# Mechanical trading systems - Weissman R.L.

ISBN 0-471-65435-3

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Since stochastics and RSI are so similar, the most obvious choice for development of a mean reversion trading system is use of the same logic as

FIGURE 2.17 Spot euro/yen with sloc stochastics extremes trading system. Data shows results from December 31, 2000, to December 31, 2003.

Note: All trade summaries include $100 round-turn trade deductions for slippage

and commissions. ©2004 CQG, Inc. All rights reserved worldwide.

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MECHANICAL TRADING SYSTEMS

illustrated in the stochastics extremes trading system. As a result, the 14-day RSI extremes trading system (see Figure 2.18) enters trades whenever the indicator closes beyond either 70 or 30. Exiting with profits occurs whenever RSI closes above 35 or below 65. The system utilizes the same 2.5 percent failsafe stop that was employed in stochastics extremes.

Differential Oscillators We have already examined several differential oscillators, including the two-moving average, the DMI, and the MACD differential oscillator. As stated, differential oscillators are based on the difference between two data series. In contrast to percentage oscillators, which range from 0 to 100, differential oscillators have no numerical limit and so determination of overbought or oversold levels is problematic. Most technicians view these oscillators as mean reversion indicators, because they lack absolute numerical ceilings or floors. So far I have used differential oscillators only in developing trend-following systems based on the indicator crossing beyond the zero line.

Momentum and Rate of Change The momentum and the rate of change (ROC) oscillators produce remarkably similar results because they both measure the closing price of x periods ago (10 periods is the most com-

FIGURE 2.18 Cash S&P 500 x 250 using RSI extremes trading system. Data show results from December 31, 1997, to December 31, 2003.

Note: All trade summaries include $100 round-turn trade deductions for slippage

and commissions. ©2004 CQG, Inc. All rights reserved worldwide.

Mathematical Technical Analysis

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monly employed default value for these indicators ) in relation to the latest closing price. (Momentum subtracts, whereas ROC divides closing price of x periods ago by the latest closing price.) If the latest closing price is above the closing price x periods ago, the oscillator is positive, if it is below the closing price x periods ago, the oscillator is negative. Subsequently, these oscillators are tailor made for a stop and reverse trend-following trading system with buy and sell signals triggered by closing beyond the zero level.

A comparison of Figures 2.19 and 2.20 shows that these systems often produce identical results and that utilization of both systems offers little benefit in terms of system diversification.

Statistical Oscillators Statistical oscillators are based on a statistical measurement known as the standard deviation (a mathematical measure of how widely dispersed a data set is from its mean). Instead of comparing current prices to past prices on a relative percentage basis, statistical oscillators compare current prices to a statistically measured amount of past price movement (deviation from the data set’s mean). These oscillators use the standard deviation of past prices over a specific period as the benchmark for “normal” price movement, and then compare the current price to the benchmark of normal price movement to measure the momentum of the

FIGURE 2.19 Spot Australian dollar/U.S. dollar with 10-day momentum. Includes data from December 31, 2002, to December 31, 2003.

Note: All trade summaries include $100 round-turn trade deductions for slippage

and commissions. ©2004 CQG, Inc. All rights reserved worldwide.

36

MECHANICAL TRADING SYSTEMS

FIGURE 2.20 Spot Australian dollar/U.S. dollar with 10-day ROC. Includes data from December 31, 2002, to December 31, 2003.

Note: All trade summaries include $100 round-turn trade deductions for slippage and commissions. ©2004 CQG, Inc. All rights reserved worldwide.

market. The benefit of this approach is that the standard to which current prices are compared changes in response to shifts in market volatility.

Bollinger Bands Bollinger bands, which were popularized by John Bollinger, who started as a market technician on CNBC, are constructed by calculating the standard deviation of prices over a specified period of time (Bollinger used 20 periods as his default value) and then adding and subtracting two standard deviations to a simple 20-period moving average. By constantly recalculating the standard deviation of recent prices, the indicator remains attuned to changes in market volatility since overbought and oversold levels will be harder to reach in a volatile market and easier to achieve in quiet markets.

Because Bollinger bands are based on two standard deviations from the 20-day moving average, they should theoretically encompass around 97 percent of all price action. When the market closes beyond the upper or lower bands, such price action is traditionally viewed as unsustainable. In fact, this often proves to be the case, and Bollinger bands are a commonly used as a building block in mean reversion trading systems (see Figure 2.21).

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