wehub-resource-sync
87 lines
3.1 KiB
C#
87 lines
3.1 KiB
C#
/*
|
|
* 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 Accord.MachineLearning.VectorMachines.Learning;
|
|
using QuantConnect.Indicators;
|
|
using System;
|
|
|
|
namespace QuantConnect.Algorithm.CSharp
|
|
{
|
|
/// <summary>
|
|
/// Machine Learning example using Accord VectorMachines Learning
|
|
/// In this example, the algorithm forecasts the direction based on the last 5 days of rate of return
|
|
/// </summary>
|
|
public class AccordVectorMachinesAlgorithm : QCAlgorithm
|
|
{
|
|
// Define the size of the data used to train the model
|
|
// It will use _lookback sets with _inputSize members
|
|
// Those members are rate of return
|
|
private const int _lookback = 30;
|
|
private const int _inputSize = 5;
|
|
private RollingWindow<double> _window = new RollingWindow<double>(_inputSize * _lookback + 2);
|
|
|
|
public override void Initialize()
|
|
{
|
|
SetStartDate(2013, 10, 07);
|
|
SetEndDate(2013, 10, 11);
|
|
SetCash(100000);
|
|
|
|
var symbol = AddEquity("SPY").Symbol;
|
|
|
|
ROC(symbol, 1, Resolution.Daily).Updated += (s, e) => _window.Add((double)e.Value);
|
|
|
|
Schedule.On(DateRules.Every(DayOfWeek.Monday),
|
|
TimeRules.AfterMarketOpen(symbol, 10),
|
|
TrainAndTrade);
|
|
|
|
SetWarmUp(_window.Size, Resolution.Daily);
|
|
}
|
|
|
|
private void TrainAndTrade()
|
|
{
|
|
if (!_window.IsReady) return;
|
|
|
|
// Convert the rolling window of rate of change into the Learn method
|
|
var returns = new double[_inputSize];
|
|
var targets = new double[_lookback];
|
|
var inputs = new double[_lookback][];
|
|
|
|
// Use the sign of the returns to predict the direction
|
|
for (var i = 0; i < _lookback; i++)
|
|
{
|
|
for (var j = 0; j < _inputSize; j++)
|
|
{
|
|
returns[j] = Math.Sign(_window[i + j + 1]);
|
|
}
|
|
|
|
targets[i] = Math.Sign(_window[i]);
|
|
inputs[i] = returns;
|
|
}
|
|
|
|
// Train SupportVectorMachine using SetHoldings("SPY", percentage);
|
|
var teacher = new LinearCoordinateDescent();
|
|
teacher.Learn(inputs, targets);
|
|
|
|
var svm = teacher.Model;
|
|
|
|
// Compute the value for the last rate of change
|
|
var last = (double) Math.Sign(_window[0]);
|
|
var value = svm.Compute(new[] {last});
|
|
if (value.IsNaNOrZero()) return;
|
|
|
|
SetHoldings("SPY", Math.Sign(value));
|
|
}
|
|
}
|
|
} |