| Date |
Summary |
| 2021.3.3 |
- Syllabus
- (FYR) FRM Study Guide 2021
- Development environment
- (FYR) Preliminary knowledge about CS
- Programming basics
- Lecture notebook: notebook with pdf
- Variables and naming
- Simple data types (float, str, bool)
- Arithmetic operators (+,-, *, /, //, %, **), assignment operator (=), rational operators (<, <=, ==, >, >=, !=), logical operators (and, or, not)
- A collection of data: list
- Branching (if-elif-else)
- Iteration (for, while)
- Applications: Monte Carlo simulation with random number generator, bisection method for root-finding
- Jump statements (break, continue, pass)
- Functions and lambda expressions
- (FYR) Kronos Research, Kronos Webinar Trilogy #1 - Quant Trading 101, 2020.5.16
|
| 2020.3.6 |
- Data acquisition, visualization, strategy development, & backtesting
- Lecture notebook: notebook1, notebook2
- Pandas: DataFrame
- Data source: financial functions for Python (ffn)
- Plotting: matplotlib, seaborn, mpl_finance, bokeh
- Technical analysis: ta
- Backtesting: backtrader
- Homework: Lab 1 due by 3.13
|
| 2021.3.10 |
- Mathematical tools
- Lecture notebook: notebook1
- Vectorization: numpy
- Matrix computation: numpy & scipy
- Interpolation: spline
- Optimization: curve fitting, root-finding
- (FYR) Jake VanderPlas, Python's data science stack, 2016
|
| 2021.3.13 |
- Mathematical tools (cont'd)
|
| 2021.3.17 |
- Modern portfolio theory
- Lecture notebook: pyf_4_modern_portfolio_theory.ipynb
- Mean-variance framework: Markowitz efficient frontier
- Capital asset pricing model (CAPM)
- Arbitrage pricing theory (APT)
- Fama-French 3-factor model
- More similar models: Barra risk factor analysis, smart beta
- Black-Litterman model: a Bayesian approach
- (FYR) Prof. Rogers, stochastic financial models, 2012
- Homework: Lab 2 due by 3.20
|
| 2021.3.20 (2h0m) |
- Financial time series analysis
- Lecture notebook: pyf_5_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
- Homework: Lab 3 due by 3.24
|
| 2021.3.24 |
- Pricing theory
- Lecture slides: pricing_theory.pdf
- Lecture notebook: pyf_6_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
|
| 2021.3.27 |
- Pricing theory (cont'd)
- Random walk: Brownian motion
- Black-Scholes formula
- Case study: option pricing with negative strikes by using Bachelier model (Chadv20-152)
- Monte Carlo simulation
- European options
- American options using least-square Monte Carlo (LSM)
- More stochastic processes with simulation
- Mean-reverting process: Ornstein-Uhlenbeck (OU) model, Vasicek model
- Mean-reverting square-root process: Cox-Ingersoll-Ross (CIR) model
- Stochastic volatility model: Heston model
- Jump-diffusion process: Merton's model
- Term-structure of interest rates: Hull-White (HW) model, Heath-Jarrow-Morton (HJM) framework, LIBOR market model
- QuantLib tutorial
- Model calibration
- Implied volatility
- CIR model
- Financial news
- Homework: Lab 4 due by 3.31
|
| 2021.3.31 |
- Risk management
- Lecture notebook: pyf_7_risk_management.ipynb
- Value at risk (VaR) and Expected shortfall (ES)
- Sensitivity analysis: Greeks
- Dynamic hedging
- Case study: VIX
- Homework: Lab 5 due by 4.7
|
| 2021.4.3 |
(no class due to long weekend)
|
| 2021.4.7 |
- Machine learning tutorial
- Lecture notebook: pyf_8_machine_learning_tutorial_1.ipynb
- Regression with regularization: ridge regression & LASSO regression
- Logistic regression
- Support vector machine (SVM)
- Decision tree, random forest, and AdaBoost
- Principal component analysis (PCA)
- K-means clustering
- Reinforcement learning: Q-learning
|
| 2021.4.10 |
- Additional topics
- Kelly formula: link
- Seasonal ARIMA with exogenous regressors: link
|