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I have timeseries for a bunch of currencies. For example, USD_NOK, EUR_USD, EUR_NOK, EUR_SEK and so forth. About 75 of them going back about 20 years in Pandas.

My goal is to isolate each currency separately. In other words I want to get a table with the movement of each currency individually. For example, how has NOK fluctuated during these 20 years?

I can see the NOK according to USD have gone up and down. But in the same time, NOK have fluctuated with EUR. And EUR have fluctuated with USD. If for example during a particular month EUR and USD has been fairly stable, and NOK have increased in comparison to USD but not according to EUR, I now that it is NOK that has changed.

Obviously I am not going to get a perfect "value" for each currency. But is there some method I can look into to isolate the effect of each column.

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The way central banks do this is to calculate the Effective Exchange Rate for the country in question. Basically this is a weighted average of the other currencies, with the weights chosen to represent the importance of each foreign country in the international trade of the domestic country.

For example for the United States, the Fed has defined the Broad Effective Exchange Rate for United States (NBUSBIS) to assess long term movements in the value of the USD. There is also something called the Dollar Index (DXY) which however is somewhat out of date in its choice of weights and no longer accurate. You can read more about how the weights are defined online.

Other central banks have defined similar indexes for their currencies.

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I have done exactly this sort of thing for my own personal use and have blogged about it on my blog at https://dekalogblog.blogspot.com

If you go to the blog and do a search in "search this blog and links" using the term "currency strength" you'll get the relevant posts, which include Octave code, some charts of the individual currencies and discussion and links to papers etc.

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