Quantopian's community platform is shutting down. Please read this post for more information and download your code.
Back to Community
Is there a way to prevent performance from being calculated while my algorithm is gathering two yrs of stats prior to first trade?
B. Student
posted May 19, 2016
API Seeking Help Getting Started Using Data

I am running an algo that gathers about two years of data before it starts trading (and from then on uses a rolling two year window going forward). I'm mainly using Zipline right now, but porting back to quantopian API soon.
As is, it seems that the performance stats (e.g. the benchmark cumulative return) are running for two years before I make a trade. Is there some way to set a specific date for that data collection to begin?
I can tweak it myself in most cases - there's got to be a better way, but I don't see it in the doc.

TIA

2 responses
B. Student
May 19, 2016

Hmmm ... don't all talk at once! Here's a clue (i think) from zipline tradesimulation.py:

See the comment and code right above the ETC: 

class AlgorithmSimulator(object):  
    def __init__(self, algo, sim_params):  
      ...  
      self.algo_start = normalize_date(self.sim_params.first_open)  
      ...  
    def transform(self, stream_in):  
        """  
        Main generator work loop.  
        """  
        ...  
        # inject the current algo  
        # snapshot time to any log record generated.  
        with ExitStack() as stack:  
            ...  
            for date, snapshot in stream_in:  
                self.simulation_dt = date  
                self.on_dt_changed(date)  
                # If we're still in the warmup period.  Use the event to  
                # update our universe, but don't yield any perf messages,  
                # and don't send a snapshot to handle_data.  
                if date < self.algo_start:  
                ....  
                ETC
B. Student
May 24, 2016

Duh.
Create a day counter.
Initialize to zero , increment daily before trading.
When it equals 1 on the first day, perform initialization to backfill historical outputs.

About Quantopian
Careers
Community
Events
Documentation
Academia
Lectures
Status
Facebook
Twitter
LinkedIn
YouTube
Terms of Use
Privacy Policy

The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian.

In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.