Source: dqindia.com
Leveraging the power of immense data analysis to enhance human expertise further, machine learning technology holds the potential to cause the next evolution in trading. Through data analytics, statistical trends accumulated over vast periods of time reveal actionable insights that can enhance decision making for individuals as well as enterprises. History repeats itself even in trading, but to identify when and how machine learning is needed.
Trading strategies involve a lot of variables to be observed over a period of time. Pattern identification and the time period within which they will be repeated are the objectives of such strategies, and it needs rigorous checks to acquire real, actionable patterns from random ones. This is where machine learning comes in.
By identifying a target variable and using historical data to train an ML model, we can predict the variable’s value as close as possible to its actual value over different time periods and market conditions. The massive scope of ML to correlate different market conditions, stock price rise/fall patterns, etc. empowers modern analysts like never before.
Machine learning has enabled optimizing portfolio execution like never before. If the base variable taken is highly relevant to the ultimate objective, then ML models have been seen to achieve high rates of success. Also, the data collected needs to enable the algorithm with an ideal predictive power that helps to identify the target variable. This data can have a massive range-Stock Price Data, Stock Trade Volume Data, Fundamental Data, Price and Volume Data of Correlated stocks, an Overall Market indicator like Stock Index Level, Price of other correlated assets etc.
Machine learning based trading systems have the ability to completely reimagine conventional trading platforms as we know them. They can effectively crunch millions upon millions of data points in an instant, and make predictive models that are the most accurate according to the information.
Today, stocks, bonds and future trading are getting heavily automated by large market participants. For a few companies that invested during the 90s and 2000s in this technology, it has resulted in immense rise in yield. As the costs of ML hardware falls, even smaller financial services companies are making a beeline to adopt such solutions. Trading, which essentially involved understanding and reacting to patterns across financial markets will not remain the same ever again.
As data sources widen and these solutions continue to grow more intelligent, the trading world finds itself at the cusp of acquiring a previously unattainable, even unimaginable level of insight into a stock’s trading ability.
