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Mechanical trading systems - Weissman R.L.

Weissman R.L. Mechanical trading systems - Wiley publishing , 2005 . - 240 p.
ISBN 0-471-65435-3
Download (direct link): mechanicaltradingsystems2005.pdf
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Table 9.2 is even more instructive in the context of diversification when we compare the performance of the 7- and 20-day and the 6- and 20-day parameter sets. Although these parameter sets retained identical longer-term moving average parameters and the shorter-term moving average
180
MECHANICAL TRADING SYSTEMS
parameter was changed only by one step, the 7- and 20-day parameter set was the year’s top performer, while the 6- and 20-day parameter set remained in the bottom half of all parameter sets analyzed.
MECHANICS OF TRADING SYSTEM DIVERSIFICATION

Diversification of negatively and/or uncorrelated trading systems is one of the most effective methods of improving rates of return without proportionately increasing the risk assumed to achieve these enhanced levels of performance. To illustrate this point, let us examine a trend-following system from Chapter 3 (MACD) with our diversified futures portfolio, and a directionally biased intermediate-term mean reversion system from Chapter 4 (RSI Extremes with the 200-day moving average filter) with our mean reversion portfolio, and then compare these results with the combined performance of both trading systems.
In comparing Tables 9.3 and 9.4 to Table 9.5, the first and most important improvement is in the profit to maximum drawdown ratio. This is due to the fact that low correlations between the trend-following and mean reversion systems led to a smoothing of equity drawdowns for the performance of the combined trading system results. Although the maximum drawdown column shown in Table 9.5 was larger than in Table 9.3 or 9.4, it represented an increase only of roughly 17 percent and 20 percent respectively. By contrast, because Table 9.5 took all signals generated by both systems, its total net profits were additive, thereby leading to an overall improvement in performance results.
TABLE 9.3 MACD totals from 1993 to 2002.
Asset # Profit Trades # Days Max Draw MDD MCL P:MD P:L Ratio %W Time %
Total 219498 175 1 42.9 - -42554 686 7 5.16 2.34 42.85 100
Note: All trade summaries include $100 round-turn trade deductions for slippage and commissions. Data source: CQG, Inc.
TABLE 9.4 RSI extremes with 200-day moving average filter and 2.5% stop. Totals from 1993 to 2002.
Asset # Profit Trades # Days Max Draw MDD MCL P:MD P:L Ratio %W Time %
Totals 100188 372 20.4 -44202 801 11 2.27 1.27 54.57 31.06
Note: All trade summaries include $100 round-turn trade deductions for slippage
and commissions. Data source: CQG, Inc.
Improving the Rate of Return 18!
TABLE 9.5 Combination of MACD totals and RSI extremes totals from 1993 to
2002.
Asset # Profit Trades # Days Max Draw MDD MCL P:MD P:L Ratio %W Time %
Totals 319686 547 59.6 53487 1084 14 5.98 1.61 50.82 53.12
Note: All trade summaries include $100 round-turn trade deductions for slippage and commissions. Data source: CQG, Inc.
In addition, combining these uncorrelated trading programs lessened many of the deficiencies of both methodologies as stand-alone systems. For example, one of the drawbacks to the trend-following system as a standalone solution is that it experiences more losing trades than winners. By contrast, by combining these two systems, the winning trade percentage increased from 42.85 percent for trading the MACD system alone to 50.82 percent.
Because these two trading systems are not highly correlated, sometimes both will generate profits; sometimes one will profit while the other loses; and sometimes both will lose. Consequently, the only way to replicate the backtested performance of these combined system results is through consistent implementation of all signals generated by all assets and/or trading systems. In other words, traders should not try to outguess the systems.
Although consistent implementation of all signals for all assets sounds like a straightforward proposition, it is complicated by the fact that both systems could be trading the same asset. In fact, this was the case for the combined trading system results generated in Table 9.5, because both the trend-following and mean reversion portfolios contained the E-mini S&P 500 futures contract. Consequently it is quite possible that these two trading systems could have generated opposite trading signals for the same instrument.
When I first started trading multiple systems with low correlations, I encountered this problem of conflicting trading signals. I failed to take a buy signal in the trend-following system because my mean reversion system had generated a sell signal for the same instrument. During the overnight trading session, my mean reversion realized its profit, which corresponded to what would have been a temporary open equity drawdown in the trend-following system (had I taken that trade). Then, almost immediately after the mean reversion system’s profitable exit, the market reversed, and I awoke to find that I had missed out on one of that year’s most profitable trend trades.
This painful lesson reinforced the fact that a prerequisite to successful implementation of diversified trading strategies is never missing a trading
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