/*
* 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 System.Collections.Generic;
using System.Linq;
using Python.Runtime;
using QuantConnect.Algorithm.Framework.Alphas;
using QuantConnect.Scheduling;
namespace QuantConnect.Algorithm.Framework.Portfolio
{
///
/// Provides an implementation of that generates percent targets based on the
/// . The target percent holdings of each Symbol is given by the
/// from the last active for that symbol.
/// For insights of direction , long targets are returned and for insights of direction
/// , short targets are returned.
/// If the sum of all the last active per symbol is bigger than 1, it will factor down each target
/// percent holdings proportionally so the sum is 1.
/// It will ignore that have no value.
///
public class InsightWeightingPortfolioConstructionModel : EqualWeightingPortfolioConstructionModel
{
///
/// Initialize a new instance of
///
/// The date rules used to define the next expected rebalance time
/// in UTC
/// Specifies the bias of the portfolio (Short, Long/Short, Long)
public InsightWeightingPortfolioConstructionModel(IDateRule rebalancingDateRules,
PortfolioBias portfolioBias = PortfolioBias.LongShort)
: base(rebalancingDateRules, portfolioBias)
{
}
///
/// Initialize a new instance of
///
/// Rebalancing func or if a date rule, timedelta will be converted into func.
/// For a given algorithm UTC DateTime the func returns the next expected rebalance time
/// or null if unknown, in which case the function will be called again in the next loop. Returning current time
/// will trigger rebalance. If null will be ignored
/// Specifies the bias of the portfolio (Short, Long/Short, Long)
/// This is required since python net can not convert python methods into func nor resolve the correct
/// constructor for the date rules parameter.
/// For performance we prefer python algorithms using the C# implementation
public InsightWeightingPortfolioConstructionModel(PyObject rebalance,
PortfolioBias portfolioBias = PortfolioBias.LongShort)
: base(rebalance, portfolioBias)
{
}
///
/// Initialize a new instance of
///
/// For a given algorithm UTC DateTime returns the next expected rebalance time
/// or null if unknown, in which case the function will be called again in the next loop. Returning current time
/// will trigger rebalance.
/// Specifies the bias of the portfolio (Short, Long/Short, Long)
public InsightWeightingPortfolioConstructionModel(Func rebalancingFunc,
PortfolioBias portfolioBias = PortfolioBias.LongShort)
: base(rebalancingFunc, portfolioBias)
{
}
///
/// Initialize a new instance of
///
/// For a given algorithm UTC DateTime returns the next expected rebalance UTC time.
/// Returning current time will trigger rebalance. If null will be ignored
/// Specifies the bias of the portfolio (Short, Long/Short, Long)
public InsightWeightingPortfolioConstructionModel(Func rebalancingFunc,
PortfolioBias portfolioBias = PortfolioBias.LongShort)
: base(rebalancingFunc, portfolioBias)
{
}
///
/// Initialize a new instance of
///
/// Rebalancing frequency
/// Specifies the bias of the portfolio (Short, Long/Short, Long)
public InsightWeightingPortfolioConstructionModel(TimeSpan timeSpan,
PortfolioBias portfolioBias = PortfolioBias.LongShort)
: base(timeSpan, portfolioBias)
{
}
///
/// Initialize a new instance of
///
/// Rebalancing frequency
/// Specifies the bias of the portfolio (Short, Long/Short, Long)
public InsightWeightingPortfolioConstructionModel(Resolution resolution = Resolution.Daily,
PortfolioBias portfolioBias = PortfolioBias.LongShort)
: base(resolution, portfolioBias)
{
}
///
/// Method that will determine if the portfolio construction model should create a
/// target for this insight
///
/// The insight to create a target for
/// True if the portfolio should create a target for the insight
protected override bool ShouldCreateTargetForInsight(Insight insight)
{
return insight.Weight.HasValue;
}
///
/// Will determine the target percent for each insight
///
/// The active insights to generate a target for
/// A target percent for each insight
protected override Dictionary DetermineTargetPercent(List activeInsights)
{
var result = new Dictionary();
// We will adjust weights proportionally in case the sum is > 1 so it sums to 1.
var weightSums = activeInsights.Where(RespectPortfolioBias).Sum(insight => GetValue(insight));
var weightFactor = 1.0;
if (weightSums > 1)
{
weightFactor = 1 / weightSums;
}
foreach (var insight in activeInsights)
{
result[insight] = (int)(RespectPortfolioBias(insight) ? insight.Direction : InsightDirection.Flat)
* GetValue(insight)
* weightFactor;
}
return result;
}
///
/// Method that will determine which member will be used to compute the weights and gets its value
///
/// The insight to create a target for
/// The value of the selected insight member
protected virtual double GetValue(Insight insight) => insight.Weight != null ? Math.Abs((double)insight.Weight) : 0;
}
}