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Hi,
Say I have a pandas dataframe filled with stocks ohlcv data. Let's say it has a format similar to the format of a History() call. So, I can easily implement functions like GetActiveStocks(date) or GetOhlcvForStock(stock, date) which in my opinions should be everything the algorithm needs to make a backtest.
How do I go about feeding my algorithm with this data? I would like it to be my Universe and source of daily ohlcv data passed to OnData.
I looked into the examples of custom data sources but they almost always use either urls to pull data or local csv files which is not a big help here.
Fred Painchaud
Hi Midra,
Sorry that I can't help further. Your questions open up very very large cans.
Fred
Midra
That's too bad :(
Out of curiosity, have you maybe backtested a universe like sp500? How fast does it work to backtest 1 year with daily resolution? I mean with some simple built-in indicators like SMA EMA etc.
Fred Painchaud
Yes, check out SP500SectorsETFUniverse and QC500UniverseSelectionModel. On daily, for a year, it should not take long - that's 253 ish data points. A couple of seconds max with little indicators…
Fred
Midra
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