Hi, I am relatively new to python. I am trying to figure out if it is possible to generate a list of symbols using the coarse and fine universe tools. I have been playing around with the universe functions and I haven't had any luck. Before I continue, I just want to know if it is even possible to do this in QuantConnect. If anyone could let me know or point me in the right direction to find out if it is possible to generate a list of symbols that fall under certain parameters, that would be a tremendous help. I do not want to run the algorithm yet, I just want to see the list of symbols that comes up. Thank you.
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Universe Selection and Creating a list of symbols
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Hello Nicholas,
It is absolutely possible to generate and display a list of symbols with coarse and fine selection on QuantConnect. The general idea is that you can select symbols through coarse and fine selection based on some factors/parameters you like, and then you can log the symbols in OnSecuritiesChanged() and finally see the list of symbols in log after backtest.
Here is an example of coarse and fine selection. In coarse selection, we select symbols based on dollar volume and price; in fine selection, we pick symbols with the highest PERatios. You may set different parameters as you like following the template. More examples and detailed explanation are shown here.
Hope it helps!
class UniverseSelection(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2019, 6, 1) # Set Start Date
self.SetEndDate(2019, 6, 15) # Set End Date
self.UniverseSettings.Resolution = Resolution.Daily
# coarse and fine selection
self.AddUniverseSelection(
FineFundamentalUniverseSelectionModel(self.SelectCoarse, self.SelectFine)
)
self.num_coarse = 200
self.num_fine = 5
def OnSecuritiesChanged(self, changes):
# selected symbols will be found in Log
self.Log(f'New Securities Added: {[security.Symbol.Value for security in changes.AddedSecurities]}')
self.Log(f'Securities Removed{[security.Symbol.Value for security in changes.RemovedSecurities]}')
def SelectCoarse(self, coarse):
sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
# select symbols with price > 5
filtered = [ x.Symbol for x in sortedByDollarVolume if x.Price > 5 ]
return filtered[:self.num_coarse]
def SelectFine(self, fine):
# select stocks with the highest PERatio
sortedByPERatio = sorted(fine, key = lambda x: x.ValuationRatios.PERatio, reverse = True)
return [ x.Symbol for x in sortedByPERatio[:self.num_fine] ]
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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 QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect 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. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.
0
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 QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect 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. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.
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