Evolutionary Money Management

  • Philip Saks
  • Dietmar Maringer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5484)


This paper evolves trading strategies using genetic programming on high-frequency tick data of the USD/EUR exchange rate covering the calendar year 2006. This paper proposes a novel quad tree structure for trading system design.

The architecture consists of four trees each solving a separate task, but mutually dependent for overall performance. Specifically, the functions of the trees are related to initiating (“entry”) and terminating (“exit”) long and short positions. Thus, evaluation is contingent on the current market position. Using this architecture the paper investigates the effects of money management. Money management refers to certain measures that traders use to control risk and take profits, but it is found that it has a detrimental effects on performance.


Exchange Rate Genetic Programming Trading Strategy Loss Aversion Foreign Exchange Market 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Allen, H., Taylor, M.P.: Charts, noise and fundamentals in the London foreign exchange market. The Economic Journal 100(400), 49–59 (1990)CrossRefGoogle Scholar
  2. 2.
    Bhattacharyya, S., Pictet, O.V., Zumbach, G.: Knowledge-intensive genetic discovery in foreign exchange markets. IEEE Transactions on Evolutionary Computation 6(2), 169–181 (2002)CrossRefGoogle Scholar
  3. 3.
    Chang, K., Osler, C.L.: Methodical madness: Technical analysis and the irrationality of exchange-rate forecasts. The Economic Journal 109, 636–661 (1999)CrossRefGoogle Scholar
  4. 4.
    Dacorogna, M.M., Gencay, R., Müller, U.A., Olsen, R.B., Pictet, O.V.: An Introduction to High-Frequency Finance. Academic Press, London (2001)Google Scholar
  5. 5.
    Dempster, M.A.H., Jones, C.M.: A real-time adaptive trading system using genetic programming. Quantitative Finance 1, 397–413 (2001)CrossRefGoogle Scholar
  6. 6.
    Fama, E.F.: Efficient capital markets: A review of theory and empirical work. Journal of Finance 25(2), 383–417 (1970)CrossRefGoogle Scholar
  7. 7.
    Jonsson, H., Madjidi, P., Nordahl, M.G.: Evolution of trading rules for the FX market or how to make money out of GP, Technical report, Institute of Theoretical Physics, Chalmers University of Technology (1997)Google Scholar
  8. 8.
    Kahneman, D., Tversky, A.: Prospect theory: An analysis of decision under risk. Econometrica 47(2), 263–291 (1979)CrossRefzbMATHGoogle Scholar
  9. 9.
    LeBaron, B.: Technical trading profitability in foreign exchange markets in the 1990’s, Technical report, Brandeis University (2002)Google Scholar
  10. 10.
    Maillet, B., Michel, T.: Further insights on the puzzle of technical analysis profitability. The European Journal of Finance 6, 196–224 (2000)CrossRefGoogle Scholar
  11. 11.
    Meese, R., Rogoff, K.: Empirical exchange rate models of the seventies, do they fit out-of-sample? Journal of International Economics 14, 3–24 (1983)CrossRefGoogle Scholar
  12. 12.
    Menkhoff, L., Taylor, M.P.: The obstinate of foreign exchange professionals: Technical analysis (2006)Google Scholar
  13. 13.
    Neely, C.J., Weller, P.A.: Intraday technical trading in the foreign exchange market, Technical report, Federal Reserve Bank of St Louis (1999)Google Scholar
  14. 14.
    Osler, C.L.: Currency orders and exchange rate dynamics: An explanation for the predictive success of technical analysis. The Journal of Finance 58(5), 1791–1819 (2003)CrossRefGoogle Scholar
  15. 15.
    Saks, P., Maringer, D.: Evolutionary money management, Technical report, Centre for Computational Finance and Economic Agents, University of Essex (2008)Google Scholar
  16. 16.
    Saks, P., Maringer, D.: Genetic programming in statistical arbitrage. In: Giacobini, M., Brabazon, A., Cagnoni, S., Di Caro, G.A., Drechsler, R., Ekárt, A., Esparcia-Alcázar, A.I., Farooq, M., Fink, A., McCormack, J., O’Neill, M., Romero, J., Rothlauf, F., Squillero, G., Uyar, A.Ş., Yang, S. (eds.) EvoWorkshops 2008. LNCS, vol. 4974, pp. 73–82. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  17. 17.
    Thaler, R., Tversky, A., Kahneman, D., Schwartz, A.: The effect of myopia and loss aversion on risk taking: An experimental test. The Quarterly Journal of Economics 112(2), 647–661 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Philip Saks
    • 1
  • Dietmar Maringer
    • 2
  1. 1.Centre for Computational Finance and Economic AgentsUniversity of EssexUK
  2. 2.Department for Quantitative Methods, Economics and Business FacultyUniversity of BaselSwitzerland

Personalised recommendations