Repo for the talk: Financial Risk Management: Analytics and Aggregation with the PyData stack
I've create a conda environment. If you go to the repository's root directory.
conda env create
source activate pyldn
pip install -r requirements.txt
#if you want to use PostgreSQL
conda install -c https://conda.binstar.org/anaconda psycopg2
#For the Spark stuff you need to install it. I am on a Mac, using Homebrew and install Spark 1.4.0
brew install apache-spark
[Backtesting with Bokeh](https://github.com/mvaz/PyData-London2015/blob/master/notebooks/Backtesting%20with%20Bokeh.ipynb)
[Spark Scenario Aggregation](https://github.com/mvaz/PyData-London2015/blob/master/notebooks/SparkScenarioAggregation.ipynb)
[Value at Risk notebook](http://nbviewer.ipython.org/github/mvaz/financial-notebooks/blob/master/Value%20at%20Risk.ipynb)
- for VaR Filtered Historical Simulation
Giovani Barone-Adesi, Frederick Bourgoin and Kostas Giannopoulos (1998) “Don’t look back”, Risk, 11, August, pp100-104
John Hull and Alan White, “Incorporate Volatility Updating into the Historical Simulation Method for Value-at-Risk”, Journal of Risk, 1998
- for Apache Spark
[Estimating Financial Risk with Spark Sandy Ryza (Cloudera)](https://spark-summit.org/east-2015/talk/estimating-financial-risk-with-spark)
[Embracing Spark as the Scalable Data Analytics Platform - Matthew Glickman - Goldman Sachs - Video](https://www.youtube.com/watch?v=9yiwhfmEfi0)
[Fast Data Analytics with Spark and Python](http://www.slideshare.net/BenjaminBengfort/fast-data-analytics-with-spark-and-python)
[Introducing DataFrames in Spark for Large Scale Data Science](http://www.slideshare.net/databricks/introducing-dataframes-in-spark-for-large-scale-data-science)