toolbox of fast mm-related funcs
To install mm_toolbox
, follow these steps:
- Optional: If you are familiar with virtual environments, create one now and activate it. If not, this step is not necessary:
$ virtualenv venv
$ source venv/bin/activate
-
Clone the repository or download the source code to your local machine.
-
With your virtual environment activated, navigate to the root directory of
mm_toolbox
(wheresetup.py
is located) and run:python setup.py install
This will install mm_toolbox
and its dependencies into your virtual environment.
After installing mm_toolbox
, you can start using it in your projects by importing the necessary modules and functions. Here's an example:
from mm_toolbox.orderbook.orderbook import Orderbook
# Example usage of the orderbook from mm_toolbox
base_orderbook = Orderbook(size=500)
Please create issues to flag bugs or suggest new features and feel free to create a pull request with any improvements.
mm_toolbox
is licensed under the MIT License. See the LICENSE file in the repository for more details.
-
Look at https://numba.pydata.org/numba-doc/dev/reference/envvars.html
- Set NUMBA_OPT: max
- Set NUMBA_ENABLE_AVX: 1
-
Read https://tbetcke.github.io/hpc_lecture_notes/simd.html, TLDR:
- Ex. Doing 2.0 rather than 2 in a long calculation
- @njit(error_model="numpy") when working with div
- Examine ASM/LLVM generation for autovectorization debugging
- @njit(fastmath=True) in for loops if execution order doesn't matter
If you have any questions/suggestions regarding the repository, or just want to have a chat, my handles are below ๐๐ผ
Twitter: @beatzXBT | Discord: gamingbeatz