This year we plan to start to research into using Artificial Intelligence (A.I) for algorithmic trading covering subjects like Machine Learning Neural Networks to forecast profits. We will dig into the internet and find all the useful information about Machine Learning, Deep Learning, AI, Big data, Analytics.
Neural networks are state-of-the-art, trainable algorithms that emulate certain major aspects of the functioning of the human brain. This gives them a unique, self-training ability, the ability to formalize unclassified information and, most importantly, the ability to make forecasts based on the historical information they have at their disposal.
It is well known that 80% of data is unstructured. Unstructured data is the messy stuff every quantitative analyst tries to traditionally stay away from.
Technical analysis uses certain chart patterns that may indicate trend changes. This helps identify price targets and time horizons for stock to reach those price targets.
Chart pattern recognition has been around for decades. Before personal computers were readily available, stock traders would literally keep books of charts plotted by hand every day. Using their chart books, traders would recognize certain chart patterns as they plotted that day's price into their books. However, this process was not only time-consuming but also limited the number of stocks that a trader could realistically follow.
Now you can automate the classic technical analysis techniques which expand the number of instruments a trader can review from a few to several thousand.
R is the world’s most powerful, and preferred, programming language for statistical computing and machine learning which fits perfectly with algorithmic trading, this blog explains how Microsoft has embraced the R-Statistical language in their enterprise-level applications.