November 2019
Beginner
394 pages
10h 31m
English
Something that should be obvious at this point is that the quality and quantity of the historical market data available is a key aspect in being able to build a profitable algorithmic trading business. For this reason, most market participants invest a lot of resources in building a market data capture and normalization process that is extremely accurate, and software implementation that is bug free and able to faithfully capture and replay live market data in historical mode to match exactly what algorithmic trading strategies will observe when they are deployed in live markets. Usually, if trading strategies are not performing in live markets as expected, this is the first place to start. By adding an extensive ...