Keynote
summary
Argument: open source code, open data APIs and cloud compute let finance move from closed, slow toolchains to reproducible, fast workflows. Examples: Python + NumPy + pandas + Jupyter runs a vectorized Monte Carlo of 10 million Monte Carlo sims in 1.33s and plots results in one line; data comes from Quandl/Yahoo via pandas_datareader; backtesting libraries like Zipline/Quantopian and a derivatives analytics library (5,500+ lines and Jupyter notebooks on GitHub) power production and training; large deployments exist (e.g. 14M+ lines in a bank quartz system).
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chapters (11)
- 0:00Introduction and background
- 2:31Strategic use of open source
- 3:39Trends in data and finance
- 11:06The Python ecosystem
- 39:33Data visualization
- 40:35Option pricing algorithms
- 44:16Financial data science tools
- 56:40Education and training
- 57:28Online learning and degrees
- 57:51Academic collaboration
- 59:04Summary and performance
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