Hedging and non-hedging trading strategies on commodities using the d-Backtest PS method. Optimized trading system hedging

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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”.

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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
    • 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