chore: import upstream snapshot with attribution
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wehub-resource-sync
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/*
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* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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using System;
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using QuantConnect.Data.Market;
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using QuantConnect.Indicators;
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using Accord.Fuzzy;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Demonstration of the Accord Fuzzy Logic library in CSharp. Using Accord to do fuzzy inference for making decisions on indicators.
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/// </summary>
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/// <meta name="tag" content="strategy example" />
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/// <meta name="tag" content="indicators" />
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/// <meta name="tag" content="machine learning" />
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/// <meta name="tag" content="fuzzy logic" />
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public class FuzzyInferenceAlgorithm : QCAlgorithm
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{
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//Indicators
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private RelativeStrengthIndex _rsi;
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private Momentum _mom;
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private string _symbol = "SPY";
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//
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// With Accord v3.3.0, we need Accord.Math referenced in other projects that use
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// this. By placing a hard reference to an Accord.Math type, the compiler
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// will properly copy the required dlls into other project bin directories.
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// Without this, consuming projects would need to hard reference the Accord dlls,
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// which is less than perfect. This seems to be the better of two evils
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//
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#pragma warning disable 0414, CA1823
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Accord.Math.Matrix3x3 _matrix = new Accord.Math.Matrix3x3();
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#pragma warning restore 0414, CA1823
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//Fuzzy Engine
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private FuzzyEngine _engine;
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public override void Initialize()
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{
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SetStartDate(2015, 01, 01); //Set Start Date
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SetEndDate(2015, 06, 30); //Set End Date
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SetCash(100000); //Set Strategy Cash
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AddEquity(_symbol, Resolution.Daily);
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_rsi = RSI(_symbol, 14, MovingAverageType.Simple, Resolution.Daily);
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_mom = MOM(_symbol, 10, Resolution.Daily, Field.Close);
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_engine = new FuzzyEngine();
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}
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public void OnData(TradeBars data)
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{
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if (_rsi.IsReady && _mom.IsReady)
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{
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try
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{
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var signal = _engine.DoInference((float)_mom.Current.Value, (float)_rsi.Current.Value);
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if (!Portfolio.Invested)
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{
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if (signal > 30)
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{
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var quantity = decimal.ToInt32(Portfolio.MarginRemaining / data[_symbol].Price);
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Buy(_symbol, quantity);
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Debug("Purchased Stock: " + quantity + " shares");
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}
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}
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else
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{
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if (signal < -10)
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{
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var quantity = decimal.ToInt32(Portfolio[_symbol].Quantity);
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Sell(_symbol, quantity);
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Debug("Sold Stock: " + quantity + " shares");
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}
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}
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}
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catch (RegressionTestException ex)
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{
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Debug("Ex: " + ex.Message);
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Debug("## rsi: " + _rsi + " mom: " + _mom);
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}
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}
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}
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}
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public class FuzzyEngine
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{
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private InferenceSystem IS;
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public FuzzyEngine()
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{
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// Linguistic labels (fuzzy sets) for Momentum
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var momDown = new FuzzySet("Down", new TrapezoidalFunction(-20, 5, 5, 5));
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var momNeutral = new FuzzySet("Neutral", new TrapezoidalFunction(-20, 0, 0, 20));
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var momUp = new FuzzySet("Up", new TrapezoidalFunction(5, 20, 20, 20));
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// Linguistic labels (fuzzy sets) for RSI
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var rsiLow = new FuzzySet("Low", new TrapezoidalFunction(0, 30, 30, 30));
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var rsiMedium = new FuzzySet("Medium", new TrapezoidalFunction(0, 50, 50, 100));
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var rsiHigh = new FuzzySet("High", new TrapezoidalFunction(70, 100, 100, 100));
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// MOM (Input)
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var lvMom = new LinguisticVariable("MOM", -20, 20);
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lvMom.AddLabel(momDown);
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lvMom.AddLabel(momNeutral);
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lvMom.AddLabel(momUp);
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// RSI (Input)
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var lvRsi = new LinguisticVariable("RSI", 0, 100);
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lvRsi.AddLabel(rsiLow);
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lvRsi.AddLabel(rsiMedium);
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lvRsi.AddLabel(rsiHigh);
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// Linguistic labels (fuzzy sets) that compose the Signal
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var fsShort = new FuzzySet("Sell", new TrapezoidalFunction(-100, 0, 0, 00));
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var fsHold = new FuzzySet("Hold", new TrapezoidalFunction(-50, 0, 0, 50));
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var fsLong = new FuzzySet("Buy", new TrapezoidalFunction(0, 100, 100, 100));
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// Output
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var lvSignal = new LinguisticVariable("Signal", -100, 100);
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lvSignal.AddLabel(fsShort);
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lvSignal.AddLabel(fsHold);
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lvSignal.AddLabel(fsLong);
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// The database
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var fuzzyDB = new Database();
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fuzzyDB.AddVariable(lvMom);
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fuzzyDB.AddVariable(lvRsi);
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fuzzyDB.AddVariable(lvSignal);
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// Creating the inference system
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IS = new InferenceSystem(fuzzyDB, new CentroidDefuzzifier(1000));
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// Rules
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IS.NewRule("Rule 1", "IF RSI IS Low AND MOM IS Down THEN Signal IS Buy");
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IS.NewRule("Rule 2", "IF RSI IS Medium AND MOM IS Down THEN Signal IS Buy");
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IS.NewRule("Rule 3", "IF RSI IS High AND MOM IS Down THEN Signal IS Hold");
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IS.NewRule("Rule 4", "IF RSI IS Low AND MOM IS Neutral THEN Signal IS Buy");
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IS.NewRule("Rule 5", "IF RSI IS Medium AND MOM IS Neutral THEN Signal IS Hold");
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IS.NewRule("Rule 6", "IF RSI IS High AND MOM IS Neutral THEN Signal IS Sell");
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IS.NewRule("Rule 7", "IF RSI IS Low AND MOM IS Up THEN Signal IS Hold");
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IS.NewRule("Rule 8", "IF RSI IS Medium AND MOM IS Up THEN Signal IS Sell");
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IS.NewRule("Rule 9", "IF RSI IS High AND MOM IS Up THEN Signal IS Sell");
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}
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public double DoInference(float mom, float rsi)
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{
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// Setting inputs
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IS.SetInput("MOM", mom);
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IS.SetInput("RSI", rsi);
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// Setting outputs
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double signal = IS.Evaluate("Signal");
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return signal;
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}
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}
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}
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