| Date |
Summary |
| 2019.5.22 |
- syllabus
- using jupyter notebook
- live code: pyf20190522.ipynb
- programming basic
- computation model (CPU and memory)
- variables and naming
- simple data types (int, float, str)
- arithmetic operators (+-*/, //, %, **)
- first built-in data structure: list with slicing
- assignment operator (=), rational operators (<, ==, >), logical operators (and/or)
- selection (if-elif-else)
- iteration (for-in, while) with jump statements (break, continue, pass)
- application: Monte Carlo simulation, bisection method for root-finding
|
| 2019.5.25 |
- live code: pyf20190525.ipynb
- data structures
- list, tuple, dictionary, and set
- looping techniques: enumerate, zip, reverse, sorted
- list/dictionary/tuple/set comprehension
- functions
- user-defined function
- variable scope
- default arguments
|
| 2019.5.29 |
- live code: pyf20190529.ipynb
- functional programming
- functional programming (map, filter)
- lambda expressions
- generator (yield)
- object-oriented programming
- class and object: ... in python, everything is an object.
- user-defined class: attributes and methods
- magic methods
- (single) inheritance and method overriding
- file I/O and with-as
- try-except-else-finally
- applications: file i/o, exceptions, datetime
- scripts, modules, import
|
| 2019.6.1 |
- notebook: pyf20190601.ipynb
- using spyder (spyder official website)
- data acquisition
- data crawlers (or you can buy financial data from those famous information suppliers)
- pandas: dataframe
- data visualization
- matplotlib, seaborn, pygal, mpl_finance
- technical analysis
|
| 2019.6.5 |
|
| 2019.6.12 |
- notebook: pyf20190612.ipynb
- statistical models
- multivariate linear regression: capital asset pricing model (CAPM), Fama-French 3-factor model
- autoregressive moving-average (ARMA) model
- generalized autoregressive conditional heteroskedasticity (GARCH) model
- vector autoregression (VAR) model
- cointegrated VAR using vector error correction (VEC) model
|
| 2019.6.15 |
- notebook: pyf20190615.ipynb
- stochastic processes
- random walk: geometric Brownian motion
- square-root and mean-reverting process: CIR model
- stochastic volatility model: Heston model
- jump-diffusion process: Merton's model
- option basics and pricing models
- valuation framework
- Monte Carlo simulation
- binomial option pricing model (BOPM)
|
| 2019.6.19 |
- notebook: pyf20190619.ipynb
- QuantLib tutorial
- model calibration
- implied volatility
- CIR model
- Heston model
|
| 2019.6.22 |
- notebook: pyf20190622.ipynb
- risk management
- value at risk (VaR)
- dynamic delta hedging
- performance issues
- row major vs. column major
- performance profiling
- multiprocess/multithreading and Amdhal's law
- dynamic compiling
|
| 2019.6.26 |
- notebook: pyf20190626.ipynb
- machine learning tutorial
- LASSO regression and ridge regression
- logistic regression
- support vector machine (SVM)
- decision tree and random forest
- principal component analysis (PCA)
- k-mean/clustering
- reinforcement learning: Q-learning
- feedback
|