Overall Statistics
from Execution.ImmediateExecutionModel import ImmediateExecutionModel
from Portfolio.EqualWeightingPortfolioConstructionModel import EqualWeightingPortfolioConstructionModel
import pandas as pd

class TransdimensionalParticleReplicator(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2019, 11, 13)  # Set Start Date
        self.SetCash(100000)  # Set Strategy Cash
        # self.AddEquity("SPY", Resolution.Minute)


        self.__numberOfSymbols = 100
        self.__numberOfSymbolsFine = 5
        self.SetUniverseSelection(FineFundamentalUniverseSelectionModel(self.CoarseSelectionFunction, self.FineSelectionFunction, None, None))

    def OnData(self, data):
        '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
                data: Slice object keyed by symbol containing the stock data

        # if not self.Portfolio.Invested:
        #    self.SetHoldings("SPY", 1)

    # sort the data by daily dollar volume and take the top 'NumberOfSymbols'
    def CoarseSelectionFunction(self, coarse):
        # sort descending by daily dollar volume
        sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
        # return the symbol objects of the top entries from our sorted collection
        return [ x.Symbol for x in sortedByDollarVolume[:self.__numberOfSymbols] ]
    # sort the data by P/E ratio and take the top 'NumberOfSymbolsFine'
    def FineSelectionFunction(self, fine):
        # sort descending by P/E ratio
        sortedByPeRatio = sorted(fine, key=lambda x: x.ValuationRatios.PERatio, reverse=True)
        f1 = pd.DataFrame.from_records(
                    'symbol': str(security.Symbol),
                    'f1': str(security.FinancialStatements.BalanceSheet.Inventory.ThreeMonths)  
                } for security in fine
        # take the top entries from our sorted collection
        return [ x.Symbol for x in sortedByPeRatio[:self.__numberOfSymbolsFine] ]