# region imports
from AlgorithmImports import *

# Import necessary librarie


# Import necessary libraries
# from quantconnect.algorithm import QCAlgorithm
# from quantconnect.indicators import SimpleMovingAverage
# import matplotlib.pyplot as plt

class SimpleMovingAverageStrategy(QCAlgorithm):

    def Initialize(self):
        # Set the cash we'd like to use for our backtest
        self.SetStartDate(2017,1,1)
        self.SetEndDate(2022,1,1)
        self.SetCash(100000)

        # Set the symbol we'd like to use for our backtest
        self.symbol = self.AddEquity("SPY").Symbol

        # Set the lookback period for our moving average
        self.lookback_period = 20

        # Create a Simple Moving Average indicator
        self.sma = SimpleMovingAverage(self.lookback_period)

        # Schedule an event to fire every trading day at market close
        self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.At(17, 0), self.Trade)

        # Set up a plot for the strategy and benchmark
        # self.strategy_plot = plt.subplot(2, 1, 1)
        # self.benchmark_plot = plt.subplot(2, 1, 2)
        # self.SetPlotOrder(self.benchmark, self.strategy)

    def Trade(self):
        # Get the current price of the asset
        current_price = self.Securities[self.symbol].Price

        # Get the value of the moving average indicator
        sma_value = self.sma.Current.Value

        # If the current price is greater than the moving average, buy the asset
        if current_price > sma_value:
            self.SetHoldings(self.symbol, 1)
        # If the current price is less than the moving average, sell the asset
        elif current_price < sma_value:
            self.SetHoldings(self.symbol, -1)
        # If the current price is equal to the moving average, do nothing
        else:
            pass