support Vector Machine is a machine learning language for categorizing data. Due to its ease of application in data categorization challenges, the language has gained wide acceptance. SVMs work by dividing data sets using decision boundaries.

SVM is used to anticipate or assess whether a market trend is bullish or bearish using this method in Forex trading. This is accomplished by establishing hyperplanes between the highs and lows of a trend. A forward hyperplane denotes an uptrend, while a backward hyperplane denotes a downtrend (hyperplanes), then ranks the new data using the hyperplanes.

Neural network

Forex Neural Network is a machine learning method that analyzes market data (values ​​of technical and fundamental indicators) and tries to anticipate the target variable (closing price, trading result, etc.). It is inspired by the functioning of human biological neurons.

In Forex, there are two main contention issues: the Forex regression problem, in which we try to predict future trends, and the Forex classification problem, in which we try to predict whether a trade will be successful or not. The neural network solves both of these problems by capturing yesterday’s high and low prices along with the high and low prices of the past seven days to predict tomorrow’s price.

Why Use Machine Learning in Forex?

In the world of Forex trading, ML can be used for a variety of purposes:

  • Using ML to monitor prices in real time has led to greater transparency. ML algorithms can automate the buying / selling of lots in the Forex market, giving traders an advantage in terms of speed and accuracy.
  • ML involves entering historical data into a system so that it can make future decisions based on it. As a result, ML uses past data, called predictor variables, to predict current monetary values, which are called target variables. To do this, the ML algorithm learns to use predictor variables to predict target variables.

With the help of a supervised ML model, the predicted upward or downward trend of the Forex rate could help traders make the right decision on Forex trades since the decisions made are based on facts, unlike human beings whose decisions are driven by emotions like fear, greed, and hope.

ML also helps increase the number of markets a trader can watch and respond to. The more marketplaces available, the more likely a trader will choose the most profitable. As a result, by implementing ML, traders can optimize their profits and decrease their risks.

Conclusion

The forex market is the world’s largest financial market, and it’s not going away anytime soon. ML has been a game-changer in Forex trading with its fast automated trading, which requires no human intervention and provides accurate analysis, forecasting and rapid trade execution. And to mitigate risk, ML plays an important role in shaping the future of Forex trading.

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Analytics Insight is an influential platform dedicated to insights, trends and opinions from the world of data-driven technologies. It monitors the developments, recognition and achievements of artificial intelligence, big data and analytics companies around the world.

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