Hedging and non-hedging trading strategies on commodities using the d-Backtest PS method. Optimized trading system hedging
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DOIhttp://dx.doi.org/10.21511/imfi.15(3).2018.29
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Article InfoVolume 15 2018, Issue #3, pp. 351-369
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Modern trading systems are mechanic, run automatically on computers inside trading platforms and decide their position against the market through optimized parameters and algorithmic strategies. These systems now, in most cases, comprise high frequency traders, especially in the Forex market.
In this research, a piece of software of an automatic high frequency trading system was developed, based on the technical indicator PIVOT (price level breakthrough). The system made transactions on hourly closing prices with weekly parameters optimization period, using the d-Backtest PS method.
Through the search and checking of the results, two findings for optimization of trading strategy were found. These findings with the order they were examined and are presented in this paper are as follows: (1) the simultaneous use of “long and short” positions, with different parameters in a hedging account, acts as a hedging strategy, minimizing losses, in relation to a “long or short” in a non-hedging account for the same time period and (2) there is weak correlation of past backtesting periods between the same systems, if they are configured for “long and short” trades, or for just “long” or for just “short”.
- Keywords
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JEL Classification (Paper profile tab)Q02, G17
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References47
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Tables10
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Figures12
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- Figure 1. The rules for executing trades
- Figure 2. Close position functions (stop loss/take profit)/open position in the system
- Figure 3. Short EURUSD and PIVOT indicator. Stop loss at ± 2ATR
- Figure 4. Long USDJPY and PIVOT indicator. Stop loss at ± 2ATR
- Figure 5. Example of long and short positions for the three systems during a sample week
- Figure 6. Test execution infrastructure
- Figure 7. Algorithm of research execution
- Figure 8. Sums of profits using three different parameter setting methods
- Figure 9. A diagram that shows the values of the backtesting periods (in number of weeks) for the three PIVOT systems for COTTON
- Figure 10. A diagram that shows the values of the backtesting periods (in number of weeks) for the three PIVOT systems for NATGAS
- Figure 11. A diagram that shows the values of the backtesting periods (in number of weeks) for the three PIVOT systems for OIL
- Figure 12. A diagram that shows the values of the backtesting periods (in number of weeks) for the three PIVOT systems for XAUUSD
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- Table 1. Percentage of weeks with profit factor > 1 for Expert Advisors running with constant default parameters
- Table 2. Sums of profits during the 79 week period for Expert Advisors running with constant default parameters
- Table 3. Percentage of weeks with profit factor > 1 for Expert Advisors running with optimized parameters (by means of the d-Backtest PS method)
- Table 4. Sums of profits during the 79 week period for Expert Advisors running with optimized parameters (by means of the d-Backtest PS method)
- Table 5. Percentage of weeks with profit factor > 1 for Expert Advisors running with parameters optimized with a priori knowledge of the best backtesting periods
- Table 6. Sums of profits during the 79 week period for Expert Advisors running with parameters optimized with a priori knowledge of the best backtesting periods
- Table 7. Table of correlation coefficients between backtesting periods of the three PIVOT systems on COTTON
- Table 8. Table of correlation coefficients between backtesting periods of the three PIVOT systems on NATGAS
- Table 9. Table of correlation coefficients between backtesting periods of the three PIVOT systems on OIL
- Table 10. Table of correlation coefficients between backtesting periods of the three PIVOT systems on XAUUSD
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- Aepli, M., Füss, R., Henriksen, T., & Paraschiv, F. (2017). Modelling the Multivariate Dynamic Dependence Structure of Commodity Futures Portfolios. Journal of Commodity Markets, 6, 66-87.
- Aitken, M., Harris, D., & Harris, F. (2015). Fragmentation and Algorithmic Trading: Joint Impact on Market Quality.
- Akansha, J. (2013). Forex Risk Management: Ways for Succeeding in Turbulent Economic Times. International Journal of Emerging Research in Management & Technology.
- Alom, F., Ward, B., & Hu, B. (2011). Spillover effects of World oil prices on food prices: evidence for Asia and Pacific countries. Proceedings from: 52nd Annual Conference New Zealand Association of Economists. Wellington, New Zealand.
- Andrieu, C., Doucet, A., & Holenstein, R. (2010). Particle Markov Chain Monte Carlo Methods. Journal of Royal Statistical Society B, 72(3), 269-342.
- Awartani, B., Aktham, M., & Cherif, G. (2016). The connectedness between crude oil and financial markets: Evidence from implied volatility indices. Journal of Commodity Markets, 4(1), 56-69.
- Brogaard, J. (2010). High Frequency Trading and its Impact on Market Quality.
- Carter, D., Rogers, D., Simkins, B., & Treanor, S. (2017). A Review of the Literature on Commodity Risk Management. Journal of Commodity Markets, 8, 1-17.
- Chang, T., & Su, H. (2010). The substitutive effect of biofuels on fossil fuels in the lower and higher crude oil price periods. Energy, 35(7), 2807-2813.
- Chaves, D., & Viswanathan, V. (2016). Momentum and mean-reversion in commodity spot and futures markets. Journal of Commodity Markets, 3(1), 39-53.
- 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.
- Dash, M., & Kumar, A. (2013). Exchange rate dynamics and Forex hedging strategies. Investment Management and Financial Innovations, 10(4), 125-129.
- Du, X., Lu, C., & Hayes, D. (2011). Speculation and volatility spillover in the crude oil and agricultural commodity markets: A Bayesian analysis. Energy Economics, 33(3), 497-503.
- Fileccia, G., & Sgarra, C. (2018). A Particle Filtering Approach to Oil Futures Price Calibration and Forecasting. Journal of Commodity Markets, 9, 21-34.
- Foucault, T., & Menkveld, A. (2008). Competition for Order Flow and Smart Order Routing Systems. The Journal of Finance, 63(1), 119-158.
- Fousekis, P., & Grigoriadis, V. (2017). Price co-movement and the crack spread in the US futures markets. Journal of Commodity Markets, 7, 57-71.
- Frino, A., Mollica, V., & Zang, S. (2015). The Impact of Tick Size on High Frequency Trading: The Case for Splits.
- Gilbert, C. (2010). How to understand high food prices. Journal of Agricultural Economics, 61(2), 398-425.
- Guilbaud, F., & Pham, H. (2015). Optimal High Frequency Trading in a Pro-Rata Microstructure with Predictive Information. Mathematical Finance, 25(3), 545-575.
- Haase, M., & Huss, M. (2018). Guilty Speculators? Range based conditional volatility in a cross-section of wheat futures. Journal of Commodity Markets, 10, 29-46.
- Haase, M., Zimmermann, Y., & Zimmermann, H. (2016). The impact of speculation on commodity futures markets – A review of the findings of 100 empirical studies. Journal of Commodity Markets, 3(1), 1-15.
- Haugom, E., & Ray, R. (2017). Heterogeneous Traders, Liquidity, and Volatility in Crude Oil Futures Market. Journal of Commodity Markets, 5, 36-49.
- Kirilenko, A., Kyle, A., Samadi, M., & Tuzun, T. (2017). The Flash Crash: High-Frequency Trading in an Electronic Market. The Journal of Finance, 72(3), 967-998.
- Kirk, C. (2014). Integration of a Predictive, Continuous Time Neural Network into Securities Market Trading Operations.
- Kocaarslan, B., Sari, R., Gormus, A., & Soytas, U. (2017). Dynamic Correlations between BRIC and U.S. Stock Markets: The Asymmetric Impact of Volatility Expectations in Oil, Gold and Financial Markets. Journal of Commodity Markets, 7, 41- 56.
- Kuck, K., & Schweikert, K. (2017). A Markov regime-switching model of crude oil market integration. Journal of Commodity Markets, 6, 16-31.
- Li, S., & Lucey, B. (2017). Reassessing the role of precious metals as safe havens – What colour is your haven and why? Journal of Commodity Markets, 7, 1-14.
- Liu, P., & Tang, K. (2011). The stochastic behavior of commodity prices with heteroskedasticity in the convenience yield. Journal of Empirical Finance, 18(2), 211-224.
- Lombardi, M., & Ravazzolo, F. (2016). On the correlation between commodity and equity returns: implications for portfolio allocation. Journal of Commodity Markets, 2(1), 45-57.
- Lübbers, J., & Posch, P. (2016). Commodities’ common factor: An empirical assessment of the markets’ drivers. Journal of Commodity Markets, 4(1), 28-40.
- Mann, J., & Sephton, P. (2016). Global relationships across crude oil benchmarks. Journal of Commodity Markets, 2(1), 1-5.
- Masteika, S., Rutkauskas, A., & Tamosaitis, A. (2012). Downtrend Algorithm and Hedging Strategy in Futures Market. International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, 6(10), 2549-2554.
- Menkveld, A. (2011). High Frequency Trading and the New Market Makers. Journal of Financial Markets, 16(4), 712-740.
- Miffre, J. (2016). Long-short commodity investing: A review of the literature. Journal of Commodity Markets, 1(1), 3-13.
- Mohaddes, K., & Raissi, R. (2017). Do Sovereign Wealth Funds Dampen the Negative Effects of Commodity Price Volatility? Journal of Commodity Markets, 8, 18-27.
- Ohashi, K., & Okimoto, T. (2016). Increasing trends in the excess comovement of commodity prices. Journal of Commodity Markets, 1(1), 48-64.
- Pirrong, C. (2017). The economics of commodity market manipulation: A survey. Journal of Commodity Markets, 5, 1-17.
- Securities and Exchange Commission (2010). Concept Release on Equity Market Structure.
- Shanker, L. (2017). New indices of adequate and excess speculation and their relationship with volatility in the crude oil futures market. Journal of Commodity Markets, 5, 18-35.
- Smales, L. (2017). Commodity market volatility in the presence of U.S. and Chinese macroeconomic news. Journal of Commodity Markets, 7, 15-27.
- Spencer, S., Bredin, D., & Conlon T., (2018). Energy and agricultural commodities revealed through hedging characteristics: Evidence from developing and mature markets. Journal of Commodity Markets, 9, 1-20.
- Thompson, M. (2016). Natural Gas Storage Valuation, Optimization, Market and Credit Risk Management. Journal of Commodity Markets, 2(1), 26- 44.
- Vezeris, D., Schinas, Ch., & Papaschinopoulos, G. (2016). Profitability Edge by Dynamic Back Testing Optimal Period Selection for Technical Parameters Optimization, in Trading Systems with Forecasting. Computational Economics, 51(4), 761-807.
- Witt, H., Schroeder, T., & Hayenga, M. (1987). Comparison of analytical approaches for estimating hedge ratios for agricultural commodities. Journal of Futures Markets, 7(2), 135-146.
- Working, H. (1960). Speculation on Hedging Markets. Food Research Institute Studies, 1, 185- 220.
- Zhang, Y., & Wei, Y. (2010). The crude oil market and the gold market: Evidence for cointegration, causality and price discovery. Resources Policy, 35(3), 168-177.
- Zhang, Z., & Zhang, H. (2016). The dynamics of precious metal markets VaR: A GARCHEVT approach. Journal of Commodity Markets, 4(1), 14-27.