Optimizing Trading Robots for the Futures Market A KNIME Approach. Jan Pospisil

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1 A KNIME Approach Robot Trading Jan Pospisil

Trading Robots and Futures Market Trading Robot Futures Market automated order execution software that automatically places orders directly into your brokers trading platform compost of many

2 Trading Robots and Futures Market Trading Robot Futures Market automated order execution software that automatically places orders directly into your brokers trading platform compost of many subsystems risk managament / money management / stop loss order processing and tracking engine prediction engine etc. overall inference engine which combines all subsystems and determines strategy (scalping, arbitrage, spread-trading, trend following) man-made trading of derivatives instead of assets agreement to exchange cash (or assets) over time mostly highly leveraged (small movements in the price of the asset cause high movements in the price of the derivative) trader can make profit taking either a long position - expecting the price going up - or a short position - expecting the price going down Page 2 (02/2010)

$) self-similar at different time scales (fractal) (Mantegna, R., & Stanley, E. 2000) at least weakly stationary (Tsay R.$

3 Prediction - Futures Market Properties Properties of Price Signals in Futures Markets noisy - SNR 1.4 in average (Siriopoulos, Leontitsis; 2002; et al.) self-similar at different time scales (fractal) (Mantegna, R., & Stanley, E. 2000) at least weakly stationary (Tsay R., 2002) models confidence decreases over time seasonal Movement - Retracement Consequences preprocessing necessary to smooth noise and to reduce data extraction of patterns and trading signals on different time scales rebuild trading signal models from time to time Page 3 (02/2010)

Prediction - What to Predict? Standard Methods exact price of the next period in the current scale (one step ahead predition)? single value prediction of a noisy signal(!

4 Prediction - What to Predict? Standard Methods exact price of the next period in the current scale (one step ahead predition)? single value prediction of a noisy signal(!) frequently leads to violation of the time line ( prediction of the previous steps!) (especially when models are not human comprehensible; e.g. ANN) many steps ahead prediction? risk to predict the past is low predicting of exact (noisy) shape not valuable Other Possibilities prediction of extreme values or turning points Movement Prediction (Traders Choice) visible on all scales can be exploited immediately (leverage) always precede trends (trend following) less noisy and easier to detect detection must happen at the beginning of a movement destinguish between false and positive outbreaks is a non trivial task Page 4 (02/2010)

5 Selection of Algorithms Standard Financial source: Metatrader4, R-Project, Rmetrics e.g. EMA, Relative Strength Index, Alligator / Gator fixed structured / partially capable of parametrisation models available in most trading platforms frequently applied by traders in a man-made market Signal Processing / Statistical - KNIME, R-Project, Rmetrics e.g. Elliptic Filters, DFT, Noise-Related, 1st & 2nd order Derivatives, Standard Deviation fixed structured / capable of parametrisation models occational available in trading platforms proven to operate very well in noisy environments Machine Learning - KNIME, Rulequest, WEKA, (Salford) e.g. GEP, Eureqa, Cubist, C5 (rules), RecBF-DDA, Random Forests, (MART (Treenet)) variable structured / capable of parametrisation models proven to operate very well in high dimensional environments Page 5 (02/2010)

6 Design of a Trading Robot in a Trading Platform (MT4) tick based CEP Engine price ticks are provided from broker in real time all preprocessing and visualization takes places in the client workflow Optimization in KNIME Tick Events Indicator Noise Reduction Indicator Feature Extraction Indicator etc Meta-Indicator Pattern Extractor Meta-Indicator Feature Extraction Movement Signal Continuous Signal Continuous Movement Signal Discrete long / short / flat Trading Signal long / short / flat Trading Signal long / short / flat Expert Advisor (EA) (Inference Engine) Order Closing (Take Profit / Stop Loss) Risk Calculation Broker Order Size Calculation Orders Order Opening (according to strategy) Page 6 (02/2010)

MT4: RealTime Simulation Historical TS Data

Statistics Rmetrics Indicators & Signals

7 MT4: RealTime Simulation Historical TS Data (CSV) Indicators, Signals Signal Proc. Statistics Rmetrics Indicators & Signals MODELS & Indicators: MQL4,.DLL KNIME: Predictor Optimization and Backtesting RandomForest TS Export (CSV) TS Export (CSV) MODEL:.tree.rules Eureqa MODEL: JAVA DATA:.names.data MODEL:.tree.rules MODEL: JAVA Page 7 (02/2010) Rulequest: C5, Cubist

8 Example Result Page 8 (02/2010)

9 Lessons Learned KNIME can easily model very complex wokflows very good to use for preprocessing good integration of R and WEKA wasn t unable to export RecBF-DDA models Modelling of Financial Time Series financial time series have a timeline and therefore are asymetrical do not rely on modelling techniques which do not give you human comprehensible models do not rely on only ONE decision algorithm for trading signal extraction take missclassification costs into account (if available) always test on unseen data Testing and Implementing always take transaction costs into account do real time simulation (against your brokers trading platform) compare your Trading Robot to a random walk within the same environment implement emergency shutdowns Page 9 (02/2010)

10 END Page 10 (02/2010)

11 One step ahead prediction Predicting the future or the past? (Swingler, 1994) Page 11 (02/2010)

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