using System;
using QuantConnect.Data.Market;
using QuantConnect.Indicators;
using Accord.Fuzzy;

namespace QuantConnect.Algorithm.CSharp
{
    public class FuzzyInferenceAlgorithm : QCAlgorithm
    {
        //Indicators
        private RelativeStrengthIndex rsi;
        private Momentum mom;
        private string symbol = "SPY";

        //
        // With Accord v3.3.0, we need Accord.Math referenced in other projects that use 
        // this. By placing a hard reference to an Accord.Math type, the compiler
        // will properly copy the required dlls into other project bin directories. 
        // Without this, consuming projects would need to hard reference the Accord dlls,
        // which is less than perfect. This seems to be the better of two evils
        //
        Accord.Math.Matrix3x3 _matrix = new Accord.Math.Matrix3x3();

        //Fuzzy Engine
        private FuzzyEngine engine;

        public override void Initialize()
        {
            SetStartDate(2016, 01, 01);  //Set Start Date
            SetEndDate(2016, 06, 30);    //Set End Date
            SetCash(100000);             //Set Strategy Cash
            AddEquity(symbol, Resolution.Daily);

            rsi = RSI(symbol, 14, MovingAverageType.Simple, Resolution.Daily);
            mom = MOM(symbol, 10, Resolution.Daily, Field.Close);

            engine = new FuzzyEngine();
        }

        public void OnData(TradeBars data)
        {
            if (rsi.IsReady && mom.IsReady)
            {
                try
                {
                    double signal = engine.DoInference((float)mom.Current.Value, (float)rsi.Current.Value);

                    if (!Portfolio.Invested)
                    {
                        if (signal > 30)
                        {
                            int quantity = Decimal.ToInt32(Portfolio.Cash / data[symbol].Price);
                            Buy(symbol, quantity);
                            Debug("Purchased Stock: " + quantity + " shares");
                        }
                    }
                    else
                    {
                        if (signal < -10)
                        {
                            int quantity = Portfolio[symbol].Quantity;
                            Sell(symbol, quantity);
                            Debug("Sold Stock: " + quantity + " shares");
                        }
                    }
                }
                catch (Exception ex)
                {
                    Debug("Ex: " + ex.Message);
                    Debug("## rsi: " + rsi + " mom: " + mom);
                }
            }

        }
    }

    public class FuzzyEngine
    {
        private InferenceSystem IS;

        public FuzzyEngine()
        {
            // Linguistic labels (fuzzy sets) for Momentum
            FuzzySet momDown = new FuzzySet("Down", new TrapezoidalFunction(-20, 5, 5, 5));
            FuzzySet momNeutral = new FuzzySet("Neutral", new TrapezoidalFunction(-20, 0, 0, 20));
            FuzzySet momUp = new FuzzySet("Up", new TrapezoidalFunction(5, 20, 20, 20));


            // Linguistic labels (fuzzy sets) for RSI
            FuzzySet rsiLow = new FuzzySet("Low", new TrapezoidalFunction(0, 30, 30, 30));
            FuzzySet rsiMedium = new FuzzySet("Medium", new TrapezoidalFunction(0, 50, 50, 100));
            FuzzySet rsiHigh = new FuzzySet("High", new TrapezoidalFunction(70, 100, 100, 100));

            // MOM (Input)
            LinguisticVariable lvMom = new LinguisticVariable("MOM", -20, 20);
            lvMom.AddLabel(momDown);
            lvMom.AddLabel(momNeutral);
            lvMom.AddLabel(momUp);

            // RSI (Input)
            LinguisticVariable lvRsi = new LinguisticVariable("RSI", 0, 100);
            lvRsi.AddLabel(rsiLow);
            lvRsi.AddLabel(rsiMedium);
            lvRsi.AddLabel(rsiHigh);

            // Linguistic labels (fuzzy sets) that compose the Signal
            FuzzySet fsShort = new FuzzySet("Sell", new TrapezoidalFunction(-100, 0, 0, 00));
            FuzzySet fsHold = new FuzzySet("Hold", new TrapezoidalFunction(-50, 0, 0, 50));
            FuzzySet fsLong = new FuzzySet("Buy", new TrapezoidalFunction(0, 100, 100, 100));

            // Output
            LinguisticVariable lvSignal = new LinguisticVariable("Signal", -100, 100);
            lvSignal.AddLabel(fsShort);
            lvSignal.AddLabel(fsHold);
            lvSignal.AddLabel(fsLong);

            // The database
            Database fuzzyDB = new Database();
            fuzzyDB.AddVariable(lvMom);
            fuzzyDB.AddVariable(lvRsi);
            fuzzyDB.AddVariable(lvSignal);

            // Creating the inference system
            IS = new InferenceSystem(fuzzyDB, new CentroidDefuzzifier(1000));

            // Rules
            IS.NewRule("Rule 1", "IF RSI IS Low AND MOM IS Down THEN Signal IS Buy");
            IS.NewRule("Rule 2", "IF RSI IS Medium AND MOM IS Down THEN Signal IS Buy");
            IS.NewRule("Rule 3", "IF RSI IS High AND MOM IS Down THEN Signal IS Hold");

            IS.NewRule("Rule 4", "IF RSI IS Low AND MOM IS Neutral THEN Signal IS Buy");
            IS.NewRule("Rule 5", "IF RSI IS Medium AND MOM IS Neutral THEN Signal IS Hold");
            IS.NewRule("Rule 6", "IF RSI IS High AND MOM IS Neutral THEN Signal IS Sell");

            IS.NewRule("Rule 7", "IF RSI IS Low AND MOM IS Up THEN Signal IS Hold");
            IS.NewRule("Rule 8", "IF RSI IS Medium AND MOM IS Up THEN Signal IS Sell");
            IS.NewRule("Rule 9", "IF RSI IS High AND MOM IS Up THEN Signal IS Sell");



        }

        public double DoInference(float mom, float rsi)
        {
            // Setting inputs
            IS.SetInput("MOM", mom);
            IS.SetInput("RSI", rsi);

            // Setting outputs
            double signal = IS.Evaluate("Signal");

            return signal;
        }
    }
}