Presentation on High Frequency Trading risks delivered during OpRisk conference in London in June 2012. Content includes an overview of key risks affecting high frequency trading.
1. Failure to meet regulatory and exchange requirements.
2. Removal of human decision making once the algorithms are finished.
3. Extreme market behaviour: Flash Crash (2010).
4. Theft or loss of Intellectual Property.
5. Errors or problems suffered by clients using Direct Market Access and Algo/HFT.
6. Business impact of latency (system errors may increase delays).
7. Limited security controls at the infrastructure level.
8. Failure of hedges. 9. Incorrect/untested strategies.
David Ramirez
IT Audit Director
In The Speed Traders, Edgar Perez, founder of the prestigious business networking community Golden Networking, opens the door to the secretive world of high-frequency trading (HFT). Inside, prominent figures of HFT drop their guard and speak with unprecedented candidness about their trade.
Today’s trading is complex and frequently involves little human intervention. Five years after the "Flash Crash," do you know how high frequency trading and dark pools work? Our new report separates fact from fiction.
EXANTE's lecture at Stockholm School of Economics in Riga.
– Objectives of algorithmic trading
– Various types of algorithms
– The process of creating one
– Testing and evaluation
– Understanding the possible pitfalls (and solutions)
In The Speed Traders, Edgar Perez, founder of the prestigious business networking community Golden Networking, opens the door to the secretive world of high-frequency trading (HFT). Inside, prominent figures of HFT drop their guard and speak with unprecedented candidness about their trade.
Today’s trading is complex and frequently involves little human intervention. Five years after the "Flash Crash," do you know how high frequency trading and dark pools work? Our new report separates fact from fiction.
EXANTE's lecture at Stockholm School of Economics in Riga.
– Objectives of algorithmic trading
– Various types of algorithms
– The process of creating one
– Testing and evaluation
– Understanding the possible pitfalls (and solutions)
Algorithmic trading, also called automated trading, black-box trading, or algo trading, is the use of electronic platforms for entering trading orders with an algorithm which executes pre-programmed trading instructions accounting for a variety of variables such as timing, price, and volume.
Order book dynamics in high frequency tradingQuantInsti
An important task of high-frequency trading is to successfully capture the dynamics in the Data. Empirical Data on Indian Exchanges show that 95% of all NEW orders are placed within 5 ticks of best-bid and best-ask. The Quantinsti® Replacement Matrix shows that most of the orders that are being replaced are among the top 3 levels and these replacements allow us to visualize and generalize about market behaviour. This matrix gives a visual representation of the cost metrics and replacement behaviour.
Execution Algorithms provide a price which is between Limit Order Execution and Market Order Execution. Market Orders guarantee execution within a certain time but the price that it may get the trader remains uncertain. Limit Order guarantees the price but it may remain un-executed if price moves away. Most Execution Algorithms balance between these two order types.
The speaker, Mr. Gaurav Raizada, discusses Quantinsti® Replacement Matrix in the webinar along with basics on order book management theory for high frequency traders.
Changing Notions of Risk Management in Financial MarketsQuantInsti
Presentation on "Changing Notions of Risk Management in Financial Markets - Impact of Proliferation of Automated Trading Systems and Technology on Financial Markets".
This presentation explains about the changing landscape of trading risk management, trends in automated trading, major automated trading risk failures, regulatory framework in India and all the risk management process phrases.
This presentation was presented by senior QI faculty and co-founder Rajib Ranjan Borah at the pre-conference workshop of the "India Risk Management Week", in Mumbai at 22nd May 2014.
This presentation will help you understand about risk management in automated trading and give you a clear picture about how automated trading is changing the way of trading in India.
Slides for speech of EXANTE Managing Partners Vladimir Maslyakov and Anatoliy Knyaze , entitled "Practical aspects of algorithmic trading and high-frequency trading", on TradeTech Russia 2011
Presentation highlights the problems associated with the development of a model (pre-trade analysis), the launch of the strategy (trading) and the post-trade analysis, as well as an overview of the algorithmic trading in general, and a small glimpse into the future.
Changing Notions of Risk Management in Financial MarketsQuantInsti
The presentation is a part of QuantInsti's Webinar on "Changing Notions of Risk Management in Current Markets" which was conducted on 10th August, 2015.
In the presentation Mr. Rajib Borah, Director and Faculty at QuantInsti, talks about a few major risk oversight issues in algorithmic trading, like:
a. How did Knight Capital lose $460 in 45 minutes?
b. Why was Deutsche Bank forced to close their Algorithmic Trading desk in Tokyo?
c. What went wrong at Infinium Capital while trading Crude ETFs and why were they fined $850,000?
d. What mistake caused HanMag Securities of Korea to lose 57 billion Korean Won in a few minutes?
e. and a few more
QuantInsti's (http://www.quantinsti.com) flagship offering is the 'Executive Programme in Algorithmic Trading' (EPAT) which is a comprehensive course covering all important aspects of Algorithmic Trading. Apart from detailed theoretical lessons, we provide our course participants in-house proprietary tools and other globally renowned applications in a simulated environment -- course participants can design, implement and test their strategies in such environment and build on their learning in the class.
Download Slides: http://www.slideshare.net/QuantInsti/...
You can contact us at: (+91) 22- 61691401 or (+91) 9920448877 or Toll Free: 1800-266-5401 for any queries you might have.
The presentation is part of a conference conducted by QuantInsti Quantitative Learning Pvt. Ltd. along with a multinational investment banking firm that engages in global investment banking, securities, investment management, and other financial services.
Indian Exchanges follow the Price-Time Priority principle in Limit Order Books. This allows for quantification of the costs that a trader is willing to pay or receive in order to Trade. In order to gain price priority, the cost is in terms of ticks paid to gain price precedence over others. In terms of Time priority, it is atleast one tick, that allows the trader's order to leapfrog others at the same price level.
You can find a detailed version of this presentation on our blog - http://www.quantinsti.com/blog/empirical-analysis-of-limit-order-books/
Connect with us:
Facebook - http://facebook.com/quantinsti
Twitter - http://twitter.com/quantinsti
Youtube - http://youtube.com/quantinsti
Learn about the different types of algorithmic trading and how it actually works. Algorithmic trading is a growing trend. I Know First has an advanced self-learning algorithm that has helped many investors achieve magnificent returns. I Know First's live portfolio returned 60.66% in 2013, beating the S&P 500 by over 30%!
High Frequency Trading & The Case For Emerging MarketsMark Finn
The increasing competition in HFT among hedge funds and other market participants will inevitably reduce alpha opportunities in developed markets and cause hedge funds to focus more on emerging markets that are less efficient.
A Flash Crash Simulator: Analyzing HFT's Impact on Market QualityYoshi S.
A 2014 CFTC report has concluded that the 2010 Flash Crash was not caused by high-frequency traders but was exacerbated by them with their market-making strategy known as the electronic liquidity provision (ELP). This paper presents a computational analysis of the impact of ELP-HFTs on core market quality during a flash crash. Specifically examined is how ELP-HFTs affect the attributes of core market quality such as liquidity, bid-ask spreads, and short-term price volatility. To investigate the question, we build a zero-intelligence limit order book (LOB) simulator from scratch, implement the ELP strategy in it, and execute simulations in which a flash crash is artificially created. Our results show that ELP-HFTs reduce bid-ask spreads, mitigate short-term volatility, and increase total trade volume. The increase in total trade volume is attributed to what is known as the “hot-potato” effect, which was also observed during the 2010 Flash Crash. However, we conclude that the ELP strategy by itself does not amplify directional price moves despite hot-potato effects.
Algorithmic trading, also called automated trading, black-box trading, or algo trading, is the use of electronic platforms for entering trading orders with an algorithm which executes pre-programmed trading instructions accounting for a variety of variables such as timing, price, and volume.
Order book dynamics in high frequency tradingQuantInsti
An important task of high-frequency trading is to successfully capture the dynamics in the Data. Empirical Data on Indian Exchanges show that 95% of all NEW orders are placed within 5 ticks of best-bid and best-ask. The Quantinsti® Replacement Matrix shows that most of the orders that are being replaced are among the top 3 levels and these replacements allow us to visualize and generalize about market behaviour. This matrix gives a visual representation of the cost metrics and replacement behaviour.
Execution Algorithms provide a price which is between Limit Order Execution and Market Order Execution. Market Orders guarantee execution within a certain time but the price that it may get the trader remains uncertain. Limit Order guarantees the price but it may remain un-executed if price moves away. Most Execution Algorithms balance between these two order types.
The speaker, Mr. Gaurav Raizada, discusses Quantinsti® Replacement Matrix in the webinar along with basics on order book management theory for high frequency traders.
Changing Notions of Risk Management in Financial MarketsQuantInsti
Presentation on "Changing Notions of Risk Management in Financial Markets - Impact of Proliferation of Automated Trading Systems and Technology on Financial Markets".
This presentation explains about the changing landscape of trading risk management, trends in automated trading, major automated trading risk failures, regulatory framework in India and all the risk management process phrases.
This presentation was presented by senior QI faculty and co-founder Rajib Ranjan Borah at the pre-conference workshop of the "India Risk Management Week", in Mumbai at 22nd May 2014.
This presentation will help you understand about risk management in automated trading and give you a clear picture about how automated trading is changing the way of trading in India.
Slides for speech of EXANTE Managing Partners Vladimir Maslyakov and Anatoliy Knyaze , entitled "Practical aspects of algorithmic trading and high-frequency trading", on TradeTech Russia 2011
Presentation highlights the problems associated with the development of a model (pre-trade analysis), the launch of the strategy (trading) and the post-trade analysis, as well as an overview of the algorithmic trading in general, and a small glimpse into the future.
Changing Notions of Risk Management in Financial MarketsQuantInsti
The presentation is a part of QuantInsti's Webinar on "Changing Notions of Risk Management in Current Markets" which was conducted on 10th August, 2015.
In the presentation Mr. Rajib Borah, Director and Faculty at QuantInsti, talks about a few major risk oversight issues in algorithmic trading, like:
a. How did Knight Capital lose $460 in 45 minutes?
b. Why was Deutsche Bank forced to close their Algorithmic Trading desk in Tokyo?
c. What went wrong at Infinium Capital while trading Crude ETFs and why were they fined $850,000?
d. What mistake caused HanMag Securities of Korea to lose 57 billion Korean Won in a few minutes?
e. and a few more
QuantInsti's (http://www.quantinsti.com) flagship offering is the 'Executive Programme in Algorithmic Trading' (EPAT) which is a comprehensive course covering all important aspects of Algorithmic Trading. Apart from detailed theoretical lessons, we provide our course participants in-house proprietary tools and other globally renowned applications in a simulated environment -- course participants can design, implement and test their strategies in such environment and build on their learning in the class.
Download Slides: http://www.slideshare.net/QuantInsti/...
You can contact us at: (+91) 22- 61691401 or (+91) 9920448877 or Toll Free: 1800-266-5401 for any queries you might have.
The presentation is part of a conference conducted by QuantInsti Quantitative Learning Pvt. Ltd. along with a multinational investment banking firm that engages in global investment banking, securities, investment management, and other financial services.
Indian Exchanges follow the Price-Time Priority principle in Limit Order Books. This allows for quantification of the costs that a trader is willing to pay or receive in order to Trade. In order to gain price priority, the cost is in terms of ticks paid to gain price precedence over others. In terms of Time priority, it is atleast one tick, that allows the trader's order to leapfrog others at the same price level.
You can find a detailed version of this presentation on our blog - http://www.quantinsti.com/blog/empirical-analysis-of-limit-order-books/
Connect with us:
Facebook - http://facebook.com/quantinsti
Twitter - http://twitter.com/quantinsti
Youtube - http://youtube.com/quantinsti
Learn about the different types of algorithmic trading and how it actually works. Algorithmic trading is a growing trend. I Know First has an advanced self-learning algorithm that has helped many investors achieve magnificent returns. I Know First's live portfolio returned 60.66% in 2013, beating the S&P 500 by over 30%!
High Frequency Trading & The Case For Emerging MarketsMark Finn
The increasing competition in HFT among hedge funds and other market participants will inevitably reduce alpha opportunities in developed markets and cause hedge funds to focus more on emerging markets that are less efficient.
A Flash Crash Simulator: Analyzing HFT's Impact on Market QualityYoshi S.
A 2014 CFTC report has concluded that the 2010 Flash Crash was not caused by high-frequency traders but was exacerbated by them with their market-making strategy known as the electronic liquidity provision (ELP). This paper presents a computational analysis of the impact of ELP-HFTs on core market quality during a flash crash. Specifically examined is how ELP-HFTs affect the attributes of core market quality such as liquidity, bid-ask spreads, and short-term price volatility. To investigate the question, we build a zero-intelligence limit order book (LOB) simulator from scratch, implement the ELP strategy in it, and execute simulations in which a flash crash is artificially created. Our results show that ELP-HFTs reduce bid-ask spreads, mitigate short-term volatility, and increase total trade volume. The increase in total trade volume is attributed to what is known as the “hot-potato” effect, which was also observed during the 2010 Flash Crash. However, we conclude that the ELP strategy by itself does not amplify directional price moves despite hot-potato effects.
EXTENT-2016: MiFID 2 Compliant Fixed Income SOR SystemIosif Itkin
EXTENT-2016: Software Testing & Trading Technology Trends
22 June, 2016, 10 Paternoster Square, London
MiFID 2 Compliant Fixed Income SOR System
Ferdinando La Posta, Co-founder and CEO, GATElab
Would like to know more?
Visit our website: extentconf.com
Follow us:
https://www.linkedin.com/company/exactpro-systems-llc?trk=biz-companies-cym
https://twitter.com/exactpro
#extent2016
#exactpro
Algo Trading – Best Algorithmic Trading Examples.pdfNazim Khan
https://pivotstocks.com/
Algo trading, or algorithmic trading, is the process of executing orders using automated, pre-programmed trading instructions that take time, price, and volume into consideration. Compared to human traders, this kind of trading aims to take advantage of computers’ speed and computational power. Algorithmic trading has become more popular in the twenty-first century among institutional and retail traders. According to a 2019 study, trading algorithms executed 92% of all trades on the Forex market, as opposed to human traders.
It is widely used by investment banks, pension funds, mutual funds, and hedge funds that may need to spread out the execution of a larger order or perform trades too fast for human traders to react to. However, it is also available to private traders using simple retail tools.
Understanding Algo Trading
Algorithmic trading and automated trading systems are frequently used similarly. These cover a wide range of trading strategies, many of which depend on specific software and are based on financial formulas and results.
Evolution over Time
Think of algo trading as the superhero upgrade of traditional trading. It started simple, executing straightforward orders, and now it’s a complex system with a bag of tricks. The “designated order turnaround” (DOT) system, launched by the New York Stock Exchange in the early 1970s, marked the beginning of the computerization of order flow in financial markets. An improved version of DOT was released in 1984 under the name SuperDOT. The electronic routing of orders to the appropriate trading post was made possible by both systems. The expert received assistance in figuring out the market clearing opening price (SOR; Smart Order Routing) from the “opening automated reporting system” (OARS).
Even so, relatively few people in India knew about the arrival of algorithmic trading in 2008. Because it is hard for humans to execute, it was designed to automatically execute a large number of market trades at exact timing and speed. Investors and dealers can conduct transactions on the stock market through automated processes thanks to algorithmic trading, often known as “algo trading.”
In India, algorithmic trading was first used by brokers and institutions and only started in 2010 or so. But with the growth of digital discount brokers and API solutions, the retail business now has unrestricted access to building algorithms with almost endless possibilities.
Reduced Human Errors
Algo trading is a well-oiled machine with key parts—smart algorithms, speedy data feeds, and slick execution methods—all working together for seamless trading. Algo trading is like the Flash of the financial world. It can make split-second decisions and grab opportunities before you blink.
Emotions can mess with your decisions. Algo trading keeps it cool, minimizing mistakes caused by human impulses. Algorithms follow a script, like a robot with a plan. This leads to accurate and consistent trading, ma
Real-time, high-frequency trading (HFT) is placing increasing pressure on regulatory compliance teams to keep up with and monitor the industry's widening pools of structured and unstructured data. Emerging technologies can help capital markets firms use big-data analytics to collect, classify and analyze high volumes of data to formulate strategies for better surveillance, compliance and spot abuse.
Not only does electronic trading continue to make our financial markets more competitive, but it has brought numerous benefits to all investors This presentation seeks to provide an overview of the evolution of electronic trading, provide clear definitions of often misused terms, and demystify electronic trading strategies like high frequency trading.
Among the topics discussed in this presentation:
The modernization of our financial markets using electronic trading
Definitions of electronic trading, algorithmic trading and high frequency trading
The Securities and Exchange Commission and high frequency trading
The Commodity Futures Trading Commission and high frequency trading
Regulatory framework in place to safeguard investors who invest in markets where electronic trading is prevalent
Trading in financial markets today is dominated by automated trading across most asset classes, but current programs are implemented using structured programming approaches which are static and represent a snapshot of the authors ideas, biases, and shortcomings at the time of implementation. Building automated trading bots that can learn from experience and can adapt to changing market conditions is changing the landscape and will deeply change trading as we know it.
In this presentation we will explore the history of automated trading, the environment in which these programs operate, current state, and challenges of the current approach. We will explore how a machine learning approach can be applied to automated trading and the forces driving this transformation. Analysis, which used to take hours or days, can now be done in seconds, back-testing over a larger length of time with fuller data now possible, and more data sources are available that can be used to build richer more accurate models.
Speaker
Diego Baez, GM Financial Services, Hortonworks
Impact 2013: How Technology is used for real-time monitoring of Dodd-Frank Tr...jKool
Presentation delivered at IBM IMPACT 2013
Dodd-Frank Trade Reporting regulations were enacted to ensure improved transparency and accountability for trade execution and reporting. However, there are key challenges such as: how do swap dealers ensure compliance and how can this be done in real-time as the windows for course correction are small.
-Real-time trade surveillance across the lifecycle of a reportable trade
- NACK Management
- How to provide visibility in real-time to actual or potential breaches in responsibility with the flexibility to change as the regulation evolves
- See the video at: http://www.nastel.com/dodd-frank-webinar.html
How tech-is-used-real-time-monitoring-dodd-frank-trade-reporting
Op Risk High Frequency Trading June 14 Final
1. High Frequency Trading
Operational Risk Issues and Mitigation Measures
David Ramirez – Director, IT Audit
14 June 2012 – London 11.10-11.50 am
2. 2
Agenda
1
• Introduction and Key Concepts
2
• Details of Algorithmic Trading and HFT
3
• Key Risks
4
• Mitigating Mechanisms
3. 3
Taxonomy of Algorithmic Trading
“The use of computer algorithms to
Algorithmi automatically make certain trading
c Trading decisions, submit orders, and manage
those orders after submission”.
(Hendershott and Riordan, 2009).
High
Frequency “Employs extremely fast automated
Trading programs for generating, routing,
cancelling , and executing orders in
electronic markets.” (Cvitani and
Kirilenko, 2010)
Trading
Strategies “Market Making, Electronic Liquidity
Provision, Statistical Arbitrage,
Liquidity Detection, Latency Arbitrage,
etc” (Gomber and Arndt, 2011)
4. 4
Agenda
1
• Introduction and Key Concepts
2
• Details of Algorithmic Trading and HFT
3
• Key Risks
4
• Mitigating Mechanisms
5. 5
Latency vs. Position Timeline
High
Traditional
Long-Term
Investment
Latency
Algorithmic Trading
HF
Low
T
Short Long
How Long Position Held
6. 6
Latency? - Key Concepts
Trading Risk Book
Market Data Trade Order
Logic Management Processing
There is some
The Algorithm (algo) Risk Management The order needs to
latency within the
Data from exchange, would need to take checks on the orders: arrive from the
exchange, tends to
news, other decisions based on size, frequency, fat system hosting the
be minimal at
participants. high volumes of fingers, VAR, short algo, to the
selling, etc. around 0.5
data. exchange.
milliseconds.
7. 7
Arbitrage:
•The practice of taking advantage of a price
difference between two or more markets: striking a
combination of matching deals that capitalize upon
the imbalance, the profit being the difference
between the market prices.
Collocation:
•Servers are hosted by the exchange (NYSE, LSE,
NASDAQ) in large data centres; access granted
directly to the exchange infrastructure.
8. 8
HFT Trading Strategies
•Market Making: Earn the •Market Neutral Arbitrage:
spread between bid and ask. Long and short; gain the
difference.
•Rebate Driven Strategies:
Leverage rebates offered by •Cross Asset/Market and
Exchange. Exchange Traded Fund
(ETF) arbitrage: Leverage
•Statistical Arbitrage: Predict
price inefficiencies across
discrepancies in the market.
asset/markets.
•Latency Arbitrage:
Predicting the ‘National Best
Bid and Offer’ value.
9. 9
Agenda
1
• Introduction and Key Concepts
2
• Details of Algorithmic Trading and HFT
3
• Key Risks
4
• Mitigating Mechanisms
10. 10
Key Risks Related to HFT Environments
1. Failure to meet regulatory and exchange
requirements.
2. Removal of human decision making once the
algorithms are finished.
3. Extreme market behaviour: Flash Crash
(2010).
4. Theft or loss of Intellectual Property.
5. Errors or problems suffered by clients using
Direct Market Access and Algo/HFT.
11. 11
Key Risks Related to HFT Environments - cont
6. Business impact of latency (system errors
may increase delays).
7. Limited security controls at the
infrastructure level.
8. Failure of hedges. Incorrect/untested
strategies.
12. 1. Failure to Meet Regulatory and Exchange 12
Requirements
•Regulators and exchanges define message structures that must be
adhered to (regulatory and contractual); this includes specific flags on
the packets (short selling, max order size, frequency on same name,
dealing on restricted names/securities).
•September 2011, the SEC announced that it would start collecting
copies of algorithms for analysis. There is also a plan to collect live
logs from all exchanges.
•Time compliance: Have you closed a trade on time? How is it being
measured? (GPS and the IEEE1588v2 Precision Time Protocol (PTP);
Financial and stock exchange data centers are increasingly deploying
GPS receivers on the roof of the data center and then distributing GPS
timing throughout the data center.)
13. 1. Failure to Meet Regulatory and Exchange 13
Requirements– cont
Securities and Exchange Act 1934 and MAS
•“For the purpose of creating a false or misleading
appearance of active trading in any security registered
on a national securities exchange, or a false or
misleading appearance with respect to the market for
any such security,
14. 2. Removal of human decision making once the
algorithms are finished.
•Algorithms will be executing instructions without
any supervision, the potential for errors increases
significantly.
•Human intervention should be available at all
times, as expected by exchanges.
15. 15
3. Extreme market behaviour: Flash Crash
(2010).
Flash Crash – May 6 2010 – Runaway Algos – Domino Effect? Wikipedia.org
•The Flash Crash, was a United States stock market crash
on May 6, 2010 in which the Dow Jones Industrial Average
plunged about 1000 points—or about nine percent—only to
recover those losses within minutes. It was the second
largest point swing, 1,010.14 points, and the biggest one-
day point decline, 998.5 points, on an intraday basis in
Dow Jones Industrial Average history.
•"'HFTs began to quickly buy and then resell contracts to
each other—generating a 'hot-potato' volume effect as the
same positions were passed rapidly back and forth.'"
16. 3. Extreme market behaviour: Flash Crash 16
(2010). - cont
High volume days tend to be high execution days for HFT – based on
network capacity it can impact traditional trading technology and pipes
assigned to that business.
Volumes can be massive and add up quickly – e.g. a bug in the code
order will become a very large order error and then lead to an error
with the exchange or network or exchange connectivity.
A coding error (which is big and means the Algo is wrong from the
start) can be (mis)understood to be a routing issue with an exchange
(which is small and easier to fix).
17. 4. Theft or loss of Intellectual Property. 17
‘Secret sauce’
• There are examples in the industry of at least four legal
cases in relation to algorithms being stolen.
•These programs are key intellectual property, it is very
easy for staff to leave the firm with the code underlying the
trading strategy.
•Firms struggle with understanding when does an Algo
become an Algo.
18. 5. Errors or problems suffered by clients using
Direct Market Access and Algo/HFT .
•Firms offer Direct Market access to prime clients,
this creates a risk as the activities of clients can
impact the compliance with exchange rules and
regulations.
19. 6. Business impact of latency (system errors 19
may increase delays).
•Latency has direct impact on the P&L, an Ultra-HFT
strategy and some forms of arbitrage will fail if latency is
higher than expected.
•Communications from the servers (collocated or not) to
the exchange must be done over low latency links.
Trading Applications
Packaged Applications Proprietary Applications
Network Network
20. 20
7. Limited controls at the infrastructure level.
•Algorithmic Trading environments tend to have a
very limited number of infrastructure controls, most
are between the local corporate network and the
HFT equipment.
•Operating systems are modified to gain speed
advantages; this has an impact on the security
configuration and layers of security available.
•There is a significant demand increases on the
underlying infrastructure.
21. 8. Failure of hedges. Incorrect/untested
strategies.
•Poorly tested algorithms or interpretation errors
could disrupt the market or drive trading losses.
The magnitude of these will be related to available
liquidity and market conditions.
22. 22
Agenda
1
• Introduction and Key Concepts
2
• Details of Algorithmic Trading and HFT
3
• Key Risks
4
• Mitigating Mechanisms
23. 23
Mitigating Measures
•Increased oversight and •Measuring latency
visibility over algorithms. across applications,
operating systems and
•Built-in and regulatory networks.
algorithmic limits/checks
(e.g., circuit breakers). •Security reviews over
Active data leakage the environment.
controls. • Robust change
management controls
and testing/validation
over new algorithms.
25. Evolution of Order Processing Time (1995-2011)
Source: NYSE Technologies – Eric Bertrand 2011
1200
1000
Latency (microseconds)
800
600
400
200
§¦ ¥¤ £ §¦ ¨¤ £
0 ¢¢ ¡
1995 2000 2005 2006 2008 2009
1 second = 1,000 millisecond =1’000,000 microseconds.
26. How Many Transactions? (Approximate Numbers!)
Number of HFT Transactions For Each Action
Blink of an Eye
Brain Recognises Human Expression
Hard Disk Read
Housefly Wing Flap
0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000
Brain Recognises Human
Housefly Wing Flap Hard Disk Read Blink of an Eye
Expression
Series1 600 800 40,000 80,000