Market Surveillance Prognoz 11 November 2015 Trading Technology Trends & Quality Assurance Conference in St. Petersburg

1. Prognoz
Market
Surveillance

2. PROGNOZ TODAY
2
1 5
A wide range of standard products for public,
corporate, and financial sectors
Offices in 7 countries, including USA, Canada,
Belgium, China, CIS
Own training center with strong methodological
support of key projects
Support of partner professional community
around the world
More than 20 years experience in the IT and
business analytics market
More than 1,500 successful implementations for
550 customers in 70 countries worldwide
Leading company in international ratings related to
business analytics and custom software
development
In-house unique software – the Prognoz Platform
62
73
84
years’ experience
in the IT market
successful implementations highly qualified programmers,
analysts, and economists
customers around
the world
countries where our offices are
located
countries we delivered projects to
70+
550+20+
71500+ 1500+

3. GARTNER MAGIC QUADRANTS
3
Business Analytics Advanced Analytics
Gartner included Prognoz in the 2015 Magic Quadrant for Business Intelligence and Analytical
Platforms and 2015 Magic Quadrant for Advanced Analytics Platforms

4. KEY CUSTOMERS IN FINANCIAL SECTOR

5. PROGNOZ MARKET SURVEILLANCE (TIMELINE)
Functionality
Features
Product Specification
 Market abuse patterns recognition (insider trading, pre-
arranged wash trades, matched orders, non-competitive
trading, market price manipulation, price control etc.)
 HFT abusive strategies detection (front-running, quote
staffing, quote smoking, layering/ spoofing, price fade,
momentum ignition)
 Statistical detection of deviations
 High-frequency data visualization engine
Tradebook and orderbook replay
 Market abuse detection: Insider trading and wash sales
 Market price manipulation
Trading data interactive visualization
 Case management
 Regulatory compliance
Front-end: 2-tier application
Data sources: Stock exchanges data (clients trades
and orders, tradelogs, orderlogs)
References: This solution is in use in Central Bank
of Russia

6. BUILD-IN DETECTION MODELS
7
1. Market abuse patterns recognition:
― insider trading
― pre-arranged wash trades
― matched orders
― non-competitive trading
― market price manipulation
― price control
2. HFT abusive strategies detection
― quote staffing
― quote smoking
― layering / spoofing
― price fade
3. Statistical detection of price deviations

7. END-OF-DAY TO INTRADAY DRILLDOWN
9
Drilldown into intraday data:
― sorting and filtering data
― news and event labels
― sorting and filtering event labels
― zooming function
― intraday market activities monitoring
― cross-transactions in group
― key statistics by trader or group
03.11.2011
Intraday 03.11.2011

8. INTRADAY DATA VISUALIZATION
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Convenient instruments for intraday data analysis:
Intraday
deals
Traders
Net position of
selected trader
Deals

9. HIGHLIGHTING DEALS
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Buy deals
(green points)
Sell deals
(red points)
Visualization of intraday dynamics:
― Labels of deals and events on the timeline
― Net position for trader or group
― Drilldown to orders and counterparty for each deal
Cross deals
(orange points)

10. ORDER BOOK VISUALIZATION AND REPLAY
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Visualization of order book:
― Bid-ask spread and orders of selected traders over historical period
― Order book visualization at the selected moment
― Order book replay: tick-by-tick or second-by-second
― Drilldown to list of orders for each price level
Historical
period
Order book
Order list
Traders

11. EXAMPLE: BID-ASK SPREAD & ORDER BOOK, 60 SEC
13
Orders of selected
trader
Historical period 60 sec. Current time = 16:48:45.000084
Volume by price
levels of selected
trades
Play mode navigator
Bid-ask
spread

12. EXAMPLE: BID-ASK SPREAD & ORDER BOOK, 1 SEC
14
16 ms
Historical period 1 sec

13. PROGNOZ.SITUATION CENTER
15
1. On-line markets and news monitoring
2. High level market health indicators
3. Interactive drilling down into the detailed trading information
4. Early warnings

14. ALGORITHMS CONFIGURATOR
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Algorithms configurator
1. High level objective language available for users
2. Binary compliable code (not interpreter)

15. DMZ Enterprise Network
ProjectProject
HOW IT WORKS
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Project
Historical Database
Oracle
API
Cache Cache
Prognoz.Situation Center
Prognoz.TimeLine
ProjectProject
Distributed
Calculation Engine t
Alerts &
Statistics
Algorithm
configurator
Architecture benefits for brokers and regulators
1. Sophisticated market abuse patterns recognition
2. Configurable algorithms by users
3. Having isolated DMZ is the stringent info security requirement of many
financial institutions
4. Insignificant (for surveillance) latency dramatically decreases costs of
solution
Batch Files
Reserved channel

16. 0.1
0.2
0.4
0.8
1.6
3.2
6.4
12.8
25.6
Count
Millions
Time
Number of trades Number of orders
Number of orders: ~ 10 M per day (median)
~ 37 M per day (in peak)
Number of trades: ~ 700 K per day (median)
ACTIVITY OF EQUITY MARKET

17. *CFTC Technology Advisory Committee, 2012
HIGH FREQUENCY TRADING (HFT)

18. “Coscia was accused of entering large orders into futures markets in 2011 that he never intended to
execute. His goal, prosecutors said, was to lure other traders to markets by creating an illusion of
demand so that he could make money on smaller trades, a practice known as spoofing. Prosecutors
said he illegally earned $1.4m (£900,000) in less than three months in 2011 through spoofing.”, The
Guardian, HFT layering, November 2015
“A unit of hedge fund Citadel LLC was fined $800,000 by U.S. regulators in June for failing to prevent
erroneous orders from being sent to several stock exchanges over a nearly three-year period”,
Reuters, HFT stuffing, 2014
“Athena is the regulator’s first market manipulation case against a firm engaged in high-frequency
trading, an industry besieged by accusations that it cheats slower investors” , Bloomberg Business,
HFT manipulations, 2014
“Navinder Singh Sarao, 36, is fighting extradition to the US where he is facing 22 charges ranging
from wire fraud to commodities manipulation, which carry sentences totalling a maximum of 380
years. Mr Sarao is alleged by US prosecutors to have made $40m over four years by spoofing the
Chicago futures market. The trader’s activities include making a $900,000 profit on May 6, 2010,
when a trading frenzy known as the flash crash saw one of the most spectacular falls ever seen in the
equity markets.” , Financial Times, 22 October 2015
MANIPULATIONS

19. TIMELINE: BUILD-IN DETECTION MODELS
23
1. Market abuse patterns recognition:
― insider trading
― pre-arranged wash trades
― matched orders
― non-competitive trading
― market price manipulation
― price control
2. HFT abusive strategies detection
― quote staffing
― quote smoking
― layering / spoofing
― price fade
3. Statistical detection of price deviations

20. Quote smoking - practice of putting a large number of quotes (creation new bids and offers) and
then immediately canceling them
Best bid
Best ask
69 ms
QUOTE SMOKING
Agent’s asks

21. Possible criteria:
• Number of orders per time interval (sec, min, hour)
• Median lifetime of order
• Best price ratio = count “best price” orders / count agent’s orders
• Median minimum distance between agent’s price orders and best prices (best
ask/best bid)
QUOTE SMOKING

22. Quote stuffing - practice of putting a large number of orders (thousands of messages) and then
immediately canceling them for creation delay in other participants.
QUOTE STUFFING

23. Possible criteria:
• Number of orders per time interval (sec, min, hour)
• Order-to-trade ratio
• Median lifetime of orders
• Range of order’s price
QUOTE STUFFING

24. Layering - practice of creation selling/buying pressure in order to make naive investor to move the
price.
Best ask
Best bid
Cancellations
of orders
Agent’s bid
Agent’s asks
Trade
LAYERING/SPOOFING

25. Possible criteria:
• High level of order imbalance = count of buy orders / all orders
• High number of orders in visible part of order book
• Ratio of agent’s volume to visible volume of order book
• Ratio of agent’s orders to count of orders in visible part of order book
• Median lifetime of orders in each part of order book
LAYERING/SPOOFING

26. Price Fade - practice of orders cancellation immediately after the trade on the same venue.
Best ask
Best bid
Cancellations
of orders
Agent’s asks
Trade
PRICE FADE

27. Possible criteria:
• Number of cancelled orders at the same time
• Range of order’s price
• Number of orders cancelled before trade (in interval x sec)
PRICE FADE

28. TIMELINE: BUILD-IN DETECTION MODELS
33
1. Market abuse patterns recognition:
― insider trading
― pre-arranged wash trades
― matched orders
― non-competitive trading
― market price manipulation
― price control
2. HFT abusive strategies detection
― quote staffing
― quote smoking
― layering / spoofing
― price fade
3. Statistical detection of price deviations

29. CIS, Eastern Europe
Moscow
+7 495 995 80 76
Western Europe
Brussels
+32 2 217 19 50
Asia
Beijing
+86 10 6566 5337
North and South America,
Canada, Australia, Africa
Washington
+1 202 955 55 20
34
CONTACTS