Building Stock Trading Strategies: 20% Faster with Hadoop
Based on complex mathematical algorithms, automated stock trading solutions take into account hundreds of factors and suggest the right time for placing buy/sell orders. Some of the systems like that can even make a deal without any human involvement. However, if an algorithm omits essential market parameters, this may bring significant loss.
In my guest post for Hortonworks, I shared a real-life example of how Hadoop and data clustering speeded up stock trading system’s performance by 20% and increased a customer’s revenues by 12%. You will learn how data clustering helped to diversify sell/buy strategies and how the right infrastructure improved the system’s performance without additional investments.