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Test Methodology for the Turning-Point Model The test methodology for this model is identical to that used for the time-reversed Slow %K model. The fact set is generated, loaded into N-TRAIN, scaled, and shuffled. A series of nets (from 3. to 4-layer ones) are trained to maximum convergence and then polished. Statistics such as shrinkage-corrected correlations are calculated. Training Results for the Turning-Point Model Bo&vn Forecaster. The structure of Table 1 l-2 is identical to that of Table 11-l. As with the net trained to predict the time-reversed Slow %K, there was a monotonic relationship between the number of connections in the network and the multiple correlation of the network s output with the target; i.e., larger nets evinced higher correlations. The net was trained on a total of 23,900 facts, which is a smaller fact set than that for the time-reversed Slow %K. The difference in number of facts resulted because the only facts used were those that contained some uncertainty about whether tomorrow s open could be a turning point. Since the facts for the bottom forecaster came from more widely spaced points in the time series, it was assumed that there would be less redundancy among them. When corrected for shrinkage, effective sample sizes of 23,919 (equal to the actual number of facts) and 8,000 (a reduced effective fact count) were assumed. In terms of the more severely adjusted correlations, the best net in this model appeared to be the largest 4-layer network; the smaller 4-layer network was also very good. Other than these two nets, only the 3.layer network with 10 middle-layer neurons was a possible choice. For tests of trading performance, the large 4-layer network (nn9.nef) and the much smaller 3-layer network (n&.net) were selected.
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Training Statistics for Neural Nets to Predict Bottom Turning Points
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Net Name INet Size ! NNI .NET NN2.NET NNB NET . .-.. .- ,. NN4.NE. NNS.NET NN8.NET NN7.NE-r NNB.NE 1841 18&i 1 &&I .--. IlIL~h4 ._ ._ . 1 &I 2-1 1818-l 1 a-20-.1 18-14-4-1
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Training Statistics
Net Name NNI .NET
NN2 NET
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Net Size 18-4-l
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0.068 1
0 na71
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Table 1 l-3 contains the statistics for the nets in this model; they were trained on 25,919 facts. Again, the correlations were directly related in size to the number of connections in the net, with a larger number of connections leading to a better model fit. When mildly corrected for shrinkage, only the smaller 4-layer network deviated from this relationship by having a higher correlation than would be expected. When adjusted under the assumption of large amounts of curve-fitting and shrinkage, only the two 4-layer networks stood out, with the largest one (nn9.net) performing best. The only other high correlation obtained was for the 1% 10-l net (nn4.nef). To maximize the difference between the nets used in the trading tests, the largest 4.layer net, which was the best shrinkage-corrected performer, and the fairly small (18-10-l) net were chosen. TRADING RESULTS FOR ALL MODELS Table 11-4 provides data regarding whole portfolio performance with the best insample parameters for each test in the optimization and verification samples. The information is presented for each combination of order type, network, and model. In the table, SAM = whether the test was on the training or verification sample (Nor OUT); 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; $TRD = the average profit/loss per trade; BARS = the average number of days a trade was held; NETL = the total net profit on long trades, in thousands of dollars; NETS = the total net profit on short trades, in thousands of dollars. Columns PI, P2, and P3 represent parameter val-
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