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Impact of Machine Learning in Trading

The way we look at trading has evolved over the years. There was a time when not many were willing to get into trading. The main reasons that stopped people from investing in stocks were the lack of knowledge, the lack of time to spend on watching the market and the complicate process involved in finding a broker and buying a stock. Technology has offered solutions to all of these problems. Trading bots are making it big in the world of trading. Trading bots are available to help even those with little to no trading knowledge to invest in stocks. And for those who have no time, there are automation tools where the trader can save time by feeding in trading strategies for automatic order placement. Finally, the fact that all these can be done without even stepping out of your house, simply by using your smartphone. AI or artificial intelligences offers a competitive edge when you are designing a technology to handle complicated decision making tasks.

Impact of Machine Learning in Trading-Mobinius

ML and trading

As a sub-field within the rapidly growing AI technology, machine learning is a data driven technology that has been used in cases where smart algorithms are to be written to enable the user to make better predictions by tapping the full computational potential of machines. When we talk about making predictions based on the identification of patterns and based on the understanding of the available data and extrapolating it to predict future data, trading is one field which sounds very relevant. So machine learning is apt to be used in trading. Machine learning is given that name because it is a technology that allows machines to learn and evolve instead of simply playing by the rules. When you design a machine learning algorithm there would be feedback fed back to the system to improve the algorithm and to help in making better predictions the next time. Pattern recognition in several forms, including facial recognition, identification of handwriting and more are performed by using Machine learning.

Why use ML in trading?

When we talk about trading statistics plays a vital role. Yes, there is a level of uncertainty attached with it. But if you look at the finer details the uncertainties might be removed after all. Trading unlike gambling, has very little amount of doubt attached when you study all the parameters and give them the right weightage. If you look at the technical analysis of a stock there are several parameters that are spoken about. These attributes would give an idea about the direction of progress of the value of the stock. But then that is not all. You would also have to compare the peer performance, the major decisions taken by the business, relationship with the financial indices of the nation and the world, as well as any economic changes that might impact the stock. It is not impossible to do all this research. But by the time you study all of this you might have lost the window. And ask any trader he would tell you how missing a single moment could cause a big profit or a big loss in some stocks. So in a place where time is money, the speed of performance of the machines can be put to good use. What takes hours for the traders can be finished in seconds by the machine that is running an ML based algorithm for trading.

Feature selection ability

One other benefit besides saving your time is the fact that when you perform a manual study you would be able to compare just a handful of technical parameters for a stock. And when you are trading multiple stocks it is even more tedious. But then a machine learning algorithm would be able to compare and analyse several technical parameters and help study multiple stocks without any hassle. So you can save your brain from exhaustion while your computer does the research for you. You can always have your control on the steering by still being the one who designs the trading strategies according to the market research data. Because there is more to trading that just comparing the RSI (relative strength index) values, Bollinger Bands and Moving Averages. The indicators to choose would also vary on the type of trading you do. It is about understanding the weights to assign to each of the indicators and this is what is called as Feature selection in ML- where the most relevant predictors are taken into account. The decision would then be validated by back testing with the historic data available in order to test the efficiency of the predictors and the prediction strategy.

Overfitting can be expensive for an investor

Overfitting is the most predominant issue while forming prediction strategies based on the analysis of the formed pattern from the data available. Though it might instantaneously help you, it would be a major hurdle in the long run and can lead to losses. Overfitting is also responsible for the errors performed by prediction algorithms. Feature selection can help prevent the negative effects of overfitting. Overfitting can also be avoided by extending the dataset available for trading. Sometimes feedback from the same loop performed too many times might end up catching instances which were actually not strong indicators of a pattern but just happened to be chance events. The problem of overfitting is avoided by using as many indicators as possible to understand the trends and this is possible with the use of machine learning to study the stocks at hand. In the field of statistics and its applications regularization is used in order to address the issue of overfitting which still continues to be one of the major factors that lead to losses for traders.

No matter how efficient a machine learning based trading algorithm is, remember that it is still only a prediction and the market risks are still applicable. Historic data is not always relevant in live markets. Keep this in mind if you plan to use ML for your trading strategies and proceed with realistic goals for enhancing the overall experience.

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