Quantinsti’s webinar on algorithmic trading

Quantinsti’s Webinar on Algorithmic
Trading
Nitesh Khandelwal
Sep 23, 2013
© Copyright 2010-2014 QuantInsti Quantitative Learning Private Limited

Webinar agenda
• Algorithmic Trading: Introduction
• The Changing Trading Environment: Skills
• QuantInsti’s Executive Program in Algorithmic trading
(E-PAT)
• Algorithmic Trading Platform Architecture
• Strategy Paradigms
• Q&A

What is Algorithmic Trading
• Electronic Trading:

• Electronic Trading: Sending orders over an electronic
network instead of calling a broker

• Algorithmic Trading:

• Algorithmic Trading: Using Algorithms to generate trading
signals and manage portfolios

• High-Frequency Trading:

• High-Frequency Trading: A special category of algorithmic
trading characterized by unusually brief position-holding
periods, low-latency response times, and high trading
volumes in a day

volumes in a day
• Quant Trading:

volumes in a day
• Quant Trading: Usage of advanced statistical methodologies
- no user discretion
– Could be low frequency / high frequency
– Could be manual / electronic

Algorithmic Trading in India
• Allowed by SEBI in April 2008
• Estimated share of Algorithmic Trading Volume in Overall
Volume:
– Index & Equity Futures & Options: 40%-50%
– Commodity Futures (Non Agri): 20%

Algorithmic Trading in India
• Important Steps taken by Exchanges and Regulators to create
conducive environment for Algorithmic Trading:
– Colocation facility
– Tick by Tick data
• Normal Vs. Bucket Feed
– High capacity interactive lines
– Smart Order Routing
– Speedy empanelment process
– New membership categories
– Compliance

Algorithmic Trading: Career

Concepts of Diverse fields
Statistics
Finance
Computer
Science
Operations
Research
Economics
Psychology
History
Mathematics
Strategy

E-PAT course structure
Core Content
Statistics and Econometrics
Financial Computing &
Technology
Algorithmic & Quantitative
Trading

E-PAT Course Structure: Statistics and
Econometrics
Core Content
Technology
Trading
 Probability and Distribution
 Statistical Inference
 Linear Regression
 Correlation vs. Co-integration
 ARIMA, ARCH-GARCH Models
 Multiple Regression
Basic Statistics
Advanced Statistics
Time Series Analysis
 Stochastic Math
 Causality
 Forecasting

E-PAT Course Structure: Financial Computing
& Technology
Core Content
Technology
Trading
 Intro to Programming Language(s)
 Programming on Algorithmic
Trading Platforms
 Linear Regression
 System Architecture
 Understanding an Algorithmic
Trading Platform
 Handling HFT Data
Programming
Technology for Algorithmic
Trading
Statistical Tools
 Excel & VBA
 Financial Modeling using R
 Using R & Excel for Back-testing

E-PAT Course Structure: Financial Computing
Core Content
Technology
Trading
 Statistical Arbitrage
 Market Making Strategies
 Execution Strategies
 Forecasting & AI Based Strategies
 Machine readable News based
 Trend following Strategies
 Option Pricing Model
 Time Structure of Volatility
 Dispersion Trading
 Volatility Forecasting & Interpretations
 Managing Risk using Greeks
 Position Analysis
 Order Book Dynamics
 Market Microstructure
Trading Strategies
Derivatives & Market
Microstructure
Managing Algo Operations
 Hardware & Network
 Regulatory Framework
 Exchange Infrastructure & Financial
Planning (Costing)
 Handling Risk Management in
Automated systems
& Technology

E-PAT course mapping

Mapping Skill Set
Trading
Knowledge
Software
Development
Quantitative Skills
Conventional
Trading
Sales Trading Algo Trading Broking
Asset
management
– Mid Office
Asset
management
– Front
Office

Program Delivery
• Weekends only program
– 3 hrs sessions on Saturday & Sunday both
– 4 months long program
– Practical Oriented
– 100 contact hours including practical sessions
• Convenience – Conducted online
• Open Source
• Virtual Classroom integration
• Student Portal

Going beyond the curriculum
• Faculty supervision
• Placement assistance
• Forum

Algorithmic Trading Platform: A Sneak Peek
Source: marketcetera

Algorithmic Trading: System Architecture
Application Server Exchange
Application
Market Data
F
I
X
Complex Event Processing
engine
Order Manager
MktData
Store
Event
History
Exchange
1
Storage
Strategy
Settings
UI
State
Mgmt (PnL
+ Position)
Order /
Execution
Monitor
Within
application
RMS
Maths
Calc
RMS
Admin
Monitor
Exchange
2
F
I
X
Data
Normalizer
Order
Router
Back
office
record
Adaptor for
third party
apps – R,
Matlab, etc
Data
Retrieval
Data Vendor
Replay
of stored
data
Simulator
exchange

Strategy Paradigms
Market
Making
 Algorithms that try to profit from the bid ask spread
 Algorithms that try to take advantage of temporary mispricing
 Algorithms that try to encash on momentum
 Algorithms based on mean reversion strategy. Mostly pair based.
Arbitrage
Momentum
Statistical
Arbitrage
M/c Readable
News
AI based
 Algorithms are used to read & interpret the news/economic data for making
trading decisions
 Algorithms are used predict very short term range at a certain confidence
interval

Coming dates
• http://www.quantinsti.com/importantdates.h
tml
Date Event Venue
27th September, 2013
Last date for Early bird registration for 17th
batch of EPAT*
Online
26th October, 2013 Classes for EPAT batch 17th start Online & offline

Q&A
• Please type your questions in the chat
window.
Important dates
Date Event Venue
27th Sep, 2013 Last date for Early bird registration for 17th batch of EPAT* Online
26th Oct, 2013 Classes for EPAT batch 17 starts Online & offline
http://www.quantinsti.com/importantdates.html
*Scholarships: http://www.quantinsti.com/scholarships.html

Thanks!
• For more information visit:
Youtube channel
LinkedIn group

Quantinsti’s webinar on algorithmic trading

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