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Jupyter Notebooks and code for Derivatives Analytics with Python (Wiley Finance) by Yves Hilpisch.
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This repository provides all Python codes and Jupyter Notebooks of the book Derivatives Analytics with Python by Yves Hilpisch.
Oder the book here http://eu.wiley.com/WileyCDA/WileyTitle/productCd-1119037999.html or here www.amazon.com/Derivatives-Analytics-Python-Simulation-Calibration/dp/1119037999/.
There are two code versions available: for Python 3.6 and Python 2.7 (not maintained anymore).
There is now a yaml file for the installation of required Python packages in the repository. This is to be used with the conda package manager (see https://conda.io/docs/user-guide/tasks/manage-environments.html). If you do not have Miniconda or Anaconda installed, we recommend to install Miniconda 3.6 first (see https://conda.io/miniconda.html).
After you have cloned the repository, do on the Linux/Mac shell:
cd dawp
conda env create -f dawp_conda.yml
source activate dawp
cd python36
jupyter notebook
On Windows, do:
cd dawp
conda env create -f dawp_conda.yml
activate dawp
cd python36
jupyter notebook
Then you can navigate to the Jupyter Notebook files and get started.
You can immediately use all codes and Jupyter Notebooks by registering on the Quant Platform under http://wiley.quant-platform.com.
Check out our Python for Algorithmic Trading Course under http://pyalgo.tpq.io.
Check out also our Python for Algorithmic Trading Certificate Program under http://certificate.tpq.io.
© Dr. Yves J. Hilpisch | The Python Quants GmbH
The Quant Platform (http://pqp.io) and all codes/Jupyter notebooks come with no representations or warranties, to the extent permitted by applicable law.
http://tpq.io | dawp@tpq.io | http://twitter.com/dyjh
Quant Platform | http://wiley.quant-platform.com
Derivatives Analytics with Python (Wiley Finance) | http://derivatives-analytics-with-python.com
Python for Finance (O'Reilly) | http://python-for-finance.com
Python for Algorithmic Trading Course | http://pyalgo.tpq.io
Python for Finance Online Training | http://training.tpq.io
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