Automated trading systems’ evaluation using d-Backtest PS method and WM ranking in financial markets
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Author(s)Dimitrios VezerisLink to ORCID Index: https://orcid.org/0000-0002-2251-5772
,
Themistoklis Kyrgos
,
Ioannis Karkanis
,
Vasiliki Bizergianidou
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DOIhttp://dx.doi.org/10.21511/imfi.17(2).2020.16
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Article InfoVolume 17 2020, Issue #2, pp. 198-215
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426 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
Given the popularity and propagation of automated trading systems in financial markets among institutional and individual traders in recent decades, this work attempts to compare and evaluate such ten systems based on different popular technical indicators in combination – for the first time – with the d-Backtest PS method for parameter selection. The systems use the technical indicators of Moving Averages (MA), Average Directional Index (ADX), Ichimoku Kinko Hyo, Moving Average Convergence/Divergence (MACD), Parabolic Stop and Reverse (SAR), Pivot, Turtle and Bollinger Bands (BB), and are enhanced by Stop Loss Strategies based on the Average True Range (ATR) indicator. Improvements in the speed of the back-testing computations used by the d-Backtest PS method over weekly intervals allowed examining all systems on a 3.5 years trading period for 7 assets in financial markets, namely EUR/USD, GBP/USD, USD/JPY, USD/CHF, XAU/USD, WTI, and BTC/USD. To evaluate the systems more holistically, a weighted metric is introduced and examined, which, apart from profit, takes into account more factors after normalization like the Sharpe Ratio, the Maximum Drawdown and the Expected Payoff, as well as a newly introduced Extended Profit Margin factor. Among the automated systems examined and evaluated using the weighted metric, the Adaptive Double Moving Average (Ad2MA) system stands out, followed by the Adaptive Pivot (AdPivot), and the Adaptive Average Directional Index (AdADX) systems.
Acknowledgments
We would like to thank Dr. Christos Schinas for his time and invaluable guidance towards the methodology of the weighted metric. We would also like to thank Michalis Foulos for the hardware setup and support and Nektarios Mitakidis for his contribution to the representation of the results.
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).
- Keywords
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JEL Classification (Paper profile tab)F31, G17
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References31
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Tables8
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Figures11
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- Figure 1. Systems’ 3.5-year balance graph (January 2016 – July 2019) with compounding capital using the d-Backtest PS method for parameter selection
- Figure A1. EUR/USD chart with signals from the Ad1MA strategy
- Figure A2. EUR/USD chart with signals from the Ad2MA strategy
- Figure A3. EUR/USD chart with signals from the Ad3MA strategy
- Figure A4. EUR/USD chart with signals from the AdADX strategy
- Figure A5. EUR/USD chart with signals from the AdIchimoku strategy
- Figure A6. EUR/USD chart with signals from the AdMACD strategy
- Figure A7. EUR/USD chart with signals from the AdParSAR strategy
- Figure A8. EUR/USD chart with signals from the AdPivot strategy
- Figure A9. EUR/USD chart with signals from the AdTurtle strategy
- Figure A10. EUR/USD chart with signals from the AdBBAntiTrend strategy
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- Table 1. Classification occurrences for all systems based on the WM values for d-Backtest PS method
- Table 2. Classification occurrences for all systems based on the WM values for best BTs
- Table 3. Classification occurrences for all systems based on the WM values for 6-month constant BT
- Table B1. Total results for Extended Profit Margin (xPM)
- Table B2. Total results for Sharpe ratio
- Table B3. Total results for Drawdown
- Table B4. Total results for Expected Payoff
- Table B5. Total results for Profit
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Dynamic relationship between equity, bond, commodity, forex and foreign institutional investments: Evidence from India
Rajeev Matha ,
Geetha E. ,
Satish Kumar ,
Raghavendra
doi: http://dx.doi.org/10.21511/imfi.19(4).2022.06
Investment Management and Financial Innovations Volume 19, 2022 Issue #4 pp. 65-82 Views: 761 Downloads: 173 TO CITE АНОТАЦІЯThe interrelationship between equity, bond, commodity and forex movements can provide investors with abundant trading opportunities regardless of whether one market is trending upward or downward. Hence, to understand the interlinkage between markets, this study examines the long-run and causal linkage between forex, G-sec bonds, oil prices, gold rates, foreign institutional investment (FII) flows, and equity market and sectoral index returns. Daily time-series data from August 2012 to August 2021 were considered for empirical analysis. Johansen’s cointegration test revealed that foreign exchanges like USD, Euro, GBP and Yen, oil and gold rates, G-bond returns and FII flows were significantly cointegrated with the stock market and sectoral indices in the long run. Further, Granger causality found a uni-directional relationship between forex rates (i.e., USD, Euro, Yen) and the market, as well as sectoral indices, except Nifty 50 and Nifty IT indices. Oil price movements were found to effectively predict future price changes of Nifty consumer durables, auto, IT indices. Gold prices are useful to predict Nifty-Auto, Bank, Financial Services, Oil & Gas and PSU. The study also found a bi-directional relationship from FII inflows to the stock market and sectoral indices. The findings suggest that forex rates, oil prices and FII flows significantly affect India’s stock market and sectoral performance. The study contributes to the existing literature by comprehensively examining the interlinkage between commodities such as oil and gold, foreign exchanges like USD, Euro, GBP and Yen, G-bond, FII flows and the stock market, and fourteen sectoral indices in the Indian context.
