MQF - Advanced Computational and Programing Methods

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When/Where

Place: Xuhui

Course Objective

1. Overall Introduction

2. Basic presentation of python (together with do’s and not do in general)
   • Data types
   • List, dictionary
   • Controls flows
   • Functions
3. Scientific packages
   • Numpy
   • Plotly
   • Scipy
   • Pandas (introduction)
4. First financial applications
   • implementation of BS model
   • Introduction to classes
   • Volatility Smile
   • Risk quantification
   • Systemic risk quantification (introduction to Monte Carlo)
5. Data analysis
   • Advanced operations with pandas
   • Timesstamps and time series manipulations
   • advanced slicing, grouping, pivoting (with applications)
   • Save data
6. Performance Control, debugging, optimizing code
   • Profiling Code
   • Visualize loops (tqdm)
   • Debugging (print!)
   • tips and tricks for code optimization
7. Curse of dimensionality Monte Carlo
   • RNG
   • Monte-Carlo implementation (when how)
   • optimal sampling
   • quasi montecarlo methods
   • applications (american options)
8. Moment Generating functions
   • Fourier transform
   • Application to pricing
   • FFT method and implementation
   • Limitations

9. Finite elements/difference methods

10. Tchebychev Methods

11. How to handle large data sets (HFT) - Database design

12. What to do when Python is just too slow (Parallel computing, interfacing with C/C++/Java)
    • identify bottlenecks and their nature
    • built in functionalities
    • Parallel processing
    • Numba and co
    • Cython
    • C interface
13. Outlook for further computational approaches