Optimization-Python
JuMP.jl
Our great sponsors
| Optimization-Python | JuMP.jl | |
|---|---|---|
| 1 | 3 | |
| 221 | 2,134 | |
| - | 1.5% | |
| 0.0 | 9.3 | |
| over 2 years ago | about 20 hours ago | |
| Jupyter Notebook | Julia | |
| MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Optimization-Python
-
What is the best DCA Strategy - Part IV (Dynamic DCA)
One approach to this problem is based on the Nobel prize winning Modern Portfolio Theory (MPT). In fact, there we can use pretty simple code available online: https://github.com/tirthajyoti/Optimization-Python/blob/master/Portfolio_optimization.ipynb. There is a one BIG difference between DCA and MPT though. Here, we do not want to do a one-time purchase and try to gain maximum profit. We are looking at a dual problem, where we want to purchase regularly, while aiming maximum accumulation.
JuMP.jl
-
Optimization
JuMP.jl is my personal go-to when solving "big" optimization problems in Julia (maybe it's overkill for your application).
-
Multiple dispatch: Common Lisp vs Julia
A 100+ contributor project
-
Julia macros
Macros are very useful if you want to create Domain Specific Languages (DSLs), see https://github.com/jump-dev/JuMP.jl or if you want to transpile a subset of Julia to another language or say GPU code.
What are some alternatives?
market-making-backtest - algo trading backtesting on BitMEX
Catalyst.jl - Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.
portfolio_allocation_js - A JavaScript library to allocate and optimize financial portfolios.
ModelingToolkit.jl - An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
cocp - Source code for the examples accompanying the paper "Learning convex optimization control policies."
ComponentArrays.jl - Arrays with arbitrarily nested named components.
Optimus * 96 - Optimus is a mathematical programming library for Scala.
NumericalAlgorithms.jl - [DEPRECATED] Statistics & Numerical algorithms implemented in Julia.
psi4numpy - Combining Psi4 and Numpy for education and development.
OMLT - Represent trained machine learning models as Pyomo optimization formulations
analisis-numerico-computo-cientifico - Análisis numérico y cómputo científico
MuladdMacro.jl - This package contains a macro for converting expressions to use muladd calls and fused-multiply-add (FMA) operations for high-performance in the SciML scientific machine learning ecosystem