Chapter 6

FactSet OnDemand Developer’s Toolkit
Programmer’s Manual & Reference
FactSet Research Systems
Version 2.6
Updated February 2014
Table of Contents
Chapter 1: Introduction ...................................................................................................... 4
1.1
Overview ................................................................................................................... 4
1.2
Server Technology .................................................................................................... 4
Chapter 2: Architecture ...................................................................................................... 5
2.1
Wrappers................................................................................................................... 5
2.2
Dynamic Link Libraries .............................................................................................. 5
2.3
Win32 API ................................................................................................................. 5
Chapter 3: Installation ........................................................................................................ 6
3.1
Accessing the Installer............................................................................................... 6
3.2
Running the Installer.................................................................................................. 6
3.3
Setup and Deployment .............................................................................................. 8
3.3.1 Setting up C++ Project in Visual Studio ........................................................... 9
3.3.2 C# .NET or VB .NET Project in Visual Studio................................................... 9
3.3.3 Java Project in Eclipse................................................................................... 10
3.3.4 Python Project ............................................................................................... 10
3.4
FactSetOnDemand Object Configuration................................................................. 11
3.4.1 SetConfigurationItem ..................................................................................... 11
3.4.2 GetConfigurationItem..................................................................................... 12
3.4.3 TestConnectivitySettings ............................................................................... 12
Chapter 4: Retrieving FactSet Data ................................................................................. 13
4.1
Single Threaded API ............................................................................................... 13
4.2
FactSet Data ........................................................................................................... 14
4.3
Extract Data from FactSet ....................................................................................... 15
4.3.1 FactSet Query Language (FQL): Time-Series Access ................................... 16
4.3.2 Screening Language: Cross- Sectional Access ............................................. 16
4.3.3 Date Format using FQL and Screening Codes .............................................. 17
4.3..1 Absolute Dates ......................................................................................... 17
4.3.3.2 Relative Dates ........................................................................................ 17
4.2
FactSet Functions to Extract Data ........................................................................... 19
4.2.1 ExtractFormulaHistory ................................................................................... 19
4.2.2 ExtractDataSnapshot ..................................................................................... 23
4.2.3 ExtractBenchmarkDetail ................................................................................ 26
4.2.4 ExtractEconData............................................................................................ 28
4.3
UploadToOFDB ....................................................................................................... 31
4.3.1 Requirements for UploadToOFDB ................................................................. 31
4.3.2 OFDB ............................................................................................................ 32
4.3.2.1 Creating a New OFDB ............................................................................ 32
4.3.2.2 Modifying an Existing OFDB ................................................................... 32
4.3.3 UploadToOFDB Syntax ................................................................................. 32
Chapter 5: Formula Lookup ............................................................................................. 34
5.1
Launching Formula Lookup ..................................................................................... 34
5.2
Searching for Data................................................................................................... 35
5.2.1 Identifier Lookup ............................................................................................ 35
5.2.2 Search for Data Formulas .............................................................................. 35
5.3
Sample Scripts ........................................................................................................ 37
Chapter 6: Factlet Object .................................................................................................. 40
Chapter 7: Create DataFrame ........................................................................................... 43
7.1
IsDataFrameComplete ............................................................................................ 45
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7.2
7.3
7.4
7.5
7.6
7.7
GetDataFrameErrorValue ........................................................................................ 45
GetDataFrameFactletRequest \ GetDataFrameFactletResult .................................. 45
GetDataFrameNumberOfRows \ GetDataFrameNumberOfCols .............................. 46
GetDataFrameColumnName \ GetDataFrameColumnType ..................................... 46
GetDataFrameColumnNative .................................................................................. 47
GetDataFrameValueAsString \ GetDataFrameValueAsInteger \
GetDataFrameValueAsDouble ............................................................................... 48
Chapter 8: Troubleshooting ............................................................................................. 49
8.1
IsDataFrameValueInvalid ........................................................................................ 49
8.2
TranslateErrorMessage \ GetFactSetErrorValue ..................................................... 49
8.3
GetListOfLivingDataFrames .................................................................................... 50
8.4
FactSet Event Logger .............................................................................................. 50
Chapter 9: Sample Code ................................................................................................... 52
9.1
C++ Sample Code ................................................................................................... 53
9.2
VB .NET Sample Code ............................................................................................ 66
9.3
Java Sample Code .................................................................................................. 72
9.4
Python Sample Code............................................................................................... 78
Appendix ........................................................................................................................... 84
Table 1: List of Configuration Items ..................................................................................... 84
Table 2: List of File Output Options ..................................................................................... 85
Table 3: Error Message Output ........................................................................................... 86
Table 4: Top 25 most commonly used FactSet Fundamentals formulas ............................. 94
Table 5: FactSet Economics Key Indicator Series for G-7 Countries ................................... 95
Table 6: UploadToOFDB Error Messages ......................................................................... 104
FactSet Glossary .............................................................................................................. 105
Contact FactSet Support: .................................................................................................. 107
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Chapter 1: Introduction
1.1
Overview
FactSet collects and integrates financial data and makes this data accessible via the
Internet. FactSet currently provides integrations to applications such as R and
MATLAB®. For clients needing to integrate data into their own system, FactSet
provides an installation program that includes a toolkit of the supporting shared
libraries, native bindings, also known as wrapper files, and individual methods called
factlets for retrieving data. Upon installation, the installer is prompted for an
individual user’s FactSet credentials. Once installed, clients can request and receive
data in formats tailored for their particular application.
The shared libraries provided in the Toolkit encapsulate common communication
and data manipulation functions, and free developers from the details of the network
connectivity tasks. This toolkit also provides debugging and troubleshooting tools.
Users can access reports from FactSet called factlets to retrieve a variety of data
sets. Factlets are functions that encapsulate business logic and data collection
procedures. A factlet can be a simple data request or can invoke complex
application logic. The technology is capable of cross referencing and dealing with
time series for a large quantity of data, which can be returned in a variety of tagged
or delimited formats.
This document provides information on how to use the FactSet provided tools so
that FactSet clients can build integrations into their own environments. FactSet
allows for this integration to be built using a variety of programming languages such
as Java, VB .NET, C#, C++ and Python. Sample code provided in Chapter 6.
1.2
Server Technology
The collection of technologies used to respond to requests over the Internet are
called DataDirect. FactSet maintains redundant data servers in its data centers
accessbile via the public Internet. The Developer’s Toolkit contains technology to
help generate requests to these DataDirect Servers. The requests will be in the form
of an HTTPS request generated by a URL statement. When this request is received
by FactSet, the DataDirect server will interpret the request and respond with the
proper data. In addition to data requests, the DataDirect servers handle
authentication of the user requests and the permissioning of data sets. Please refer
to the FactSet DataDirect Summary Document for additional information.
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Chapter 2: Architecture
FactSet provides tools to build an application to extract FactSet data by utilizing
programming languages such as Java, VB .NET, C#, C++ and Python. The main
tool is Dynamic Link Libraries (DLL) – a Microsoft shared library technology. The
main FactSet DLL is titled kratos.dll.
2.1
Wrappers
Programming languages store and use data differently. To compensate for these
differences, FactSet has conversion files called wrappers. These wrappers have four
responsibilities:

Locate the kratos.dll on the hard drive and load it.

Make function calls into kratos.dll and convert between C data types and the
native ones.

Standardize the way functions\methods are called in the varying applications.

Provide the FactSet integrated documentation.
FactSet uses the Microsoft Windows Registry to store its configuration information
and to store its location on the hard drive. Wrappers will search the Windows
Registry for the kratos.dll location and then load it from the found directory.
2.2
Dynamic Link Libraries
The utilized DLLs are statically linked as part of the FactSet integration. These DLLs
are:

cURL is an open source library\application that handles internet related
communication protocols such as HTTP and FTP. FactSet uses one DLL
from cURL called ‘libcurl.dll’.

OpenSSL is another open source library which handles security protocols
over the internet such as HTTPS. cURL and OpenSSL are often compiled
together to perform HTTPS requests. FactSet uses three DLLs from
OpenSSL called ‘libeay32.dll’, ‘libssl32.dll’, and ‘ssleay32.dll’.
Note: The FactSet integration includes software developed by the OpenSSL
Project for use in the OpenSSL Toolkit.
2.3
Win32 API
The Microsoft Windows Application Programming Interface, or Win32API, is a set of
functions that allow for the operating system to be utilized for accomplishing such
tasks as drawing a Window on the screen for users. This Win32API exists on
practically all Windows Platforms. The FactSet integration utilizes the Win32API for
numerous elements such as task parallelization.
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Chapter 3: Installation
The FactSet OnDemand Developer’s Toolkit integration allows users to retrieve
FactSet data by building their own integrations into their environment. FactSet allows
for this integration to be built using a variety of programming languages such as
Java, VB .NET, C#, C++ and Python.
Note: The FactSet OnDemand Developer’s Toolkit Installer is retired 18 months
after release. The Plugin will continue to work after 18 months, but from 15 months
and on users are prompted to upgrade.
3.1
Accessing the Installer
The FactSet OnDemand Developer’s Toolkit Installer can be accessed from:
www.factset.com/download/statlink
The installer is compressed in a folder named FactSet OnDemand Developers
Toolkit. Depending on the computer architecture that a user is running, the user will
choose the FactSetOnDemandDevelopersToolkitx86Installer.msi version if they are
using a 32-bit machine and the FactSetOnDemandDevelopersToolkitx64Installer.msi
for a 64-bit machine. Both versions are compatible with Windows XP, Windows Vista
& Windows 7.
Note: The .NET framework 2.0 is the minimal requirement necessary to install.
3.2
Running the Installer
To begin the installation process, the user can double-click on the .msi file. The
Windows Installer will then open the OnDemand Toolkit Setup Wizard. The user
should select next to continue the install or cancel the install process.
In the first step, enter the FactSet Username and Password credentials. To connect
to the FactSet server, a username and password are required. These credentials are
different than the FactSet workstation login. New credentials will be provided by a
FactSet representative upon trial or purchase of the OnDemand Developer’s Toolkit
integration.
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In the second step, if applicable, enter the proxy server configuration information,
otherwise, it may be left blank and a proxy server will not be used.
In the next step, select the install location to apply this OnDemand Toolkit. The
installer will suggest a location such as “C:\Program Files\FactSetOnDemand\”: The
user may choose to specify a different location by selecting Browse.
Once the install is completed, the installer will run startup scripts to finalize the
installation.
In order to upload and download data from FactSet, there is an OnDemand file
created in a temp directory. The location of those files is automatically generated,
but in some instances (such as running through a server) those generated files may
be inaccessible at run time.
If you suspect this to be a problem, it is possible to specify for the Plugin where to
create its upload/download files by setting configuration item number 16:
(pseudocode, changes according to language)
FactSetOnDemand.setConfigurationItem(16, newDirectory)
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3.3
Setup and Deployment
For including the FactSet integration into the client’s own system, there are two
components to consider for most projects, the library and the native language
bindings. These native language bindings are included as source files in the project.
The FactSet dynamic library is typically included in one of three ways - on the load
library search path, by using the registry to store its location or locally.
The following table describes the details for including the dynamic library in all three
specified ways.
Method of
Inclusion
Locally
Description
This is the simplest method of including the dynamic library. The
user simply places the kratos.dll in the same directory as the
executable. When the library is initialized, it will be found in the local
directory.
Library Path This method involves placing the library in a directory where DLLs
are commonly looked for when applications are loaded. One such
example is the ‘C:\Windows\System’ directory. These directories are
typically listed in the computers PATH environment variable.
Registry
This method is the most versatile. There is a Windows application
Loading
called regsvr32. This application is responsible for informing the
windows registry of the location of the library on the hard disk.
When this regsvr32 is called with the library’s location as an
argument, it register’s it.
The library’s CLSID is registered under:
Computer\HKEY_CURRENT_USER\Software\FactSet\OnDemand\
CLSID
The library’s location is then stored under:
Computer\HKEY_CURRENT_USER\Software\Classes\CLSID\{CLS
ID}\InprocServer64
Or
Computer\HKEY_CURRENT_USER\Software\Classes\CLSID\{CLS
ID}\InprocServer32
This is the first method that the wrapper class will attempt to use to
load the library.
The following sections detail the steps for setting up the FactSet integration using a
number of different programming languages.
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3.3.1 Setting up C++ Project in Visual Studio
1. Create a new Visual Studio Project
 The project may be either 32 or 64 bit architecture.
 The project may use Win32, MFC or ATL libraries, if needed.
 The project may support Unicode or not, but note that the FactSet
integration only supports ANSI C Strings.
 The project may have any calling convention as long as function calls
to FactSet are set up as __cdecl function calls.
 The project may use precompiled headers, if needed.
 The project may use any of the four Microsoft Runtime Libraries.
 The project may support Common Language Runtime.
2. Include the header files for the FactSet integration
 FactSetOnDemand.h and FactSetOnDemand.cpp are compiled in the
project.
3. To dynamically load the FactSet integration at runtime, simple call the
FactSetOnDemand constructor as shown in the Main.cpp example file.
 Note: No static linking is required for the FactSet integration to be
included
4. Compile the project and execute the application
 Kratos.dll must be either in the same directory as the calling
application, on the library load path, or registered in the Windows
Registry using regsvr32.
 The correct architecture version of the DLL must be included. 64-bit
applications may not dynamically load 32-bit DLLs at runtime and vice
versa.
The static library included in the binary section of the integration, titled kratos.lib, can
also be utilized. This static library allows the user to link against the project without
using the FactSetOnDemand headers. The static function has a similar functionality
as the headers, but will only allow the user to use the simple C interface that it
provides. The C header is named FactSetOnDemandCAPI.h and is located in the
header directory of the developer’s toolkit.
3.3.2 C# .NET or VB .NET Project in Visual Studio
The FactSet integration uses the Java Native Interface to allow Java to call functions
from FactSet.
1. Create a new Visual Studio Project (either C# or VB)
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 May use any version of the .NET framework.
 Does not need to allow ‘unsafe’ code.
 Project may be either Debug or Release configuration.
2. Add the source code file
 In VB, FactSetOnDemand.vb should be included
 In C#, FactSetOnDemand.cs should be included
3. To dynamically load Kratos at runtime, simply call the FactSetOnDemand
constructor as shown in the Main.cs\Main.vb example file.
4. Compile the project and execute the application
 Ensure the appropriate version of Kratos.dll is available to be loaded.
The architecture of the DLL required is the one that matches the
installed version of the .NET runtime libraries. Most of the time, the
architecture of .NET installed matches the operating system
architecture.
3.3.3 Java Project in Eclipse
The FactSet integration uses the Java Native Interface to allow Java to call functions
from FactSet.
1. Create a new Java Project in Eclipse.
2. Import the source file binding for Java, FactSetOnDemand.java.
3. To dynamically load the integration at runtime, FactSetOnDemand
constructor as shown in the Main.java example file.
4. Compile the Java Byte Code and execute the application
 Ensure that the appropriate version of Kratos.dll is available to be
loaded. The architecture of the DLL required is the one that matches
the installed version of the Java runtime libraries. In most casees, the
architecture of the Java Runtime Libraries installed matches the
operating system architecture.
 Note: Loading DLLs in the JVM requires the DLL to be on the JVM
path.
3.3.4 Python Project
In order to interface with FactSet, the integration uses the ctypes library from
Python. Similarly to the other projects, the Kratos DLL is dynamically loaded at
runtime by creating the FactSetOnDemand object.
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3.4
FactSetOnDemand Object Configuration
The FactSet OnDemand Object, typically with a class name ‘FactSetOnDemand’, is
a wrapper class for using FactSet’s functions. Typically when this object is created
or constructed, the software will locate the Kratos.DLL and dependencies and load
them into memory for use. The software searches the Windows Registry for the
location and then searches the local directory. Assuming that the DLL is located and
loaded successfully, an initialization route is run to prepare the DLL:
C++
FactSet::FactSetOnDemand* factSetOnDemand = new FactSet::FactSetOnDemand ();
Java
FactSetOnDemand factSetOnDemand = new FactSetOnDemand( );
VB
Dim factSetOnDemand As FactSet.FactSetOnDemand = New FactSet.FactSetOnDemand()
C#
FactSet::FactSetOnDemand* factSetOnDemand = new FactSet::FactSetOnDemand ();
3.4.1 SetConfigurationItem
There are several dozen configuration options stored in the Registry on behalf of
FactSet. These items can be viewed in the Windows Registry using the Windows
Registry Editor at the following location:
“Computer\HKEY_CURRENT_USER\Software\FactSet\OnDemand”
Configuration items are stored as strings in the Windows Registry. The user can
access them as need be. The following are examples of commands to set
configuration items:
C++
factSetOnDemand->setConfigurationItem(FactSet::FactSetOnDemand::DATADIRECTUSERNAME,
"EXAMPLE_001_SERVICES" );
Java
factSetOnDemand.setConfigurationItem(FactSetOnDemand.DATADIRECTUSERNAME,
"EXAMPLE_001_SERVICES");
VB
factSetOnDemand.setConfigurationItem(FactSet.FactSetOnDemand.DATADIRECTUSERNAME,
"EXAMPLE_001_SERVICES")
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C#
factSetOnDemand.setConfigurationItem(FactSet.FactSetOnDemand.DATADIRECTUSERNAME,
"EXAMPLE_001_SERVICES");
3.4.2 GetConfigurationItem
The GetConfirgurationItem method is the complement to SetConfigurationItem. This
allows the user to get the configuration item as a character string. Note that these
configuration items use ‘TRUE’ or ‘FALSE’ for Boolean logical configuration and
standard integer only numbers for numerical items. Table 1 in Appendix lists the
available configuration items.
C++
std::string datadirectusername = factSetOnDemand->getConfigurationItem(
FactSet::FactSetOnDemand::DATADIRECTUSERNAME);
Java
String datadirectusername = factSetOnDemand.setConfigurationItem(
FactSetOnDemand.DATADIRECTUSERNAME);
VB
Dim datadirectusername As String = factSetOnDemand.getConfigurationItem(
FactSet.FactSetOnDemand.DATADIRECTUSERNAME )
C#
string datadirectusername = factSetOnDemand.getConfigurationItem(
FactSet.FactSetOnDemand.DATADIRECTUSERNAME );
3.4.3 TestConnectivitySettings
The TestConnectivity method tests making a short query to DataDirect and attempts
to extract the price of IBM. If the given configuration settings are correct, this will
return a true Boolean, otherwise, it will return false.
C++
bool isConnected = factSetOnDemand->testConnectivitySettings();
Java
boolean isConnected = factSetOnDemand.testConnectivitySettings();
VB
Dim isConnected As Boolean = factSetOnDemand.testConnectivitySettings()
C#
bool isConnected = factSetOnDemand.testConnectivitySettings();
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Chapter 4: Retrieving FactSet Data
4.1
Single Threaded API
The externally facing API for the OnDemand Developer’s Toolkit Integration is
designed to be driven by a single thread. All function calls into the API will return as
quickly as possible. The external facing function calls dispatch commands to
FactSet’s internal objects that will handle processing and return data when
requested. The externally facing API can be called by multiple threads but it is
blocking in the event of multiple threads simultaneously attempting to access it.
The API is designed to allow the user to leverage the power of parallel processing
while only calling functions from a single threaded environment. Inherently single
threaded environments can create up to 25 simultaneous HTTPS connections at
once. The single threaded environment may then poll the API to see if the desired
request is finished.
The following diagram details the single threaded data request execution process.
As depicted, the data requests are made via the Web. When a request is created
from the client’s application, using the FactSet DataDirect technology a URL call is
made, which goes to a FactSet OnDemand API. CreateDataFrame is the first
method call used in fetching and parsing data. This method sends an HTTPS
request, allocates memory to receive the data and parses the data. This method
also checks the entire process for errors. Each CreateDataFrame spawns a new
thread so that these actions can occur in parallel. The is DataFrameComplete
method then polls and returns true if the given DataFrame response is complete.
This processing includes fetching the data, parsing the response into a matrix and
checking for errors. And the GetDataFrameFactletResult method gets the ASCII
result string returned as a result of the Factlet String. These 3 steps are discussed in
further detail in Chapter 6.
The requests that are thus made are passed through the created DataFrame and
are queued in the Thread Pool. These functions make up the Kratos DLL, a
collection of common functions and data entities which a program may refer to at run
time, without requiring the function code or data to be duplicated within the program
file itself. The Kratos DLL is dynamically loaded at runtime by creating the
FactSetOnDemand object.
The client data request is thus sent over the internet as an HTTPS request to the
FactSet OnDemand Webservers, and that is sent to the FactSet databases, which
include the wide range of data, such as Fundamentals, Estimates, Ownership,
Economics, or Ownership among other data. The fetched data is is then returned
and parsed along the same path to the client’s application.
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Given that the FactSet integration allows for parallel processing, it must spawn
multiple threads to complete the tasks at hand. Often, using many threads can be
taxing on system resources. Therefore an artificial multiple request limit of 25 is
imposed. An internal Thread Pool is available that increases the amount of threads
as new requests are created. In response to the number of requests executing, the
thread pool will release resources appropriately, as well. If there are more requests
than resources available, the requests are queued in the order that they were
submitted.
4.2
FactSet Data
FactSet integrates over 200 databases to analyze markets, companies and
industries. For a comprehensive list of databases available on FactSet go to:
www.factset.com/data
The available FactSet databases include the following:

FactSet Fundamentals is updated intra-day and is a comprehensive global
database with extensive coverage and data history. The database provides
coverage of more than 66,000 public and private companies with annual
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history going back to 1980 in developed market and the early 1990s in
emerging markets.

FactSet Estimates is updated intra-day and provides consensus- and detaillevel estimates and statistics from leading investment banks and research
firms. According to the Wall Street Journal’s “Best on the Street” Analysts
Survey, a quantitative evaluation of analysts’ performance based on the
accuracy of buy/sell recommendations, the top analysts evaluated for the
survey are identified using broker recommendation data sourced from
FactSet Estimates.

FactSet clients have access to Equity and Fixed Income Benchmarks, which
include Dow Jones, FTSE, MSCI, Russell, S&P, Barclays, and
BofA Merrill Lynch, among a number of others. FactSet Market Aggregates
(FMA), combines data from FactSet Fundamentals, Estimates and Prices to
calculate ratios and per share values on an aggregate level. It provides
access to over 50 metrics for more than 3,500 commercial and exchange
indices.

FactSet Economics database is updated intra-day and provides
comprehensive global coverage. Database includes details such as economic
information, exchange rates, commodities, and interest rates.
4.3
Extract Data from FactSet
As outlined above, FactSet integrates a range of data sets - company accounts,
security data such as price and return, consensus and detail broker estimates, and
economic data are a few of the examples. FactSet stores all of the available data in
proprietary database structures on FactSet computers. This allows FactSet to adjust,
when necessary, the way data is stored, so that clients can access data as efficiently
as possible.
This range of data is stored on FactSet in such a way as to make powerful crosssectional and time-series analysis possible. FactSet provides two different data
access methods. The FactSet Query Language is used for time-series requests and
the FactSet Screening Language allows extraction of data for a large universe of
securities as of a single date.
The selected data access method depends on the type of data a user wants to get
from FactSet, and what they want to do with that data.
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4.3.1 FactSet Query Language (FQL): Time-Series Access
The first access method is oriented around time-series data fetches using the
FactSet Query Language (FQL). FQL is a proprietary data retrieval language used to
access historical FactSet data. Given an entity (i.e. a security or an index) and a
data item (i.e. market closing price) or macroeconomic data (i.e. the last 5 years of
industrial production for the BRIC countries) the entire history can be fetched using
FQL. For more information on FQL, see Online Assistant page 1961 in the FactSet
workstation.
Some advantages of FQL include:

The ability to specify dates for any database using the same
formats.
With FQL, date formats are flexible. You can use a number of
consistent date formats (defined by FQL) for all databases which
makes using and combining data from different databases easier than
ever.

The ability to iterate items, formulas, and functions at any
frequency.
With FQL, you can iterate items, formulas, and functions at any
frequency. For example, you can request a series of weekly price to
earnings ratios.
To request a time-series of data, a start date, end date and frequency needs to be
specified. If a date is not specified, data is returned from the most recent time period.
To request data for a time series, the start date is the first date of the requested time
series of data. The end date is the last date of the time series. The dates can be
designated as absolute dates or relative dates.
4.3.2 Screening Language: Cross- Sectional Access
Alternatively, the extraction of multiple items for a list of ids for 1 date, both for equity
and fixed income securities, is done using the Screening Language. The FactSet
Screening Language is a way to efficiently facilitate quick access to a data item as of
single time period for a universe of securities.
By default, the Screening Language does not allow iteration and therefore cannot be
used to return a time series of data with a single request code. To request data as of
a single historical date, an absolute or relative date must be specified.
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4.3.3 Date Format using FQL and Screening Codes
4.3.3.1
Absolute Dates
Absolute dates indicate a specific day, month-end, fiscal quarter-end, calendar
quarter-end, fiscal year-end, or calendar year-end as depicted in the examples
below:

Day: MM/DD/YYYY i.e. 7/11/1999 Note: DD/MM/YYYY is not a valid format.

Month-end: MM/YYYY i.e. 6/1999

Fiscal quarter-end: YY/FQ or YYYY/FQ i.e. 1999/1F, 2000/3F, 2001/2F

Calendar quarter-end: YY/CQ or YYYY/CQ i.e. 1999/1C, 00/3C, 2001/1C

Fiscal year-end: YY or YYYY i.e. 2000, 01, 1999
4.3.3.2
Relative Dates
Relative dates represent a date relative to the most recently updated period. For
example, 0 (zero) represents the most recently updated period and -1 represents the
time period prior to the most recently updated.
The zero date is determined by the default time period or the natural frequency of
the data being requested. Zero (0) when used with monthly data indicates the most
recent month end. Negative one (-1) when used with annual data indicates one year
prior to the most recently updated year.
List of Relative Date Arguments:
D
0D is the most recent trading day, -1D is one trading day prior.
AW
0AW is the most recent trading day, -1AW is the one actual week (7 days)
prior to the most recent trading day.
W
0W is the last day of the most recent trading week (usually Friday), -1W is the
last trading day of the prior week.
AM
0AM is the most recent trading day, -1AM is the same day, one actual month
ago.
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M
0M is the last trading day of the most recent month, -1M is the last trading day
of the prior month.
AQ
0AQ is the most recent trading day, -1AQ is the same day 3 months prior
Q
0Q is the last trading day of the company’s most recent fiscal quarter, -1Q is
the last day of the prior fiscal quarter.
CQ
0CQ is the last trading day of the most recent calendar quarter (March, June,
September, or December), -1CQ is the last trading day of the prior calendar
quarter.
AY
0AY is the most recent trading day, -1AY is one actual year (365 days) prior
Y
0Y is the last trading day of the company’s most recent fiscal year, -1Y is the
last trading day of the prior fiscal year.
CY
0CY is the last trading day of the most recent calendar year (the last trading
day in December), -1CY is the last trading day of the prior calendar year.
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4.2
FactSet Functions to Extract Data
FactSet has written a number of functions, called factlets that retrieve FactSet data
using the FQL or Screening languages. These factlets are application components
which encapsulate business logic and data collection procedures. The data items
that are requested, along with an id (identifier) and date, are stored in a structure.
Entities (Security, Index, etc.) and the time dimensions are stored in arrays. The
factlets are used to create URL strings. The string specifies several key parameters
by providing name-value pairs.
Depending on the format of the factlet, date items may be returned in a variety of
formats. Pipe format is the most commonly used format. If a DataFrame successfully
parses in the PIPE format, it stores the date as an integer value in YYYYMMDD
format. If the date value is returned as an integer or a float, the value is returned in
YYMMDD format. If the date value is returned in string format, it will return in ‘DDMMM-YYYYY’ format such as ’12-Nov-2012’. The name-value pair “format=pipe” is
added to the URL string by default if another format is not specified.
The following examples highlight a number of the functions that allow for time series
and point in time data extraction. For a comprehensive list of factlets that are
available, refer to Online Assistant page 17337 in the FactSet workstation.
4.2.1 ExtractFormulaHistory
The ExtractFormulaHistory function is used for extracting one or more items for one
security, an index or a list of securities over time. The function is using FQL.
The syntax for the ExtractFormulaHistory function is:
factlet=ExtractFormulaHistory&ids=&items=&dates=&currency=&
where,
ids
CellString array with a list of one or more security identifiers.
Item(s)
CellString array with a list of one or more FactSet data items in
FQL.
Start date
The start date for the data.
End date
The End date for the data.
Frequency
String with a valid FactSet frequency (e.g. ‘d’dates Alternate
method of entering dates entered in start:end:freq format.
Currency
The currency in which the data is to be returned, using a string
with the three character ISO code (e.g. ‘USD’ or ‘EUR’).
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For databases available on FactSet, there are a set of data items that represent the
piece of information on the database, such as sales, EPS and market value. These
FQL codes are used to specify the items parameter. Formulas that begin with FG_
are FactSet Global formulas and are some of the most commonly used formulas to
retrieve prices, estimates, valuation, growth, and fundamentals. The formulas return
data from multiple database sources, based on a client's FactSet subscription.
In database specific codes, the database is specified in the FQL code, such as in
P_PRICE where the P_ element of the code denotes the pricing database and
FF_SALES denotes the FactSet Fundamentals database with the FF_ element of
the code. Table 4 in the Appendix lists the top 25 mostly commonly used FactSet
Fundamentals formulas. For links to more comprehensive lists of the items available
from the FactSet Fundamentals database, refer to Online Assistant page 15099 in
the FactSet workstation.
FQL codes used in the item parameter may have their arguments in different
positions of their respective codes, depending on if it’s a commonly used code,
which will start with an FG_ or a database specific code.
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Example 1:
In this example, extract the last 6 quarters EPS for Exxon Mobile (ticker XOM) using
the FQL code FG_EPS.
factlet=ExtractFormulaHistory&ids=XOM&items=fg_eps(0Q,-5Q,Q)&dates=0Q:5Q:Q&
Here, the names of the parameters, such as ids and items, specify the name in the
name value pair. The variable “ids” is given the value “XOM” and the name value
pair “ids=XOM” is appended in the URL string.
In this example, the date argument is using relative rather than absolute dates. To
specify relative dates, enter the number of periods and a period code, such as D for
days, W for weeks, or Q for quarters and Y for years. When using relative dates, "0"
refers to the most recent time period. Therefore, 0Q refers to the most recent quarter
end, while -1Q refers to two quarters ago.
Example 2:
In this example, extract the price for a number of securities, Microsoft and IBM,
using the FQL code FG_PRICE. The date arguments used here are absolute dates.
factlet=ExtractFormulaHistory&ids=MSFT,IBM&items=fg_price&dates=201012
15:20110115:D&
Example 3:
In this example, extract the last 6 periods of pricing and sales data for Statoil (ticker
STL-NO) using the FQL code for price data P_PRICE from the Pricing database and
the FQL code for sales data FF_SALES from the FactSet Fundamentals database.
Both P_PRICE and FF_SALES in this example are used in the items parameter.
Because these are database specific codes and not the most commonly used FG_
codes demonstrated in Examples 1 and 2, the arguments for elements such as
currency are positioned differently in the code. For example, in the P_PRICE code,
currency is the 4th argument and in the FF_SALES code, currency is the 6th
argument.
factlet=ExtractFormulaHistory&ids=stl-no&items=p_price(5,0,Q,USD),ff_sales(QTR,-5,0,Q,,USD)&dates=-5:0:Q&
Note: When specifying the items in the request syntax, to ensure most effectively
that that the dates for the items, such as price and sales, align correctly with the
dates field, enter the dates in the items portion of the request syntax as specified
above. For example p_price(-5,0,Q).
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Example 4:
In this example, retrieve the last 6 quarters of EPS and price for the current
constituents of the S&P 500 using the FQL codes FF_SALES and P_PRICE and the
names of the companies using the FQL code PROPER_NAME. The universe code,
which is specified as the S&P 500 and is based on the FQL code ISON_SP500,
returns the current constituents of the S&P 500.
factlet=ExtractFormulaHistory&ison=sp500&items=proper_name,ff_eps(qtr,0,5,Q),p_price(0,-5,q)&
Example 5:
In this example, extract for Microsoft as of most recent dividends pay date the date
that is plus 1 actual quarter forward. The specified DATE function in this example
returns the date requested as a string, formatted as MM/DD/YY. Non-trading days
are not recognized by the DATE function.
factlet=ExtractFormulaHistory&ids=MSFT&items=
DATE(P_DIVS_PD(0,,,,EXDATE)%plus1AQ)&
Note: When using a hex symbol in the request, such as +1AQ in this case, it is
necessary to use %plus instead.
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4.2.2 ExtractDataSnapshot
The ExtractDataSnapshot function is used for efficiently extracting multiple items as
of a single date, for a universe of both equity and fixed income securities. This
function is using the FactSet Screening Language, to efficiently extract data for a
large universe of securities as of a single date.
The syntax for the ExtractDataSnapshot function is:
factlet=ExtractDataSnapshot&ids=&item=&date=&currency=&
where,
ids
CellString array with a list of one or more security identifiers.
Item(s)
CellString array with a list of one or more FactSet data items in
the Screening language.
Date
The date as of which the data is retrieved. If no date is
specified, data is returned from the most recent time period.
Currency
The currency in which the data is to be returned, using a string
with the three character ISO code (e.g. ‘USD’ or ‘EUR’).
Table 4 in Appendix lists the top 25 most frequently used data items from the
FactSet Fundamentals database using the Screening syntax. For links to more
comprehensive lists of the items available from the FactSet Fundamentals database,
refer to Online Assistant page 15099 in the FactSet workstation.
23 | P a g e
Example 1:
In this example, retrieve the quarterly sales value from the FactSet Fundamentals
database for IBM as of 04/01/2011 using the FactSet Screening code
FF_SALES(QTR,20110401).
factlet=ExtractDataSnapshot&ids=ibm&items=ff_sales(QTR,20110401)&
Example 2:
In this example, retrieve the quarterly sales values in USD from the FactSet
Fundamentals database for Exxon Mobile and Statoil as of 04/01/2011, using the
FactSet Screening code FF_SALES(QTR,20110401).
factlet=ExtractDataSnapshot&ids=xom,stlnos&items=ff_sales(QTR,20110401,,USD)&
Note: All Screening codes used in the item parameter have their arguments in
different positions depending on the source of the code. For example, the currency
argument for the price code from the pricing database using P_PRICE has it in the
2ND position P_PRICE(-5D,USD) , while the sales code from FactSet Fundamentals
using the code FF_SALES(QTR,-1,RP,USD) has it in the 4th position.
Example 3:
In this example, instead of specifying securities in the ids field, as was done in
examples 1 and 2 above, an index universe is specified and the items, price and
sales, are extracted for all of the constituents of the S&P 500. The syntax to extract
the price from the Pricing database using the code P_PRICE(20110401) and sales
from the FactSet Fundamentals database using the code
FF_SALES(QTR,20110401) for the current constituents of the S&P 500 using the
code ISON_SP500 is the following:
factlet=ExtractDataSnapshot&ison=sp500&ff_sales(QTR,20110401),p_price(20
110401)&
The universe is specified at the end of the code with the ison & sp500 arguments,
which are broken down from the actual Screening syntax, which is ISON_SP500. In
the output with this code, the ID field brings back the CUSIPs of the companies in
the S&P 500.
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Example 4:
In this example, extract the decile ranking of the S&P 500 companies based on the
most recently reported quarterly earnings per share (EPS) using the FactSet
Fundamentals formula FF_EPS as of the most recent quarter end. The FactSet
DECILE function returns the decile rank (1-10) of the formula evaluated for each
company in the specified universe of companies, in this case the S&P 500.
factlet=ExtractDataSnapshot&ison=sp500&items=decile(ff_sales(QTR,0))&
Example 5:
In this example utilize the backtest functionality to retrieve the latest quarterly sales
value and the corresponding fiscal period end date for a list of securities lagged by
45 days. To retrieve sales the FactSet Fundamentals code is FF_SALES and for
retrieving the Fiscal Period End date in MM/YYYY format the FactSet Fundamentals
code is FF_FP_END_DATE.
When using the lag argument (lag options: specified number of days, weeks,
months, quarters, or years), FactSet retrieves data as it was known at the time of the
rebalance date. For example, if returns are calculated for the universe rebalanced in
December 1990, a custom P/E formula can be set up using the price in December
1990, then lag the date of the earnings (to the most-recently reported quarter at that
point in time), for example, to September. By lagging EPS, it is possible to use the
P/E that was known to the public in December 1990.
A lag argument does not need to be added to pricing and shares formulas to avoid
look-ahead bias, because these are bits of information that would have been publicly
known at the time of the backtest date.
factlet=ExtractDataSnapshot&ids=hd,pg,fds,fit&items=FF_FP_END_DATE(QTR,0 L45D),FF_SALES(QTR,0
L45D)&date=20110131&
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4.2.3 ExtractBenchmarkDetail
The ExtractBenchmarkDetail function is used for extracting multiple data items for a
benchmark. Benchmark data can be retrieved using other functions, such as with
ExtractFormulaHistory, but the ExtractBenchmarkDetail function allows a user to
retrieve a more comprehensive overview of the index constituent data, without
additional codes or calculations. In the default output, identifiers are sorted in
descending order by weight in the index and each row shows the index id, company
id, date, ticker, and weight. Additional items are displayed at the end.
FactSet clients have access to Equity and Fixed Income Benchmarks, which include
Dow Jones, FTSE, MSCI, Russell, S&P, Barclays, and BofA Merrill Lynch, among a
number of others. Access to benchmarks is based on client subscription to various
benchmark providers.
In addition, FactSet Market Aggregates (FMA), combines data from FactSet
Fundamentals, Estimates and Prices to calculate ratios and per share values on an
aggregate level. FMA comprises over 3,500 benchmarks including S&P, Russell,
MSCI Global, FTSE, STOXX, TOPIX, and many local exchanges. Benchmarks also
include specific sector and industry level indices. This number is constantly
expanding based on client demand.
The syntax for the ExtractBenchmarkDetail function is:
factlet=ExtractBenchmarkDetail&ids=&item(s)=&dates=&cutoff=&
where,
ids
CellString array with a list of the benchmark identifiers.
Item(s)
CellString array with a list of one or more FactSet data items in
FQL.
Dates
The option argument dates can be entered in start:end:freq
format. (e.g. ‘20101215:20110115:d’)
Cutoff
Optional number of holdings to display, based on Market Cap.
Default displays to all holdings.
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Example 1:
In this example, extract the securities that make up the S&P 500, and their
corresponding Company Name using the code FG_COMPANY_NAME and Market
Cap using the code FG_MKT_VALUE.
factlet=ExtractBenchmarkDetail&ids=SP50&items=FG_COMPANY_NAME,FG_
MKT_VALUE&
Example 2:
In this example, extract the top 10 holdings for the S&P 500 and display the
securities corresponding price using the pricing database with the code P_PRICE
and the company name using the code PROPER_NAME.
factlet=ExtractBenchmarkDetail&ids=SP50&items=p_price,proper_name&cuto
ff=10&
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4.2.4 ExtractEconData
The ExtractEconData function is used to extract a broad array of macroeconomic
content, interest rates and yields, country indices and various exchange rate
measures from both the FactSet Economics and the Standardized Economic
databases.
FactSet Economics is a database of primary-sourced information on the global
economy. The database includes specialty sources such as Eurostat, ICIS, IMF,
LME, NYMEX, and OECD. For a comprehensive list of economic databases
available on FactSet please refer to http://www.factset.com/data/data/economic.
Each data series from the database has a mnemonic identifying that requested
series. For example, GDP published by the National Bureau of Statistics of China
Seasonally Adjusted, will have an id or mnemonic assigned to it that’s different from
a mnemonic assigned for China’s GDP value that is not seasonally adjusted. To find
the mnemonic for a series, use the Series Lookup.
Note: Table 5 in Appendix lists the Key Indicator series from the FactSet Economics
database for the G-7 Countries. For a comprehensive list of data series available
from the FactSet Economics database, refer to the Series Lookup in the FactSet
workstation. A Series Lookup overview is available in Online Assistant page 15694.
The syntax for the ExtractEconData function is:
factlet=ExtractEconData&item=DB_SOURCE_DATA(‘mnemonic’, start
date, end date, frequency, conv, dist, nfb)&
where,
item
String specifying the economic series.
dbsource
Pertains to the FactSet Economics database.
Note: After specifying a database source, the syntax should be
_ DATA for each. For example, FDS_ECON_DATA.
FDS_ECON = FactSet Economics
SPEC_ID
= FactSet Commodities & Interest Rates
FDS_COM = FactSet Commodities
IMF_IFS
= IMF
OECD_MEI = OECD
EURO_STAT = EuroStat
TCB_CCI
= Conference Board
ONS_ECON = UK Office for National Statistics
mnemonic
String specifying the economic series mnemonic, i.e. US GDP
the mnemonic BEANIPAA191RL1@US from series
FDS_ECON[BEANIPAA191RL1@US]
Note: Table 5 in Appendix lists the Key Indicator series for G-7.
28 | P a g e
start date
start date entered in mm/dd/yyyy format.
Alternate method of entering dates entered in start:end:freq
format. (i.e. ‘20101215:20110115:d’).
end date
end date entered in mm/dd/yyyy format.
Alternate method of entering dates entered in start:end:freq
format. (i.e. ‘20101215:20110115:d’).
frequency
String with a valid FactSet frequency (i.e. ‘d’,’w’,’m’,’q’,’y’).
Note: For economic request codes a frequency argument is
necessary to retrieve the data.
conv
The conversion technique for aggregating periods, when a data
series of a higher frequency is converted to a lower frequency;
for example, converting a quarterly series to an annual series.
(i.e. ‘SUM’, ‘AVERAGE’, or ‘AVERAGENP’ which excludes
NAs).
dist
The distribution technique for spreading over periods when a
data series of a lower frequency is distributed to a higher
frequency; for example, distributing an annual series to a
quarterly series. (i.e. ‘STEP’, ‘EVEN’, ‘NONE’)
nfb
NFB is the optional "no feel back" argument in FQL codes. If
you do not use the NFB argument, the returned data series will
contain NAs where the data is not available (default is NFB=1).
If you want the data to "feel back" over NAs to find the last
actual data point and carry this data forward, set the NFB
argument to either 0 or 2.
29 | P a g e
Example 1:
In this example, retrieve industrial production data for the United States using the
FactSet Economics database as identified by the FDS_ECON argument in the code,
starting 122 months ago (denoted with -121) until 2 months ago (denoted with -1).
factlet=ExtractEconData&item=FDS_ECON_DATA(‘FRBIPSB50001’,-121,1,m,step,average)&
Example 2:
In this example, retrieve the last 30 days of WTI commodity data using the FactSet
Economics series SPEC_ID[WTI-FDS:FG_PRICE].
factlet=ExtractEconData&item=SPEC_ID_DATA(‘WTI-FDS:FG_PRICE’,0,-30,d)&
30 | P a g e
4.3
UploadToOFDB
The UploadToOFDB functionality allows clients to upload data into an OFDB file
stored in Data Central in the FactSet workstation.
OFDB, which stands for Open FactSet Database, is a high-performance multidimensional database system used to securely store proprietary numeric and textual
data on FactSet and can be time-series or non-time series databases. OFDB is ideal
for users who manage large portfolios or maintain extensive historical proprietary
databases. OFDB optimizes large, multi-dimensional databases, giving FactSet
users highly flexible, fast access to large volumes of complex data that can be used
in many different applications.
Note: The optimal use of the UploadToOFDB functionality is for ad-hoc and smaller
scale data uploads and would not replace a client’s needs for FTP processes or
production services, for larger scale or holdings uploads into FactSet.
4.3.1 Requirements for UploadToOFDB
The following are the necessary requirements to upload data into an OFDB:
 Data set must have at least 1 ID, Date and Items field;
o Fields uploaded can be iterated of any frequency or non-iterated;
o Date types can be High Precision, Integer, or Text;
o Dates need to be uploaded as integers in YYYYMMDD format.
 Data Central subscription in the FactSet workstation is necessary;
o Basic data storage access available to all clients with a premium
FactSet workstation in Data Central is 1GB of storage space.
Additional data storage is available and should be discussed with a
FactSet sales representative.
o If attempting to upload data to a full OFDB file and thus exceeding data
storage space access, there will be an error message after running an
upload notifying that data storage access is full. The error message "Client Data Space is Full" will be displayed.
o FactSet does not need to be launched when uploading data into an
OFDB.
Note: A Comprehensive list of error messages is available in Table 5 in the
Appendix.
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4.3.2 OFDB
4.3.2.1
Creating a New OFDB
The following details are regarding the behavior of an OFDB file that is created
through the Developer’s Toolkit:
 If the OFDB does not already exist, it will be created.
 If created by UploadToOFDB, all fields will be Iterated with Daily Frequency
and data type High Precision for numbers and Text(32) for strings.
 OFDB schemas define the database. If another schema is required for the
OFDB file, it should first be created in Data Central. For more details
regarding creating or editing schemas refer to Online Assistant page 11502
within the FactSet workstation.
 Once an OFDB is created, no changes to the schema can be made through
the UploadToOFDB functionality. A new OFDB would need to be created to
make the necessary changes.
4.3.2.2
Modifying an Existing OFDB
The following details are regarding modifying an OFDB file that is created through
the Developer’s Toolkit, when adding additional dates or values to that file:
 Data for an additional date can be appended for existing securities in an
OFDB file.
 Data for additional securities can be appended to an OFDB file for the
existing dates in the file or for a new date range.
 New Data items cannot be appended to an existing OFDB through the
UploadtoOFDB functionality.
4.3.3 UploadToOFDB Syntax
In order to upload data into an OFDB, it is initially necessary to instantiate an upload
factlet.
The “UploadFactlet.MakeUploadFactlet” method takes an OFDB name, the column
names separated by commas, and the column types separated by commas (so in
this example, Id is an Id, Date is a Date, my_price is a Double, and my_volume is a
Double).
32 | P a g e
It is necessary to use the addDataPoint method in the uploadFactlet that is created
to add points one at a time.
The next step is to call the Upload method in the UploadFactlet to tell it to upload.
Subsequently getErrorMessage or getErrorValue is called on to see if it was a
success. “Successful” will be returned by getErrorMessage and getErrorValue will
return 0 if nothing went wrong.
C#:
uFactlet =
FactSet.UploadFactlet.MakeUploadFactlet("TestOFDB_CSharp",
"Id,Date,my_price,my_volume", "Id,Date,Double,Double"); // Make an
upload factlet
string[] dates = { "04/10/2013", "04/11/2013", "04/12/2013" };
double[] fds_price = { 93.33, Double.NaN, 92.65 };
double[] fds_volume = { 388.205, Double.NaN, 359.396 };
double[] xom_price = { 88.68, Double.NaN, 89.22 };
double[] xom_volume = { 14826.0, Double.NaN, 14980.0 };
for (int i = 0; i < dates.Length; i++)
{
uFactlet.addDataPoint("FDS");
// Add one piece of data
at a time, column by column
uFactlet.addDataPoint(dates[i]);
uFactlet.addDataPoint(fds_price[i]);
uFactlet.addDataPoint(fds_volume[i]);
}
for (int i = 0; i < dates.Length; i++)
{
uFactlet.addDataPoint("XOM");
uFactlet.addDataPoint(dates[i]);
uFactlet.addDataPoint(xom_price[i]);
uFactlet.addDataPoint(xom_volume[i]);
}
uFactlet.upload(); // Upload
Console.WriteLine(uFactlet.getErrorMessage()); // check
success or failure
33 | P a g e
Chapter 5: Formula Lookup
Sidebar is a formula lookup that provides a robust way of searching for FactSet data
formulas along with formula details and definitions. The integration of Sidebar into
the Developer’s Toolkit provides a seamless way of finding FactSet Query Language
(FQL) and Screening request codes. Upon launching the GUI, it is possible to
search for identifiers and formulas.
Note: Sidebar integration with the Developer’s Toolkit is for searching for codes, it
cannot be used for inserting formulas into the necessary programming language.
5.1
Launching Formula Lookup
Formula Lookup can be launched from the Sidebar - OnDemand icon, which gets
created when installing the Developer’s Toolkit Plugin.
Note: It is necessary to be logged into the FactSet workstation for Sidebar to load. If
not already logged in, will be prompted to log into the FactSet workstation when
selecting Sidebar - OnDemand.
The following Sidebar GUI will launch:
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5.2
Searching for Data
FactSet’s formula lookup allows users to streamline the process of creating FactSet
formulas. It features targeted search results, type-ahead search functionality for
identifiers, and function-building capability.
5.2.1 Identifier Lookup
The process of searching for and extracting data in Sidebar involves entering
identifiers for equities, indices and yields, among other categories. The identifier can
be entered directly in the Enter ID field which utilizes type-ahead functionality or
searched for by launching the Identifier Lookup magnifying glass, as displayed in the
screenshot below.
5.2.2 Search for Data Formulas
Data Items are searched for by entering keywords in the Formula Lookup section.
There is a thesaurus built in, so that the data item search is not a text search for the
entered keyword. By default, the returned list of formulas will match all of the
databases based on a user’s subscription.
To limit search results to FactSet Recommended formulas only, click the FactSet
Recommended button
. To control which formula libraries are being searched,
click the Filter button . The option is available to toggle between FQL and
Screening codes.
35 | P a g e
Once a formula is selected, a Formula Arguments section loads, in which it is
possible to specify the report basis, date, frequency, currency, and other formula
arguments that are specific to the data item selected, using the drop-down menus.
A Definition section can also be expanded to view the formula name, data source,
and description. Once an identifier and data item are selected, the Formula
Workspace section can be expanded to add a function.
36 | P a g e
5.3
Sample Scripts
The following examples detail selected data items in Sidebar and the corresponding
script.
C++
factSet::Factlet* factlet; // Create a factlet pointer variable to use
factlet = FactSet::Factlet::ExtractFormulaHistory(<string representing
ids>, <string that matches what is in sidebar formula workspace window>);
Java
Factlet factlet; // Create a factlet variable to use
factlet = Factlet.ExtractFormulaHistory(<string representing ids>, <string
that matches what is in sidebar formula workspace window>);
VB
Dim factlet As Factlet ' Create a factlet pointer variable to use
factlet = FactSet.Factlet.ExtractFormulaHistory(<string representing ids>,
<string that matches what is in sidebar formula workspace window>)
C#
FactSet.Factlet factlet; // Create a factlet pointer variable to use
factlet = FactSet.Factlet.ExtractFormulaHistory(<string representing ids>,
<string that matches what is in sidebar formula workspace window>);
Python
# creating a factlet variable is not necessary
factlet = Factlet.ExtractFormulaHistory(<string representing ids>, <string
that matches what is in sidebar formula workspace window>);
37 | P a g e
Example 1:
In this example, the selected security in Sidebar is Exxon Mobil, with identifier XOM.
The selected item in the FQL mode is EPS from the FactSet Fundamentals
database, with the formula arguments specifying 5 years of annual history. The
Formula Workspace section previews the code as FF_EPS(ANN,0,-5AY,FY).
C++
factlet = FactSet::Factlet::ExtractFormulaHistory("XOM_US",
"FF_EPS(ANN,0,-5AY,FY)");
Java
factlet = Factlet.ExtractFormulaHistory("XOM_US", "FF_EPS(ANN,0,5AY,FY)");
VB
factlet = FactSet.Factlet.ExtractFormulaHistory("XOM_US", "FF_EPS(ANN,0,5AY,FY)")
C#
factlet = FactSet.Factlet.ExtractFormulaHistory("XOM_US", "FF_EPS(ANN,0,5AY,FY)");
Python
factlet = Factlet.ExtractFormulaHistory("XOM_US", "FF_EPS(ANN,0,5AY,FY)");
38 | P a g e
Example 2:
In this example, the selected index is the Russell 3000 and the selected item in the
FQL code is price history, with the formula arguments specifying one year of daily
history. The Formula Workspace section previews the code as FG_PRICE(0,1AY,D).
C++
factlet = FactSet::Factlet::ExtractFormulaHistory("R_3000", "FG_PRICE(0,1AY,D)");
Java
factlet = Factlet.ExtractFormulaHistory("R_3000", "FG_PRICE(0,-1AY,D)");
VB
factlet = FactSet.Factlet.ExtractFormulaHistory("R_3000", "FG_PRICE(0,1AY,D)")
C#
factlet = FactSet.Factlet.ExtractFormulaHistory("R_3000", "FG_PRICE(0,1AY,D)");
Python
factlet = Factlet.ExtractFormulaHistory("R_3000", "FG_PRICE(0,-1AY,D)");
39 | P a g e
Chapter 6: Factlet Object
The fetching and execution of the factlet object requests discussed in section 4.2 is
done using the FactSetOnDemand API. A user should create one
FactSetOnDemandAPI, otherwise the FactSet shared libraries can become
corrupted. The static method ‘GetInstance’ can be used in all of the
FactSetOnDemand classes to provide access to the API.
The following is a comprehensive list of available methods to access the fetched
data once the factlet request is executed:
 isComplete
This method returns a Boolean result indicating if the factlet has finished executing
and parsing. The result true indicates that the factlet has terminated parsing and
execution. This method returns instantly and does not wait for the Factlet to
terminate execution.
 getFactletResult
This method returns a string representation of the factlet report in ASCII text. This
string can be very large, sometimes up to 15 megabytes or more. It is recommended
that if the Factlet\DataFrame was created and parsed in pipe-delimited format, that
data is accessed through the value assessors below. This method does wait for the
factlet to terminate execution if it is called before completion.
 getFactletRequest
This method returns a string representation of the factlet request in ASCII text. This
string is the same string of name-value pairs that was created when the factlet object
was instantiated. This method does wait for the factlet to terminate execution if it is
called before completion.
 getNumberOfColumns
This method returns an integer representation of the number of columns in the given
factlet DataFrame. If the DataFrame does not parse or fails to parse due to an error,
this result will be negative one (-1). Before accessing any information from a
DataFrame, the user should check to see if the DataFrame has an error value
associated with it. This method does wait for the factlet to terminate execution if it is
called before completion.
 getNumberOfRows
This method returns an integer representation of the number of rows in the given
factlet DataFrame. If the DataFrame does not parse or fails to parse due to an error,
this result will be negative one (-1). Before accessing any information from a
DataFrame, the user should check to see if the DataFrame has an error value
associated with it. This method does wait for the factlet to terminate execution if it is
called before completion.
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 getValueAsInteger
This method returns the value of the data point at the given DataFrame index as an
integer. If the data point is a native double, it is truncated. If the data point is a native
date, it is usually returned in YYYYMMDD format as an integer. If the data point is a
string, a conversion is attempted; in the event of failure, a non-signaling C++ NaN is
returned. This method does wait for the factlet to terminate execution if it is called
before completion.
 getValueAsDouble
This method returns the value of the data point at the given DataFrame index as a
double. If the data point is a native integer, it is casted to a double type and returned.
If the data point is a native date, it is usually returned in YYYYMMDD format as an
integer casted to double. If the data point is a string, a conversion is attempted; in
the event of failure, a non-signaling C++ NaN is returned. This method does wait for
the factlet to terminate execution if it is called before completion.
 getValueAsString
This method returns the value of the data point at the given DataFrame index as a
string. If the data point is a native integer or double, it is converted to an ASCII
representation. If the data point is a native date, it is usually returned in an ASCII
string in the format “DD-mmm-YYYY”. This method does wait for the factlet to
terminate execution if it is called before completion.
 hasError
This method returns a Boolean value that indicates if the factlet has encountered
any type of error fetching or parsing. The method returns true if an error has been
found. This method does wait for the factlet to terminate execution if it is called
before completion.
 getErrorMessage
This method returns an ASCII string which gives a short explanation of the error
found in the DataFrame. This method does wait for the factlet to terminate execution
if it is called before completion.
 getColumnName
This method returns an ASCII string of the column name of the specified column in
the DataFrame. This name is the name given by the factlet report. This method does
wait for the factlet to terminate execution if it is called before completion.
 getColumnType
This method returns an ASCII string of the column type of the specified column in
the DataFrame. This name is the type given by the factlet report. This method does
wait for the factlet to terminate execution if it is called before completion.
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 getColumnNative
This method returns an enumerated integer of the column native storage type of the
specified column in the DataFrame. This name is the name given by the factlet
report. See DataFrame native types for values. This method does wait for the factlet
to terminate execution if it is called before completion.
 Cleanup
This method is available in languages that do not have destructors such as VB and
Java. This method will delete the factlet’s DataFrame from the FactSet OnDemand
API. If the user attempts to access data from the DataFrame after this method is
called, an exception will be thrown. This method does not wait for the factlet to
terminate execution if it is called before completion.
 toString
This method prints out an ASCII string of the DataFrame. This method prints out
each value to string in a CSV format. This string can be extremely large if the
DataFrame is large. It is not recommended to use this method on non-parsed and
large factlets. The first two lines of the ASCII summary will be the column name and
type, respectively. This method does wait for the factlet to terminate execution if it is
called before completion.
 getSummary
This method prints out an ASCII summary of the factlet. Each column will be briefly
detailed for name, type and native storage, as can be seen in the output example
below:
Factlet with ID : 2
Known Error Code : 0
Number of Rows
: 3
Number of Cols
: 4
Col
Name
0
Id
1
Date
2
p_price
3
p_volume
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Type
Entity
Date
Float
Float
Native
String
Date
Double
Double
Chapter 7: Create DataFrame
CreateDataFrame is the first method call used in fetching and parsing data. This
method sends an HTTPS request to DataDirect, allocates memory to receive the
data and parses the data. This method also checks the entire process for errors.
Each CreateDataFrame spawns a new thread so that these actions can occur in
parallel. If the HTTPS throttle is set high enough, the user may use multiple threads
for fetching and parsing.
The first argument of the method is always the DataFrame Factlet Request. Other
possible parameters are optional and include file output, data transformations and
file path redirection. Once a DataFrame is created, it begins fetching and parsing
and other DataFrames may be created.
Some highlight properties of DataFrames are the following:








Rectangular Form – DataFrames parse typically into rectangular matrices.
Column Normal – DataFrame’s columns have a name, a type and a native
storage type.
Error Content - DataFrames report errors if they are detected.
Zero Indexed – The DataFrame is indexed starting at zero and ending at
its size minus one.
Parse Independently – DataFrames may finish out of order depending on
the request.
Unique ID – DataFrames are assigned a unique ID at creation.
Deleted by user – The user must call DELETEDATAFRAME to delete it.
Can make files – DataFrames may output files during parsing time if
requested.
The parameters for CreateDataFrame are outlined below:
Parameter Name
factletString
Parameter
Type
String
dataFrameFileOutput Integer
transformMode
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Integer
Description
This string is an ASCII string that describes a Factlet
to be run. Note, at present time this string should
contain a name-value pair of ‘format=pipe’ if the user
desires it to be parsed.
This is a field that allows the user to write the Factlet
to a file. There are several types of outputs possible
such as text files and Excel 2.0 format files. Table 2
in Appendix provides a comprehensive list of file
output types.
This is a reserved field for matrix transfers such as a
matrix transpose if applicable. At this time, this is not
implemented.
fileName
String
This is a string for the desired filename output. This
string should not have a file extension. If the user
gives a file name with an absolute path and name the
file will be written to the location with the chosen
output type’s extension. If the file name is relative, the
file will be written from the current working directory of
the application.
The following example uses an overloaded method that only uses the ‘factletString’
parameter.
Note: In this example, the ExtractDataSnapshot factlet discussed in section 4.2.2 is
used.
If no format is requested in the Factlet string, the report is retuned as a XML text file.
For FactSet to properly parse the file for the user, it must be specified as a
‘pipe’ format. This means the report is pipe-delimited. If the user desires to retrieve
the XML format, they may simply not specify the pipe format and set the transform
mode to NO_PARSE_NO_CHECK. The XML text can be retrieved by simply calling
GetDataFrameFactletResult.
C++
int dataFrameID = factSetOnDemand->createDataFrame(
"factlet=extractdatasnapshot&ids=IBM&items=p_price&dates=&format=pipe&");
Java
int dataFrameID = factSetOnDemand.createDataFrame(
"factlet=ExtractDataSnapshot&ids=IBM&items=p_price&dates=&format=pipe&");
VB
Dim dataFrameID As Integer = factSetOnDemand.createDataFrame(
"factlet=extractdatasnapshot&ids=ibm&items=p_price&dates=&format=pipe&")
C#
int dataFrameID = factSetOnDemand.createDataFrame(
"factlet=extractdatasnapshot&ids=ibm&items=p_price&dates=&format=pipe&");
44 | P a g e
7.1
IsDataFrameComplete
This method returns true if the given DataFrame response is complete. This
processing includes fetching the data over HTTPS, parsing the response into a
matrix and checking for errors. If the DataFrame is still parsing or does not exist, this
function returns false. This function could be used in a loop that sleeps while the
DataFrame is not complete. If the user is running multiple Frames simultaneously,
the method could check different frames and handle them as they complete.
C++
bool isComplete = factSetOnDemand->isDataFrameComplete(dataFrameId);
\
Java
boolean isComplete = factSetOnDemand.isDataFrameComplete(dataFrameID);
VB
Dim isComplete As Boolean = factSetOnDemand.isDataFrameComplete(dataFrameID);
C#
bool isComplete = factSetOnDemand.isDataFrameComplete(dataFrameID);
7.2
GetDataFrameErrorValue
Once the DataFrame has finished parsing, it should be checked for an error value. If
there are no errors returned, the result will be zero. Table 3 in the Appendix provides
a comprehensive list of error messages and what the error indicates.
7.3
GetDataFrameFactletRequest \ GetDataFrameFactletResult
GetDataFrameFactletRequest method gets the specified DataFrame’s request
string. This is the same string passed into the ‘factletString’ parameter in
CreateDataFrame. GetDataFrameFactletResult method gets the ASCII result string
returned as a result of the Factlet String. This result can vary widely in size from a
few kilobytes to a few megabytes depending on the factlet request. This string may
even be obtained if the DataFrame fails to parse.
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C++
std::string factletRequest= factSetOnDemand->getDataFrameFactletRequest(dataFrameID);
std::string factletResult = factSetOnDemand->getDataFrameFactletResult(dataFrameID);
Java
String factletRequest = factSetOnDemand.getDataFrameFactletRequest(dataFrameID);
String factletResult = factSetOnDemand.getDataFrameFactletResult(dataFrameID);
VB
Dim factletRequest As String = factSetOnDemand.getDataFrameFactletRequest(dataFrameID)
Dim factletResult As String = factSetOnDemand.getDataFrameFactletResult(dataFrameID)
C#
string factletRequest = factSetOnDemand.getDataFrameFactletRequest(dataFrameID);
string factletResult = factSetOnDemand.getDataFrameFactletResult(dataFrameID);
7.4
GetDataFrameNumberOfRows \ GetDataFrameNumberOfCols
These methods return the dimensions of the DataFrames in terms of the number of
Rows and Columns respectively. Note that in a column the data type is always the
same.
C++
int numRows = factSetOnDemand->getDataFrameNumberOfRows(dataFrameID);
int numCols = factSetOnDemand->getDataFrameNumberOfCols(dataFrameID);
Java
int numRows = factSetOnDemand.getDataFrameNumberOfRows(dataFrameID);
int numCols = factSetOnDemand.getDataFrameNumberOfCols(dataFrameID);
VB
Dim numRows As Integer = factSetOnDemand.getDataFrameNumberOfRows(dataFrameID)
Dim numCols As Integer = factSetOnDemand.getDataFrameNumberOfCols(dataFrameID)
C#
int numRows = factSetOnDemand.getDataFrameNumberOfRows(dataFrameID);
int numCols = factSetOnDemand.getDataFrameNumberOfCols(dataFrameID);
7.5
GetDataFrameColumnName \ GetDataFrameColumnType
GetDataFrameColumnName and GetDataFrameColumnType both return string
representatives of the column. The column name typically is a descriptor such as
‘Id’, ‘Date’ or ‘Price’. The column type is a representation of how the data should be
interpreted such as ‘Date’, ‘String’, ‘Double’, or ‘Float’.
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C++
std::string colName = factSetOnDemand->getDataFrameColumnName(dataFrameID, col);
std::string colType = factSetOnDemand->getDataFrameColumnType(dataFrameID, col);
Java
String colName = factSetOnDemand.getDataFrameColumnName(dataFrameID, col);
String colType = factSetOnDemand.getDataFrameColumnType(dataFrameID, col);
VB
Dim colName As String = factSetOnDemand.getDataFrameColumnName(dataFrameID, col)
Dim colType As String = factSetOnDemand.getDataFrameColumnType(dataFrameID, col)
C#
string colName = factSetOnDemand.getDataFrameColumnName(dataFrameID, col);
string colType = factSetOnDemand.getDataFrameColumnType(dataFrameID, col);
7.6
GetDataFrameColumnNative
This method gets the native storage type of the given column. This native storage
type is one of three C data types, a double precision floating point value, an integer
value or a character string. Requesting data in the native storage type for use is the
fastest way to return it. Otherwise, conversions occur.
C++
int colNative = factSetOnDemand->getDataFrameColumnNative(dataFrameID, col);
Java
int colNative = factSetOnDemand.getDataFrameColumnNative(dataFrameID, col);
VB
Dim colNative As Integer = factSetOnDemand.getDataFrameColumnNative(dataFrameID, col)
C#
int colNative = factSetOnDemand.getDataFrameColumnNative(dataFrameID, col);
The following is the list of native storage types that can occur:
Type Name
UNKNOWN
STRINGVALUE
DOUBLEVALUE
INTEGERVALUE
DATEVALUE
47 | P a g e
Type Index
0
3
1
2
4
7.7
GetDataFrameValueAsString \ GetDataFrameValueAsInteger \
GetDataFrameValueAsDouble
These three methods are used to get the values of particular cells in a DataFrame. It
is advised that the corresponding method is called based on the native column type.
Despite this advice, the user may attempt to convert any value into any type but
there will be a performance penalty depending on the cast. Larger performance
penalties occur in string to numeric and numeric to string conversions.
C++
std::string strVal=factSetOnDemand->getDataFrameValueAsString(dataFrameID, col, row);
int integerVal
=factSetOnDemand->getDataFrameValueAsInteger(dataFrameID, col, row);
double doubleVal = factSetOnDemand->getDataFrameValueAsDouble(dataFrameID, col, row);
Java
std::string strVal=factSetOnDemand.getDataFrameValueAsString(dataFrameID, col, row);
int integerVal
=factSetOnDemand.getDataFrameValueAsInteger(dataFrameID, col, row);
double doubleVal = factSetOnDemand.getDataFrameValueAsDouble(dataFrameID, col, row);
VB
Dim strVal As String=factSetOnDemand.getDataFrameValueAsString(dataFrameID, col, row)
Dim intValAs Integer=factSetOnDemand.getDataFrameValueAsInteger(dataFrameID,col, row)
Dim doblValAs Double=factSetOnDemand.getDataFrameValueAsDouble(dataFrameID, col, row)
C#
string strVal
= factSetOnDemand.getDataFrameValueAsString (dataFrameID, col, row);
int integerVal = factSetOnDemand.getDataFrameValueAsInteger(dataFrameID, col, row);
double doubleVal= factSetOnDemand.getDataFrameValueAsDouble (dataFrameID, col, row);
48 | P a g e
Chapter 8: Troubleshooting
8.1
IsDataFrameValueInvalid
This method returns if the specified DataFrame value is invalid. Invalid can mean
that the value does not exist in FactSet’s database. An example of this would be
Apple’s dividends before 2012. Also, an invalid value could mean that the data was
unable to be parsed properly. For example, if the library tried to convert the string ‘q’
into an integer.
C++
bool isInvalid = factSetOnDemand->isDataFrameValueInvalid(dataFrameId,col,row);
Java
boolean isInvalid = factSetOnDemand.isDataFrameValueInvalid(dataFrameID,col,row);
VB
Dim isInvalid As Boolean = factSetOnDemand.isDataFrameInvalid(dataFrameID, col, row)
C#
bool isInvalid = factSetOnDemand.isDataFrameValueInvalid(dataFrameID, col, row );
8.2
TranslateErrorMessage \ GetFactSetErrorValue
Both of these methods are meant to be used together to help the user identify errors
and resolve them quickly. GetFactSetErrorValue returns a general error message
that would not be typically associated with any one DataFrame. An example of this
would be a system resources allocation error. When a user gets an error message
from either GetFactSetErrorValue or GetDataFrameErrorValue, they may translate it
into a string type error message using TranslateErrorMessage, the preferred method
for translating.
C++
int errorVal = factSetOnDemand->getFactSetErrorValue( );
std::string errorMsg = factSetOnDemand->translateErrorMessage(errorVal);
Java
int errorVal = factSetOnDemand.getFactSetErrorValue( );
String errorMsg = factSetOnDemand.translateErrorMessage(errorVal);
VB
Dim errorVal As Integer = factSetOnDemand.getFactSetErrorValue( )
Dim errorMsg As String = factSetOnDemand.translateErrorMessage(errorVal)
C#
int errorVal = factSetOnDemand.getFactSetErrorValue( );
string errorMsg = factSetOnDemand.translateErrorMessage(errorVal);
Table 3 in Appendix has a comprehensive list of error codes.
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8.3
GetListOfLivingDataFrames
This method returns a list of all the DataFrames that have not been deleted. This
method is useful for obtaining a list of DataFrame IDs and parsing them in order of
completion.
C++
std::vector<int> listOfDataFrameIDs = factSetOnDemand->getListOfLivingDataFrames( );
Java
int[] listOfDataFrameIDs = factSetOnDemand.getListOfLivingDataFrames();
VB
Dim listOfDataFrameIDs As Integer() = factSetOnDemand.getListOfLivingDataFrames()
C#
int[] listOfDataFrameIDs = factSetOnDemand.getListOfLivingDataFrames();
8.4
FactSet Event Logger
The FactSet Event Logger (FFEL) is installed as a part of the FactSet OnDemand
Developers Toolkit. This feature provides reporting services that will allow FactSet to
troubleshoot more efficiently errors in requests for FactSet data.
The EventLogger binds references to all major objects in the code base and can be
exposed for additional trouble-shooting information. The EventLogger is generated
as a HTML report and typically is opened with the client’s web browser automatically
when generated. The EventLogger has three key functions:
1. Inventories – Lists of items\events
a) Object Inventory – This is a list of all the central objects in the FactSet
integration.
b) Thread Registry – This is a list of all the threads created in the
FactSet integration along with statistics.
c) HTTPS Inventory – This is a list of all HTTPS request made along
with statistics.
d) DataFrame Inventory – This is a list of all the DataFrames and
performance statistics.
2. Report Manager – A collection of operating information and configuration
information
a) Screen Shot – A bitmap image of the clients PC at time of report
creating the event log.
50 | P a g e
b) Configuration – A list of configuration variables and current values.
c) Directory Listings – Lists of files in various important directories.
d) Environment Analysis – A brief exploration of environment settings
and hardware.
3. EventLogger – A collection of events and errors at runtime.
a) External – Any events that occurred outside the Kratos.DLL
b) Internal – Any events that occurred at the system call level (Win32API
calls).
c) Object – Any events that occurred in the Kratos.DLL organized by
object
The EventLogger report is generated in the location prescribed by the
TEMP_DIRECTORY configuration setting. When the FactSet OnDemand Toolkit is
installed, by default, this temporary directory is set in the user’s application data.
This may be set to any writable location such as the desktop. This method
dispatches a thread to create a report. If the Kratos library is dynamically unloaded
while the EventLogger report is running, the report will be canceled.
There are two options available with the EventLogger report. The first option is to
display the report and second is to change the report’s path. By default, the display
option is enabled.
Note: Even if the EVENTLOGGERON configuration setting is set to false, the
EventLogger Report will still get generated, but any events during the
EVENTLOGGERON setting being false are not recorded.
In order to generate the EventLogger Report:
C++
factSetOnDemand->generateEventLoggerReport( true, “” );
Java
factSetOnDemand.generateEventLoggerReport( true, “”);
VB
factSetOnDemand.generateEventLoggerReport( TRUE, “”)
C#
factSetOnDemand.generateEventLoggerReport( true, “” );
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Chapter 9: Sample Code
The following sample code examples across several languages show the steps from
creating a new FactSet OnDemand Object until the data is requested and the old
DataFrame is deleted once the data is accessed.
The first step entails creating a new FactSet OnDemand Object, which goes into the
registry and checks where this library is located and attempts to load it. If the library
is not found, it will load it locally and put it into memory. The FactSet credentials can
then be stored and any other configuration information that is necessary, such as
timeout or throttle details.
In the second step, a factlet object is created, as a pointer variable to use. Various
factlets are run to examine the unique Ids and Dates in the returned data.
In the third step, the createDataFrame should be run before running a factlet. Note,
the specified format should be set to pipe for FactSet to properly parse the file for
the user. The createDataFrame portion the code sends a request to DataDirect and
queues the requests, depending on the specified throttle setting. In this example,
multi-threading is used.
In the getListOfLivingDataFrames portion of the code, a number is returned for all of
the created data frames. Subsequently, listOfDataFrames specifies that, when the
data request runs and is completed, check the data for an error, parse it, and then
delete it from memory.

isDataframe complete > if it’s complete, then it’s ready for data to be
accessed and deleted.

getDataFrameErrorValue > error is checked if it’s an error

TranslateErrorMessage > shows what the error issue is, such as no
company ids were entered in the factlet request code.
In the fourth step, the getDataFrameFactletRequest allows a user to see a raw text
file of data without parsing.
In the fifth step, to display the data it is printed to screen and then deleteDataFrame
ensures that the old DataFrame is deleted when accessing the data is completed.
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9.1
C++ Sample Code
#ifndef FACTSETONDEMAND_H_
#include "FactSetOnDemand.h"
#endif
#ifndef WIN32
#include <unistd.h>
#endif
int main(int argc, char** argv) {
// First, we get a handle to the FactSet OnDemand API.
// This handle creation loads FactSet's DLL into memory
FactSet::FactSetOnDemand* factSetOnDemand = FactSet::FactSetOnDemand::GetInstance();
//***********************************************************************//
//
CONFIGURATION DEMO USE
//
//***********************************************************************//
if( argc > 1 ){
factSetOnDemand->setConfigurationItem( FactSet::FactSetOnDemand::DATADIRECTUSERNAME,
argv[1]);
}
if( argc > 2 ){
factSetOnDemand->setConfigurationItem( FactSet::FactSetOnDemand::DATADIRECTPASSWORD,
argv[2]);
}
if( argc > 3 ){
factSetOnDemand->setConfigurationItem( FactSet::FactSetOnDemand::PROXYSERVER, argv[3]
);
}
if( argc > 4 ){
factSetOnDemand->setConfigurationItem( FactSet::FactSetOnDemand::PROXYPORT, argv[4]
);
}
if( argc > 5 ){
factSetOnDemand->setConfigurationItem( FactSet::FactSetOnDemand::PROXYUSERNAME,
argv[5] );
}
if( argc > 6 ){
factSetOnDemand->setConfigurationItem( FactSet::FactSetOnDemand::PROXYPASSWORD,
argv[6] );
}
//***********************************************************************//
//
FACTLET OBJECT DEMO USE
//
//***********************************************************************//
FactSet::Factlet* factlet; // Create a factlet pointer variable to use
// In this example, we will run ExtractFormulaHistory and examine the unique
// IDs and Dates in the returned data
factlet = FactSet::Factlet::ExtractFormulaHistory("xom,stl-no,fds,ibm,efc,appl,goog",
"fg_eps(0,-1m,d)");
printf("%s\n", factlet->getSummary().c_str() );
std::vector<std::string> uniqueIDs = factlet->getUniqueIDs();
printf("%s\n","Printing list of unique IDs:\n");
for( unsigned int i = 0; i < uniqueIDs.size(); i++ )
{
printf("\t%s\n",uniqueIDs[i].c_str() );
}
std::vector<int> uniqueDates = factlet->getUniqueDates();
printf("%s\n","Printing list of unique dates:\n");
for( unsigned int i = 0; i < uniqueDates.size(); i++ )
{
printf("\t%d\n",uniqueDates[i] );
}
53 | P a g e
delete factlet; // Note that we must delete the factlet after we are done to prevent memory
leaks
// Extract Alpha Testing Snapshot
factlet = FactSet::Factlet::ExtractAlphaTestingSnapshot("N","N","Factset:Calculation Example
Model","CONSTITUENTS","ALL","M");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet = FactSet::Factlet::ExtractAlphaTestingSnapshot("N","N","Factset:Calculation Example
Model","CONSTITUENTS","5,7","M");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet = FactSet::Factlet::ExtractAlphaTestingSnapshot("N","N","Factset:Calculation Example
Model","CONSTITUENTS","Weight,Market_Capitalization","M");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet = FactSet::Factlet::ExtractAlphaTestingSnapshot("N","N","Factset:Calculation Example
Model","CONSTITUENTS","all","M","","08865810");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet = FactSet::Factlet::ExtractAlphaTestingSnapshot("Y","N","Factset:Calculation Example
Model","PERIODS","ALL","M","","","RS","DEFAULT","A","1");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
// Extract Benchmark Detail
factlet =
FactSet::Factlet::ExtractBenchmarkDetail("SP50","items","FG_COMPANY_NAME,FG_MKT_VALUE");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet =
FactSet::Factlet::ExtractBenchmarkDetail("SP50","items","p_price,proper_name","cutoff","10");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet =
FactSet::Factlet::ExtractBenchmarkDetail("SP50","items","p_price","dates","20110115:20110120:d");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
// Extract Data Snapshot
factlet = FactSet::Factlet::ExtractDataSnapshot("ibm","ff_sales(QTR,20110401,RF,EUR)");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet = FactSet::Factlet::ExtractDataSnapshot("xom,stl-no","ff_sales(QTR,20110401,,USD)");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet = FactSet::Factlet::ExtractDataSnapshot("","ff_sales(QTR,20110401),p_price(20110401)",
"ison","sp500");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet =
FactSet::Factlet::ExtractDataSnapshot("","ff_sales(QTR,0)","ison","msci_eafe","isonParams","0,1");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet = FactSet::Factlet::ExtractDataSnapshot("","decile(ff_sales(QTR,0))", "ison","sp500");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
54 | P a g e
factlet = FactSet::Factlet::ExtractDataSnapshot("ibm,hd,pg,fds,f-it","FF_FP_END_DATE(QTR,0
L45D),FF_SALES(QTR,0 L45D)","date","20110131");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
// Extract Econ Data
factlet = FactSet::Factlet::ExtractEconData("","","FDS_ECON_DATA('FRBIPSB50001',-121,1,m,step,average)");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet = FactSet::Factlet::ExtractEconData("","","SPEC_ID_DATA('WTI-FDS:FG_PRICE',0,-30,d)");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet = FactSet::Factlet::ExtractEconData("","","IMF_IFS_DATA('11199Z_F',0,9,y),IMF_IFS_DATA('11299Z_F',0,-9,y)","dataitemname",",,US,UK","heading",",,US,UK");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet = FactSet::Factlet::ExtractEconData("","","EURO_STAT_DATA('CONSCONFBAL@EUZ',0,11,M),TCB_CCI_DATA('CCI',0,-11,M)");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet =
FactSet::Factlet::ExtractEconData("","","ECON_EXPR_DATA('ZSCORE(FDS_ECON[GRLM0347861])',10,0,m,step,average)");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet =
FactSet::Factlet::ExtractEconData("","","ECON_EXPR_DATA('RETURNS(FDS_ECON[BLSCUSR0000SA0L1E], 1AY)',0,0/0/-10,M)");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet =
FactSet::Factlet::ExtractEconData("","","ECON_EXPR_DATA('RETURNS(EURO_STAT[GDPEURNS@GR], 1AY)',0,0/0/-10,Y)");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet = FactSet::Factlet::ExtractEconData("","CC_GB","FDS_ECON_BOPCURR_Y","dates","-10:1:Y");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet = FactSet::Factlet::ExtractEconData("","CC_US,CC_GR","FDS_ECON_GDP_USD_Y","dates","10:-1:Y");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
// Extract Formula History
factlet = FactSet::Factlet::ExtractFormulaHistory("xom","fg_eps(0Q,-5Q,Q)","dates","0Q:5Q:Q");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet =
FactSet::Factlet::ExtractFormulaHistory("goog","fg_price","dates","20101215:20110115:D");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet = FactSet::Factlet::ExtractFormulaHistory("stl-no","p_price(-5,0,Q,USD),ff_sales(QTR,5,0,Q,,USD)","dates","-5:0:Q");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
55 | P a g e
factlet = FactSet::Factlet::ExtractFormulaHistory("XOM","BETA('SP50',0,-59M,M)");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet = FactSet::Factlet::ExtractFormulaHistory("GE","AVG(FF_EPS(QTR,0,5,Q)),MAX(FF_EPS(QTR,0,-5,Q)),MIN(FF_EPS(QTR,0,-5,Q)),STD(FF_EPS(QTR,0,-5,Q))");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet = FactSet::Factlet::ExtractFormulaHistory("","proper_name,ff_eps(qtr,0,5,Q),p_price(0,-5,q)","ison","sp500");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
// Extract Options Snapshot
factlet =
FactSet::Factlet::ExtractOptionsSnapshot("P_OPT_UNDERLYING_PRICE","","P_OPT_UNDERLYING_SECURITY=","IBM
","","","","");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet =
FactSet::Factlet::ExtractOptionsSnapshot("P_OPT_UNDERLYING_PRICE,P_OPT_CLOSE_PRICE","","P_OPT_UNDERLYI
NG_SECURITY=","GE","","","","");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet =
FactSet::Factlet::ExtractOptionsSnapshot("P_OPT_UNDERLYING_PRICE","","P_OPT_UNDERLYING_SECURITY=","GE"
,"","20100513","","");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
// Extract Estimates Actuals
factlet = FactSet::Factlet::FactSetEstimatesActuals("msft","eps,sales","NOW","fiscalperiod","1,0","periodtype","annual");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet =
FactSet::Factlet::FactSetEstimatesActuals("msft,ibm","eps,sales","NOW","fiscalperiod","1,0","periodtype","annual");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet =
FactSet::Factlet::FactSetEstimatesActuals("xom","eps,sales","12/31/2010","fiscalperiod","1,0","periodtype","quarterly_roll");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet =
FactSet::Factlet::FactSetEstimatesActuals("xom","eps,sales","12/31/2010","fiscalperiod","1,0","periodtype","quarterly","fields","fe_actual");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
// Estimates Broker Detail
factlet = FactSet::Factlet::FactSetEstimatesBrokerDetail("msft","eps","0D","1AY","fiscalperiod","1");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet = FactSet::Factlet::FactSetEstimatesBrokerDetail("msft,ibm","eps","0D","1AY","fiscalperiod","1");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
56 | P a g e
factlet = FactSet::Factlet::FactSetEstimatesBrokerDetail("xom","sales","0Y","0Y2Q","fiscalperiod","2","periodtype","quarterly");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet = FactSet::Factlet::FactSetEstimatesBrokerDetail("xom,stl-no,cvx","eps","0D","2Q","fiscalPeriod","1","periodtype","quarterly","fields","FE_ANALYSTNAME,FE_BROKERNAME,FE_ESTIMATE,FE_
EST_REV_VAL_ARROW");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet = FactSet::Factlet::FactSetEstimatesBrokerDetail("xom","sales","0Y","0Y2Q","fiscalperiod","2","periodtype","quarterly","display","histo");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
// Estimates Consensus
factlet =
FactSet::Factlet::FactSetEstimatesConsensus("msft","eps,capex","NOW","fiscalperiod","1,2","periodtype"
,"annual");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet =
FactSet::Factlet::FactSetEstimatesConsensus("msft,ibm","eps,capex","12/31/2011","fiscalperiod","1,2","
periodtype","quarterly");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet =
FactSet::Factlet::FactSetEstimatesConsensus("msft","eps,bvps","NOW","fiscalperiod","1","periodtype","a
nnual","fields","fe_mean");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet =
FactSet::Factlet::FactSetEstimatesConsensus("aapl","eps,capex","NOW","fiscalperiod","1","periodtype","
annual","previousDates","-30","fields","prev");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
// Estimates Guidance
factlet =
FactSet::Factlet::FactSetEstimatesGuidance("FTI","EPS","NOW","fiscalperiod","1,2","periodtype","annual
");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet =
FactSet::Factlet::FactSetEstimatesGuidance("fti,oii","eps","NOW","fiscalperiod","1","periodtype","annu
al");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet =
FactSet::Factlet::FactSetEstimatesGuidance("oii","eps","12/31/2011","fiscalperiod","1","periodtype","a
nnual");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet =
FactSet::Factlet::FactSetEstimatesGuidance("FTI","eps","NOW","fiscalperiod","1","periodtype","annual",
"fields","fe_mean,Guidance Mean");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
// Estimates Suprise
57 | P a g e
factlet =
FactSet::Factlet::FactSetEstimatesSurprise("IBM","eps","1/19/2012","fiscalperiod","0","periodtype","an
nual","statistic","mean");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet =
FactSet::Factlet::FactSetEstimatesSurprise("blk,bac","eps","1/18/2012","fiscalperiod","0","periodtype"
,"quarterly","statistic","mean");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
factlet =
FactSet::Factlet::FactSetEstimatesSurprise("IBM","eps","1/19/2012","fiscalperiod","0","periodtype","an
nual","statistic","mean","fields","Surprise_Before,Surprise_After");
printf("%s\n", factlet->getSummary().c_str() );
delete factlet;
//***********************************************************************//
//
FACTSET ONDEMAND API OBJECT DEMO USE
//
//***********************************************************************//
// Demo fetching a Factlet using the OnDemand API - simply print out the report text
FactSet::DataFrameID firstDataFrame = factSetOnDemand->createDataFrame(
"factlet=extractdatasnapshot&ids=fds&items=p_price&dates=&format=pipe&");
while (!factSetOnDemand->isDataFrameComplete(firstDataFrame)){
#ifdef WIN32
Sleep( 10 );
#else
usleep( 10*1000 );
#endif
}
std::string firstFactletString = factSetOnDemand->getDataFrameFactletResult(firstDataFrame);
factSetOnDemand->deleteDataFrame( firstDataFrame ); // Delete DataFrame when done
printf("Got first factlet result \n\"%s\"\n", firstFactletString.c_str() );
// Create many factlets and parse - allow them to queue - mutlithreaded approach
factSetOnDemand>createDataFrame("factlet=ExtractDataSnapshot&ids=ibm&items=p_price&dates=&format=pipe&");
factSetOnDemand>createDataFrame("factlet=ExtractDataSnapshot&ids=ibm&items=p_price&dates=&format=pipe&");
factSetOnDemand>createDataFrame("factlet=ExtractDataSnapshot&ids=ibm,msft&items=p_price&dates=&format=pipe&");
factSetOnDemand>createDataFrame("factlet=ExtractDataSnapshot&ids=ibm,msft,efc&items=p_price,ff_sales,p_volume,fg_eps&
dates=&format=pipe&");
factSetOnDemand>createDataFrame("factlet=ExtractDataSnapshot&ids=&items=p_price,ff_sales,p_volume,fg_eps&dates=&forma
t=pipe&");
factSetOnDemand>createDataFrame("factlet=ExtractDataSnapshot&ids=imb&items=&dates=&format=pipe&");
// Process the frames and print to console
std::vector<FactSet::DataFrameID> listOfDataFrames = factSetOnDemand>getListOfLivingDataFrames();
FactSet::DataFrameID dataFrameID;
for( unsigned int i = 0; i < listOfDataFrames.size(); i++ )
{
dataFrameID = listOfDataFrames.at(i);
printf("\n\n\nGetting Data Frame with ID = %d\n", dataFrameID );
while (!factSetOnDemand->isDataFrameComplete(dataFrameID))
{
#ifdef WIN32
Sleep( 100 );
#else
usleep( 100*1000 );
#endif
}
58 | P a g e
printf("%s\n", factSetOnDemand->getDataFramePropertyValue(dataFrameID,
FactSet::FactSetOnDemand::DATAFRAME_SUMMARY ).c_str() );
factSetOnDemand->deleteDataFrame( dataFrameID );
}
// Demo XML fetch - Do not use DataFrame parsing
// Note: for XML, there is no FORMAT=PIPE argument
dataFrameID = factSetOnDemand->createDataFrame(
"factlet=ExtractFormulaHistory&ids=ibm,efc&items=p_price(0,-5d,d)&dates=&",
FactSet::FactSetOnDemand::NONE,
FactSet::FactSetOnDemand::NO_PARSE_NO_CHECK);
while( !factSetOnDemand->isDataFrameComplete( dataFrameID ) ){
#ifdef WIN32
Sleep( 10 );
#else
usleep( 10*1000 );
#endif
}
int errorCode = factSetOnDemand->getDataFrameErrorValue(dataFrameID);
if(errorCode != 0) {
std::string errorCodeTranslation = factSetOnDemand->translateErrorMessage( errorCode
);
printf("Detected Error Code = %d = '%s'\n",errorCode, errorCodeTranslation.c_str() );
}
std::string XMLResult = factSetOnDemand->getDataFrameFactletResult( dataFrameID );
factSetOnDemand->deleteDataFrame( dataFrameID );
printf("XML Demo \n\n%s", XMLResult.c_str());
// Unload library
FactSet::FactSetOnDemand::CleanUp();
return 0;
}
C# Sample Clode
using System;
using System.Collections.Generic;
using System.Text;
using FactSet;
namespace KratosCSharpTest {
class Program {
static int Main(string[] args) {
// First, we get a handle to the FactSet OnDemand API.
// This handle creation loads FactSet's DLL into memory
FactSet.FactSetOnDemand factSetOnDemand = FactSet.FactSetOnDemand.GetInstance();
//***********************************************************************//
//
CONFIGURATION DEMO USE
//
//***********************************************************************//
if (args.Length > 0) {
factSetOnDemand.setConfigurationItem(FactSet.FactSetOnDemand.DATADIRECTUSERNAME,args[0]);
}
if (args.Length > 1) {
factSetOnDemand.setConfigurationItem(FactSet.FactSetOnDemand.DATADIRECTPASSWORD,
args[1]);
}
if (args.Length > 2) {
factSetOnDemand.setConfigurationItem(FactSet.FactSetOnDemand.PROXYSERVER, args[2]);
}
if (args.Length > 3) {
factSetOnDemand.setConfigurationItem(FactSet.FactSetOnDemand.PROXYPORT, args[3]);
}
if (args.Length > 4) {
factSetOnDemand.setConfigurationItem(FactSet.FactSetOnDemand.PROXYUSERNAME, args[4]);
}
if (args.Length > 5) {
factSetOnDemand.setConfigurationItem(FactSet.FactSetOnDemand.PROXYPASSWORD, args[5]);
}
//***********************************************************************//
59 | P a g e
//
FACTLET OBJECT DEMO USE
//
//***********************************************************************//
FactSet.Factlet factlet; // Create a factlet pointer variable to use
// In this example, we will run ExtractFormulaHistory and examine the unique
// IDs and Dates in the returned data
factlet = FactSet.Factlet.ExtractFormulaHistory("xom,stl-no,fds,ibm,efc,appl,goog",
"fg_eps(0,-1m,d)");
System.Console.Write(factlet.getSummary());
String[] uniqueIDs = factlet.getUniqueIDs();
System.Console.Write("Printing list of unique IDs:\n");
foreach (string ID in uniqueIDs)
{
System.Console.Write("\t" + ID + "\n");
}
int[] uniqueDates = factlet.getUniqueDates();
System.Console.Write("Printing list of unique dates:\n");
foreach (int date in uniqueDates)
{
System.Console.Write("\t" + Convert.ToString(date) + "\n");
}
factlet.cleanUp(); // Note that we must clean up in C# because it is garbage
collected
// Extract Alpha Testing Snapshot
factlet = FactSet.Factlet.ExtractAlphaTestingSnapshot("N","N","Factset:Calculation
Example Model","CONSTITUENTS","ALL","M");
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet = FactSet.Factlet.ExtractAlphaTestingSnapshot("N","N","Factset:Calculation
Example Model","CONSTITUENTS","5,7","M");
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet = FactSet.Factlet.ExtractAlphaTestingSnapshot("N","N","Factset:Calculation
Example Model","CONSTITUENTS","Weight,Market_Capitalization","M");
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet = FactSet.Factlet.ExtractAlphaTestingSnapshot("N","N","Factset:Calculation
Example Model","CONSTITUENTS","all","M","","08865810");
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet = FactSet.Factlet.ExtractAlphaTestingSnapshot("Y","N","Factset:Calculation
Example Model","PERIODS","ALL","M","","","RS","DEFAULT","A","1");
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
// Extract Benchmark Detail
factlet =
FactSet.Factlet.ExtractBenchmarkDetail("SP50","items","FG_COMPANY_NAME,FG_MKT_VALUE");
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet =
FactSet.Factlet.ExtractBenchmarkDetail("SP50","items","p_price,proper_name",new
string[]{"cutoff","10"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet = FactSet.Factlet.ExtractBenchmarkDetail("SP50","items","p_price",new
string[]{"dates","20110115:20110120:d"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
// Extract Data Snapshot
factlet = FactSet.Factlet.ExtractDataSnapshot("ibm","ff_sales(QTR,20110401,RF,EUR)");
60 | P a g e
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet = FactSet.Factlet.ExtractDataSnapshot("xom,stlno,fds,ibm,efc,appl,goog","ff_sales(QTR,20110401,,USD)");
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet =
FactSet.Factlet.ExtractDataSnapshot("","ff_sales(QTR,20110401),p_price(20110401)", new
string[]{"ison","sp500"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet = FactSet.Factlet.ExtractDataSnapshot("","ff_sales(QTR,0)",new
string[]{"ison","msci_eafe","isonParams","0,1"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet = FactSet.Factlet.ExtractDataSnapshot("","decile(ff_sales(QTR,0))", new
string[]{"ison","sp500"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet = FactSet.Factlet.ExtractDataSnapshot("ibm,hd,pg,fds,fit","FF_FP_END_DATE(QTR,0 L45D),FF_SALES(QTR,0 L45D)",new string[]{"date","20110131"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
// Extract Econ Data
factlet = FactSet.Factlet.ExtractEconData("","","FDS_ECON_DATA('FRBIPSB50001',-121,1,m,step,average)");
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet = FactSet.Factlet.ExtractEconData("","","SPEC_ID_DATA('WTI-FDS:FG_PRICE',0,30,d)");
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet = FactSet.Factlet.ExtractEconData("","","IMF_IFS_DATA('11199Z_F',0,9,y),IMF_IFS_DATA('11299Z_F',0,-9,y)",new string[]{"dataitemname",",,US,UK","heading",",,US,UK"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet = FactSet.Factlet.ExtractEconData("","","EURO_STAT_DATA('CONSCONFBAL@EUZ',0,11,M),TCB_CCI_DATA('CCI',0,-11,M)");
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet =
FactSet.Factlet.ExtractEconData("","","ECON_EXPR_DATA('ZSCORE(FDS_ECON[GRLM0347861])',10,0,m,step,average)");
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet =
FactSet.Factlet.ExtractEconData("","","ECON_EXPR_DATA('RETURNS(FDS_ECON[BLSCUSR0000SA0L1E], 1AY)',0,0/0/-10,M)");
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet =
FactSet.Factlet.ExtractEconData("","","ECON_EXPR_DATA('RETURNS(EURO_STAT[GDPEURNS@GR], -1AY)',0,0/0/10,Y)");
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
61 | P a g e
factlet = FactSet.Factlet.ExtractEconData("","CC_GB","FDS_ECON_BOPCURR_Y",new
string[]{"dates","-10:-1:Y"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet = FactSet.Factlet.ExtractEconData("","CC_US,CC_GR","FDS_ECON_GDP_USD_Y",new
string[]{"dates","-10:-1:Y"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
// Extract Formula History
factlet = FactSet.Factlet.ExtractFormulaHistory("xom","fg_eps(0Q,-5Q,Q)",new
string[]{"dates","0Q:-5Q:Q"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet = FactSet.Factlet.ExtractFormulaHistory("goog","fg_price",new
string[]{"dates","20101215:20110115:D"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet = FactSet.Factlet.ExtractFormulaHistory("stl-no","p_price(5,0,Q,USD),ff_sales(QTR,-5,0,Q,,USD)",new string[]{"dates","-5:0:Q"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet = FactSet.Factlet.ExtractFormulaHistory("XOM","BETA('SP50',0,-59M,M)");
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet = FactSet.Factlet.ExtractFormulaHistory("GE","AVG(FF_EPS(QTR,0,5,Q)),MAX(FF_EPS(QTR,0,-5,Q)),MIN(FF_EPS(QTR,0,-5,Q)),STD(FF_EPS(QTR,0,-5,Q))");
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet = FactSet.Factlet.ExtractFormulaHistory("","proper_name,ff_eps(qtr,0,5,Q),p_price(0,-5,q)",new string[]{"ison","sp500"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
// Extract Options Snapshot
factlet =
FactSet.Factlet.ExtractOptionsSnapshot("P_OPT_UNDERLYING_PRICE","","P_OPT_UNDERLYING_SECURITY=","IBM",
"","","","");
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet =
FactSet.Factlet.ExtractOptionsSnapshot("P_OPT_UNDERLYING_PRICE,P_OPT_CLOSE_PRICE","","P_OPT_UNDERLYING
_SECURITY=","GE","","","","");
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet =
FactSet.Factlet.ExtractOptionsSnapshot("P_OPT_UNDERLYING_PRICE","","P_OPT_UNDERLYING_SECURITY=","GE","
","20100513","","");
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
// Extract Estimates Actuals
factlet = FactSet.Factlet.FactSetEstimatesActuals("msft","eps,sales","NOW",new
string[]{"fiscalperiod","-1,0","periodtype","annual"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet = FactSet.Factlet.FactSetEstimatesActuals("msft,ibm","eps,sales","NOW",new
string[]{"fiscalperiod","-1,0","periodtype","annual"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
62 | P a g e
factlet = FactSet.Factlet.FactSetEstimatesActuals("xom","eps,sales","12/31/2010",new
string[]{"fiscalperiod","-1,0","periodtype","quarterly_roll"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet = FactSet.Factlet.FactSetEstimatesActuals("xom","eps,sales","12/31/2010",new
string[]{"fiscalperiod","-1,0","periodtype","quarterly","fields","fe_actual"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
// Estimates Broker Detail
factlet = FactSet.Factlet.FactSetEstimatesBrokerDetail("msft","eps","0D","-1AY",new
string[]{"fiscalperiod","1"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet = FactSet.Factlet.FactSetEstimatesBrokerDetail("msft,ibm","eps","0D","1AY",new string[]{"fiscalperiod","1"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet = FactSet.Factlet.FactSetEstimatesBrokerDetail("xom","sales","0Y","0Y-2Q",new
string[]{"fiscalperiod","2","periodtype","quarterly"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet = FactSet.Factlet.FactSetEstimatesBrokerDetail("xom,stl-no,cvx","eps","0D","2Q",new
string[]{"fiscalPeriod","1","periodtype","quarterly","fields","FE_ANALYSTNAME,FE_BROKERNAME,FE_ESTIMAT
E,FE_EST_REV_VAL_ARROW"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet = FactSet.Factlet.FactSetEstimatesBrokerDetail("xom","sales","0Y","0Y-2Q",new
string[]{"fiscalperiod","2","periodtype","quarterly","display","histo"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
// Estimates Consensus
factlet = FactSet.Factlet.FactSetEstimatesConsensus("msft","eps,capex","NOW",new
string[]{"fiscalperiod","1,2","periodtype","annual"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet =
FactSet.Factlet.FactSetEstimatesConsensus("msft,ibm","eps,capex","12/31/2011",new
string[]{"fiscalperiod","1,2","periodtype","quarterly"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet = FactSet.Factlet.FactSetEstimatesConsensus("msft","eps,bvps","NOW",new
string[]{"fiscalperiod","1","periodtype","annual","fields","fe_mean"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet = FactSet.Factlet.FactSetEstimatesConsensus("aapl","eps,capex","NOW",new
string[]{"fiscalperiod","1","periodtype","annual","previousDates","-30","fields","prev"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
// Estimates Guidance
factlet = FactSet.Factlet.FactSetEstimatesGuidance("FTI","EPS","NOW",new
string[]{"fiscalperiod","1,2","periodtype","annual"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet = FactSet.Factlet.FactSetEstimatesGuidance("fti,oii","eps","NOW",new
string[]{"fiscalperiod","1","periodtype","annual"});
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System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet = FactSet.Factlet.FactSetEstimatesGuidance("oii","eps","12/31/2011",new
string[]{"fiscalperiod","1","periodtype","annual"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet = FactSet.Factlet.FactSetEstimatesGuidance("FTI","eps","NOW",new
string[]{"fiscalperiod","1","periodtype","annual", "fields","fe_mean,Guidance Mean"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
// Estimates Suprise
factlet = FactSet.Factlet.FactSetEstimatesSurprise("IBM","eps","1/19/2012",new
string[]{"fiscalperiod","0","periodtype","annual","statistic","mean"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet = FactSet.Factlet.FactSetEstimatesSurprise("blk,bac","eps","1/18/2012",new
string[]{"fiscalperiod","0","periodtype","quarterly","statistic","mean"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
factlet = FactSet.Factlet.FactSetEstimatesSurprise("IBM","eps","1/19/2012",new
string[]{"fiscalperiod","0","periodtype","annual","statistic","mean","fields","Surprise_Before,Surpris
e_After"});
System.Console.Write(factlet.getSummary());
factlet.cleanUp();
//***********************************************************************//
//
FACTSET ONDEMAND API OBJECT DEMO USE
//
//***********************************************************************//
// Demo fetching a Factlet using the OnDemand API - simply print out the
report text
int firstDataFrame = factSetOnDemand.createDataFrame(
"factlet=ExtractDataSnapshot&ids=fds&items=p_price&dates=&format=pipe&");
while (!factSetOnDemand.isDataFrameComplete(firstDataFrame)){
System.Threading.Thread.Sleep(100);
}
String firstFactletString =
factSetOnDemand.getDataFrameFactletResult(firstDataFrame);
factSetOnDemand.deleteDataFrame( firstDataFrame ); // Delete DataFrame when
done
System.Console.Write("Got first factlet result \n" + firstFactletString +
"\n" );
// Create many factlets and parse - allow them to queue - mutlithreaded
approach
factSetOnDemand.createDataFrame("factlet=ExtractDataSnapshot&ids=ibm&items=p_price&dates=&form
at=pipe&");
factSetOnDemand.createDataFrame("factlet=ExtractDataSnapshot&ids=ibm&items=p_price&dates=&form
at=pipe&");
factSetOnDemand.createDataFrame("factlet=ExtractDataSnapshot&ids=ibm,msft&items=p_price&dates=
&format=pipe&");
factSetOnDemand.createDataFrame("factlet=ExtractDataSnapshot&ids=ibm,msft,efc&items=p_price,ff
_sales,p_volume,fg_eps&dates=&format=pipe&");
factSetOnDemand.createDataFrame("factlet=ExtractDataSnapshot&ids=&items=p_price,ff_sales,p_vol
ume,fg_eps&dates=&format=pipe&");
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factSetOnDemand.createDataFrame("factlet=ExtractDataSnapshot&ids=imb&items=&dates=&format=pipe
&");
// Process the frames and print to console
int[] listOfDataFrameIDs = factSetOnDemand.getListOfLivingDataFrames();
int dataFrameID;
for( int i = 0; i < listOfDataFrameIDs.Length; i++ )
{
dataFrameID = listOfDataFrameIDs[i];
System.Console.Write("\n\n\nGetting Data Frame with ID = " +
dataFrameID );
while (!factSetOnDemand.isDataFrameComplete(dataFrameID))
{
System.Threading.Thread.Sleep(100);
}
System.Console.Write(factSetOnDemand.getDataFramePropertyValue(dataFrameID,
FactSetOnDemand.DATAFRAME_SUMMARY) + "\n");
factSetOnDemand.deleteDataFrame( dataFrameID );
}
// Demo XML fetch - Do not use DataFrame parsing
// Note: for XML, there is no FORMAT=PIPE argument
dataFrameID = factSetOnDemand.createDataFrame(
"factlet=ExtractFormulaHistory&ids=ibm,efc&items=p_price(0,5d,d)&dates=&",
FactSetOnDemand.NO_FILE,
FactSetOnDemand.NO_PARSE_NO_CHECK, null);
while( !factSetOnDemand.isDataFrameComplete( dataFrameID ) ){
System.Threading.Thread.Sleep(100);
}
int errorCode = factSetOnDemand.getDataFrameErrorValue(dataFrameID);
if(errorCode != 0) {
string errorCodeTranslation = factSetOnDemand.translateErrorMessage(
errorCode );
System.Console.Write("Detected Error Code = " + errorCode + " = " +
errorCodeTranslation );
}
String XMLResult = factSetOnDemand.getDataFrameFactletResult( dataFrameID );
factSetOnDemand.deleteDataFrame( dataFrameID );
System.Console.Write("XML Demo \n\n" + XMLResult );
// Unload library
FactSet.FactSetOnDemand.CleanUp();
return 0;
}
}
}
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9.2
VB .NET Sample Code
Module Main
Sub Main(ByVal cmdArgs() As String)
' First, we get a handle to the FactSet OnDemand API.
' This handle creation loads FactSet's DLL into memory
Dim factSetOnDemand As FactSet.FactSetOnDemand = factSetOnDemand.GetInstance()
'***********************************************************************//
'
CONFIGURATION DEMO USE
//
'***********************************************************************//
If cmdArgs.Length > 0 Then
factSetOnDemand.setConfigurationItem(FactSet.FactSetOnDemand.DATADIRECTUSERNAME,
cmdArgs.ElementAt(0))
End If
If cmdArgs.Length > 1 Then
factSetOnDemand.setConfigurationItem(FactSet.FactSetOnDemand.DATADIRECTPASSWORD,
cmdArgs.ElementAt(1))
End If
If cmdArgs.Length > 2 Then
factSetOnDemand.setConfigurationItem(FactSet.FactSetOnDemand.PROXYSERVER,
cmdArgs.ElementAt(2))
End If
If cmdArgs.Length > 3 Then
factSetOnDemand.setConfigurationItem(FactSet.FactSetOnDemand.PROXYPORT,
cmdArgs.ElementAt(3))
End If
If cmdArgs.Length > 4 Then
factSetOnDemand.setConfigurationItem(FactSet.FactSetOnDemand.PROXYUSERNAME,
cmdArgs.ElementAt(4))
End If
If cmdArgs.Length > 5 Then
factSetOnDemand.setConfigurationItem(FactSet.FactSetOnDemand.PROXYPASSWORD,
cmdArgs.ElementAt(5))
End If
'***********************************************************************//
'
FACTLET OBJECT DEMO USE
//
'***********************************************************************//
Dim factlet As Factlet ' Create a factlet pointer variable to use
' In this example, we will run ExtractFormulaHistory and examine the unique
' IDs and Dates in the returned data
factlet = FactSet.Factlet.ExtractFormulaHistory("xom,stl-no,fds,ibm,efc,appl,goog",
"fg_eps(0,-1m,d)")
System.Console.Write(factlet.getSummary())
Dim uniqueIDs As String() = factlet.getUniqueIDs()
Dim uniqueDates As Integer() = factlet.getUniqueDates()
System.Console.Write("Printing list of unique IDs:\n")
For Each ID As String In uniqueIDs
System.Console.Write(ControlChars.Tab + ID + ControlChars.NewLine)
Next
System.Console.Write("Printing list of unique dates:\n")
For Each dateInt As Integer In uniqueDates
System.Console.Write(ControlChars.Tab + Convert.ToString(dateInt) + ControlChars.NewLine)
Next
factlet.CleanUp()
' Extract Alpha Testing Snapshot
factlet = factlet.ExtractAlphaTestingSnapshot("N", "N", "Factset:Calculation Example Model",
"CONSTITUENTS", "ALL", "M")
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
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factlet = factlet.ExtractAlphaTestingSnapshot("N", "N", "Factset:Calculation Example Model",
"CONSTITUENTS", "5,7", "M")
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.ExtractAlphaTestingSnapshot("N", "N", "Factset:Calculation Example Model",
"CONSTITUENTS", "Weight,Market_Capitalization", "M")
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.ExtractAlphaTestingSnapshot("N", "N", "Factset:Calculation Example Model",
"CONSTITUENTS", "all", "M", "", "08865810")
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.ExtractAlphaTestingSnapshot("Y", "N", "Factset:Calculation Example Model",
"PERIODS", "ALL", "M", "", "", "RS", "DEFAULT", "A", "1")
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
' Extract Benchmark Detail
factlet = factlet.ExtractBenchmarkDetail("SP50", "items", "FG_COMPANY_NAME,FG_MKT_VALUE")
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.ExtractBenchmarkDetail("SP50", "items", "p_price,proper_name", New String()
{"cutoff", "10"})
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.ExtractBenchmarkDetail("SP50", "items", "p_price", New String() {"dates",
"20110115:20110120:d"})
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
' Extract Data Snapshot
factlet = factlet.ExtractDataSnapshot("ibm", "ff_sales(QTR,20110401,RF,EUR)")
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.ExtractDataSnapshot("xom,stl-no", "ff_sales(QTR,20110401,,USD)")
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.ExtractDataSnapshot("", "ff_sales(QTR,20110401),p_price(20110401)", New
String() {"ison", "sp500"})
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.ExtractDataSnapshot("", "ff_sales(QTR,0)", New String() {"ison",
"msci_eafe", "isonParams", "0,1"})
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.ExtractDataSnapshot("", "decile(ff_sales(QTR,0))", New String() {"ison",
"sp500"})
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.ExtractDataSnapshot("ibm,hd,pg,fds,f-it", "FF_FP_END_DATE(QTR,0
L45D),FF_SALES(QTR,0 L45D)", New String() {"date", "20110131"})
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
' Extract Econ Data
factlet = factlet.ExtractEconData("", "", "FDS_ECON_DATA('FRBIPSB50001',-121,1,m,step,average)")
System.Console.Write(factlet.getSummary())
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factlet.CleanUp()
factlet = factlet.ExtractEconData("", "", "SPEC_ID_DATA('WTI-FDS:FG_PRICE',0,-30,d)")
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.ExtractEconData("", "", "IMF_IFS_DATA('11199Z_F',0,9,y),IMF_IFS_DATA('11299Z_F',0,-9,y)", New String() {"dataitemname", ",,US,UK", "heading", ",,US,UK"})
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.ExtractEconData("", "", "EURO_STAT_DATA('CONSCONFBAL@EUZ',0,11,M),TCB_CCI_DATA('CCI',0,-11,M)")
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.ExtractEconData("", "", "ECON_EXPR_DATA('ZSCORE(FDS_ECON[GRLM0347861])',10,0,m,step,average)")
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.ExtractEconData("", "",
"ECON_EXPR_DATA('RETURNS(FDS_ECON[BLSCUSR0000SA0L1E], -1AY)',0,0/0/-10,M)")
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.ExtractEconData("", "", "ECON_EXPR_DATA('RETURNS(EURO_STAT[GDPEURNS@GR], 1AY)',0,0/0/-10,Y)")
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.ExtractEconData("", "CC_GB", "FDS_ECON_BOPCURR_Y", New String() {"dates", "10:-1:Y"})
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.ExtractEconData("", "CC_US,CC_GR", "FDS_ECON_GDP_USD_Y", New String()
{"dates", "-10:-1:Y"})
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
' Extract Formula History
factlet = factlet.ExtractFormulaHistory("xom", "fg_eps(0Q,-5Q,Q)", New String() {"dates",
"0Q:-5Q:Q"})
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.ExtractFormulaHistory("goog", "fg_price", New String() {"dates",
"20101215:20110115:D"})
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.ExtractFormulaHistory("stl-no", "p_price(-5,0,Q,USD),ff_sales(QTR,5,0,Q,,USD)", New String() {"dates", "-5:0:Q"})
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.ExtractFormulaHistory("XOM", "BETA('SP50',0,-59M,M)")
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.ExtractFormulaHistory("GE", "AVG(FF_EPS(QTR,0,-5,Q)),MAX(FF_EPS(QTR,0,5,Q)),MIN(FF_EPS(QTR,0,-5,Q)),STD(FF_EPS(QTR,0,-5,Q))")
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.ExtractFormulaHistory("", "proper_name,ff_eps(qtr,0,-5,Q),p_price(0,-5,q)",
New String() {"ison", "sp500"})
System.Console.Write(factlet.getSummary())
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factlet.CleanUp()
' Extract Options Snapshot
factlet = factlet.ExtractOptionsSnapshot("P_OPT_UNDERLYING_PRICE", "",
"P_OPT_UNDERLYING_SECURITY=", "IBM", "", "", "", "")
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.ExtractOptionsSnapshot("P_OPT_UNDERLYING_PRICE,P_OPT_CLOSE_PRICE", "",
"P_OPT_UNDERLYING_SECURITY=", "GE", "", "", "", "")
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.ExtractOptionsSnapshot("P_OPT_UNDERLYING_PRICE", "",
"P_OPT_UNDERLYING_SECURITY=", "GE", "", "20100513", "", "")
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
' Extract Estimates Actuals
factlet = factlet.FactSetEstimatesActuals("msft", "eps,sales", "NOW", New String()
{"fiscalperiod", "-1,0", "periodtype", "annual"})
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.FactSetEstimatesActuals("msft,ibm", "eps,sales", "NOW", New String()
{"fiscalperiod", "-1,0", "periodtype", "annual"})
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.FactSetEstimatesActuals("xom", "eps,sales", "12/31/2010", New String()
{"fiscalperiod", "-1,0", "periodtype", "quarterly_roll"})
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.FactSetEstimatesActuals("xom", "eps,sales", "12/31/2010", New String()
{"fiscalperiod", "-1,0", "periodtype", "quarterly", "fields", "fe_actual"})
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
' Estimates Broker Detail
factlet = factlet.FactSetEstimatesBrokerDetail("msft", "eps", "0D", "-1AY", New String()
{"fiscalperiod", "1"})
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.FactSetEstimatesBrokerDetail("msft,ibm", "eps", "0D", "-1AY", New String()
{"fiscalperiod", "1"})
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.FactSetEstimatesBrokerDetail("xom", "sales", "0Y", "0Y-2Q", New String()
{"fiscalperiod", "2", "periodtype", "quarterly"})
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.FactSetEstimatesBrokerDetail("xom,stl-no,cvx", "eps", "0D", "-2Q", New
String() {"fiscalPeriod", "1", "periodtype", "quarterly", "fields",
"FE_ANALYSTNAME,FE_BROKERNAME,FE_ESTIMATE,FE_EST_REV_VAL_ARROW"})
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.FactSetEstimatesBrokerDetail("xom", "sales", "0Y", "0Y-2Q", New String()
{"fiscalperiod", "2", "periodtype", "quarterly", "display", "histo"})
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
' Estimates Consensus
factlet = factlet.FactSetEstimatesConsensus("msft", "eps,capex", "NOW", New String()
{"fiscalperiod", "1,2", "periodtype", "annual"})
69 | P a g e
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.FactSetEstimatesConsensus("msft,ibm", "eps,capex", "12/31/2011", New
String() {"fiscalperiod", "1,2", "periodtype", "quarterly"})
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.FactSetEstimatesConsensus("msft", "eps,bvps", "NOW", New String()
{"fiscalperiod", "1", "periodtype", "annual", "fields", "fe_mean"})
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.FactSetEstimatesConsensus("aapl", "eps,capex", "NOW", New String()
{"fiscalperiod", "1", "periodtype", "annual", "previousDates", "-30", "fields", "prev"})
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
' Estimates Guidance
factlet = factlet.FactSetEstimatesGuidance("FTI", "EPS", "NOW", New String() {"fiscalperiod",
"1,2", "periodtype", "annual"})
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.FactSetEstimatesGuidance("fti,oii", "eps", "NOW", New String()
{"fiscalperiod", "1", "periodtype", "annual"})
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.FactSetEstimatesGuidance("oii", "eps", "12/31/2011", New String()
{"fiscalperiod", "1", "periodtype", "annual"})
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.FactSetEstimatesGuidance("FTI", "eps", "NOW", New String() {"fiscalperiod",
"1", "periodtype", "annual", "fields", "fe_mean,Guidance Mean"})
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
' Estimates Suprise
factlet = factlet.FactSetEstimatesSurprise("IBM", "eps", "1/19/2012", New String()
{"fiscalperiod", "0", "periodtype", "annual", "statistic", "mean"})
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.FactSetEstimatesSurprise("blk,bac", "eps", "1/18/2012", New String()
{"fiscalperiod", "0", "periodtype", "quarterly", "statistic", "mean"})
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
factlet = factlet.FactSetEstimatesSurprise("IBM", "eps", "1/19/2012", New String()
{"fiscalperiod", "0", "periodtype", "annual", "statistic", "mean", "fields",
"Surprise_Before,Surprise_After"})
System.Console.Write(factlet.getSummary())
factlet.CleanUp()
'***********************************************************************//
'
FACTSET ONDEMAND API OBJECT DEMO USE
//
'***********************************************************************//
' Demo fetching a Factlet using the OnDemand API - simply print out the report text
Dim firstDataFrame As Integer = factSetOnDemand.createDataFrame( _
"factlet=ExtractDataSnapshot&ids=fds&items=p_price&dates=&format=pipe&")
While Not (factSetOnDemand.isDataFrameComplete(firstDataFrame))
System.Threading.Thread.Sleep(10)
End While
Console.Out.WriteLine(factSetOnDemand.getDataFrameFactletResult(firstDataFrame))
factSetOnDemand.deleteDataFrame(firstDataFrame)
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' Create many factlets and parse - allow them to queue - mutlithreaded approach
factSetOnDemand.createDataFrame(
"factlet=ExtractDataSnapshot&ids=ibm&items=p_price&dates=&format=pipe&")
factSetOnDemand.createDataFrame(
"factlet=ExtractDataSnapshot&ids=ibm&items=p_price&dates=&format=pipe&")
factSetOnDemand.createDataFrame(
"factlet=ExtractDataSnapshot&ids=ibm,msft&items=p_price&dates=&format=pipe&")
factSetOnDemand.createDataFrame(
"factlet=ExtractDataSnapshot&ids=ibm,msft,efc&items=p_price,ff_sales,p_volume,fg_eps&dates=&format=pip
e&")
factSetOnDemand.createDataFrame(
"factlet=ExtractDataSnapshot&ids=&items=p_price,ff_sales,p_volume,fg_eps&dates=&format=pipe&")
factSetOnDemand.createDataFrame(
"factlet=ExtractDataSnapshot&ids=ibm&items=&dates=&format=pipe&")
' Process the frames and print to console
Dim listOfLivingFrames As Integer() = factSetOnDemand.getListOfLivingDataFrames()
' Process the frames and print to console
For Each dataFrameID As Integer In listOfLivingFrames
While Not (factSetOnDemand.isDataFrameComplete(dataFrameID))
System.Threading.Thread.Sleep(10)
End While
Console.Out.WriteLine(factSetOnDemand.getDataFramePropertyValue(dataFrameID,
FactSet.FactSetOnDemand.DATAFRAME_SUMMARY))
factSetOnDemand.deleteDataFrame(dataFrameID)
Next
' Demo XML fetch - Do not use DataFrame parsing
' Note: for XML, there is no FORMAT=PIPE argument
Dim dataFrameXML = factSetOnDemand.createDataFrame( _
"factlet=ExtractFormulaHistory&ids=ibm,efc&items=p_price(0,-5d,d)&dates=&", _
FactSet.FactSetOnDemand.NO_FILE, _
FactSet.FactSetOnDemand.NO_PARSE_NO_CHECK)
While Not (factSetOnDemand.isDataFrameComplete(dataFrameXML))
System.Threading.Thread.Sleep(10)
End While
Dim errorCode = factSetOnDemand.getDataFrameErrorValue(dataFrameXML)
If (errorCode <> 0) Then
Dim errorCodeTranslation = factSetOnDemand.translateErrorMessage(errorCode)
Console.Out.WriteLine("Detected Error Code = " + errorCode.ToString() + " = " +
errorCodeTranslation)
End If
Dim XMLResult = factSetOnDemand.getDataFrameFactletResult(dataFrameXML)
factSetOnDemand.deleteDataFrame(dataFrameXML)
Console.Out.WriteLine("XML Demo " + ControlChars.NewLine + XMLResult)
' Unload library
Console.Out.WriteLine("Exiting successfully")
End Sub
End Module
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9.3
Java Sample Code
import FactSet.FactSetOnDemand;
import FactSet.Factlet;
public class Main {
public static void main(String[] args) throws Exception {
// First, we get a handle to the FactSet OnDemand API.
// This handle creation loads FactSet's DLL into memory
FactSetOnDemand factSetOnDemand = FactSetOnDemand.GetInstance();
//***********************************************************************//
//
CONFIGURATION DEMO USE
//
//***********************************************************************//
if( args.length > 0 ){
factSetOnDemand.setConfigurationItem(FactSetOnDemand.DATADIRECTUSERNAME, args[0]);
}
if( args.length > 1 ){
factSetOnDemand.setConfigurationItem(FactSetOnDemand.DATADIRECTPASSWORD, args[1]);
}
if( args.length > 2 ){
factSetOnDemand.setConfigurationItem(FactSetOnDemand.PROXYSERVER, args[2]);
}
if( args.length > 3 ){
factSetOnDemand.setConfigurationItem(FactSetOnDemand.PROXYPORT, args[3]);
}
if( args.length > 4 ){
factSetOnDemand.setConfigurationItem(FactSetOnDemand.PROXYUSERNAME, args[4]);
}
if( args.length > 5 ){
factSetOnDemand.setConfigurationItem(FactSetOnDemand.PROXYPASSWORD, args[5]);
}
//***********************************************************************//
//
FACTLET OBJECT DEMO USE
//
//***********************************************************************//
Factlet factlet; // Create a factlet variable to use
// In this example, we will run ExtractFormulaHistory and examine the unique
// IDs and Dates in the returned data
factlet = Factlet.ExtractFormulaHistory("xom,stl-no,fds,ibm,efc,appl,goog", "fg_eps(0,1m,d)");
System.out.print(factlet.getSummary());
String[] uniqueIDs = factlet.getUniqueIDs();
System.out.print("Printing list of unique IDs:\n");
for( int i = 0; i < uniqueIDs.length; i++)
{
System.out.print("\t" + uniqueIDs[i] + "\n");
}
Integer[] uniqueDates = factlet.getUniqueDates();
System.out.print("Printing list of unique dates:\n");
for( int i = 0; i < uniqueDates.length; i++)
{
System.out.print("\t" + uniqueDates[i] + "\n");
}
factlet.cleanUp(); // Note that we must clean up in Java because it is garbage collected
// Extract Alpha Testing Snapshot
factlet = Factlet.ExtractAlphaTestingSnapshot("N", "N", "Factset:Calculation Example Model",
"CONSTITUENTS", "ALL", "M");
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.ExtractAlphaTestingSnapshot("N", "N", "Factset:Calculation Example Model",
"CONSTITUENTS", "5,7", "M");
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System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.ExtractAlphaTestingSnapshot("N", "N", "Factset:Calculation Example Model",
"CONSTITUENTS", "Weight,Market_Capitalization", "M");
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.ExtractAlphaTestingSnapshot("N", "N", "Factset:Calculation Example Model",
"CONSTITUENTS", "all", "M", "", "08865810");
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.ExtractAlphaTestingSnapshot("Y", "N", "Factset:Calculation Example Model",
"PERIODS", "ALL", "M", "", "", "RS", "DEFAULT", "A", "1");
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
// Extract Benchmark Detil
factlet = Factlet.ExtractBenchmarkDetail("SP50", "items", "FG_COMPANY_NAME,FG_MKT_VALUE");
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.ExtractBenchmarkDetail("SP50", "items", "p_price,proper_name", new String[]
{"cutoff", "10"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.ExtractBenchmarkDetail("SP50", "items", "p_price", new String[] {"dates",
"20110115:20110120:d"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
// Extract Data Snapshot
factlet = Factlet.ExtractDataSnapshot("ibm", "ff_sales(QTR,20110401,RF,EUR)");
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.ExtractDataSnapshot("xom,stl-no", "ff_sales(QTR,20110401,,USD)");
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.ExtractDataSnapshot("", "ff_sales(QTR,20110401),p_price(20110401)", new String[]
{"ison", "sp500"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.ExtractDataSnapshot("", "ff_sales(QTR,0)", new String[] {"ison", "msci_eafe",
"isonParams", "0,1"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.ExtractDataSnapshot("", "decile(ff_sales(QTR,0))", new String[] {"ison",
"sp500"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.ExtractDataSnapshot("ibm,hd,pg,fds,f-it", "FF_FP_END_DATE(QTR,0
L45D),FF_SALES(QTR,0 L45D)", new String[] {"date", "20110131"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
// Extract Econ Data
factlet = Factlet.ExtractEconData("", "", "FDS_ECON_DATA('FRBIPSB50001',-121,-1,m,step,average)");
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.ExtractEconData("", "", "SPEC_ID_DATA('WTI-FDS:FG_PRICE',0,-30,d)");
System.out.print(factlet.getSummary() + "\n");
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factlet.cleanUp();
factlet = Factlet.ExtractEconData("", "", "IMF_IFS_DATA('11199Z_F',0,9,y),IMF_IFS_DATA('11299Z_F',0,-9,y)", new String[] {"dataitemname", ",,US,UK", "heading",
",,US,UK"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.ExtractEconData("", "", "EURO_STAT_DATA('CONSCONFBAL@EUZ',0,11,M),TCB_CCI_DATA('CCI',0,-11,M)");
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.ExtractEconData("", "", "ECON_EXPR_DATA('ZSCORE(FDS_ECON[GRLM0347861])',10,0,m,step,average)");
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.ExtractEconData("", "", "ECON_EXPR_DATA('RETURNS(FDS_ECON[BLSCUSR0000SA0L1E], 1AY)',0,0/0/-10,M)");
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.ExtractEconData("", "", "ECON_EXPR_DATA('RETURNS(EURO_STAT[GDPEURNS@GR], 1AY)',0,0/0/-10,Y)");
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.ExtractEconData("", "CC_GB", "FDS_ECON_BOPCURR_Y", new String[] {"dates", "-10:1:Y"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.ExtractEconData("", "CC_US,CC_GR", "FDS_ECON_GDP_USD_Y", new String[] {"dates",
"-10:-1:Y"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
// Extract Formula History
factlet = Factlet.ExtractFormulaHistory("xom", "fg_eps(0Q,-5Q,Q)", new String[] {"dates", "0Q:5Q:Q"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.ExtractFormulaHistory("goog", "fg_price", new String[] {"dates",
"20101215:20110115:D"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.ExtractFormulaHistory("stl-no", "p_price(-5,0,Q,USD),ff_sales(QTR,-5,0,Q,,USD)",
new String[] {"dates", "-5:0:Q"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.ExtractFormulaHistory("XOM", "BETA('SP50',0,-59M,M)");
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.ExtractFormulaHistory("GE", "AVG(FF_EPS(QTR,0,-5,Q)),MAX(FF_EPS(QTR,0,5,Q)),MIN(FF_EPS(QTR,0,-5,Q)),STD(FF_EPS(QTR,0,-5,Q))");
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.ExtractFormulaHistory("", "proper_name,ff_eps(qtr,0,-5,Q),p_price(0,-5,q)", new
String[] {"ison", "sp500"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
// Extract Options Snapshot
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factlet = Factlet.ExtractOptionsSnapshot("P_OPT_UNDERLYING_PRICE", "",
"P_OPT_UNDERLYING_SECURITY=", "IBM", "", "", "", "");
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.ExtractOptionsSnapshot("P_OPT_UNDERLYING_PRICE,P_OPT_CLOSE_PRICE", "",
"P_OPT_UNDERLYING_SECURITY=", "GE", "", "", "", "");
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.ExtractOptionsSnapshot("P_OPT_UNDERLYING_PRICE", "",
"P_OPT_UNDERLYING_SECURITY=", "GE", "", "20100513", "", "");
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
// Extract Estimates Actuals
factlet = Factlet.FactSetEstimatesActuals("msft", "eps,sales", "NOW", new String[]
{"fiscalperiod", "-1,0", "periodtype", "annual"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.FactSetEstimatesActuals("msft,ibm", "eps,sales", "NOW", new String[]
{"fiscalperiod", "-1,0", "periodtype", "annual"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.FactSetEstimatesActuals("xom", "eps,sales", "12/31/2010", new String[]
{"fiscalperiod", "-1,0", "periodtype", "quarterly_roll"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.FactSetEstimatesActuals("xom", "eps,sales", "12/31/2010", new String[]
{"fiscalperiod", "-1,0", "periodtype", "quarterly", "fields", "fe_actual"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
// Estimates Broker Detail
factlet = Factlet.FactSetEstimatesBrokerDetail("msft", "eps", "0D", "-1AY", new String[]
{"fiscalperiod", "1"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.FactSetEstimatesBrokerDetail("msft,ibm", "eps", "0D", "-1AY", new String[]
{"fiscalperiod", "1"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.FactSetEstimatesBrokerDetail("xom", "sales", "0Y", "0Y-2Q", new String[]
{"fiscalperiod", "2", "periodtype", "quarterly"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.FactSetEstimatesBrokerDetail("xom,stl-no,cvx", "eps", "0D", "-2Q", new String[]
{"fiscalPeriod", "1", "periodtype", "quarterly", "fields",
"FE_ANALYSTNAME,FE_BROKERNAME,FE_ESTIMATE,FE_EST_REV_VAL_ARROW"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.FactSetEstimatesBrokerDetail("xom", "sales", "0Y", "0Y-2Q", new String[]
{"fiscalperiod", "2", "periodtype", "quarterly", "display", "histo"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
// Estimates Consensus
factlet = Factlet.FactSetEstimatesConsensus("msft", "eps,capex", "NOW", new String[]
{"fiscalperiod", "1,2", "periodtype", "annual"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
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factlet = Factlet.FactSetEstimatesConsensus("msft,ibm", "eps,capex", "12/31/2011", new String[]
{"fiscalperiod", "1,2", "periodtype", "quarterly"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.FactSetEstimatesConsensus("msft", "eps,bvps", "NOW", new String[]
{"fiscalperiod", "1", "periodtype", "annual", "fields", "fe_mean"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.FactSetEstimatesConsensus("aapl", "eps,capex", "NOW", new String[]
{"fiscalperiod", "1", "periodtype", "annual", "previousDates", "-30", "fields", "prev"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
// Estimates Guidance
factlet = Factlet.FactSetEstimatesGuidance("FTI", "EPS", "NOW", new String[] {"fiscalperiod",
"1,2", "periodtype", "annual"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.FactSetEstimatesGuidance("fti,oii", "eps", "NOW", new String[] {"fiscalperiod",
"1", "periodtype", "annual"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.FactSetEstimatesGuidance("oii", "eps", "12/31/2011", new String[]
{"fiscalperiod", "1", "periodtype", "annual"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.FactSetEstimatesGuidance("FTI", "eps", "NOW", new String[] {"fiscalperiod", "1",
"periodtype", "annual", "fields", "fe_mean,Guidance Mean"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
// Estimates Suprise
factlet = Factlet.FactSetEstimatesSurprise("IBM", "eps", "1/19/2012", new String[]
{"fiscalperiod", "0", "periodtype", "annual", "statistic", "mean"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.FactSetEstimatesSurprise("blk,bac", "eps", "1/18/2012", new String[]
{"fiscalperiod", "0", "periodtype", "quarterly", "statistic", "mean"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
factlet = Factlet.FactSetEstimatesSurprise("IBM", "eps", "1/19/2012", new String[]
{"fiscalperiod", "0", "periodtype", "annual", "statistic", "mean", "fields",
"Surprise_Before,Surprise_After"});
System.out.print(factlet.getSummary() + "\n");
factlet.cleanUp();
//***********************************************************************//
//
FACTSET ONDEMAND API OBJECT DEMO USE
//
//***********************************************************************//
// Demo fetching a Factlet using the OnDemand API - simply print out the report text
int firstDataFrame = factSetOnDemand.createDataFrame(
"factlet=extractdatasnapshot&ids=fds&items=p_price&dates=&format=pipe&");
while (!factSetOnDemand.isDataFrameComplete(firstDataFrame)){ // Wait until complete
Thread.sleep(100);;
}
String firstFactletString = factSetOnDemand.getDataFrameFactletResult(firstDataFrame);
factSetOnDemand.deleteDataFrame( firstDataFrame ); // Delete DataFrame when done
System.out.print("Got first factlet result \n" + firstFactletString + "\n" );
// Create many factlets and parse - allow them to queue - mutlithreaded approach
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factSetOnDemand.createDataFrame("factlet=ExtractDataSnapshot&ids=ibm&items=p_price&dates=&format=pipe&
");
factSetOnDemand.createDataFrame("factlet=ExtractDataSnapshot&ids=ibm&items=p_price&dates=&format=pipe&
");
factSetOnDemand.createDataFrame("factlet=ExtractDataSnapshot&ids=ibm,msft&items=p_price&dates=&format=
pipe&");
factSetOnDemand.createDataFrame("factlet=ExtractDataSnapshot&ids=ibm,msft,efc&items=p_price,ff_sales,p
_volume,fg_eps&dates=&format=pipe&");
factSetOnDemand.createDataFrame("factlet=ExtractDataSnapshot&ids=&items=p_price,ff_sales,p_volume,fg_e
ps&dates=&format=pipe&");
factSetOnDemand.createDataFrame("factlet=ExtractDataSnapshot&ids=imb&items=&dates=&format=pipe&");
// Process the frames and print to console
int[] listOfDataFrameIDs = factSetOnDemand.getListOfLivingDataFrames();
int dataFrameID;
for( int i = 0; i < listOfDataFrameIDs.length; i++ )
{
dataFrameID = listOfDataFrameIDs[i];
System.out.print("\n\nGetting Data Frame with ID = " + String.valueOf(dataFrameID) + "\n");
while (!factSetOnDemand.isDataFrameComplete(dataFrameID))
{
Thread.sleep(100);
}
System.out.print(factSetOnDemand.getDataFramePropertyValue(dataFrameID,
FactSetOnDemand.DATAFRAME_SUMMARY) + "\n");
factSetOnDemand.deleteDataFrame(dataFrameID);
}
// Demo XML fetch - do not use DataFrame parsing
// Note: for XML, there is no FORMAT=PIPE argument
dataFrameID = factSetOnDemand.createDataFrame(
"factlet=ExtractFormulaHistory&ids=ibm,efc&items=p_price(0,-5d,d)&dates=&",
FactSetOnDemand.NO_FILE,
FactSetOnDemand.NO_PARSE_NO_CHECK, null);
while( !factSetOnDemand.isDataFrameComplete( dataFrameID ) )
{
Thread.sleep(100);
}
int errorCode = factSetOnDemand.getDataFrameErrorValue(dataFrameID);
if(errorCode != 0) {
String errorCodeTranslation = factSetOnDemand.translateErrorMessage( errorCode );
System.out.print("Detected Error Code = " + errorCode + " = " + errorCodeTranslation
);
}
String XMLResult = factSetOnDemand.getDataFrameFactletResult( dataFrameID );
factSetOnDemand.deleteDataFrame( dataFrameID );
System.out.print("XML Demo \n\n%s" + XMLResult);
// Unload library
FactSetOnDemand.CleanUp();
return;
}
}
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9.4
Python Sample Code
from FactSetOnDemand import FactSetOnDemand, DataFrame, Factlet
import os
import time
import sys
try:
# First, we get a handle to the FactSet OnDemand API.
# This handle creation loads FactSet's DLL into memory
factSetOnDemand = FactSetOnDemand.GetInstance()
#***********************************************************************//
#
CONFIGURATION DEMO USE
//
#***********************************************************************//
if( len(sys.argv) > 1 ):
factSetOnDemand.setConfigurationItem( factSetOnDemand.DATADIRECTUSERNAME,
sys.argv[1]);
if( len(sys.argv) > 2 ):
factSetOnDemand.setConfigurationItem( factSetOnDemand.DATADIRECTPASSWORD,
sys.argv[2]);
if( len(sys.argv) > 3 ):
factSetOnDemand.setConfigurationItem( factSetOnDemand.PROXYSERVER, sys.argv[3] );
if( len(sys.argv) > 4 ):
factSetOnDemand.setConfigurationItem( factSetOnDemand.PROXYPORT, sys.argv[4] );
if( len(sys.argv) > 5 ):
factSetOnDemand.setConfigurationItem( factSetOnDemand.PROXYUSERNAME, sys.argv[5] );
if( len(sys.argv) > 6 ):
factSetOnDemand.setConfigurationItem( factSetOnDemand.PROXYPASSWORD, sys.argv[6] );
#***********************************************************************//
#
FACTLET OBJECT DEMO USE
//
#***********************************************************************//
factlet = [] # Create a factlet pointer variable to use
# In this example, we will run ExtractFormulaHistory and examine the unique
# IDs and Dates in the returned data
factlet = Factlet.ExtractFormulaHistory("xom,stl-no,fds,ibm,efc,appl,goog", "fg_eps(0,1m,d)");
print factlet.getSummary() + "\n"
uniqueIDs = factlet.getUniqueIDs();
print "Printing list of unique IDs:\n"
for i in xrange(len(uniqueIDs)):
print "\t" + uniqueIDs[i]
uniqueDates = factlet.getUniqueDates();
print "Printing list of unique dates:\n"
for i in xrange(len(uniqueDates)):
print "\t" + str(uniqueDates[i])
del factlet # Note that we must delete the factlet after we are done to prevent memory leaks
# Extract Alpha Testing Snapshot
factlet = Factlet.ExtractAlphaTestingSnapshot("N","N","Factset:Calculation Example
Model","CONSTITUENTS","ALL","M")
print factlet.getSummary() + "\n"
del factlet
factlet = Factlet.ExtractAlphaTestingSnapshot("N","N","Factset:Calculation Example
Model","CONSTITUENTS","5,7","M")
print factlet.getSummary() + "\n"
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del factlet
factlet = Factlet.ExtractAlphaTestingSnapshot("N","N","Factset:Calculation Example
Model","CONSTITUENTS","Weight,Market_Capitalization","M");
print factlet.getSummary() + "\n"
del factlet
factlet = Factlet.ExtractAlphaTestingSnapshot("N","N","Factset:Calculation Example
Model","CONSTITUENTS","all","M","","08865810");
print factlet.getSummary() + "\n"
del factlet
factlet = Factlet.ExtractAlphaTestingSnapshot("Y","N","Factset:Calculation Example
Model","PERIODS","ALL","M","","","RS","DEFAULT","A","1");
print factlet.getSummary() + "\n"
del factlet
# Extract Benchmark Detil
factlet = Factlet.ExtractBenchmarkDetail("SP50",["items","FG_COMPANY_NAME,FG_MKT_VALUE"]);
print factlet.getSummary() + "\n"
del factlet
factlet =
Factlet.ExtractBenchmarkDetail("SP50",["items","p_price,proper_name","cutoff","10"]);
print factlet.getSummary() + "\n"
del factlet
factlet =
Factlet.ExtractBenchmarkDetail("SP50",["items","p_price","dates","20110115:20110120:d"]);
print factlet.getSummary() + "\n"
del factlet
# Extract Data Snapshot
factlet = Factlet.ExtractDataSnapshot("ibm","ff_sales(QTR,20110401,RF,EUR)");
print factlet.getSummary() + "\n"
del factlet
factlet = Factlet.ExtractDataSnapshot("xom,stl-no","ff_sales(QTR,20110401,,USD)");
print factlet.getSummary() + "\n"
del factlet
factlet = Factlet.ExtractDataSnapshot("","ff_sales(QTR,20110401),p_price(20110401)",
["ison","sp500"]);
print factlet.getSummary() + "\n"
del factlet
factlet =
Factlet.ExtractDataSnapshot("","ff_sales(QTR,0)",["ison","msci_eafe","isonParams","0,1"]);
print factlet.getSummary() + "\n"
del factlet
factlet = Factlet.ExtractDataSnapshot("","decile(ff_sales(QTR,0))", ["ison","sp500"]);
print factlet.getSummary() + "\n"
del factlet
factlet = Factlet.ExtractDataSnapshot("ibm,hd,pg,fds,f-it","FF_FP_END_DATE(QTR,0
L45D),FF_SALES(QTR,0 L45D)",["date","20110131"]);
print factlet.getSummary() + "\n"
del factlet
# Extract Econ Data
factlet = Factlet.ExtractEconData("","","FDS_ECON_DATA('FRBIPSB50001',-121,1,m,step,average)");
print factlet.getSummary() + "\n"
del factlet
factlet = Factlet.ExtractEconData("","","SPEC_ID_DATA('WTI-FDS:FG_PRICE',0,-30,d)");
print factlet.getSummary() + "\n"
del factlet
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factlet = Factlet.ExtractEconData("","","IMF_IFS_DATA('11199Z_F',0,9,y),IMF_IFS_DATA('11299Z_F',0,-9,y)",["dataitemname",",,US,UK","heading",",,US,UK"]);
print factlet.getSummary() + "\n"
del factlet
factlet = Factlet.ExtractEconData("","","EURO_STAT_DATA('CONSCONFBAL@EUZ',0,11,M),TCB_CCI_DATA('CCI',0,-11,M)");
print factlet.getSummary() + "\n"
del factlet
factlet = Factlet.ExtractEconData("","","ECON_EXPR_DATA('ZSCORE(FDS_ECON[GRLM0347861])',10,0,m,step,average)");
print factlet.getSummary() + "\n"
del factlet
factlet = Factlet.ExtractEconData("","","ECON_EXPR_DATA('RETURNS(FDS_ECON[BLSCUSR0000SA0L1E],
-1AY)',0,0/0/-10,M)");
print factlet.getSummary() + "\n"
del factlet
factlet = Factlet.ExtractEconData("","","ECON_EXPR_DATA('RETURNS(EURO_STAT[GDPEURNS@GR], 1AY)',0,0/0/-10,Y)");
print factlet.getSummary() + "\n"
del factlet
factlet = Factlet.ExtractEconData("","CC_GB","FDS_ECON_BOPCURR_Y",["dates","-10:-1:Y"]);
print factlet.getSummary() + "\n"
del factlet
factlet = Factlet.ExtractEconData("","CC_US,CC_GR","FDS_ECON_GDP_USD_Y",["dates","-10:-1:Y"]);
print factlet.getSummary() + "\n"
del factlet
# Extract Formula History
factlet = Factlet.ExtractFormulaHistory("xom","fg_eps(0Q,-5Q,Q)",["dates","0Q:-5Q:Q"]);
print factlet.getSummary() + "\n"
del factlet
factlet = Factlet.ExtractFormulaHistory("goog","fg_price",["dates","20101215:20110115:D"]);
print factlet.getSummary() + "\n"
del factlet
factlet = Factlet.ExtractFormulaHistory("stl-no","p_price(-5,0,Q,USD),ff_sales(QTR,5,0,Q,,USD)",["dates","-5:0:Q"]);
print factlet.getSummary() + "\n"
del factlet
factlet = Factlet.ExtractFormulaHistory("XOM","BETA('SP50',0,-59M,M)");
print factlet.getSummary() + "\n"
del factlet
factlet = Factlet.ExtractFormulaHistory("GE","AVG(FF_EPS(QTR,0,-5,Q)),MAX(FF_EPS(QTR,0,5,Q)),MIN(FF_EPS(QTR,0,-5,Q)),STD(FF_EPS(QTR,0,-5,Q))");
print factlet.getSummary() + "\n"
del factlet
factlet = Factlet.ExtractFormulaHistory("","proper_name,ff_eps(qtr,0,-5,Q),p_price(0,5,q)",["ison","sp500"]);
print factlet.getSummary() + "\n"
del factlet
# Extract Options Snapshot
factlet =
Factlet.ExtractOptionsSnapshot("P_OPT_UNDERLYING_PRICE","","P_OPT_UNDERLYING_SECURITY=","IBM","","",""
,"");
print factlet.getSummary() + "\n"
del factlet
80 | P a g e
factlet =
Factlet.ExtractOptionsSnapshot("P_OPT_UNDERLYING_PRICE,P_OPT_CLOSE_PRICE","","P_OPT_UNDERLYING_SECURIT
Y=","GE","","","","");
print factlet.getSummary() + "\n"
del factlet
factlet =
Factlet.ExtractOptionsSnapshot("P_OPT_UNDERLYING_PRICE","","P_OPT_UNDERLYING_SECURITY=","GE","","20100
513","","");
print factlet.getSummary() + "\n"
del factlet
# Extract Estimates Actuals
factlet = Factlet.FactSetEstimatesActuals("msft","eps,sales","NOW",["fiscalperiod","1,0","periodtype","annual"]);
print factlet.getSummary() + "\n"
del factlet
factlet = Factlet.FactSetEstimatesActuals("msft,ibm","eps,sales","NOW",["fiscalperiod","1,0","periodtype","annual"]);
print factlet.getSummary() + "\n"
del factlet
factlet = Factlet.FactSetEstimatesActuals("xom","eps,sales","12/31/2010",["fiscalperiod","1,0","periodtype","quarterly_roll"]);
print factlet.getSummary() + "\n"
del factlet
factlet = Factlet.FactSetEstimatesActuals("xom","eps,sales","12/31/2010",["fiscalperiod","1,0","periodtype","quarterly","fields","fe_actual"]);
print factlet.getSummary() + "\n"
del factlet
# Estimates Broker Detail
factlet = Factlet.FactSetEstimatesBrokerDetail("msft","eps","0D","-1AY",["fiscalperiod","1"]);
print factlet.getSummary() + "\n"
del factlet
factlet = Factlet.FactSetEstimatesBrokerDetail("msft,ibm","eps","0D","1AY",["fiscalperiod","1"]);
print factlet.getSummary() + "\n"
del factlet
factlet = Factlet.FactSetEstimatesBrokerDetail("xom","sales","0Y","0Y2Q",["fiscalperiod","2","periodtype","quarterly"]);
print factlet.getSummary() + "\n"
del factlet
factlet = Factlet.FactSetEstimatesBrokerDetail("xom,stl-no,cvx","eps","0D","2Q",["fiscalPeriod","1","periodtype","quarterly","fields","FE_ANALYSTNAME,FE_BROKERNAME,FE_ESTIMATE,FE
_EST_REV_VAL_ARROW"]);
print factlet.getSummary() + "\n"
del factlet
factlet = Factlet.FactSetEstimatesBrokerDetail("xom","sales","0Y","0Y2Q",["fiscalperiod","2","periodtype","quarterly","display","histo"]);
print factlet.getSummary() + "\n"
del factlet
# Estimates Consensus
factlet =
Factlet.FactSetEstimatesConsensus("msft","eps,capex","NOW",["fiscalperiod","1,2","periodtype","annual"
]);
print factlet.getSummary() + "\n"
del factlet
factlet =
Factlet.FactSetEstimatesConsensus("msft,ibm","eps,capex","12/31/2011",["fiscalperiod","1,2","periodtyp
e","quarterly"]);
81 | P a g e
print factlet.getSummary() + "\n"
del factlet
factlet =
Factlet.FactSetEstimatesConsensus("msft","eps,bvps","NOW",["fiscalperiod","1","periodtype","annual","f
ields","fe_mean"]);
print factlet.getSummary() + "\n"
del factlet
factlet =
Factlet.FactSetEstimatesConsensus("aapl","eps,capex","NOW",["fiscalperiod","1","periodtype","annual","
previousDates","-30","fields","prev"]);
print factlet.getSummary() + "\n"
del factlet
# Estimates Guidance
factlet =
Factlet.FactSetEstimatesGuidance("FTI","EPS","NOW",["fiscalperiod","1,2","periodtype","annual"]);
print factlet.getSummary() + "\n"
del factlet
factlet =
Factlet.FactSetEstimatesGuidance("fti,oii","eps","NOW",["fiscalperiod","1","periodtype","annual"]);
print factlet.getSummary() + "\n"
del factlet
factlet =
Factlet.FactSetEstimatesGuidance("oii","eps","12/31/2011",["fiscalperiod","1","periodtype","annual"]);
print factlet.getSummary() + "\n"
del factlet
factlet =
Factlet.FactSetEstimatesGuidance("FTI","eps","NOW",["fiscalperiod","1","periodtype","annual",
"fields","fe_mean,Guidance Mean"]);
print factlet.getSummary() + "\n"
del factlet
# Estimates Suprise
factlet =
Factlet.FactSetEstimatesSurprise("IBM","eps","1/19/2012",["fiscalperiod","0","periodtype","annual","st
atistic","mean"]);
print factlet.getSummary() + "\n"
del factlet
factlet =
Factlet.FactSetEstimatesSurprise("blk,bac","eps","1/18/2012",["fiscalperiod","0","periodtype","quarter
ly","statistic","mean"]);
print factlet.getSummary() + "\n"
del factlet
factlet =
Factlet.FactSetEstimatesSurprise("IBM","eps","1/19/2012",["fiscalperiod","0","periodtype","annual","st
atistic","mean","fields","Surprise_Before,Surprise_After"]);
print factlet.getSummary() + "\n"
del factlet
#***********************************************************************//
#
FACTSET ONDEMAND API OBJECT DEMO USE
//
#***********************************************************************//
# Demo fetching a Factlet using the OnDemand API - simply print out the report text
firstDataFrame = factSetOnDemand.createDataFrame(
"factlet=extractdatasnapshot&ids=fds&items=p_price&dates=&format=pipe&");
while( not( factSetOnDemand.isDataFrameComplete(firstDataFrame) ) ):
time.sleep(1)
firstFactletString = factSetOnDemand.getDataFrameFactletResult(firstDataFrame);
factSetOnDemand.deleteDataFrame( firstDataFrame ); # Delete DataFrame when done
print "Got first factlet result \n" + firstFactletString + "\n"
82 | P a g e
# Create many factlets and parse - allow them to queue - mutlithreaded approach
factSetOnDemand.createDataFrame("factlet=ExtractDataSnapshot&ids=ibm&items=p_price&dates=&form
at=pipe&");
factSetOnDemand.createDataFrame("factlet=ExtractDataSnapshot&ids=ibm&items=p_price&dates=&form
at=pipe&");
factSetOnDemand.createDataFrame("factlet=ExtractDataSnapshot&ids=ibm,msft&items=p_price&dates=
&format=pipe&");
factSetOnDemand.createDataFrame("factlet=ExtractDataSnapshot&ids=ibm,msft,efc&items=p_price,ff
_sales,p_volume,fg_eps&dates=&format=pipe&");
factSetOnDemand.createDataFrame("factlet=ExtractDataSnapshot&ids=&items=p_price,ff_sales,p_vol
ume,fg_eps&dates=&format=pipe&");
factSetOnDemand.createDataFrame("factlet=ExtractDataSnapshot&ids=imb&items=&dates=&format=pipe
&");
# Process the frames and print to console
listOfLivingFrames = factSetOnDemand.getListOfLivingDataFrames()
dataFrameID = []
for i in xrange(len(listOfLivingFrames)):
dataFrameID = listOfLivingFrames[i]
print "\n\n\nGetting Data Frame with ID = " + str( dataFrameID ) + "\n"
while( not( factSetOnDemand.isDataFrameComplete(dataFrameID) ) ):
time.sleep(1)
print factSetOnDemand.getDataFramePropertyValue(dataFrameID,
factSetOnDemand.DATAFRAME_SUMMARY )
factSetOnDemand.deleteDataFrame( dataFrameID );
# Demo XML fetch - Do not use DataFrame parsing
# Note: for XML, there is no FORMAT=PIPE argument
dataFrameID = factSetOnDemand.createDataFrame( \
"factlet=ExtractFormulaHistory&ids=ibm,efc&items=p_price(0,-5d,d)&dates=&", \
factSetOnDemand.NONE, \
factSetOnDemand.NO_PARSE_NO_CHECK);
while( not( factSetOnDemand.isDataFrameComplete(dataFrameID) ) ):
time.sleep(1)
errorCode = factSetOnDemand.getDataFrameErrorValue(dataFrameID);
if(errorCode != 0):
errorCodeTranslation = factSetOnDemand.translateErrorMessage( errorCode );
print "Detected Error Code = " + str(errorCode) + errorCodeTranslation
XMLResult = factSetOnDemand.getDataFrameFactletResult( dataFrameID );
factSetOnDemand.deleteDataFrame( dataFrameID );
print "XML Demo \n\n" + XMLResult
factSetOnDemand = None
finally:
FactSetOnDemand.CleanUp()
exit(0)
83 | P a g e
Appendix
Table 1: List of Configuration Items
Item Name
DATADIRECTUSERNAME
DATADIRECTPASSWORD
DATADIRECTURL
PROXYSERVER
PROXYPORT
PROXYUSERNAME
PROXYPASSWORD
NTLM
TIMEOUT
THROTTLE
EVENTLOGGERON
EVENTLOGGERVERBOSE
EVENTLOGGERMAXLOGS
EVENTLOGGERREDUCTION
APPDATA_DIRECTORY
TEMP_DIRECTORY
OUTPUT_DIRECTORY
ROOT_DIRECTORY
IMAGES_DIRECTORY
REFERENCES_DIRECTORY
BINARY_DIRECTORY
GUIACTIVE
84 | P a g e
Item Index
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
22
Item Type
String
String
String
String
Integer
String
String
NA
Integer
Integer
Boolean
Boolean
Integer
Integer
String
String
String
String
String
String
String
Boolean
Table 2: List of File Output Options
Output Name
NONE
PLAINTEXT_OPEN_NODELETE
Output
Index
0
1
PLAINTEXT_NOOPEN_NODELETE
2
PLAINTEXT_NOOPEN_DELETE
3
PLAINTEXT_NOOPEN_DELETE_OPEN_EXCEL 4
EXCEL_OPEN_NODELETE
5
EXCEL_NOOPEN_NODELETE
6
85 | P a g e
Output Description
No file output is made.
A plain text file of the
Factlet result is created
and then opened in the
default text editor. The
file is not deleted.
A plain text file of the
Factlet result is created
and is not deleted.
A plain text file of the
Factlet result is created
and is deleted when the
DataFrame is called for
deletion.
A plain text file of the
Factlet result is created
and is deleted when the
DataFrame is called for
deletion. Also, an excel
file is created and
opened and not deleted.
An excel file of the
Factlet result is created
and not deleted. The file
is opened in the default
excel application.
An excel file of the
Factlet result is created
and not deleted.
Table 3: Error Message Output
Error Message
Error
Value
Error Description
SUCCESS
0 Successful
FACTLET_ERROR_DNE
-1
FACTLET_ERROR_IDS
-2
FACTLET_ERROR_ITEMS
-3
FACTLET_ERROR_DATES
-4
FACTLET_ERROR_CREDENTIALS
-5
FACTLET_ERROR_APPLICATION
-6
FACTLET_ERROR_CONNECTION
-7
FACTLET_ERROR_PARSE_FAILURE
-8
FACTLET_ERROR_UNKNOWN
-9
DATAFRAME_ERROR_UNABLE_TO_TRANSFORM
-10
DATAFRAME_ERROR_INSUFFICIENT_ROWS
-11
DATAFRAME_ERROR_BAD_COL_INDEX
-12
DATAFRAME_ERROR_BAD_ROW_INDEX
-13
DATAFRAME_ERROR_BAD_VALUE_INDEX
-14
DATAFRAMECOL_ERROR_BAD_INDEX
-15
DATAFRAMECOL_ERROR_BAD_ASSIGNMENT
-16
DATAFRAMEMAN_ERROR_NO_FRAME
-17
CURL_ERROR_LIBRARY_LOAD
-18
CURL_ERROR_LIBRARY_FUNCTION
-19
CURL_RESULT_UNSET
-22
DATA_TABLE_NOT_RECTANGULAR
-23
DATA_TABLE_NOT_ENOUGH_ROWS
-24
DATA_TABLE_PARSE_EXCEPTION
-25
FACTLET_ERROR_NO_DATA
-26
86 | P a g e
Programatic Error : Item Does Not
Exist
FactSet Query Error : No Ids were
selected.
FactSet Query Error : No Items were
selected.
FactSet Query Error : Date Range is
invald.
FactSet Query Error : Invalid
Credentials.
FactSet Query Error : No Application
Access or Does Not Exist.
Connection Error : HTTPS
Connection Failed. Check
configuration
FactSet Query Error : Unable To
Parse. Check Factlet.
Unknown Error
Data Frame Error : Unable to
transform
FactSet Data Frame Error : Unable to
parse factlet result.
FactSet Data Frame Error : Bad
column index.
FactSet Data Frame Error : Bad row
index.
FactSet Data Frame Error : Bad value
index.
FactSet Data Frame Column Error :
Bad value index.
FactSet Data Frame Column Error :
Bad value assignment.
FactSet Data Frame Manager Error :
Attempted to spawn non-existent
frame.
FactSet Library Error : Failed to load
cURL library.
FactSet Library Error : cURL Library
Function failed.
Connection Error : Unknown
HTTPS Error.
FactSet Data Frame Error : Unable to
parse factlet result - result not tabular.
FactSet Data Frame Error : Unable to
parse factlet result - not enough rows.
FactSet Data Frame Error : Unable to
parse factlet result - unknown parse
error.
FactSet Query Error : No Data Exists.
DATA_TABLE_MALLOC_VALUE_ERROR
-27
DATA_TABLE_MALLOC_COLUMN_ERROR
-28
DATA_TABLE_COLUMN_RESERVE_ERROR
-29
DATA_TABLE_COLUMN_ITERATOR_OFLOW
-30
DATA_TABLE_DELETE_EXCEPTION
-31
JOB_EXECUTION_NULL_JOB
-32
JOB_ASSIGNEMENT_BAD
-33
JOB_EXECUTION_BUSY_THREAD
-34
EXECUTIONER_THREAD_NOT_IN_SERVICE
-35
EXECUTIONER_CANNOT_SPAWN
-36
EXECUTIONER_CANNOT_ADD_NEW_THREAD
-37
EXECUTIONER_DELETE_THREAD_FAILURE
-38
EXECUTIONER_THREAD_NOT_FOUND
-39
EXECUTIONER_THREAD_COUNT_WRONG
-41
EXECUTIONER_JOB_COUNT_WRONG
-42
EXECUTIONER_NO_FREE_THREADS
-43
EXECUTIONER_NO_JOBS_TO_EXECUTE
-44
EXECUTIONER_THREAD_HAS_JOB
-45
EXECUTIONER_FAILED_TO_ASSIGN
-46
EXECUTIONER_THREAD_ALREADY_REG
-47
EXECUTIONER_CANNOT_FIND_EXEC
-48
EXECUTIONER_WASTEFUL_CALL
-49
DATAFRAMEMANAGER_FRAME_EXISTS
-50
DATAFRAMEMANAGER_FRAME_DNE
-51
DATAFRAMEMANAGER_ASK_FAILURE
-52
DATATABLE_COLUMN_DNE
-53
87 | P a g e
FactSet Data Frame Error : Value
unable to be created.
FactSet Data Frame Error : Column
unable to be created.
FactSet Data Frame Error : Column
unable to be reserved.
FactSet Data Frame Error : Column
unable to be deleted due to iterator
overflow.
FactSet Data Frame Error : Column
delete exception.
FactSet Job Execute Error : Null Job
Created.
FactSet Job Assign Error : Job
assigned to null thread.
FactSet Job Assign Error : Job
assigned to busy thread.
FactSet Executioner Error :
Attempted to assign job to out of
service thread.
FactSet Executioner Error : Failed to
spawn thread.
FactSet Executioner Error : Failed to
add new thread.
FactSet Executioner Error : Failed to
delete thread.
FactSet Executioner Error : Failed to
find thread record.
FactSet Executioner Error : Thread
count is wrong.
FactSet Executioner Error : Job
count is wrong.
FactSet Executioner Error : Failed to
find free thread.
FactSet Executioner Error :
Attempted to execute jobs when none
exist.
FactSet Executioner Error :
Attempted to assign job to busy
thread.
FactSet Executioner Error : Failed to
assign job.
FactSet Executioner Error : Thread
already marked free.
FactSet Executioner Error : Cannot
find executable.
FactSet Executioner Error : Wasteful
call.
FactSet DataFrameManager Error :
DataFrame already exists.
FactSet DataFrameManager Error :
DataFrame does not exist.
FactSet DataFrameManager Error :
Unable to inquire to service needed.
FactSet Data Frame Table Error :
Column does not exist.
FACTSET_DATE_RANGE
-54
FACTSET_DATE_CONVERSION_FAILED
-55
FACTSET_DONT_KNOW_DATE_FORMAT
-56
DATACOLUMN_ABSTRACT_COLUMN
-57
DATATABLE_BAD_COLUMN_INDEX
-59
DATATABLE_UNABLE_TO_DETERMINE_TYPE
-60
CONFIGURATION_THROTTLE_VALUE
-61
CONFIGURATION_TIMEOUT_VALUE
-62 Configuration Setting Error :
Timeout must be in range 1 to
3600.
-63 Configuration Setting Error :
Event Logger must be set 'TRUE'
or 'FALSE'.
-64 Configuration Setting Error :
Event Logger settings must be in
range 20 to 1000.
-65 Configuration Setting Error : GUI
must be set 'TRUE' or 'FALSE'.
-66 Configuration Setting Error :
Directory does not exist.
-67 Configuration Setting Error : This
item must be four characters or
longer.
-68 Configuration Setting Error :
DataDirect Username is too
short.
-69 Configuration Setting Error :
DataDirect Password is too short.
-70 Configuration Setting Error :
Proxy Server Username is too
short.
-71 Configuration Setting Error :
Proxy Server Password is too
short.
-72 Configuration Setting Error :
Proxy Server Port is too short.
-73 Configuration Settinsg Error :
Proxy Server Name is too short.
-74 Data Frame Error : Cannot make
Excel file without parse. Check
Tranform mode.
CONFIGURATION_EVENT_LOGGER_VALUE
CONFIGURATION_EVENT_RANGE
CONFIGURATION_GUI_ON
CONFIGURATION_DIRECTORY_DNE
CONFIGURATION_ITEM_MUST_BE_FOUR_CHAR
CONFIGURATION_DD_USER_LENGTH
CONFIGURATION_DD_PASS_LENGTH
CONFIGURATION_P_USER_LENGTH
CONFIGURATION_P_PASS_LENGTH
CONFIGURATION_P_PORT_LENGTH
CONFIGURATION_P_SERV_LENGTH
DATAFRAME_CANNOT_MAKE_EXCEL_WO_PARS
E
88 | P a g e
FactSet Date Range Error : Date out
of range.
FactSet Date Range Error : Date
conversion failed.
FactSet Date Range Error : Date
conversion format not known.
FactSet DataColumn Error : Abstract
column.
FactSet Data Frame Table Error :
Bad column index.
FactSet Data Frame Table Error :
Unable to determine type.
Configuration Setting Error : Throttle
must be in range 1 to 25.
EXECUTABLE_ABORTED
-75 Executable Job Error : Aborted.
KRATOS_NOT_INITIALIZED
-76 Kratos Error : Kratos not
initialized. Please Run
InitializeFactSetOnDemand.
-77 Matrix Builder : First item not
entity.
-78 Matrix Builder : Second item not
date.
-79 Matrix Builder : Too many items
selected for this transform.
-80 Configuration Setting Error :
NTLM not supported in this
version.
-81 FactSet Data Frame Table Error :
Unable to determine factlet type.
-82 FactSet DataFrame Inspector :
Cannot find Id Column.
-83 FactSet DataFrame Inspector :
Cannot find Date Column.
-84 FactSet DataFrame Inspector :
Cannot find Requested Column.
-85 Configuration Setting Error :
SIDEBAR must be set 'TRUE' or
'FALSE'.
-86 Process Creation Error : Pipes
Failed.
-87 Process Creation Error : Process
Creation Failed.
-88 IP Message Error : Unable to
process message
-89 IP Message Error : Process no
longer alive.
-90 Calling Application Error : This
function is not supported in this
application.
-91 Calling Application Error :
External Application Error.
-93 Calling Application Error : Unable
to load library.
-94 Calling Application Error : Unable
to locate library symbol.
-95 Configuration Setting Error :
Usage Data must be set 'TRUE' or
'FALSE'.
MATRIX_BUILDER_FIRST_ITEM_NOT_ENTITY
MATRIX_BUILDER_SECOND_ITEM_NOT_DATE
MATRIX_BUILDER_TOO_MANY_ITEMS
CONFIGURATION_NTLM_VALUE
DATAFRAME_UNABLE_TO_DETERMINE_FACTLE
T
DF_INSPECTOR_ID_COL
DF_INSPECTOR_DATE_COL
DF_INSPECTOR_FIND_COL
CONFIGURATION_SIDEBAR_OFF
PIPE_CREATION_FAILED
PROCESS_CREATION_FAILED
UNABLE_TO_PARSE_IPMESSAGE
PROCESS_NO_LONGER_ALIVE
FUNCTION_NOT_SUPPORTED
EXTERNAL_APPLICATION_ERROR
UNABLE_TO_LOCATE_LIBRARY
UNABLE_TO_LOCATE_LIBRARY_EXTERNAL
CONFIGURATION_USAGE_DATA_VALUE
89 | P a g e
CONFIGURATION_HTTPSRETRY_DATA_VALUE
CANNOT_SEND_EMPTY_MESSAGE
-96 Configuration Setting Error :
HTTPS Retries must be in range 0
to 10.
-98 Inventory Error : Inventory Item
Not Found.
-99 Configuration Setting Error :
Extention must be set 'TRUE' or
'FALSE'.
-100 Configuration Setting Error : Last
Update must be set a positive
integer.
-101 Configuration Setting Error :
Master Slave must be set 'TRUE'
or 'FALSE'.
-102 Master Message Executor Error :
Bad message format.
-103 Resource Tracker Error :
Attempted to add duplicate pID'.
-104 Resource Tracker Error :
Attempted to remove bad pID'.
-105 Configuration Setting Error :
Check in period must be in range
20 to 300.
-106 Configuration Setting Error :
Usage Data Upload is disabled in
this configuration.
-107 Configuration Setting Error :
Master Collection must be set
'TRUE' or 'FALSE'.
-108 Warning : Master is not
collecting.
-109 Cannot send empty message.
MESSAGE_FORMAT_BAD
-110 Message Format Bad.
INTERPROCESSMESSAGE_NOT_FOUND
-111 Interprocess message not found.
SIDEBAR_EXECUTABLE_NOT_LOCATED
-112 Sidebar Executable Not Located.
SIDEBAR_NOT_SUPPORTED_THIS_PLATFROM
-113 Sidebar is not supported on this
platform.
-114 Sidebar is not enabled presently.
INVENTORY_ITEM_NOT_FOUND
CONFIGURATION_EXTENTION_OFF
CONFIGRATIONLASTUPDATE_VALUE
CONFIGURATION_MASTERSLAVE_OFF
MME_FAILED_TO_DECYPHER_TYPE
RT_ADD_DUPLICATE_PID
RT_REM_BAD_PID
CHECKINPERIOD_EVENT_RANGE
USAGEDATA_UPLOAD_DISABLED
CONFIGURATION_MASTERCOLLECTION_OFF
MASTER_NO_COLLECTING
SIDEBAR_NOT_ENABLED
CONFIGURATION_IPMESSAGE_VALUE
CONFIGURATION_FILEDELETIONWINDOW
90 | P a g e
-115 Configuration Setting Error : IP
Message format must be in range
1 to 1.
-116 Configuration Setting Error :
Temp file deletion window must
be 1 to 30 days.
CONFIGURATION_DEVELOPERSMODE_VALUE
CONFIGURATION_CONSOLE_LOAD
CONFIGURATION_SIDEBAR_COPY_TO_CLIPBOAR
D
CONFIGURATION_SIDEBAR_COPY_TO_CONSOLE
CONFIGURATION_SIDEBAR_SIMULATE_TYPE
NOT_IN_DEVELOPERS_MODE
FACTLET_SERVER_ERROR
FILE_POINTER_IS_INVALID
FACTLETPARSER_NO_FORMULAS
FACTLETPARSER_NO_MATCH
CONFIGURATION_SUPPLIMENTAL_CODE_ENABL
ED
CONFIGURATION_ITEM_DOES_NOT_EXIST
OFDB_UPLOAD_CONFIG_INVALID
PARAMETER_TYPE_CHECKING_VALUE
AUTO_ESCAPE_FOR_DATADIRECT_VALUE
MENU_KEEP_OPEN_VALUE
OFDB_ALLOWED_FIELD_CHARS_VALUE
OFDB_ALLOWED_FIELD_LENGTH_VALUE
91 | P a g e
-117 Configuration Setting Error :
Developer Mode must be set
'TRUE' or 'FALSE'.
-118 Configuration Setting Error :
Console Load must be set 'TRUE'
or 'FALSE'.
-119 Configuration Setting Error :
SideBarCopyToClipboard must be
set 'TRUE' or 'FALSE'.
-120 Configuration Setting Error :
SideBarCopyToConsole must be
set 'TRUE' or 'FALSE'.
-121 Configuration Setting Error :
SideBarSimulateType must be set
'TRUE' or 'FALSE'.
-122 Function is only available in
developer's mode.
-123 Connection Error : FactSet
internal server error.
-124 File I/O Error : File Pointer is
Invalid.
-125 FQL\\Screening Error : No
formula provided.
-126 FQL\\Screening Error : Unable to
match Factlet.
-127 Configuration Setting Error :
SupplementalCodeEnabled must
be set 'TRUE' or 'FALSE'.
-128 Configuration Setting Error : Item
does not exist.
-133 Invalid value for OFDB Upload
Threshold.
-134 Invalid value for Parameter Type
Checking - must be 'TRUE' or
'FALSE'.
-135 Invalid value for Auto Escape must be 'TRUE' or 'FALSE'.
-138 Configuration Setting Error :
OFDB Column Limit must be in
range 3 to 600.
-139 Configuration Setting Error :
OFDB Character Limit must be in
range 25 to 252.
-140 Configuration Setting Error : Keep
Menu Open must be set 'TRUE' or
'FALSE'.
DATADIRECTTESTURL_VALUE
FAILED_CREATING_FILE
-141 outString=Configuration Setting
Error : More characters required.
-142 outString=Configuration Setting
Error : OFDB Character Limit
must be in range 10 to 128.
-143 outString=Configuration Setting
Error : Invalid URL String.
-144 outString=Configuration Setting
Error : Using Test URL must be
set 'TRUE' or 'FALSE'.
-1000004 Library error : Serious Application
Error. Unable to locate libcurl.dll,
libeay32.dll and/or ssleay32.lib.
Check that these files are the
correct architecture and in the
C:\\Windows\\System32 folder.
-1000005 Connection Error : Failed to
resolve proxy. Check Proxy Server
Name.
-1000006 Connection Error : Failed to
resolve host. Check DataDirect
URL.
-1000007 Connection Error : Failed to
connect. Check Proxy Server
Settings.
-1000028 Connection Error : Connection
Timeout. Adjust timeout setting
or decompose query into smaller
queries.
-1000056 Connection Error : Failed to
receive data from network. Check
Proxy Authentication and
Connection.
-2000009 Windows API Registry Failure :
Failed to write string.
-2000010 Windows API Registry Failure :
Strings don't match.
-2000036 Failed creating file.
FAILED_LOCKING_FILE
-2000037 Failed locking file.
FAILED_WRITING_FILE
-2000038 Failed writing file.
FAILED_READING_FILE
-2000039 Failed reading file.
FAILED_UNLOCKING_FILE
-2000040 Failed unlocking file.
FAILED_CLOSING_HANDLE
-2000041 Failed closing file handle.
USING_DATADIRECT_TEST_URL_VALUE
USING_POST_BUILDER_VALUE
POST_BUILDER_CHAR_LIMIT_VALUE
CURL_ERROR_NOT_BUILT_IN
CURL_ERROR_RESOLVEPROXY
CURL_ERROR_RESOLVEHOST
CURL_ERROR_CONNECTIONFAILURE
CURL_ERROR_QUERYTIMEOUT
CURL_ERROR_FAILEDTORECIEVE
OS_WRITE_REG_VAL_FAIL
OS_WRITE_STRINGS_DONT_MATCH
92 | P a g e
FAILED_SETTING_FILE_POINTER
-2000042 Failed setting file pointer.
FAILED_SETTING_END_OF_FILE
-2000043 Failed setting end of file.
COM_COINITIALIZED_FAILED
-2000045 COM Error : COM CoInitialize
Failed.
-2000046 COM Error : COM CoCreate
Failed.
-2000047 COM Error : FDS API Fetch Failed.
COM_COCREATE_FAILED
COM_FETCH_FAILED
COM_VARIANT_TYPE_UNKNOWN
KEYBOARD_INSERT_TOO_SOON
-2000048 COM Error : Variant type not
known.
-2000049 Simulated type too soon.
THREAD_NOT_RETURNED
-4000000 Thread still executing.
93 | P a g e
Table 4: Top 25 most commonly used FactSet Fundamentals formulas
Description
ExtractDataSnapshot Syntax
ExtractFormulaHistory Syntax
Acquisition of Business
(Cash Flow)
Capital Expenditures
(Fixed Assets)
Capital Expenditures
(Other Assets)
Cash Dividends Paid
FF_ACQ_BUS_CF(QTR,0)
FF_ACQ_BUS_CF(QTR,0,-4,,,USD)
FF_CAPEX_FIX(ANN,0)
FF_CAPEX_FIX(ANN,0,-5AY,,,USD)
FF_CAPEX_OTH(ANN,0)
FF_CAPEX_OTH(ANN,0,-5AY,,,USD)
FF_DIV_CF(ANN,0)
FF_DIV_CF(ANN,0,-5AY,,,USD)
Cash Flow from
Financing Activity - Net
(Cash Flow)
Cash Flow from
Investing Activity - Net
(Cash Flow)
Cash Flow from
Operating Activity - Net
(Cash Flow)
Changes in Working
Capital
Common Equity (Total)
FF_FIN_CF(ANN,0)
FF_FIN_CF(ANN,0,-5AY,,,JPY)
FF_INVEST_CF(ANN,0)
FF_INVEST_CF(ANN,0,-5AY,,,USD)
FF_FUNDS_OPER_GROSS(ANN,0)
FF_FUNDS_OPER_GROSS(ANN,0,5AY,,,USD)
FF_WKCAP_CHG(ANN,0)
FF_WKCAP_CHG(ANN,0,-5AY,,,USD)
FF_COM_EQ(QTR,0)
FF_COM_EQ(QTR,-10,0,,,EUR)
Cost of Goods Sold
Excluding Depreciation
& Amortization
Depreciation &
Amortization (Cash
Flow)
Extraordinary Items
(Cash Flow)
Funds from Operations
FF_COGS_XDEP(ANN,0)
FF_COGS_XDEP(ANN,0,-5AY,,,JPY)
FF_DEP_EXP_CF(ANN,0)
FF_DEP_EXP_CF(ANN,0,-5AY,,,USD)
FF_XORD_CF(ANN,0)
FF_XORD_CF(ANN,0,-5AY,,,CAD)
FF_FUNDS_OPER_GROSS(ANN,0)
Issuance of LT Debt
FF_DEBT_LT_ISS_CF(ANN,0)
FF_FUNDS_OPER_GROSS(ANN,0,5AY,,,USD)
FF_DEBT_LT_ISS_CF(ANN,0,-5AY,,,USD)
Long Term Debt
excluding Capital Lease
Obligations
Minority Interest Accumulated
Preferred Stock Carrying Value
Price - Close for
Calendar Period End
Purchase of
Investments
Reduction of LT Debt
FF_DEBT_LT_XCAP(ANN,0)
FF_DEBT_LT_XCAP(ANN,0,-5AY,,,USD)
FF_MIN_INT_ACCUM(ANN,0)
FF_MIN_INT_ACCUM(ANN,0,-5AY,,,USD)
FF_PFD_STK(QTR,0)
FF_PFD_STK(QTR,0,-10,,,USD)
FF_PRICE_CLOSE_CP(ANN,0)
Repurchase of
Common & Pref Stock
Sale of Common & Pref
Stock
Sales or Revenues Net
Short-Term Debt (incl.
Current Portion of LTD)
FF_STK_PURCH_CF(ANN,0)
FF_PRICE_CLOSE_CP(ANN,0,5AY,,,USD)
FF_INVEST_PURCH_CF(ANN,0,5AY,,,USD)
FF_DEBT_LT_REDUCT_CF(ANN,0,5AY,,,USD)
FF_STK_PURCH_CF(ANN,0,-5AY,,,USD)
FF_STK_SALE_CF(ANN,0)
FF_STK_SALE_CF(ANN,0,-5AY,,,USD)
FF_SALES(ANN,0)
FF_SALES(ANN,0,-5AY,,,USD)
FF_DEBT_ST(ANN,0)
FF_DEBT_ST(ANN,0,-5AY,,,USD)
94 | P a g e
FF_INVEST_PURCH_CF(ANN,0)
FF_DEBT_LT_REDUCT_CF(ANN,0)
Table 5: FactSet Economics Key Indicator Series for G-7 Countries
DBSOURCE
ITEM-MNEMONIC
COUNTRY
DESCRIPTION
Average hourly wage rate, Total
employees, CAD - Canada
Average Weekly Earnings, Industrial
aggregate excl. unclassified, CAD, SA Canada
BOP, Current Account, Balance, CAD,
SA - Canada
Building Permits, Total, CAD, SA Canada
Business leading indicators, Composite
index of 10 indicators, 1992=100,
Smoothed - Canada
Capacity Utilization, Total industrial,
Percent - Canada
Core CPI (All items excl. the 8 most
volatile components), 2002=100, NSA Canada
Corporation profits after taxes, CAD,
SAAR - Canada
CPI, All items, 2002=100, NSA - Canada
FDS_CANSIM
V2415197
Canada
FDS_CANSIM
V1597104
Canada
FDS_CANSIM
V114421
Canada
FDS_CANSIM
V4667
Canada
FDS_CANSIM
V7688
Canada
FDS_CANSIM
V4331081
Canada
FDS_CANSIM
V41692942
Canada
FDS_CANSIM
V499681
Canada
FDS_CANSIM
V41690973
Canada
FDS_CANSIM
V2062811
Canada
FDS_CANSIM
V41590956
Canada
FDS_CANSIM
V41881175
Canada
FDS_CANSIM
V499722
Canada
FDS_CANSIM
V498086
Canada
FDS_CMHC
CMHCDT00001@CA
Canada
FDS_CANSIM
V41590548
Canada
FDS_CANSIM
V53384992
Canada
FDS_CANSIM
V1409153
Canada
FDS_CANSIM
V803227
Canada
FDS_CANSIM
V800913
Canada
FDS_CANSIM
V800450
Canada
FDS_CANSIM
V800450
Canada
FDS_CANSIM
V191490
Canada
FDS_CANSIM
V183406
Canada
95 | P a g e
Employment, Total, Persons, SA Canada
Export Price Index, Total, Paasche
current weighted, 2002=100, NSA Canada
GDP by Industry, All industries, Chained
2002 dollars, SAAR - Canada
Gross domestic product (GDP) at market
prices, CAD, NSA - Canada
Gross domestic product (GDP) at market
prices, CAD, SAAR - Canada
Housing Starts, Total Dwelling Units, All
Areas, Canada, Thousands of Units,
SAAR - Canada
Import Price Index, Total, Paasche
current weighted, 2002=100, NSA Canada
Industrial Product Prices, Total, All
Commodities, 2002=100 - Canada
Labour Productivity, Business Sector,
Labour productivity, 2002=100, SA Canada
Manufacturers' Inventories, All
Manufacturing Industries, CAD, SA Canada
Manufacturers' New Orders, All
Manufacturing Industries, CAD, SA Canada
Manufacturers' Shipments, All
Manufacturing Industries, CAD, SA Canada
Manufacturers' Shipments, All
Manufacturing Industries, CAD, SA Canada
Merchandise Exports, Total, CAD, SA Canada
Merchandise Imports, Total, CAD, SA -
Canada
FDS_CANSIM
V41552786
Canada
Money Supply, M2, CAD - Canada
FDS_CANSIM
V53600422
Canada
FDS_CANSIM
V42169912
Canada
FDS_CANSIM
V53434748
Canada
FDS_CANSIM
V52367097
Canada
FDS_CANSIM
V52367119
Canada
FDS_CANSIM
V2062815
Canada
FDS_ECON
FRBP0013337
France
EURO_STAT
ECONSENTNR2@FR
France
FDS_ECON
FRSU0013609
France
FDS_ECON
FRSU0013621
France
FDS_ECON
FRSU0111884
France
FDS_ECON
FRSU1335083
France
FDS_ECON
FRPR0403230
France
FDS_ECON
FRPR0067552
France
FDS_ECON
FRLM0066761
France
FDS_ECB
ECBGGDEF%GDP@FR
France
EURO_STAT
GDS13%GDP@FR
France
FDS_ECON
FRPR1172435
France
New housing price index, Total (house
and land), 2007=100 - Canada
New motor vehicle sales, Total, Units,
SA - Canada
Raw Material Prices, Total, 2002=100 Canada
Retail sales, All trade groups, NAICS,
CAD, SA - Canada
Retail sales, Motor vehicle and parts
dealers, NAICS, CAD, SA - Canada
Unemployment Rate, Total, Percent, SA
- Canada
Balance of Payments, Current Account,
Net, Mil Eur
Business Survey, Economic sentiment
indicator, Nace R2, SA - France
Business Survey, Industry, Total,
Business sentiment indicator, SA,% France
Business Survey, Industry, Total,
Capacity utilisation rate, SA,% - France
Business Survey, Manufacturing
industry, Synthetic Index - France
Consumer Confidence Survey,
Consumer prices, Synthetic index,
Balance, WDA/SA, % - France
CPI, Total excluding energy, 1998=100,
Index - France
CPI, Total, NSA, 1998=100, Index France
Employment, Total (excluding
agriculture), SA - France
General govt deficit/surplus, incl. UMTS
proceeds, Non-financial flows, Current
Prices (ESA95, % of GDP) - France
Government Consolidated Gross Debt,
General Government, % of GDP France
Harmonized CPI, Index - France
FDS_ECON
FRIN0066438
France
FDS_ECON
FRFT0414480
France
FDS_ECON
FRNA0062321
France
FDS_ECON
FRNA0061958
France
FDS_ECON
FRNA0061974
France
FDS_ECON
FRNA0988432
France
FDS_ECON
FRSA0312432
France
96 | P a g e
Industrial Production, Industry, SA/WDA
(2005=100) - France
International Trade, Balance, Total
FOB/FOB (including military equipment),
Million EUR, SA/WDA - France
NA, Household consumption
expenditure, Total, chain-linked volumes,
Bil. EUR, TD/SA - France
National Accounts, Gross Domestic
Product, Chained Prices, WDA/SA, Bil
EUR - France
National Accounts, Gross Domestic
Product, Current Prices, WDA/SA, Bil
EUR - France
National Accounts, Households
consumption expenditures,
Manufactured goods, Base 2005, EUR France
New Passenger Cars Registration, Total,
Numbers - France
FDS_ECON
FRSA0111775
France
FDS_ECON
FRPR0111776
France
FDS_ECON
FRSU0013519
France
FDS_ECON
FRLM0111772
France
FDS_ECON
FRLM0988436
France
FDS_ECON
FRLM0111773
France
FDS_ECON
DEBP0386292
Germany
FDS_ECON
DEBP0386292
Germany
FDS_ECON
DEBP0386308
Germany
FDS_ECON
DEBP0386310
Germany
FDS_ECON
DEBP0386263
Germany
EURO_STAT
UCAP@DE
Germany
EURO_STAT
ECONSENTNR2@DE
Germany
FDS_ECON
DESU0083141
Germany
FDS_ECON
DESU0933978
Germany
EURO_STAT
CONSCONF@DE
Germany
FDS_ECON
DEPR0988455
Germany
FDS_ECON
DEPR0422832
Germany
FDS_ECON
DELM0987186
Germany
FDS_ECB
ECBGGDEF%GDP@DE
Germany
EURO_STAT
GDS13%GDP@DE
Germany
FDS_ECON
DEPR0422980
Germany
FDS_ECON
DENA0436208
Germany
FDS_ECON
WDSA0161561
Germany
FDS_ECON
DENA0435181
Germany
FDS_ECON
DEPR0992887
Germany
FDS_ECON
DEIN0992522
Germany
FDS_ECON
DEIN0992528
Germany
97 | P a g e
New vehicles registrations, SA/WDA,
Thous EUR - France
PPI, Total Industry , 2005=100 - France
Retail Trade Survey, Total Trade, Value,
SA, Index - France
Unemployment Rate by Sex and Age,
Total, SA, % - France
Unemployment Rate, Total, Metropolitan
and DOM, Percent - France
Unemployment, Total Population, SA,
Thous of Persons - France
BOP, Balance On Current Account, Mil.
EUR - Germany
BOP, Balance On Current Account, Mil.
EUR - Germany
BOP, Exports, Total, Thou. EUR Germany
BOP, Imports, Total, Thou. EUR Germany
BOP, Trade In Goods, External Trade,
Balance, Mil. EUR - Germany
Business Survey - Industry, Current level
of capacity utilization (%), Balance, SA Germany
Business Survey, Economic sentiment
indicator, Nace R2, SA - Germany
Business Surveys, IFO, Main Index,
Business Climate, Trade and Industry
(2005=100), Index, SA - Germany
Business Surveys, ZEW, Economic
Expectations, Germany (ZEW Indicator),
Balance, % - Germany
Consumer Survey - Consumer
confidence indicator, Balance, SA Germany
CPI, All Items excluding fuel, NSA, Index
2005=100 - Germany
CPI, Total (2005=100) - Germany
Employment, Overall, SA, Thous Germany
General govt deficit/surplus, incl. UMTS
proceeds, Non-financial flows, Current
Prices (ESA95, % of GDP) - Germany
Government Consolidated Gross Debt,
General Government, % of GDP Germany
HICP, Total, 2005=100 - Germany
NA, Real, Consumption & Investment,
Private Consumption Expenditure Households, SA/CA, X-12-ARIMA, Chain
Index, 2000=100 - Germany
New Passenger Car Registrations,
Germany - World
Nominal GDP, Total, NSA, Bil EUR,
2005=100 - Germany
PPI, Total Industry, 2005=100 Germany
Production, Industry, SA/CA, 2005=100 Germany
Production, Manufacturing Excluding
Construction, SA/CA, 2005=100 -
Germany
FDS_ECON
DENA0435202
Germany
FDS_ECON
DEPR1422754
Germany
FDS_ECON
DESA0988452
Germany
FDS_ECON
DELM0987184
Germany
FDS_ECON
ITBP0062460
Italy
EURO_STAT
UCAP@IT
Italy
EURO_STAT
ECONSENTNR2@IT
Italy
FDS_ECON
ITSU1026964
Italy
EURO_STAT
CONSCONFBAL@IT
Italy
FDS_ECON
ITPR0027835
Italy
FDS_ECON
ITPR0027834
Italy
FDS_ECON
ITLM0073816
Italy
FDS_ECON
ITFT0069219
Italy
FDS_ECON
ITFT0069225
Italy
FDS_ECON
ITFT0069229
Italy
FDS_ECB
ECBGGDEF%GDP@IT
Italy
EURO_STAT
GDS13%GDP@IT
Italy
FDS_ECON
ITLM0069240
Italy
FDS_ECON
ITIN0069198
Italy
FDS_ECON
ITIN0036245
Italy
FDS_ECON
ITNA0027276
Italy
FDS_ECON
ITNA0027318
Italy
FDS_ECON
ITNA0027318
Italy
FDS_ECON
WDSA0161564
Italy
FDS_ECON
ITPR0069203
Italy
FDS_ECON
ITPR1294953
Italy
98 | P a g e
Real GDP, Total, SA/CA X12-ARIMA,
2005=100 - Germany
Retail Prices, Retail and Motor Trade,
Including VAT, 2005=100, Index Germany
Retail Sales, Total Excluding Motor
Vehicles, Real, NSA, Index 2005=100 Germany
Unemployment Rate, Overall, SA, % Germany
BOP, Current Account Balance, Mil EUR
- Italy
Business Survey - Industry, Current level
of capacity utilization (%), Balance, SA Italy
Business Survey, Economic sentiment
indicator, Nace R2, SA - Italy
Business Surveys, Manufacturing, Total,
Confidence Indicator (2005=100), SA Italy
Consumer Survey - Consumer
confidence indicator, Balance, SA - Italy
CPI, Total (excl. tobacco) (2010=100) Italy
CPI, Total (including tobacco)
(2010=100) - Italy
Employment, by Geographical Area &
Sex, Total, Thousands - Italy
Foreign trade, Balances, Total balance,
Total, Millions of Eur - Italy
Foreign trade, Exports, Total exports,
Total, Millions of Eur - Italy
Foreign trade, Imports, Total imports,
Total, Millions of Eur - Italy
General govt deficit/surplus, incl. UMTS
proceeds, Non-financial flows, Current
Prices (ESA95, % of GDP) - Italy
Government Consolidated Gross Debt,
General Government, % of GDP - Italy
Hourly wages, per employee, Total - Italy
Industrial new orders, Total new orders
index, Total industry excluding
Construction (2005=100) - Italy
Industrial production, Total industry
excluding construction, SA (2005=100) Italy
National Accounts, Chained Volumes,
Gross domestic product at market
prices, Eur, SA - Italy
National Accounts, Current Prices,
Gross domestic product at market
prices, Eur, SA - Italy
National Accounts, Current Prices,
Gross domestic product at market
prices, Eur, SA - Italy
New Passenger Car Registrations, Italy World
PPI, Total industry excluding
Construction (2005=100) - Italy
Prices, HICP, Base 2005=100, Index -
Italy
FDS_ECON
ITSA0069242
Italy
FDS_ECON
ITLM0074794
Italy
FDS_ECON
JPBP0577771
Japan
FDS_ECON
JPSU0311674
Japan
FDS_ECON
JPSU0087044
Japan
FDS_ECON
JPSU0590365
Japan
FDS_ECON
JPSU0590299
Japan
FDS_ECON
JPPR0582659
Japan
FDS_ECON
JPPR0988546
Japan
FDS_ECON
JPPR0988545
Japan
FDS_ECON
JPPR0382789
Japan
FDS_ECON
JPPR0382786
Japan
FDS_ECON
JPNA0068561
Japan
FDS_ECON
JPIN0590229
Japan
FDS_ECON
JPIN0322658
Japan
FDS_ECON
JPIN0943453
Japan
FDS_ECON
JPFT0558218
Japan
FDS_ECON
JPFT0558219
Japan
FDS_ECON
JPLI0420280
Japan
FDS_ECON
JPLI0087084
Japan
FDS_ECON
JPLM0988459
Japan
FDS_ECON
JPLM0938143
Japan
FDS_ECON
JPLM0938144
Japan
FDS_ECON
JPMA0337151
Japan
BOP, Current Account, Current AccountSa - Japan
Business Sentiment, Small And MediumSized Companies, All Industries, Index Japan
Consumer Confidence Survey, Total,
SA, Index - Japan
Consumer Surveys, Family Income &
Expenditures, Workers HHs, Average
Propensity to Consume, % - Japan
Consumer Surveys, Real Family Income
& Expenditures, Workers HHs,
Consumption, 2005=100, SA - Japan
Corporate Goods Price Index, Domestic,
All commodities, 2005=100 - Japan
CPI, Tokyo, Total Ex. Fresh Food,
2010=100, Index - Japan
CPI, Tokyo, Total, 2010=100, Index Japan
CPI, Total Ex. Fresh Food, 2010=100,
Index - Japan
CPI, Total, NSA, 2010=100, Index Japan
GDP Deflator, GDP Total, 2000=100 Japan
Industrial Orders, Machinery, Total Ex.
Volatile Orders - Japan
Industrial Production Index(IPI),
Production, By Goods, Mining &
manufacturing, (2005=100), Sa - Japan
Industry Activity Indices, Indices of All
Industry Activity (except
Agriculture,Forestry and Fisheries), SA,
2005=100 - Japan
International Trade, Exports, Total, NSA,
JPY - Japan
International Trade, Imports, Total, NSA,
JPY - Japan
Leading Index, Total Composite,
2005=100 - Japan
Leading Indicators, Sales Forecast D.I.
of Small Businesses, % - Japan
LFS, Unemployment Rate, %, SA,
Percent - Japan
LM, Active Job Opening, Persons Japan
LM, New Job Openings. Number Of Japan
Money Stock, M1, Average - Japan
FDS_ECON
JPMA0337149
Japan
Money Stock, M2, Average - Japan
FDS_ECON
JPNA0067856
Japan
FDS_ECON
JPNA0068194
Japan
FDS_ECON
JPBC0587691
Japan
Nominal GDP, Expenditure Approach,
Total - Japan
Nominal GDP, Expenditure Approach,
Total, SA - Japan
Outstanding of Deposits and Loans,
Loans and Discounts, Total of Banks
99 | P a g e
Retail Sales, Total, NSA (2005=100) Italy
Unemployment Rate, Total SA - Italy
and Shinkin Banks - Japan
FDS_ECON
JPGV0341530
Japan
FDS_ECON
JPGV0341530
Japan
FDS_ECON
JPNA0068239
Japan
FDS_ECON
JPNA0068239
Japan
FDS_ECON
JPSU0353978
Japan
FDS_ECON
JPSU0354014
Japan
ONS_ECON
IKBL
United
Kingdom
ONS_ECON
HBOP
United
Kingdom
FDS_BOE
BOELPVTVJ
United
Kingdom
ONS_ECON
D7BT
United
Kingdom
ONS_ECON
DKC9
United
Kingdom
ONS_ECON
NMRY
United
Kingdom
ONS_ECON
YBGB
United
Kingdom
ONS_ECON
YBHA
United
Kingdom
ONS_ECON
YBHA
United
Kingdom
ONS_ECON
ABJR
United
Kingdom
ONS_ECON
NRJS
United
Kingdom
FDS_ECON
GBHC1137972
United
Kingdom
ONS_ECON
CGBZ
United
Kingdom
ONS_ECON
BEAO
United
Kingdom
ONS_ECON
MGRZ
United
Kingdom
ONS_ECON
MGSX
United
Kingdom
ONS_ECON
LRYC
United
Kingdom
100 | P a g e
Public Finance, National Government
Debt, Total - Japan
Public Finance, National Government
Debt, Total - Japan
Real GDP, Expenditure Approach, Total,
SA - Japan
Real GDP, Expenditure Approach, Total,
SA - Japan
Tankan Business Conditions Diffusion
Index, Large Enterprises, Manufacturing,
Actual result - Japan
Tankan Business Conditions Diffusion
Index, Large Enterprises,
Nonmanufacturing, Actual result - Japan
Balance of Payments: Imports: Total
Trade in Goods & Services: CVM SA United Kingdom
BoP Current Account Balance SA Mil. of
GBP - United Kingdom
Changes of Total Sterling Net Secured
Lending to Individuals & Housing
Associations, Mil. GBP, SA - United
Kingdom
CPI INDEX 00, ALL ITEMS- estimated
pre-97 2005=100 - United Kingdom
CPI INDEX: Excluding energy &
seasonal food (SP) 2005=100 - United
Kingdom
General Government: Final consumption
expenditure: P3: CVM SA - United
Kingdom
Gross Domestic Product (Expenditure)
at market prices deflator: SA - United
Kingdom
Gross Domestic Product at market
prices: Current price: Seasonally
adjusted - United Kingdom
Gross Domestic Product at market
prices: Current price: Seasonally
adjusted - United Kingdom
Household final consumption
expenditure, National concept CVM
NAYear SA - United Kingdom
Households & NPISH: Households
saving ratio: CP SA - United Kingdom
Housing and Construction, Construction
Output, Total output, Total, 2005 Prices,
SA, Mil GBP - United Kingdom
Income based: Gross operating surplus
of corporations, Total: CP SA - United
Kingdom
LFS: ILO redundancy level: UK: All: SA United Kingdom
LFS: In employment: UK: All: Aged 16&:
000s: SA: Annual = Spring qtr (Mar to
May) - United Kingdom
LFS: Unemployment rate: UK: All: Aged
16 and over: %: SA - United Kingdom
Manufacturing PNFCs net rate of return
(%) - United Kingdom
FDS_ECON
GBSA0313873
United
Kingdom
FDS_BOE
BOELPVTVX
United
Kingdom
ONS_ECON
LRWM
United
Kingdom
ONS_ECON
K646
United
Kingdom
ONS_ECON
J5C4
United
Kingdom
ONS_ECON
LRYQ
United
Kingdom
ONS_ECON
BCJD
United
Kingdom
ONS_ECON
RLMH
United
Kingdom
ONS_ECON
AP2Y
United
Kingdom
ONS_ECON
DYDC
United
Kingdom
ONS_ECON
LNNK
United
Kingdom
FDS_ECON
BLSCES0000000001
United
States
FDS_ECON
BLSCES0500000008
United
States
FDS_ECON
FFFL152090005
United
States
FDS_BEA
BEABOPT1L76@US
United
States
FDS_FRB
FRBUTLB50001
United
States
FDS_BEA
BEANIPADPCCRG3@US
United
States
FDS_BEA
BEANIPADPCERG3@US
United
States
TCB_BCI
G0M910
United
States
FDS_FRB
CRED@US
United
States
FDS_BLS
BLSCUSR0000SA0L1E
United
States
FDS_BLS
BLSCUSR0000SA0
United
States
FDS_BLS
BLSCUSR0000SA0
United
States
FDS_ECON
BLSCIS1010000000000I
United
States
FDS_ECON
FFLA314104005
United
States
FDS_BEA
BEANIPAA191RV1@US
United
States
101 | P a g e
New Car Registrations, Total - United
Kingdom
Number of Total Sterling Approvals for
House Purchase to Individuals (Thous),
SA - United Kingdom
PNFCs net operating surplus in Mil. of
GBPillion - United Kingdom
PPI: 6207000050: All mfg (materials &
fuel purchased) (Net sector input prices)
- United Kingdom
RSI: Value Seasonally Adjusted: All
Retailers inc fuel: All Business Index United Kingdom
Service sector PNFCs net rate of return
(%) - United Kingdom
Total Claimant count SA (UK) thousands - United Kingdom
Total consumer credit: Net lending: SA United Kingdom
UK Vacancy Survey C-O - avg 3 month
level - United Kingdom
UK Workforce jobs (SA), Total thousands (LMT table B11) - United
Kingdom
Unit Wage Costs, whole economy SA:
Index 2002=100: UK - United Kingdom
All Employees, Total Nonfarm Payroll,
Thousands of Persons, SA - United
States
Average Hourly Earnings Of Production
And Nonsupervisory Employees Total
Private SA - United States
Balance sheet of households & nonprofit
organizations - Net worth, B.100 - United
States
BOP Current Account balance, SA United States
Capacity Utilization, Total index, NSA United States
Chain-Type Price Index for PCE, Excl.
Food & Energy, Price Index, 2005=100,
SA - United States
Chain-Type Price Index for PCE, Price
Index, 2005=100, SA - United States
Composite index of 10 leading indicators
(2004=100)
Consumer Credit Outstanding, Total, SA
(Bil. Dollars) - United States
CPI-U All Items Less Food And Energy
U.s. City Average SA 1982-84=100 United States
CPI-U All Items U.s. City Average SA
1982-84=100 - United States
CPI-U All Items U.s. City Average SA
1982-84=100 - United States
Employment Cost Index Total
Compensation All Civilian All Workers
SA Dec. 2005=100 - United States
Federal government - Credit market
instruments, D.3 - United States
Gross Domestic Product, %Chg P/P in
Price Index, SAAR - United States
FDS_BEA
BEANIPAA191RC1@US
United
States
FDS_BEA
BEANIPAA191RL1@US
United
States
FDS_CENSUS
CENHAUTHBP@US
United
States
FDS_CENSUS
CENSTARTS@US
United
States
FDS_FRB
FRBIPSB50001
United
States
FDS_ISM
ISMPMI@M
United
States
FDS_ISM
ISMNMI@NM
United
States
FDS_FRB
FRBM2@US
United
States
FDS_BEA
BEANIPAA051RC1@US
United
States
FDS_CENSUS
CENAMTMNO
United
States
FDS_CENSUS
CENHSOLDTOT@US
United
States
FDS_FRB
FRBJXRATEM@US
United
States
FDS_BEA
BEANIPAA466RC1@US
United
States
FDS_ECON
BLSPRS85006092
United
States
FDS_BEA
BEANIPADPCERC1@US
United
States
FDS_BEA
BEANIPADPCERL1@US
United
States
FDS_BEA
BEANIPAA065RC1@US
United
States
FDS_BEA
BEANIPAA072RC1@US
United
States
TCB_BCI
G0M940
United
States
FDS_FRB
FRBJXRATEMR@US
United
States
FDS_CENSUS
CENRETAIL@US
United
States
FDS_CENSUS
CENRETAIL&FS@US
United
States
FDS_CENSUS
CEN10010
United
States
FDS_CENSUS
CENAMTMTI
United
States
FDS_CENSUS
CENTRBT@US
United
States
FDS_CENSUS
CENTRXT@US
United
States
FDS_ECON
BLSLNS14000000
United
States
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Gross Domestic Product, Bil. $, SAAR United States
Gross Domestic Product, Real %Chg
P/P - United States
Housing Units Authorized by Building
Permits, Total, SA - United States
Housing Units Started, Total, SA - United
States
Industrial Production, Total index, SA United States
ISM (NAPM) Manufacturing, Purchasing
Managers Index - United States
ISM (NAPM) Non-Manufacturing Index United States
M2 Money Supply, SA - United States
National Income, Corporate Profits with
IVA & CCAdj, Bil. $, SAAR - United
States
New Orders, NAICS, Total
Manufacturing, SA - United States
New Residential Sales, New Houses
Sold, Total, SA - United States
Nominal Trade-Weighted Exchange
Rate Index, Major Currencies,
3/1973=100 - United States
Nonfinancial Corporate Business, Profits
After Tax (Without IVA & CCAdj), Bil. $,
SAAR - United States
Output Per Hour Nonfarm Business All
Persons % Change Quarter Ago, At
Annual Rate SA - United States
Personal Consumption Expenditures, Bil.
$, SAAR - United States
Personal Consumption Expenditures,
Real %Chg P/P - United States
Personal Income, Bil. $ - United States
Personal Income, Personal Saving As A
Percentage Of Disposable Personal
Income, Bil. $, SAAR - United States
Ratio, coincident index to lagging index
(1996=100)
Real Trade-Weighted Exchange Rate
Index, Major Currencies, 3/1973=100 United States
Retail Sales, NAICS, Total, SA (Incl. in
Advance Release) - United States
Retail Sales, Retail & Food Service
Sales, NAICS, SA (Incl. in Advance
Release) - United States
Total Construction Put in Place, Mil.
USD, SA - United States
Total Inventories, NAICS, Total
Manufacturing, SA - United States
U.S. International Trade - Balance Total
G&S, BOP Basis, Mil. USD, SA - United
States
U.S. International Trade - Exports Total
G&S, BOP Basis, Mil. USD, SA - United
States
Unemployment Rate - Percent, SA United States
FDS_ECON
BLSLNS14000000
United
States
FDS_BEA
BEADETSAARD3@US
United
States
FDS_BEA
BEADETSAARF3@US
United
States
FDS_ECON
BLSPRS85006112
United
States
FDS_BEA
BEADETTEMG3@US
United
States
FDS_CENSUS
CENAMTMVS
United
States
FDS_BLS
BLSWPSSOP3520
United
States
FDS_BLS
BLSWPSSOP3000
United
States
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Unemployment Rate - Percent, SA United States
Unit Auto Sales, Domestic, Millions of
Vehicles, SAAR - United States
Unit Auto Sales, Foreign, Millions of
Vehicles, SAAR - United States
Unit Labor Costs Nonfarm Business All
Persons % Change Quarter Ago, At
Annual Rate SA - United States
Unit Truck Sales, Light Trucks (14,000
Lbs. GVW & Under), Imports, Millions of
Vehicles, SAAR - United States
Value of Shipments, NAICS, Total
Manufacturing, SA - United States
WPI Stage Of Processing, Finished
Goods Less Energy SA 1982=100 United States
WPI Stage Of Processing, Finished
Goods SA 1982=100 - United States
Table 6: UploadToOFDB Error Messages
"One or more required parameters are not set"
"First column must be labeled 'Id' and be type 'Id'"
"Second column must be labeled 'Date' and be type 'Date'"
"Client Data Space is Full"
"Invalid data type in column #: <TYPE>"
"Invalid dates (#) starting on row #: '<VALUE>'"
"Invalid field names for this OFDB starting with: <FIELDNAME>"
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FactSet Glossary
Factlet =
A factlet is a FactSet report that retrieves a raw table of data. These reports or
functions are application components that encapsulate business logic and data
collection procedures. Factlets consist of built in multiple result formats that a user
can choose from (i.e. XML, Delimited, Excel). The data can then be cleaned up (i.e.
converted to data set).
FactSet DataDirect =
DataDirect provides synchronous access to FactSet data via the standard HTTPS
protocol. Data can be returned in a number of formats, such as XML or CSV file.
There are many reports and services available. A user can make custom requests
by changing the request URL to contain the needed parameters.
DataDirect uses HTTP basic authentication over Secure Sockets Layer (SSL).
FactSet Economics =
FactSet database of primary-sourced information on the global economy. Includes
specialty sources such as Eurostat, ICIS, IMF, LME, NYMEX, and OECD.
Data from this database can be retrieved most effectively using the ExtractEconData
function.
FactSet Fundamentals =
FactSet database includes coverage of more than 66,600 companies from over 70
countries, 20 years of historical data and up to 2,000 data elements on each
company record. Comprised of annual and interim/quarterly data, detailed historical
financial statement content, per share data, calculated ratios, and pricing.
Data from this database can be retrieved most effectively using the
ExtractFormulaHistory function for a time-series of data request and using the
ExtractDataSnapshot function for a single date data request.
FQL (FactSet Query Language) =
FQL is a proprietary data retrieval language used to access FactSet data.
Requesting data with FQL is powerful, flexible, and versatile. Some advantages of
FQL include:

The ability to specify dates for any database using the same formats.
With FQL, date formats are flexible. You can use a number of consistent date
formats (defined by FQL) for all databases which makes using and combining
data from different databases easier than ever.
105 | P a g e

The ability to iterate items, formulas, and functions at any frequency.
With FQL, you can iterate items, formulas, and functions at any frequency.
For example, you can request a series of weekly price to earnings ratios
To download the Introduction to FQL manual see the FactSet Online Assistant page
1961.
FactSet Workstation =
FactSet platform that is installed on a client’s desktop and provides an interface to
access content and analytical tools to easily examine a range of data, including
prices, financials, estimates, research reports, news, and a client’s proprietary data.
The data accessible in a workstation can be extracted using the factlet reports.
Analytical tools include an interface for performing company analysis, viewing
market data in real time, generating investment ideas, and managing portfolios.
Online Assistant =
Online Assistant is a utility available in the FactSet workstation and is a rich
information network designed to provide a one-stop source for comprehensive
documentation on all of FactSet's applications and databases.
Screening Language =
The FactSet Screening syntax is way to efficiently extract data for a large universe of
securities. By default, the Screening syntax does not allow iteration and therefore
cannot be used to return a time series of data with a single request code. To request
data as of a single historical date, a date must be specified. If a date is not specified,
data is returned from the most recent time period.
106 | P a g e
Contact FactSet Support:
United States and Canada
+1.877.FACTSET
[email protected]
India
000.800.440.1703
[email protected]
Spain
900.811.921
[email protected]
United Kingdom
0800.169.5954
[email protected]
Italy
800.510.858
[email protected]
South Africa
0800 166 509
[email protected]
Australia
1800.33.28.33
[email protected]
Japan
0120.779.465
[email protected]
South Korea
080.411.0880
[email protected]
Belgium
080094108
[email protected]
Mexico
+1.888.542.9899
[email protected]
Sweden
0200.110.263
[email protected]
Brazil
0800 8917850
[email protected]
Middle Eastern
+44.(0)20.7374.4445
[email protected]
Switzerland
0800.881.720
[email protected]
Denmark
8060 1698
[email protected]
Netherlands
0800.228.8024
[email protected]
United Arab Emirates
80004440014
[email protected]
France
0800.484.414
[email protected]
Norway
800.30365
[email protected]
Other Asian Countries
+852.3011.4888
[email protected]
Germany
0800.200.0320
[email protected]
Republic of Ireland
1800.409.937
[email protected]
Other European + Russia
+44.(0)20.7374.4445
[email protected]
Hong Kong
3011.4888
[email protected]
Singapore
800.61.61.724
[email protected]
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