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Trading system optimization using mu...

Swain, Jacob A.

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Trading system optimization using multiple entry point strategies.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Trading system optimization using multiple entry point strategies./
作者:	Swain, Jacob A.
面頁冊數:	90 p.
附註:	Source: Masters Abstracts International, Volume: 52-03.
Contained By:	Masters Abstracts International52-03(E).
標題:	Information Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1524095
ISBN:	9781303512483

Trading system optimization using multiple entry point strategies.
Swain, Jacob A.

Trading system optimization using multiple entry point strategies. - 90 p.

Source: Masters Abstracts International, Volume: 52-03.

Thesis (M.S.)--University of Houston-Clear Lake, 2013.

Investors pour trillions of dollars into the stock markets annually [1-4]. Many of these investors make decisions to buy and sell securities by analyzing historical price data, corporate financial statements, and global economic conditions. Although many successful investors exist, implementing a profitable trading strategy is easier said than done [1,3-8]. Recent developments in the field of financial data mining may provide investors with tools to implement successful strategies and maximize the returns.

ISBN: 9781303512483Subjects--Topical Terms:

1017528
Information Science.

Trading system optimization using multiple entry point strategies.
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500 $a Source: Masters Abstracts International, Volume: 52-03.
500 $a Adviser: Gary Boethicher.
502 $a Thesis (M.S.)--University of Houston-Clear Lake, 2013.
520 $a Investors pour trillions of dollars into the stock markets annually [1-4]. Many of these investors make decisions to buy and sell securities by analyzing historical price data, corporate financial statements, and global economic conditions. Although many successful investors exist, implementing a profitable trading strategy is easier said than done [1,3-8]. Recent developments in the field of financial data mining may provide investors with tools to implement successful strategies and maximize the returns.
520 $a Results from several academic papers suggest that financial data mining techniques can produce profitable trading strategies [5-9]. These results look very promising. However, many of these approaches overlook real-world issues that impact actual trading; such as the role of position sizing in maximizing returns [1,3,9,10]. Additionally, current research typically shows models trading in single trades that commit 100 percent of their equity into each trade. The question of whether staggering entry into a position will produce superior results remains unanswered.
520 $a This research applies a genetic algorithm (GA) to develop trading models that use multiple entry points to stagger a trader's entry into a trading position. The algorithm builds models for trading S& P 500 E-Mini futures contracts using historical data from an online brokerage where traders buy and sell these contracts. To determine if a multiple-entry-point (MEP) approach outperforms a single-entry-point (SEP) approach, the GA generates 32 models optimized for both SEP trading and 32 models for MEP trading.
520 $a Statistical analysis of the results shows the average MEP model producing superior returns compared to SEP models. SEP based models increased equity by 3.9% after transaction costs while MEP based models increased equity by 10%. Additional analysis using a Monte Carlo simulation of an MEP model shows the strategy producing profitable returns 90% of the time.
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