Building Stock Trading Strategies: 20% Faster with Hadoop
Speeding up a stock trading platform
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 a significant loss.
In this guest blog post, experts at Altoros shared a real-life example of how Hadoop and data clustering speeded up stock the performance of a trading system by 20% and increased customer’s revenues by 12%.
The article also explores how data clustering helped to diversify sell/buy strategies, and how the right infrastructure can improve the system’s performance without additional investments.
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