Trader Tech

Artificial intelligence and machine learning is complex and extremely expensive, but understanding how to use the R Programming Language can help bring it into focus.

Supervised learning methods include back propagation neural networks, support vector machines and machine induction algorithms such as C.4.5 and rough sets.

When trading moved from raucous pits to computer screens, it created efficiencies, but what was lost was a sense of community.

In our FINtech Focus section we will profile one of these firms. While FINtech is a broad category, we will be focusing on firms that specifically cater to the alternative investment industry. This week we take a look at Trendrating.
In our FINtech Focus section we will profile one of these firms. While FINtech is a broad category, we will be focusing on firms that specifically cater to the alternative investment industry. This week we take a look at Trendrating.

The classic approach to developing trading systems is to develop a set of trading rules and parameters that work well on one particular market.

In our new section, FINtech Focus, we will profile one of these firms. While FINtech is a broad category, we will be focusing on firms that specifically cater to the alternative investment industry. This week we take a look at Trendrating.
Neural networks are powerful tools made more powerful by the technology leaps of recent years. Indeed, they can even be used to create rules from data. Here’s how.
New technology is unlocking greater capabilities of support vector machines. Here, we will explore the intricacies of how they can be integrated into a trading system.
Today’s advanced hardware is easing computational restrictions on advanced market analysis. Here, we continue our look at hybrid trading systems and how to apply them.