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MovAvglma2,mal,matype,aVglen,nb) if(modeltype == 3 11 modeltype
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s-bar m*t %K StochOac(stoch,hi,lo,cls,1,9,nb); // break; default: nrerrorl"TRAPSMOD: invalid modeltype"1; ) /I step through bare (days) to simulate actual trading forlcb = 1; cb L- nb; cb++) ( // take no trades before the in-sample period // ,.. same aa Tradestation's MaxBarsBack setting if(dt[cbl < IS-DATE) ( eqcls[cb] = 0.0; continue; ) /f execute any pending orders and save closing equity rc 3 ts.updaee,opnIcbl, bilcbl, loIcbl. clelcbl, cb); if(rc I= 0) nrerror("Trade buffer overflow"); eqcls[cbl = te.curr.nte~ify(EQ_CLOSETOTAL);
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// avoid placing orders on possibly limit-locked days if(hi[cb+ll == lo[cb+ll) continue; /! generate entry aignala, stop prices and limit prices // for all seasonality-based entry models signal - 0; switchtmodeltype) ( case 1: // basic thresholded momentum entry model k = cb + disp; tmp = thresh * mallkl; ifleavglk] 5 tmp &(r savglk-11 c= tmp) signal = 1; else if(savg[kl < -tmp &h aavglk-II >= -tmp) signal * -1; break; case 2: // basic crossover entry model k - cb + disp;
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After declaring local variables and arrays, the first major block of code copies various parameters to local variables for more convenient and understandable reference. The parameters are described in comments in this block of code. The next block of code performs all computations that are carried out on complete time series. The 50-bar average true range is calculated and saved in a vector (exiratr); it will be used later for the placement of money management stops and profit targets in the standard exit strategy. The average true range in this vector (or data series) is also used to normalize the price changes in the code that immediately follows. After calculating the average true range, normalized and clipped price changes are calculated. Each bar in the seriespchg reflects the change in price from the close of the previous bar to the close of the current bar. The price changes are normalized by dividing them by the average true range. They are then clipped to limit the influence of sharp spikes or statistical oudiers. Normalization is performed because markets change in their volatility over time, sometimes very dramatically. For example, the current S&P 500 has a price almost five or more times its price 15 years ago, with a corresponding increase in the size of the average daily move. If the price changes were not normalized and represented in terms of recent volatility, any seasonal estimate conducted over a number of years would be biased. The years with greater volatility would contribute more to the estimate than the years with lesser volatility. In the case of the S&P 500, the most recent years would almost totally dominate the picture. Using normalized price changes, each year contributes about equally. Clipping to remove outhers is performed so the occasional, abnormal price change does not skew the estimates. Clipping is performed at -2 and f2 average true range units. The modekype selection then determines which calculations occur next. A modeltype of 1 selects the basic momentum model. The seasonals are computed from the clipped and normalized price changes, the jackknife is used on the insample period, and the all-past-years technique is used for the out-of-sample period. These calculations are accomplished with a call to the function called SeasonalAvg. The series of seasonal estimates is then smoothed using a moving average of the type, specified by the parameter matype, and of a length set by avglen, another parameter. A series of average absolute deviations of the seasonal momentums is then computed. This series is nothing more than a lOO-bar simple moving average of the absolute values of seasonal momentum, which is used in later computations of threshold values. The modeltypes of 2,3, and 4 all represent variations of the crossover model. The seasonals are computed, and then the seasonal estimates of price change for every bar are integrated (a running sum is calculated), creating a new series that behaves almost like a price series. The synthesized, price-like series represents the movement in prices based on typical behavior in previous and perhaps future years. Two moving averages are then computed: ma1 (a moving average of the integrated seasonal time series of
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