Hi all,

I have been working on an implementation of the continuous futures prices for a universe of futures and in the process have found myself using "dirty hacks" so thought the community might help to achieve a better design.

My current implementation is the following (also in the attached backtest):

  1. Declare universe and store top-level contract names in a dictionary
  2. Inside OnData for each symbol add Daily consolidator for N most popular contracts, as advised here
  3. Inside DailyHandler, I create a pandas' Series object that contains Symbol, Close Price, Open Interest and Date and then append it to the db property of the algo class.
  4. A function scheduled to run daily then iterates through unique symbols in the db and for each symbol it creates a continuous price series.
    The particular methodology is not that important because at this step one can use any method. In this case, I use Perpetual Series based on Open Interest as described here. For each symbol I contruct 2 pivot tables, the first one with Open Interest (normalized to 1 for each row, as it is used as weights) and the second one with Close Price. Then I take element-wise product of the 2 tables and sum columns to get a Series of weighted average prices.
    All individual price series are then concatenated into a single data frame and written into algo's property cont∈uousfuture. 
  5. Now self.cont∈uousfuture can be used for longer term technical analysis.

There are several issues with that implementation:

  1. Price data is stored outside the Slice object, and not linked to a Symbol. This means that QC's rich toolkit, that includes Charting, Indicators and Insights cannot be used as it always requires Symbol object. I wonder if I create symbol manually through Symbol.Create method, can I then update data associated with this symbol, so that it is reflected in Slice later?
  2. This method generally is extremely slow. Not least because it uses pandas but I can't think of a cleaner way of creating the continuation logic without it. So any suggestion on how to speed it up would be great.
  3. Any other feedback is much appreciated.

  

Author

Aram Darakchian

November 2020