Imagine being able to predict the movements on the stock market accurately. This thought may have sounded like science fiction in the past but today we know that it’s certainly possible. Making investment decisions is not an easy task, and it comes with many big risks. And making the wrong decision can have disastrous consequences.
Nevertheless, in recent years, financial trading has been evolving thanks to data science. And, as a result, today even beginner trader can dip their toes into the world of investments, and benefit from it.
While it’s still far from an exact science, access to large amounts of data and data analysis, allows both institutional and individual traders to make more accurate predictions, and consequently, less risky and more informed decisions.
The simple explanation behind these changes is that due to the improvement of various pieces of software designed to observe patterns, it’s now possible to develop algorithms capable of processing and analyzing large amounts of data.
This provides you with invaluable insights that can help you predict future developments based on what has happened in the past. This can minimize the financial risk, and it can get higher returns.
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Volatility Surfaces
Data science has created many opportunities across industries. In financial trading, it has allowed access to quality data and much more complete metrics and calculations.
Whether you are trading with your own money, or you work for a company or a financial organization, you can analyze historical implied volatility data fast and more accurately, and decide which strategy is best for a particular market.
Based on volatility, as well as other important technical indicators, such as price movements and trends, volume oscillation, and so on, traders can find patterns and develop their strategies. This essentially gives them a clearer picture of the risks and possible returns, helping them be more confident in their decisions.
Volume of Trade
Another important way in which data science can help traders make better decisions is by analyzing trading volume. Volume can be a very important indicator to use in trading.
This calculates the total quantity of financial transactions of securities, which have been made between two entities – buyers and sellers – in a certain period of time. And it can be used in various ways.
Trade volume can tell you what big traders, those that can move the prices, are doing, like whether they are selling or buying. For example, high volume during a big price movement can indicate the strength of a given trend.
Real-Time Predictive Analysis
Data gathered from different relevant sources and the ability to analyze them is crucial in finance. Financial traders use these methods to ensure they are making the right decision and build an investment strategy. However, data analysis and pattern recognition can happen much faster now, thanks to data science.
Machine learning is one of the most exciting technological advancements and it is only possible because of data science. In simple words, it uses algorithms to recognize patterns by analyzing large amounts of data from various sources in real-time. Also called bots, the algorithms will work by following rules that are previously set.
Machine learning also automates the most complex processes, so the trader can identify patterns instantly, identify financial risk, and the current market regime. Thanks to this, even though the trader will choose which patterns are relevant, the decision-making process isn’t influenced by human emotions.
Training and Testing
Thanks to data science, you can also use historical data to create a machine learning model. This is done by selecting a portion of the data, training the model, and then testing it. Once you have the model and you test whether it performs well or not.
A machine learning model should help you predict future movements, based on past data. For instance, to predict the price of a stock in the future, you can give it data for the previous year.
Final Thoughts
Analyzing historical data from different sources is a common practice in trading. Thanks to data science and machine learning, nowadays it can be done fast and more accurately. This allows traders to make better and less risky decisions.
Having large amounts of data from different sources to use to make trading decisions, used to be a privilege of big funds. However, as a result of the process of democratization of data, and thanks to data science, everyday people can now take advantage of quality sets of data and use them to start investing.
