Machine learning enables a computer to learn itself without the help of human input. Also similar to artificial intelligence, machine learning does not need any explicit programming to gather the required knowledge.
Application of machine learning can be found in software engineering, medical research, advertising, economics, financial market, and a wide range of other fields. Forex machine learning although quite unpopular requires substantial knowledge for a successful deployment.
There are still no worthy publications that can present adequate machine learning models in Forex trading. One of the factors affecting a successful integration of machine learning in Forex could certainly be the nature of an unpredictable market.
Implementing machine learning in Forex trading requires building algorithms based on historical data. These algorithms identify patterns in the market and predict what the future it holds. Similarly, identifying support and resistance lines is how a machine learning algorithm can work.
A prediction says that by 2020 machine learning will replace human traders. Although currently, the application doesn’t find ground due to extensive time consumption and high expense; rumors suggest that several trading establishments use machine learning covertly. The only players investing in machine learning Forex prediction are either banks or financial institutions.
Presently Forex machine learning only finds application in the 4 major currencies of USD, EUR, GPB, and CHF. Speculations suggest with the advent of machine learning, all world currencies will come under its umbrella shortly.
However, as of today, professional traders are still not convinced of the successful positive outcome of machine learning. On top of that, these traders have no interest in helping software developers create an algorithm because of their skepticism. These traders believe that machine learning algorithms cannot determine the correlations of minuscule anomalies that a human can easily find.
Even if machine learning can identify these anomalies or correlations in short-term trading, in long-term trading, they find difficulties.
Machine learning for Forex trading presents traders with the following features:
Traders implementing a strategy with machine learning can optimize it using a wide range of parameters. These parameters can include entry and exit points, indicators selections, stop loss and trading stop locations, take profit conditions, etc. Combining all these parameters in real-life might encounter several difficulties, but machine learning eases them out.
With machine learning Forex prediction, traders can combine multiple indicators while also deploying secondary once in case of convergence. Price action; fundamental, or technical traders all can implement any indicators with this approach.
- Live Market Trading
A machine learning algorithm continuously adapts to live market conditions and learns from a trader’s actions. However, backtesting the algorithm before using it in a market is of prime importance as not knowing how to operate, might cause unfavorable outcomes in live trading.
Forex machine learning is still an untamed technology that is yet to receive recognition. However, with the constant development of Forex market, the future seems bright for it.
The content of this article reflects the author’s opinion and does not necessarily reflect the official position of LiteForex. The material published on this page is provided for informational purposes only and should not be considered as the provision of investment advice for the purposes of Directive 2004/39/EC.