Degrees of freedom in moving average crossover strategies with varying parameters

External question — Quantitative Finance Stack Exchange Author: MMsmithH Original post: https://quant.stackexchange.com/questions/80601 License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/ Adaptation: HTML converted to plain text; contact email addresses removed. How might variables, such as using high price vs low price or Simple moving average (SMA) vs exponential moving average (EMA), influence a determination of degrees of freedom or data points that are consumed by a moving crossover strategy. How many degrees of freedom are consumed by a moving average crossover strategy in the following 4 cases? SMA (10, high), SMA (20, high) SMA (10, low), SMA (20, low) SMA (10, high), EMA (20, high) SMA (10, high), EMA (20, low) As defined by Pardo degrees of freedom = whole data sample – rules and conditions – data consumed by rules and conditions I realize that there might be different answers depending on a more conservative or liberal approach.

External answer — Quantitative Finance Stack Exchange Author: Content_Quantinsti Original post: https://quant.stackexchange.com/a/80960 License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/ Adaptation: HTML converted to plain text; contact email addresses removed. Defined for both conservative and liberal approach MA (10, high) vs SMA (20, high): 20 data points (conservative and liberal). SMA (10, low) vs SMA (20, low): 20 data points (conservative and liberal). SMA (10, high) vs EMA (20, high): 54 data points (conservative), possibly 20 (liberal). SMA (10, high) vs EMA (20, low): 54 data points (conservative), possibly 20 (liberal). In all cases, the exact degrees of freedom also depend on the number of rules and conditions applied to the strategy (e.g., entry/exit conditions, filters).