I am a software engineer specializing in Fintech risk monitoring systems. Previously, I worked as a macroeconomic analyst/trader, focusing on trading opportunities based on a global macro framework, particularly in currency and commodity markets.
These materials were initially prepared by me for new-hire training at my previous institution, where I also served as chief macro analyst and quantitative instructor. We organized internal training sessions for interns, new-hires and even university students, typically held from 7pm-11pm in our conference room. The notes are designed to be approachable, requiring only a basic understanding of freshman-level math.
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| Course | Description |
|---|---|
| Linear Algebra with Python | This training will walk you through all the must-know concepts that set the foundation of data science or advanced quantitative skill sets. Suitable for statisticians, econometricians, quantitative analysts, data scientists, etc. to quickly refresh linear algebra with the assistance of Python computation and visualization. Core concepts covered are: linear combination, vector space, linear transformation, eigenvalues and -vector, diagnolization, singular value decomposition, etc. |
| Basic Statistics with Python | These notes aim to refresh the essential concepts of frequentist statistics, such as descriptive statistics, parameter estimations, hypothesis testing, ANOVA and etc. All codes are straightforward to understand. We were spending roughly three hours in total to cover all sections. |
| Econometrics with Python | This is a crash course for reviewing the most important concepts and techniques of econometrics. The theories are presented lightly without hustles of mathematical derivation and Python codes are mostly procedural and straightforward. Core concepts covered: single and multi-linear regression, logistic model, dummy variable, simultaneous equations model, panel data model and time series analysis. |
| Financial Engineering | This is a compound training sessions of time series analysis, financial engineering and algorithmic trading. The Part I covers the basics of sell-side financial engineering such stochastic processes, partial differential equations, Black-Scholes model, mixed jump-diffusion model, and etc. The Part II will cover the buy-side financial engineering such portfolio-optimization, multi-factor modeling, Black-Litterman model, etc. |
| Bayesian Statistics with Python | Bayesian statistics is the last pillar of quantitative framework, also the most challenging subject. The course will explore the algorithms of Markov chain Monte Carlo (MCMC), specifically Metropolis-Hastings, Gibbs Sampler and etc., we will build up our own toy model from crude Python functions. In the meanwhile, we will cover the PyMC3, which is a library for probabilistic programming specializing in Bayesian statistics. |