Driving Digital Innovation through Robotics
Robotic Refactoring the Workplace
The "Cambrian" Robotic Explosion has Begun
Position Sensors for Robotics
Robot-Proof: Higher Education in the Age of Artificial Intelligence
Joseph E. Aoun, President, Northeastern University
From Robotics to Big Data, What the Future Holds for Manufacturing
Karl Rosenblum, Head, Global Capacity and Risk Strategy, Alcon
Deep Learning and Future of Healthcare
Sanjib Basak, Former Director of Data Science & Artificial Intelligence, Carlson Wagonlit Travel
Robots: A New Source of Business Data
John Santagate, Research Director, Service Robotics, IDC
Automating Trades with Robots
Human civilization is at the cusp of a breakthrough, regarding technology, knowledge, data and unraveling human DNA. But at the heart of this shift is artificial intelligence wave which the world behave is changing beliefs, religion, governments, and organizations around. Advancements in artificial intelligence are driving this change. Stakeholders are providing services via bots; Singapore and Dubai are leading the change. The idea of building personalized bots has permeated to the financial industry. Traders and software engineers are increasingly making customized bots for trading.
What separates individuals is the approach and beliefs they carry. Building personalized bots have caught the imagination of traders and software engineers around the world. But, building personalized bots requires systematic steps.
Firstly, preliminary research focuses on developing a strategy that suits one’s characteristics. To build a personal approach, businesses need to gauge personal risk profile, time commitment, and trading capital. After these calculations, the next step is to move to test these characteristics. Backtesting is about validating trading robot also responds to the same trading characteristics, this step includes checking the code to make sure it does what that individual wants and understand how it works in different time frames, asset classes or market conditions, especially in black swan events such as the global financial crisis in 2008.
After backtesting requires optimizing the system, fixing the bugs-maximizing the performance while minimizing the overfitting bias. Overfitting bias occurs when one’s robot is strictly based on past data; such a robot will give off the illusion of high performance.
The key to achieving success is the live execution of a robot by using real money. Some technical issues exist, like selecting a broker and implementing mechanisms to manage both market risks and operational risks such as potential hackers and technology downtime.
Algorithmic trade can be rewarding, but understanding is the key to success.