Table Of ContentStocks & Commodities V. 4:5 (172-175): How useful are stochastics for trading? by Jack D. Schwager and Norman Strahm
How useful are stochastics for trading?
by Jack D. Schwager and Norman Strahm
I
n the current popular trading systems terminology, a stochastic refers to a measure of the placement of
the current price within a recent range. If the current price is near the top part of the recent range, the
stochastic will be high; if it is near the low end of the range, the stochastic will be low. Specifically, the
stochastic can be defined as follows:
C - L
S = t
N H-L
Ct = current closing price
H = high price during past N days
L = low price during past N days
N = number of days used to calculate the stochastic value
S = the stochastic's value on the Nth day
This definition uses daily price data, but any time interval can be used.
Testing stochastic signals
Stochastics seem to have become relatively popular in recent years, although much of the evidence
regarding the forecasting ability of this measure has been anecdotal. To rigorously test the usefulness of
the stochastic, the following simple system was devised:
1. Cover long and go short if the short-term moving average of the stochastic moves below the long-term
moving average of the stochastic for a specified number of consecutive days.
2. Cover short and go long if the short-term moving average of the stochastic moves above the long-term
moving average of the stochastic for a specified number of consecutive days.
The specific system included four parameters:
N1 = number of days used to calculate the stochastic
N2 = number of days in short-term moving average of stochastic
N3 = number of days in long-term moving average of stochastics
N4 = number of consecutive days for which crossover must hold to provide a signal.
The system was tested for a range of parameter sets. An analogous system was tested using weekly data.
The systems were tested using a hypothetical $1.3 million portfolio—a portfolio size which would only
be relevant for pools, funds, or extremely wealthy individuals. The reason for this fund size was to allow
for adjustments in position sizes to compensate for extremely wide differences in volatility between
markets.
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Stocks & Commodities V. 4:5 (172-175): How useful are stochastics for trading? by Jack D. Schwager and Norman Strahm
For example, the tested portfolio contains 25 contracts of corn, but only four contracts of coffee. This
type of approach provides a much better test of a system than simply assuming that all contracts are
traded in a one-unit size. However, based on our experience, the general conclusions at the end of the
article would also apply to similar portfolios trading single contracts for all markets.
The simulation program charged $150 per trade for transaction costs. Although this figure may seem
high, it is important to realize that transaction costs are much greater than commission costs. On average,
there is slippage on both entry and exit of trades. For example, if one assumes only a one point slippage
factor in bonds on entry and exit, slippage alone would equal $62.50. Admittedly, the $150 per-trade
transaction cost may be a bit conservative, but there is a strong argument for using a figure at least equal
to $100 per trade. In any case, any system which cannot make money at $150 per trade should be viewed
with some skepticism.
Results
The results using this system were disappointing (see Figure 1). Over the 1976-1984 test period, six of
the 16 parameter sets tested for the weekly version of the crossover stochastic system actually lost
money. The best parameter set registered only a 27.7 percent average gain, with a Sharpe Ratio of 0.65
and a Gain to Retracement Ratio of 2.19. (For a definition of this measure, see "Alternative to Sharpe
Ratio Better Measure of Performance," Futures magazine, March 1985).
The daily version of the system did somewhat better (Figure 2). Sixteen of the 22 parameter sets tested
were profitable, but in most cases, the gains were mediocre. The best performing parameter set registered
a 36.3 percent average annual gain, with a Sharpe Ratio of 0.88 and a Gain to Retracement Ratio of 4.26.
Even the much maligned crossover moving average system
performed significantly better than the stochastic crossover
system described.
While the performance of the best parameter set tested was respectable, it must be emphasized that it was
derived on the basis of hindsight. A trader using the type of stochastic system described would likely
have experienced far less favorable results since it is unlikely that he would have picked the optimum
parameter set.
It should be pointed out that the parameter sets which performed the best used much slower moving
averages of the stochastic than suggested by popular literature (i.e.,3-day moving average, and 3-day
moving average of the 3-day moving average). While this particular combination was not tested, the
performance of similar parameter sets which were tested suggests that the use of fast moving averages in
a stochastic crossover system would be a consistent money drain (the opposite of the proverbial money
machine). Unfortunately, one cannot get rich trading the reverse system, since in each case (i.e., trading
or fading the system), performance is decimated by the extreme burden of transaction costs. (Of course
"Disneyland simulations," which do not include transaction costs, might show such systems as being
viable. Any trader using actual money would know better.)
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Stocks & Commodities V. 4:5 (172-175): How useful are stochastics for trading? by Jack D. Schwager and Norman Strahm
The average trader should not automatically assume the value of
stochastics before rigorously testing their past performance....
Finally, it should be noted that even the much maligned crossover moving average system performed
significantly better than the stochastic crossover system described above (Figure 3). For example, a
10/30-day crossover combination witnessed a 32.5 percent average gain during the test period, with a
Sharpe Ratio of 0.85 and a Gain to Retracement Ratio of 3.94. Although these performance figures are
slightly below those scored by the best performing parameter set tested in the stochastic crossover
system, it should be emphasized that the 10/30 combination is not the optimum parameter set for the
crossover moving average system. Overall, a wide scattering of parameter sets for a straightforward
crossover system would perform better than the parameter sets of the stochastic crossover system. For
comparison purposes, tables are included summarizing the performance of the daily crossover stochastic
system, weekly crossover stochastic system, and 1:3 moving average crossover combinations (e.g., 3/9,
4/12, etc.). Each table indicates the portfolio performance for a range of parameter sets for each system.
Conclusion
The test described above suggests that the generally perceived value of the stochastic as a technical
indicator may be overstated. It appears particularly doubtful whether fast stochastic moving averages can
be useful as a sole input for trading decisions. Nevertheless, all we have proven is that a particular system
using a stochastic measure provides poor to mediocre results. Obviously, one can never prove that a
given technical indicator is useless, since there are an infinite number of ways an indicator could be used.
Certainly, some more creative individuals might be able to construct a methodology which successfully
uses the stochastic as a trading tool. However, the average trader should not automatically assume the
value of stochastics before rigorously testing their past performance for the given application.
Jack D. Schwager is Director of Research and Managed Trading for Paine Webber, and author of A
Complete Guide to the Futures Markets, John Wiley & Sons, 1984. Norman Strahm is the Managed
Trading Strategist for Paine Webber Futures Department.
Figure 1:
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