A recommended approach for interviewing candidates is to draft a specific list of questions before the interview begins that are pertinent to the position that needs to be filled. These questions should be based on the candidate’s QA accomplishments and skill sets as documented in the resume. Below are some sample questions that can help demonstrate a candidate’s experience in the areas of quality assurance, and also their creativity and ability to comprehend basic testing concepts. The questions below can serve as criteria to screen out inexperienced candidates during the interviewing process.

Describing Testing/QA Terms

An experienced testing candidate with several years of experience will exhibit knowledge and understanding of well-established testing principles and testing terminologies. It behooves the test manager to ask the testing candidate to describe these concepts. Some suitable examples would be:

What is the objective of a peer review?
What is the Unified Modeling Language?
What are the components of a test plan?
What are the benefits of automated testing?
What are the benefits of Configuration Management?
What are the characteristics of a good test requirement?
What is a requirements traceability matrix is and why it is necessary?
What is the criterion for composing a test readiness review list (TRR)?
Provide descriptions for testing approaches (i.e. white box versus black box, etc).

The test manager needs to ascertain if the testing candidate is familiar with industry accepted terminologies that are commonly used within the project where the tester is being considered.

Thinking On Their Feet

In addition to understanding the testing requirements, a good testing candidate should have creativity and ingenuity when testing a software application. A tester should always be alert to potential scenarios that could cause an application to fail or yield defects and/or errors; even if such scenarios are not documented or presented in requirements. A thorough tester executes a particular test scenario with different sets of data, and conducts boundary testing to ensure that an application would not be deployed into production with overlooked problems.
A suggested question to discern a candidate’s testing meticulousness is to have a candidate provide use cases and test cases for a commonly used machine, or a self service application. An example would be: what test cases and use cases can the candidate think of for operating a beverage dispensing machine, or for purchasing books via a website. The candidate should generate an extensive list of test cases and use cases for the two aforementioned examples.

Development of Test Scenarios and Test Scripts A well written test scenario has information about: pre-conditions, post-conditions, traceability to a requirement, description, identification of authorship, a peer review and sign off section, roles to be tested, etc. A test script or test procedure on the other hand has detailed test steps with valid data values and expected results for each test step. In addition, a test script provides information about the test execution results for “passes/failures” and mapped requirements for the test step.
The test manager can have the tester provide information as to how test cases and test scripts were documented at the previous project. What level of detail was presented for the test cases and test scripts? What exactly was documented for the test cases and test scripts? Another suggestion is to have the tester send a sample test case and test script that he/she documented at a previous project for review. A well-documented test case and test script will demonstrate the tester’s attention to detail.

Life Cycle Methodologies

An experienced tester should have experience working with one or more IT methodologies such as waterfall spiral, evolutionary, incremental, rapid prototyping, etc. Some methodologies are more appropriate when requirements are well known/defined, or when requirements are not well known, or when the project has high risks, etc. The tester vying for the position should understand what the differences are between the main software development lifecycle methodologies. The test manager can present the tester with different hypothetical scenarios of IT projects and ask the tester what methodology would be most fitting based on the presented scenario.

Test Procedures and Test Standards

What testing standards and procedures the candidate has been exposed to is of paramount importance in determining whether the candidate would either be a good fit or adapt to a new testing environment. Did the candidate come from a regimented and disciplined type work environment like a CMM work environment with repeatable and defined processes? Or, did the candidate work in a chaotic test team that did not have any standards, procedures or defined processes?

Testers that come from a loose testing environment sometimes have difficulty adjusting to regimented testing environments that have defined processes and standards for things such as: lessons learned, test plans, naming standards, version control, test case templates, test execution matrix, test logs, test folders, reporting of test results, etc. Conversely, a tester that follows strict testing standards and procedures may struggle in a work environment that does not have defined processes, well documented scenarios, no structure for test cases, or templates for test scripts, etc. Questions that identify what the candidate’s experience is with testing standards and procedures are critical for assessing how well the candidate would adjust to the current QA environment.

Defects

It’s advisable to learn what sorts of defects a tester has identified and reported on in previous projects. Based on the candidate’s answers, the test manager can learn whether the tester focuses on cosmetic/minor defects or defects that are show stoppers and would have caused havoc for a released or production deployed application.

The test manager can have the candidate expound on a significant defect that was discovered and, what the impact of the defect would have been had the candidate not caught it. In particular, the test manager can focus on the tester’s approach for identifying the defect and how the tester re-tested the defect subsequently leading to the closing of the defect.

Test Script Automation Many candidates list in their resumes that they have experience with various automated testing tools. However, when confronted with technical questions about their experience with automated testing tools many candidates respond that their experience with automated test tools is limited to capturing and playback. Candidates that manifest to have only recorded and played back test scripts are in fact devoid of significant experience with automation test tools. Even candidates who claim to hold certifications with test tools should have their background probed and examined with technical questions.

The test manager should find out whether the candidates understand what a data driven test script is, how to create a parameterized test script, and why it would be necessary to construct a data driven script. The test manager should also ask the candidates to explain what data correlation is, how to create a driver script, how to synchronize scripts, why it is necessary to synchronize scripts, and how to report the execution results from a test script. Other key questions would be techniques for identifying and verifying recorded objects including providing examples of object’s attributes that can be verified, how to create checkpoints, how to debug an automated test script, and in what modes a recorded script can be re-played. The candidate should also know how to map and learn custom objects that the recording test tool does not identify or recognize.

The test manager can present more technical questions based on the nuances of the recording test tool that is present at the project site. The main objective of technical questions is to ensure that a candidate has experience that goes beyond merely recording and playing back scripts.

Rather than limiting this to a formally technical process used to investigate and measure quality of the developed computer software in terms of correctness, completeness and security, it is imperative to test the software components and the software as a whole for integrity, capability, reliability, efficiency, portability, maintainability, compatibility and usability of the software. This is particularly important in an operating environment where it is planned to be implemented, and testing will need to adhere to the technical requirements as described under ISO standard ISO 9126.

Test Management

Planning a Test Project

A test project includes the creation of a test plan, collecting test scenarios, writing test cases, executing test cases, evaluating and reporting the test results and managing the software testers.

It is also imperative to ensure which items are in the scope of testing and which are out of scope, maintaining scheduling and training. We need to establish a strategy to be followed in each of the testing phases. All of these phases come under test management.

Managing Test Data

Effective testing requires the presence of adequate and suitable test data. Where test results are specified, it is presumed that the inputs will be consistent from text execution to execution. In situations where automated testing methodologies are employed, a standardized test bed of data may be required to facilitate the automated assessment of “passed”.

Depending on the test being executed, test data may be copied from a production system, or may be developer generated, or a combination of both. In situations where extreme testing precision is required, the test data may be specifically created, saved and copied into the environment at the outset of every testing cycle to ensure a consistent test execution.

Test data needs to be considered when developing some of the larger tests (User Acceptance Testing, Regression Testing and System Testing). As plans are developed for test execution, the creation, modification or even restoration of test data are critical concerns that must be effectively managed.

Testing Methodology

The best approach is to conduct end to end testing. Testing methodology is applied in two major phases: Functional Testing and Non-Functional Testing.

But also the context where a system is deployed will influence the Testing Methodology. There will be a difference between tests for a commercial software package to be sold to consumers and embedded software or a server solution for a website of a one-off logistics solution.

Functional Testing

1. Unit Testing : Testing each component of the application separately.
2. System Testing : Testing the system as a whole.
3. Integration Testing : Testing the system with other interfaces.
4. Regression Testing : Testing changes made to the system and ensuring that new problems are not introduced as a result of the changes.
5. User Acceptance Testing : Testing the system to ensure it meets the user requirements.
6. Sociability Testing : Testing the system with other applications in the platform.
7. Security testing : Test the software from external damages it meets the user requirements.
8. Performance testing : Testing that is performed to determine how fast some aspect of a system performs under a particular workload.

Non-Functional Testing

1. Usability Testing : Usability testing focuses on determining if the product is easy to learn, satisfying to use and contains the functionality that the users desire.
2. Stress and Performance Testing : The purpose of this testing is to predict the system behavior and performance.
3. Accessibility Testing : to determine the extent to which the end user interacts with the application.

Automated Testing

One of the purposes of automated testing is to ease regression testing. Once the system is stable, then automated testing can be started.

Once automated tests exist, they can and should be used to help build better and more stable software ready for any manual testing and user acceptance testing.

Preferrably, automated tests can be run overnight on software builds, rather than the live version. This will help give a clearer indication to the stability of the build the following morning. Hopefully, leading to more time being available for the rest of the testing and development.

Test Framework

A set of ideas and tools that revolve around optimizing the test-effort. When implemented using the supporting tools, these ideas render the maintenance of the automated testing scripts or other entities easier.

JUnit Test Tool

The JUnit test tool is designed to assist in unit testing of java objects. It can be useful in testing a non-java environment (eg. by wrapping the test target inside a java object).

From Main Frame testing to ebusiness testing

* Testing the MainFrame System
* Testing the Client Server Application
* Testing the Internet Application
* Testing Web Services

Component Performance Criteria

Testing is a major part of agile development processes, but until recently, the vast majority of effort has been focused on functional testing. In the world of integrated systems, many projects are undertaken to speed up processes. Traditional development processes teach that optimisation should not be carried out until a performance problem is identified, thus aiding maintainability. Unfortunately, this has led to the postponement of performance testing until after integration testing. This can often make it too late to address fundamental problems as time is short or key resources have moved on to other projects.

Component Performance Criteria is about breaking down the performance requirement for the whole system and attributing it to individual components. Admittedly, it does not guarantee the absence of performance problems later in the project, but it will highlight if they are already there.

Example: if a system must process five transactions per second, and consists of 10 major components, and an individual component has been measured to take 0.5 seconds to process a single transaction and can only process one transaction simultaneously, a bottleneck is already present and resolution of the problem can begin immediately.

Optimally, performance testing should be carried out regularlly with the fewest potential issues. This is effectively done with automation.

Know The Client
The holy grail of any project, satisfy your client. For that you need to know, what is in his mind regarding his requirements. He is the best person to tell you, what they want. While skilled and experienced software engineers can fill in any incomplete, ambiguous or contradictory information.

Specification
Specification means precisely describing the software to be written in a rigorous way.

Software architecture
The architecture of a software system refers to an abstract representation of that system. Architecture is concerned with making sure the software system will meet the requirements of the product, as well as ensuring that future requirements can be addressed.

Implementation (or coding)
Reducing a design to code may be the most obvious part of the software engineering job, but it is not necessarily the largest portion.

Testing
Testing of parts of software is done by a software engineer to check out any bug or error.

Documentation
An important (and often overlooked) task is documenting the internal design of software for the purpose of future maintenance and enhancement.

Black-Box testing

* Examines the fundamental aspect of the system with little or no regard to the internal logical structure of the system.
* Demonstrates that software functions are operational and the input is properly accepted and output is correct produced.
* Integrity of the external information (eg. Database) is maintained.

BLACK BOX TESTING STRATEGY - would involve different types of Testing. When the Build is for the first time to test - we start with Validations and Verifications wrt the Scope/SRS document or Mapped Test Cases. We even start tracing the Traceability Matrix. We mark the observations as in Severity and Priority and publish a Testing Report. Next Build onwards we start with Smoke Testing Phase - if test cases report FAIL STATUS then we reject the build saying it Non Testable. If the cases are of PASS STATUS we start with System Testing, Include Regression Testing - verifying the old bugs and though Impact Analysis of each Bug. If new functionalities are there then we do Integration Testing. IN ALL the Builds we incorporate Usability Testing - checking UI, Messages, and other User perspective points. We incorporate Database Testing - through SQL Commands to check the Data Driven Scenarios.Last but not the least cover the Security Testing phase - especially in cases of web applications.

White-Box testing / Glass-Box testing

* Examines the fundamental aspect of the system with complete information and access to the internal logical structure, code and algorithms.
* Logical paths are tested. Test cases which tests the loops (DO LOOP) and conditions (IF STATEMENTS) are explicitly designed and used to test the logical paths.

http://sourceforge.net/projects/humaitrader
http://blogtrader.org/

AIOTrade (formerly Humai Trader Platform) is a free, open source stock technical analysis platform built on pure java. Its pluggable architecture is also ideal for custom features extending, such as indicators and charts. It Requires JRE 1.5.0+.

Auge
http://sourceforge.net/projects/auge
http://auge.sourceforge.net/

Auge is an easy-to-use financial portfolio management application. Auge will help you monitor and analyze your stock and mutual fund positions, providing powerful insight into your entire investment portfolio.

Data Visualizer
http://sourceforge.net/projects/dataviews
http://dataviews.sourceforge.net/

Modular environment for graphical visualization of stock market type data

CCAPI2
http://www.activestocks.eu/?q=node/1
http://www.activestocks.eu/

The open source finance library on the net.
A java library for automated stock trading, sub fields of financial engineering and automated financial
instrument analysis. A java financial library. The CCAPI It is also a algorithm trading application framework.
CCAPI is the premium open source java library for developing stock exchange related applications on the net.
Various common indicators, methods for creating charts and direct trade interfaces to selected brokers are
available for your fingertips.

EclipseTrade
http://sourceforge.net/projects/eclipsetrader/
http://eclipsetrader.sourceforge.net/

Stock exchange analysis system, featuring shares pricing watch, intraday and history charts with technical analysis indicators, level II/market depth view, news watching, automated trading systems, integrated trading. Based on Eclipse RCP framework.

JSystemTrader
http://www.myjavaserver.com/~nonlin…stemTrader.html

JSystemTrader is a fully automated trading system (ATS) that can trade various types of market securities during the trading day without user monitoring.
All aspects of trading, such as obtaining historical and real time quotes, analyzing price patterns, making trading decisions, placing orders, monitoring order executions, and controlling the risk are automated according to the user preferences.
The central idea behind JSystemTrader is to completely remove the emotions from trading, so that the trading system can systematically and consistently follow a predefined set of rules.

Market Analysis System
http://sourceforge.net/projects/eiffel-mas
http://eiffel-mas.sourceforge.net/

System for analysis of financial markets using technical analysis. Includes facilities for stock charting and futures charting, as well as automated generation of trading signals based on user-selected criteria. Operates on both daily and intraday data.

Marketcetera
http://trac.marketcetera.org/
http://www.marketcetera.com/

Marketcetera LLC is building a new software platform committed to providing fast, flexible and reliable securities trading tools to financial services professionals. Our mission is to make world-class order-management and risk-management software available and affordable to individuals and to institutions of all sizes. Marketcetera focuses on building the key trading functions that are common to all organizations, thus freeing our clients to concentrate on proprietary trading algorithms and other specialized software that provide a competitive advantage.

Matrex
http://sourceforge.net/projects/matrex/
http://matrex.sourceforge.net/

Use Matrex, the un-spreadsheet, instead of spreadsheets when working with vectors (e.g. database data, charts) and matrices. The perfect desktop tool for mathematical, statistical models and complex calculations. Adapters to matlab, scilab, octave, R.

Merchant of Venice
http://sourceforge.net/projects/mov
http://mov.sourceforge.net/

Venice is a stock market trading programme that supports portfolio management, charting, technical analysis, paper trading and genetic programming. Venice runs in a graphical user interface with online help and has full documentation.

Open Java Trading System
http://sourceforge.net/projects/ojts/
http://ojts.sourceforge.net/

The Open Java Trading System (OJTS) is meant to be a common infrastructure to develop (stock) trading systems. There are four parts: gathering of raw data over the internet, recognition of trading signals, a visualisation module and trading with banks.

Oropuro trading system
http://sourceforge.net/projects/oropuro
http://www.oropuro.org

Complete technical analysis & trading system, full set of features: retrieve, analyze EOD stocks data; manage multiple portfolios; technical analysis & graphical rendering; neural networks for generation of trading signals; support trader community,

SFL Java Trading System Enviroment
http://sourceforge.net/projects/sfljtse
http://www.sflweb.org/index.php?blog=sfljtse

The SFL Java Trading System Enviroment is a java application built on KISS principle (Keep It Simple,Stupid) and its aim is to provide a fast and platform indipendent infrastructure to develop and execute trading systems.

TrueTrade
http://code.google.com/p/truetrade/
http://groups.google.com/group/TrueTrade-Gen
http://groups.google.com/group/TrueTrade-Dev

TrueTrade is a framework for developing, testing and running automatic trading systems. It is intended to provide support for a wide range of orders, financial instruments and time scales. It provides tooling for backtesting the strategy against historical data, and a separate tool for running the strategies in live mode. Strategies currently require some Java coding experience, though this may change at a later date.
It is currently in pre-alpha mode and should not be used against a live trading account.

http://www2.wealth-lab.com/WL5WIKI/MainPage.ashx

The Wealth-Lab Wiki hosts a Knowledge Base for Wealth-Lab Version 5 (.NET) as well as expanded documentation for Wealth-Lab Standard Indicators and TASC Indicators.

Open Source Code!
This Wiki also serves as our Community portal for open-source Indicators, Components, and their documentation.

That was 1989. Today, Hull’s company is one of the world’s premier market-making firms, setting the bid-ask price on stocks listed on 28 exchanges in nine countries. In some venues, the Hull Group trades nearly a quarter of the entire daily market volume. That is no small feat, considering that other American market-making firms have consistently tried and failed to set up shop overseas. Hull is an aggressive firm in this country, too; its trades account for 8 percent of the volume in U.S. equity-index options in the Standard & Poor’s 500 Stock Index, the Russell 2000, and the Dow Jones Industrial Average. And Hull buys and sells as much as 1 percent of all shares moving on the NYSE.

What distinguishes Hull’s firm from other trading giants, such as Goldman Sachs, is that it does an extremely high volume of small trades that pay small amounts each, while a Goldman does fewer trades for more money. We do about 30,000 trades a day, Hull explains, involving on average about 700 shares each. Speed, of course, is essential in executing Hull’s trades efficiently.

Hull has a staff top-heavy with Ph.D.’s drawn from the sciences. Lately, the firm’s massive computer network has been sorting through patterns in stocks according to genetic algorithms. Our view is that if you have a mouse in your hand you’re too late, Hull says. Indeed, his computers trade stocks faster than the human eye can see them scroll on a screen. The system is designed to predict patterns that will unfold in the market over periods as short as two minutes.

In a business that isn’t normally modest about its technological savvy, Hull and his automated trading shop are the envy of Wall Street’s top firms. So much so that, in July, Goldman Sachs paid $531 million for the whole shebang. They told us they bought us because it would have taken them two years to build a similar system, Hull says, and that by then, we’d be two years ahead.

Mr. Tepper’s hedge-fund firm has racked up about $7 billion of profit so far this year—with Mr. Tepper on track to earn more than $2.5 billion for himself, according to people familiar with the matter. That is among the largest one-year takes in recent years.

Behind the wins: a bet worth billions of dollars that America would avoid a repeat of the Great Depression.

Through February and March, Mr. Tepper scooped up beaten-down bank shares as many investors were running for the exits. Day after day, Mr. Tepper bought Bank of America Corp. shares, then trading below $3, and Citigroup Inc. preferred shares, when that stock was under $1. One of his investors insisted more carnage loomed. Friends who shared his bullish beliefs were wary of aping his moves amid speculation that the government was about to nationalize the big banks.

“I felt like I was alone,” Mr. Tepper recalls. On some days, he says, “no one was even bidding.”

The bets paid off. A resurgent market has helped Mr. Tepper’s firm, Appaloosa Management, gain about 120% after the firm’s fees, through early December. Thanks to those gains, Mr. Tepper, who specializes in the stocks and bonds of troubled companies, manages about $12 billion, a sum that makes Appaloosa one of the largest hedge funds in the world.

Mr. Tepper, whose office overlooks the parking lot of a Hilton hotel in Short Hills, N.J., across from an upscale mall, now is taking aim at a new target. He’s purchased about $2 billion of beaten-down commercial mortgage-backed securities. Among his purchases are bonds backed by chunks of the debt of Peter Cooper Village & Stuyvesant Town and 666 Fifth Ave. in New York, two high-profile real-estate deals that have fallen in value over the past two years.

Some experts predict more bad news for commercial real estate—and say that if Mr. Tepper’s move doesn’t pan out, it could jeopardize a chunk of his recent gains. Mr. Tepper says he remains optimistic.

Hedge funds, once darlings of well-heeled investors, suffered dearly in 2008, dropping 19%. Nearly 1,500 funds, or 16% of the total, shuttered last year. This year, hedge funds are clawing back, with gains of 19% through November, on pace for their best annual gains in a decade, according to Hedge Fund Research Inc.

A handful of funds—including Everest Capital’s emerging-market funds and the stock-focused Glenview Capital—have racked up fat gains this year. In sheer dollars, though, none appear to have come close to matching Appaloosa’s winnings.

Mr. Tepper grew up in a middle-class neighborhood in Pittsburgh, the son of an accountant who worked seven days a week and once won a $715,000 lottery payout. In the late 1980s, he helped run junk-bond trading at Goldman Sachs. Mr. Tepper wears jeans and sneakers to work, and can be self-deprecating, playing down his successes. He claims to have popularized on Wall Street the phrase “it is what it is” to explain the need to adjust a portfolio if facts on the ground shift.

After he was repeatedly passed over for a partnership, Mr. Tepper left Goldman to start Appaloosa in 1993. By 2008, he had a track record of annual gains averaging about 30% and a net worth estimated at about $2 billion.

Mr. Tepper lives in a two-story home in New Jersey he bought in 1990 for $1.2 million. He recently purchased an ownership stake in the Pittsburgh Steelers football team, and flies to every home game. In 2004 he gave $55 million to Carnegie Mellon University’s business school, his alma mater, which renamed itself the Tepper School of Business.

The husky, bespectacled trader laughs easily, but employees say he can quickly turn on them when he’s angry. Mr. Tepper keeps a brass replica of a pair of testicles in a prominent spot on his desk, a present from former employees. He rubs the gift for luck during the trading day to get a laugh out of colleagues.

His biggest scores over the years have come from buying large chunks of out-of-favor investments. When Asian markets crumbled in 1997, Mr. Tepper added Korean stocks to a portfolio laden with Russian debt. The moves led to hundreds of millions of dollars in profits when markets rebounded two years later. He scored big on junk bonds in 2003, and his 2007 wager on steel, coal and other resource companies paid off in 2008 when commodity prices soared.

But because he sometimes places more than half of his portfolio in a single trade idea, Mr. Tepper also is prone to brutal, abrupt losses.

That approach cost him more than $1 billion last year. In January 2008, Societe General SA trader Jerome Kerviel was revealed to have lost €5 billion ($7.2 billion), one of the world’s largest trading loss. Mr. Tepper sold large chunks of his holdings, fearing a market tumble. Prices held up, though, hurting Appaloosa. In the spring of last year, he turned bullish on large-company stocks and did some buying, but suffered as markets declined.

Mr. Tepper made a big wager on Delphi in 2006. But in April of last year he and a group of investors withdrew from a deal to inject as much as $2.6 billion in the bankrupt auto-parts supplier, sparking a nasty legal battle that was resolved this summer. Appaloosa lost almost $200 million on its investment in Delphi.

Mr. Tepper’s largest fund dropped 25% for 2008, worse than the industry’s 19% average decline.

“Investing with David is like flying, with hours of boredom followed by bouts of sheer terror,” says Alan Shealy, a client of more than 18 years. “He’s the quintessential opportunist, investing in any asset class, but you have to have a cast-iron stomach.”

Mr. Tepper entered 2009 cautiously, with more than 30% of his firm’s assets in cash, or more than $2 billion. He itched to do some buying. Mr. Tepper explains his investment philosophy with a line from Allan Meltzer, a professor at his alma mater: “Trees grow.” In other words, growth is the natural state of economies, so optimism usually is rewarded.

On Feb. 10 of this year, Mr. Tepper read that the Treasury Department was introducing the so-called Financial Stability Plan. It included a commitment by the government to inject capital into banks by buying their preferred stock, or shares that carry less chance of reward but also less risk than common stock.

At the time, investors worried that the government ultimately would have to nationalize big banks. U.S. officials said they had no intention of such a move, which could wipe out common shareholders, but investors were dubious.

The news from the Treasury Department struck Mr. Tepper as proof that the government would stand behind the banks. He directed his traders to begin buying bank stock and debt.

Few investors were feeling as optimistic. The Dow Jones Industrial Average fell more than 382 points on the day Treasury Secretary Timothy Geithner introduced the plan, nearly 5%. Bank shares continued to tumble in the days that followed. Bank of America shares fell as low as $2.53 on Feb. 20. By March 5, Citigroup traded as low as 97 cents.

“This is ridiculous, it’s nuts, nuts, nuts!” Mr. Tepper recalls saying to Michael Lukacs, one of his partners, on the firm’s small trading floor. “Why would the government break its word? They’re not going to let these banks go under, people aren’t being logical!”

Mr. Tepper huddled with Mr. Lukacs and Jim Bolin, another top Appaloosa executive. Mr. Tepper insisted that stimulus spending and low interest rates would boost the economy. He said he estimated there was only a 20% chance that the U.S. would nationalize banks such as Citigroup.

Mr. Bolin, who people at the firm say tends to be more conservative than Mr. Tepper, was bullish about banks, but still thought it safer to stick to bank debt than to riskier shares. Mr. Tepper says he listened to the arguments, but said it was time to place a big bet.

Over several weeks, Mr. Tepper’s team bought a variety of bank investments, including debt, preferred shares and common shares. Just months earlier, the government had injected billions of dollars to keep companies such as American International Group Inc. going, much as they were now doing with the banks. But that didn’t prevent shares of those companies from tumbling.

At one point in March, the firm was down about 10% for the year, or about $600 million. Mr. Tepper got on the phone to make more trades, something he often left to subordinates. This time, he wanted to talk directly to Wall Street brokers to test how bad things really were.

The answer: really bad. Mr. Tepper says he was told that he was the only big investor doing much buying.

“Clients were nervous that the game had changed and capitalism wouldn’t be the same. There was real fear,” recalls Timothy Ghriskey, chief investment officer at Solaris Asset Management, a $2 billion investment firm, who says he only bought a small amount of bank shares during this period.

One day in late winter, Mr. Tepper heard from a skeptical client of his own, Mr. Shealy.

“This thing is far from over,” Mr. Shealy recalls saying, referring to the bank problems. Still, Mr. Shealy, who runs an investment firm in Boise, Idaho, stuck with Mr. Tepper. “I figured the positions were fairly liquid, so if he was wrong, he would get out.”

Mr. Tepper hadn’t paid his investors’ nerves much heed since 2000. That year, he bet that the tech-heavy Nasdaq index would fall. But so many investors complained that Mr. Tepper was straying from his roots in debt investing that he canceled his bets. When the Nasdaq collapsed months later, Mr. Tepper fumed.

By late March of 2009, Citigroup shares had tripled, and Mr. Tepper’s other holdings, including junk bonds, were rising. He and his team bought more, spending more than $1 billion, when various banks conducted share sales. Mr. Tepper says his average cost for shares of Citigroup was 79 cents; for Bank of America it was $3.72.

At one point in the summer, Mr. Tepper had recorded about $1 billion of profits in shares of just Citigroup and Bank of America, and his overall gains soared past $4.5 billion, or 70%, since January.

After Mr. Bolin, the Appaloosa executive, urged caution, Mr. Tepper did some selling to lock in gains. But the firm remains a big holder of both Bank of America and Citigroup shares, which now trade at $15.03 and $3.40, respectively.

Mr. Tepper remains upbeat. He says he expects interest rates to stay low, and argues that stocks and bonds are reasonably priced.

This belief is driving another risky bet. At the end of each quarter this year, Mr. Tepper noticed that investors were dumping holdings of troubled bonds backed by commercial properties. He had never dabbled in these investments, but he and his 10-person team did some research and judged them attractive, with some seemingly safe debt trading at yields above 15%.

Mr. Tepper slowly spent more than $1 billion to gain ownership of between 10% and 20% of highly rated slices of commercial mortgage-backed securities, or CMBS. He focused on debt backed by loans of properties including Stuyvesant Town and 666 Fifth Ave. in New York.

His bet: If the economy improves, he’ll earn hefty interest payments on the bonds. But if the properties can’t make their payments, Mr. Tepper believes he owns so much of the debt that he’ll have a big say in how the properties get restructured. That means he could ultimately end up ahead.

He’s taking a big risk, some analysts warn. The value of commercial real estate continues to fall. Owners of debt classes don’t always have much power to influence a commercial real-estate restructuring. And because the debt of these big properties was carved into many pieces, and many investors are involved, any battle for control will be complicated.

Mr. Tepper says the worrywarts have it wrong: “If you think the economy will be fine, as we do, then we’re going to do very well.”

Write to Gregory Zuckerman at gregory.zuckerman@wsj.com

Corrections & Amplifications

David Tepper has purchased bonds backed partly by debt of 666 Fifth Ave. in New York. A previous version of this story described the property as 666 West 57th Street in New York.

From the top of my head can name a few companies that develop accounting software for hedge funds and FoHFs:

Cogency Softwre

FundCount

Advent

PennyItWorks

To be continued…

“We believe there is a huge opportunity to bring an integrated solution to the market that will combine portfolio analytics and advanced risk management capabilities to the investment management and hedge fund space,” said Michael Rosen, Managing Director at Concept Capital. “This solution will give investors increased confidence in receiving risk reports that will be produced by an independent third party using industry-leading software from SAS.”

Added Dan Connell, Project Manager at Concept Capital, who will be driving the initiative, “With the heightened requirements on risk reporting, security pricing and portfolio transparency that are now pervasive in the industry, it is critical that hedge fund managers have access to the necessary tools. By teaming with SAS, a leader in risk management software, CONCEPTONE will be meeting this demand.”

With SAS, Concept Capital will offer a hosted portfolio analytics and risk management portal to the hedge fund market. The solution includes a client Web portal with access to customized risk reports and interactive analytics that will permit a multitude of views – including value at risk (VaR), factor analysis, customized stress testing, profit and loss (P&L) time-series analysis, performance attribution and measurement, and production of ex-post risk indicators coupled with more traditional portfolio appraisal reports – all on a multicustodial basis.

“With SAS, CONCEPTONE will be able to deliver a high level of auditable risk intelligence to clients that are now a requirement in today’s market,” said David Wallace, Global Financial Services Marketing Manager for SAS. “Hedge fund managers will now be able to move beyond ‘black box’ analytics to have fine-grained control of their risk analysis. Through this partnership with Concept Capital, SAS continues to help reshape the capital markets’ risk practices in achieving a strong and stable recovery.”

SAS risk management software has garnered many accolades this past year. In November, SAS was ranked the top company in Chartis Research’s RiskTech100TM. SAS placed in the Leaders quadrant of the Magic Quadrant for Operational Risk Management Software for Financial Services by Gartner Inc. in September. Also, in July 2009, Chartis ranked SAS as the leader in its Credit Risk Management Systems 2009 report for the third straight year; and for a fifth straight year, SAS was first in Chartis Research’s Operational Risk Management Systems 2009 report in June.

About Concept Capital

Founded in 1995, Concept Capital is a leading institutional broker and total solutions provider for global investment managers. Concept Capital provides a full suite of prime brokerage services, proprietary research, fund administration, real-time risk management and portfolio analytics. Concept Capital also provides an experienced, hands-on institutional trading desk for traditional buy-side customers, hedge funds and registered investment advisors. Concept Capital specializes in providing clients with experienced, in-depth market knowledge combined with advanced proprietary systems, enabling clients to quickly launch their businesses in the most cost-effective manner possible. Concept Capital is headquartered in Garden City, NY, and has offices in New York; Greenwich, CT; Washington; Chicago; and Bernardsville, NJ. Concept Capital is currently a division of SMH Capital Inc., member FINRA, SIPC. Concept Capital is forming an independent broker-dealer and, once approved, will operate as an independent company, of which Sanders Morris Harris Group Inc. will be a minority shareholder. www.conceptcapital.com

About SAS

SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions delivered within an integrated framework, SAS helps customers at more than 45,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world THE POWER TO KNOW®. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. Copyright © 2009 SAS Institute Inc. All rights reserved.

Press Release Contact Details:

SAS Kris Balic, 919-531-0624 Kris.Balic@sas.com Visit the SAS Press Center www.sas.com/presscenter