<commit_before># PyData-London2015



Repo for the talk:
[Financial Risk Management: Analytics and Aggregation with the PyData stack](http://london.pydata.org/schedule/presentation/13/)




## Setup environment

    conda env create
    source activate pyldn
    pip install -r requirements.txt


## References

  1. for VaR Filtered Historical Simulation

    <NAME>, <NAME> and <NAME> (1998) “Don’t look back”, Risk, 11, August, pp100-104

    <NAME> and <NAME>, “Incorporate Volatility Updating into the Historical Simulation Method for Value-at-Risk”, Journal of Risk, 1998<commit_msg>add more references on Spark<commit_after># PyData-London2015



Repo for the talk:
[Financial Risk Management: Analytics and Aggregation with the PyData stack](http://london.pydata.org/schedule/presentation/13/)




## Setup environment

    conda env create
    source activate pyldn
    pip install -r requirements.txt


## References

  1. for VaR Filtered Historical Simulation

    <NAME>, <NAME> and <NAME> (1998) “Don’t look back”, Risk, 11, August, pp100-104

    <NAME> and <NAME>, “Incorporate Volatility Updating into the Historical Simulation Method for Value-at-Risk”, Journal of Risk, 1998

  2. for Apache Spark

    [Embracing Spark as the Scalable Data Analytics Platform - <NAME> - 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)
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