# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2020 QuantConnect Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import statistics as stat

class DynamicCalibratedGearbox(QCAlgorithm):

    def Initialize(self):
        # we need 3 extra years to warmup our ROA values
        self.SetStartDate(2012, 9, 1)  # Set Start Date
        self.SetCash(100000)  # Set Strategy Cash
        
        self.SetBrokerageModel(AlphaStreamsBrokerageModel())
        self.SetAlpha(ConstantAlphaModel(InsightType.Price, InsightDirection.Up, timedelta(days=31)))
        self.SetExecution(ImmediateExecutionModel())
        self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel(lambda time:None))
        
        self.AddUniverseSelection(
            FineFundamentalUniverseSelectionModel(self.CoarseFilter, self.FineFilter)
        )
        self.UniverseSettings.Resolution = Resolution.Daily
        
        self.curr_month = -1
        
        # store ROA of tech stocks
        self.tech_ROA = {}
        self.quarters = 0
        
    def CoarseFilter(self, coarse):
       
        if self.curr_month == self.Time.month:
            return Universe.Unchanged
        
        self.curr_month = self.Time.month
        
        # we only want to update our ROA values every three months
        if self.Time.month % 3 != 1:
            return Universe.Unchanged
        
        self.quarters += 1
        
        return [c.Symbol for c in coarse if c.HasFundamentalData]
      
    def FineFilter(self, fine):
        # book value == FinancialStatements.BalanceSheet.NetTangibleAssets (book value and NTA are synonyms)
        # BM (Book-to-Market) == book value / MarketCap
        # ROA == OperationRatios.ROA
        # CFROA == FinancialStatements.CashFlowStatement.OperatingCashFlow / FinancialStatements.BalanceSheet.TotalAssets
        # R&D to MktCap == FinancialStatements.IncomeStatement.ResearchAndDevelopment / MarketCap
        # CapEx to MktCap == FinancialStatements.CashFlowStatement.CapExReported / MarketCap
        # Advertising to MktCap == FinancialStatements.IncomeStatement.SellingGeneralAndAdministration / MarketCap
        #   note: this parameter may be slightly higher than pure advertising costs
        
        tech_securities = [f for f in fine if f.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Technology and
                                                f.OperationRatios.ROA.ThreeMonths]
        
        for security in tech_securities:
            symbol = security.Symbol
            if symbol not in self.tech_ROA:
                # 3 years * 4 quarters = 12 quarters of data
                self.tech_ROA[symbol] = RollingWindow[float](12)
            self.tech_ROA[symbol].Add(security.OperationRatios.ROA.ThreeMonths)
            
        # we want to rebalance in the fourth month after the (fiscal) year ends
        #   so that we have the most recent quarter's data
        if self.Time.month != 4 or self.quarters < 12:
            return Universe.Unchanged
        
        # make sure our stocks has these fundamentals
        tech_securities = [x for x in tech_securities if x.OperationRatios.ROA.OneYear and
                                                        x.FinancialStatements.CashFlowStatement.OperatingCashFlow.TwelveMonths and
                                                        x.FinancialStatements.BalanceSheet.TotalAssets.TwelveMonths and
                                                        x.FinancialStatements.IncomeStatement.ResearchAndDevelopment.TwelveMonths and
                                                        x.FinancialStatements.CashFlowStatement.CapExReported.TwelveMonths and
                                                        x.FinancialStatements.IncomeStatement.SellingGeneralAndAdministration.TwelveMonths and
                                                        x.MarketCap]
                                                        
        # compute the variance of the ROA for each tech stock
        tech_VARROA = {symbol:stat.variance(list(ROA)) for symbol, ROA in self.tech_ROA.items() if ROA.IsReady}
        tech_VARROA_median = stat.median(tech_VARROA.values())
        
        
        # we will now map tech Symbols to various fundamental ratios, 
        #   and compute the median for each ratio
        
        # ROA 1-year
        tech_ROA1Y = {x.Symbol:x.OperationRatios.ROA.OneYear for x in tech_securities}
        tech_ROA1Y_median = stat.median(tech_ROA1Y.values())
        
        # Cash Flow ROA
        tech_CFROA = {x.Symbol: (
            x.FinancialStatements.CashFlowStatement.OperatingCashFlow.TwelveMonths 
            / x.FinancialStatements.BalanceSheet.TotalAssets.TwelveMonths
            ) for x in tech_securities}
        tech_CFROA_median = stat.median(tech_CFROA.values())
        
        # R&D to MktCap
        tech_RD2MktCap = {x.Symbol: (
            x.FinancialStatements.IncomeStatement.ResearchAndDevelopment.TwelveMonths / x.MarketCap
            ) for x in tech_securities}
        tech_RD2MktCap_median = stat.median(tech_RD2MktCap.values()) 
            
        # CapEx to MktCap
        tech_CaPex2MktCap = {x.Symbol: (
            x.FinancialStatements.CashFlowStatement.CapExReported.TwelveMonths / x.MarketCap
            ) for x in tech_securities}
        tech_CaPex2MktCap_median = stat.median(tech_CaPex2MktCap.values())  
        
        # Advertising to MktCap
        tech_Ad2MktCap = {x.Symbol: (
            x.FinancialStatements.IncomeStatement.SellingGeneralAndAdministration.TwelveMonths / x.MarketCap
            ) for x in tech_securities}
        tech_Ad2MktCap_median = stat.median(tech_Ad2MktCap.values())
        
        # sort fine by book-to-market ratio, get lower quintile
        has_book = [f for f in fine if f.FinancialStatements.BalanceSheet.NetTangibleAssets.TwelveMonths and f.MarketCap]
        sorted_by_BM = sorted(has_book, key=lambda x: x.FinancialStatements.BalanceSheet.NetTangibleAssets.TwelveMonths / x.MarketCap)[:len(has_book)//4]
        # choose tech stocks from lower quintile
        tech_symbols = [f.Symbol for f in sorted_by_BM if f in tech_securities]
        
        ratioDicts_medians = [(tech_VARROA, tech_VARROA_median), (tech_ROA1Y, tech_ROA1Y_median), 
                                (tech_CFROA, tech_CFROA_median), (tech_RD2MktCap, tech_RD2MktCap_median),
                                (tech_CaPex2MktCap, tech_CaPex2MktCap_median), (tech_Ad2MktCap, tech_Ad2MktCap_median)]
        
        def compute_g_score(symbol):
            g_score = 0
            if tech_CFROA[symbol] > tech_ROA1Y[symbol]:
                g_score += 1
            for ratio_dict, median in ratioDicts_medians:
                if symbol in ratio_dict and ratio_dict[symbol] > median:
                    g_score += 1
            return g_score
        
        # compute g-scores for each symbol    
        g_scores = {symbol:compute_g_score(symbol) for symbol in tech_symbols}
        
        return [symbol for symbol, g_score in g_scores.items() if g_score >= 5]