HipsyclImplementation of SYCL for CPUs, AMD GPUs, NVIDIA GPUs
Stars: ✭ 377 (-87.11%)
lubeckHigh level linear algebra library for Dlang
Stars: ✭ 57 (-98.05%)
VuhVulkan compute for people
Stars: ✭ 264 (-90.97%)
BayaderaHigh-performance Bayesian Data Analysis on the GPU in Clojure
Stars: ✭ 342 (-88.31%)
ClojureclClojureCL is a Clojure library for parallel computations with OpenCL.
Stars: ✭ 266 (-90.91%)
NeanderthalFast Clojure Matrix Library
Stars: ✭ 927 (-68.31%)
Quant NotesQuantitative Interview Preparation Guide, updated version here ==>
Stars: ✭ 180 (-93.85%)
WebclglGPGPU Javascript library 🐸
Stars: ✭ 313 (-89.3%)
PicongpuParticle-in-Cell Simulations for the Exascale Era ✨
Stars: ✭ 452 (-84.55%)
GpurR interface to use GPU's
Stars: ✭ 208 (-92.89%)
Morpheus CoreThe foundational library of the Morpheus data science framework
Stars: ✭ 203 (-93.06%)
Riskfolio LibPortfolio Optimization and Quantitative Strategic Asset Allocation in Python
Stars: ✭ 305 (-89.57%)
QuantdomPython-based framework for backtesting trading strategies & analyzing financial markets [GUI ]
Stars: ✭ 449 (-84.65%)
Cuda Api WrappersThin C++-flavored wrappers for the CUDA Runtime API
Stars: ✭ 362 (-87.62%)
OcbarrageiOS 弹幕库 OCBarrage, 同时渲染5000条弹幕也不卡, 轻量, 可拓展, 高度自定义动画, 超高性能, 简单易上手; A barrage render-engine with high performance for iOS. At the same time, rendering 5000 barrages is also very smooth, lightweight, scalable, highly custom animation, ultra high performance, simple and easy to use!
Stars: ✭ 589 (-79.86%)
Strata Open source analytics and market risk library from OpenGamma
Stars: ✭ 598 (-79.56%)
QlibQlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant investment strategies. An increasing number of SOTA Quant research works/papers are released in Qlib.
Stars: ✭ 7,582 (+159.21%)
ArraymancerA fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends
Stars: ✭ 793 (-72.89%)
Awesome QuantA curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)
Stars: ✭ 8,205 (+180.51%)
1833718.337 - Parallel Computing and Scientific Machine Learning
Stars: ✭ 834 (-71.49%)
PortbalanceDetermine optimal rebalancing of a passive stock portfolio.
Stars: ✭ 31 (-98.94%)
CekirdeklerMulti-device OpenCL kernel load balancer and pipeliner API for C#. Uses shared-distributed memory model to keep GPUs updated fast while using same kernel on all devices(for simplicity).
Stars: ✭ 76 (-97.4%)
NyuziprocessorGPGPU microprocessor architecture
Stars: ✭ 1,351 (-53.81%)
MlfinlabMlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.
Stars: ✭ 2,676 (-8.51%)
NnpackAcceleration package for neural networks on multi-core CPUs
Stars: ✭ 1,538 (-47.42%)
OcbarrageiOS 弹幕库 OCBarrage, 同时渲染5000条弹幕也不卡, 轻量, 可拓展, 高度自定义动画, 超高性能, 简单易上手; A barrage render-engine with high performance for iOS. At the same time, rendering 5000 barrages is also very smooth, lightweight, scalable, highly custom animation, ultra high performance, simple and easy to use!
Stars: ✭ 294 (-89.95%)
DeepnetDeep.Net machine learning framework for F#
Stars: ✭ 99 (-96.62%)
Pyhpc BenchmarksA suite of benchmarks to test the sequential CPU and GPU performance of most popular high-performance libraries for Python.
Stars: ✭ 119 (-95.93%)
StocksharpAlgorithmic trading and quantitative trading open source platform to develop trading robots (stock markets, forex, crypto, bitcoins, and options).
Stars: ✭ 4,601 (+57.3%)
ClojurecudaClojure library for CUDA development
Stars: ✭ 158 (-94.6%)
Ta LibPython wrapper for TA-Lib (http://ta-lib.org/).
Stars: ✭ 6,034 (+106.29%)
Go Finance⚠️ Deprecrated in favor of https://github.com/piquette/finance-go
Stars: ✭ 536 (-81.68%)
ResearchNotebooks based on financial machine learning.
Stars: ✭ 714 (-75.59%)
Stdgpustdgpu: Efficient STL-like Data Structures on the GPU
Stars: ✭ 531 (-81.85%)
Kubernetes Gpu GuideThis guide should help fellow researchers and hobbyists to easily automate and accelerate there deep leaning training with their own Kubernetes GPU cluster.
Stars: ✭ 740 (-74.7%)
PresentationsSlide show presentations regarding data driven investing.
Stars: ✭ 162 (-94.46%)
PaiResource scheduling and cluster management for AI
Stars: ✭ 2,223 (-24%)
BlisBLAS-like Library Instantiation Software Framework
Stars: ✭ 859 (-70.63%)
QlnetQLNet C# Library
Stars: ✭ 252 (-91.38%)
QuantQuantitative Analysis Research see more at https://teddykoker.com
Stars: ✭ 76 (-97.4%)
QuantsbinQuantitative Finance tools
Stars: ✭ 74 (-97.47%)
EmuThe write-once-run-anywhere GPGPU library for Rust
Stars: ✭ 1,350 (-53.85%)
PycudaCUDA integration for Python, plus shiny features
Stars: ✭ 1,112 (-61.98%)
Bulbea🐗 🐻 Deep Learning based Python Library for Stock Market Prediction and Modelling
Stars: ✭ 1,585 (-45.81%)
Strategems.jlQuantitative systematic trading strategy development and backtesting in Julia
Stars: ✭ 106 (-96.38%)
LibflameHigh-performance object-based library for DLA computations
Stars: ✭ 197 (-93.26%)
HeteroflowConcurrent CPU-GPU Programming using Task Models
Stars: ✭ 57 (-98.05%)
PyportfoliooptFinancial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
Stars: ✭ 2,502 (-14.46%)
Alpha Mindquantitative security portfolio analysis. The analysis pipeline including data storage abstraction, alpha calculation, ML based alpha combining and portfolio calculation.
Stars: ✭ 171 (-94.15%)
bcnnA minimalist Deep Learning framework for embedded Computer Vision
Stars: ✭ 39 (-98.67%)
LuisaRenderHigh-Performance Multiple-Backend Renderer Based on LuisaCompute
Stars: ✭ 47 (-98.39%)
Nvidia libs testTests and benchmarks for cudnn (and in the future, other nvidia libraries)
Stars: ✭ 36 (-98.77%)
TuringtraderThe Open-Source Backtesting Engine/ Market Simulator by Bertram Solutions.
Stars: ✭ 132 (-95.49%)
Compute.scalaScientific computing with N-dimensional arrays
Stars: ✭ 191 (-93.47%)