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As a result, they are susceptible to making a mistake in choosing their first job. That mistake could lead to later giving up on the industry, when in fact you may be well suited for success. Without experience, it is easy to make an error; but I want to provide some guidance to help you avoid some common pitfalls.

Finally, it is never a bad idea to double check with friends or classmates that are now in the quantitative trading industry. This summary is an attempt to shed some light on modern quantitative trading since there is limited information available for people who are not already in the industry. Most quantitative trading firms have converged on roughly the same basic organizational framework so this is a reasonably accurate description of the roles at any established quantitative trading firm.

The product of a quantitative trading company is an automated software program that buys and sells electronically traded securities to make a profit. This software program is supported by many systems designed to maintain and optimize it. Most companies are roughly divided into 3 main groups: strategy research, core development, and operations. Employees generally start and stay in one of these groups throughout a career.

This guide focuses on strategy research and core development roles. The software components of a quantitative trading system are built by one of these two teams. The majority of the components are built in-house at most major trading firms, so below is a list of the programs you could expect to build or maintain if you were on the research or dev teams.

The industry has generally settled on three main types of strategies that are sustainable because they provide real economic value to the market:. Market taking requires predictive signals and relatively low-latency because you pay to cross the spread.

A common low-latency market taking strategy would be to attempt to buy the remaining liquidity at a price after a large buy trade. Some firms have FPGAs configured to send orders as soon as they see a trade message matching the right conditions more on this later.

Market Making: Posting passive non-marketable buy and sell orders with the goal to profit from the spread. Market makers are compensated for the risk that there may be more buyers than sellers or vice versa for an extended time, such as during times of market stress.

Basic trading system design. In between is the strategy algorithm. The input to a trading system is tick-by-tick market data. The input is handled in an event loop. The events are the packets sent by the exchange that are read off the network and normalized by the market data parser.

Each packet gives information about the current supply and demand for a security and the current price. A packet can tell you one of three things:. For example, a few packets look like this for a more detailed, real example see section 4 appendix 1 of this spec :. If the trading system adds up all the AddOrder packets and subtracts CancelOrder and Trade packets, it can see what the order book currently looks like.

The order book shows the aggregate visible supply and demand currently available at each price. The order book is an industry-standard normalization layer. Some companies have each member of their intern classes program a strategy like this as a teaching project during a summer. This strategy calculates some signals using the order book as input, and buys or sells when the aggregate signals are strong enough.

A signal is an algorithm that takes market data as the input and outputs a theoretical price for a security. Market micro-structure signals generally rely on price, size, and trade data coming directly from data feeds. Please reference the order book state provided previously as we walk through the following signal examples. The book pressure and trade impulse signal are enough to create a market taking strategy. After the sell trade for 9, the remaining quantity on the book is:. That is a high-level overview of a simple quantitative strategy, and provides a basic understanding of the flow from the input market data to the output orders.

If you ran the market taking strategy from the previous section live in a real trading system, you would likely find that your orders rarely get filled. State of the art latency, as of , can be achieved by putting the trading logic on an FPGA.

The old trading system is now only responsible for calculating hypothetical scenarios. Instead of sending the order, it notifies the FPGA what hypothetical condition needs to be met to send the order.

Using the same case as before, it could hypothetically evaluate the signal for a range of trade quantities:. With any sell trade of quantity 7 or more, the theoretical price would cross below the threshold of the best bid For example, the message from the exchange could look like the following struct:. Because of the relative ease of this setup, it has become a very competitive trade — some trading firms can make these types of trade decisions in less than one microsecond.

Unfortunately, if you only have one shared connection, and broadcast data internally with a switch, the switch might introduce too much latency to be competitive. Many companies will now pay for multiple connections which raises their costs significantly. As I mentioned above, the simple 3-signal trading strategy could have made money several years ago. These are large and interesting topics which are now well understood inside and outside the industry.

Market micro-structure signal based strategies, as described above before the two digressions, are just one type of strategy. Here are some other example trading strategy algorithm components used by many major quantitative trading companies:. The job of a researcher is to optimize the settings of the trading system and to ensure it is behaving properly. Working for an established company, this whole software system will likely already be in place, and your job would be to make it better.

With that in mind, here are some more details about 4 other main software components I listed above that are programmed and used by the research team to optimize and analyze the trading strategy:. Most people who are new to the industry think that researchers primarily work on new signal development, and developers primarily optimize latency. The most important skills for success are actually very close attention to detail, hard work, and trading intuition.

On top of that it should be clear that having strong programming skills is essential. All of these systems are tailor-made in-house and have to be constantly tweaked and improved by the users themselves — you. The information above is a collection of some helpful information to shed some light on what a quantitative trading firm does and what you could be doing if you worked at one. The information, although intended to be helpful to you, should not be relied on and is not represented to be accurate or current.

Please note this is by no means an exhaustive description of what goes on at a quantitative trading firm. Nor should this be taken as covering industry best practices or everything you need to know to start trading quantitatively. This is simply a very high overview of information I think those considering joining a quantitative trading firm may find useful as they navigate the interview process.

Different technology decisions and antiquated infrastructure have resulted in trading idiosyncrasies. There are many publicly available discussions of the effects of these idiosyncrasies. Here are a few interesting items:. Are brainteasers, gambling, poker, or mental math questions used in the interview process? Will you have a 2 year noncompete? Will you be blocked from accessing any part of the source code?

As a researcher, will you be the primary on-call trader monitoring any live trading processes? Will you be blocked from viewing the PnL of any strategies that utilize your research? Are strategy parameters manually changed based on judgment calls during the day? Are there other employees in the company in direct competition with you? Are brainteasers, gambling, poker, or mental math questions used in the interview? If a company asks these types of questions, it is potentially a sign they value manual, non-automated trader intuition and decision making more than quantitative, algorithmic, and research-driven approaches.

If your background is quantitative, you will want a company that will value those skills the most highly. Will you have a 2 year or greater noncompete? Non-compete agreements are a fact of life for quants working in the trading industry, but the lengths of those agreements vary widely the standard term is one year.

However, one year should be sufficient to protect your work. With a 2 year noncompete, other companies may be much less willing to hire you. Some companies encrypt or password-protect parts of their source code. This goes beyond taking adequate steps to protect proprietary property, like securing an internal filesystem from external intruders or preventing employees from copying files off the company network- these companies even prevent their own full-time employees from seeing parts of the existing codebase.

However, it is actually quite common in quantitative trading firms. Having parts of the source code blocked limits your ability to learn, collaborate with coworkers, and make an impact. Companies that highly value research will have separate dedicated operations and trading teams to handle the majority of the routine day-to-day tasks of running and monitoring an automated trading system.

While this might seem exciting at first, monitoring live trading, monitoring system health, and ensuring system functionality is a full time responsibility and will severely reduce your time available to concentrate and do high quality research. Will you be blocked from viewing the PnL revenue of any strategies that utilize your research?

One of the main attractions of working in trading is the fast feedback you get on your research. You can think of an idea, implement the idea, and then see the results within a few days. This tight feedback loop compares favorably to, say, a Physics department, where a single idea could take years to validate. However, some companies separate alpha signal researchers from strategy developers.

Companies might do this to prevent their secrets from leaking out easily, but there are plenty of successful companies that trust their employees and encourage loyalty in other ways. Okay, final question! This is important to ask, because some companies have employees or teams directly competing with each other so that the company is diversified in its revenue streams.

But for your career, you want your company to invest fully in you. Programs for live production trading: Market data parser: Dev. Receives normalized data, decides whether to buy or sell. Order gateway: Dev.


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