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Model Calibration and Automated Trading Agent for Euro Futures


Germán Creamer


Stevens Institute of Technology - Wesley J. Howe School of Technology Management

March 22, 2012

Quantitative Finance, Vol. 12, No. 4, pp. 531-545, 2012

Abstract:     
We explored the application of a machine learning method, Logitboost, to automatically calibrate a trading model using different versions of the same technical analysis indicators. This approach takes advantage of boosting's feature selection capability to select an optimal combination of technical indicators and design a new set of trading rules. We tested this approach with high frequency data of the Dow Jones EURO STOXX 50 Index Futures (FESX) and the DAX Futures (FDAX) for March 2009. Our method was implemented with different learning algorithms and outperformed a combination of the same group of technical analysis indicators using the parameters typically recommended by practitioners.

We incorporated this method of model calibration in a trading agent that relies on a layered structure consisting of the machine learning algorithm described above, an online learning utility, a trading strategy, and a risk management overlay. The online learning layer combines the output of several experts and suggests a short or long position. If the expected position is positive (negative), the trading agent sends a buy (sell) limit order at prices slightly lower (higher) than the bid price at the top of the buy (sell) order book less (plus) transaction costs. If the order is not 100% filled within a fixed period (i.e. 1 minute) of being issued, the existent limit orders are cancelled, and limit orders are reissued according to the new experts' forecast. As part of its risk management capability, the trading agent eliminates any weak trading signal.

The trading agent algorithm generated positive returns for the two major European index futures (FESX and FDAX) and outperformed a buy and hold strategy.

Number of Pages in PDF File: 27

Keywords: Automated trading, machine learning, algorithmic trading, agent based economics, trading agents, boosting

JEL Classification: G11, G12, G13, G15, G17

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Date posted: March 26, 2012 ; Last revised: February 20, 2013

Suggested Citation

Creamer, Germán, Model Calibration and Automated Trading Agent for Euro Futures (March 22, 2012). Quantitative Finance, Vol. 12, No. 4, pp. 531-545, 2012. Available at SSRN: http://ssrn.com/abstract=2028677

Contact Information

German (Herman) G. Creamer (Contact Author)
Stevens Institute of Technology - Wesley J. Howe School of Technology Management ( email )
1 Castle Point on Hudson
Hoboken, NJ 07030
United States
2012168986 (Phone)
HOME PAGE: http://www.creamer-co.com

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