Advertisement

Optimization of Backtesting Techniques in Automated High Frequency Trading Systems Using the d-Backtest PS Method

  • 16 Accesses

Abstract

Trading strategies intended for high frequency trading in Forex markets are executed by cutting-edge automated trading systems. Such systems implement algorithmic trading strategies and are configured with predefined optimized parameters in order to generate entry and exit orders and execute trades on trading platforms. Three high-frequency automated trading systems were developed in the current research, using the MACD (oscillator), the SMA (moving average) and the PIVOT points (price crossover) technical indicators. The systems traded on hourly time frames, employing historical data of closing prices and the parameter optimization for each system was done using the d-Backtest PS method over weekly periods. With this work we intend to extend the methods of parameter selection for automated trading systems in high frequency trading. Through this research and the interpretation and evaluation of its results, we conclude that backtesting parameters’ optimization, especially through the d-Backtest PS method, is much more profitable than the default values of the parameters and that the optimization of parameters yields the highest profits through the implementation of restrictive relationships among them. It is also observed that the selection of the most profitable parameters of a trading system can be unrestricted, rendering the validation of the minor divergence occurring among slightly varying prices redundant. Meanwhile, other conclusions that can be drawn are that the most profitable classification system employed by the d-Backtest PS method is calibrated by means of two validation periods and that the most efficient profitability ratio between historical data period and validation period is 6:1 (in- and out-of-the-sample ratio).

This is a preview of subscription content, log in to check access.

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 99

This is the net price. Taxes to be calculated in checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

References

  1. Achelis, S. B. (1995). Technical analysis from A to Z: Covers every trading tool from the absolute breadth index to the zig zag. Chicago, IL: Probus Publisher.

  2. Appel, G. (2005). Technical analysis: Power tools for active investors. Upper Saddle River: FT Press.

  3. Bailey, D. H., Ger, S., Lopez de Prado, M., Sim, A., & Wu, K. (2014). Statistical overfitting and backtest performance. In Risk-Based and Factor Investing. Quantitative Finance, Elsevier.

  4. Bailey, D., & Lopez de Prado, M. (2014). The deflated sharpe ratio: Correcting for selection bias, backtest overfitting and non-normality. Journal of Portfolio Management,40(5), 94–107.

  5. Bank for International Settlements. (2016). Triennial Central Bank Survey of foreign exchange and OTC derivatives markets in 2016.

  6. Briza, A., & Naval, P. (2011). Stock trading system based on the multi-objective particle swarm optimization of technical indicators on end-of-day market data. Applied Soft Computing,11(1), 1191–1201.

  7. Byrnes, D. (2015). Extreme value theory and backtest overfitting in Finance. Honors Projects. Paper 24.

  8. Candelon, B., Colletaz, G., Hurlin, C., & Tokpavi, S. (2011). Backtesting value-at-risk: A GMM duration-based test. Journal of Financial Econometrics,9(2), 314–343.

  9. Castermans, G., Martens, D., Gestel, T., Hamers, B., & Baesens, B. (2010). An overview and framework for PD backtesting and benchmarking. Journal of the Operational Research Society,61(3), 359–373.

  10. Ceffer, A., Levendovszky, J., & Fogarasi, N. (2017). Applying independent component analysis and predictive systems for algorithmic trading. Computational Economics,54(1), 281–303.

  11. Chang, P., Liao, T., Lin, J., & Fan, Ch. (2011). A dynamic threshold decision system for stock trading signal detection. Applied Soft Computing,11(5), 3998–4010.

  12. Chavarnakul, T., & Enke, D. (2009). A hybrid stock trading system for intelligent technical analysis-based equivolume charting. Neurocomputing,72(2009), 3517–3528.

  13. Comelli, F. (2012). Emerging market sovereign bond spreads: Estimation and back-testing. Emerging Markets Review,13(4), 598–625.

  14. Cornaglia, A., & Morone, M. (2009). Rating philosophy and dynamic properties of internal rating systems: A general framework and an application to backtesting. Journal of Risk Model Validation, 3(4), 61–88.

  15. Dai, S., Wu, X., Pei, M., & Du, Z. (2017). Big data framework for quantitative trading system. Journal of Shanghai Jiaotong University (Science),22(2), 193–197.

  16. Depei, B., & Zehong, Y. (2018). Intelligent stock trading system by turning point confirming and probabilistic reasoning. Expert Systems with Applications,34(1), 620–627.

  17. Dowd, K. (2012). Back-testing market risk models. In Encyclopedia of Financial Models III. Wiley. ISBN: 9781118539835.

  18. Dowd, K., Cairns, A., Blake, D., Coughlan, G., Epstein, D., & Khalaf-Allah, M. (2010). Backtesting stochastic mortality models: An ex-post evaluation of multi-period-ahead density forecasts. North American Actuarial Journal,14(3), 281–298.

  19. Dymova, L., Sevastianov, P., & Bartosiewicz, P. (2010). A new approach to the rule-base evidential reasoning: Stock trading expert system application. Expert Systems with Applications,37(8), 5564–5576.

  20. Ehling, P., Gallmeyer, M., Srivastava, S., Tompaidis, S., & Yang, C. (2017). Portfolio tax trading with carryover losses. Management Science,64(9), 4156–4176.

  21. Escanciano, C., & Olmo, J. (2010a). Backtesting parametric value-at-risk with estimation risk. Journal of Business & Economic Statistics,28(1), 36–51.

  22. Escanciano, C., & Olmo, J. (2010b). Robust backtesting tests for value-at-risk models. Journal of Financial Econometrics,9(1), 132–161.

  23. Escanciano, C., & Pei, P. (2012). Pitfalls in backtesting historical simulation VaR models. Journal of Banking & Finance,36(8), 2233–2244.

  24. Fastrich, B., & Winker, P. (2013). Combining forecasts with missing data: Making use of portfolio theory. Computational Economics,44, 127–152.

  25. Holt, C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting,20(1), 5–10.

  26. Huschens, S., Karmann, A., Maltritz, D., & Vogl, K. (2007). Country default probabilities: Assessing and backtesting. The Journal of Risk Model Validation,1, 3–26.

  27. Janetzko, D., Krauss, J., & Nann, S. (2016). Differential effects of buy and sell rules in sentiment-informed EUR/USD trading. https://ssrn.com/abstract=2922485. Accessed July 15, 2016.

  28. Karathanasopoulos, A., Mitra, S., Skindilias, K., & Chun, Lo C. (2016). Modelling and trading the English and German stock markets with novelty optimization techniques. Journal of Forecasting, 36(8), 974–988.

  29. Kirk, C. (2014). Integration of a predictive, continuous time neural network into securities market trading operations. In: Computational Finance (q-fin.CP); Computational Engineering, Finance, and Science (cs.CE); Neural and Evolutionary Computing (cs.NE), arXiv:1406.0968.

  30. Lopez de Prado, M. (2014). Optimal trading rules without backtesting. https://ssrn.com/abstract=2502613. Accessed September 28, 2014.

  31. Löw, R., Maier-Paape, S., & Platen A. (2015). Correctness of Backtest Engines, Trading and Market Microstructure (q-fin.TR); Computational Finance (q-fin.CP), arXiv:1509.08248.

  32. Maier-Paape, S., & Platen, A. (2016). Backtest of trading systems on candle charts, trading and market microstructure. IFTA Journal (pp. 10–17), arXiv:1412.5558.

  33. Martinez, L., da Hora, D., Palotti, J., Meira, W., & Pappa, G. (2009). From an artificial neural network to a stock market day-trading system: A case study on the BM&F BOVESPA. In Neural Networks, 2009, IJCNN 2009, International joint conference on neural networks.

  34. Meucci, A., Ardia, D., & Colasante, M. (2014). Portfolio construction and systematic trading with factor entropy pooling. Risk Magazine,27(5), 56–61.

  35. Murphy, J. J. (1999). Technical analysis of the financial markets. Paramus, NJ: New York Institute of Finance.

  36. Pajhede, T. (2015). Backtesting value-at-risk: A generalized Markov framework. Univ. of Copenhagen Dept. of Economics Discussion Paper No. 15–18.

  37. Person, J. (2011). Candlestick and pivot point trading triggers: Setups for stock, forex, and futures markets. New York: Wiley.

  38. Quan, D. M. (2016). Best statistic profile: An efficient parameter tuning algorithm for systematic trading methods. International Journal of Computational Economics and Econometrics,6(4), 337–350.

  39. Rachlin, G., Last, M., Alberg, D., & Kandel, A. (2007). ADMIRAL: A data mining based financial trading system. In 2007 IEEE symposium on computational intelligence and data mining, Honolulu, HI, 2007 (pp. 720–725).

  40. Sepp, A. (2016). Volatility modelling and trading. Global derivatives workshop global derivatives trading & risk management, Budapest.

  41. Song, Q., Liu, A., Yang, S. Y., Deane, A., & Datta, K. (2015). An extreme firm-specific news sentiment asymmetry based trading strategy. In 2015 IEEE symposium series on computational intelligence, Cape Town (pp. 898–904).

  42. Teixeira, L., & Oliveira, A. (2010). A method for automatic stock trading combining technical analysis and nearest neighbor classification. Expert Systems with Applications,37(10), 6885–6890.

  43. Vezeris, D., Schinas, C., & Papaschinopoulos, G. (2016). Profitability edge by dynamic back testing optimal period selection for technical parameters optimization. Trading Systems with Forecasting, Computational Economics,54(4), 791–807.

  44. Virdi, N. K. (2011). A review of backtesting methods for evaluating value-at-risk. International Review of Business Research Papers,7(4), 14–24.

  45. Wiecki, T., Campbell, A., Lent, J., & Stauth, J. (2016). All that glitters is not gold: Comparing backtest and out-of-sample performance on a large cohort of trading algorithms. The Journal of Investing, 25(3), 69–80.

  46. Wong, W. (2008). Backtesting trading risk of commercial banks using expected shortfall. Journal of Banking & Finance,32(7), 1404–1415.

  47. Wong, J., Souroutzidis, Y., Lai, M., Mei, E., & Sagwal, A. (2016). Fundamental signals for algorithmic trading MS&E 448 final project. The Journal of Investing,25(3), 69–80.

  48. Xiuquan, L., Zhidong, D., & Jing, L. (2009). Trading strategy design in financial investment through a turning points prediction scheme. Expert Systems with Applications,36(4), 7818–7826.

Download references

Acknowledgements

We would like to thank COSMOS4U for providing the nessesary infrastructure for this research. We would also like to thank the anonymous reviewers for their valuable comments on our work. This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH—CREATE—INNOVATE (project code:T1EDK-02342).

figureaa

Author information

Correspondence to D. Th. Vezeris.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix 1

PIVOT

Returns by implementing the PIVOT system, with fixed previous week BT period optimization parameters and dynamic optimization parameters.

SymbolAvg(PF)Max (PF)Min (PF)PF > 1% PF > 1Max (SharpeRatio)Min (DD)Avg (Profit)Max (Profit)Min (Profit)Sum (Profit)Weeks (2015.02.22–2017.08.27)
FOREX returns of a classic PIVOT(24) trading system
AUDUSD39.3386905440.9125.1110.78− 57.39796.95− 681.54− 7575.23132
EURUSD49.881096.140665012.149.36− 2.3742.18− 528.39− 303.61132
GBPUSD57.521640.506045.454.10− 9.26821.14− 527.68− 1222.03132
USDCAD27.32657.8306146.2112.610− 27.23382.35− 412.54− 3594.05132
USDJPY46.891079.6105340.1516.187.38− 21.49884.46− 1131.38− 2836.09132
XAUUSD76.3306305843.946.6311.09− 41.85931.7− 621.74− 5524.23132
figurea
Symbol Avg(PF) Max(PF) Min (PF) PF > 1 % PF > 1 Max (SharpeRatio) Min (DD) Avg (Profit) Max (Profit) Min (Profit) Sum (Profit) Weeks (2015.02.22–2017.08.27)
FOREX returns of an adaptive AdPIVOT (x w-1 ) trading system, using previous week’s best BT period for parameters’ optimization
AUDUSD 88.53 1372.13 0 57 43.18 6.54 4.87 − 26.88 767.83 − 587.16 − 3548.62 132
EURUSD 92.74 1656.81 0 62 46.97 4.56 8.54 − 15.39 701.14 − 716.47 − 2031.48 132
GBPUSD 116.21 2391.03 0 51 38.64 9.13 0 − 35.56 761.81 − 525.54 − 4693.53 132
USDCAD 92.03 1371.1 0 63 47.73 5.8 0 − 25.6 548.04 − 525.78 − 3379.71 132
USDJPY 101.69 1603.95 0 55 41.67 17.77 7.38 − 34.61 1104.06 − 1276.02 − 4568.46 132
XAUUSD 236.51 3063 0 62 46.97 4.67 9.39 11.96 989.6 − 867.49 1578.17 132
figureb
Symbol d-BackTest PS method Avg (PF) Max (PF) Min (PF) PF > 1 % PF > 1 Max (Sharpe Ratio) Min (DD) Avg (Profit) Max (Profit) Min (Profit) Sum s(Profit) Weeks (2016.02.28–2017.08.27)
FOREX returns of an adaptive AdPIVOT (x dyn ) trading system, using d-Backtest PS method for parameters’ optimization
AUDUSD wa vp > 2 82.49 1372.13 0 59 44.7 25.11 4.87 − 21.11 767.83 − 580.75 − 2786.13 132
EURUSD ea 56.81 1647.81 0 62 46.97 4.56 8.37 − 14.8 592.85 − 689.62 − 1954.23 132
GBPUSD avg vp > 2 150.1 2513.98 0 57 43.18 9.13 0 − 16.22 1457.53 − 525.54 − 2141.05 132
USDCAD avg ccbt vp > 2 121.04 1237.6 0 67 50.76 12.61 0 1.63 494.64 − 525.78 214.77 132
USDJPY ea vp > 2 116.74 1603.95 0 61 46.21 15.38 7.38 − 16.85 1104.06 − 1162.57 − 2224.66 132
XAUUSD wa ccbt vp > 2 276.15 3173.82 0 63 47.73 1.91 11.09 23.09 1078.76 − 867.49 3048.04 132
figurec

SMA

Forex returns of theSMA trading system, utilizing fixed parameters for previous week BT period optimization and dynamic optimization parameters.

SymbolAvg (PF)Max (PF)Min (PF)PF > 1% PF > 1Max (Sharpe ratio)Min (DD)Avg (Profit)Max (Profit)Min (Profit)Sum (Profit)Weeks (2015.02.22–2017.08.27)
FOREX returns of a classic SMA(200,5) trading system
AUDUSD90.971372.1304634.851.589.24− 75.84767.83− 661.65− 10010.36132
EURUSD150.291656.8105440.914.458.36− 27.46596.09− 527.55− 3625.08132
GBPUSD214.192391.0305440.910.967.07− 22.53857.99− 542.53− 2973.82132
USDCAD149.251371.106246.972.2710.13− 19.67548.04− 591.54− 2596.38132
USDJPY185.431822.405944.71.526.617.23728.56− 494.31954.91132
XAUUSD370.843169.06066505.352.7910.181077.14− 712.771343.2132
figured
Symbol Avg (PF) Max (PF) Min (PF) PF > 1 % PF > 1 Max (Sharpe Ratio) Min (DD) Avg (Profit) Max (Profit) Min (Profit) Sum (Profit) Weeks (2015.02.22–2017.08.27)
FOREX returns of an adaptive AdSMA (x w-1 ,y w-1 ) trading system, using previous week’s best BT period for parameters’ optimization
AUDUSD 58.05 1372.13 0 56 42.42 1.91 4.87 − 54.79 787.84 − 648.98 − 7231.83 132
EURUSD 88.48 1656.81 0 49 37.12 4.56 8.54 − 47.9 596.09 − 550.55 − 6322.86 132
GBPUSD 142.29 2513.98 0 55 41.67 4.41 7.87 − 29.16 1457.53 − 622.19 − 3848.67 132
USDCAD 96.57 1124.05 0 59 44.7 3.72 7.36 − 38.46 449.22 − 690.66 − 5076.77 132
USDJPY 111.04 1603.95 0 58 43.94 17.84 7.38 − 26.62 702.99 − 1276.02 − 3514.02 132
XAUUSD 260.48 3169.06 0 59 44.7 1.29 2.79 − 18.04 1077.14 − 885.03 − 2381.71 132
figuree
Symbol d-BackTest PS method Avg (PF) Max (PF) Min (PF) PF > 1 % PF > 1 Max (Sharpe Ratio) Min (DD) Avg (Profit) Max (Profit) Min (Profit) Sum (Profit) Weeks (2015.02.22–2017.08.27)
FOREX returns of an adaptive AdSMA (x dyn ,y dyn ) trading system, using d-Backtest PS method for parameters’ optimization
AUDUSD ea ccbt vp > 2 68.83 1372.13 0 56 42.42 3.84 9.24 − 26.3 767.83 − 568.92 − 3471.09 132
EURUSD ea vp > 1 103.58 1656.81 0 62 46.97 2.74 8.54 − 15.35 596.09 − 511.75 − 2026.57 132
GBPUSD avg 149.59 2513.98 0 57 43.18 1.7 8.13 − 17.75 1457.53 − 509.9 − 2342.72 132
USDCAD avg vp > 2 120.45 1237.6 0 62 46.97 5.8 7.36 − 13.68 494.64 − 690.66 − 1805.62 132
USDJPY avg vp > 1 123.57 1603.95 0 55 41.67 17.77 7.38 − 9.94 735.11 − 1162.57 − 1311.94 132
XAUUSD wa vp > 1 232.11 3061.57 0 62 46.97 1.59 2.79 − 18.59 987.86 − 812.79 − 2453.65 132
figuref

Appendix 2

AdMACD

In this appendix we expand on the returns of the AdMACD trading system, by implementing various restrictions among parameters and we display their profitability results.

fast <>slow

Symbol Condition Avg (PF) Max (PF) Min (PF) PF > 1 % PF > 1 Max (Sharpe Ratio) Min (DD) Avg (Profit) Max (Profit) Min (Profit) Sum (Profit) Weeks (2016.02.28–2017.08.27) Max profit Best profit among condition Sum profit of all assets
AUDUSD fast <>slow 48.16 1102.1 0 38 48.1 14.24 3.33 9.53 925.74 − 687.76 752.9 79 1   
EURUSD fast <>slow 30.62 827.64 0 30 38 2.56 7.1 − 33.1 343.37 − 907.64 − 2614.7 79    
GBPUSD fast <>slow 89.57 2423 0 40 50.6 3.89 9.59 9.43 726.61 − 1162.7 745.12 79    
USDCAD fast <>slow 50.89 745.23 0 47 59.5 16.25 4.01 29.5 679.18 − 383.78 2330.42 79 1   
USDJPY fast <>slow 61.03 2201 0 38 48.1 2.49 0.58 − 8.95 352.08 − 330.35 − 706.89 79    
XAUUSD fast <>slow 93.58 1347.9 0 42 53.2 3.44 6.08 5.91 644.12 − 963.49 466.74 79   2 973.6
figureg

fast < slow

Symbol Condition Avg (PF) Max (PF) Min (PF) PF > 1 % PF > 1 Max (Sharpe Ratio) Min (DD) Avg (Profit) Max (Profit) Min (Profit) Sum (Profit) Weeks (2016.02.28–2017.08.27) Max profit Best profit among condition Sum profit of all assets
AUDUSD fast < slow 67.82 1102.1 0 33 41.8 14.24 4.8 − 39.32 572.57 − 581.15 − 3106.6 79    
EURUSD fast < slow 31.19 827.64 0 32 40.5 2.56 7.1 − 26.72 504.17 − 907.64 − 2110.7 79    
GBPUSD fast < slow 87.73 2423 0 45 57 3.89 4.67 24.66 726.61 − 1162.7 1948.3 79    
USDCAD fast < slow 54.82 745.23 0 48 60.8 16.25 3.19 9.46 439.1 − 511.54 747.28 79    
USDJPY fast < slow 82.84 2201 0 40 50.6 3.1 0.53 − 6.15 352.08 − 330.35 − 486.11 79    
XAUUSD fast < slow 88.99 1347.9 0 44 55.7 1.55 9.45 29.61 644.12 − 952.6 2339.29 79   0 − 668.5
figureh

fast < slow & signal < fast

Symbol Condition Avg(PF) max(PF) min(PF) PF > 1 % PF > 1 Max (Sharpe Ratio) Min (DD) Avg (Profit) Max (Profit) Min (Profit) Sum (Profit) Weeks (2016.02.28–2017.08.27) Max profit Best profit among condition Sum profit of all assets
AUDUSD fast < slow & signal < fast 67.79 1102.1 0 32 40.5 14.24 4.8 − 39.99 572.57 − 581.15 − 3159 79    
EURUSD fast < slow & signal < fast 31.32 827.64 0 32 40.5 2.56 7.1 − 27.76 558.86 − 907.64 − 2193 79    
GBPUSD fast < slow & signal < fast 87.62 2423 0 44 55.7 3.89 8.49 25.96 726.61 − 1162.7 2051.08 79 1   
USDCAD fast < slow & signal < fast 54.8 745.23 0 48 60.8 16.25 3.19 5.31 439.1 − 511.54 419.23 79    
USDJPY fast < slow & signal < fast 82.86 2201 0 40 50.6 3.1 0.53 − 5.43 352.08 − 330.35 − 428.75 79    
XAUUSD fast < slow & signal < fast 89.21 1347.9 0 46 58.2 1.55 9.45 34.41 644.12 − 952.6 2718.43 79 1 2 − 591.99
figurei

fast < slow < (fast x 4)

Symbol Condition Avg (PF) Max (PF) Min (PF) PF > 1 % PF > 1 Max (SharpeRatio) Min (DD) Avg (Profit) Max (Profit) Min (Profit) Sum (Profit) Max profit Best profit among condition Sum profit of all assets
AUDUSD fast < slow < (fast x4) 33.63 1102.1 0 27 34.2 14.24 4.8 − 80.65 572.57 − 581.15 − 6371.4    
EURUSD fast < slow < (fast x4) 31.3 528.21 0 31 39.2 3.35 7.1 − 18.72 504.17 − 680.76 − 1479.2    
GBPUSD fast < slow < (fast x4) 87.45 2423 0 37 46.8 3.28 9.59 − 14.78 726.61 − 1162.7 − 1167.4    
USDCAD fast < slow < (fast x4) 46.04 745.23 0 47 59.5 2.66 3.19 17.45 583.28 − 381.73 1378.18    
USDJPY fast < slow < (fast x4) 32.84 816 0 38 48.1 28.72 0.53 − 5.07 317.79 − 330.35 − 400.16    
XAUUSD fast < slow < (fast x4) 45.51 1172.3 0 42 53.2 1.55 9.45 15.4 644.12 − 992.5 1216.83   0 − 6823.15
figurej

fast < slow < (fast x 4) & signal < fast

Symbol Condition Avg (PF) Max (PF) Min (PF) PF > 1 % PF > 1 Max (Sharpe Ratio) Min (DD) Avg (Profit) Max (Profit) Min (Profit) Sum (Profit) Weeks (2016.02.28–2017.08.27) Max profit Best profit among condition Sum profit of all assets
AUDUSD fast < slow < (fast x4) & signal ≤ fast 33.65 1102.1 0 27 34.2 14.24 4.8 − 78.1 572.57 − 581.15 − 6170.3 79    
EURUSD fast < slow < (fast x4) & signal ≤ fast 31.66 528.21 0 32 40.5 3.35 7.1 − 18.28 558.86 − 680.76 − 1443.8 79 1   
GBPUSD fast < slow < (fast x4) & signal ≤ fast 90.14 2423 0 37 46.8 3.28 4.55 − 17.52 726.61 − 1162.7 − 1384.1 79    
USDCAD fast < slow < (fast x4) & signal ≤ fast 46.02 745.23 0 47 59.5 2.66 3.19 13.97 583.28 − 417.93 1103.72 79    
USDJPY fast < slow < (fast x4) & signal ≤ fast 32.85 816 0 38 48.1 28.72 0.53 − 5.15 317.79 − 330.35 − 406.91 79    
XAUUSD fast < slow < (fast x4) & signal ≤ fast 45.73 1172.3 0 45 57 1.55 9.45 20.42 644.12 − 992.5 1613.57 79   1 − 6687.8
figurek

(fast x 2) < slow

Symbol Condition Avg (PF) Max (PF) Min (PF) PF > 1 % PF > 1 Max (Sharpe Ratio) Min (DD) Avg (Profit) Max (Profit) Min (Profit) Sum (Profit) Weeks (2016.02.28–2017.08.27) Max Profit Best profit among condition Sum profit of all assets
AUDUSD (fast x2) < slow 32.57 1102.1 0 28 35.4 14.24 4.8 − 49.18 925.74 − 637.36 − 3885.6 79    
EURUSD (fast x2) < slow 16.16 475.81 0 36 45.6 2.35 9.22 − 24.77 634.47 − 907.64 − 1956.5 79    
GBPUSD (fast x2) < slow 45.08 984 0 33 41.8 2.53 6.33 − 45.57 550.48 − 1199.1 − 3600.1 79    
USDCAD (fast x2) < slow 31.03 745.23 0 45 57 2.9 7.67 27.32 703.42 − 357.84 2157.9 79    
USDJPY (fast x2) < slow 100.2 2201 0 35 44.3 4.35 0.58 − 7.99 450.7 − 332.53 − 631.29 79    
XAUUSD (fast x2) < slow 128.7 3156.9 0 42 53.2 1.63 6.08 − 1.51 946.77 − 1171.4 − 119.47 79   0 − 8035.09
figurel

(fast x 2) < slow & signal < fast

Symbol Condition Avg (PF) Max (PF) Min (PF) PF > 1 % PF > 1 Max (Sharpe Ratio) Min (DD) Avg (Profit) Max (Profit) Min (Profit) Sum (Profit) Weeks (2016.02.28–2017.08.27) Max profit Best profit among condition Sum profit of all assets
AUDUSD (fast x2) < slow) & signal ≤ fast 63.07 1102.1 0 32 40.5 11.85 4.8 − 43.29 572.57 − 581.15 − 3420.2 79    
EURUSD (fast x2) < slow) & signal ≤ fast 31.69 827.64 0 34 43 2.56 7.1 − 35.4 304.71 − 907.64 − 2796.5 79    
GBPUSD (fast x2) < slow) & signal ≤ fast 100.6 2423 0 39 49.4 3.89 6.33 15.89 726.61 − 1179.2 1255.4 79    
USDCAD (fast x2) < slow) & signal ≤ fast 50.79 745.23 0 48 60.8 16.25 3.19 12.22 439.1 − 511.54 965.65 79    
USDJPY (fast x2) < slow) & signal ≤ fast 79.91 2201 0 38 48.1 3.1 0.53 − 4.73 352.08 − 342.86 − 373.67 79    
XAUUSD (fast x2) < slow) & signal ≤ fast 91.68 1347.9 0 45 57 1.54 9.45 30.34 637.3 − 952.6 2397.11 79   0 − 1972.18
figurem

(fast x 2) < slow < (fast x 4)

Symbol Condition Avg (PF) Max (PF) Min (PF) PF > 1 % PF > 1 Max (Sharpe Ratio) Min (DD) Avg (Profit) Max (Profit) Min (Profit) Sum (Profit) Weeks (2016.02.28–2017.08.27) Max profit Best profit among condition Sum profit of all assets
AUDUSD (fast x2) < slow < (fast x4) 28.97 1102.1 0 28 35.4 11.85 4.8 − 83.24 572.57 − 581.15 − 6576.1 79    
EURUSD (fast x2) < slow < (fast x4) 25.18 475.81 0 34 43 2.56 7.1 − 19.81 363.55 − 612.75 − 1565.2 79    
GBPUSD (fast x2) < slow < (fast x4) 88.03 2423 0 33 41.8 3.28 9.59 − 21.86 726.61 − 1179.2 − 1727.3 79    
USDCAD (fast x2) < slow < (fast x4) 42.14 745.23 0 47 59.5 2.66 3.19 19.4 583.28 − 381.73 1532.41 79    
USDJPY (fast x2) < slow < (fast x4) 32.34 816 0 38 48.1 28.72 0.53 − 2.75 317.79 − 293.61 − 217.51 79    
XAUUSD (fast x2) < slow < (fast x4) 47.02 1172.3 0 43 54.4 1.55 9.45 17.74 644.12 − 992.5 1401.53 79   0 − 7152.07
figuren

(fast x 2) < slow < (fast x 4) & signal < fast

Symbol Condition Avg (PF) Max (PF) Min (PF) PF > 1 % PF > 1 Max (Sharpe Ratio) Min (DD) Avg (Profit) Max (Profit) Min (Profit) Sum (Profit) Weeks (2016.02.28–2017.08.27) Max profit Best profit among condition Sum profit of all assets
AUDUSD (fast x2) < slow < (fast x4) & signal ≤ fast 28.98 1102.1 0 29 36.7 11.85 4.8 − 76.32 572.57 − 581.15 − 6029.3 79    
EURUSD (fast x2) < slow < (fast x4) & signal ≤ fast 25.32 475.81 0 34 43 2.56 7.1 − 31.63 394.37 − 612.75 − 2498.7 79    
GBPUSD (fast x2) < slow < (fast x4) & signal ≤ fast 103.2 2423 0 35 44.3 3.28 4.55 − 17.55 726.61 − 1179.2 − 1386.6 79    
USDCAD (fast x2) < slow < (fast x4) & signal ≤ fast 38.64 745.23 0 47 59.5 2.66 3.19 18.48 583.28 − 417.93 1459.88 79    
USDJPY (fast x2) < slow < (fast x4) & signal ≤ fast 32.35 816 0 39 49.4 28.72 0.53 − 0.04 317.79 − 293.61 − 3.12 79 1   
XAUUSD (fast x2) < slow < (fast x4) & signal ≤ fast 48.23 1172.3 0 44 55.7 1.54 9.45 22.04 637.3 − 992.5 1741.29 79   1 − 6716.56
figureo

SMA

x ≥ 5 & y ≥ 2

Symbol Condition Avg (PF) Max (PF) Min (PF) PF > 1 % PF > 1 Max (SharpeRatio) Min (DD) Avg (Profit) Max (Profit) Min (Profit) Sum (Profit) Weeks (2015.02.22–2017.08.27) Max profit Best profit among condition Sum profit of all assets
AUDUSD x ≥ 5 & y ≥ 2 63.69 1372.1 0 50 37.9 1.91 9.24 − 56.47 767.83 − 568.92 − 7453.97 132    
EURUSD x ≥ 5 & y ≥ 2 91.77 1656.8 0 61 46.2 2.74 9.57 − 30.15 596.09 − 550.55 − 3980.18 132    
GBPUSD x ≥ 5 & y ≥ 2 130.5 2514 0 59 44.7 6.01 8.13 − 19.24 1457.5 − 622.19 − 2539.39 132    
USDCAD x ≥ 5 & y ≥ 2 119.1 1237.6 0 59 44.7 5.8 7.36 − 23.02 494.64 − 690.66 − 3038.14 132 1   
USDJPY x ≥ 5 & y ≥ 2 100.8 1604 0 53 40.2 8.71 9.27 − 33.12 735.11 − 1276 − 4371.7 132    
XAUUSD x ≥ 5 & y ≥ 2 261 3169.1 0 55 41.7 1.29 2.79 − 35.7 1077.1 − 812.79 − 4711.74 132 1 2 − 26095.12
figurep

x > y

Symbol Condition Avg (PF) Max (PF) Min (PF) PF > 1 % PF > 1 Max (SharpeRatio) Min (DD) Avg (Profit) Max (Profit) Min (Profit) Sum (Profit) Weeks (2015.02.22–2017.08.27) Max profit Best profit among condition Sum profit of all assets
AUDUSD x > y 63.69 1372.1 0 50 37.9 1.91 9.24 − 56.47 767.83 − 568.92 − 7453.97 132    
EURUSD x > y 91.77 1656.8 0 61 46.2 2.74 9.57 − 30.15 596.09 − 550.55 − 3980.18 132    
GBPUSD x > y 130.5 2514 0 59 44.7 6.01 8.13 − 19.24 1457.5 − 622.19 − 2539.39 132    
USDCAD x > y 119.1 1237.6 0 59 44.7 5.8 7.36 − 23.02 494.64 − 690.66 − 3038.14 132 1   
USDJPY x > y 100.8 1604 0 53 40.2 8.71 9.27 − 33.12 735.11 − 1276 − 4371.7 132    
XAUUSD x > y 261 3169.1 0 55 41.7 1.29 2.79 − 35.7 1077.1 − 812.79 − 4711.74 132 1 2 − 26095.12
figureq

y ≥ x

Symbol Condition Avg (PF) Max (PF) Min (PF) PF > 1 % PF > 1 Max (SharpeRatio) Min (DD) Avg (Profit) Max (Profit) Min (Profit) Sum (Profit) Weeks (2015.02.22–2017.08.27) Max profit Best profit among condition Sum profit of all assets
AUDUSD x < y 54.79 1372.1 0 52 39.4 25.11 9.24 − 39.27 787.71 − 514.05 − 5183.86 132 1   
EURUSD x < y 62.86 1606.1 0 63 47.7 2.74 9.36 − 10.81 728.35 − 485.73 − 1427.47 132 1   
GBPUSD x < y 73.98 1325.4 0 55 41.7 8.14 8.95 − 19.2 821.14 − 511.15 − 2534.71 132 1   
USDCAD x < y 60.9 1237.6 0 60 45.5 12.61 7.97 − 26.38 494.64 − 690.66 − 3481.57 132    
USDJPY x < y 50.18 992.7 0 60 45.5 20.65 7.38 − 7.93 961.26 − 1162.6 − 1046.2 132 1   
XAUUSD x < y 81.44 1836.1 0 45 34.1 1.69 2.79 − 78.66 717.52 − 867.92 − 10383.5 132   4 − 24057.27
figurer

PIVOT

Symbol Condition Avg (PF) Max (PF) Min (PF) PF > 1 % PF > 1 Max (Sharpe Ratio) Min (DD) Avg (Profit) Max (Profit) Min (Profit) Sum (Profit) Weeks (2015.02.22–2017.08.27) Max profit Best profit among condition Sum profit of all assets
AUDUSD f ≥ 4 80.99 1372.1 0 61 46.2 6.54 9.24 − 25.47 767.83 − 580.75 − 3361.7 132 1   
EURUSD f ≥ 5 116.7 1656.8 0 56 42.4 4.56 8.54 − 34.01 597.12 − 716.47 − 4489.4 132    
GBPUSD f ≥ 6 145.5 2514 0 56 42.4 9.13 0 − 19.39 1457.5 − 525.54 − 2559.5 132    
USDCAD f ≥ 7 121.2 1237.6 0 67 50.8 12.61 0 0.84 494.64 − 525.78 110.59 132 1   
USDJPY f ≥ 8 90.58 1604 0 52 39.4 20.94 7.38 − 39.46 1104.1 − 1276 − 5209 132    
XAUUSD f ≥ 9 274.2 3173.8 0 58 43.9 1.91 9.39 9.07 1078.8 − 867.49 1197.77 132   2 − 14311.3
figures

Appendix 3

figuret
figureu
figurev
figurew

Appendix 4

AdMACD

The following table displays the returns of the implementation of the AdMACD trading system in detail, while sorting procedure involves 24 d-Backtest PS methods.

W1NmW2NmW3NmW4NmW5NmW6NmFinal sortingNm
avg1avg20avg22avg22avg19avg14avg98
avg vp > 16avg vp > 124avg vp > 117avg vp > 112avg vp > 122avg vp > 112avg vp > 193
avg vp > 29avg vp > 222avg vp > 26avg vp > 223avg vp > 210avg vp > 24avg vp > 274
avg ccbt24avg ccbt23avg ccbt13avg ccbt18avg ccbt4avg ccbt8avg ccbt90
avg ccbt vp > 122avg ccbt vp > 119avg ccbt vp > 115avg ccbt vp > 121avg ccbt vp > 113avg ccbt vp > 19avg ccbt vp > 199
avg ccbt vp > 24avg ccbt vp > 216avg ccbt vp > 28avg ccbt vp > 220avg ccbt vp > 22avg ccbt vp > 25avg ccbt vp > 255
ea19ea10ea19ea13ea6ea24ea91
ea vp > 116ea vp > 115ea vp > 114ea vp > 12ea vp > 121ea vp > 122ea vp > 190
ea vp > 214ea vp > 28ea vp > 27ea vp > 29ea vp > 217ea vp > 218ea vp > 273
ea ccbt12ea ccbt4ea ccbt23ea ccbt3ea ccbt15ea ccbt19ea ccbt76
ea ccbt vp > 17ea ccbt vp > 17ea ccbt vp > 121ea ccbt vp > 11ea ccbt vp > 123ea ccbt vp > 113ea ccbt vp > 172
ea ccbt vp > 25ea ccbt vp > 23ea ccbt vp > 212ea ccbt vp > 28ea ccbt vp > 220ea ccbt vp > 211ea ccbt vp > 259
pf17pf9pf4pf16pf16pf20pf82
pf vp > 12pf vp > 121pf vp > 12pf vp > 117pf vp > 124pf vp > 115pf vp > 181
pf vp > 218pf vp > 211pf vp > 21pf vp > 224pf vp > 28pf vp > 26pf vp > 268
pf ccbt10pf ccbt18pf ccbt3pf ccbt19pf ccbt11pf ccbt3pf ccbt64
pf ccbt vp > 18pf ccbt vp > 113pf ccbt vp > 15pf ccbt vp > 114pf ccbt vp > 114pf ccbt vp > 12pf ccbt vp > 156
pf ccbt vp > 213pf ccbt vp > 25pf ccbt vp > 210pf ccbt vp > 25pf ccbt vp > 29pf ccbt vp > 21pf ccbt vp > 243
wa23wa12wa11wa11wa1wa23wa81
wa vp > 121wa vp > 117wa vp > 116wa vp > 110wa vp > 15wa vp > 121wa vp > 190
wa vp > 220wa vp > 214wa vp > 220wa vp > 215wa vp > 27wa vp > 217wa vp > 293
wa ccbt15wa ccbt6wa ccbt24wa ccbt7wa ccbt12wa ccbt16wa ccbt80
wa ccbt vp > 111wa ccbt vp > 12wa ccbt vp > 118wa ccbt vp > 16wa ccbt vp > 118wa ccbt vp > 110wa ccbt vp > 165
wa ccbt vp > 23wa ccbt vp > 21wa ccbt vp > 29wa ccbt vp > 24wa ccbt vp > 23wa ccbt vp > 27wa ccbt vp > 227

Sorted by means of frequency of profitability occurrence they are on display as follows:

Final sum of sortingPointsNormalized
avg ccbt vp > 19955.00
avg9854.44
avg vp > 19351.67
wa vp > 29351.67
ea9150.56
avg ccbt9050.00
ea vp > 19050.00
wa vp > 19050.00
pf8245.56
pf vp > 18145.00
wa8145.00
wa ccbt8044.44
ea ccbt7642.22
avg vp > 27441.11
ea vp > 27340.56
ea ccbt vp > 17240.00
pf vp > 26837.78
wa ccbt vp > 16536.11
pf ccbt6435.56
ea ccbt vp > 25932.78
pf ccbt vp > 15631.11
avg ccbt vp > 25530.56
pf ccbt vp > 24323.89
wa ccbt vp > 22715.00

AdPIVOT

The following tables expand on returns obtained through the implementation of the AdPIVOT trading system, while sorting procedure involves 24 d-Backtest PS methods.

W1PointsW2PointsW3PointsW4PointsW5PointsW6PointsFinal sortingPoints
avg1avg2avg5avg2avg3avg10avg23
avg vp > 19avg vp > 17avg vp > 17avg vp > 115avg vp > 15avg vp > 17avg vp > 150
avg vp > 219avg vp > 212avg vp > 26avg vp > 221avg vp > 212avg vp > 212avg vp > 282
avg ccbt18avg ccbt9avg ccbt2avg ccbt9avg ccbt2avg ccbt2avg ccbt42
avg ccbt vp > 121avg ccbt vp > 114avg ccbt vp > 11avg ccbt vp > 113avg ccbt vp > 111avg ccbt vp > 13avg ccbt vp > 163
avg ccbt vp > 222avg ccbt vp > 221avg ccbt vp > 23avg ccbt vp > 220avg ccbt vp > 215avg ccbt vp > 215avg ccbt vp > 296
ea6ea1ea11ea1ea13ea21ea53
ea vp > 14ea vp > 18ea vp > 120ea vp > 114ea vp > 114ea vp > 124ea vp > 184
ea vp > 214ea vp > 25ea vp > 222ea vp > 218ea vp > 221ea vp > 220ea vp > 2100
ea ccbt16ea ccbt6ea ccbt24ea ccbt6ea ccbt9ea ccbt5ea ccbt66
ea ccbt vp > 111ea ccbt vp > 115ea ccbt vp > 123ea ccbt vp > 116ea ccbt vp > 119ea ccbt vp > 114ea ccbt vp > 198
ea ccbt vp > 220ea ccbt vp > 219ea ccbt vp > 212ea ccbt vp > 212ea ccbt vp > 217ea ccbt vp > 217ea ccbt vp > 297
pf3pf3pf17pf5pf24pf11pf63
pf vp > 17pf vp > 113pf vp > 119pf vp > 111pf vp > 123pf vp > 19pf vp > 182
pf vp > 28pf vp > 217pf vp > 221pf vp > 223pf vp > 216pf vp > 216pf vp > 2101
pf ccbt17pf ccbt22pf ccbt18pf ccbt3pf ccbt4pf ccbt8pf ccbt72
pf ccbt vp > 110pf ccbt vp > 124pf ccbt vp > 113pf ccbt vp > 18pf ccbt vp > 11pf ccbt vp > 118pf ccbt vp > 174
pf ccbt vp > 212pf ccbt vp > 223pf ccbt vp > 28pf ccbt vp > 217pf ccbt vp > 210pf ccbt vp > 223pf ccbt vp > 293
wa2wa4wa10wa4wa18wa22wa60
wa vp > 15wa vp > 120wa vp > 114wa vp > 122wa vp > 120wa vp > 119wa vp > 1100
wa vp > 213wa vp > 210wa vp > 215wa vp > 224wa vp > 222wa vp > 213wa vp > 297
wa ccbt23wa ccbt11wa ccbt16wa ccbt19wa ccbt6wa ccbt1wa ccbt76
wa ccbt vp > 115wa ccbt vp > 116wa ccbt vp > 19wa ccbt vp > 17wa ccbt vp > 18wa ccbt vp > 14wa ccbt vp > 159
wa ccbt vp > 224wa ccbt vp > 218wa ccbt vp > 24wa ccbt vp > 210wa ccbt vp > 27wa ccbt vp > 26wa ccbt vp > 269

Sorted by means of frequency of profitability occurrence they are on display as follows:

PIVOT final sortingPointsNormalized
pf vp > 210156.11
ea vp > 210055.56
wa vp > 110055.56
ea ccbt vp > 19854.44
ea ccbt vp > 29753.89
wa vp > 29753.89
avg ccbt vp > 29653.33
pf ccbt vp > 29351.67
ea vp > 18446.67
avg vp > 28245.56
pf vp > 18245.56
wa ccbt7642.22
pf ccbt vp > 17441.11
pf ccbt7240.00
wa ccbt vp > 26938.33
ea ccbt6636.67
avg ccbt vp > 16335.00
pf6335.00
wa6033.33
wa ccbt vp > 15932.78
ea5329.44
avg vp > 15027.78
avg ccbt4223.33
avg2312.78

AdSMA

The following tables display detailed returns’ data emerging through the implementation of the AdSMA trading system, while sorting procedure involves 24 d-Backtest PS methods.

W1PointsW2PointsW3PointsW4PointsW5PointsW6PointsFinal sortingPoints
avg9avg4avg2avg18avg18avg7avg58
avg vp > 17avg vp > 18avg vp > 13avg vp > 115avg vp > 110avg vp > 12avg vp > 145
avg vp > 212avg vp > 22avg vp > 21avg vp > 217avg vp > 211avg vp > 24avg vp > 247
avg ccbt11avg ccbt5avg ccbt5avg ccbt10avg ccbt1avg ccbt1avg ccbt33
avg ccbt vp > 13avg ccbt vp > 13avg ccbt vp > 112avg ccbt vp > 111avg ccbt vp > 17avg ccbt vp > 114avg ccbt vp > 150
avg ccbt vp > 215avg ccbt vp > 21avg ccbt vp > 211avg ccbt vp > 216avg ccbt vp > 216avg ccbt vp > 219avg ccbt vp > 278
ea22ea6ea6ea12ea3ea20ea69
ea vp > 121ea vp > 115ea vp > 121ea vp > 11ea vp > 18ea vp > 115ea vp > 181
ea vp > 28ea vp > 223ea vp > 222ea vp > 27ea vp > 212ea vp > 213ea vp > 285
ea ccbt14ea ccbt24ea ccbt23ea ccbt2ea ccbt2ea ccbt12ea ccbt77
ea ccbt vp > 119ea ccbt vp > 122ea ccbt vp > 124ea ccbt vp > 15ea ccbt vp > 117ea ccbt vp > 13ea ccbt vp > 190
ea ccbt vp > 224ea ccbt vp > 216ea ccbt vp > 220ea ccbt vp > 214ea ccbt vp > 215ea ccbt vp > 211ea ccbt vp > 2100
pf2pf14pf7pf23pf4pf21pf71
pf vp > 14pf vp > 112pf vp > 118pf vp > 122pf vp > 114pf vp > 124pf vp > 194
pf vp > 210pf vp > 219pf vp > 210pf vp > 221pf vp > 219pf vp > 217pf vp > 296
pf ccbt5pf ccbt7pf ccbt19pf ccbt19pf ccbt13pf ccbt6pf ccbt69
pf ccbt vp > 11pf ccbt vp > 110pf ccbt vp > 19pf ccbt vp > 120pf ccbt vp > 120pf ccbt vp > 122pf ccbt vp > 182
pf ccbt vp > 26pf ccbt vp > 29pf ccbt vp > 214pf ccbt vp > 224pf ccbt vp > 224pf ccbt vp > 223pf ccbt vp > 2100
wa20wa11wa4wa13wa5wa16wa69
wa vp > 123wa vp > 118wa vp > 18wa vp > 14wa vp > 19wa vp > 18wa vp > 170
wa vp > 213wa vp > 220wa vp > 213wa vp > 29wa vp > 221wa vp > 210wa vp > 286
wa ccbt16wa ccbt21wa ccbt16wa ccbt3wa ccbt6wa ccbt9wa ccbt71
wa ccbt vp > 118wa ccbt vp > 117wa ccbt vp > 117wa ccbt vp > 16wa ccbt vp > 123wa ccbt vp > 15wa ccbt vp > 186
wa ccbt vp > 217wa ccbt vp > 213wa ccbt vp > 215wa ccbt vp > 28wa ccbt vp > 222wa ccbt vp > 218wa ccbt vp > 293

Sorted by means of frequency of profitability occurrence they are on display as follows:

SMA final sortingPointsNormalized
ea ccbt vp > 210055.56
pf ccbt vp > 210055.56
pf vp > 29653.33
pf vp > 19452.22
wa ccbt vp > 29351.67
ea ccbt vp > 19050.00
wa vp > 28647.78
wa ccbt vp > 18647.78
ea vp > 28547.22
pf ccbt vp > 18245.56
ea vp > 18145.00
avg ccbt vp > 27843.33
ea ccbt7742.78
pf7139.44
wa ccbt7139.44
wa vp > 17038.89
ea6938.33
pf ccbt6938.33
wa6938.33
avg5832.22
avg ccbt vp > 15027.78
avg vp > 24726.11
avg vp > 14525.00
avg ccbt3318.33

Appendix 5

AdMACD

The following tables expand on returns emerging through the implementation of the AdMACD trading system for the six verification periods.

Symbold-BackTest PS methodAvg (PF)Max (PF)Min (PF)PF > 1% PF > 1Max (SharpeRatio)Min (DD)Avg (Profit)Max (Profit)Min (Profit)Sum (Profit)Weeks (2016.02.28–2017.08.27)Verification period (VP)
AUDUSDwa vp > 252.481102.103949.3712.832.7419.02880.29− 595.593002.7279W1
EURUSDwa vp > 233.33827.6403341.772.769.22− 24.11614.5− 907.64− 404.6179W1
GBPUSDwa vp > 267.641620.503746.843.899.11− 18.07544− 1427.8272.8379W1
USDCADwa vp > 241.35745.2304759.4916.255.179.06425.13− 390.442215.4879W1
USDJPYwa vp > 273.97220103341.772.880.58− 17.08352.08− 330.35150.5179W1
XAUUSDwa vp > 2104.391347.9104354.433.446.083.65644.12− 1628.751788.2779W1
Symbold-BackTest PS methodAvg (PF)Max (PF)Min (PF)PF > 1% PF > 1Max (SharpeRatio)Min (DD)Avg (Profit)Max (Profit)Min (Profit)Sum (Profit)Weeks (2016.02.28–2017.08.27)Verification period (VP)
AUDUSDwa vp > 241.631102.102936.7112.832.74− 50.74583.92− 687.76− 2508.7679W2
EURUSDwa vp > 225.64539.9303645.573.647.18.07388.03− 907.642137.3979W2
GBPUSDwa vp > 247.331086.0903645.573.897.86− 36.49538.69− 1162.67− 1382.7379W2
USDCADwa vp > 251.87745.2304455.72.94.4420.27652.42− 383.783101.0279W2
USDJPYwa vp > 271.98220104151.94.90.490.22280.76− 238.591517.3379W2
XAUUSDwa vp > 281.91347.9103848.14.716.08− 22.54644.12− 1628.75− 280.2979W2
Symbold-BackTest PS methodAvg (PF)Max (PF)Min (PF)PF > 1% PF > 1Max (SharpeRatio)Min (DD)Avg (Profit)Max (Profit)Min (Profit)Sum (Profit)Weeks (2016.02.28–2017.08.27)Verification period (VP)
AUDUSDwa vp > 246.681102.103645.5712.834.8− 25.47630.36− 687.76− 512.3679W3
EURUSDwa vp > 217.29513.0803341.772.359.44− 17.68614.5− 907.64103.579W3
GBPUSDwa vp > 275.291620.504151.93.896.330.59550.48− 1170.991546.7179W3
USDCADwa vp > 253.32764.3704455.72.94.448.9652.42− 383.782202.7879W3
USDJPYwa vp > 281.69220104151.94.90.61− 5.3352.08− 266.491081.5179W3
XAUUSDwa vp > 254.261439.9103645.574.716.08− 24.1644.12− 1241.43− 403.9979W3
Symbold-BackTest PS methodAvg (PF)Max (PF)Min (PF)PF > 1% PF > 1Max (SharpeRatio)Min (DD)Avg (Profit)Max (Profit)Min (Profit)Sum (Profit)Weeks (2016.02.28–2017.08.27)Verification period (VP)
AUDUSDwa vp > 236.951102.103544.312.834.8− 19.76630.36− 499.21− 61.1379W4
EURUSDwa vp > 219.94827.6404050.632.356.69− 13.61385.11− 907.64424.8479W4
GBPUSDwa vp > 244.091620.503645.572.229.78− 45.25527.15− 1502.01− 2075.1379W4
USDCADwa vp > 248.71745.2304253.162.95.0614.18679.18− 396.482620.2579W4
USDJPYwa vp > 281.27220103848.18.40.61− 4.76391.13− 342.861124.3479W4
XAUUSDwa vp > 270.681347.9103746.844.716.08− 19.65644.12− 1241.43− 52.4779W4
Symbold-BackTest PS methodAvg (PF)Max (PF)Min (PF)PF > 1% PF > 1Max (SharpeRatio)Min (DD)Avg (Profit)Max (Profit)Min (Profit)Sum (Profit)Weeks (2016.02.28–2017.08.27)Verification period (VP)
AUDUSDwa vp > 217.66883.1103240.5112.834.89− 35.61880.29− 499.21− 313.579W5
EURUSDwa vp > 211.42539.9303645.573.646.86− 13.79634.47− 907.64410.2379W5
GBPUSDwa vp > 260.21620.503848.13.289.78− 12.83538.69− 1162.67486.7879W5
USDCADwa vp > 259.45764.3704354.432.565.0631.09679.18− 314.823955.8479W5
USDJPYwa vp > 287.76220104253.163.390.61− 2.96352.08− 342.861265.8879W5
XAUUSDwa vp > 287.111347.9104050.631.636.08− 17.13644.12− 952.6146.7579W5
Symbold-BackTest PS methodAvg (PF)Max (PF)Min (PF)PF > 1% PF > 1Max (SharpeRatio)Min (DD)Avg (Profit)Max (Profit)Min (Profit)Sum (Profit)Weeks (2016.02.28–2017.08.27)Verification period (VP)
AUDUSDwa vp > 234.551102.103139.2412.834.8− 39.28925.74− 637.36− 1603.4479W6
EURUSDwa vp > 213.38492.9303240.513.187.52− 38.3634.47− 907.64− 1525.3379W6
GBPUSDwa vp > 256.261620.503645.572.539.18− 15.15538.69− 1162.67302.7679W6
USDCADwa vp > 258.93764.3704354.432.95.0626.64703.42− 357.843604.7179W6
USDJPYwa vp > 257.4899003240.514.350.61− 0.87450.7− 332.531431.1779W6
XAUUSDwa vp > 279.961439.9104455.71.636.0816.45644.12− 952.62799.8179W6

AdPIVOT

The following tables expand on returns emerging through the implementation of the AdPIVOT trading system for the six verification periods.

Symbold-BackTest PS methodAvg (PF)Max (PF)Min (PF)PF > 1% PF > 1Max (SharpeRatio)Min (DD)Avg (Profit)Max (Profit)Min (Profit)Sum (Profit)Weeks (2015.02.22–2017.08.27)Verification period (VP)
AUDUSDwa vp > 282.491372.1305944.725.114.87− 21.11767.83− 580.75− 2786.131321 week
EURUSDwa vp > 288.41656.8105944.74.568.54− 34.26597.12− 689.62− 4522.641321 week
GBPUSDwa vp > 2115.262391.0305642.421.70− 27.19717.01− 525.54− 3589.31321 week
USDCADwa vp > 289.011237.606448.485.80− 24.79494.64− 612.34− 3272.61321 week
USDJPYwa vp > 2110.191603.9506246.9715.387.38− 23.41104.06− 1162.57− 3088.181321 week
XAUUSDwa vp > 2263.463173.8205843.941.9111.09− 10.761078.76− 867.49− 1420.571321 week
Symbold-BackTest PS methodAvg (PF)Max (PF)Min (PF)PF > 1% PF > 1Max (SharpeRatio)Min (DD)Avg (Profit)Max (Profit)Min (Profit)Sum (Profit)Weeks (2015.02.22–2017.08.27)Verification period (VP)
AUDUSDwa vp > 267.331372.1304937.1225.119.44− 61.41796.95− 576.59− 8106.671322 weeks
EURUSDwa vp > 2110.621656.8106146.214.568.37− 20.07597.12− 623.28− 2649.481322 weeks
GBPUSDwa vp > 2110.712391.0305642.421.70− 22.75717.01− 525.54− 3003.341322 weeks
USDCADwa vp > 269.99898.1305843.9412.610− 43.19358.85− 612.34− 5701.621322 weeks
USDJPYwa vp > 2105.791603.9506045.4520.947.38− 21.21735.11− 1162.57− 2800.131322 weeks
XAUUSDwa vp > 2278.713173.8206246.974.749.390.491078.76− 867.4964.241322 weeks
Symbold-BackTest PS methodAvg (PF)Max (PF)Min (PF)PF > 1% PF > 1Max (SharpeRatio)Min (DD)Avg (Profit)Max (Profit)Min (Profit)Sum (Profit)Weeks (2015.02.22–2017.08.27)Verification period (VP)
AUDUSDwa vp > 285.681372.1305340.1515.329.24− 50.12796.95− 617.87− 6616.231323 weeks
EURUSDwa vp > 2105.971656.8105642.424.568.54− 11.86701.14− 716.47− 1565.811323 weeks
GBPUSDwa vp > 2140.582513.9805541.671.890− 19.11457.53− 525.54− 2521.421323 weeks
USDCADwa vp > 2107.341237.606347.7312.610− 12.92494.64− 612.34− 1705.31323 weeks
USDJPYwa vp > 2106.591603.9506347.7320.947.38− 20.5735.11− 1162.57− 2706.321323 weeks
XAUUSDwa vp > 2268.913173.8205743.184.7411.09− 11.591078.76− 867.49− 1529.571323 weeks
Symbold-BackTest PS methodAvg (PF)Max (PF)Min (PF)PF > 1% PF > 1Max (SharpeRatio)Min (DD)Avg (Profit)Max (Profit)Min (Profit)Sum (Profit)Weeks (2015.02.22–2017.08.27)Verification period (VP)
AUDUSDwa vp > 298.641372.1305138.6425.119.24− 36.22796.95− 617.87− 4780.611324 weeks
EURUSDwa vp > 288.511656.8105138.644.568.54− 31.86701.14− 716.47− 4205.741324 weeks
GBPUSDwa vp > 2112.662391.0305743.181.70− 20.19761.81− 525.54− 2665.161324 weeks
USDCADwa vp > 2115.771237.60665012.6102.27494.64− 525.78299.691324 weeks
USDJPYwa vp > 275.611603.9505944.720.947.38− 8.98735.11− 1162.57− 1185.211324 weeks
XAUUSDwa vp > 2251.543173.8206045.454.749.39− 6.181078.76− 867.49− 815.251324 weeks
Symbold-BackTest PS methodAvg (PF)Max (PF)Min (PF)PF > 1% PF > 1Max (SharpeRatio)Min (DD)Avg (Profit)Max (Profit)Min (Profit)Sum (Profit)Weeks (2015.02.22–2017.08.27)Verification period (VP)
AUDUSDwa vp > 298.091372.1305340.159.329.24− 45.19796.95− 617.87− 5964.521325 weeks
EURUSDwa vp > 2100.311647.8106146.214.568.54− 19.81701.14− 716.47− 2614.411325 weeks
GBPUSDwa vp > 2110.942391.0305541.671.350− 24.24761.81− 525.54− 3199.51325 weeks
USDCADwa vp > 2115.991237.606549.2412.610− 11.43494.64− 525.78− 1509.351325 weeks
USDJPYwa vp > 2118.151603.9505642.4220.947.38− 10.75735.11− 1276.02− 1418.471325 weeks
XAUUSDwa vp > 2237.393173.8206246.974.7411.093.451140.33− 867.49455.641325 weeks
Symbold-BackTest PS methodAvg (PF)Max (PF)Min (PF)PF > 1% PF > 1Max (SharpeRatio)Min (DD)Avg (Profit)Max (Profit)Min (Profit)Sum (Profit)Weeks (2015.02.22–2017.08.27)Verification period (VP)
AUDUSDwa vp > 272.821372.1305037.881.589.24− 67.62767.83− 617.87− 8926.211326 weeks
EURUSDwa vp > 2109.711656.8105843.944.568.54− 26.49701.14− 716.47− 3496.241326 weeks
GBPUSDwa vp > 2114.322391.0305642.425.180− 20.71761.81− 525.54− 2733.881326 weeks
USDCADwa vp > 2115.541237.606750.7612.610− 6.51494.64− 543.58− 858.881326 weeks
USDJPYwa vp > 288.261603.9505440.9117.7710.6− 41.03641.18− 1276.02− 5415.521326 weeks
XAUUSDwa vp > 2276.513173.8206146.214.7411.0913.581078.76− 867.491792.941326 weeks

AdSMA

The following tables expand on returns emerging through the implementation of the AdSMA trading system, for the six verification periods.

Symbold-BackTest PS methodAvg (PF)Max (PF)Min (PF)PF > 1% PF > 1Max (SharpeRatio)Min (DD)Avg (Profit)Max (Profit)Min (Profit)Sum (Profit)Weeks (2016.02.28–2017.08.27)Verification period (VP)
AUDUSDwa vp > 266.421372.1305440.9125.114.87− 37.79767.83− 568.92− 4988.361321 week
EURUSDwa vp > 295.121656.8106146.212.428.54− 30.72596.09− 550.55− 4054.691321 week
GBPUSDwa vp > 2113.052391.0305541.676.017.87− 38.13717.01− 522.71− 5033.621321 week
USDCADwa vp > 295.591124.0505440.915.86.87− 26.25461.55− 690.66− 3464.661321 week
USDJPYwa vp > 2118.711603.9505239.3917.777.38− 23.81735.11− 1276.02− 3142.761321 week
XAUUSDwa vp > 2235.43061.5706146.211.292.79− 29.91987.86− 791.04− 3948.441321 week
Symbold-BackTest PS methodAvg (PF)Max (PF)Min (PF)PF > 1% PF > 1Max (SharpeRatio)Min (DD)Avg (Profit)Max (Profit)Min (Profit)Sum (Profit)Weeks (2016.02.28–2017.08.27)Verification period (VP)
AUDUSDwa vp > 274.171372.1305541.6725.119.24− 32.53767.83− 577.98− 4293.321322 weeks
EURUSDwa vp > 275.291656.8106146.214.449.57− 30.68596.09− 514.48− 4049.661322 weeks
GBPUSDwa vp > 2147.732391.0306347.736.018.22− 9.34857.99− 509.9− 1232.971322 weeks
USDCADwa vp > 2116.971237.606146.214.197.36− 15.48494.64− 690.66− 2043.61322 weeks
USDJPYwa vp > 2134.511603.9505743.1817.847.38− 15.18735.11− 647.62− 2003.451322 weeks
XAUUSDwa vp > 2239.793169.0605541.671.292.79− 37.391077.14− 812.79− 4934.891322 weeks
Symbold-BackTest PS methodAvg (PF)Max (PF)Min (PF)PF > 1% PF > 1Max (SharpeRatio)Min (DD)Avg (Profit)Max (Profit)Min (Profit)Sum (Profit)Weeks (2016.02.28–2017.08.27)Verification period (VP)
AUDUSDwa vp > 275.821372.1305541.6725.119.24− 41.35949.56− 582.82− 5458.091323 weeks
EURUSDwa vp > 276.281348.6405642.422.749.55− 18.99561.13− 550.55− 2506.131323 weeks
GBPUSDwa vp > 2125.242513.9805843.946.017.87− 16.621457.53− 504.92− 2194.281323 weeks
USDCADwa vp > 2117.381237.605541.6712.617.36− 29.91494.64− 690.66− 3948.161323 weeks
USDJPYwa vp > 2141.011603.9505743.1817.847.38− 13.62735.11− 623.73− 1797.721323 weeks
XAUUSDwa vp > 2239.763169.0605944.71.592.79− 36.041077.14− 863.66− 4756.71323 weeks
Symbold-BackTest PS methodAvg (PF)Max PF)Min (PF)PF > 1% PF > 1Max (SharpeRatio)Min (DD)Avg (Profit)Max (Profit)Min (Profit)Sum (Profit)Weeks (2016.02.28–2017.08.27)Verification period (VP)
AUDUSDwa vp > 266.441372.1305541.679.024.87− 40.2767.83− 651.05− 5306.61324 weeks
EURUSDwa vp > 274.421656.8105440.912.749.57− 29.14701.14− 550.55− 3846.691324 weeks
GBPUSDwa vp > 2115.482513.9805541.672.317.87− 37.781457.53− 622.19− 4987.41324 weeks
USDCADwa vp > 2130.971237.605843.944.197.36− 35.58494.64− 690.66− 4696.451324 weeks
USDJPY 123.391603.9505340.1517.847.38− 23.83702.99− 1162.57− 3146.211324 weeks
XAUUSDwa vp > 2253.993169.0605340.150.722.79− 46.311077.14− 812.79− 6112.681324 weeks
Symbold-BackTest PS methodAvg (PF)Max (PF)Min (PF)PF > 1% PF > 1Max (SharpeRatio)Min (DD)Avg (Profit)Max (Profit)Min (Profit)Sum (Profit)Weeks (2016.02.28–2017.08.27)Verification period (VP)
AUDUSDwa vp > 270.241372.1305138.649.024.87− 48.6767.83− 519.48− 6415.71325 weeks
EURUSDwa vp > 293.31656.8105541.672.748.54− 19.28701.14− 550.55− 2544.761325 weeks
GBPUSDwa vp > 2133.012513.9805843.942.317.87− 17.971457.53− 428.03− 2371.561325 weeks
USDCADwa vp > 2131.961237.606347.734.197.36− 20.51494.64− 690.66− 2707.591325 weeks
USDJPYwa vp > 2108.751560.605642.4217.847.38− 22.15702.99− 1162.57− 2923.991325 weeks
XAUUSDwa vp > 2204.142745.0605037.880.762.79− 64.08987.86− 687.42− 8458.481325 weeks
Symbold-BackTest PS methodAvg (PF)Max (PF)Min (PF)PF > 1% PF > 1Max (SharpeRatio)Min (DD)Avg (Profit)Max (Profit)Min (Profit)Sum (Profit)Weeks (2016.02.28–2017.08.27)Verification period (VP)
AUDUSDwa vp > 256.62116905239.391.919.24− 68.08654.08− 651.05− 8986.11326 weeks
EURUSDwa vp > 286.381656.8105541.671.888.54− 24.91701.14− 522.92− 3287.761326 weeks
GBPUSDwa vp > 2140.032513.9805743.181.77.87− 29.171457.53− 458.47− 3849.81326 weeks
USDCADwa vp > 2109.661124.0506448.484.197.36− 25.42461.55− 690.66− 3355.621326 weeks
USDJPYwa vp > 2122.71603.9506146.2117.847.38− 15.28702.99− 1162.57− 2017.341326 weeks
XAUUSDwa vp > 2247.013169.0605541.670.962.79− 26.911077.14− 678.56− 3552.111326 weeks

Appendix 6

Implementation of the Lag Method

figurex

Implementation of Different Verification Periods

figurey

Implementation of Different Neighbourhood Check

figurez

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Vezeris, D.T., Schinas, C.J., Kyrgos, T.S. et al. Optimization of Backtesting Techniques in Automated High Frequency Trading Systems Using the d-Backtest PS Method. Comput Econ (2019). https://doi.org/10.1007/s10614-019-09956-1

Download citation

Keywords

  • d-Backtest PS method
  • Optimal historical data period selection
  • Backtesting optimization algorithms
  • Expert backtesting systems
  • MACD
  • SMA
  • PIVOT
  • Forex
  • Exchange rates (AUDUSD, EURUSD, GBPUSD, USDCAD, USDJPY)
  • Metals (XAUUSD)