Algorithmic Trading Workshop In MDP By NSE

Date: June 27 and 28, 2015

Time: 10:00 AM to 5:30 PM

Venue: NSE, Exchange Plaza, C-1, Block-G, Bandra Kurla Complex, Mumbai- 400051

Event Overview

Technology has revolutionized the way financial markets function and the way financial assets are traded. Technology development across global markets has necessitated a multidimensional approach for understanding the Importance of Algorithmic Trading.

It is imperative to develop domain knowledge expertise in quantitative and qualitative algorithmic trading skills. It helps to understand the market in a better manner and often allows us to frame difference strategies as per the market movements.

Given the current market scenario and dynamism, Algorithmic Trading has attracted attention more than ever before. The concepts are multi-fold and are applicable across all financial markets: equities, fixed income, currencies-domestic or global.

In view of this, NSE in association with QuantInsti presented a comprehensive workshop on Algorithmic Trading for analysts, dealers, traders, consultants, and other market practitioners as part of their Management Development Program (MDP)

Management Development Program by NSE

NSE's MDPs aims to enhance the competitiveness of executives of all levels in the financial industry. It is designed to assist professionals to take on a leadership role in their position individually and collectively while improving their knowledge. NSE's MDPs are vital for practising professionals and managers who are keen to take on leadership roles with their organizations.

QuantInsti’s Association with NSE

As the pioneer institute for learning Algorithmic Trading in Asia, our vision at QuantInsti is to expand the reach of Algorithmic Trading to each and every retail and institutional trader, by providing the right knowledge, skills, tools and attitude required to successfully trade in the markets. Our mission is to help every trader across the world to get initiated into the world of Automated Trading and benefit from technological innovations.

Thus our faculty established this association with NSE so that attendees can learn from both the rich practical experience of the faculty, as well as from the diverse experience of fellow learners. This was an ideal platform for gaining new insights in order to be successful.

Rajib Borah at NSE MDP Rajib Borah at Algorithmic Trading Management Development Program by NSE in 2014

In our previous Algorithmic Trading workshop in MDP, our faculty has shed light various automated trading strategies, tool, performance evaluation techniques, portfolio management techniques, rules and regulations. This time our faculty discussed new generation strategies and changes in the trading ecosystem in past few months.

Program Content of 2 Day Workshop

Day 1 Demystifying Algorithmic Trading

Decoding the jargon: Quant Trading, Algorithmic Trading, Automated Trading, High-Frequency Trading, Ultra-High Frequency Trading
Evolution of algorithmic trading - Globally
Evolution of algorithmic trading - India
Why should you do Algorithmic Trading - Benefits of Algorithmic Trading?
Global & Indian trends - volumes generated, etc

System Architecture and its impact on trading performance

Internal components of an algorithmic trading platform ( OMS, CEP, RMS, Adaptors, tickStore, eventStore, etc) and their interaction
External components - adaptor communication with destinations, communication standards and protocols (FIX, etc), TAP servers, multi TAP and invitation management
Technological setup for Indian markets - network connectivity (scenarios, message rates); different trading environments (mock, test); colocation vs non-colocation; tbt vs snapshot; native api vs FIX connectivity
Build vs buy decision ('building tools in house' vs 'buying off the shelf products')

Technological innovations for algorithmic trading

Latency, methods of measuring latency, standard latency benchmark figures
Software innovations - low latency codes,
Hardware innovations - cpu affinity vs scalability, FPGA vs ASIC, strategy on hardware, hardware configurations
Tools available for Indian markets - software, hardware, etc

Tool-box set of Algorithmic Trading

Statistics, Quant Finance, Computing
Key Statistical concepts relevant for designing algorithmic trading strategies

Different types of algorithmic trading strategies

High/Ultra high frequency strategies
Execution strategies: TWAP, VWAP, IS, etc
Alpha seeking strategies: - market-making, arbitrage
Different types of arbitrage strategies (structural and statistical)
Equity segment strategies: Index Arbitrage, Mean reversion, momentum, technical analysis, pair trading
Option Strategies: Dispersion, Volatility Spreads, Variance Swaps, Jelly Rolls, Skew trades
Multiple exchange strategies: Smart Order Routing strategies
Order Book Dynamics based trading strategies
What are different global firms doing?

Process of developing an algorithmic trading strategy

Entire life-cycle involved in designing & operating an algorithmic trading strategy
Working with high frequency data - managing tick databases
Normalizing and cleansing data
Hypothesis formulation
Machine learning methodologies to automate strategy development

Rules and Regulations

Auditing Process and Requirements (NSE defined)
SEBI recommendations on audit
Exchange audits
Technology and System audits
Compliance Requirements
Strategy approval process for Indian exchanges
Global trends in regulations

Day 2 Working on Algorithmic Trading Platforms - I

Complex Event Processing on Algorithmic
Trading Platforms
Working with exchange simulators and testing strategies

Risk Management specific to Algorithmic Trading

Risk Management for Trading Operations - different sources of risk, evaluation methodologies to quantify and set limits
Additional Risk Management issues in Automated Trading
Common errors encountered in Algorithmic Trading
Case studies of all major failures globally on Algorithmic Trading
Risk Management requirements for Indian Exchanges

Working with Quant Tools

Statistical big-data analytics using R
Using R with Excel

Performance Evaluation and Portfolio Management

Determining profitability of strategies using Sharpe ratio, Sortino ratio, Jensen's alpha, RaROC, Treynor Ratio, etc
Leverage Space Theory to allocate resources across strategies

Building Quant Tools

Practical exercise to build Options Portfolio Management Tools for algorithmic trading

What next

Exchange innovations
Competitive Landscape
New generation strategies - Machine Readable News based strategies, etc

Future studies

Literature review of books, study material and research papers on algorithmic trading

Speakers

Gaurav Raizada is a Director at iRageCapital Advisory Private Ltd., leads the firm’s advisory practice in India on the Systems, Performance and Strategies. He has consulted extensively with core focus on strategy development and execution including trading systems development, latency reduction, optimization and transaction cost analysis. [/ststaff] Kunal Kumar works in the Trading Strategy Team at iRageCapital Advisory, responsible for developing new strategy along with fine-tuning existing ones. His focus area extends to process automation and maintaining trading infrastructure. Prior to that, he worked at ICICI Bank headquarters in their Treasury team, helping International Institutional clients with their FX, Bonds and Derivatives requirements. He has also interned with Religare Capital Markets in Investment Banking Division and Tata Steel. [/ststaff] Nilesh Koshe works as Quantitative Associate at iRageCapital Advisory in their Derivative strategy team. His focus area is data modelling using machine learning techniques, automating trading decision for algo trading. Prior to iRage Capital, he worked with one of the leading Investment Bank as Derivative Strategist, proving derivatives solution to buy side clients including insurance funds, pension funds etc. He served in Asian and European markets. [/ststaff] Rajib Ranjan Borah is a co-founder and director at iRageCapital & QuantInsti. In the past 10 years, he has worked in various key roles related to automated trading in US, Europe & Asia with pioneering firms from the industry like Bloomberg LP, Optiver LLP, iRageCapital – in roles spanning from designing and trading HFT strategies, technology for low latency trading, and strategy advisory to a consortium for starting a new commodity derivative exchange. A national Olympiad finalist, Rajib has twice represented India at the World Puzzle Championships.

Extensive Courses for Learning Algorithmic Trading and Quantitative Finance

Post completion of this workshop attendees received a lot of theoretical and practical insights about automated trading from our faculty. In order to learn and implement these best practices and strategies, one can join our Executive Programme in Algorithmic Trading (EPAT). It is a 6-month program conducted on the weekends (online as well as in a classroom). We also provide our participants access to various tools and test market data for project purposes.
Event Photos