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Figure 14-2

when corrected for 100 tests or generations) that chance produced the observed return Almost every combination of parameters examined generated profits on the long side and losses on the short side The average trade for the best parameter set was held for 6 bars and yielded a profit of $4,675 Only 240 trades were present in the optimization period, about 45% of which were winners Compared to previous tests, the smaller number of trades, and the higher percentage of winners, are explained by breakout thresholds placed further from current price levels The average trade lost $7,371 in the verification sample and only 25% of the 112 trades were profitable Both long positions and short positions lost about the same amount Almost all gain in equity occurred from August 1987 to December 1988, and then from December 1992 to August 1993.

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Code 39 is a discrete and self-checking symbology which has variable data length. It is also called Alpha39, Code 3 of 9 , Type 39 , USS Code 39 and USD-3. This barcode is widely adopted in non-retail fields. Customers are free to download this evaluation version of KA. Barcode for Java .
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Code 39 is a discrete and self-checking symbology which has variable data length. It is also called Alpha39, Code 3 of 9 , Type 39 , USS Code 39 and USD-3. This barcode is widely adopted in non-retail fields. Customers are free to download this evaluation version of KA. Barcode for Java .

Equity declined from October 1985 through July 1986, from August 1989 through May 1992, and from May 1995 to December 1998 Excessive optimization may have contributed to deteriorated performance in the verification sample Nevertheless, given the number of parameters and parameter combinations tested, a good entry model should have generated a greater insample return than was seen and better statistics, capable of withstanding correction for multiple tests without total loss of significance In other words, excessive optimization may not be the central issue: Despite optimization, this model generated poor in-sample returns and undesirably few trades Like the others, this model may simply have worked better in the past As before, currencies were generally profitable Oddly, the oil complex, which traded profitably in most earlier tests, became a serious loser in this one Coffee and Lumber traded well in-sample, but poorly out-of-sample, the reverse of previous findings.

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Some of these results might be due to the model s limited number of trades Test 8: Vokztility Breakout with Entry on Limit This model attempts to establish a long position on the next bar using a limit order when the close of the current bar is greater than the current price level plus a multiple of the average true range It attempts to establish a short position on the next bar using a limit order when the close of the current bar is less than the current price level minus the same multiple of the average true range The current price level is determined by an exponential moving average of length malen calculated for the close The multiplier for the average true range is referred to as bw, and the period of the average true range as atrlen.

Price for the limit order to be posted on the next bar is set to the midpoint price of the current or breakout bar Optimization was carried out exactly as in Test 7 For all parameter combinations, long positions were more profitable (or lost less) than short positions The best in-sample results were achieved with a bandwidth of 37, a moving average length of 22, and a period of 41 for the average true range measure of volatility; these parameter values produced a 483% annualized return Results this good should occur less than twice in one-thousand.

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