1. A computer-implemented method comprising: receiving first input that specifies a first market instrument; receiving second input that specifies a start date and an end date of a first time series associated with the first market instrument; receiving third input that specifies a feature set computer and the values of one or more parameters of the feature set computer, that define a particular time series feature; retrieving the first time series from a data repository; determining a first feature set associated with the first market instrument by applying the feature set computer to the first time series based on the values of the one or more parameters, wherein the first feature set includes first one or more features that existed for the first market instrument between the start date and the end date; wherein the feature set computer applied to the first time series comprises a set of instructions which, when executed by one or more processors, computes each of the first one or more features based on the values of the one or more parameters as a first set of data that represents one or more characteristics of the first time series; retrieving, from the data repository, a second time series associated with a second market instrument, wherein the second time series is bounded by the start date and the end date; determining a second feature set associated with the second market instrument by applying the feature set computer to the second time series based on the values of the one or more parameters, wherein the second feature set includes second one or more features that existed for the second market instrument between the start date and the end date; wherein the feature set computer applied to the second time series comprises the set of instructions which, when executed by the one or more processors, computes each of the second one or more features based on the values of the one or more parameters as a second set of data that represents one or more characteristics of the second time series; correlating the second feature set to the first feature set to determine a similarity score associated with the second market instrument, wherein the similarity score is a measure of the similarity between the one or more characteristics of the second time series of the second market instrument and the one or more characteristics of the first time series of the first market instrument; and storing the similarity score in computer data storage; wherein the method is performed by one or more computer systems; wherein correlating the second feature set to the first feature set comprises: determining a first array of values that represents the first feature set, wherein each value in the first array is a real number value within a range of values; determining a second array of values that represents the second feature set, wherein each value in the second array is a real number value within the range of values; and determining the similarity score based on the first array of values and the second array of values; wherein: the first one or more features in the first feature set and the second one or more features in the second feature set are peaks/troughs features; and the range of values is bounded by the values of “−1” and “+1”, wherein in the first array of values and in the second array of values: a trough point is mapped to an array value of “−1”; feature points leading to a trough point on either side are mapped to array values between the values of “0” and “−1”; a peak point is mapped to an array value of “+1”; and feature points leading to a peak point on either side are mapped to array values between the values of “0” and “+1”.