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prices vector Ll. .*I of low prices - vector [l..nbl Of c1oaing prices vector [I. .*I Of volumes vector [i..nbl of open interest numbers - vector [l..nh] of average dollar volatilities number of bars in data series or vectors trading simulator class instance vector Il..*1 of closing equity levels
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// declare int local rc, cb. scratch variables maxhold, signal, ranseed;
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static float stpa, stpb, matp, ptlim, limprice, stpprice; static int entryposted, entrybar, modeltype; static float exitatrM+XBAR+ll, mum, entryprice, tmp, at*; static long iseed; ,, copy parameters to local variables for clearer reference mmstp = parms[ll; // used to set the initial stop = parms[2]; // additional stop parameter stpa stp!J = parms131; I/ additional stop parameter ptlim = parma[61; I/ profit target limit in at* units modeltype = parms~71; // type of dynamic stop to use = parmsL81; // maximum holding period in days maxhold = parms~91 ; ,, used to Select random seed ranseed I/ perform whole-series calculations ~~gTruenange~~exitatr,hi,lo,cls.50,*~;
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// ATR for exit
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,, seed the random number generator // _._ use a different seed for each tradeable I/ t*.modelO returns a market index CSP=l, iwed = -(ranseed + 10 * ts.modelo,; mum = rarOC&iseed);
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break; case 2: = min(stpprice, cls[cbl+stpa*a~r~; stpprice break; case 3:
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tmp = (lolcbl < 0.0) + stpa l += atrl stptz * stpprice; elnp;
if~mp break; )
stpprice
tS.exit6hortlimit( * , ts .exitdlortstop iftch-entrybar 1 I ) I ,/ process next bar ( J >= ,
limprice~; stpprice1 i
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ts.exitshortclose( H );
The code above implements the MSES with the original fixed stop replaced with one of the three more responsive, dynamic stops, The parameter modeltype selects the stop, and depending on which is to be tested, up to three other controlling parameters are set and adjusted. For the two-bar highest-high/lowestlow (HHLL) stop, the parameter (mmsrp) is the multiple of the average true range that is added to or subtracted from the entry price to obtain the stop price for the entry bar. The stop price on the entry bar is set to either the lowest low of the last two bars or the entry price plus or minus the specified multiple of the average true range, whichever is further away from the current market price. For the other two kinds of stops (ATR-based and MEMA), the stop price on the entry bar is initialized to the usual value, i.e., the entry price minus (long) or plus (short) the money management stop parameter (mmsrp) multiplied by the average true range. On each bar after the entry bar, the stop price is adjusted. The adjustment used depends on the particular type of stop being employed, as selected by the modeltype parameter. For the HHLL stop, the highest high or lowest low is calculated according to whether the position is short or long (respectively). If the result of the calculation is closer to the current market price than the current stop price is, then the current stop price is replaced with the new value. For the next model (dynamic ATR-based stop), a second money management stop parameter (srpa) is multiplied by the average true range. Then the resultant number is subtracted from (long) or added to (short) the current closing price. If the resultant number is closer to the market than the current value of the stop price, the stop price is replaced with that number and, therefore, moved in closer to the market. For the third type of stop (MEMA), a stop parameter (stpa) is multiplied by the average true range, and then subtracted from the current high (longs) or added to the current low (shorts) as a kind of offset. The stop price is then subtracted from
the resultant number. The result of this last calculation is placed in a variable (nap). The stop price is updated on successive bars by adding tmp multiplied by another parameter (sfpb, a correction rate coefficient) to the existing stop price. The current stop price is adjusted, however, only if the adjustment will move the stop closer to the current price level. The computations are identical to those used when calculating an exponential moving average (EMA). The only difference is that, in a standard exponential moving average, the stop price would be corrected regardless of whether the correction was up or down and there would be no offset involved. In this model, stpb determines the effective length of an exponential moving average that can only move in one direction, in toward the prices. Test of Highest-High/Lowest-Low Stop In this test (modeltype = l), tire initial money management stop parameter controls the maximum degree of the stop s tightness on the first bar. It is stepped from 0.5 to 3.5 in increments of 0.5. Each row in Table 14-2 presents data on the in-sample performance of each of the values through which the parameter was stepped (ZNSTP). The last row describes the behavior of the model with the best parameter value (as found in the stepping process) retested on the out-of-sample data. Table 14-2 may be interpreted in the same way as the other optimization tables presented in this book, in the table, DRAW represents drawdown, in thousands of dollars. This stop appears to have been consistently too tight, as evidenced by a decreased percentage of winning trades when compared with the baseline MSFS model. In the previous test, the best solution (mmcrp parameter 1.5, ptlim parameter 4.5) had 39% wins, had a risk-to-reward ratio of - 1.46, and lost an average of $1,581 per trade. In the current test, the best solution has only 28% of the trades winning in-sample and 29% out-of-sample. Many potentially profitable trades (some of the trades that would have been profitable with the basic MSES, using an optimal fixed stop) were converted to small losses. The tightness of this stop is also demonstrated by the total number of bats the average trade was held (4), compared with the usual 6 to 8 bars. The average risk-to-reward ratio (-2.52 in-sample and -2.38 out-of-sample) and the average loss per trade ($1,864 in-sample, $1,818 out-of-sample) have also significantly worsened when compared with the optimal fixed stop. The 2-bar HI-ILL stop is obviously no great shakes, and one would be better served using a fixed, optimally placed stop, like that which forms part of the MSES when used with optimized parameters, such as those discovered and shown in the test results in Table 14-1. Test of the Dynamic ATR-Based Stop In this model, the two parameters (represented in the code as mmsip and apa) are multipliers for the average true range. They are used when calculating placement of the stop on the entry bar and later bars, respectively. The entry bar stop parameter
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