GitPlanet

Cheap and reliable Node.js hosting starts at $3/month, and $1/month static HTML hosting

Created with love in Canada, visit hostnodejs.com today

Feel like to post an Ad? Learn Details
All Projects → lakshmiDRIP → DROP

lakshmiDRIP / DROP

Licence: Apache-2.0, Apache-2.0 licenses found Licenses found Apache-2.0 LICENSE Apache-2.0 LICENCE.txt
Fixed Income Analytics, Portfolio Construction Analytics, Transaction Cost Analytics, Counter Party Analytics, Asset Backed Analytics

Programming Languages

HTML
75241 projects
java
68154 projects - #9 most used programming language

Labels

statistical-learningbondportfolio-optimizationfxmarket-riskloansinterest-ratesasset-allocationtreasuryfixed-incometransaction-cost-analyticscva-dva-fva-kva-xvaasset-backedcredit-riskcounterparty-riskcorporatesmunicipalsemerging-marketinflation-linked

Projects that are alternatives of or similar to DROP

DRIP
Fixed Income Analytics, Portfolio Construction Analytics, Transaction Cost Analytics, Counter Party Analytics, Asset Backed Analytics
Stars: ✭ 44 (-49.43%)
Mutual labels:  bond, asset-allocation, fixed-income, transaction-cost-analytics
okama
Investment portfolio and stocks analyzing tools for Python with free historical data
Stars: ✭ 87 (+0%)
Mutual labels:  portfolio-optimization, asset-allocation
ahead
Univariate and multivariate time series forecasting
Stars: ✭ 15 (-82.76%)
Mutual labels:  statistical-learning
Statistical Learning Method
《统计学习方法》笔记-基于Python算法实现
Stars: ✭ 1,643 (+1788.51%)
Mutual labels:  statistical-learning
BayesHMM
Full Bayesian Inference for Hidden Markov Models
Stars: ✭ 35 (-59.77%)
Mutual labels:  statistical-learning
ANTsR
Advanced Normalization Tools in R
Stars: ✭ 101 (+16.09%)
Mutual labels:  statistical-learning
ISLR-Python
Notes and implementations in Python for ISLR.
Stars: ✭ 14 (-83.91%)
Mutual labels:  statistical-learning
GDLibrary
Matlab library for gradient descent algorithms: Version 1.0.1
Stars: ✭ 50 (-42.53%)
Mutual labels:  statistical-learning
M2cgen
Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies
Stars: ✭ 1,962 (+2155.17%)
Mutual labels:  statistical-learning
sldm4-h2o
Statistical Learning & Data Mining IV - H2O Presenation & Tutorial
Stars: ✭ 26 (-70.11%)
Mutual labels:  statistical-learning
An Introduction To Statistical Learning
This repository contains the exercises and its solution contained in the book "An Introduction to Statistical Learning" in python.
Stars: ✭ 1,843 (+2018.39%)
Mutual labels:  statistical-learning
r4sl
📈 Machine Learning from the perspective of a Statistician using R
Stars: ✭ 63 (-27.59%)
Mutual labels:  statistical-learning
JCLAL
JCLAL is a general purpose framework developed in Java for Active Learning.
Stars: ✭ 22 (-74.71%)
Mutual labels:  statistical-learning
Islr Python
An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code
Stars: ✭ 3,344 (+3743.68%)
Mutual labels:  statistical-learning
BoostSRL
BoostSRL: "Boosting for Statistical Relational Learning." A gradient-boosting based approach for learning different types of SRL models.
Stars: ✭ 31 (-64.37%)
Mutual labels:  statistical-learning
Esl Cn
The Elements of Statistical Learning (ESL)的中文翻译、代码实现及其习题解答。
Stars: ✭ 1,855 (+2032.18%)
Mutual labels:  statistical-learning
data-science
Lecture Slides for Introduction to Data Science
Stars: ✭ 22 (-74.71%)
Mutual labels:  statistical-learning
A- Guide -to Data Sciecne from mathematics
It is a blueprint to data science from the mathematics to algorithms. It is not completed.
Stars: ✭ 25 (-71.26%)
Mutual labels:  statistical-learning
Jupyter-Notebooks-Statistic-Walk-Throughs-Using-R
Jupyter notebooks with examples of statistical methods and analyses using R.
Stars: ✭ 21 (-75.86%)
Mutual labels:  statistical-learning
SparseRegression.jl
Statistical Models with Regularization in Pure Julia
Stars: ✭ 37 (-57.47%)
Mutual labels:  statistical-learning
View All Similar Projects ➔

DROP

v5.82 3 April 2023

DROP implements libraries targeting analytics/risk, transaction cost analytics, asset liability analytics, capital, exposure, and margin analytics, valuation adjustment analytics, and portfolio construction analytics within and across fixed income, credit, commodity, equity, FX, and structured products. It also includes auxiliary libraries for graph algorithms, numerical analysis, numerical optimization, spline builder, model validation, statistical learning, and computational support

DROP is composed of three modules.

  • Product Core Module => Fixed Income Product Analytics, Loan Analytics, and Transaction Cost Analytics.
  • Portfolio Core Module => Portfolio Contruction and Asset Liability, along with Exposure, Margin, XVA, and Capital Analytics.
  • Computation Core Module => Algorithm/Computation Support, Function Analysis, Model Validation, Numerical Analysis, Numerical Optimizer, Spline Builder, Graph Algorithms, and Statistical Learning.

Pointers

Travis CircleCI CircleCI Build status
Git
Stack Overflow Git
Join the chat at https://gitter.im/lakshmiDRIPDROP

Codacy Badge Codacy Badge codecov.io Coverage Status Coverity Status
Documentation Status Javadoc Other

Average time to resolve an issue Percentage of issues still open

Awesome

Repo Structure

Module, Library, and Project Layout

Project Home Issues Library Module
alm README Git Asset Liability Analytics Portfolio
analytics README Git Fixed Income Analytics Product
capital README Git Capital Analytics Portfolio
dynamics README Git Fixed Income Analytics Product
execution README Git Transaction Cost Analytics Product
exposure README Git Exposure Analytics Portfolio
feed README Git Computation Support Computational
function README Git Numerical Analysis Computational
graph README Git Numerical Analysis Computational
historical README Git Computation Support Computational
json README Git Computation Support Computational
learning README Git Statistical Learning Computational
loan README Git Loan Analytics Product
market README Git Fixed Income Analytics Product
measure README Git Numerical Analysis Computational
numerical README Git Numerical Analysis Computational
optimization README Git Numerical Optimizer Computational
param README Git Fixed Income Analytics Product
portfolio construction README Git Asset Allocation Analytics Portfolio
pricer README Git Fixed Income Analytics Product
product README Git Fixed Income Analytics Product
regression README Git Computation Support Computational
sequence README Git Statistical Learning Computational
service README Git Computation Support Computational
simm README Git Margin Analytics Portfolio
spaces README Git Statistical Learning Computational
special function README Git Function Analysis Computational
spline README Git Spline Builder Computational
state README Git Fixed Income Analytics Product
template README Git Fixed Income Analytics Product
validation README Git Model Validation Computational
xva README Git XVA Analytics Portfolio

Installation

Installation is as simple as building a jar and dropping into the classpath. There are no other dependencies.

Samples

Java Samples | Excel Samples | Test Data

Documentation

Javadoc API | DROP Specifications | Release Notes | User guide is a work in progress!

Misc

JUnit Tests | Jacoco Coverage | Jacoco Session | Credit Attributions | Version Specifications

Contact

[email protected]

codecov.io codecov.io codecov.io codecov.io

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].