# region imports
from AlgorithmImports import *
# endregion
def get_r_o_a_score(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.operation_ratios.ROA.three_months
    # 1 score if ROA datum exists and positive, else 0
    score = 1 if roa and roa > 0 else 0
    return score

def get_operating_cash_flow_score(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.financial_statements.cash_flow_statement.cash_flow_from_continuing_operating_activities.three_months
    # 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 get_r_o_a_change_score(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.operation_ratios.ROA
    if not roa.three_months or not roa.one_year:
        return 0

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

def get_accruals_score(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.financial_statements.cash_flow_statement.cash_flow_from_continuing_operating_activities.three_months
    total_assets = fine.financial_statements.balance_sheet.total_assets.three_months
    roa = fine.operation_ratios.ROA.three_months
    # 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 get_leverage_score(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.operation_ratios.long_term_debt_equity_ratio
    if not long_term_debt_ratio.three_months or not long_term_debt_ratio.one_year:
        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.three_months < long_term_debt_ratio.one_year else 0
    return score

def get_liquidity_score(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.operation_ratios.current_ratio
    if not current_ratio.three_months or not current_ratio.one_year:
        return 0

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

def get_share_issued_score(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.financial_statements.balance_sheet.share_issued
    if not shares_issued.three_months or not shares_issued.twelve_months:
        return 0

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

def get_gross_margin_score(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.operation_ratios.gross_margin
    if not gross_margin.three_months or not gross_margin.one_year:
        return 0

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

def get_asset_turnover_score(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.operation_ratios.assets_turnover
    if not asset_turnover.three_months or not asset_turnover.one_year:
        return 0

    # 1 score if asset turnover is higher in the current year, else 0 score
    score = 1 if asset_turnover.three_months > asset_turnover.one_year 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.set_start_date(2020, 7, 1)  # Set Start Date
        self.set_end_date(2023, 7, 1)  # Set Start Date
        self.set_cash(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.get_parameter("fscore_threshold", 7)

        ### Reality Modeling ###
        # Interactive Broker Brokerage fees and margin
        self.set_brokerage_model(BrokerageName.INTERACTIVE_BROKERS_BROKERAGE, AccountType.MARGIN)
        # Custom security initializer
        self.set_security_initializer(CustomSecurityInitializer(self.brokerage_model, FuncSecuritySeeder(self.get_last_known_prices)))

        ### Universe Settings ###
        self.universe_settings.resolution = Resolution.MINUTE

        # Our universe is selected by Piotroski's F-Score
        self.add_universe_selection(FScoreUniverseSelectionModel(self, fscore_threshold))
        # Assume we want to just buy and hold the selected stocks, rebalance daily
        self.add_alpha(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.set_portfolio_construction(SectorWeightingPortfolioConstructionModel())
        # Avoid placing orders with big bid-ask spread to reduce friction cost
        self.set_execution(SpreadExecutionModel(0.01))       # maximum 1% spread allowed
        # Assume we do not have any risk management measures
        self.add_risk_management(NullRiskManagementModel())

    def on_securities_changed(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.set_slippage_model(VolumeShareSlippageModel())

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

class FScoreUniverseSelectionModel(FineFundamentalUniverseSelectionModel):

    def __init__(self, algorithm, fscore_threshold):
        super().__init__(self.select_coarse, self.select_fine)
        self.algorithm = algorithm
        self.fscore_threshold = fscore_threshold

    def select_coarse(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.has_fundamental_data and x.price > 1]
        return filtered

    def select_fine(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.get_piotroski_f_score(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 get_piotroski_f_score(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 += get_r_o_a_score(fine)
        fscore += get_operating_cash_flow_score(fine)
        fscore += get_r_o_a_change_score(fine)
        fscore += get_accruals_score(fine)
        fscore += get_leverage_score(fine)
        fscore += get_liquidity_score(fine)
        fscore += get_share_issued_score(fine)
        fscore += get_gross_margin_score(fine)
        fscore += get_asset_turnover_score(fine)
        return fscore