/* * 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 } }