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Tables 13-1, 13-2, and 13-3 present the portfolio performance that resulted from trading random entries with the standard exit strategy. Each of the numbers in the column RAND represents a seed modifier (ranseed) that causes the RNG to generate a different sequence of random entries. NET = the total net profit, in thousands of dollars. NETL = the total net profit for the longs. NETS = the total net profit for the shorts, PFAC = the profit factor. ROA% = the annualized return on account. ARRR = the annualized risk-reward ratio. PROB = the statistical significance or probability, DRAW = the drawdown, in thousands of dollars. TRDS = the number of trades. WIN% = the percentage of wins. AVTR = the average trade, in dollars. TRDB = the average bars per trade, rounded to the nearest integer. VER zz the performance for the random sequence that provided the best in-sample performance when this sequence is continued and then tested on the verification sample. AVG = the average in-sample value of the numbers in rows 1 through 10. STDEV = the standard deviation for the in-sample performance data shown in rows 1 through 10. Test I: SES with Random Entries at the Open. This system did not perform very well in-sample. The average trade, over all 10 randomizations, lost $2,243, with a
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TABLE 13-I
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Results for the Standard Exit with Random Entries at the Open
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Results for the Standard Exit with Random Entries on a Limit
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standard deviation of $304. In terms of average dollars-per-trade. the less attractive systems that were tested when studying entries were on par with the results for the current system. In fact, some of them performed significantly worse than chance. The percentage of wins was very stable, with the average at 36.91% and a standard deviation of only 0.7%. The total number of trades taken per commodity, over the lo-year in-sample period, was 3,703, about the number it should be, given the probability of taking a random entry on any bar.
Out-of-sample, performance was within the expected range, consistent with in-sample performance. The percentage of wins was 37%, very close to that for insample results. The average loss per trade was $1,883, which is within 1 standard deviation of the in-sample estimate. Obviously, the standard exit was unable to pull a profit out of trades entered randomly. Test 2: SES with Random Entries on Limit. Table 13-Z is identical to Table 131, except that it shows portfolio behavior for the standard exit with random entries taken on a limit order. In-sample, the average loss per trade of $1,930 was somewhat less, demonstrating the effect of the limit order in reducing transaction costs. The standard deviation was somewhat higher: $477. The percentage of winning trades (38.73%) was just under 2% better, due to the better prices achieved with a limit entry. As was anticipated, other than such small changes as those described, nothing else in Table 13-Z is of any great interest. Out-of-sample, the system lost $3,056 per trade, just over 2 standard deviations worse than in-sample behavior. For whatever reason, the limit order may have had some real effect with random entries, making the system perform more poorly in recent yearn. The percentage of wins was also slightly worse, i.e., 37%, just under 2 standard deviations below in-sample behavior, but on par with entty at the open. Test 3: SES with Random Enhies on Stop. Table 13-3 contains information about the portfolio behavior for the standard exit when trades were entered randomly on a stop. In terms of dollars-per-trade, in-sample performance was between that of the other two orders, with a loss per trade of $2,039. The standard deviation was $391. The percentage of wins was lower, 36.36%, with a standard deviation of 1.12%. The lower percentage of wins probably reflects the poorer entry prices achieved using the stop. Out-of-sample, the average trade and percentage of wins were within 2 Standard deviations plus or minus the in-sample figures, demonstrating that out-ofsample performance was within the statistically expected range and fundamentally no different from in-sample performance. The performance figures in Tables 13-l through 13-3 provide a baseline (in the form of means and standard deviations) that can serve as a yardstick when evaluating ,the entries studied in Part Il of this book. For this purpose the $TRD and WIN% figures are the best ones to use since they are not influenced by the number of trades taken by a system. Market-by-Market Performance for SES with Random Entries at Open, In Table 13-4, the behavior of the standard exit with random entries taken at the open is broken down by market and by sample. The particular randomization used was the one that produced the best in-sample performance (in terms of annualized riskto-reward ratio) in Test 1. The leftmost column (SYM) contains the commodity
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