I have to agree that I tend to think of concatenating contracts for markets where it seems to make more sense (ie where there is no/less seasonality – like gold for example – although it could well exhibit serious backwardation in the future, similarly to how oil was showing serious contango at the beginning of the year). As you highlight, agriculturals with different seasonal crops is even worse (I like your comparison with trading different stocks).

I see what you are saying with trading something close to the spot market – which is basically what I have in mind when generating a continuous contract – but as you rightly point out, it is true that we might be missing some opportunities in the other contracts.

In any case thanks again for taking the time to post your views – which will definitely benefit my reflections on that matter (and hopefully so for other readers)

I’ll definitely check PowerST – thanks.

The logic supporting abnormal returns in the futures markets argues against rolling on volume or open interest. Trend followers are supposed to be providing insurance to hedgers by purchasing rising markets and selling falling markets (trading against hedgers). But if you wait until AFTER the open interest or volume is so high in a contract that it exceeds that of any other contract you are choosing to avoid most of the hedging activity (since that’s what produced the open interest and volume). If you wait until the hedgers are already in (already hedged) to participate, all that’s left is the beta of the market. That’s close to just trading the spot market. Not saying that can’t work…but it should be considered for what it is.

I’ve tried manually concatenating contracts using various different methods (OI, Vol, time, delta of each, etc.) As expected, the results are different.

At this point I’m inclined to use un-adjusted data, if only to be intellectually honest. This is a problem for most backtesting software programs. But most backtesting software programs don’t produce realistic results anyway. TradingBlox, Mechanica, TradersStudio, PowerST are a few that are worthy. It is inconvenient to use individual contracts. But in this business you do not want to be a payer of the “convenience premium”…because it tends to be very high.

I use PowerST http://www.powerst.com

Ultimately I would like to implement my own Futures contract concatenation program so that I would have control over all the variables I need (including adjusting the point value, also storing actual rollover dates to take rollover costs+slippage into consideration, etc.).

One thing that CSI does not seem to provide either is historical “market characteristics” (ie meta-data such as min Tick move, margin requirements, etc.). I have been unlucky so far to even locate a source for this data. I’d be glad to hear if you have? They would obviously need to be adjusted similarly to the point value.

I would be interested to hear on what method you have settled to concatenate your Futures data (if you have elected one)?

As of today I only use a set of continuous Futures data as it is more practical. But in the future – and before I go “live” with a trading strategy I fully intend to back-test the version on pure raw data (se my comment just above in response to tito’s comment). When I come to that stage I’ll post an update on how the results compare between raw vs concatenated data (actually that could be an idea for a future post – before I find that “killer” trading strategy ;-)

I agree with what you are saying.

This is only a compromise and will not beat any “straight clean data” back-testing. Its disadvantage is potential distortions (although – as per my examples – the idea is to minimise them) but its advantage is that it produces a format much easier to process. For example I don’t know of any back-testing systems that handles individual contract files for strategy testing. So you have to do something similar if you want to use these testing platforms – which speed up the testing process from my point of view.

The way I envisage the testing process is a 3-step one:
- research, test, optimise and choose strats in a rapid manner in a back-testing platform (like in TradersStudio or TradeStation)
- Validate the elected system(s) by doing a “more serious” testing on raw contracts data (i.e. applying skepticism to results found above) by coding up the strategy from scratch and not using the back-testing platform.
- Implement/deploy the code from step 2 in live if the results are satisfactory.

That way, the distortions would only affect initial systems testing/selection but the decision of putting a system “live” is still based on raw data back-tests.

That said, for my money a much better approach is to leave the futures data alone and smarten up your strategies so they don’t look to trade a specific contract but instead trade the current most active contract. This avoids all the data mangling issues you mention as well as providing a strategy that can be deployed directly into the market. It also enables you to backtest strategies which are trading the curve instead of just the most active or front contract.

For me, any approach that involves mangling your source data should be looked at with skepticism as it will inevitably introduce distortions to your backtested results.

–Matt