35 hours (usually 5 days including breaks)
This course provides a comprehensive introduction to the MATLAB technical computing environment + an introduction to using MATLAB for financial applications. The course is intended for beginning users and those looking for a review. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course. Topics include:
A Brief Introduction to MATLAB
Objectives: Offer an overview of what MATLAB is, what it consists of, and what it can do for you
Working with the MATLAB User Interface
Objective: Get an introduction to the main features of the MATLAB integrated design environment and its user interfaces. Get an overview of course themes.
Variables and Expressions
Objective: Enter MATLAB commands, with an emphasis on creating and accessing data in variables.
Analysis and Visualization with Vectors
Objective: Perform mathematical and statistical calculations with vectors, and create basic visualizations. See how MATLAB syntax enables calculations on whole data sets with a single command.
Analysis and Visualization with Matrices
Objective: Use matrices as mathematical objects or as collections of (vector) data. Understand the appropriate use of MATLAB syntax to distinguish between these applications.
Automating Commands with Scripts
Objective: Collect MATLAB commands into scripts for ease of reproduction and experimentation. As the complexity of your tasks increases, entering long sequences of commands in the Command Window becomes impractical.
Working with Data Files
Objective: Bring data into MATLAB from formatted files. Because imported data can be of a wide variety of types and formats, emphasis is given to working with cell arrays and date formats.
Multiple Vector Plots
Objective: Make more complex vector plots, such as multiple plots, and use color and string manipulation techniques to produce eye-catching visual representations of data.
Logic and Flow Control
Objective: Use logical operations, variables, and indexing techniques to create flexible code that can make decisions and adapt to different situations. Explore other programming constructs for repeating sections of code, and constructs that allow interaction with the user.
Matrix and Image Visualization
Objective: Visualize images and matrix data in two or three dimensions. Explore the difference in displaying images and visualizing matrix data using images.
Objective: Perform typical data analysis tasks in MATLAB, including developing and fitting theoretical models to real-life data. This leads naturally to one of the most powerful features of MATLAB: solving linear systems of equations with a single command.
Objective: Increase automation by encapsulating modular tasks as user-defined functions. Understand how MATLAB resolves references to files and variables.
Objective: Explore data types, focusing on the syntax for creating variables and accessing array elements, and discuss methods for converting among data types. Data types differ in the kind of data they may contain and the way the data is organized.
Objective: Explore the low-level data import and export functions in MATLAB that allow precise control over text and binary file I/O. These functions include textscan, which provides precise control of reading text files.
Note that the actual delivered might be subject to minor discrepancies from the outline above without prior notification.
Overview of the MATLAB Financial Toolbox
Objective: Learn to apply the various features included in the MATLAB Financial Toolbox to perform quantitative analysis for the financial industry. Gain the knowledge and practice needed to efficiently develop real-world applications involving financial data.
Asset Allocation and Portfolio Optimization
Objective: perform capital allocation, asset allocation, and risk assessment.
Risk Analysis and Investment Performance
Objective: Define and solve portfolio optimization problems.
Fixed-Income Analysis and Option Pricing
Objective: Perform fixed-income analysis and option pricing.
Financial Time Series Analysis
Objective: analyze time series data in financial markets.
Regression and Estimation with Missing Data
Objective: Perform multivariate normal regression with or without missing data.
Technical Indicators and Financial Charts
Objective: Practice using performance metrics and specialized plots.
Monte Carlo Simulation of SDE Models
Objective: Create simulations and apply SDE models
Objectives: Summarise what we have learned
Note: the actual content delivered might differ from the outline as a result of customer requirements and the time spent on each topic.
Tomasz (the trainer) was knowledgeable and friendly and made the training very interesting. He helped me learnt a lot about a subject I was very new to.
Paul Cox - Network Rail
Trainer took the initiative to cover additional content outside our course materials to improve our learning.
Chia Wu Tan - SMRT Trains Ltd
Exercises were most beneficent thing in the sessions
Students interact to solve problems
chengyang cai - 东风康明斯
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