Results of the 18-14-4-l Network. This network provided trading performance in Software

Creation UPC Code in Software Results of the 18-14-4-l Network. This network provided trading performance

Results of the 18-14-4-l Network. This network provided trading performance
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that showed more improvement in-sample than out-of-sample. In-sample, returns
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Equity Growth for Reverse Slow %K 18-6-l Net, with Entry on a Stop
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ranged from a low of 328.9% annualized (stop order, Test 6) to 534.7% (entry at open, Test 4). In all cases, there was greater than $6,000 profit per trade. As usual, the longs were more profitable than the shorts. Out-of-sample, every order type produced losses. However, as noted in the previous set of tests, the losses were smaller than typical of losing systems observed in many of the other chapters: i.e., the losses were about $1,000 per trade, rather than $2,000. This network also took many more trades than the previous one. The limit order performed best (Test 5). The long side evidenced smaller losses than the short side, except in the case of the stop order, where the short side had relatively small losses. The better in-sample performance and worsened out-of-sample performance are clear evidence of curve-fitting. The larger network, with its 320 parameters, was able to capitalize on the idiosyncrasies of the training data, thereby increasing its performance insample and decreasing it out-of-sample. In-sample, virtually every market was profitable across every order. There were only three exceptions: Silver, the Canadian Dollar, and Cocoa. These markets seem hard to trade using any system. Out-of-sample, several markets were profitable across all three order types: the Deutschemark, the Canadian Dollar, Light Crude, Heating Oil, Palladium, Feeder Cattle, Live Cattle, and Lumber. A few other markets traded well with at least one of the order types. The equity curve showed perfectly increasing equity until the out-of-sample period, at which point it mildly declined. This is typical of a curve resulting from overoptimization. Given a sample size of 88,092 facts, this network may have been too large.
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Trading Results for the Bottom Turning-Point Model
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The two networks that were selected, on the basis of their corrected multiple correlations with the target, as most likely to hold up out-of-sample are analyzed for trading performance below. The tist network was the smaller of the two, having 3 layers (1810-I network). The second nehvork was a network with 4 layers (18-20-6-I network).
Results of the 18-10-I Nefwork.
In-sample, this network performed exceptionally well-nothing unusual, given the degree of curve-fitting involved. Out-ofsample, there was a return to the scenario of a heavily losing system. For all three order types (at open, on limit, and on stop, or Tests 7, 8, and 9, respectively), the average loss per trade was in the $2,000 range, typical of many of the losing models tested in previous chapters. The heavy per-trade losses occurred although this model was only trading long positions, which have characteristically performed better than shorts. In-sample, only four markets did not perform well: the British Pound, Silver, Live Cattle, and Corn. Silver was a market that also gave all the previously tested networks problems. Out-of-sample, the network was profitable across all three
order types for the S&P 500, the Japanese Yen, Light Crude, Unleaded Gasoline, Palladium, Soybeans, and Bean Oil. A number of other markets were also profitable with one or two of the orders. The equity curve showed strong steady gains in-sample and losses out-ofsample.
Results of&e 18-20-6-I Nehvork.
These results were derived from Tests l&12 (at open, on limit, and on stop, respectively). In-sample performance for this network soared to unimaginable levels. With entry at open, the return was 768% annualized, with 83% of the 699 trades taken being profitable. The average trade produced $18,588 profit. Surprisingly, despite the larger size of this network (therefore, the greater opportunity for curve-fitting), the out-of-sample performance, on a dollars-per-trade basis, was better than the smaller network, especially in the case of the stop entry, where the loss per trade was down to $518. All markets were profitable across all orders in-sample, without exception. Out-of-sample, the S&P 500, the British Pound, Platinum, Palladium, Soybean Meal, Wheat, Kansas Wheat, Minnesota Wheat, and Lumber were profitable across all three order types.
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