Large Discrepancy in Continuous vs Front Month Futures

The discrepancy you're seeing between the continuous /MES and the front month contract could be due to the way QuantConnect handles futures contracts. When you add a futures contract in QuantConnect, you can specify the data normalization mode and the contract mapping mode. These settings can affect the price of the contract.

For data normalization, QuantConnect offers several modes:

• ForwardPanamaCanal: Eliminates price jumps between two consecutive contracts, adding a factor based on the difference of their prices. The first contract is the true one, with a factor of 0.
• BackwardsPanamaCanal: Similar to ForwardPanamaCanal, but the last contract is the true one, with a factor of 0.
• BackwardsRatio: Eliminates price jumps between two consecutive contracts, multiplying the prices by their ratio. The last contract is the true one, with a factor of 1.
• Raw: No price adjustment is made.

For contract mapping, QuantConnect offers several modes:

• LastTradingDay: The contract maps on the previous day of expiration of the front month.
• FirstDayMonth: The contract maps on the first date of the delivery month of the front month. If the contract expires prior to this date, then it rolls on the contract's last trading date instead.
• OpenInterest: The contract maps when the back month contract has a higher traded volume than the current front month.

You can specify these settings when you add the futures contract. Here's an example in Python:

self._continuousContract = self.AddFuture(Futures.Indices.SP500EMini,
                                          dataNormalizationMode = DataNormalizationMode.BackwardsRatio,
                                          dataMappingMode = DataMappingMode.LastTradingDay,
                                          contractDepthOffset= 0, extendedMarketHours=False)

If you're still seeing a discrepancy, it could be due to other factors, such as missing data or a delay in receiving data for the security. If you believe it's a data issue, you can report it.

Source: Forum Discussion

So, how do you then have your indicators only use front month raw data and pick when to roll the current symbol into them? How can you use a history request to make sure happens properly?

To use front month raw data and decide when to roll the current symbol into your indicators, you can use the AddFuture method with the DataNormalizationMode.Raw setting. This will ensure that the raw data is used for your indicators.

When it comes to rolling the current symbol, you can use the OnSecuritiesChanged method. This method is called whenever the securities in your universe have changed. You can check if the future contract has changed and update your indicators accordingly.

Here's an example in Python:

class MyAlgorithm(QCAlgorithm):
    def Initialize(self):
        self.future = self.AddFuture(Futures.Indices.SP500EMini, Resolution.Minute)
        self.future.SetDataNormalizationMode(DataNormalizationMode.Raw)
        self.sma = SimpleMovingAverage(14)

    def OnData(self, data):
        if not self.sma.IsReady: return
        if not data.ContainsKey(self.contract.Symbol): return
        self.sma.Update(self.Time, data[self.contract.Symbol].Close)

    def OnSecuritiesChanged(self, changes):
        for security in changes.AddedSecurities:
            if security.Type == SecurityType.Future:
                self.contract = security

In this example, the OnSecuritiesChanged method is used to update the self.contract variable whenever the future contract changes. The OnData method then uses this contract to update the SMA indicator.

As for history requests, you can use them to warm up your indicators. Here's how you can do it:

history = self.History(self.contract.Symbol, 14, Resolution.Daily)
for bar in history.itertuples():
    self.sma.Update(bar.Index[1], bar.close)

This will request the last 14 days of data for the current contract and use it to warm up the SMA indicator.

Please note that these are just examples and you might need to adjust them according to your specific needs.

Sources:

• QuantConnect Documentation: Data Feeds
• QuantConnect Documentation: History Requests
• QuantConnect Documentation: Manual Indicators