QF101 by CEV Aryavarta
This course is an initiative by CEV to equip talented and enthusiastic engineers for the world of Quantitative Finance. Post 2008 crisis data world has made this field very famous and lucrative. Although Indian Universities haven’t yet offered QF majors, except a few, mostly IITs and ISI, almost every top T and B schools in the West have been teaching them since before the ‘08 crisis. The courses go by varied names – Quantitative Finance, Mathematical Finance, Financial Engineering, etc. all mostly pertaining to the same fields with almost similar curricula (with subtle differences, of course)
What is Quantitative Finance?
Quant Finance is the field wherein emphasis is given on math models, probability, statistics, and quantitative models to predict future prices of various Financial instruments like equities and derivatives. The field differs from traditional Finance in a way that we do not pay much attention to the financials or other regular finance 101 predictors and solely rely on these “Quant Models” to make buying/selling decisions. In a way, it is mostly an application of Pure Mathematics rather than Finance.
Why should you pursue this course?
The candidates are mostly engineering undergrads or pure science majors (math, physics), which are purely quantitative fields. It is highly likely that engineers have already fallen in love with math and algorithms. Hence, it becomes fascinating to pursue such a course, which is a direct application of math and coding. Apart from this, there is a steep rise in the number of engineers interested in Data Science, and Quant Finance is similar – In a way, Data Science applied to Finance! We are sure if Mathematics, Technology, Algorithms, Models are your thing, you will find the course exciting. Again, you don’t have to be experts, just passionate. We’ll handle the rest!
A brief idea
This course is designed in such a way that we will explore the required math and coding skills in parallel. The math models, methods which you’ll learn in a week will be applied in a programmatic way using Python. The course flow can be viewed here.
We will cover :
- Python and its required libraries
- Basics of Global Financial Markets and Instruments
- Linear Algebra
- Probability Theory
- Stochastic Processes
- Technical Indicators
- Algorithmic Strategies
- Volatility, VAR Models and Pricing, etc.
More information will be shared as the course proceeds. Mathematical part of the course will directly follow the MIT OCW course which goes by the name Topics in Mathematics with Applications in Finance
The course will try to follow this flow. Please have a look.
- Week 1 : Brushing up coding skills, Python
Basic hands on coding, Python with emphasis on its libraries :
Matplotlib, Pandas, NumPy, SciPy, Seaborn. Apply the learning
on a Data Analytical Exercise (Just to gain some Data intuition)
- Week 2 : Finance 101 – Basics of Markets
Idea of how Finances work, Concept of Money, Markets –
Commodities, Equities, Derivatives, Government Bonds, etc.
Basics of Stock Market and developing a compatibility with
World Business/Financial Market news.
- Week 3 : Mathematical Finance and Python – I
: Linear Algebra, Probability Theory, Stochastic Processes. Python
: Using Python for basic Financial Data Analysis.
- Week 4 : Mathematical Finance and Python – II
: Regression Analysis, VAR Models, Time Series Analysis Python
: Applying Python to Risk-Return Concepts, Markowitz
and other Portfolio Optimization models.
- Week 5 : Mathematical Finance and Python – III
: Volatility Modelling, Regularized Pricing and Risk Models,
TS Analysis. Python
: Practice of Algorithmic Trading, 5 Technical Indicators,
Introduction to Strategies.
- Week 6 : Assignments and Tests
Testing on the basis of what we learn in 5 Weeks. Top Achievers
will be carefully noted and will be considered for important
Positions of Responsibility which we are soon going to open up
within CEV and CEV Aryavarta.
Course Assignments Tutorials/assignments will be uploaded as the course proceeds. Keep checking this tab as and when instructed.
As the course proceeds, resources will also be updated. Currently only Week 1 resources are updated. The course will follow the material posted here. For further reading, understanding, materials will be discussed in the communication groups. Each week has its own objectives. At the end of the week, you should make sure that you check most/all of the objectives.
Regards, course instructors.
Welcome to the course! We are happy that you decided to pick this course up. We hope that it will be an enriching experience for all of us. Week 1 will deal with getting started
with coding. This will include setting up your system, learning the basics of Python and its related libraries.
Week 1 Objectives
- Code any given algorithm/problem statement using Python.
- Learn some vital data libraries in Python.
- Able to quickly search for a functionality on the web. For example, a statistical function needed in an analytical project.
- Data intuition. Play around with data, use your creativity and analytical skills to find information out of structured Data Sets intuitively.
Week 1 Resources
- Introduction to Python Programming Language.
We believe it’s better to learn coding by doing. If you’re a complete newbie, you may solve the first few questions available at HackerRank Python.
You’d have to make an account if you don’t have one.
Solve the first 12 problems (till “Tuples”).
Note: You may skip this if you’re already comfortable with the very basics of Python.
- Setup and install Anaconda for Python
The process can be quickly learned on the web, in case of any doubt, you may ask us in the communication groups.
Setup a Kaggle Account. Pick one Dataset and start analysing using the week’s learning!
- This Week’s done. If still some days are left, watch some good movies 🙂 Contact us we have a wonderful list of movies with us at CEV.
Course Instructor (2020)
The Instructor for this course is Viraj Mohile. Final year undergrad (class of 2021), he has in the past handled the responsibility of Director at Cutting Edge Visionaries. Along with his team, he founded the first Quant Finance club of NIT Surat, CEV Aryavarta in December 2019. He is extremely passionate about data analytics, statistics, economics and quantitative methods in Finance. For further support, you can connect with him on LinkedIn