Date 
Summary 
2019.8.7 
 syllabus
 coding platform
 programming basics
 variables and naming
 simple data types (float, str, bool)
 arithmetic operators (+*/, //, %, **), assignment operator (=), rational operators (<, ==, >), logical operators (and/or)
 builtin data structures: list, dictionary, tuple
 selection (ifelifelse)
 iteration (forin, while) with jump statements (break, continue, pass)
 looping techniques: enumerate, zip, reverse, sorted
 application: Monte Carlo simulation with random number generator, bisection method for rootfinding
 list/dictionary/tuple/set comprehension

2019.8.10 
 programming basics (cont'd)
 functions
 userdefined function
 default arguments
 functional programming
 functional programming (map, filter)
 lambda expressions
 generator (yield)
 objectoriented programming
 class and object
 applications: file i/o (withas), exceptions (tryexceptelsefinally), datetime
 data acquisition and visualization
 package: ffn
 customized data crawlers (or you can buy financial data from those famous information suppliers)
 highlevel data structure: pandas (mainly dataframe)
 plotting: matplotlib, seaborn, mpl_finance, pygal, bokeh
 backtesting
 signal generation: technical analysis
 performance evaluation: net present value, maximum draw down (MDD), Sharpe ratio

2019.8.17 
 numerical and scientific packages
 vectorization
 matrix computation
 interpolation
 regression
 optimization
 statistics

2019.8.21 
 modern portfolio theory
 capital asset pricing model (CAPM)
 FamaFrench 3factor model
 BlackLitterman model

2019.8.24 
 financial time series analysis
 autocorrelation
 autoregressive movingaverage (ARMA) model
 generalized autoregressive conditional heteroskedasticity (GARCH) model
 vector autoregression (VAR) model
 cointegrated VAR using vector error correction (VEC) model
 Granger causality: granger.ipynb

2019.8.28 
 pricing theory
 valuation framework: fundamental theorem of asset pricing
 arbitragefree principle
 complete market
 efficient market hypothesis
 martingale
 binomial option pricing model (BOPM)
 Euorpean options
 American options
 stochastic processes
 random walk: geometric Brownian motion
 squareroot and meanreverting process: CIR model
 stochastic volatility model: Heston model
 jumpdiffusion process: Merton's model
 Monte Carlo simulation
 QuantLib tutorial
 model calibration
 implied volatility
 CIR model
 Heston model

2019.8.31 
 risk management
 value at risk (VaR)
 Sensitivity analysis: Greeks
 static/dynamic hedging

2019.9.4 
 machine learning tutorial
 regression with regularization: ridge regression & LASSO regression
 logistic regression
 support vector machine (SVM)

2019.9.7 
 machine learning tutorial (cont'd)
 decision tree and random forest
 principal component analysis (PCA)

2019.9.11 
 machine learning tutorial (cont'd)
 kmeans clustering
 reinforcement learning: Qlearning
 performance issues
 row major vs. column major
 performance profiling
 multiprocessing/multithreading and Amdahl's law
 dynamic compiling
