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initialization and exits for longs on entry day
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1 e1ae if~entryposted < 0) ( /I initialization and exits for shorts on entry day limprice - entryprice - ptlim * exitatr[cbl; stpprice = entryprice + mmstp * exiratr[cbl; ta.exitshortlimit('o', limprice); t~.exiCshortst~p('E', atpprice); if(prdLcbl < loo.o-thresh, te.exirshortclose('P'l: ) else (
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The code fragment above implements the logic of the exit strategy. The parameters ptlim and mmsrp are set to 4.5 and 1.5, respectively; these are the values that gave the best overall portfolio performance (see Table 14-1, 14). The thresh parameter, i.e., the threshold used to generate exits based on the neural forecasts, is optimized. The logic of the additional exit can be seen in the if statements that compare the prediction of the network with the threshold and that post an exit at close order based on the comparison. The parameter thresh is stepped from 50 to 80 in increments of 2. RESULTS OF THE NEURAL EXIT TEST Baseline Results Table 15-l contains data on the baseline behavior of the MSES. The threshold ..was set high enough to prevent any net-based exits from occurring. The numbers in this table are the same as those reported in 14 (Table 14-I) for an opti-
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ma1 fixed stop and profit target. The abbreviations in Table 15-l may be interpreted as follows: SAMP = whether the test was on the training or verification sample (IN or OUT); NETL = the total net profit on long trades, in thousands of dollars; NETS = the total net profit on short trades, in thousands of dollars; PFAC = the profit factor; ROA% = the annualized return-on-account; ARRR = the annualized risk-to-reward ratio; PROB = the associated probability or statistical significance; TRDS = the number of trades taken across all commodities in the portfolio; WIN% = the percentage of winning trades; AVTR = the average profit/loss per trade; and TRDB = the average number of bars or days a trade was held. There was great consistency between in- and out-of-sample performance: The average trade lost $1,581 in-sample and $1,580 out-of-sample; both samples had 39% winning trades; and the risk-to-reward ratios were - 1.46 in-sample and - 1.45 out-of-sample. Neural Exlt Porlfolio Results
Table 15-2 is the standard optimization table. It shows the in-sample portfolio performance for every threshold examined and the out-of-sample results for the threshold that was the best performer during the in-sample period. In-sample, an improvement in overall results was obtained from the use of the additional neural network exit. The average trade responded to the threshold in a consistent manner. A threshold of 54 produced the best results, with an average trade losing $832. There were 41% wins and an annualized risk-to-reward ratio of -0.87. The numbers represent a dramatic improvement over those for the baseline presented in Table 15-l. Out-of-sample, however, no improvement was evident: Performance was not too different from that of the optimal MSES without the neural signal element. In the tests conducted using the neural net for entries, performance deteriorated very significantly when moving from in-sample to out-ofsample data. The same thing appears to have happened in the current test, where the same net was used as an element in an exit strategy.
Baseline Performance Data for the Modified Standard Exit Strategy to Be Used When Evaluating the Addition of a Neural Forecaster Signal Exit
SAW 1 NETI. 1 NETS 1 PFAC 1 RCA% / ARRR I PRO6 I l-R06 1 WIN% [ AVTR ITRDB IN I -19761 -4073) 0.831 -10.31 -1.461 1.0000~ 38261 391 -15811 6 OUT I -9741 -163zl 0.641 -21.61 -1.451 O.BS651 16491 391 -15501 8
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