Interactive Analysis of (Large) Financial Data Sets
summary
Shows concrete results: uses NumPy, pandas, PyTables/HDF5, IPython/Jupyter and multiprocessing to do portfolio optimization, PCA and Monte Carlo; runs 2,500 random portfolios and plots efficient frontier, finds first ten principal components explain ~75% of variance, and parallelizes simulations across 8 cores 64 GB RAM. Demonstrates IO and timing: writing 100 million rows CSV takes ~22 minutes, converting CSV→HDF5 with chunked reads takes ~5 minutes (PyTables ~4m28s), and column aggregations run in seconds.
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chapters (12)
- 0:00Interactive financial data analysis
- 1:54Python and IPython in finance
- 5:49NumPy for financial data
- 8:14Pandas and time-series finance
- 12:05Portfolio optimization
- 19:50Monte Carlo simulation
- 22:15PCA for index replication
- 26:32Parallel processing
- 29:45Efficient CSV processing
- 37:54HDF5 and PyTables
- 38:59Python workflow advantages
- 40:24Q&A
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