Issues accessing Open Interest on Future Options! Oi is always 0

Based on the information provided in the extracts, it seems that you are correctly adding the future and future options in your algorithm. However, as you mentioned, the open interest data is only available at daily resolution.

In the QuantConnect platform, the resolution of the future options is determined by the resolution of the future. If you set the future resolution to daily, then the future option resolution should also be daily. This is mentioned in the US Future Options documentation.

However, it's important to note that the Slice object may not contain data for your Future Options at every time step. This is also mentioned in the same documentation.

Here is an example of how to access the open interest data from the Slice object:

def OnData(self, slice: Slice) -> None:
    for canonical_fop_symbol, chain in slice.OptionChains.items():
        for contract in chain:
            self.Log(f"{contract.Symbol} price at {slice.Time}: {contract.LastPrice}")

If you are still facing issues, it might be a good idea to post this question on the QuantConnect Forum for more specific help.

The answers provided by the AI (Mia) dont help. I also cant figure it out looking through the documentation. I have been stumped by this for 2 months now.  Can anyone please help? :/

I'm sorry to hear that you're having trouble with this issue. Based on the information provided in the extracts, it's difficult to pinpoint the exact cause of the problem.

However, it's worth noting that in live trading, bars are built using the exchange timestamps with microsecond accuracy. This microsecond-by-microsecond processing of the ticks can mean that the individual bars between live trading and backtesting can have slightly different ticks. As a result, it's possible for a tick to be counted in different bars between backtesting and live trading, which can lead to bars having slightly different open, high, low, close, and volume values. This is mentioned in the Future Options documentation.

If you're still having trouble, I would recommend reaching out to the QuantConnect community on the QuantConnect Forum. They might be able to provide more specific help.

Hey James,

Not sure if this is still an issue for you, how about making a history request from some time delta in the past for the minutely volume up to the point in time you need the options volume for and then summing up the values in the returned data frame?

history: DataFrame = algorithm.history(symbol=contract, timedelta=timedelta(), resolution=Resolution.MINUTE, fill_forward=True,
                data_normalization_mode=DataNormalizationMode.RAW)

				previous_open = util.previous_trading_day(algorithm, algorithm.securities[contract])
                delta = (algorithm.time - previous_open)
                difference = math.floor(delta.seconds / 60) + 390 * delta.days
                
                history: DataFrame = algorithm.history(contract, difference, Resolution.MINUTE, 
                    False, False, None, DataNormalizationMode.RAW)
                
                sum_volume = 0
                for row in history.itertuples():
                    if not math.isnan(row.volume):
                        sum_volume += row.volume

Hey Michael, thanks for your guidance above! I never got to the bottom of it and moved on. However, I might try to pick this strategy up again and see if your suggestion works. My intuition was to select assets with unusually high vol/OI ratios, and then overlay any lagging retail indicator (ie stochastic oscilator) to try to trade reversals. There are a few other routes the strategy could go, but this is a good starting point!