Building Stock Trading Strategies: 20% Faster with Hadoop

Sofia Parfenovich

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.

  • What I think is trading was there but there but the way of working means the strategies of working was having huge changes as well people were accepting the same and working on it.Its not because they liked it but may be we can say actually they all were getting profit from it.

    • This is possible. In any case, Hadoop is a great tool for speeding up stock trading strategies. Our article not only describes how it works but shows what kind of results you might expect.

Benchmarks and Research

Subscribe to new posts

Get new posts right in your inbox!