def GetROAScore(fine):
    '''Get the Profitability - Return of Asset sub-score of Piotroski F-Score
    Arg:
        fine: Fine fundamental object of a stock
    Return:
        Profitability - Return of Asset sub-score'''
    # Nearest ROA as current year data
    roa = fine.OperationRatios.ROA.ThreeMonths
    # 1 score if ROA datum exists and positive, else 0
    score = 1 if roa and roa > 0 else 0
    return score

def GetOperatingCashFlowScore(fine):
    '''Get the Profitability - Operating Cash Flow sub-score of Piotroski F-Score
    Arg:
        fine: Fine fundamental object of a stock
    Return:
        Profitability - Operating Cash Flow sub-score'''
    # Nearest Operating Cash Flow as current year data
    operating_cashflow = fine.FinancialStatements.CashFlowStatement.CashFlowFromContinuingOperatingActivities.ThreeMonths
    # 1 score if operating cash flow datum exists and positive, else 0
    score = 1 if operating_cashflow and operating_cashflow > 0 else 0
    return score

def GetROAChangeScore(fine):
    '''Get the Profitability - Change in Return of Assets sub-score of Piotroski F-Score
    Arg:
        fine: Fine fundamental object of a stock
    Return:
        Profitability - Change in Return of Assets sub-score'''
    # if current or previous year's ROA data does not exist, return 0 score
    roa = fine.OperationRatios.ROA
    if not roa.ThreeMonths or not roa.OneYear:
        return 0

    # 1 score if change in ROA positive, else 0 score
    score = 1 if roa.ThreeMonths > roa.OneYear else 0
    return score

def GetAccrualsScore(fine):
    '''Get the Profitability - Accruals sub-score of Piotroski F-Score
    Arg:
        fine: Fine fundamental object of a stock
    Return:
        Profitability - Accruals sub-score'''
    # Nearest Operating Cash Flow, Total Assets, ROA as current year data
    operating_cashflow = fine.FinancialStatements.CashFlowStatement.CashFlowFromContinuingOperatingActivities.ThreeMonths
    total_assets = fine.FinancialStatements.BalanceSheet.TotalAssets.ThreeMonths
    roa = fine.OperationRatios.ROA.ThreeMonths
    # 1 score if operating cash flow, total assets and ROA exists, and operating cash flow / total assets > ROA, else 0
    score = 1 if operating_cashflow and total_assets and roa and operating_cashflow / total_assets > roa else 0
    return score

def GetLeverageScore(fine):
    '''Get the Leverage, Liquidity and Source of Funds - Change in Leverage sub-score of Piotroski F-Score
    Arg:
        fine: Fine fundamental object of a stock
    Return:
        Leverage, Liquidity and Source of Funds - Change in Leverage sub-score'''
    # if current or previous year's long term debt to equity ratio data does not exist, return 0 score
    long_term_debt_ratio = fine.OperationRatios.LongTermDebtEquityRatio
    if not long_term_debt_ratio.ThreeMonths or not long_term_debt_ratio.OneYear:
        return 0

    # 1 score if long term debt ratio is lower in the current year, else 0 score
    score = 1 if long_term_debt_ratio.ThreeMonths < long_term_debt_ratio.OneYear else 0
    return score

def GetLiquidityScore(fine):
    '''Get the Leverage, Liquidity and Source of Funds - Change in Liquidity sub-score of Piotroski F-Score
    Arg:
        fine: Fine fundamental object of a stock
    Return:
        Leverage, Liquidity and Source of Funds - Change in Liquidity sub-score'''
    # if current or previous year's current ratio data does not exist, return 0 score
    current_ratio = fine.OperationRatios.CurrentRatio
    if not current_ratio.ThreeMonths or not current_ratio.OneYear:
        return 0

    # 1 score if current ratio is higher in the current year, else 0 score
    score = 1 if current_ratio.ThreeMonths > current_ratio.OneYear else 0
    return score

def GetShareIssuedScore(fine):
    '''Get the Leverage, Liquidity and Source of Funds - Change in Number of Shares sub-score of Piotroski F-Score
    Arg:
        fine: Fine fundamental object of a stock
    Return:
        Leverage, Liquidity and Source of Funds - Change in Number of Shares sub-score'''
    # if current or previous year's issued shares data does not exist, return 0 score
    shares_issued = fine.FinancialStatements.BalanceSheet.ShareIssued
    if not shares_issued.ThreeMonths or not shares_issued.TwelveMonths:
        return 0

    # 1 score if shares issued did not increase in the current year, else 0 score
    score = 1 if shares_issued.ThreeMonths <= shares_issued.TwelveMonths else 0
    return score

def GetGrossMarginScore(fine):
    '''Get the Leverage, Liquidity and Source of Funds - Change in Gross Margin sub-score of Piotroski F-Score
    Arg:
        fine: Fine fundamental object of a stock
    Return:
        Leverage, Liquidity and Source of Funds - Change in Gross Margin sub-score'''
    # if current or previous year's gross margin data does not exist, return 0 score
    gross_margin = fine.OperationRatios.GrossMargin
    if not gross_margin.ThreeMonths or not gross_margin.OneYear:
        return 0

    # 1 score if gross margin is higher in the current year, else 0 score
    score = 1 if gross_margin.ThreeMonths > gross_margin.OneYear else 0
    return score

def GetAssetTurnoverScore(fine):
    '''Get the Leverage, Liquidity and Source of Funds - Change in Asset Turnover Ratio sub-score of Piotroski F-Score
    Arg:
        fine: Fine fundamental object of a stock
    Return:
        Leverage, Liquidity and Source of Funds - Change in Asset Turnover Ratio sub-score'''
    # if current or previous year's asset turnover data does not exist, return 0 score
    asset_turnover = fine.OperationRatios.AssetsTurnover
    if not asset_turnover.ThreeMonths or not asset_turnover.OneYear:
        return 0

    # 1 score if asset turnover is higher in the current year, else 0 score
    score = 1 if asset_turnover.ThreeMonths > asset_turnover.OneYear else 0
    return score

# region imports
from AlgorithmImports import *
from security_initializer import CustomSecurityInitializer
from universe import FScoreUniverseSelectionModel
# endregion

class PensiveFluorescentYellowParrot(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2020, 7, 1)  # Set Start Date
        self.SetEndDate(2023, 7, 1)  # Set Start Date
        self.SetCash(10000000)  # Set Strategy Cash

        ### Parameters ###
        # The Piotroski F-Score threshold we would like to invest into stocks with F-Score >= of that
        fscore_threshold = self.GetParameter("fscore_threshold", 7)

        ### Reality Modeling ###
        # Interactive Broker Brokerage fees and margin
        self.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage, AccountType.Margin)
        # Custom security initializer
        self.SetSecurityInitializer(CustomSecurityInitializer(self.BrokerageModel, FuncSecuritySeeder(self.GetLastKnownPrices)))

        ### Universe Settings ###
        self.UniverseSettings.Resolution = Resolution.Minute

        # Our universe is selected by Piotroski's F-Score
        self.AddUniverseSelection(FScoreUniverseSelectionModel(self, fscore_threshold))
        # Assume we want to just buy and hold the selected stocks, rebalance daily
        self.AddAlpha(ConstantAlphaModel(InsightType.Price, InsightDirection.Up, timedelta(1)))
        # Avoid overconcentration of risk in related stocks in the same sector, we invest the same size in every sector
        self.SetPortfolioConstruction(SectorWeightingPortfolioConstructionModel())
        # Avoid placing orders with big bid-ask spread to reduce friction cost
        self.SetExecution(SpreadExecutionModel(0.01))       # maximum 1% spread allowed
        # Assume we do not have any risk management measures
        self.AddRiskManagement(NullRiskManagementModel())

    def OnSecuritiesChanged(self, changes):
        # Log the universe changes to test the universe selection model
        # In this case, the added security should be the same as the logged stocks with F-score >= 7
        self.Log(changes)

# region imports
from AlgorithmImports import *
# endregion

class CustomSecurityInitializer(BrokerageModelSecurityInitializer):

    def __init__(self, brokerage_model: IBrokerageModel, security_seeder: ISecuritySeeder) -> None:
        super().__init__(brokerage_model, security_seeder)

    def Initialize(self, security: Security) -> None:
        # First, call the superclass definition
        # This method sets the reality models of each security using the default reality models of the brokerage model
        super().Initialize(security)
        
        # We want a slippage model with price impact by order size for reality modeling
        security.SetSlippageModel(VolumeShareSlippageModel())

# region imports
from AlgorithmImports import *
from f_score import *
# endregion

class FScoreUniverseSelectionModel(FineFundamentalUniverseSelectionModel):

    def __init__(self, algorithm, fscore_threshold):
        super().__init__(self.SelectCoarse, self.SelectFine)
        self.algorithm = algorithm
        self.fscore_threshold = fscore_threshold

    def SelectCoarse(self, coarse):
        '''Defines the coarse fundamental selection function.
        Args:
            algorithm: The algorithm instance
            coarse: The coarse fundamental data used to perform filtering
        Returns:
            An enumerable of symbols passing the filter'''
        # We only want stocks with fundamental data and price > $1
        filtered = [x.Symbol for x in coarse if x.HasFundamentalData and x.Price > 1]
        return filtered

    def SelectFine(self, fine):
        '''Defines the fine fundamental selection function.
        Args:
            algorithm: The algorithm instance
            fine: The fine fundamental data used to perform filtering
        Returns:
            An enumerable of symbols passing the filter'''
        # We use a dictionary to hold the F-Score of each stock
        f_scores = {}

        for f in fine:
            # Calculate the Piotroski F-Score of the given stock
            f_scores[f.Symbol] = self.GetPiotroskiFScore(f)
            if f_scores[f.Symbol] >= self.fscore_threshold:
                self.algorithm.Log(f"Stock: {f.Symbol.ID} :: F-Score: {f_scores[f.Symbol]}")

        # Select the stocks with F-Score higher than the threshold
        selected = [symbol for symbol, fscore in f_scores.items() if fscore >= self.fscore_threshold]

        return selected

    def GetPiotroskiFScore(self, fine):
        '''A helper function to calculate the Piotroski F-Score of a stock
        Arg:
            fine: MorningStar fine fundamental data of the stock
        return:
            the Piotroski F-Score of the stock
        '''
        # initial F-Score as 0
        fscore = 0
        # Add up the sub-scores in different aspects
        fscore += GetROAScore(fine)
        fscore += GetOperatingCashFlowScore(fine)
        fscore += GetROAChangeScore(fine)
        fscore += GetAccrualsScore(fine)
        fscore += GetLeverageScore(fine)
        fscore += GetLiquidityScore(fine)
        fscore += GetShareIssuedScore(fine)
        fscore += GetGrossMarginScore(fine)
        fscore += GetAssetTurnoverScore(fine)
        return fscore