Post Graduate Program in Algorithmic Trading (PGP-AT)
6 Months (Part Time)

The Financial Markets the world over have seen a major paradigm shift in how trading is done. Algorithmic Trading (abv. Algo Trading) also known as Program Trading or Automated Trading, essentially implies that the trading is done by computer programs. Currently a vast majority of the trades in some of the markets are algorithmic in nature.

These algorithms depend on quantitative techniques for formulating trading strategies, detection of profitable trade opportunities, generating trade signals, generating the trades and trade order execution. At each stage there is extensive use of technologies.

Algorithm Trading, both High-Frequency as well as Low Frequency, using Quantitative Methods is now a very lucrative career. A breed of traders known as the Algo-Traders or Quant-Traders has emerged who have certain skill-sets that are much sought after in the industry.

Course Highlights

Highly qualified industry practitioner faculty
Advanced Curriculum
Thoroughly hands-on training in programming algorithmic trading strategies in Python
Training on industry leading algorithmic trading platforms
Training in Simulation Lab
Live Trading experience in real market

About the Course

The PGPAT course conducted by IIQF is taught by highly qualified and experienced market practitioners and is a job-oriented course that aims to produce industry-ready Algo-Traders, who can join trading desks of various financial institutions or setup their own independent algorithmic prop trading desks.

Who Should Attend

Fresh Graduates	Management Students	Finance Professionals
Dealers	Prop Traders	Arbitrageurs
Retail Traders

Course Details

Duration	6 Months (Part Time)
Schedule	Saturdays and Sundays
Start Date	15th January, 2022
Course Fee	INR 95000 / USD 1400 for participants registering from outside India
Eligibility	Graduate degree in science / economics / commerce / engineering / managament (with mathematics as one of the subjects)

Brief Course Outline

Introduction to Algorithmic and Quantitative Trading	Technical Trading Strategies
Options Trading Strategies	Strategy Development and Back-testing
Money Management and Risk Management	Algorithm Trading Infrastructure Setup
Algorithmic System Design and Implementation	EWMA, ARMA, ARIMA, GARCH
Machine Learning for Quantitative Trading Using Python	Quantitative Directional Strategies
Statistical Arbitrage Strategies	Index Arbitrage
Spread Arbitrage	Gamma Scalping
Volatility Trading	Risk Reversal / Volatility Skew Trading
Dispersion Trading	Electronic Market Making Strategies
Working with Tick Data	Market Microstructure and Concepts
Order Book Dynamics	Bid-Ask Spread
Bid-Ask Bounce	Latency Considerations
Execution Algorithms

Algorithmic Trading Lab

Both Simulated Trading Lab as well as Live Trading Lab fully equipped with advanced algorithmic trading platforms and statistical analysis systems.

Faculty

Abhijit Biswas, is the founding Director and Head of Product Development at Risk Infotech Solutions, India’s pioneering company in Portfolio Risk Management Software Products. He is currently consultant to HPC Links which is involved in the development of Quantitative Finance solutions and services using High Performance Parallel Computing technologies in Algorithmic Trading, Risk Analytics, etc. He is also consultant to financial institutions for Quantitative Volatility Trading systems. He is also the founding Director of IIQF. He has been a consultant to major global financial institutions in risk management domain. More...

Dr. Hari, Ph.D in Applied and Computational Mathematics from IISc Bangalore. He is a Quantitative Researcher and Data Scientist who has Worked as trader, researchers, data scientist, developer on different projects.

Dr. Narayana Darapaneni, Ph.D. in Mathematics from University of Paris VI and Indian Institute of Science (Bangalore, India) and a M.S. in Mathematics from Pondicherry University (India). His expertise is in parallel multilevel Monte Carlo methods, machine learning, time series analysis, optimization, trading strategies, risk management, R, Python and Big Data technologies. He has worked as Research Consultant with a leading HFT firm where he was involved in developing machine learning and statistical techniques for algorithmic trading and HFT strategies design and development. Prior to that he has worked with Edelweiss Securities as Deputy Vice President and was responsible for Running High frequency trading (HFT) book using machine learning and statistical techniques and previously have also worked at Deep Value Technology as Senior Quantitative Analyst responsible for Indicators design and development, HFT strategies design and development, portfolio optimization and risk management.

Learning Outcomes

Successful completion of the course will train the candidates to:

	Carry out Statistical Analysis of Data using Statistical Packages to formulate Algorithmic Trading Strategies
	Build, Back-test, Optimize and Implement Quantitative Algorithmic Trading Strategies
	Integrate the Trading Strategies with Algorithmic Trading Platforms

Course Calendar

Centre	Start Date
Online	15th January, 2022

Registration Process

You may register online for the course by filling up the form given here .

Contact Details

Mumbai Office:
Module No. 624, Mastermind IV,
Royal Palms IT Park, Goregaon (E),
Mumbai - 400065

Office Hours:
Weekdays : 10:00 AM - 7:30 PM
Saturdays & Sundays : 2:30 PM - 7:30 PM

Contact Person:
Nitish Mukherjee : +91-22-28797660 / +91-9769860151