Review of Python Courses (Part 28)

In Part 27, I summarized my Datacamp courses 80-82. Today I will continue with the next three.

As a reminder, I introduced you to my recent work learning Python here.

My course #83 was GARCH Models in Python. This course covers:

• Why do we need GARCH models?
• What are ARCH and GARCH (from statsmodels.graphics.tsaplots import plot_acf)?
• How to implement GARCH models in Python (from arch import arch_model)?
• Distribution assumptions
• Mean model specifications
• Volatility models for asymmetric shocks
• GARCH rolling window forecast
• Significance testing of model parameters
• Validation of GARCH model assumptions (from statsmodels.stats.diagnostic import acorr_ljungbox)
• Goodness of fit measures
• GARCH model backtesting (from sklearn.metrics import mean_absolute_error, mean_squared_error)
• VaR in financial risk management
• Dynamic covariance in portfolio optimization
• Dynamic beta in portfolio management

My course #84 was Cleaning Data in Python. Topics covered in this course include:

• Data type constraints (dataframe preprocessing)
• Data range constraints (use .loc[] to assign column values)
• Uniqueness constraints (needs further review)
• Membership constraints
• Categorical variables
• Cleaning text data
• Uniformity
• Cross field validation
• Completeness [count .isna(); import missingno as msno]
• Comparing strings (from fuzzywuzzy import fuzz, process)
• Generating pairs (import recordlinkage)
• Linking dataframes

My course #85 was Quantitative Risk Management in Python. This was deep and needs more study. The course covers:

• What is quantitative risk management?
• Risk management and the financial crisis (import statsmodels.api as sm)
• Modern portfolio theory (from pypfopt.expected_returns import mean_historical_return)
• Measuring risk (from pypfopt.risk_models import CovarianceShrinkage)
• Risk exposure and loss (from scipy.stats import t)
• Risk management using VaR and CVaR (from pypfopt.objective_functions import negative_cvar)
• Portfolio hedging: offsetting risk
• Parametric estimation (from scipy.stats import norm, anderson, skewnorm, skewtest)
• Historical and Monte Carlo simulation
• Structural breaks
• Volatility and extreme values
• Extreme value theory (from scipy.stats import genextreme)
• Kernel density estimation (from scipy.stats import gaussian_kde)
• Neural network risk management (from keras.models import Sequential; from keras.layers import Dense)

I will review more courses next time.