What are the differences between traders, quants, and

analysts?
What kind of education/training does each typically require? What are major
differences in lifestyles, salaries, and career paths (i.e. working for a hedge fund vs.
climbing the ladder at an investment bank)?

7 Answers

Michael Toth, Quant Risk & Investment Strategy

Written 2 Dec 2012
Traders

A trader is a person who executes trades for a bank, trading firm, or hedge fund.
They could trade based on a variety of strategies, from fundamental (examining
financial statements to determine a company's value) to scientific/quantitative
(determining market trends based upon statistical analysis). A trader may also be
an execution trader, who simply executes the strategies of others and attempts to
get "best execution" by trading lots of securities strategically to get the best possible
prices over a period of time. Traders are generally only required to have an
undergraduate degree (often in a related field such as finance, economics,
mathematics, etc but not necessarily). Compensation can be salaried, but it
generally also has a commission component where the trader earns more money
depending on the level of profits he generates for the firm. For this reason skilled
traders can make very large amounts of money at a young age, but there is also less
job security as traders can be quickly fired for poor performance.

Quants

A quant generally refers to somebody who is a quantitative trader or a quantitative

modeler. A quantitative trader works to develop computer-based trading strategies
that execute trades when certain market conditions are met in order to generate
value based on statistical analysis. This may or may not include fast-paced
algorithmic trading. A quantitative modeler creates financial models to determine
either security-based value and risk or the movement of macroeconomic factors
such as interest rates. Generally they would focus on one product type and create
models based on statistical analysis of historical data. In order to be a quantitative
modeler or a quantitative trader, an advanced degree is generally, but not always,
required. Common degrees include PhD in mathematics, physics, or computer
science. Quantitative/mathematical finance and financial engineering masters
degrees are also growing in popularity for these roles. Compensation for quant
roles is generally salaried.

Analysts

An analyst typically refers to the lowest-level financial employee in a trading/quant

role. It can also refer to a senior person in an equity research or fixed-income
research role. People in research roles perform fundamental research on equity
and fixed income placements of companies that are generally based on a company's
financial performance and the market environment. To get a role in equity research
typically only requires an undergraduate degree in a related field, but to rise to the
 level of analyst will require years of quality work performance and may also require
an MBA.
31.7k Views · View Upvotes
Related QuestionsMore Answers Below

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 How do you tell the difference between a quant and a "technical chartist"?
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Hunter Johnson, applies machine learning to financial markets

Written 12 Feb
These definitions are general and by no means should be taken so as to apply for
everyone, everywhere.

Quants - have STEM background sufficient to understand advanced math and

programming. Quant fund/HFT jobs are very competitive with many PhDs and the
advancement opportunities aren't that great for quants at most investment banks
relative to traders. Many leave for Silicon Valley or elsewhere in tech to pursue
other opportunities.

Traders - come from wide variety of educational backgrounds, especially for

long/short equities+indices as opposed to more mathematically complex
instruments. Greater chance to directly make a lot of money for yourself and the
firm, especially long term as a portfolio manager.

Analysts - come from wider variety of educational backgrounds, perform a wide

variety of jobs in different product or coverage groups at a bank or fund and
typically use Excel and Powerpoint instead of R or C++. As far as investment banks
are concerned, many analysts leave after several years for an MBA and/or to pursue
opportunities in consulting, hedge funds, private equity & venture capital firms.
4.8k Views · View Upvotes

Lukasz Wrobel
Written 2 Dec 2012
Traders: trade what they see, no matter what they think
Quants: trade what their model tells them and that's often what they think
Analyst: trade what they are sure of (which means they don't trade at all), they are
paid to read a lot and are good at constructing the big picture view
9.8k Views · View Upvotes

Anonymous
Written 4 Dec 2012
Traders- Attention Span- 5 seconds (Average time taken to arrive at a decision)

Quants- Attention Span- 5 minutes (They sometimes take less time to create some
models)

Analyst- Attention Span- 5 hours (Yes they sometimes take more time to write the
reports)

Traders give a lot of reasons as to why the trade was done on the hindsight. As a
trader myself, I used to bullshit my risk-manager at times why I crossed my daily
limits and most of the times, he was convinced.
9.1k Views · View Upvotes

George P. Adams, I refer to PayScale.com's data for my answers

Written 18 Mar
The career and salary research site PayScale will have the information you need to
differentiate these careers.

I've looked it up and found these:

PayScale - Salary Search: trader

PayScale - Salary Search: quant

PayScale - Salary Search: analyst

Since traders, quants, and analysts are too broad, you will need to check each job on
the type for more information.
2.7k Views · View Upvotes

Joseph Wang, ex-VP Quant, Big bank

Written Nov 14, 2013
The trader drives the race car. The quant builds the engine for the race car. The
analyst puts together the television ads and posters to tell people to come and watch
the race.
9.6k Views · View Upvotes

Amol Wagh, Author, Blogger, Digital Marketer

Written May 14, 2015
I have came across one interesting article recently that pin points the core
differences between the Traders Vs the Reseach Analysts.

I hope you will find it helpful - Traders V/s Research Analysts – 6 Differences You
Must Know!
 In this article I'm going to introduce you to some of the basic
concepts which accompany an end-to-end quantitative
trading system. This post will hopefully serve two audiences. The
first will be individuals trying to obtain a job at a fund as a
quantitative trader. The second will be individuals who wish to try
and set up their own "retail" algorithmic trading business.
Quantitative trading is an extremely sophisticated area of quant
finance. It can take a significant amount of time to gain the
necessary knowledge to pass an interview or construct your own
trading strategies. Not only that but it requires extensive
programming expertise, at the very least in a language such as
MATLAB, R or Python. However as the trading frequency of the
strategy increases, the technological aspects become much more
relevant. Thus being familiar with C/C++ will be of paramount
importance.
A quantitative trading system consists of four major components:
 Strategy Identification - Finding a strategy, exploiting an edge and deciding on

trading frequency

 Strategy Backtesting - Obtaining data, analysing strategy performance and

removing biases

 Execution System - Linking to a brokerage, automating the trading and minimising

transaction costs

 Risk Management - Optimal capital allocation, "bet size"/Kelly criterion and trading

psychology

We'll begin by taking a look at how to identify a trading strategy.

Strategy Identification
 All quantitative trading processes begin with an initial period of
research. This research process encompasses finding a strategy,
seeing whether the strategy fits into a portfolio of other strategies
you may be running, obtaining any data necessary to test the
strategy and trying to optimise the strategy for higher returns and/or
lower risk. You will need to factor in your own capital requirements if
running the strategy as a "retail" trader and how any transaction
costs will affect the strategy.
Contrary to popular belief it is actually quite straightforward to
find profitable strategies through various public sources. Academics
regularly publish theoretical trading results (albeit mostly gross of
transaction costs). Quantitative finance blogs will discuss strategies
in detail. Trade journals will outline some of the strategies employed
by funds.
You might question why individuals and firms are keen to discuss
their profitable strategies, especially when they know that others
"crowding the trade" may stop the strategy from working in the long
term. The reason lies in the fact that they will not often discuss
the exact parameters and tuning methods that they have carried out.
These optimisations are the key to turning a relatively mediocre
strategy into a highly profitable one. In fact, one of the best ways to
create your own unique strategies is to find similar methods and
then carry out your own optimisation procedure.
Here is a small list of places to begin looking for strategy ideas:

 Social Science Research Network - www.ssrn.com

 arXiv Quantitative Finance - arxiv.org/archive/q-fin

 Seeking Alpha - www.seekingalpha.com

 Elite Trader - www.elitetrader.com

 Nuclear Phynance - www.nuclearphynance.com

 Quantivity - quantivity.wordpress.com

Many of the strategies you will look at will fall into the categories
of mean-reversion and trend-following/momentum. A mean-reverting
strategy is one that attempts to exploit the fact that a long-term
mean on a "price series" (such as the spread between two
correlated assets) exists and that short term deviations from this
mean will eventually revert. A momentum strategy attempts to
exploit both investor psychology and big fund structure by "hitching
a ride" on a market trend, which can gather momentum in one
direction, and follow the trend until it reverses.
Another hugely important aspect of quantitative trading is
the frequency of the trading strategy. Low frequency trading (LFT)
generally refers to any strategy which holds assets longer than a
trading day. Correspondingly, high frequency trading (HFT)
generally refers to a strategy which holds assets intraday. Ultra-high
frequency trading (UHFT) refers to strategies that hold assets on the
order of seconds and milliseconds. As a retail practitioner HFT and
UHFT are certainly possible, but only with detailed knowledge of the
trading "technology stack" and order book dynamics. We won't
discuss these aspects to any great extent in this introductory article.
Once a strategy, or set of strategies, has been identified it now
needs to be tested for profitability on historical data. That is the
domain of backtesting.

Strategy Backtesting
The goal of backtesting is to provide evidence that the strategy
identified via the above process is profitable when applied to both
historical and out-of-sample data. This sets the expectation of how
the strategy will perform in the "real world". However, backtesting is
NOT a guarantee of success, for various reasons. It is perhaps the
most subtle area of quantitative trading since it entails numerous
biases, which must be carefully considered and eliminated as much
as possible. We will discuss the common types of bias
including look-ahead bias, survivorship bias and optimisation
bias (also known as "data-snooping" bias). Other areas of
importance within backtesting include availability and cleanliness of
historical data, factoring in realistic transaction costs and deciding
upon a robust backtesting platform. We'll discuss transaction costs
further in the Execution Systems section below.
Once a strategy has been identified, it is necessary to obtain the
historical data through which to carry out testing and, perhaps,
refinement. There are a significant number of data vendors across
all asset classes. Their costs generally scale with the quality, depth
and timeliness of the data. The traditional starting point for beginning
quant traders (at least at the retail level) is to use the free data set
from Yahoo Finance. I won't dwell on providers too much here,
rather I would like to concentrate on the general issues when
dealing with historical data sets.

The main concerns with historical data include accuracy/cleanliness,

survivorship bias and adjustment for corporate actions such as
dividends and stock splits:
 Accuracy pertains to the overall quality of the data - whether it contains any errors.

Errors can sometimes be easy to identify, such as with a spike filter, which will pick

out incorrect "spikes" in time series data and correct for them. At other times they

can be very difficult to spot. It is often necessary to have two or more providers and

then check all of their data against each other.

 Survivorship bias is often a "feature" of free or cheap datasets. A dataset with

survivorship bias means that it does not contain assets which are no longer trading.

In the case of equities this means delisted/bankrupt stocks. This bias means that

any stock trading strategy tested on such a dataset will likely perform better than in

the "real world" as the historical "winners" have already been preselected.

 Corporate actions include "logistical" activities carried out by the company that

usually cause a step-function change in the raw price, that should not be included

in the calculation of returns of the price. Adjustments for dividends and stock splits

are the common culprits. A process known as back adjustment is necessary to be

carried out at each one of these actions. One must be very careful not to confuse a

stock split with a true returns adjustment. Many a trader has been caught out by a

corporate action!

In order to carry out a backtest procedure it is necessary to use

a software platform. You have the choice between dedicated
backtest software, such as Tradestation, a numerical platform such
as Excel or MATLAB or a full custom implementation in a
programming language such as Python or C++. I won't dwell too
much on Tradestation (or similar), Excel or MATLAB, as I believe in
creating a full in-house technology stack (for reasons outlined
below). One of the benefits of doing so is that the backtest software
and execution system can be tightly integrated, even with extremely
advanced statistical strategies. For HFT strategies in particular it is
essential to use a custom implementation.
When backtesting a system one must be able to quantify how well it
is performing. The "industry standard" metrics for quantitative
strategies are the maximum drawdown and the Sharpe Ratio. The
maximum drawdown characterises the largest peak-to-trough drop
in the account equity curve over a particular time period (usually
annual). This is most often quoted as a percentage. LFT strategies
will tend to have larger drawdowns than HFT strategies, due to a
number of statistical factors. A historical backtest will show the past
maximum drawdown, which is a good guide for the future drawdown
performance of the strategy. The second measurement is the
Sharpe Ratio, which is heuristically defined as the average of the
excess returns divided by the standard deviation of those excess
returns. Here, excess returns refers to the return of the strategy
above a pre-determined benchmark, such as the S&P500 or a 3-
month Treasury Bill. Note that annualised return is not a measure
usually utilised, as it does not take into account the volatility of the
strategy (unlike the Sharpe Ratio).
Once a strategy has been backtested and is deemed to be free of
biases (in as much as that is possible!), with a good Sharpe and
minimised drawdowns, it is time to build an execution system.

Execution Systems
An execution system is the means by which the list of trades
generated by the strategy are sent and executed by the broker.
Despite the fact that the trade generation can be semi- or even fully-
automated, the execution mechanism can be manual, semi-manual
(i.e. "one click") or fully automated. For LFT strategies, manual and
semi-manual techniques are common. For HFT strategies it is
necessary to create a fully automated execution mechanism, which
will often be tightly coupled with the trade generator (due to the
interdependence of strategy and technology).
The key considerations when creating an execution system are
the interface to the brokerage, minimisation of transaction
costs (including commission, slippage and the spread)
and divergence of performance of the live system from backtested
performance.
There are many ways to interface to a brokerage. They range from
calling up your broker on the telephone right through to a fully-
automated high-performance Application Programming Interface
(API). Ideally you want to automate the execution of your trades as
much as possible. This frees you up to concentrate on further
research, as well as allow you to run multiple strategies or even
strategies of higher frequency (in fact, HFT is essentially impossible
without automated execution). The common backtesting software
outlined above, such as MATLAB, Excel and Tradestation are good
for lower frequency, simpler strategies. However it will be necessary
to construct an in-house execution system written in a high
performance language such as C++ in order to do any real HFT. As
an anecdote, in the fund I used to be employed at, we had a 10
minute "trading loop" where we would download new market data
every 10 minutes and then execute trades based on that information
in the same time frame. This was using an optimised Python script.
For anything approaching minute- or second-frequency data, I
believe C/C++ would be more ideal.
In a larger fund it is often not the domain of the quant trader to
optimise execution. However in smaller shops or HFT firms, the
traders ARE the executors and so a much wider skillset is often
desirable. Bear that in mind if you wish to be employed by a fund.
Your programming skills will be as important, if not more so, than
your statistics and econometrics talents!

Another major issue which falls under the banner of execution is that
of transaction cost minimisation. There are generally three
components to transaction costs: Commissions (or tax), which are
the fees charged by the brokerage, the exchange and the SEC (or
similar governmental regulatory body); slippage, which is the
difference between what you intended your order to be filled at
versus what it was actually filled at; spread, which is the difference
between the bid/ask price of the security being traded. Note that the
spread is NOT constant and is dependent upon the current liquidity
(i.e. availability of buy/sell orders) in the market.

Transaction costs can make the difference between an extremely

profitable strategy with a good Sharpe ratio and an extremely
unprofitable strategy with a terrible Sharpe ratio. It can be a
challenge to correctly predict transaction costs from a backtest.
Depending upon the frequency of the strategy, you will need access
to historical exchange data, which will include tick data for bid/ask
prices. Entire teams of quants are dedicated to optimisation of
execution in the larger funds, for these reasons. Consider the
scenario where a fund needs to offload a substantial quantity of
trades (of which the reasons to do so are many and varied!). By
"dumping" so many shares onto the market, they will rapidly depress
the price and may not obtain optimal execution. Hence algorithms
which "drip feed" orders onto the market exist, although then the
fund runs the risk of slippage. Further to that, other strategies "prey"
on these necessities and can exploit the inefficiencies. This is the
domain of fund structure arbitrage.
The final major issue for execution systems concerns divergence of
strategy performance from backtested performance. This can
happen for a number of reasons. We've already discussed look-
ahead bias and optimisation bias in depth, when considering
backtests. However, some strategies do not make it easy to test for
these biases prior to deployment. This occurs in HFT most
predominantly. There may be bugs in the execution system as well
as the trading strategy itself that do not show up on a backtest but
DO show up in live trading. The market may have been subject to
a regime change subsequent to the deployment of your strategy.
New regulatory environments, changing investor sentiment and
macroeconomic phenomena can all lead to divergences in how the
market behaves and thus the profitability of your strategy.

Risk Management
The final piece to the quantitative trading puzzle is the process
of risk management. "Risk" includes all of the previous biases we
have discussed. It includes technology risk, such as servers co-
located at the exchange suddenly developing a hard disk
malfunction. It includes brokerage risk, such as the broker becoming
bankrupt (not as crazy as it sounds, given the recent scare with MF
Global!). In short it covers nearly everything that could possibly
interfere with the trading implementation, of which there are many
sources. Whole books are devoted to risk management for
quantitative strategies so I wont't attempt to elucidate on all possible
sources of risk here.
Risk management also encompasses what is known as optimal
capital allocation, which is a branch of portfolio theory. This is the
means by which capital is allocated to a set of different strategies
and to the trades within those strategies. It is a complex area and
relies on some non-trivial mathematics. The industry standard by
which optimal capital allocation and leverage of the strategies are
related is called the Kelly criterion. Since this is an introductory
article, I won't dwell on its calculation. The Kelly criterion makes
some assumptions about the statistical nature of returns, which do
not often hold true in financial markets, so traders are often
conservative when it comes to the implementation.
Another key component of risk management is in dealing with one's
own psychological profile. There are many cognitive biases that can
creep in to trading. Although this is admittedly less problematic
with algorithmic trading if the strategy is left alone! A common bias is
that of loss aversion where a losing position will not be closed out
due to the pain of having to realise a loss. Similarly, profits can be
taken too early because the fear of losing an already gained profit
can be too great. Another common bias is known as recency bias.
This manifests itself when traders put too much emphasis on recent
events and not on the longer term. Then of course there are the
classic pair of emotional biases - fear and greed. These can often
lead to under- or over-leveraging, which can cause blow-up(i.e. the
account equity heading to zero or worse!) or reduced profits.

Summary
As can be seen, quantitative trading is an extremely complex, albeit
very interesting, area of quantitative finance. I have literally
scratched the surface of the topic in this article and it is already
getting rather long! Whole books and papers have been written
about issues which I have only given a sentence or two towards. For
that reason, before applying for quantitative fund trading jobs, it is
necessary to carry out a significant amount of groundwork study. At
the very least you will need an extensive background in statistics
and econometrics, with a lot of experience in implementation, via a
programming language such as MATLAB, Python or R. For more
sophisticated strategies at the higher frequency end, your skill set is
likely to include Linux kernel modification, C/C++, assembly
programming and network latency optimisation.
If you are interested in trying to create your own algorithmic
trading strategies, my first suggestion would be to get good at
programming. My preference is to build as much of the data
grabber, strategy backtester and execution system by yourself as
possible. If your own capital is on the line, wouldn't you sleep better
at night knowing that you have fully tested your system and are
aware of its pitfalls and particular issues? Outsourcing this to a
vendor, while potentially saving time in the short term, could be
extremely expensive in the long-term.