#region imports
from AlgorithmImports import *
#endregion


class PriceEarningsAnamoly(QCAlgorithm):

    def initialize(self):
        self.set_start_date(self.end_date - timedelta(5*365))        
        self.set_cash(100000)
        self.set_benchmark("SPY")
        self.settings.seed_initial_prices = True
        # Define some parameters.
        self._liquidity_filter_size = 200
        self._universe_size = 10
        # Add a universe of US Equities.
        self._date_rule = self.date_rules.year_start('SPY')
        self.universe_settings.schedule.on(self._date_rule)
        self._universe = self.add_universe(self._select_assets)
        # Add a warm-up period so the algorithm trades on deployment
        # instead of waiting for the new year.
        self.set_warm_up(timedelta(400))
        
    def _select_assets(self, fundamentals):       
        # Select the most liquid stocks.
        filtered = [f for f in fundamentals if f.price > 5 and f.has_fundamental_data]
        filtered = sorted(filtered, key=lambda x: x.dollar_volume)[-self._liquidity_filter_size:]
        # Select the subset of stocks with the lowest PE ratios.
        filtered = [f for f in filtered if f.valuation_ratios.pe_ratio > 0]
        filtered = sorted(filtered, key=lambda x: x.valuation_ratios.pe_ratio)[:self._universe_size]
        return [f.symbol for f in filtered]
    
    def on_warmup_finished(self):
        # Add a Scheduled Event to rebalance the portfolio yearly.
        self.schedule.on(self._date_rule, self.time_rules.at(8, 0), self._rebalance)
        # Rebalance today too.
        self._rebalance()
    
    def _rebalance(self):
        # Form an equal-weighted portfolio.
        targets = [PortfolioTarget(symbol, 1/self._universe_size) for symbol in self._universe.selected]
        self.set_holdings(targets, True)