Quantitative Finance by CEV
An open-source community course on Quantitative Finance developed by the members of Cutting Edge Visionaries, NIT Surat. You may visit us here.
QF101, by Viraj Mohile, Head of Aryavarta 2020 & Director of CEV 2019-2020
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.
Course structure
The course will try to follow this flow. Please have a look.- Week 1 : Brushing up coding skills, Python
- Week 2 : Finance 101 – Basics of Markets
- Week 3 : Mathematical Finance and Python – I
- Week 4 : Mathematical Finance and Python – II
- Week 5 : Mathematical Finance and Python – III
- Week 6 : Assignments and Tests
Course Assignments
Tutorials/assignments will be uploaded as the course proceeds. Keep checking this tab as and when instructed.Course Resources
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.
Week 1
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.
- Get accustomed to Jupyter Notebooks.
- Learn Python Libraries
- Pandas
- Matplotlib
- NumPy and NumPy official Docs
- Seaborn Playlist
- SciPy
- Data Intuition
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.
Introducrion
The Course Resources are sufficient for you to sail through, however, in case you are passionate to explore more, you may find some additional resources here. This page will be regularly updated.Table of Contents
- Stock Prices
- Market Mechanics
- Data Processing
- Stock Returns
- Momentum Trading
- Quant Workflow
- Outliers & Filtering
- Regression
- Time Series Modeling
- Volatility
- Pairs Trading & Mean Reversion
- Breakout Strategy
- Stock, Indices & Funds
- ETFs
- Portfolio Risk & Returns
- Portfolio Optimization
- Smart Beta & Portfolio Optimization
- Factors
- Factor Models & Factor Types
- Risk Factor Models – Time Series & Cross Sectional
- Risk Factor Models with PCA
- Alpha Factors
- Alpha Factor Research Methods
- Advanced Portfolio Optimization
- Multi-factor Model
Tutorials
- Algorithmic Trading Strategies
- Artificial Intelligence for Trading
- Computational Investing, Part I
- Financial Engineering and Risk Management Part I
- Financial Engineering and Risk Management Part II
- MIT Open Courseware – Analytics of Finance
- MIT Open Courseware – Investments
- MIT Open Courseware – Topics in Mathematics with Applications in Finance
- Machine Learning and Reinforcement Learning in Finance Specialization
- Machin Learning for Trading
- Model a Quantitative Trading Strategy in R
- Trading Strategies in Emerging Markets Specialization
- Time Series with R
- Quantitative Analyst with R
Articles
- 10 Things to Know About Every Cash Flow Statement
- The 15 Stock Diversification Myth
- The Limitations of Ratio Analysis
- The Right Way and the Wrong Way to Benchmark a Diversified Portfolio
- Performance Measurement: The What, Why, and How of the Investment Management Process
- Utility Theory and Attitude toward Risk (Explained With Diagram)
- The Guide to Diversification
- Diversification: How much is too much?
- Successful Backtesting of Algorithmic Trading Strategies – Part I
- Successful Backtesting of Algorithmic Trading Strategies – Part II
Research Papers
- Are Markets Efficient?
- Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers
- Do Stock Prices Fully Reflect Information in Accruals and Cash Flows about Future Earnings?
- Betting Against Beta
- Momentum
- Separating Winners from Losers Among Low Book-to-Market Stocks Using Financial Statement Analysis
- The 101 Ways to Measure Portfolio Performance
- Does the Composition of the Market Portfolio Really Matter?
- Pairs Trading: Performance of a Relative Value Arbitrage Rule
Books
- Algorithmic Trading and DMA: An introduction to direct access trading strategies
- Building Winning Algorithmic Trading Systems, + Website: A Trader’s Journey From Data Mining to Monte Carlo Simulation to Live Trading (Wiley Trading)
- Finding Alphas: A Quantitative Approach to Building Trading Strategies
- Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
- Inside the Black Box: A Simple Guide to Quantitative and High Frequency Trading
- Python for Finance: Analyze Big Financial Data
- Technical Analysis Explained, Fifth Edition: The Successful Investor’s Guide to Spotting Investment Trends and Turning Points
- Quantitative Investing: Strategies to exploit stock market anomalies for all investors
Communities
- Advanced Risk and Portfolio Management
- Certificate in Quantitative Finance
- The Python Quants Group
- Quantor
- Quantopian
- QuantConnect
- QuantNet
- QuantStart
Aryavarta Resource Section
You may further check the CEV Aryavrata resource sectionCourse 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