//Copyright HardingSoftware.com, 2021. Released to the public domain.
//Use entirely at your own risk.
//This algorithm contains open source code from other sources,
//no claim is being made to such code.
//Do not remove this copyright notice.
using System;
using System.Collections.Generic;
using System.Linq;
using QuantConnect.Data.Market;
using QuantConnect.Data.UniverseSelection;

namespace QuantConnect.Algorithm.CSharp
{
    public class TrendSingle : QCAlgorithm
    {
        string ticker = "AMZN";
        Symbol symbol;
		Resolution resolution=Resolution.Hour;
		int period = 10;
		decimal exponent = 1.5m;
		List<decimal> prices = new List<decimal>();
        decimal trend = 0;

        public override void Initialize()
        {
            UniverseSettings.Resolution = resolution;

            SetStartDate(2016, 6, 10);
            //SetEndDate(2017, 8, 30);
            SetCash(100000);

			symbol = AddEquity(ticker, resolution).Symbol;
        }

        public void OnData(TradeBars data)
        {
        	if (data.ContainsKey(symbol))
        	{
            	TradeBar bar=data[symbol];
            	prices.Add(bar.Close);
            	if (prices.Count > period)
            	{
            		prices.RemoveAt(0);
            	}
            	if (prices.Count == period)
            	{
					trend = Trend(prices.ToArray(), exponent);
            	}
        	}
            
    		if (trend > 0 && Portfolio[symbol].Quantity <= 0)
    		{
				SetHoldings(symbol, 0.99m);
    		}
    		else if (trend < 0 && Portfolio[symbol].Quantity >= 0)
    		{
				SetHoldings(symbol, -0.99m);
    		}

        }
        
        public static decimal Trend(decimal[] values, decimal exponent)
		{
			decimal[] changes = ChangesPercent(values);
			return ExponentialMovingAverage(changes, exponent);
		}

        public static decimal[] ChangesPercent(decimal[] values)
        {
            List<decimal> lOut = new List<decimal>();
            for (int i = 0; i < values.Length - 1; i++)
            {
                lOut.Add((values[i + 1] - values[i]) / values[i]);
            }
            return lOut.ToArray();
        }
        
        public static decimal[] ExponentialWeights(int length, decimal exponent)
        {
            List<decimal> weights = new List<decimal>();
            double e = Convert.ToDouble(exponent);
            for (int i=0;i<length;i++)
            {
                double w = (double)(i + 1);
                weights.Add(Convert.ToDecimal(Math.Pow(w, e)));
            }
            return weights.ToArray();
        }

        public static decimal ExponentialMovingAverage(decimal[] values, decimal exponent)
        {
            return WeightedAverage(values, ExponentialWeights(values.Length, exponent));
        }
        
        public static decimal WeightedAverage(decimal[] values, decimal[] weights)
        {
            return values.Zip(weights, (x, y) => x * y).Sum() / weights.Sum();
        }
    }
}