extent-2015: prognoz market surveillance
TRANSCRIPT
Prognoz Market Surveillance
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PROGNOZ TODAY
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’ experiencein 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+
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GARTNER MAGIC QUADRANTS
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
KEY CUSTOMERS IN FINANCIAL SECTOR
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 salesMarket price manipulation Trading data interactive visualizationCase managementRegulatory 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
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BUILD-IN DETECTION MODELS
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
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END-OF-DAY TO INTRADAY DRILLDOWN
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
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INTRADAY DATA VISUALIZATION
Convenient instruments for intraday data analysis:
Intraday deals
Traders
Net position of selected trader
Deals
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)
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
EXAMPLE: BID-ASK SPREAD & ORDER BOOK, 60 SEC
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Orders of selected trader
Historical period 60 sec. Current time = 16:48:45.000084
Volume by price levels of selected
tradesPlay mode navigator
Bid-ask spread
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EXAMPLE: BID-ASK SPREAD & ORDER BOOK, 1 SEC
16 ms
Historical period 1 sec
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PROGNOZ.SITUATION CENTER
1. On-line markets and news monitoring2. High level market health indicators3. Interactive drilling down into the detailed trading information4. Early warnings
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ALGORITHMS CONFIGURATOR
Algorithms configurator1. High level objective language available for users2. Binary compliable code (not interpreter)
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DMZ Enterprise Network
ProjectProject
HOW IT WORKS
Project
Historical DatabaseOracle
API
Cache Cache
Prognoz.Situation Center
Prognoz.TimeLine
ProjectProjectDistributed Calculation Engine t
Alerts & Statistics
Algorithm configurator
Architecture benefits for brokers and regulators1. Sophisticated market abuse patterns recognition 2. Configurable algorithms by users3. Having isolated DMZ is the stringent info security requirement of many
financial institutions 4. Insignificant (for surveillance) latency dramatically decreases costs of
solution
Batch FilesReserved channel
1/3/2012
4/3/2012
7/3/2012
10/3/2012
1/3/2013
4/3/2013
7/3/2013
10/3/2013100000
1000000
10000000
Number of trades Number of orders
Time
Coun
t
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
*CFTC Technology Advisory Committee, 2012
HIGH FREQUENCY TRADING (HFT)
“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
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TIMELINE: BUILD-IN DETECTION MODELS
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
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
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
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
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
Layering - practice of creation selling/buying pressure in order to make naive investor to move the price.
Best ask
Best bid
Cancellationsof orders
Agent’s bid
Agent’s asks
Trade
LAYERING/SPOOFING
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
Price Fade - practice of orders cancellation immediately after the trade on the same venue.
Best ask
Best bid
Cancellationsof orders
Agent’s asks
Trade
PRICE FADE
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
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TIMELINE: BUILD-IN DETECTION MODELS
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
CIS, Eastern EuropeMoscow
+7 495 995 80 76
Western EuropeBrussels
+32 2 217 19 50AsiaBeijing
+86 10 6566 5337
North and South America, Canada, Australia, AfricaWashington
+1 202 955 55 20
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CONTACTS