Measuring Degrees of Freedom in ea strategy

it is known that Whenever you give a strategy enough degrees of freedom an optimization
will yield curve fitted results.
The less complexity (rules) and less parameters available within a given strategy the less probable it is
that it will ever be curve fitted as systems that don’t have complex
criteria tend to be unable to “fit” to the data if a true inefficiency
is not present.
It therefore becomes extremely important to code
simple “elegant” strategies in order to avoid added complexity which
will result in curve fitted solutions.

consider a four data-point, 2 year, price history composed of opens,
highs, lows, and closes, or a total of 2,080 data points.
my trading strategy uses cross ma strategy
it uses a 10-day average of highs
and a 50-day average of lows.
Average one uses 11 degrees of freedom:10 highs plus 1 more as a rule.
Average two uses 51 degrees of freedom:50 lows plus 1 as a rule.
i have 3 more exit rules, that is 3 degrees of freedom
The total is 65 degrees of freedom used. (10+1+50+1+3)

To convert that to a percentage, i will divide degrees of freedom used by
data points.

The result is 3 %.(65/2080)
is the calculation right?

Aren't the 10 day average high and 50 average low one degree of freedom each?

The values of 10 and 50 can be changed so they are your degrees of freedom if you want them to be.