/*
* 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.
*/
namespace QuantConnect.Indicators
{
///
/// Defines the different types of Correlation
///
public enum CorrelationType
{
///
/// Pearson Correlation (Product-Moment Correlation):
/// Measures the linear relationship between two datasets. The coefficient ranges from -1 to 1.
/// A value of 1 indicates a perfect positive linear relationship, -1 indicates a perfect
/// negative linear relationship, and 0 indicates no linear relationship.
/// It assumes that both datasets are normally distributed and the relationship is linear.
/// It is sensitive to outliers which can affect the correlation significantly.
///
Pearson,
///
/// Spearman Correlation (Rank Correlation):
/// Measures the strength and direction of the monotonic relationship between two datasets.
/// Instead of calculating the coefficient using raw data, it uses the rank of the data points.
/// This method is non-parametric and does not assume a normal distribution of the datasets.
/// It's useful when the data is not normally distributed or when the relationship is not linear.
/// Spearman's correlation is less sensitive to outliers than Pearson's correlation.
/// The coefficient also ranges from -1 to 1 with similar interpretations for the values,
/// but it reflects monotonic relationships rather than only linear ones.
///
Spearman
}
}