# region imports
from AlgorithmImports import *

import itertools
# endregion

class WalkForwardOptimizationGridSearchAlgorithm(QCAlgorithm):

    def initialize(self):
        self.set_start_date(2000, 1, 1)
        self.set_end_date(2024, 1, 1)
        self.set_cash(100_000)
        self.settings.automatic_indicator_warm_up = True

        self._security = self.add_equity("SPY", Resolution.DAILY)
        self._symbol = self._security.symbol
        self._short_ema = None
        self._long_ema = None
        
        # Set the optimization search space.
        self._parameter_sets = self._generate_parameter_sets(
            {
                'short_ema': (10, 50, 10),  # min, max, step
                'long_ema': (60, 200, 10)
            }
        )

        # Define the optimization objective function.
        objective = self._cumulative_return

        # Schedule periodic optimization sessions.
        self.train(
            self.date_rules.month_start(self._symbol),
            self.time_rules.midnight,
            lambda: self._do_wfo(self._optimization_func, max, objective)
        )

        # Set a warm-up period so we hit one of the optimization sessions
        # before we start trading.
        self.set_warm_up(timedelta(45))

    def _generate_parameter_sets(self, search_space):
        # Create ranges for each parameter.
        ranges = {
            parameter_name: np.arange(min_, max_ + step_size, step_size) 
            for parameter_name, (min_, max_, step_size) in search_space.items()
        }
        
        # Create list of dictionaries for parameter sets.
        return [
            dict(zip(ranges.keys(), combination)) 
            for combination in list(itertools.product(*ranges.values()))
        ]

    def _do_wfo(self, optimization_func, min_max, objective):
        # Get the historical data we need to calculate the scores.
        prices = self.history(
            self._symbol, timedelta(365), Resolution.DAILY
        ).loc[self._symbol]

        # Calculate the score of each parameter set.
        scores = [
            optimization_func(prices, parameter_set, objective)
            for parameter_set in self._parameter_sets
        ]
        
        # Find the parameter set that maximizes the objective function.
        optimal_parameters = self._parameter_sets[scores.index(min_max(scores))]

        # Record the grid search results.
        for i, score in enumerate(scores):
            self.log(
                f"{self.time}; Parameters: {self._parameter_sets[i]}; Score: {score}"
            )
        for name, value in optimal_parameters.items():
            self.plot('Parameters', name, value)

        # Adjust the algorithm's logic.
        self._update_algorithm_logic(optimal_parameters)

    def _optimization_func(self, data, parameter_set, objective):
        p1 = parameter_set['short_ema']
        p2 = parameter_set['long_ema']
        short_ema = data['close'].ewm(p1, min_periods=p1).mean()
        long_ema = data['close'].ewm(p2, min_periods=p2).mean()
        exposure = (short_ema - long_ema).dropna().apply(np.sign)\
            .replace(0, pd.NA).ffill().shift(1)
        # ^ shift(1) because we enter the position on the next day.
        asset_daily_returns = data['open'].pct_change().shift(-1) 
        # ^ shift(-1) because we want each entry to be the return from 
        # the current day to the next day.
        strategy_daily_returns = (exposure * asset_daily_returns).dropna()
        return objective(strategy_daily_returns)

    def _cumulative_return(self, daily_returns):
        return (daily_returns + 1).cumprod()[-1] - 1

    def _update_algorithm_logic(self, optimal_parameters):
        # Remove the old indicators.
        if self._short_ema:
            self.deregister_indicator(self._short_ema)
        if self._long_ema:
            self.deregister_indicator(self._long_ema)
        # Create the new indicators.
        self._short_ema = self.ema(
            self._symbol, optimal_parameters['short_ema'], Resolution.DAILY
        )
        self._long_ema = self.ema(
            self._symbol, optimal_parameters['long_ema'], Resolution.DAILY
        )

    def on_data(self, data):
        if self.is_warming_up:
            return

        # Case 1: Short EMA is above long EMA
        if (self._short_ema > self._long_ema and 
            not self._security.holdings.is_long):
            self.set_holdings(self._symbol, 1)
        # Case 2: Short EMA is below long EMA
        elif (self._short_ema < self._long_ema and 
              not self._security.holdings.is_short):
            self.set_holdings(self._symbol, 0)