# Tag: algorithmic trading

# All About Automated Trading: What It Is and What It Isn’t

*[This is a guest post from a reader who currently works in an arbitrage development team. He wanted to clear up a few points about what “automated trading” is and isn’t.]*

Ah, taking a mid-day nap and waking up with extra money in your trading account… who wouldn’t want to make money while sleeping?

That promise of set-it-and-forget-it money draws lots of traders into the field and attracts computer science and engineering students who suddenly “discover their interest in finance.”

Only one problem: **“automated trading” is far from automated cash flow, and you always need human intervention**.

To find out why and to learn all about algorithmic trading, arbitrage and other forms of automated trading, read on.

**Lexicon Confusion?**

Part of the problem stems from all the terms used to describe “computer-assisted trading”:

- “Algos”
- “Trading machines”
- “High-frequency trading”
- “Black-box trading”

People use these interchangeably but are referring to different concepts – so let’s clear that up.

**Algorithmic Trading vs. Trade Origination**

Here’s the key question you need to ask:

- Is a human making the trading
**decisions**and simply having a computer help with the**execution**, or is the machine handling the execution*and*making the trading decisions?

The first category – where the computer only assists with the execution – is called **algorithmic trading**.

The second category – where the computer actually makes decisions – can be called **trade origination**, although that is not a canonical name (there isn’t any that I’m aware of).

**Algorithmic Trading**

So now we’ve cleared up the first misconception about algorithmic trading.

The second misconception: that algorithmic trading is about making the most money possible.

It’s actually about **losing as little money as possible** and reducing your costs.

To illustrate this, let’s walk through an example of how an algorithmic trading system might work:

Let’s say that you’re a pension fund manager and you’ve decided to sell 1 million of Company X’s shares that you currently own.

Notice how **you** – the human – make the initial decision here based on your analysis.

You want to get the best price available on the exchange that Company X’s stock is traded on, and you know that the highest bid price – the highest price at which someone else is willing to buy the stock – is $20.

So ideally, when you sell those 1 million shares you will get $20 million.

But not quite.

The problem is **volume** – most likely, that bid order for $20 was for far less than 1 million shares; it might have been your next-door neighbor trading 20 shares in his E*Trade account.

If that’s the case, then you’ll end up selling only 20 shares for $20 – and the remaining 999,980 shares will go for whatever the next best price after $20 is.

If the highest anyone else is willing to pay is $10, then you might only get around $10 million from your 1 million shares rather than $20 million – even though the bid price was $20 according to your trading software.

**Limited Liquidity**

This problem is called “limited liquidity” in trading circles – **you receive less than the “paper value” of your position** because there aren’t enough bid orders at the price you thought you were getting.

As a human, you could simply monitor the market all day long and be on the lookout for those $20 bid orders.

That’s extremely time-consuming and labor-intensive, but that was exactly what agency execution traders did in the “old days.”

And that’s why **algorithmic trading** was invented – to manage the trading process over an extended period of time and get as close to the “paper value” as possible.

A trading algorithm for this scenario might divide the order up into many smaller pieces – 1,000-share blocks rather than 1 million all at once – and execute them over the course of a day or longer.

This is one of the key reasons why algorithmic trading has become so popular: there’s a high upfront investment, but a single machine can replace tens of pure agency execution traders – so you start seeing huge cost savings once you’ve been up and running for awhile.

**Trade Origination**

Algorithmic trading saves traders a lot of time and money, but there’s a small problem: **you still have to make your own decisions**.

To make the process truly automated (in theory), various systems to originate trades were created.

There’s a huge variety in the strategies these systems use – just think about all the hedge funds and prop trading firms out there and all the different trading strategies they use.

To give a concrete example of how these systems work, though, we’ll focus on just one for now: non-statistical arbitrage.

**Non-Statistical Arbitrage**

**Arbitrage** refers to buying and selling multiple securities at the same time in the hope of making a profit.

The simplest type is non-statistical, or deterministic, arbitrage, where you find and exploit price discrepancies between 2 or more securities whose prices should be related. Ideally (ignoring technical issues), this kind of arbitrage is risk-free.

(Statistical arbitrage, by contrast, deals with *expected* values of securities over the long-term. There’s no guarantee that the future will behave like the past and so this is not risk-free in any form.)

Here’s how you might apply non-statistical arbitrage, and then how a computer could make it more effective:

The S&P 500 index has a **futures contract** associated with it – that just promises to deliver the stocks at a certain point in the future and is traded on an exchange.

The underlying stocks of the S&P 500 trade on exchange as well, so you can take their prices, figure out how much it would cost to hold the stocks until the future expires, and based on that decide whether the future is a bargain or rip-off at the current price.

So let’s say you think the future is too expensive – it’s $1,000 but the underlying stocks are worth only $990 and it will cost you $5 to hold them until the future’s expiration.

You can then **sell the future and buy the underlying stocks** – you deliver the stocks when the future expires and then make a profit based on the difference between what you thought the future was worth and the higher price you sold it for.

**Does That Actually Work?**

This is a very simple example, and it would never work in real-life because everyone else is looking for the same price discrepancies.

And even if you’re Rain Man, it would still take at least a few seconds to spot this type of price discrepancy…

…which is where machines come in. They can spot arbitrage opportunities like this in milliseconds rather than seconds or minutes, and make trading decisions far more quickly than any human.

If you were creating an algorithm like this, you might program in the specific securities or trends to look for in the market and then give exact instructions on what to buy and what to sell when certain conditions are met.

These days algorithms have become far more advanced and go well beyond just looking at prices – some actually try to scan news stories to determine “sentiment” for or against a company and make trading decisions based on that.

**Still Confused?**

So going back to the terms at the top of this article, what does each one actually mean?

- “Algos” – Short for “algorithms” – Could be either algorithmic trading or trade origination.
- “Trading machines” – Generally trade origination.
- “High-frequency trading” – A type of trade origination system where securities are held for milliseconds (or less) rather than hours or days.
- “Black-box trading” – Might refer to either algorithmic trading or trade origination.

Many of these terms could refer to either variety of “computer-assisted trading,” so you need to dig in and ask what’s really going on when you see them.

**Time to Retire to the Beach?**

So you have a trade origination system set up and you’re making a lot of money with no intervention or decision-making on your part… time to retire?

Not so fast.

All types of automated trading systems must be supervised, checked, and updated constantly.

Even if the software itself is correct and has no bugs, **market conditions themselves can be a “bug.”**

We saw this back in 2008 during the start of the financial crisis when hedge funds started blowing up – supposedly “once-in-a-lifetime” events started happening every day and breaking all the old algorithms.

So no matter how great your algorithm is, it will only be effective until the next crisis, the next “unusual market condition,” or until everyone else starts copying you.

The top banks have spent a small fortune developing trading algorithms, and the tens of millions of dollars (or more) you need for such technology puts it well out-of-reach of anyone small.

And then there’s the small matter that no software is ever bug-free – especially when the algorithm is new, you need a human to monitor it all the time.

So even if your new automated trading program is making bank, you might want to hold off on buying that beach bungalow.

**For Further Learning**

This was just intended as a brief overview of automated trading – if you want to find out more, get a copy of Algorithmic Trading and DMA: An Introduction to Direct Access Trading Strategies by Barry Johnson.

Good luck with your trading, and let us know if you ever do make it to the beach for early retirement.

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# It’s Not Rocket Science: Why You Should Stop Learning Partial Differential Equations If You Want to Break Into Investment Banking

*“Hi, I was wondering which class I should take to break into investment banking: Advanced Partial Differential Equations or Quantum Field Theory. Do you think it will ruin my future if I only learn up through Multivariable Calculus?”*

No, I don’t make this stuff up: I get emails like this all the time.

Sometimes they’re from undergraduates, sometimes they’re from MBA students, and sometimes they’re from the occasional MD or PhD candidate.

But my answer is always the same:

**It doesn’t matter.**

You don’t do “real” math in investment banking, so stop worrying about it and spend your time more wisely.

**Got Obsession?**

So why is there such an obsession with learning advanced math / winning the Nobel Prize before you start working as an investment banking analyst or associate?

**You’re Good at Math**

If you’re interested in finance to begin with, there’s a good chance that you’re already good at math and have taken a lot of math classes. You’ve either:

- Been interested in finance for a long time and have taken a lot of finance/math classes; or
- You were an engineer or science-type who got bored of that and wanted to move into business.

Yes, there are bankers with liberal arts backgrounds as well but bankers in categories #1 and #2 outnumber them.

**We Like to Blame Other People**

It’s the same reason we believe so strongly in the myth of the career path.

If you can get into finance simply by calling hundreds of people and being very aggressive with networking, failure to break in would reflect poorly on you.

But if you couldn’t break in because you didn’t have that class on quantum physics, then you have the perfect alibi.

**We Like to Stay In Our Comfort Zone**

Getting out there, talking to people, and meeting them in-person is uncomfortable. It’s way easier just to sit at home watching TV…

…or to sit at home completing your math homework.

Going through dozens of advanced math classes also gives us the illusion of progress without actually requiring us to make any progress. It’s part of the 80% you should be eliminating.

**The Truth About Math**

There are 3 points you need to know about math in investment banking:

- You
**don’t use it that much**. - The math you do use is
**very simple**. As in, arithmetic. - Therefore, you don’t have to be a math genius – but you do have to be
**good with numbers**.

**Say What?**

You don’t use math that much because you don’t do that much modeling work, even in “technical” groups like M&A.

Think “administrative work,” emailing people and updating lists of information – just look at a few days in the life of an investment banker if you don’t believe me.

And when you do use math, 90% of the time you’re working with existing templates or simple models rather than creating everything from scratch.

Yes, it’s cool to be able to say you can create a hyper-advanced LBO model from a blank spreadsheet, but in the real world no one has time for that – so you use templates.

**But What About Modeling?!!**

Even when you *are* working with financial models, none of the math is complex.

There’s addition, subtraction, multiplication, and division… and occasionally built-in Excel functions like IRR, Mean, and Median.

You never use calculus or differential equations or even geometry / trigonometry. Just arithmetic and sometimes algebra.

Think about all the basic formulas in accounting: Revenue – Expenses = Profit. Revenue – Cost of Goods Sold = Gross Profit… and so on.

Notice how there are no integrals anywhere in those equations.

**So Why Do You Still Need to Be Good With Numbers?**

If the math is so simple, why do you need to be good with numbers at all?

Although the individual mathematical operations are simple, you can end up working with **huge spreadsheets where a lot of calculations are linked together**.

1 + 1 = 2 is simple, but now let’s say you have 100 similar calculations, and the input of each one is linked to the output of another calculation.

That’s exactly what you run into in investment banking, and it gets tricky to trace everything – especially when it’s someone else’s model.

**Exceptions & Other Fields**

In other fields of finance the math can get more advanced.

The main example is trading, where some funds may use advanced algorithms and higher-level math to make trading decisions – so if you go into one of those, advanced math classes might actually be helpful.

For hedge funds, it depends on what strategy the fund uses: long-term fundamental investing has less math than algorithmic trading.

Also in trading, mental math (17 * 35) is more important because you need to make quick decisions.

Outside of those, the math in other industries like private wealth management is as simple as it is banking.

**So What Should You Do About It?**

Stop taking advanced math classes – especially if they hurt your GPA.

Bankers look at the overall difficulty of your major but they don’t go in and analyze every single class – a 3.8 GPA with easier classes is much better than a 3.3 GPA with “tough” classes.

Plus, taking such advanced classes takes away from time you could be spending on internships, school-year internships, networking, and activities.

When reading your resume, bankers pay attention to the school you attended, your internships, and your GPA – not individual classes.

**Beyond Undergraduate**

Despite rumors to the contrary, sometimes you have to do work to get through business school.

At this level, taking “more advanced” classes is an even *worse* use of time because:

- At the MBA-level networking is even more important.
- Hardly any “math-intensive” finance positions hire directly from business schools – you don’t need an MBA to be a top trader. You just need to make a lot of money.

So if you’re at this stage and you’re serious about breaking into investment banking, forget about advanced statistics / financial math classes and do the bare minimum.

**Summer School?**

I also get a lot of questions on whether “finance summer school” or taking classes during the summer instead of an internship is worth it, and the answer is always “No, unless you have no better options.”

Bankers don’t like taking risks, and they always prefer to hire someone with investment banking internship experience over a newbie.

**What About Your PhD / MD?**

Bankers tend to look down on advanced degree holders.

They want people who can burn the midnight oil and who are aggressive enough to find ways to make or save money – and they don’t think that advanced degree holders fall into this category.

Getting these degrees is far more difficult than anything you do in banking, but most bankers don’t like to acknowledge this.

So if you’re already deep into one of these programs, cut your losses and get out early or take the path of least resistance if you’re too far in to drop out now.

**Improve Your Communication Skills**

If you really want to improve your skills before you start working, forget about math and focus on your writing and speaking skills.

There are tons of analysts who are good at math, but few can describe what they did and how it helped their bank make money in plain English.

And if you want to move up, you need to interact with senior bankers a lot – so getting to the point without rambling or stuttering is essential.

**And If You Really Want to Improve Your Math Skills…**

If you’re still set on improving your skills, forget about classes and have a friend in the industry send you a complex model with many different tabs.

Then, try to “reverse engineer” it and figure out what the key drivers are and how they affect the output.

Creating a model yourself is relatively easy because you control everything – the real challenge is looking at someone else’s model and figuring out how it works in the first place and how to modify it.

So spend some time playing around with complex models and get used to the process of tracing individual formulas and outputs.

And please, no more partial differential equations.

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