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using System;

using QuantConnect.Data.Market;

using QuantConnect.Indicators;

using AForge.Fuzzy;

namespace QuantConnect.Algorithm.CSharp

{

public class FuzzyInferenceAlgorithm : QCAlgorithm

{

//Indicators

private RelativeStrengthIndex rsi;

private Momentum mom;

private string symbol = "SPY";

//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;

}

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