18mar14 find the hidden signal in market data noise webinar

Find the Hidden Signal in Market Data Noise

Revolution Analytics webinar, 2014-03-18

Andrie de Vries

Business Services Director (Europe)

@RevoAndrie

[email protected]

Revolution Analytics Webinar, 13 March 2013

Agenda

Find the Hidden Signal in Market Data Noise

Louis Lovas

Onetick

Revolution Analytics, the R project and Financial

applications

Andrie de Vries

Revolution Analytics

THE R PROJECT AND

FINANCIAL APPLICATIONS


- Started by Robert Gentleman &

Ross Ihaka, 1993

- Version 1.0 in 2000

- 2.5 Million Global Users

- 5000+ “Packages”

- R in Universities = New Talent

- Open Source = Access To

Innovation

- Programming Agility

- Huge range of predictive

analytics

Open source R


Image source: http://www.quantmod.com/gallery/

Poll Question

What are you connecting to in order to

access your data? (please check all that

apply)

A) RDBMS

B) Spreadsheet

C) Time Series / Tick DB

D) non-relational / no-SQL database


Revolution Analytics is a visionary


Gartner magic quadrant

Advanced Analytics, 2014

Leaders Challengers

Visionaries Other players

Source: http://inside-bigdata.com/2014/02/25/gartner-reveals-magic-quadrant-advance-analytics/

Big Data In-memory bound Hybrid memory & disk

scalability

Operates on bigger volumes

& factors

Speed of

Analysis

Single threaded Parallel threading Shrinks analysis time

Enterprise

Readiness

Community support Commercial support Delivers full service

production support

Analytic Breadth

& Depth

5000+ innovative

analytic packages

Leverage open source

packages plus Big Data

ready packages

Supercharges R

Commercial

Viability

Risk of deployment of

open source

Commercial license Eliminate risk with open

source

Enhancing R for Enterprise deployment


Poll Question

What is your usual hardware set up?

A) Workstation

B) Server

C) Grid / Cluster

D) GPU (graphical processing unit)

E) Hadoop


Revolution R Enterprise


Language

Interpreter and

Standard R

Algorithm Suites

Development &

Deployment Tooling

Big Data Distributed

Execution Platform R +

CR

AN

Revo

R DistributedR

ConnectR

ScaleR

DevelopR Deploy

R

Revolution R Enterprise

Big Data Big Analytics Ready

– Enterprise readiness

– High performance analytics

– Multi-platform architecture

– Data source integration

– Development tools, Deployment tools

ScaleR: high performance analytics


• Text formats

• SAS

• SPSS

• Teradata

• Netezza

• Greenplum

• Hadoop

• ODBC

• DataStep

• Clean

• Transform

• Refactor

• Sort

• De-duplicate

• Split

• Merge / Join

• Cube

• Summarise

• Significance

test

• Histogram

• Parallelise

(rxExec)

• Regression

• Logistic

Regression

• GLM’s

• Clustering

• Decision trees /

Forests

• Classification

trees

• Predict

• Residual

analysis

• ROC (cum

gain curve)

• Simulation

• Online

• Web API

• BI tools

• Export to

database

• Score in-

database

Import Pre-process Analyse Model Score Deploy

Distil / combine structured and unstructured

Build models that where legacy apps can’t

Iterate and innovate at speed

Operate on bigger data – work inside Hadoop with no M/R programming

More effective models = Better business decisions

R and empirical finance

The CRAN Taskview (Empirical Finance) a rich source of recommendations for tools and packages in the field of

finance

Topics include: Regression models

Time series

Finance

Risk management

Data and date management

Other relevant task views: Econometrics

Optimization

Time Series

Social sciences

Robust statistical methods Image source:

http://timelyportfolio.github.io/rCharts_time_series/history.html

http://cran.r-project.org/web/views/Finance.html




http://cran.r-project.org/web/views/Econometrics.html

http://cran.r-project.org/web/views/Optimization.html

http://cran.r-project.org/web/views/TimeSeries.html

http://cran.r-project.org/web/views/SocialSciences.html

http://cran.r-project.org/web/views/Robust.html

Poll Question

What is your preferred statistical

programming platform?

A) MATLAB

B) STATA

C) SAS

D) R / RRE

E) NAG (Numerical Algorithm Group)

F) C++, JAVA, PYTHON


FIND THE HIDDEN SIGNAL IN

MARKET DATA NOISE


Find the Hidden signal in market data noise

Director of Solutions at OneMarketData

Louis Lovas

© 2014 OneMarketData LLC 1

ONE TICK®

Accelerating Quant Research and Trading

About OneMarketData, LLC

Founded in 2005, Profitable in 2008

Self-Funded & Self-Directed.

No venture capital / Cash-flow positive

Our Pedigree

President and Founder, Leonid Frants

Technology Built by Wall Street experts –

Leader in Financial Data Management Technology – OneTick™

Comprehensive solution financial big data management

90+ Clients Worldwide

Hedge Funds/Prop Traders, Banks & Brokers, Market Makers,

Marketplaces & Exchanges

Broad range of financial use cases

Trading model back-testing & Quant Research,

Pricing Models, Pre/Post Trade TCA, …

Bloomberg

http://www.standardchartered.com/home/en/index.html

http://en.wikipedia.org/wiki/File:Soci%C3%A9t%C3%A9_G%C3%A9n%C3%A9rale.svg

http://www.invesco.com/portal/site/global


ONE TICK®


About ONETICK

X

CEP & Database Engine

Tick Server Clients

Programming APIs

C++ C# Java

Business Intelligence

Spotfire / Tableau

Visual Dashboards

Panopticon

Analytics

R language

Reporting

ODBC/SQL

Analytics

filter

enrich

aggregate

transform

correlate

Historical

Data

In-memory Database

Reference

Data

Historical

Data

Trading Systems,

Web Portals,

Messaging

Biz Intelligence,

Programming

100+ Built-In High Performance/High Precision Analytical Operators +

Direct support for Corporate Actions , Corrections, Cancels, Symbol Maps,…

Historical

Data

Real-Time

Feeds

Price & Volume Analytics, Historical Volatility, …

Pricing modeling, Spread Trading signaling, Portfolio Analytics, …

Consolidated (Reuters, Bloomberg, etc).

Exchange feeds.

ASCII/Binary/SQL sources

3rd party(NYSE TAQ, CME, …)


ONE TICK®


Delivering on timely business insights from market analysis

True price discovery, volume and trading patterns…

Revealing unique observations and patterns

Deriving precise analytics

Market Data Quality is Key to outcomes

... result in improved trade & pricing models

x

Historical

Data

Reference

Data

Historical

Data Market Data + Analytics ( Equities, Options ) Pricing/Trading models

Markets… ONETICK Time Series Database and CEP

Market Analysis from Streaming data Equity Underliers – analytical models

Option pricing and risk models

… Predictive Models

Effective Market Analytics and Quantitative Research

Analytics


ONE TICK®


Industry Advantages Where Your Success Counts

ONETICK Product Demonstration

Introduction to …

OneTick Analytical Query Design

Integration with R analytics

Using OneTick and R for Options

QUESTION SESSION
