NSE MDP Workshop June 2015
QI Director Mr. Rajib Borah at NSE MDP Workshop June 2015
QI Director Mr. Gaurav Raizada at NSE MDP Workshop June 2015
Mr. Rajib Borah at NSE MDP Workshop June 2015
Mr. Kunal Kumar at NSE MDP Workshop June 2015
Mr. Kunal Kumar Explaining Option Strategies
Mr. Kunal Kumar Explaining Pair Ratio
Invitation post on May 27, 2015:
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
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 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
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
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
- Exchange innovations
- Competitive Landscape
- New generation strategies – Machine Readable News based strategies, etc
- Literature review of books, study material and research papers on algorithmic trading
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.
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.
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.
Rajib Ranjan Borah
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.