# region imports
from AlgorithmImports import *
from datetime import datetime
import math
# endregionfs


class InverseVolatilityFuturesAlgorithm(QCAlgorithm):

    def initialize(self):
        self.set_start_date(self.end_date - timedelta(5*365))
        self.settings.seed_initial_prices = True
        # Add a universe of Futures.
        tickers = [
            Futures.Indices.VIX,
            Futures.Indices.SP_500_E_MINI,
            Futures.Indices.NASDAQ_100_E_MINI,
            Futures.Indices.DOW_30_E_MINI,
            Futures.Energies.BRENT_CRUDE,
            Futures.Energies.GASOLINE,
            Futures.Energies.HEATING_OIL,
            Futures.Energies.NATURAL_GAS,
            Futures.Grains.CORN,
            Futures.Grains.OATS,
            Futures.Grains.SOYBEANS,
            Futures.Grains.WHEAT
        ]
        self._futures = []
        for ticker in tickers:
            future = self.add_future(ticker, extended_market_hours=True)
            # Add a volaility indicator on the continuous contract.
            future.vol = IndicatorExtensions.of(
                StandardDeviation(30), 
                self.roc(future, 1, Resolution.DAILY)
            )
            self._futures.append(future)
        # Warm up the indicators.
        self.set_warmup(31, Resolution.DAILY)
        # Add a Scheduled Event to rebalance the portfolio.
        self.schedule.on(
            self.date_rules.week_start(),
            self.time_rules.at(10, 0, 0),
            self._rebalance
        )

    def _rebalance(self):
        if self.is_warming_up:
            return
        # Select the 5 Futures with the lowest volatility.
        futures = [
            future for future in self._futures
            if (future.mapped and
                future.vol.is_ready and 
                future.exchange.hours.is_open(self.time, True))
        ]
        selected = sorted(futures, key=lambda future: future.vol)[:5]
        # Sum the inverse of volatilities.
        inverse_sum = sum([1/future.vol.current.value for future in selected])
        if inverse_sum == 0:
            return 
        # Form the inverse-volaility weighted portfolio.
        targets = [
            PortfolioTarget(future.mapped, 1/future.vol.current.value/inverse_sum/2)
            for future in selected
        ]
        self.set_holdings(targets, True)