Workshop PyConf Hyderabad
Time Series Analysis with Python
With Applications of Machine Learning Algorithms
Session by Dr. Yves J. Hilpisch | The Python Quants GmbH
Hyderabad, 07. October 2017
(short link to this Gist: https://goo.gl/Dvwhe8)
"Pichai said that as an 'AI first' company, this is a 'unique moment in time' for Google to combine hardware, software and artificial intelligence. 'It's radically rethinking how computing should work', he said."
Business Standard, "Google Ramps up Hardware Business", 06. October 2017.
PyConf Hyderabad 50% Special under http://hydpy.tpq.io
You find the introductory & overview slides under http://hilpisch.com/hydpy_workshop.pdf
If you have either Miniconda or Anaconda already installed, there is no need to install anything new.
We are using Python 3.6. Download and install Miniconda 3.6 from https://conda.io/miniconda.html if you do not have
conda already installed.
In any case, you should execute the following lines on the shell/command prompt to create a new environment with the needed packages:
conda create -n hydpy python=3.6 (source) activate hydpy conda install numpy pandas=0.19 scikit-learn matplotlib conda install ipython jupyter jupyter notebook
Read more about the management of environments under https://conda.io/docs/using/envs.html
We are going to cover the following topics:
- Reading Financial Time Series Data with pandas
- Working with pandas DataFrame objects
- Formulating a Financial Trading Strategy
- Vectorized Backtesting of the Trading Strategy
- Getting More Realistic by Considering Bid-Ask Spread
- Random Walk Hypothesis
- Prediction based on Classification Algorithm
- Out-of-Sample Performance of Fitted Model
You find the data set used under http://hilpisch.com/eurusd.csv (as provided by FXCM Forex Capital Markets Ltd.).
Use this link to get a 10 USD starting credit on DigitalOcean when signing up for a new account.
Good book about everything important in Python data analysis: Python Data Science Handbook, O'Reilly
Good book covering object-oriented programming in Python: Fluent Python, O'Reilly