V.18:6 (20-32):Warehousing Data For Better Trading Systems by Joseph M. Fisher, M.D.,Ph.D.
Product Description
Warehousing Data For Better Trading Systems
by Joseph M. Fisher, M.D., Ph.D.
Technically based trading systems are generally back-tested with tens to hundreds of entry signals. Here's a method to develop robust trading systems using more than 2.5 million signals by applying data warehousing technology.
In today's markets, the development and implementation of robust trading strategies is no simple task. One way to do this is to develop a set of trading rules and then backtest them on a small number of stocks (less than 50), in the expectation that the system will perform similarly with the universe of securities that the system has yet to see. Some software products have been designed specifically for this approach - to test indicators, buy signals, and trading systems on one stock at a time and then tabulate the results for review. Unfortunately, many systems that look promising when tested on small numbers of stocks fail in real-world situations due to curve-fitting, insufficient sample sizes, the peculiarities of the time series under study, or a changing market environment. I'll look at a way to test entry signals and trading systems on 500 stocks simultaneously using a large relational database known as a data warehouse.
by Joseph M. Fisher, M.D., Ph.D.
Technically based trading systems are generally back-tested with tens to hundreds of entry signals. Here's a method to develop robust trading systems using more than 2.5 million signals by applying data warehousing technology.
In today's markets, the development and implementation of robust trading strategies is no simple task. One way to do this is to develop a set of trading rules and then backtest them on a small number of stocks (less than 50), in the expectation that the system will perform similarly with the universe of securities that the system has yet to see. Some software products have been designed specifically for this approach - to test indicators, buy signals, and trading systems on one stock at a time and then tabulate the results for review. Unfortunately, many systems that look promising when tested on small numbers of stocks fail in real-world situations due to curve-fitting, insufficient sample sizes, the peculiarities of the time series under study, or a changing market environment. I'll look at a way to test entry signals and trading systems on 500 stocks simultaneously using a large relational database known as a data warehouse.
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