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Posted by karlsen

2011. 8. 11. 10:42 Market Data

TickBase

TickBase is a real-time database server for super-high-speed capture, storage and retrieval of financial market data.

Capture Real-Time Data and Gain Control

Market feeds provide traders with enormous volumes of real-time information. Yet traditional databases simply aren't fast enough to capture and store this avalanche of tick data, leaving most firms unable to fully utilize the tick data they already pay for. Leading Market Technologies has developed a solution to this problem - TickBase.

TickBase provides super-high-speed, real-time capture, storage and retrieval of tick data from a wide range of sources, architectures, external feeds and client internal sources. This server captures market data as it comes down the wire, stores that data, and then makes it available for immediate or subsequent analysis. You can use TickBase to:

  • Filter out consecutive, redundant, and outlier tick values from instruments showing huge volumes of activity but little movement.
  • Save space using Open-High-Low-Close (OHLC) format for instruments that you are only peripherally monitoring.
  • Capture important news headlines and internal messages.
  • Capture your firm's own trades.
Because TickBase achieves a more accurate and manageable database, reduces CPU load, and keeps you informed, TickBase places you in control.


TickBase Benefits

High speed

Traders and analysts demand access to the most recent data, especially during periods of high-volume trading and significant market changes. TickBase can capture very high rates of data -- enough to handle the most demanding market volumes. In contrast, commonly used SQL and other relational databases can get swamped in periods of peak market activity. Only TickBase can deliver the information you need -- now.

Faster data access

TickBase is efficient. It can be used to capture data on over 50,000 different instruments simultaneously on a dedicated mid-range UNIX server. TickBase serves up intra-day tick data to client application "on demand" -- on any instrument, without limit and without the time delays of going to external sources. A typical TickBase configuration will serve up one megabyte of tick history in one second. People use TickBase to help make crucial decisions faster.

Scalable growth 

The TickBase is parallel and scalable. You can process a virtually limitless amount of information by daisy-chaining TickBase servers together.

Data protection

TickBase is deployed on a server, not at the client application level, so the data reservoir remains safeguarded from power outages and other accidents occurring on end-users' machines.

Flexibility

LMT designed TickBase to provide fully open data access. Use pre-installed interfaces for the most popular real-time data distribution platforms or create a custom interface using the open TickBase API to link TickBase to any data source.

Easy access

Access TickBase using MS Excel, LMT's EXPO, TickView, or use TickBase's flexible API to bring data into the analytical package of your choice as well as your own applications.



TickBase Features

Disk Handling

TickBase employs a proprietary architecture and data storage strategy for speed and space efficiency. TickBase makes it possible for you to write to many disk partitions simultaneously, creating scalable databases distributed over many hard drives on a single machine.

Filters

TickBase can be easily configured to filter out bad or missing data. Among TickBase's filter types are:

  • intervals between ticks
  • context-sensitive percentage change between ticks
  • absolute value (high and/or low)
  • removal of identical values

Interfaces

LMT designed TickBase as an open client/server solution. It integrates easily with in-house proprietary systems as well as off-the-shelf software. TickBase provides pre-installed interfaces to:

  • Reuters TRIARCH SSL
  • Tibco TIB
An open, portable, C source code API allows you to create a custom interface to any data source such as:
  • Tick-by-tick market feeds
  • Internally created information
  • News wires
  • Trades and inventory
  • Client transaction

Database cleansing

The TickBase database administrator facility clean up "dirty" data upon command. For example, define an outlier value, and TickBase will remove all ticks greater than, less than, or equal to the specified value for a specified time frames.

Database merging

You can merge data from multiple databases into TickBase easily because TickBase recognizes a wide range of ASCII-type data file formats. Just define a record type and modify the API to deliver that database to TickBase for storage. If you choose, you can specify a file or list of date/time/ value/ records for merging into TickBase.

Automatic database transfers

Transfer data to or from TickBase automatically by setting TickBase to call another process at periodic intervals.

Customization 

If you wish to modify TickBase, this can be done by taking advantage of its programming interface in source code form.

Warehousing Market Data 

TickBase lets you specify the number of days after which you will archive data, on an instrument-by-instrument basis.

Standard Storage Options for Tick Data 

Using TickBase you can store market and trade data as raw ticks, bars of arbitrary period, the closing value of the day, the CHLO of the day, and/or an average value for the period. You can also conduct "one-shot" queries to acquire a tick value at a given time or list of times during the day.

Platform Availability

TickBase currently runs on the following:

  • SUN Solaris
  • HP PA-RISC 9000 Series running HP-UX

 

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Algorithmic trading is a machine-driven approach to trading, with the goal of reducing commission and other costs and, ideally, improving the time of execution by reducing latency. At its simplest, it means placing a buy or sell order of a defined quantity of a given asset into a quantitative model that automatically generates the timing and the size of the order based on goals specified by the parameters and constraints of the algorithm.
Increased liquidity in the electronic marketplace is key. The acceleration of exchange consolidation, and growth in program and algorithmic trading all are contributing to a dramatic change in how firms find and tap liquidity. An array of technologies, including Thomson Reuters Enterprise Platform for Velocity Analytics, support this continuing shift. Event-driven architectures and applications will draw on real-time, integrated market data to identify liquidity sources.
Increasingly sophisticated buy-side firms (especially hedge funds) are looking to build their own algorithms and are a target for Velocity Analytics as well.

HOW VELOCITY ANALYTICS MEETS THESE NEEDS

Algorithmic trading requires lots of streaming market data to be compared in real-time against historical data, as well as high-speed analysis and data handling.
Because of its proprietary technology, Velocity Analytics can load all this data (which is updated in milliseconds) into its persistence database, calculate preconfigured algorithms (such as VWAP) or customers’ own algorithms, shoot out trade signals to traders or orders directly to an OMS system, and store the market data.

Velocity Analytics comes pre-loaded with standard analytics (VWAP) enabling a customer to get up and running quickly. We deliver a ‘white box’ VWAP giving customers the easy ability to re-configure time frames and include or exclude any instrument type. Easy integration with Thomson Reuters Enterprise Platform for Real Time allows these calculated VWAPs, based upon streaming market data, to be used by other applications in a low latency environment.

With accurate market data captured and easily accessible, customers can also build new algorithmic trading strategies choosing from Velocity Analytics’ library of application programming interfaces (APIs) and scripting and interfaces to statistical packages including Excel, MATLAB and S-PLUS. And with its easy integration to third-party or proprietary, customer-developed analytics, customers can back-test these strategies as well. 
customers : 고객, 단골, 거래처, 놈, 녀석

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2011. 8. 11. 10:14 Market Data

TREP-VA for TCA

In order to improve execution performance it is necessary to measure the actual outcome of your trades against your expectations of that execution. Simply put, post-trade analysis helps you make better trading decisions.
Most post-trade analysis is focused on Transaction Cost Analysis (TCA) to uncover the total cost of trade decisions. An holistic approach to TCA includes not only commissions paid, specified benchmarks (e.g. VWAP) against realized execution and market impact, but also a comprehensive analysis of whether the execution ultimately meets the strategic goal of the portfolio manager. And the results of post-trade analysis are fed back into future trading decisions to fine-tune the trading process.

EXAMPLES OF POST-TRADE ANALYSIS

REAL-TIME CORRECTION AND FEEDBACK BASED ON POST-TRADE ANALYSIS 
Post-trade analysis is being used in realtime by the sell side. The result – immediate
results on whether you obtained best execution, as well as instantaneous feedback while working an order, allowing an order to be re-worked if a strategy is not successful. During the hours it can take to complete the order, the strategy can be switched or a wholly new approach used.

TRANSACTION COST ANALYSYS
Transaction Cost Analysis isn’t simply a matter of comparing trades against where the market was when the trade was executed Thomson Reuters Enterprise Platform for Velocity Analytics can certainly enable the buy side and sell side to monitor executed trades against the market; but it also delivers a platform where you can store and analyze trade and market data to provide an holistic view of TCA based on your strategy objectives

THOMSON REUTERS VWAP MODULE 
Velocity Analytics comes pre-loaded with standard analytics (VWAP) enabling a customer to get up and running quickly. We deliver a ‘white box’ VWAP giving customers the ability to reconfigure time frames easily and include or exclude any instrument type. And with Velocity Analytics’ patented technology for high performance, industry-strength application programming interfaces (APIs) and scripting, customers can build their own ultra low-latency in-process analyses, making it possible to develop customized post-trade analytics.

HOW VELOCITY ANALYTICS MEETS THESE NEEDS 
Post-trade analysis demands the ability to process massive volumes of streaming tick data, compare it to trade data, and perform high-speed analysis and data handling. Velocity Analytics’ delivers these functions easily to an existing market data environment. 

Even more relevant to post-trade analysis is the Velocity Analytics persistence database and Velocity Analytics’ scalability, which enables a customer to store almost any amount of market data for any amount of time. Many customers will need to look at the full order book for U.S. equities. This represents 30 gigabytes of data per day. Combining streaming market data from major market centers with corporate action data from Thomson Reuters Tick History creates a customer specific, tick-by-tick database suitable for any type of post-trade analysis. 

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2011. 8. 11. 09:59 Market Data

TREP-VA for Compliance

In today’s marketplace, trading professionals face two challenges: 

BEST EXECUTION/DATA STORAGE/MARKET ABUSE 
How can you get the best price and how can you prove that you did get the best price? Customers need to capture and store real-time tick data and trades, and run post-trade analysis to satisfy the regulators and customers that they achieved best execution. Additionally, firms need to be able to monitor and track insider trading and trading abuse.

TRADING/EXECUTION ALGORITHMS
Customers need market-sweeping algorithms looking at the top of the book across major market centers for a given instrument; intraday snapshots looking at where the market was at a given time; and a record of whether they got the best price for internal queries and reporting purposes.

HOW VELOCITY ANALYTICS MEETS THESE NEEDS 
Thomson reuters enterprise Platform for Velocity analytics – supported by the thomson reuters enterprise Platform for real-time – can help you meet these tough new requirements. Velocity analytics captures and stores tick, order book and internal trade data for as long as you need, helping to build long and deep trade information suitable for compliance reporting. combined with the powerful publishing capabilities of thomson reuters enterprise Platform for real time, Velocity analytics helps meet your quote and trade data transparency obligations.
With Velocity analytics’ extensible persistence database, multiple order books can be searched at once, scanning the market in real-time for best execution. a market-sweeping algorithm is employed to find the best price across different market centers. its real-time order book analysis supports smart order routing. 
Velocity analytics’ order Book analyzer for compliance uses level 2 data, providing a query-based, intra-day snapshot of where the markets were at a given time, and answering the question: “Did i get the best price?” it is an accurate and costeffective means of producing execution performance analytics and transaction cost research for compliance and customer reporting purposes.
We deliver Velocity analytics preloaded with VWAP, the most widely used performance measurement of best execution. Customers can easily re-configure time frames and include or exclude any trade type. Easy integration with the thomson reuters enterprise Platform for real-time allows these calculated VWAPs, based upon streaming market data, to be used by other applications in a low-latency environment.
And Velocity analytics’ ability to stream trade signals to an OMs through the low-latency thomson reuters enterprise Platform for real-time means that the risk of not getting best execution on a given security because of a slow OMS response time is reduced. When this ability is combined with low-latency thomson reuters Direct Feeds, as well as low-latency analysis with Velocity analytics’ persistent database, achieving and reporting on best execution is a realistic goal.

MARKET USE
REG NMS AND MIFID 
New regulations have clearly transformed trading operations on both sides of the atlantic. With pre- and post-trade transparency requirements for equity markets, MiFiD requires european firms to store trading data for years. the market structure reforms imposed by REG NMS, meanwhile, could signal the beginning of the end for the auction trading system in the united states as it hastens the demise of floor trading.
In both regions, meeting the new requirements requires firms to improve data storage, data integration and order management capabilities. 

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Posted by karlsen
Execution algorithms refer to the algorithms used during the execution process in order to transact the purchase/sale once the trading decision has been made. They are designed to reduce market impact and minimize execution costs.
Products are getting increasingly correlated on different exchanges or across assets, resulting in skyrocketing market data rates which increase the cost and complexity of data capture, processing, and storage. It is now even more critical to find data and tools that work across all these issues while saving cost, hence the demand for normalized data from multiple sources.
High-performance, low-latency technology are becoming a basic necessity for the ways in which most trading firms assess opportunities, execute trades and manage risk.

HOW VELOCITY ANALYTICS MEETS THESE NEEDS
Thomson Reuters enterprise Platform for Velocity Analytics helps clients with low latency market data collection, normalization and analysis. It fixes the problem of overloaded algorithm trading engines, stores massive amounts of data with instantaneous retrieval, and collects and analyzes full order books including consolidation of order books from multiple venues. data heavy computing can be offloaded to Velocity Analytics and the results sent to the algo trading engine.
Velocity Analytics is easy to manage and maintain, as well as scalable to keep up with ever-increasing load. It is easy to build interfaces between Velocity Analytics and EMS/OMS systems. It allows for flexible summarization and publishing and rich pre-programmed analytics (VWAP, TWAP, etc.). clients can add custom analytics into the server using Velocity Analytics plug-in framework. the system also supports corporate actions, cancellations and corrections. 

MARKET USE
If you are a sell-side algo execution desk or if you are actually looking to develop your own algorithms on the buy-side, it is very important to make sure that you pick the right algorithms to most effectively fill your orders. so once an order has been decided or once you have made a buy or sell decision, how do you go about executing that order?  
that’s where execution algorithms come in. there are hundreds and hundreds of different types of algorithms; picking the right one and having confidence that you are picking the right one is very important. you can marry Velocity Analytics with our data in order to ensure that you are picking the one that meets the market conditions.
For the buy-side, it’s important from an algo execution perspective to be able to compare and contrast the different algorithms that are being provided by the sell-side, therefore having the right tools to conduct this testing on your own is a key advantage. 

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Posted by karlsen
HigH Frequency Data ManageMent and Analytics

실시간, 히스토리컬 tick 데이터를 관리
trade and execution 전략 수행
research and analytics
시장 속도에 맞추어 미리 설정된 분석을 계산할 수 있음
=> 시장 상황의 변화에 대응할 수 있으며, 전략이 효율적으로 수행될 수 있도록 함

Velocity Analytics를 이용하여 
market-making trading strategies을 개발할수 있으며
실시간 시장 모니터링, 규제 컴플라이언스 체크를 수행하고
실시간 트랜젝션 코스트 모델을 만들어 execution costs를 줄일 수 있다.

글로벌 헤지펀드, proprietary arbitrage trading desks, brokerage firms, exchanges, regulatory agencies, third party transaction cost analysis firms, algo execution desks, and many other types of businesses in the trading community가 사용한다.

VA는 톰슨 로이터의 데이터 저장소에 접근가능하다: tick data, corporate actions, exchange metadata, condition codes, direct feeds, etc., as well as other third party data sources (e.g., exchanges, brokers, and market data vendors) and internal proprietary firm data
=> 모든 데이터 분석의 표준화가 가능하다

Low Latent, Best in Quality execution capabilities 
Velocity Analytics seamlessly integrates into your trading environment, supporting real-time distribution of streaming trade signals to multiple in-house or third party applications simultaneously. For example, Velocity Analytics can generate trade signals to your order Management system (OMS), facilitating program trading based on algorithmic strategies, so you can respond very quickly to short-lived market discrepancies and opportunities, and then 
accept executed trade data or other types of derived data back into the platform.

Examples of smarter, faster Execution:
• A new strategy is validated in Velocity Analytics before being deployed in production
• A measurable market profile (e.g., volatility) is detected in real-time by Velocity Analytics, triggering an alert to the OMS/execution platform to deploy a particular strategy that is typically 
successful in a volatile market
• A proprietary benchmark developed in Velocity Analytics is matched by realtime market data; it sends a Fix message directly to your OMS, which then forwards the Fix message for execution

-------------------------
DATA integration
The right technology for managing high velocity executions must not only be fast and reliable but also seamless in integrating different types of content sets. Maintaining data quality throughout the trading lifecycle is a major hurdle. Velocity Analytics is your strategic solution for bringing together multiple types of datasets (e.g., tick data and corporate actions data) for real-time and historical analytics, thereby giving you a complete time series for research and signal generation.

streaming global cross-asset data – real-time feeds, corporate actions and tick History: 

Thomson Reuters has the best-in-class global cross-asset content. our ultra-low latent data feeds, tick history and corporate actions reference data are brought together with the Velocity Analytics solution.
our solution enables you to run quantitative research and analysis on deep history, coupled with corporate action reference data to indentify market anomalies or correlations that you can leverage. We bring together historical and real-time content to offer a comprehensive platform for quantitative trading and transaction cost analysis.
 
internal and third party data sources:
 
Many of our customers are already using Thomson Reuters enterprise platform for Real-Time. it is an ultra-low latency market data integration platform providing resilience, permissioning and publishing infrastructure to banks, brokers and hedge funds worldwide. Velocity Analytics integrates with the enterprise platform to access internal and third party data. Thomson Reuters enterprise platform for Real-Time permissioning also enables tracking and compliance. Velocity Analytics leverages the enterprise platform distribution to publish streaming analytics to multiple downstream applications.

Hosted offering
Thomson Reuters has hosted data centers around the world. Velocity Analytics can be hosted in any of these data centers as a dedicated service and add-on to your firm’s infrastructure. connectivity to any of our real-time low latency direct exchange feeds and historical tick and reference data can be completely managed by Thomson Reuters.

Testing Algorithms And Third party integration
Trade and quote history from the Thomson Reuters Tick History service is integrated into Velocity Analytics, complementing streaming market data. This creates comprehensive market views, allowing you to develop and test strategies without being slowed down by querying traditional databases that are not optimized for market data.
our platform allows historical trade data to be queried and analyzed ‘on the fly’, uncovering historical trends. using these trends, you can build trading strategies that react to discrepancies in current markets. Buildingblock analytics allow you to get started or quickly create your own proprietary approach.
With accurate market data captured and easily accessible, you can build new algorithmic trading strategies, choosing from our library of application programming interfaces (Apis) and scripting. in addition, Velocity Analytics provides ODBC and JDBC connectivity to allow integration with statistical packages such as excel, MATLAB and s-PLUS.
And with easy integration to third party or proprietary customer-developed analytics, you can backtest these strategies as well.
-써드파티와의 연계는 고려하지 않아도 될듯

DATABASE CAPABILITIES
• capture and store every tick of data – global cross-asset class
• capture streaming real-time ticks and integrate historical ticks
• Lose zero ticks with fully resilient fault tolerant system
• store and apply corporate actions 
• store raw data formats and derived data analytics
• Apply data compression to reduce total cost of ownership
• enable faster time to production with easy installation
 
CUSTOM AND PRE-BUILT ANALYTICS
• send trade alerts to order management systems and execution platforms
• publish streaming analytics such as Volume Weighted Average price (VWAP) or TWAP to trading desks and applications
• consolidate multiple venues to create custom National Best Bid Offer (NBBO) data
• Leverage order book analytics
• calculate P&L in real-time throughout day
• generate underlying asset prices intraday
• Query historical data against real-time data to simulate new trading strategies
• plug into existing content, analytics and execution platforms 

INTEGRATED PLATFORM
• Analyze exponentially increasing volume of market data in milliseconds
• ensure accuracy and compatibility with Thomson Reuters or hundreds of other third party data feeds
• plug in Thomson Reuters Tick History for global cross-asset, full tick data back to 1996
• plug in corporate actions from Thomson Reuters or other third party sources
• Leverage Thomson Reuters enterprise platform for Real-Time for content integration, permissioning and publishing streaming analytics
• easily process cancellations and corrections, symbol maps, futures rolls, etc. 


PRIMARY BUSINESS USE CASES
• Alpha generation and Arbitrage
• Transaction cost Analysis
• Algorithmic execution
• compliance and surveillance

THOMSON REUTERS 24X7 SERVICE
Thomson Reuters global presence means that our support is world-class. Thomson Reuters global service centers can meet your 24x7 support needs, and Thomson Reuters account teams are local and available to respond rapidly to any of your concerns. We can provide one-stop problem resolution for Velocity Analytics, as well as the associated data and platforms.

 













 

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2011. 8. 10. 10:25 Market Data

고려할것


1. managing real-time and historical tick data
2. deriving trade and execution strategies
3. generating research and analytics
4. 
Calculate your customized or pre-configured analytics at the speed of the market
5. 
Develop market-making trading strategies
6. 
Perform real-time market monitoring and regulatory compliance checks
7.
Build real-time transaction cost models to decrease execution costs
8. fault tolerance & stream persistence
9. "no tick loss" source mirroring
10. Operational robustness
11. strategy validation
12. 
Market useA measurable market profile (e.g., volatility) is detected in real-time
13. OMS와의 연계(ex. FIX or faster way)
14. calculate P&L in real-time throughout day
15. Analyze exponentially increasing volume of market data in milliseconds
16. picking the right one and having confidence that you are picking the right one
17. How can you get the best price and how can you prove that you did get the best price?  : run post-trade analysis
18. extensible persistence database
19. REAL-TIME CORRECTION AND FEEDBACK BASED ON POST-TRADE ANALYSIS 
20. TRANSACTION COST ANALYSYS
21. 용량이 컴팩트하고 검색속도를 더 빠르게 하기 위한 저장소
22. Queuing/Event 처리(republish)에는 RabbitMQ, AnyMQ , AnyEvent 등을 사용
23. 분석에는 R, Visualization/charting에는 Protovis( http://vis.stanford.edu/protovis/ )
24. 참조 : http://www.slideshare.net/clkao/trading-with-opensource-tools-two-years-later
25. json-rpc
26. Strategy : GeniusTrader, TradingSpring
27. Analysis : R, protovis
28. Haskell, Smalltalk
29. Kdb
30. http://www.marketcetera.com/ 
31. 
StreamBase, Marketcetera, Drool
32.  
 

*로이터는 다음과 같이 다양하고 방대한 데이터를 보유하고 있기 때문에 더욱 다양한 분석 및 전략이 가능할 것이다.
Velocity Analytics integrates a wealth of data repositories from Thomson Reuters, including tick data, corporate actions, exchange metadata, condition codes, direct feeds, etc., as well as other third party data sources (e.g., exchanges, brokers, and market data vendors) and internal proprietary firm data.
=> 어떠한 우위를 점할 수 있을까?


 

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Realtime Data Warehousing
http://www.lmtech.com/lmt/products/TICKBASE.asp

THOMSON REUTERS ENTERPRISE PLATFORM FOR VELOCITY ANALYTICS : http://goo.gl/zydy2

 

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Posted by karlsen
이전버튼 1 2 이전버튼

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Pricing, hedging, risk-managing a complex derivative product
karlsen

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