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Moonshot Strategy

This notebook introduces the Moonshot calendar spread trading strategy code and runs an example backtest.

Moonshot strategy code

The calendar spread strategy code is provided in calspread.py.

Code highlights

In prices_to_signals, we use the function get_contract_nums_reindexed_like to isolate the bids and asks of the contract months from which we wish to create the calendar spread:

CONTRACT_NUMS = (1,2)
...

# Get a DataFrame of contract numbers and a Boolean mask of the 
# contract nums constituting the spread
contract_nums = get_contract_nums_reindexed_like(bids, limit=max(self.CONTRACT_NUMS))
are_month_a_contracts = contract_nums == self.CONTRACT_NUMS[0]
are_month_b_contracts = contract_nums == self.CONTRACT_NUMS[1]

# Get a Series of bids and asks for the respective contract months by
# masking with contract num and taking the mean of each row (relying on
# the fact that the mask leaves only one observation per row)
month_a_bids = bids.where(are_month_a_contracts).mean(axis=1)
month_a_asks = asks.where(are_month_a_contracts).mean(axis=1)

month_b_bids = bids.where(are_month_b_contracts).mean(axis=1)
month_b_asks = asks.where(are_month_b_contracts).mean(axis=1)

To reflect the fact that we must buy at the ask and sell at the bid, we compute the spread differently for the purpose of identifying long vs short opportunities:

# Buying the spread means buying the month A contract at the ask and
# selling the month B contract at the bid
spreads_for_buys = month_a_asks - month_b_bids

... 

# Selling the spread means selling the month A contract at the bid
# and buying the month B contract at the ask
spreads_for_sells = month_a_bids - month_b_asks

In positions_to_gross_returns, we model buying at the ask and selling at the bid:

are_buys = positions.diff() > 0
are_sells = positions.diff() < 0
midpoints = (bids + asks) / 2
trade_prices = asks.where(are_buys).fillna(
    bids.where(are_sells)).fillna(midpoints)

gross_returns = trade_prices.pct_change() * positions.shift()

Install strategy file

Install the strategy by moving it to the /codeload/moonshot directory:

# make directory if doesn't exist
!mkdir -p /codeload/moonshot

!mv calspread.py /codeload/moonshot/

Example Backtest

The moonshot file contains an example subclass for backtesting the strategy with CL contract months 1 and 2:

class CLCalendarSpreadStrategy(CalendarSpreadStrategy):

    CODE = "calspread-cl"
    UNIVERSES = "cl-fut"
    DB = "cl-1min-bbo"
    CONTRACT_NUMS = (1, 2)
    BBAND_LOOKBACK_WINDOW = 60
    BBAND_STD = 2
    COMMISSION_CLASS = NymexCommission

There is also a "frictionless" version that ignores commissions and assumes fills at the bid-ask midpoint.

class FrictionlessCLCalendarSpreadStrategy(CLCalendarSpreadStrategy):

    CODE = "calspread-cl-frictionless"
    COMMISSION_CLASS = None
    FILL_AT_MIDPOINT = True

Now run the backtest (using the version with transaction costs):

We run the backtest in monthly segments (segment="M"), which mirrors the sharding logic of our database.

from quantrocket.moonshot import backtest
backtest("calspread-cl", 
         start_date="2019-01-01", 
         end_date="2019-08-31",
         segment="M",
         filepath_or_buffer="calspread_cl_results.csv")

And view the tear sheet:

from moonchart import Tearsheet
Tearsheet.from_moonshot_csv("calspread_cl_results.csv")

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