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Let us assume that a trading system performed 100 trades, of which 47 were profitable,
33 were unprofitable, and 20 were scratches. If d is the number of defective, or unprofit-
able, trades, then p d / n is called the fraction defective. If the number of unprofit able
trades d n. p is more than expected or traditionally acceptable, the process is out of
control. The acceptable limits for d can be shown on a control chart. Like control charts
for variables, a d chart can be constructed from given standards obtained from the backtest.
The control limits for d are
UCL
LCL
dd
dd
3
3
Upper and lower control limits (UCL and LCL) represent bands three times the stan-
dard deviation above and below the mean of a variable. Upper and lower specification
limits (USL and LSL) are bands above and below the mean which define acceptable
performance of a process.
Sometimes the fraction defective, p , is controlled. The control limits for p can be
easily derived:
UCL
LCL
p
pp
n
p
pp
n
3
1
3
1
()
()
respectively, where p is the mean of the fraction defective for the whole population.
d and p charts are both useful, but the p chart is more robust when different sample sizes
are used.
28.3.3. Other Charts
There are many other statistics-based tools that organizations can employ including:
● SPC control for other known distributions.
● Pareto charts to separate the important few factors from the trivial many.
● Check sheets to display collected data in a manner that is easily interpreted.
● Histograms to chart the frequency of occurrence. Looking at the pattern of a histo-
gram will tell you:
- Whether the process is normally distributed.
- Where the process is centered.
● Scatter diagrams to determine correlation between variables.
● Run charts to display process results over time.
● Control charts (SPC) to determine whether process variation is the result of the
process ’ s inherent capability or caused by some controllable external factor.
● Cause and effect diagrams.
● Stratification.
● Scatter diagrams.
28.3. SPC CONTROL CHARTS