py_lets_be_quickly_rational is an updated version of py_lets_be_rational. It currently speeds up calculations as much as 80% while still passing the test suite. This repo also exists as a place of continued development since py_lets_be_rational seems to not be actively maintained.
The below table provides some comparisons in the various libraries
| Feature | py_lets_be_quickly_rational |
py_lets_be_rational |
lets_be_rational |
|---|---|---|---|
| Python Version Compatibility | 2.7 and 3.x | 2.7 and 3.x | 2.7 only |
| Source Language | Python + All Numba | Python + Some Numba | C with Python SWIG Wrapper |
| Optional Dependencies | Numba | Numba | None |
py_lets_be_quickly_rational uses numba to speedup calculations on every function. py_lets_be_rational also does this, but on a smaller scale. Therefore, py_lets_be_quickly_rational is several times faster at calculating implied volatility than py_lets_be_rational or lets_be_rational. The below results were done on my macbook pro, and the script can be found in tests. The script calls each function with a thousand fake values, and averages the time over 100 runs.
| Feature | py_lets_be_rational |
py_lets_be_quickly_rational |
Improvement |
|---|---|---|---|
| Normalised IV Transformed Rational Guess | 16.2s | 2.6s | ~83% |
| Normalised IV Transformed Rational Guess Limited Iterations | 13.7s | 2.7s | ~78% |
Can be installed with pip
pip3 install py-lets-be-quickly-rational
Right now the code takes advantage of numba JIT compiling, but that can only get us so far. Several common stats functions are implemented in a custom way, orignially done in the C++ implementation since they were faster than the C++ versions. However, even optimized python code is slower than native code, and numpy provides several of those functions natively. This, combined with taking advantage of numpys vectorization support, could lead to performance impacts.
Numba is an optional dependency. Because Numba installation can be tricky and OS-dependent, we decided to leave it up to each user to decide how and whether to install Numba. If Numba is present, execution speed will be faster. If not, the code will still run -- just much slower.
py_lets_be_quickly_rational optionally depends on numba which in turn depends on llvm-lite. llvm-lite wants LLVM 3.9
pip install numba
For other operating systems, please refer to the llvm-lite and numba documentation.
Fork our repository, and make the changes on your local copy:
git clone [email protected]/your_username/py_lets_be_quickly_rational
cd py_lets_be_quickly_rational
pip install -e .
pip install -r dev-requirements.txt
When you are done, push the changes, and create a pull request on your repo.
"Let's Be Rational" is a paper by Peter Jäckel showing "how Black's volatility can be implied from option prices with as little as two iterations to maximum attainable precision on standard (64 bit floating point) hardware for all possible inputs."
The paper is accompanied by the full C source code, which resides at www.jaeckel.org/LetsBeRational.7z.
Copyright © 2013-2014 Peter Jäckel.
Permission to use, copy, modify, and distribute this software is freely granted,
provided that this notice is preserved.
WARRANTY DISCLAIMER
The Software is provided "as is" without warranty of any kind, either express or implied,
including without limitation any implied warranties of condition, uninterrupted use,
merchantability, fitness for a particular purpose, or non-infringement.
MIT