The contrarian trading rules SG 4, SG 5 and SG 6 can then be applied to this normalized index in the same way as to the moving average oscillator and the momentum oscillator, respectively.
I shall now describe which models are selected and how their profitability is calculated.
The study investigates a great variety of technical models. In the case of moving average models all combinations of a short-term moving average (MAS) between 1 and 12 days and a long-term moving average (MAL) between 6 and 40 days are tested under the restriction that the lengths of MAL and MAS differ by at least 5 days. This restriction excludes those models which produce too many signals due to the similarity of the two moving averages. Hence, 354 moving average models are tested for each of the six types of signal generation, for a total of 2124 models (=6⁎354). In the case of momentum models and RSIN models the time span runs from 3 to 40 days (38 models per type of signal generation).
An upper (lower) bound the value 0,3 (−0,3) is chosen for all types of models and trading rules. In the case of RSIN models an additional upper(lower) boundof 0,4(−0,4)is testedfor thesignalgeneration4 to 6 (SG 1 to 3 are not used in the case of RSIN models) so that the number of RSIN models tested in this study (228=2⁎3⁎38) is the same as the number of momentum models (228=6⁎38). In total, the performance of 2580 different technical trading systems is simulated in the study.
The main criterion for the selection of the parameter ranges was to cover those modelsthat are used inpractice. Hence theselection is based on informal interviews with stock dealers as well as on the literature on technical analysis (however, there remains always an ad hoc element since one cannot know the universe of all trading rules used in practice).
The simulated trading is based on the following assumptions. With regard to the market for stock index futures the most liquid contract is traded. Hence, it is assumed that the technical trader rolls over his open position on the 10th day of the expiration month from the near-by contract to the contact which is to expire three months later. In order to avoid a break in the signal generating price series, the price of the contract which expires in the following quarter is indexed with the price of the near-by contract as a base (software for technical trading in the futures markets also provide such “price shifts at contract switch”). This “synthetic” price series is, however, only used for the generation of trading signals, the execution of the signals is simulated on the basis of the actually observed prices.
When simulating the performance of daily trading systems the open price is used for both the generation of trading signals as well as for the calculation of the returns from each position.[5] Using open prices ensures that the price at which a trade is executed is very close to that price which triggered off the respective trading signal (this would not be the case if one used the daily close price).
Commissionsand slippage costs are estimated under the assumption that the technical models are used by a professional trader for trading at electronic exchanges like Globex (Mini S&P 500 futures contract). This implies commissions per transaction of roughly 0.002%.[6] Slippage costs are put at 0.008%.[7]
For these reasons the simulation of technical stock futures trading operates under the assumption of overall transaction costs of 0.01% (per trade).[8]
The profitability of the trading systems is calculated in the following way. The single rate of return (SRRi) from any position i opened at time t and closed at t+n is
SRRi = Pt + n −Pt=Pt4100 for long positions Pt + n is the sell price SRRi = Pt −Pt + n=Pt4100 for short positions ðPt is the sell priceÞ
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