Finding seasonal spreads ~ forex trading systems for sale
I am pleased to introduce guest blogger Paul Teetor for todays article.
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Finding Seasonal Spreads
By Paul Teetor
Finding Seasonal Spreads
By Paul Teetor
A seasonal spread is a spread which follows a regular pattern from year to year, such as generally falling in the Spring or generally rising in October. To find seasonal spreads, Ive been using ANOVA, which stands for analysis of variance. ANOVA is a well-established statistical technique which, given several groups of data, will determine if the groups have different averages. Importantly, it determines if the differences are statistically significantly.
I start with several years of spread data, compute the spreads daily changes, then group the daily changes by their calendar month, giving me 12 groups. The ANOVA analysis tells me if the groups (months) have significantly different averages. If so, I know the spread is seasonal since it is consistently up in certain months and consistently down in others.
The beauty is that I can automate the process, scanning my entire database for seasonal spreads. A recent scan identified the spread between crude oil (CL) and gasoline (RB), for example. The initial ANOVA analysis indicated the CL/RB spread is very likely to be seasonal. This bar chart of each months average daily change demonstrates the seasonality. (Click on the graph to enlarge it.)
The lines show the confidence interval for each months average. Notice how May and June are definitely "up" months because their confidence interval is entirely positive (above the axis). Likewise, November and December are definitely "down" months. For all other months, we cannot be certain because the confidence interval crosses zero, so the true average change could be either negative or positive. The conclusion: Be long the spread during May and June; be short during November and December.
For more details, please see my on-line paper regarding ANOVA and seasonal spreads.
- Paul Teetor
I start with several years of spread data, compute the spreads daily changes, then group the daily changes by their calendar month, giving me 12 groups. The ANOVA analysis tells me if the groups (months) have significantly different averages. If so, I know the spread is seasonal since it is consistently up in certain months and consistently down in others.
The beauty is that I can automate the process, scanning my entire database for seasonal spreads. A recent scan identified the spread between crude oil (CL) and gasoline (RB), for example. The initial ANOVA analysis indicated the CL/RB spread is very likely to be seasonal. This bar chart of each months average daily change demonstrates the seasonality. (Click on the graph to enlarge it.)
The lines show the confidence interval for each months average. Notice how May and June are definitely "up" months because their confidence interval is entirely positive (above the axis). Likewise, November and December are definitely "down" months. For all other months, we cannot be certain because the confidence interval crosses zero, so the true average change could be either negative or positive. The conclusion: Be long the spread during May and June; be short during November and December.
For more details, please see my on-line paper regarding ANOVA and seasonal spreads.
- Paul Teetor
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