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FuzzyInferenceAlgorithm.cs
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/*
* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
using System;
using QuantConnect.Data.Market;
using QuantConnect.Indicators;
using Accord.Fuzzy;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Demonstration of the Accord Fuzzy Logic library in CSharp. Using Accord to do fuzzy inference for making decisions on indicators.
/// </summary>
/// <meta name="tag" content="strategy example" />
/// <meta name="tag" content="indicators" />
/// <meta name="tag" content="machine learning" />
/// <meta name="tag" content="fuzzy logic" />
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(2015, 01, 01); //Set Start Date
SetEndDate(2015, 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
{
var signal = _engine.DoInference((float)_mom.Current.Value, (float)_rsi.Current.Value);
if (!Portfolio.Invested)
{
if (signal > 30)
{
var quantity = decimal.ToInt32(Portfolio.MarginRemaining / data[_symbol].Price);
Buy(_symbol, quantity);
Debug("Purchased Stock: " + quantity + " shares");
}
}
else
{
if (signal < -10)
{
var quantity = decimal.ToInt32(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
var momDown = new FuzzySet("Down", new TrapezoidalFunction(-20, 5, 5, 5));
var momNeutral = new FuzzySet("Neutral", new TrapezoidalFunction(-20, 0, 0, 20));
var momUp = new FuzzySet("Up", new TrapezoidalFunction(5, 20, 20, 20));
// Linguistic labels (fuzzy sets) for RSI
var rsiLow = new FuzzySet("Low", new TrapezoidalFunction(0, 30, 30, 30));
var rsiMedium = new FuzzySet("Medium", new TrapezoidalFunction(0, 50, 50, 100));
var rsiHigh = new FuzzySet("High", new TrapezoidalFunction(70, 100, 100, 100));
// MOM (Input)
var lvMom = new LinguisticVariable("MOM", -20, 20);
lvMom.AddLabel(momDown);
lvMom.AddLabel(momNeutral);
lvMom.AddLabel(momUp);
// RSI (Input)
var lvRsi = new LinguisticVariable("RSI", 0, 100);
lvRsi.AddLabel(rsiLow);
lvRsi.AddLabel(rsiMedium);
lvRsi.AddLabel(rsiHigh);
// Linguistic labels (fuzzy sets) that compose the Signal
var fsShort = new FuzzySet("Sell", new TrapezoidalFunction(-100, 0, 0, 00));
var fsHold = new FuzzySet("Hold", new TrapezoidalFunction(-50, 0, 0, 50));
var fsLong = new FuzzySet("Buy", new TrapezoidalFunction(0, 100, 100, 100));
// Output
var lvSignal = new LinguisticVariable("Signal", -100, 100);
lvSignal.AddLabel(fsShort);
lvSignal.AddLabel(fsHold);
lvSignal.AddLabel(fsLong);
// The database
var 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;
}
}
}