Date 
Summary 
2019.8.7 
 syllabus
 using jupyter notebook
 programming basic
 computation model (CPU and memory)
 variables and naming
 simple data types (int, float, str)
 arithmetic operators (+*/, //, %, **)
 first builtin data structure: list with slicing
 assignment operator (=), rational operators (<, ==, >), logical operators (and/or)
 selection (ifelifelse)
 iteration (forin, while) with jump statements (break, continue, pass)
 application: Monte Carlo simulation, bisection method for rootfinding

2019.8.10 
 programming basic (cont'd)
 data structures
 list, tuple, dictionary, and set
 looping techniques: enumerate, zip, reverse, sorted
 list/dictionary/tuple/set comprehension
 functions
 userdefined function
 variable scope
 default arguments
 functional programming
 functional programming (map, filter)
 lambda expressions
 generator (yield)
 objectoriented programming
 class and object: ... in python, everything is an object.
 userdefined class: attributes and methods
 magic methods
 (single) inheritance and method overriding
 file I/O and withas
 tryexceptelsefinally
 applications: file i/o, exceptions, datetime
 misc: scripts, modules, import

2019.8.14 
 using spyder (spyder official website)
 data processing
 data acquisition
 data crawlers (or you can buy financial data from those famous information suppliers)
 pandas: dataframe
 data visualization
 backtesting
 net present value
 maximum draw down (MDD)
 Sharpe ratio

2019.8.17 
 numerical and scientific packages
 vectorization
 matrix computation
 interpolation
 optimization
 statistics
 numerical integration
 application: modern portfolio theory

2019.8.21 
 financial time series analysis
 multivariate linear regression: capital asset pricing model (CAPM), FamaFrench 3factor model
 autoregressive movingaverage (ARMA) model
 generalized autoregressive conditional heteroskedasticity (GARCH) model
 vector autoregression (VAR) model
 cointegrated VAR using vector error correction (VEC) model

2019.8.24 
 stochastic processes
 random walk: geometric Brownian motion
 squareroot and meanreverting process: CIR model
 stochastic volatility model: Heston model
 jumpdiffusion process: Merton's model
 option basics and pricing models
 valuation framework
 Monte Carlo simulation
 binomial option pricing model (BOPM)
 QuantLib tutorial

2019.8.28 
 model calibration
 implied volatility
 CIR model
 Heston model

2019.8.31 
 risk management
 value at risk (VaR)
 dynamic delta hedging

2019.9.4 
 machine learning tutorial
 LASSO regression and ridge regression
 logistic regression
 support vector machine (SVM)
 decision tree and random forest
 principal component analysis (PCA)
 kmean/clustering
 reinforcement learning: Qlearning

2019.9.7 
automated execution (?)
performance issues
 row major vs. column major
 performance profiling
 multiprocess/multithreading and Amdahl's law
 dynamic compiling
