| Date |
Summary |
| 2020.2.5 notebook |
- Syllabus
- Coding platform
- Programming basics
- Lecture slides: lecture1.pdf
- Variables and naming
- Simple data types (float, str, bool)
- Arithmetic operators (+,-, *, /, //, %, **), assignment operator (=), rational operators (<, <=, ==, >, >=, !=), logical operators (and, or, not)
- Built-in data structures: list, dictionary, tuple
- Branching (if-elif-else)
- Iteration (for, while)
- Application: Monte Carlo simulation with random number generator, bisection method for root-finding
|
| 2020.2.8 |
no class due to personal excuse
|
| 2020.2.12 notebook |
- Programming basics (cont'd)
- Flow controls
- Jump statements (break, continue, pass)
- Looping techniques: enumerate, zip, reverse, sorted
- Comprehensions
- Functions
- User-defined function
- Variable scope
- Default arguments
- Positional & keyword arguments
- Lambda expressions
- Functional programming (map, filter)
- Advanced usage: iterator (next) & generator (yield)
- Object-oriented programming
- class & object
- Applications: string, file i/o (with-as), date & time, exceptions (try-except-else-finally)
|
| 2020.2.15 notebook |
- Data acquisition, visualization & backtesting
- Customized data crawlers (or you can buy financial data from those famous information suppliers)
- High-level data structure: Dataframe of Pandas
- Another package based on Pandas: financial functions for Python (ffn)
- Plotting: matplotlib, seaborn, mpl_finance, pygal, bokeh
- Signal generation: technical analysis using ta
- Backtesting
|
| 2020.2.19 |
- (FYR) Jake VanderPlas, Python's data science stack, 2016
- Numerical & scientific packages: numpy & scipy
- Lecture notebook: pyf_math.ipynb
- Vectorization
- Matrix computation
- Interpolation
- Optimization
- Statistics
- Regression (by using statsmodels)
|
| 2020.2.22 |
|
| 2020.2.26 |
- Financial time series analysis
- Lecture notebook: pyf_financial_time_series_analysis.ipynb
- Autocorrelation
- Stationaryness
- Autoregressive moving-average (ARMA) model
- Generalized autoregressive conditional heteroskedasticity (GARCH) model
- Vector autoregression (VAR) model
- Cointegrated VAR using vector error correction (VEC) model
- Granger causality
|
| 2020.3.4 |
- Pricing theory
- Lecture slides: lecture6.pdf
- Lecture notebook: pyf_pricing_theory.ipynb
- Arbitrage-free principle
- Complete market
- Valuation framework: fundamental theorem of asset pricing
- Binomial option pricing model (BOPM)
- Stochastic calculus: Wiener process & Ito's formula
- Random walk: Brownian motion
- Black-Schole formula
- Monte Carlo simulation
|
| 2020.3.7 |
- Pricing theory (cont'd)
- Monte Carlo simulation (cont'd)
- American options using least-square Monte Carlo (LSM)
- More stochastic processes & simulation
- Mean-reverting process: CIR model
- Stochastic volatility model: Heston model
- Jump-diffusion process: Merton's model
- QuantLib tutorial
- Model calibration
- Risk management
|
| 2020.3.11 |
- Risk management (cont'd)
- Sensitivity analysis: Greeks
- Dynamic hedging
- Machine learning tutorial
- Lecture notebook: pyf_machine_learning_tutorial_1.ipynb
- Regression with regularization: ridge regression & LASSO regression
- Logistic regression
- Support vector machine (SVM)
- Decision tree and random forest
|
| 2020.3.14 |
- Machine learning tutorial
- Principal component analysis (PCA)
- K-means clustering
- Reinforcement learning: Q-learning
- Lecture notebook: pyf_machine_learning_tutorial_2.ipynb
- Recurrent neural network (RNN)
- Long-short term memory (LSTM) model
- Performance issues
- Lecture notebook: pyf_misc.ipynb
- Row major vs. column major
- Performance profiling
- Multiprocessing/multithreading and Amdahl's law
- Dynamic compiling
- TODO
- Cointegration: change API to vecm
- Corrected MC simulation for square-root process
- Write lecture slides
- Organize the contents of machine learning
- Reorganize all notebooks and slides by topics (one notebook for one topic)
|