I am trying to use Quandl data futures for backtesting some trading scenarios, specifically Wiki Continuous Futures.

Following the documentation, I understand that the data-set contains continuous contracts named by the following convention CHRIS/{EXCHANGE}_{CODE}{NUMBER}. For example for crude oil (CL symbol) I will find various datasets depending on the market and on the back month contract. Eg. CHRIS/CME_CL1 will be the front month of Crude Oil Futures from the CME exchange. Whilst, CHRIS/CME_CL12 will be the 12th back month of the same future, the same market.

Can I assume that given that these are continuous contracts they are already adjusted? But then again, in the documentation there is this statement:

Of course, one must take care when analyzing and interpreting Continuous Contract data spanning decades, because the impact of multiple Rolls over such long time frames can be quite significant.

LE: I would want to backtest my algorithm splitting the data into windows and applying a non-anchored forward window strategy. Each window should contain approximately 4 years of data: 3 for model training and 1 for testing, therefore unseen data.

My question is: how can I transform the data (ratio adjust algorithms) in order to not introduce any bias in the data? Given that in the documentation there is no indication when the contracts roll.

  • $\begingroup$ What do you mean by "transform the data"? $\endgroup$
    – user42108
    Oct 25, 2021 at 17:06
  • $\begingroup$ I have added further clarifications to the question. $\endgroup$ Oct 25, 2021 at 17:14
  • $\begingroup$ "in the documentation there is no indication when the contracts roll" - the exchanges list the expiry rules. You can configure your own roll based on volume, open interest, fixed days to expiry (etc.) as appropriate. $\endgroup$
    – user42108
    Oct 25, 2021 at 19:31
  • $\begingroup$ I am interested in both aspects: the implication of time and window slicing and the types of roll and adjustments needed. More specifically, if I need to split the time into different study roll-forward time windows and then each window will be split into training, validation, and test, how do I ensure a continuity of data without risking the data from test to leak into training and validation if I do a backward panama. $\endgroup$ Oct 26, 2021 at 10:37
  • $\begingroup$ I have read the documentation on the same site but a different data set that requires a premium contract. data.nasdaq.com/databases/SCF/documentation. Here, in Splice Codes section can be found valuable information of types of data adjustments can be done. But I cannot find anywhere information about the time-series problem that would arise in a forecasting context or any type of modeling with an in-sample and out-of-sample setup. $\endgroup$ Oct 26, 2021 at 10:42

1 Answer 1


You cannot assume they are back adjusted unless explicitly stated and then you would need to know the algorithm used to adjust so you can take that into account in your back test.


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