Tulip Indicators
AI-Toolbox
| Tulip Indicators | AI-Toolbox | |
|---|---|---|
| 4 | 2 | |
| 802 | 641 | |
| 0.7% | - | |
| 5.2 | 5.1 | |
| 3 months ago | 4 months ago | |
| C | C++ | |
| GNU Lesser General Public License v3.0 only | GNU General Public License v3.0 only |
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.
Tulip Indicators
-
Technical Analysis libraries
TA-lib and pandas-ta have already been mentioned, so just for the sake of alternatives, Tulip indicators
-
IBKR API doesn't provide technical indicators, is there any alternative?
Broker APIs usually don't provide those. You'll want to generate them using a library. The most popular are TA Library (linked by another commenter), TuliPy, and TA-Lib. All easy to use.
-
Here’s the gist of my algorithm. I want to go big with my algorithm and I need some help from you.
I use Tulip Indicators and my codes run on NodeJS and I maintain data on MariaDB. I can of course transform them to any other programming language, if given a chance.
-
C Deep
Tulip Indicators - Library of functions for technical analysis of financial data. LGPL-3.0-or-later
AI-Toolbox
-
Impact of using sockets to communicate between Python and RL environment
Makes sense. I was just wondering if someone had any comparisons to share. I will create a toy environment in Unreal and compare integrating RL C++ libraries (looking at AI-Toolbox and mlpack) vs using Python with socket communication.
-
Greedy AI agents learn to cooperate
I maintain a repository of many implementations of classical (tabular) RL algorithms [1] which you might enjoy playing with when starting out. I use it for both research and for student projects. The advantage of avoiding NNs when starting out is that it is much simpler to inspect the inner workings of an algorithm to see whether it's working or not.
I'm always happy to help if something is unclear or difficult so feel free to open issues there :)
[1]: https://github.com/Svalorzen/AI-Toolbox
What are some alternatives?
Tulip Cell - TulipCell is an Excel add-in providing 100+ technical analysis indicators.
Recast/Detour - Industry-standard navigation-mesh toolset for games
btsk - Behavior Tree Starter Kit
Veles - Distributed machine learning platform
CNTK - Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
BayesOpt - BayesOpt: A toolbox for bayesian optimization, experimental design and stochastic bandits.
ANNetGPGPU - A GPU (CUDA) based Artificial Neural Network library
tensorflow - An Open Source Machine Learning Framework for Everyone
frugally-deep - Header-only library for using Keras (TensorFlow) models in C++.
tiny-cnn - header only, dependency-free deep learning framework in C++14
Deeplearning4j - Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learning using automatic differentiation.
nano