from AlgorithmImports import *
from QuantConnect.DataSource import *

class ExtractAlphaEstimizeAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2019, 1, 1)
        self.set_end_date(2020, 12, 31)
        self.set_cash(100000)
        
        # A variable to control the next rebalance time
        self.last_time = datetime.min
        
        self.add_universe(self.my_coarse_filter_function)
        self.universe_settings.resolution = Resolution.MINUTE
        
    def my_coarse_filter_function(self, coarse: List[CoarseFundamental]) -> List[Symbol]:
        # Select the non-penny stocks with the highest dollar volume, since they have more stable price (lower risk) and more informed insights from high market activities
        sorted_by_dollar_volume = sorted([x for x in coarse if x.has_fundamental_data and x.price > 4], 
                                key=lambda x: x.dollar_volume, reverse=True)
        selected = [x.symbol for x in sorted_by_dollar_volume[:500]]
        return selected

    def on_data(self, slice: Slice) -> None:
        if self.last_time > self.time: return
    
        # Accessing Estimize data to collect the crowd-sourced insighrt as trading signals
        consensus = slice.Get(EstimizeConsensus)
        estimate = slice.Get(EstimizeEstimate)
        release = slice.Get(EstimizeRelease)
        
        if not estimate: return
        
        # Long the ones with highest earning estimates and short the ones with lowest earning estimates, assuming the fundamental factor significance
        sorted_by_eps_estimate = sorted([x for x in estimate.items() if x[1].eps], key=lambda x: x[1].eps)
        long_symbols = [x[0].underlying for x in sorted_by_eps_estimate[-10:]]
        short_symbols = [x[0].underlying for x in sorted_by_eps_estimate[:10]]
        
        # Liquidate the ones that fall out of the earning extremes
        for symbol in [x.symbol for x in self.portfolio.Values if x.invested]:
            if symbol not in long_symbols + short_symbols:
                self.liquidate(symbol)
        
        # Invest equally and dollar-neutral to evenly dissipate capital risk and hedge systematic risk
        long_targets = [PortfolioTarget(symbol, 0.05) for symbol in long_symbols]
        short_targets = [PortfolioTarget(symbol, -0.05) for symbol in short_symbols]
        self.set_holdings(long_targets + short_targets)

        # Update the rebalance time to next month start
        self.last_time = Expiry.END_OF_MONTH(self.time)
        
    def on_securities_changed(self, changes: SecurityChanges) -> None:
        for security in changes.added_securities:
            # Requesting data for trading signal generation
            estimize_consensus_symbol = self.add_data(EstimizeConsensus, security.symbol).symbol
            estimize_estimate_symbol = self.add_data(EstimizeEstimate, security.symbol).symbol
            estimize_release_symbol = self.add_data(EstimizeRelease, security.symbol).symbol

            # Historical Data
            history = self.history([estimize_consensus_symbol,
                                    estimize_estimate_symbol,
                                    estimize_release_symbol
                                    ], 10, Resolution.DAILY)
            self.debug(f"We got {len(history)} items from our history request")