Python for Quantitative Analysis and Algorithmic Trading

Algorithmic trading refers to the computerised, automated trading of financial instruments based on some algorithm or rule with little or no human intervention during trading hours. Almost any kind of financial instrument — be it stocks, currencies, commodities, credit products or volatility — can be traded in such a fashion. Python has becoming the programming language for Algorithmic trading. This course teach you the basics of algorithmic trading and quantitative Analysis using Python. The topics include:

• Option Trading with Python
• Exotics
• Time Series Analysis

Module 1. Vanilla Options and Strategies

• What is Vanilla Option?
• Call and Put Options
• European vs American Options
• Black-Scholes Model
• Python Implementation of BS Model
• Greek Options
• Option Strategies
• Binomial Options Pricing Model

Module 2. Exotic Options

• What is Exotic Options?
• Bermudan Option
• Chooser Option
• Shout Call Option
• Binary Option
• Barrier Option

Module 3. Monte Carlo Simulation

• What is Monte Carlo Simulation?
• Random Number Generation
• Stock Price Simulation
• MC Simulation for Black-Scholes Model

Module 4. Time Series Analysis

• Download Finance Data from Web
• Correlation between time series
• Autocorrelation
• ARIMA

Module 5. Volatility and Implied Volatility

• Implied Volatility

Module 6. Volue at Risk (Optional)

Sanjay is a Director at Alpha Alternatives Advisors, a Fund Management Company in Singapore, responsible for execution of investment mandates. Sanjay has over 15 years’ experience in Investment Banking and Asset Management across New York, London and Singapore.

Sanjay started his career in Finance as a trading desk quant at Lehman Brothers, New York. He worked closely with traders to develop and test equity derivatives trading strategies to exploit arbitrage, mean-reversion and other fundamental relationships. He was instrumental in building a Risk Management Advisory practice (Quant Advisory) with Ernst & Young in New York and London. He executed several key credit & market risk mandates with major investment banks like Morgan Stanley and Citigroup Subsequently he changed roles to become a Capital Markets Banker with Standard Chartered Bank (SCB) from 2007 to 2013 initially based in London before moving to Singapore in 2010.

Sanjay holds a Masters in Computational Finance (Financial Engineering) from Tepper School of Business, Carnegie Mellon University, Pittsburgh USA. He also holds a Bachelors in Electrical Engineering (Hons).

Sanjay is an engineer at heart and has extensive programming experience beginning with COBOL on mainframes, C/C++ on Unix and subsequently on modern languages like Java. He was one of the first Sun Certified Java Developer in 2000. His quantitative specialities include:

• Machine Learning/Deep-learning Toolkits: PyTorch and Tensorflow, Python and its associated popular libraries like Pandas, Scikit-learn, Excel/VBA
• Visualization and Statistics using Python, R, Tableau and Excel/VBA
• Algorithms for machine learning: Regression Modeling, Decision Trees, Clustering, Text Mining, Simulation methods and Time Series

Johnson Ang has a Masters in IT Analytics from SMU and is certified and a member of GARP, CQF andFSM. He has more than 8 years of working experience, of which 4 years of experience in the Banking Wide Operations and has been particularly involved in mega IT Analytic banking projects within the Risk Management and Financial Instrument sphere.

He can offer varied solid perspectives and has experience working with folks from all sorts of background, such as Developers, Traders/Dealers, CxOs, Programmers, Project Managers, Auditors, Head/Country Directors, Regulators, RMs, which is particularly during his stint in the Front, Middle and Back Office, as well as industry such as Real Estate, FMCG, Construction and Education Sector.

Not only he is familiar with typical banking software, but also has also been acquainted with popular tools such as SAS, JMP, Oracle, VBA, Access, Python, R, C, Java and many more. He has also developed a strong passion for Mathematics, Trading and Computing at a young age and has been a self-professed Algo-Trader having a Sharpe ratio of >3 and a passionate Math Tutor having taught over hundreds of students from Primary to University level.

Dr. Cao Yongxin (Steve) has more than five years of solid industrial and research experiences in data analytics. He held PdD on operations research in National University of Singapore. After his Phd study, he worked as data analyst and scientist in manufacturing and commodity trading companies. He is specialized in analyzing, visualizing and mining complex time series data using Python tools sucha as Pandas, Numpy, Matplotlib, SK-Learn, Tensorflow etc. He did research on predictive analytics on various systems e.g., Forex and commodity trading, system performance prediction, machine maintenance, etc. Besides data analytics using Python, he also extended his expertise in Microsoft Excel VBA, Hadoop big data platforms such as AWS and Microsoft Azure. In addition, he is strong in mathematical modelling and has cross-disciplinary domain knowledge in operations research, currency and commodity trading and industrial automation.