In research notebooks, you have unfettered access to the data. You can execute your ad hoc analysis with the data using the
asof_date as the index for your time series. Doing so will avoid any perceived holes in the data set.
In backtesting, we enforce access to the data using the
timestamp field so if the CBOE was late to post their data or Quandl was down or Quantopian had difficulty processing the data and the data wasn't available in our system on that particular day at 8:45 (when
before_trading_start() runs), then we forward fill the data with the last, best known data point.
With this choice, we're enforcing 'point-in-time' access to the data. So sometimes the data firms update data post-facto and sometimes data firms have operational issues (like those described above). The backtester simulates the best known data point that Quantopian's data processing system knew at the time. I think this is a valid use case -- modeling and simulating both revisions to data and operational hiccups that inevitably happen in the real world.
There's a strong case (as Simon has made in the past) for a different mode in the backtester that ignores these processing problems in the backtester for certain classes of analysis. We've not yet added that to the product.
But you can certainly execute analyses outside the backtester in a research notebook that ignores the point-in-time aspect.
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