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To test the original SE& the random entry method is used (described in the Introduction to Part III). The exits are the usual standard exits (with exit restricted
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on the close) that were used throughout the study of entries. The rules for the SES are as follows: If, at the close, the market is more than some multiple (the money management stop parameter) of the 50-bar average true range below the entry price, then exit; this is the money management stop. If the price at the close is greater than some other multiple (the profit tzuget limit parameter) of the same average true range above the entry price, then exit; this is the profit target limit. These rules are for long positions, and the exit is restricted to the close. For short positions, the placement of the thresholds are reversed, with the money management exit placed above the entry price and the profit target exit placed below. If, after 10 days, neither the money management stop nor profit target limit has been hit, close out the trade with a market-at-close order. The code that follows implements these rules, as well as the random entry. There are three tests, one for each type of entry order (at open, on limit, and on stop). The standard software test platform and portfolio are used. s t a t i c void Model (float *parms, float dt, float *op. float *hi, float *lo. float *cls. float *vol. float *oi, float *dlrv, int nb, TRDSIM 6rts. float *eqclsl { ,, I/ // /I // // // // // // ,, I/ // // Implements the random encry model with the Standard exit File = xl9modOl.c vector [I. .MAxPRMl Of parameters pa=ms vector ,l..rlb, Of dates i n YYMMDD form dt vector Cl. .nbl of opening prices oPn vector [I. .nbl of high prices hi vector [I. . bl Of low prices 10 vector [I. .nbl of closing prices ClS YeCtor L1. .nbl O f Ol mes vo1 vector Il..nbl Of open interest numbers oi dlrv vector Ll..nbl of average dollar volatilities number Of bars in data series or vectors nb Erading siln latOl class i n s t a n c e ts vector [I..*1 of closing equity levels eqc1s
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// declare local scratch variables static int rc, c b , ncontracts, maxhold. ordertype, signal; s t a t i c f l o a t mmstp, ptlim, stpprice, limprice, tmp; s t a t i c float eritatrbmxBAR+1,, mum; static int ranseed; static long iseed; // copy parameters to local variables for ranseed = parms~81; ,, used t o select ordertype = parmsL91; // e n t r y : kopen, maxhold = 10; ,, maximum holding clearer reference random seed 2dimit. 3=stop period
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// perform whole-series calculations A gTr eRangeS(exitatr,hi,lo,cls,50,nb);
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,, ATR for exit
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case 2: tS.buylimitc 2 , limprice, *contracts,; break; case 3: ts.buystop~~3 . stpprice, ncontractsl; break; default: nrerrOr~ I* alid buy order selected ); I I e l s e if~ts.positionO >= 0 && s i g n a l = = -1) ( switch(ordertype) ( I/ select desired order type case 1: ts.sellopen~~4 , ncontractsl; break; case 2 : ts.selllimitl S , limprice, ncontracts); b r e a k ; case 3: t ..6e11*tOp~ 6 , stpprice, ncontracts~; break; default: nrerror~ lnvalid sell order selected ); ) I // instruct simulator to employ standard exit strategy tmp = exitatrLcb1 ; ts. Sb3exitC1s C X , ptlim*tmp, mmstp*tmp. maxholdh ) ,, process next bar 1
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The code is similar to that presented in earlier chapters. Only the generation of the entry signals has changed. Entry signals are now issued using a pseudo-random number generator (RNG). Before entering the loop that steps through bars to simulate the process of trading, the RNG is initialized with a unique seed. The initialization seed is determined by the market number and by a parameter (ranseed). By changing the parameter, a totally different sequence of random entries is generated. The exact seed values are irrelevant in that, for every seed, a unique series will be generated because the period of the RNG is extremely large. The RNG used is the one described as ran2 in Numen cal Recipes in C (Press et al., 1992). The period of this RNG is greater than 2 multiplied by 10 . This RNG is by far better than those that normally come as part of a programming language library. Inside the loop, where trading actually takes place, signals are generated based on random numbers. The steps are very simple. On each bar, a uniformly distributed random number between 0 and 1 is obtained from the RNG. If the random number is less than 0.025, a short signal is generated. The probability of a short being generated on any bar is 0.025; i.e., a short signal should occur every 40 bars, on average. If the random number is greater than 0.975, a long signal is issued, these signals should also occur, on average, every 40 bars. In other words, on average, a long or a short trading signal should occur every 20 bars. The limit and stop prices are calculated in the usual way. Orders are placed and exits specified in the usual manner. The steps that are used to conduct each of the three tests are as follows: On the in-sample data and for each entry order type, 10 distinct series of random
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entries are generated and traded. The best of those sequences are then examined on the verification or out-of-sample data. The process mimics that of optimizing a parameter in a real system by stepping it from, e.g., 1 to 10; here the parameter simply selects a totally different series of random entries for each value.
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