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Currency Trading Using genetic programming

Ron Bond November 2012. Currency Trading Using genetic programming. Foreign Exchange. Currencies are priced in currency pairs First symbol = Base Currency Second symbol = Quote Currency Trading a pair = simultaneously buying one currency and selling the other.

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Currency Trading Using genetic programming

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  1. Ron Bond November 2012 Currency Trading Using genetic programming

  2. Foreign Exchange • Currencies are priced in currency pairs • First symbol = Base Currency • Second symbol = Quote Currency • Trading a pair = simultaneously buying one currency and selling the other

  3. Pairs can be though of as a unit: • Buy pair if BASE will appreciate vs QUOTE • Sell pair if QUOTE will appreciate vs BASE

  4. Analytical Models • Fundamental Analysis • In-depth analysis of an asset’s value’s underlining factors • (Currencies) country’s fiscal policies, economic climate, financial data, etc… • Assumes that an assets current (and future) price depends on its intrinsic value and anticipated return on investment

  5. Analytical Models • Technical Analysis • Uses charts of: price, volume, and other statistical indicators to predict future price movement. • rests on the assumption that history repeats itself and that future market direction can be determined by examining past prices.

  6. Technical Indicators • Metrics derived from generic price activity • Used to determine future value/direction

  7. GP - Tree Approach • Myszkowski and Bicz (2010) • Tree structure • Technical indicator evaluations linked by logical operators • Represents a trading rule • Individuals consisted of two trees • Buy Signal Tree • Sell Signal Tree

  8. Language and Operators

  9. GP Parameters • 200 runs per parameter test • Parameter with best average profit used • “Best” approximated over different data sets (Time Frames)

  10. Data Set • Data consisted of “time frames” • Frame includes 2 weeks of 10 min interval price data • Frames were categorized based on characteristics in price movement • Falling (Ln) (Bearish) • Neutral (Nn) (Ranging) • Rising (Hn) (Bullish)

  11. Data Set

  12. Experiment • 200 runs per experiment • Strategy generated by training on frame Gn and evaluated using frame Tn • Always-in-market trading style • Paper does not mention how they dealt with conflicting signals/no signal within an individual

  13. Experiment • Example training result

  14. Data Set

  15. Fitness Function • Maximizing points function • Net profits – transaction cost – penalty • Transaction cost = 4 points • Penalty for under-trading • -8 points/trade under 4 trades

  16. Data Set

  17. Results

  18. Combined Strategy • Ensemble Learning approach • Trade based on majority rule

  19. Analysis • Capital Risk Ignored • No close strategy other than reversal signal • Fitness does not factor account drawdown • Returns in study unadjusted for risk

  20. References • Investopedia • http://www.investopedia.com/terms/c/currencypair.asp#axzz2DLUK7Gwl • http://www.investopedia.com/terms/t/technicalindicator.asp#axzz2DLUK7Gwl • Inter-day Foreign Exchange Trading using Linear Genetic Programming • http://www.cs.mun.ca/~banzhaf/papers/GECCO2010p1139.pdf • Evolutionary Algorithm in Forex Trade Strategy generation • http://www.proceedings2010.imcsit.org/pliks/143.pdf

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