Quantitative Trading - 1. Introduction
Based on notes of MFIN 7037 Quantitative Trading of HKU.
Recent Posts
Macroeconomics, Economic Principles in the Real World - Part 2
Notes for The Power of Macroeconomics: Economic Principles in the Real World from coursera.
Macroeconomics, Economic Principles in the Real World - Part 1
Notes for The Power of Macroeconomics: Economic Principles in the Real World from coursera.
Can Tweets Explain Stocks
A blog post by ‘Group Researches’. In this blog, we summarize our findings on the topic of ‘Analyzing the relationship between tweets about SP500 and the rea...
Fixed Income Securities and Interest Rate Models
Notes for MFIN 7036 Fixed Income Securities and Interest Modelling. Also add some important information from other sources.
Visualization of high-dimensional datasets using PCA and t-SNE
Use PCA and T-SNE to reduce the dimension of dataset, for better visualization result, as well as better accuracy for machine learning models.
Ho-Lee Model (a Binomial Tree Model)
An implementation of Ho-Lee model with Bionimal Tree Framework. It can be used for pricing interest rate derivatives and bonds.
Asset Pricing Methods
Some notes about different approches for asset pricing.
Twint - Twitter Intelligence Tool
An advanced Twitter scraping & OSINT tool written in Python that doesn’t use Twitter’s API, allowing you to scrape a user’s followers, following, Tweets ...
Time-independent Models for estimating Value at Risk
Parametic and historical method for calculation of VARs. Both time-independent and time-dependent models are included.
Return to RiskMetrics (notes)
An update and restatement of the mathematical models in the 1996 RiskMetrics Technical Document, now known as RiskMetrics Classic. RiskMetrics Classic was th...
Statistics - HKU Bootcamp
Basic topics of statsitics convered in the HKU stats bootcamp.
MathJax basic tutorial and quick reference
Some tricks about mathjax and the block enviroment for theorems, lemmas and proofs, etc.
Quantitative Financial Risk Management
Anoter book from Michael B. Miller, which is organized around topics in risk management, such as market risk, credit risk and liquidity risk.
Mathematics and Statistics for Financial Risk Management—Part 3
Notes for the book by Michael B. Miller. It includes basic concepts in mathemtacs and statistics which are commonly used in the risk mangement process.
Mathematics and Statistics for Financial Risk Management—Part 2
Notes for the book by Michael B. Miller. It includes basic concepts in mathemtacs and statistics which are commonly used in the risk mangement process.
Mathematics and Statistics for Financial Risk Management—Part 1
Notes for the book by Michael B. Miller. It includes basic concepts in mathemtacs and statistics which are commonly used in the risk mangement process.
How to Speak by Patrick Winston
Patrick Winston’s How to Speak talk has been an MIT tradition for over 40 years. Offered every January, the talk is intended to improve your speaking ability...
Machine Learning With Python
Review for the implementation of machine learning algorithms with python.
Data Visualization
Introduction to basic data visualization tools.
Data Analysis With Python
Framework for data analysis form IBM certificate corse.
Machine Learning Part 7：Large Scale Machine Learning
Gradient descent and data parallelism for large scale machine learning problems.
Machine Learning Part 6：Recommender System
About how to use the collaborative filtering algorithm to build a recommemder system.
Machine Learning Part 5：Clustering
Introduction to several useful unsupervised algorithms for clustering, dimensionality reduction and anomaly detection.
Machine Learning Part 4：Support Vector Machine (SVM)
The algorithm of SVM for linear classfication. Kernal functions are introduced for the application of SVM in non-linear classfications.
Machine Learning Part 3：Evaluating and Designing
An introductin to evaluating and designing machine learning algorithms. Specifically, we talk about the balance between bias and variances and the learning c...
Machine Learning Part 2： DNN
Problem representation and learning algorithms for neural networks.
Machine Learning Part 1： Basics
A brief introduction to machine learning. Foundamental concepts and algorithms for regression and classification are given.