Continuous Futures are an abstraction of the chain of consecutive contracts for the same underlying commodity or asset. Additionally, they maintain an ongoing reference to the active contract on the chain. Continuous futures make it much easier to maintain a dynamic reference to contracts that you want to order, and get historical series of data. In this lesson, we will explore some of the ways in which we can use continuous futures to help us in our research.
In order to create an instance of a ContinuousFuture in Research, we need to use the continuous_future function. Similar to history, we need to import it from research's experimental library:
In [1]:
from quantopian.research.experimental import continuous_future, history
To create a continuous future, we just need to supply a root_symbol to the continuous_future function. The following cell creates a continuous future for Light Sweet Crude Oil.
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cl = continuous_future('CL')
cl
Out[2]:
We can use history to get pricing and volume data for a particular ContinuousFuture in the same way we do for Futures. Additionally, we can get the reference to its currently active Future contract by using the contract field.
Running the next cell will get pricing data for our CL continuous future and plot it:
In [3]:
# Pricing data for CL `ContinuousFuture`.
cl_pricing = history(
cl,
fields='price',
frequency='daily',
start_date='2015-10-21',
end_date='2016-06-01'
)
cl_pricing.plot()
Out[3]:
To better understand the need for continuous futures, let's use history to get pricing data for the chain of individual contracts we looked at in the previous lesson and plot it.
In [4]:
cl_contracts = symbols(['CLF16', 'CLG16', 'CLH16', 'CLJ16', 'CLK16', 'CLM16'])
# Pricing data for our consecutive contracts from earlier.
cl_consecutive_contract_pricing = history(
cl_contracts,
fields='price',
frequency='daily',
start_date='2015-10-21',
end_date='2016-06-01'
)
cl_consecutive_contract_pricing.plot();
The price difference between contracts at a given time is not considered to be an increase in value in the future. Instead, it is associated with the carrying cost and the opportunity cost of holding the underlying commodity or asset prior to delivery. This concept is covered more in depth in the Introduction to Futures Contracts lecture from our Lecture Series.
Next, let's look at the price history for active contracts separately. We will notice that this difference in price creates discontinuities when a contract expires and the reference moves to the next contract:
In [5]:
# Pricing and contract data for unadjusted CL `ContinuousFuture`.
# Adjustments are covered in the next section.
cl_unadjusted = continuous_future('CL', adjustment=None)
cl_history = history(
cl_unadjusted,
fields=['contract', 'price'],
frequency='daily',
start_date='2015-10-21',
end_date='2016-06-01'
)
cl_active_contract_pricing = cl_history.pivot(index=cl_history.index, columns='contract')
cl_active_contract_pricing.plot();
Part of the job of our continuous future abstraction is to account for these discontinuities, as we will see next by plotting our CL continuous future price against the price history for individual active contracts.
In [6]:
cl_active_contract_pricing.plot()
cl_pricing.plot(style='k--')
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The above plot is adjusted for the price jumps that we see between contracts. This allows us to get a price series that reflects the changes in the price of the actual underlying commodity/asset.
In the next section, we will explore the options for adjusting historical lookback windows of continuous futures.
As we just saw, continuous future historical data series are adjusted to account for price jumps between contracts by default. This can be overridden by specifying an adjustment argument when creating the continuous future. The adjustment argument has 3 options: 'mul' (default), 'add', and None.
The 'mul' option multiplies the prices series by the ratio of consecutive contract prices. The effect from each jump is only applied to prices further back in the lookback window.
Similarly, the 'add' technique adjusts by the difference between consecutive contract prices.
Finally, passing None means that no adjustments will be applied to the lookback window.
In the previous lesson we saw that trading activity jumps from one contract in the chain to the next as they approach their delivery date. A continuous future changes its reference from the active contract to the next bassed on its roll attribute.
A 'calendar' roll means that the continuous future will point to the next contract in the chain when it reaches the auto_close_date of the current active contract.
The volume roll (default) means that the continuous future will begin pointing to the next contract when the trading volume of the next contract surpasses the volume of the current contract. The idea is to roll when the majority of traders have moved to the next contract. If the volume swap doesn't happen before the auto_close_date, the contract will roll at this date. Note: volume rolls will not occur earlier than 7 trading days before the auto_close_date.
Let's get the volume history of our CL continuous future and plot it against the individual contract volumes we saw before.
In [7]:
cl_consecutive_contract_data = history(
cl_contracts,
fields='volume',
frequency='daily',
start_date='2015-10-21',
end_date='2016-06-01'
)
cl_continuous_volume = history(
cl,
fields='volume',
frequency='daily',
start_date='2015-10-21',
end_date='2016-06-01'
)
cl_consecutive_contract_data.plot()
cl_continuous_volume.plot(style='k--');
The volume for the CL ContinuousFuture is essentially the skyline of the individual contract volumes. As the volume moves from one contract to the next, the continuous future starts pointing to the next contract. Note that there are some points where the volume does not exactly match, most notably in the transition from CLK16 to CLM16 between April and May. This is because the rolls are currently computed daily, using only the previous day's volume to avoid lookahead bias.
The offset argument allows you to specify whether you want to maintain a reference to the front contract or to a back contract. Setting offset=0 (default) maintains a reference to the front contract, or the contract with the next soonest delivery. Setting offset=1 creates a continuous reference to the contract with the second closest date of delivery, etc.
In [8]:
print continuous_future.__doc__