Embrace the Mayhem: An evidence based approach to trading success

Table of contents :
Contents
PREFACE
Nothing in or about the markets is “obvious”.
Some personal experiences
How this book is organised
Why should you listen to us?
CHAPTER 1 DECEPTION AND CONFUSION
The consequences of false assumptions
Subtle yet dangerous false assumptions
Coming back down
Main points
CHAPTER 2 WHAT DOES IT TAKE TO BE A TRADER?
What does it take to be a successful trader?
Time
Obsession
Skills and knowledge
The top-down approach
Applying the top-down approach to trading
Experience, humility, and dealing with uncertainty
Capital
Ways to bypass capital limitations
Opportunities outside the office?
So how can you actually get started trading?
Main points
CHAPTER 3 WHAT’S YOUR TRADING TRAJECTORY?
Can you get the lifestyle you want through trading?
What success in trading looks like
That’s my story, what does James credit for his success in the markets?
The independent trader
The professional equities fund
Proprietary trading
What does this all mean for you?
Main Points
CHAPTER 4 TRADE WHAT WORKS, DITCH WHAT DOESN’T
Learning to trade is not learning technical analysis.
Is technical analysis really 100% useless?
Technical analysis is, at best, a sub-optimal tool for modeling the markets.
What really generates profit in the market?
Risk premia
Market edges
Instruments
Factor styles
Other inefficiencies
Strategies
Trade small, trade broad, trade lots and trade humble
Useful tools for trading
What about training neural networks so you can set it and go to the beach?
Main points
CHAPTER 5 HOW TO EMBRACE THE MAYHEM AND WIN
Leverage your greatest edge
Embrace the Mayhem! Get Comfortable with Uncertainty
Your reason to trade
Robustness counts for more than backtest performance
The Lure of Complexity
Finding and Evaluating Trading Ideas
The Recipe for Trading Success
Main Points
CONCLUSION
FINAL WORD
Appendix - Backtesting the RSI Strategy from Chapter 1

Citation preview

Copyright Robot Wealth Pty Ltd © 2019

Embrace the Mayhem Edition 1

This book or parts thereof may not be reproduced or distributed in any form, either electronically or in print, without prior written permission of the publisher. For permission requests, write to [email protected]. www.robotwealth.com

Disclaimer: Any views, opinions or examples presented in this book are for educational purposes only. We do not provide financial advice. Robot Wealth Pty Ltd accepts no liability or responsibility for any loss or damages which may occur from your personal or professional trading.

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Contents PREFACE........................................................................................................................................ 5 Nothing in or about the markets is “obvious”. .......................................................................................... 6 How this book is organised ....................................................................................................................... 9 Why should you listen to us?.................................................................................................................. 10 CHAPTER 1: DECEPTION AND CONFUSION ........................................................................... 12 The consequences of false assumptions.................................................................................................. 14 Subtle yet dangerous false assumptions................................................................................................... 15 Main points ............................................................................................................................................. 19 CHAPTER 2: WHAT DOES IT TAKE TO BE A TRADER? ......................................................... 20 What does it take to be a successful trader? ........................................................................................... 21 Experience, humility, and dealing with uncertainty ................................................................................. 29 So how can you actually get started trading? ........................................................................................... 32 Main points ............................................................................................................................................. 33 CHAPTER 3: WHAT’S YOUR TRADING TRAJECTORY? .......................................................... 34 What success in trading looks like .......................................................................................................... 37 What does this all mean for you? ........................................................................................................... 41 Main points ............................................................................................................................................. 42 CHAPTER 4: TRADE WHAT WORKS, DITCH WHAT DOESN’T ............................................. 43 Learning to trade is not learning technical analysis. ................................................................................ 44 What really generates profit in the market? ............................................................................................ 48 Other inefficiencies ................................................................................................................................. 55 Trade small, trade broad, trade lots and trade humble ........................................................................... 59 Useful tools for trading ........................................................................................................................... 60 Main points ............................................................................................................................................. 63 CHAPTER 5: HOW TO EMBRACE THE MAYHEM AND WIN ................................................. 64 Leverage your greatest edge .................................................................................................................... 65 Embrace the mayhem! Get comfortable with uncertainty ....................................................................... 71 Finding and evaluating trading ideas ....................................................................................................... 78 The recipe for trading success ................................................................................................................ 81 Main points ............................................................................................................................................. 82 CONCLUSION ............................................................................................................................. 83 FINAL WORD .............................................................................................................................. 87 APPENDIX - BACKTESTING THE RSI STRATEGY FROM CHAPTER 1 ................................... 88

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PREFACE

If the idea of improving your financial future gets you all fired up, then the markets can be immensely attractive. Who hasn’t dreamed of tapping into the almost bottomless streams of wealth that flow through the world’s financial centres every single day? Surely, you can carve out your own little corner of profit from all that, and create a comfortable - no, luxurious - life for yourself and your loved ones.

Can’t you? On the face of it, sure you can! Many people have made a good living in the financial markets. Some of them have earned enough for a hundred lifetimes. You can bet it’s certainly not the case that every single one of those people is smarter or harder working than you. So, how do you tap into the markets to create this exciting life you dream of for yourself? Well, if you’ve read trading books or forums it all sounds simple. You’ve probably read all about technical indicators and fundamental analysis. These concepts seem to make it obvious when you should buy and when you should sell.

“Buy good quality stocks with a low price-to-earnings ratio that are trending up”, the books say. “Don’t risk more than 2% of your account on any one trade”, you’re told with such confidence it has to be right. The social media ads say it’s a piece of cake. Follow this foolproof, step-by-step system. Copy these signals we’ll text you. Watch the stacks of money flood your bank account hot off the press. It’s easy. It’s obvious. Why are you wasting time? Click here and buy, dammit. With all that in mind, and to set the theme for this book, this first point will make-or-break your trading career so let it sink in:

Nothing in or about the markets is “obvious”. The world’s financial markets are extremely efficient. Almost all changes in price are literally indistinguishable from randomness. Markets are hard to understand because they just don’t make sense on a moment-by-moment basis. This is the mayhem that you must embrace. Once you accept and embrace the moment-by-moment madness of the markets, you gain the ability to take a broader perspective. From this vantage point you’ll see loose and noisy patterns which you can profit from.

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But consider that some of the smartest people on the planet are competing to make money at each others’ expense. Does it seem plausible that a rigid prescription from a trading book, blog or unknown social media guru is all you need to be competitive in that arena? Real, experienced traders get a good chuckle out of claims like these. The truth is, there is no step-by-step formula or set-and-forget software for making money in the markets. You certainly don’t get to where you want to be by doing what everyone else on your favourite trading forum is doing. In fact, the path you envision towards trading glory, and all the riches that come with it, is likely foggier and steeper than you imagine or have heard elsewhere. You can get there and it’s a goal I’d set my sights on over and over again, but pack your bags for a more rugged and unpredictable climb than you thought possible — it’s no evening stroll. The reality is that the journey to trading success is one seriously tough endeavour, yet the climb towards the top is one of the most rewarding parts of the whole deal. It’s a charactershaping adventure that’s full of challenge, mastery and best of all, companionship - since you can’t realistically do all this alone. That’s nice to know, but where do you start? How do you overcome all the retail-level limitations like costs and execution limitations? If most of what you read online is BS or completely false, what do successful traders really trade and how do they do it? In this book, we’re going to poke at these questions under a magnifying glass. We’ll be frank about realistic expectations and what profitable trading looks and feels like. We’ll chat about the myths we all fall prey to, and we’ll explore specific steps you should consider when beginning your exciting journey — based on evidence and experience. We’ll have a good bit of fun doing all that, too. In spite of all the fun we’ll have, there’s a chance that you might not like all of what we have to say. In fact, you might hate it. That’s because this book will very likely clash with what you’ve heard from sources giving out a comfortable, prescriptive view of trading. That’s perfectly fine. The purpose here isn’t to tell you a feel-good story that sees you go absolutely nowhere near any traces of profit. I’m here to lay out a vision for what you, an aspiring trader, can realistically build in terms of a trading capability — and the experience-based roadmap you can follow to get there. That vision is incredibly exciting. It’s also daunting because it’s not what you expect. But the good news is you will come out the other side equipped with the approach of someone who has been kicked around the markets for years, without having to endure it yourself.

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Equally, the approach of someone who has built a strong financial future for themselves, their firm and their family. What better result is there?

Some personal experiences I (Kris) was chatting to a friend about trading and markets in general recently, and they asked me why I get so fired up about this line of work. What is it about trading that inspires such passion and motivation, even throughout the most gruelling of times? Trading is challenging on multiple levels: the technical, human, emotional and even the personal. Which is what makes it so fascinating . At the time of writing this, I’d been in the markets for a decade and trading full-time for only half that. Before this, I was an engineer for over a decade and if I had to guess, I’d say my professional working hours would be split 60% in engineering and 40% in the markets. Yet the markets account for over 90% of my net worth. Which is what makes trading so exciting. But nothing was obvious, easy or fast in my experience of the markets. Everything took time: from learning the basics of how different exchanges work, to honing the prerequisite skills of automated trading, to researching and developing trading strategies, to working out how to execute them profitably. Sometimes I’d look at my alarm clock and it would scream “It’s 3am! Get to sleep!” But despite being dog tired from working all day, then working on a trading problem until the early hours, sleep was scarce because when I closed my eyes all I’d see were potential solutions to an unsolved problem. I can’t count the number of times I switched the light back on, scribbled down an idea I was petrified of forgetting in the morning, then laying back down to sleep, only to find myself flipping the light back on five minutes later. Such is the life to which the aspiring trader signs up — yet I wouldn’t change a thing. My goal in writing this book is to cut through the deceptive “trader lifestyle” Instagram posts of laptops and cocktails on golden beaches, diamond-encrusted watches and RollsRoyces. I want to tell you about the sleep you’ll sacrifice to problem-solve on a strategy, the feeling of rejection when another idea is trashed, and the awkwardness of talking about margin calls with your broker. And, beyond all the misconceptions and roadblocks, to show you how you can begin building a profitable and fulfilling trading capability as a retail trader.

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How this book is organised You can imagine this book is split into two halves: The first half is all about arming you with skepticism, diligence and healthy expectations. Its purpose is to protect you from expensive failed strategies built on bad advice or assumptions. It’s to guard you against greedy brokers and subtle deceptions in “harmless” trading content. It’s crucial you understand this before moving on. So, please be patient in going through these initial chapters. The second half of this book is where we unravel the good stuff. We’ll talk about exactly what works in the markets, including the strategies, inefficiencies and approach we’ve used to grow capital into the 8-figures for ourselves and our firms. We’ll also talk about how you can harness this approach even in the face of retail-level restrictions, and the next steps you may want to consider to begin this journey on the right foot. It’s hard to communicate just how much of a head start this will give you, especially over where I started out. If your excitement evaporates after we lay out this vision, then consider this time well spent. You’ve been spared valuable years and hard-earned dollars on a pursuit that requires complete, obsessive devotion over a long period of time in order to succeed. If on the other hand you are more excited than ever after discovering the vision and roadmap we lay out, then let’s get after it! You are among a rare breed of individuals destined for a fascinating, stimulating and potentially very profitable lifelong journey. But before we do that fun stuff, you probably want to know why you should listen to us. Good on you for being skeptical. Here’s a little about us.

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Why should you listen to us? I understand how hypocritical it would be to say “don’t listen to them, listen to us”, without giving you any solid reason to do so. You’d simply be guessing, otherwise. Well, Robot Wealth is a proprietary trading firm first and foremost, meaning we trade and grow our own capital so there’s no conflict of interest (we don’t depend on advertising or product revenue to survive). What also makes us different is that we do all this in full view of our members, to help them grow their own retail trading capability. They see everything. Every win, loss, line of code and nugget of intuition. When it comes to teaching, we believe in show, don’t tell. The humans behind the rich robot are Kris Longmore (me, left) and James Hodges (right), two systematic traders who have made our livings from the market for over two decades between us. We’re supported by Michael and Danilo (as well as our long-suffering wives), who work on tech and infrastructure in the background but are nevertheless instrumental to Robot Wealth’s success. I have an engineering background, but discovered a passion for the markets that led me into professional funds management and later proprietary trading. James has a physics background, and likewise worked in institutional finance and later proprietary trading, including setting up his own trading desk. Between us, we’ve generated well over eight figures for our firms, clients and personal accounts. In short, we’re traders, not marketers. Neither of us particularly like wearing suits or hanging out in the fluorescentlit halls of institutional finance. We prefer to trade in our shorts, without a boss, from the comfort of home. We also get a massive kick out of helping retail traders create a better life for themselves and their families. We were both retail traders before we made the big leagues, so we can relate to the frustrating dead ends and information overload you’re going through. Sharing our experience with Robot Wealth members is as fun and exciting as running our own trading desk. With that in mind, what do you say we take a journey through the markets?

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Let’s discover how to embrace the mayhem inherent in them, and adopt an approach built upon evidence and experience to ultimately become successful retail traders together. Let’s get started.

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CHAPTER 1

DECEPTION AND CONFUSION

……………………… …….

You probably want to cut straight to the nitty-gritty strategy stuff. You want to know where to start, how to make your money grow. We’ll get to that soon. First, there’s a step before you even touch your trading setup that is vital to your future success. Without it, you’ll fall into countless trading sinkholes that will suck your capital dry. This is not so much something you gain, but something you reset. Chances are you’ve picked up some trading baggage before coming across this book. This is the type of baggage you don’t even realise you’re carrying, yet it’ll weigh you down and cost you more than you can believe. So much so that you won’t really go anywhere near success. I’m talking about false assumptions. I won’t pretend this first point is any kind of revelation, but here it is: there’s a bottomless pit of trading research and content available at your fingertips, and there’s no way you’ll ever get through it all. You won’t even crack the surface. I certainly haven’t. But, what isn’t as obvious is that only a tiny minority of this content is useful to you as a trader. Most trading-related content online is not so great. In fact, I can tell you from experience that most of it tends to fall into one of two categories: 1. A deliberate and calculated deception, or 2. Confused or misinformed, but otherwise well-intended information. Interestingly, the first category isn’t as “Wolf of Wall Street” as it might sound. It’s not necessarily a direct effort to rip you off. Though, anyone who’s spent 30 minutes browsing the web for trading content will stumble upon the signals bot scams, Instagram’s FX insider circles, and similar garbage all over the place. Blatant scams aside, the more subtle deceptive content might not beat you over the head with a nauseating sales pitch. Rather it deliberately plants a rosy, enticing, yet false picture of trading into your mind for their gain. What do they want? Usually, there’s a couple of things. One, they’re offering brokerage services and they want you to open an account for you to spend (lose) money on. Two, their revenue comes from advertising, and the more fresh meat they get on their website the better. Your eyeballs have monetary value! But you’re likely smart enough not to click on these weird ads or to spontaneously dump your capital in random trading accounts, so what’s the problem?

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The insidious part is that even if you don’t open any accounts or click on any ads, these websites and authors can still plant false assumptions in your mind about trading. Though they talk about techniques and theory that sounds plausible, it’s usually bogus. On the extreme end, they’ll even tug on your heartstrings with promises of early retirement and caviar on a Greek island. These guys are marketers, not traders, and their job is to sell to you by any means necessary. Anyone who’s been kicked around by the markets for any length of time knows what it’s really like - they nauseous whenever they see this stuff around. I won’t spend long on this, but there is really good news! These false assumptions aren’t your fault, and they certainly aren’t permanent. You can recognise and reset your false assumptions in order to hone a realistic, experience-based approach to trading. It will, quite literally, pay for itself many times over when you come to trade. Once you reset your own false assumptions to discover the reality of trading, you’ll have opened up a whole new world. It’s like taking off a blindfold you didn’t know you were wearing. Sure, you may realise trading isn’t as straightforward as you initially thought, but at least now you can see where you’re going much more clearly — you should be excited. To do all that, we need to know why these false assumptions are so dangerous. Let’s do a little digging and see what these look like.

The consequences of false assumptions I’ll spare you the doom and gloom, I know you want to get to what works. But you’re at risk of losing your hard-earned money, so it’s smart to spend a little time on this before we get to the actionable steps of real, practical trading. There is light ahead, but first, the dark and damp tunnel must be navigated. The general trading discourse that you find on blogs, forums and social media is rarely grounded in reality. Typically, it’s oversimplified or obscured. Sometimes, this is simply through ignorance. Other times, it’s designed to get you emotionally attached to the idea of easily-obtained wealth through trading. And once an emotion attaches itself to an idea,

it becomes a device used to manipulate. The problem isn’t that it’s impossible to become wealthy through trading. That most definitely is possible. The problem is the ideas and concepts presented to you take on a life of their own before you have the experience to see them for what they are - an unrealistic view of how profitable traders operate. We are all susceptible to becoming emotionally attached to the idea of a simple path to wealth, and to our better judgment being hijacked as a result.

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When the seed of an idea takes root in our brains and grows into an immense tree of expectations, it’s really difficult to reassess the fundamental assumptions built into the original seed. If they’re wrong, you have to start over - and unwinding all the misinformation you picked up along the way is exceedingly difficult.

Seeds, trees, Rolexes …. what on earth does this look like in practical trading terms? Well, you might read an article describing a simple rule for a trade signal based on some indicator from technical analysis. Such articles often assert or imply that the rule leads to profit generation. I’m sure you know these sort of articles. Our brains then subconsciously compare the article’s premise to other simple cause-and-effect rules we know from life experience. We then make the link between the assumptions in the article and the logical outcome (psychologists call this the Representativeness Heuristic):

“If I do this, I will get money. If I do this more, I will get more money. It is a good idea to be a trader. Let me open up this account and deposit…” ..STOP. Remember, the assumptions you make today directly influence your actions and expectations of tomorrow. Since all of your assumptions are based on your own experience and the information you take in, there are knock-on effects of adopting bad information now that induce false assumptions down the line. Doing this costs you time, capital, and sometimes even your passion for trading. But isn’t it easy to sense when someone’s trying to trick you so blatantly?

Subtle yet dangerous false assumptions Most of us can smell a scam artist from miles away. But, the subtle deceptions we’re talking about are a lot harder to catch. What do these look like? When I searched the web for “RSI trading strategies”, I came across dozens of articles with titles like “7 RSI Strategies that will Boost your Trading Prowess ” and “7 RSI Trading Strategies That Can Tune Up Your Forex Trading .” What is it with these guys and the number 7? These were far from the scummiest articles I found. They didn’t promise quick riches or make outlandish, ‘iron-clad’ guarantees. Yet they’re misleading as a result of their implicit messaging. Let take a look. First, check out this “RSI Trading Strategy”. This is fairly common fare on many of the trading sites I checked out:

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“If the RSI is less than 30, it means that the market is oversold and that the price will probably eventually increase. Once the reversal is confirmed, a buy trade can be placed. Conversely, if the RSI is more than 70, it means that it's overbought and that the price will probably soon decline. After confirmation of the reversal, a sell trade can be placed. The 50 level is the midline that separates the upper (Bullish) and lower (Bearish) territories. In an uptrend, the RSI is usually above 50, while in a downtrend, it is below 50.”

That all sounds incredibly simple. Just wait until the RSI crosses 30, confirm the reversal, then buy! Wow, who knew it was so straightforward. Sign me up. Yet there is so much wrong with this that I barely know where to begin. First off, the RSI is nothing more than a simple mathematical transformation of recent price data that normalises it to its own range. In simple terms, it just tells you where the price was in relation to its recent past. Is it rational to believe this thing predicts future price movement? The article implies this without any proof. It’s simply assumed. Job done, let’s go to the beach. Also, notice the words “price will probably eventually increase.” That’s like saying nothing at all. Take pretty much any market in any financial product without knowing where it’s currently priced and you could safely say “price will probably eventually increase.” Even if this prediction had merit, over what time period would we expect it play out? How much can we stomach an adverse move? The irrationality of the strategy’s logic aside, there’s absolutely no mention of any of the details around the practicalities of trading: there’s nothing about position sizing, no mention of trade execution, and asset selection isn’t considered. Even if the article is just providing ideas for you to develop further, it still paints trading in an extremely simplistic light. Can you see how easy it is for false, or at the very least untested assumptions to lodge in your mind, based on what’s implied in plausible-sounding articles like this? So I’ve just rubbished this strategy. We should at least give the author the benefit of the doubt and backtest it so we have some hard numbers. Backtesting is the process of simulating trade logic on past market data to get an estimate of how it would have performed in the past. Backtesting is a big topic that we won’t go into

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here, but you can find a more detailed breakdown of this particular backtest, including its computer code, in the appendix at the end of the book. Here we’ll just present the results. Here’s the return plot that results from simulating this strategy over a bunch of major foreign exchange rates (the article originally appeared on an FX website):

The strategy loses about 30% between 2010 and 2017, before recovering slightly to finish 2018 underwater to the tune of about 15%. So, if you know some programming and the basics of simulation, you can easily show that the strategy has zero merit. Time and money saved, move on? Not quite. See, the assumptions lodged in our brains after reading articles like this suggests there’s a simple solution. Maybe try changing some parameters, using a different period for the RSI calculation, finding an asset on which the strategy “worked” in backtest, or performing some other superficial tweaks. This is a fast way of getting absolutely nowhere. Or worse, finding something that happens to exhibit a decent equity curve just by random coincidence (try enough variations and you’ll eventually find something exciting just by chance). In that case, your assumptions compel you to take the strategy live, and you most likely end up losing money as you have no edge and significant trading costs. To avoid this situation altogether, you absolutely must revisit your assumptions about the markets and approach trading from a totally different perspective. What does that look like in practice? We explore that in chapter 2.

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Here are some concluding remarks from one of these articles about trading with the RSI, before I get off my podium and show you what works:

So I hope that these RSI ideas have given you a few things that you can test. Just remember, there isn't just one way to trade the RSI. You have to find the one that works best for your trading style. But sometimes you just need a few ideas to get your creative juices flowing.

To be honest, I can’t even fathom what is meant by ‘trading style’, yet you’ll find those words plastered all over the typical trading website. My only trading style is making more money than I lose, and maybe the fact that I do it in beach shorts.

Why “trading style” is BS There are only two ways you can possibly make money trading the financial markets: ●

Taking on risk that others won’t, and

●

Exploiting fleeting market inefficiencies.

Consider a game of chance that is well understood, like blackjack. If someone wanted to systematically beat the casino at blackjack, they can’t just come up with a style of playing that suits them. They simply do not have that luxury. Ed Thorpe laid out exactly what is needed in his book Beat the Dealer in 1962, and it’s clear that there’s only one thing that works. You have exactly three choices: 1.

Do that thing that Thorpe describes

2.

Not play

3.

Lose money

Most people who go to casinos choose the latter option because they’re not playing to systematically beat the casino; they’re playing for entertainment. Others think they have a genuine edge, but they are wrong. The remaining few know what they are doing. They might not like playing the way they do, but they must play that way to make money. The markets are a lot like this. Our job is to become the first guy and “beat the dealer”, by using a systematic approach that works, whether it is comfortable or not. .

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Coming back down You’ve seen how easy it is for misinformation to seep into your trading. There are far more misleading claims than the RSI example above, too. But, as we’ve shown, you can dismantle and test these claims when armed with the right tools. We’re going to cover these tools in more depth shortly. To be honest, all this frustrating misinformation makes my head hurt. Now that you’re more sceptical of the information you’re taking in, let’s get away from the industry BS and onto what real trading looks like, what it requires and the stuff that really works. By stuff that works, I mean the intuition and approach that myself, my prop firm and my clients have used to build 7- and even 8-figure portfolios over years of experimenting — and eventually success. Let’s get to it!

Main points ● Most of what you read about trading is either misguided or a calculated deception. ● That deception usually involves the planting of false assumptions about how simple and easy trading is. ● Articles about trading often contain a lot of implicit messaging underpinned by false assumptions. It’s easy for these false assumptions to be transferred to the reader. ● Backtesting and simulation is an effective tool for testing assumptions about the markets. But backtesting is a big topic, and it’s easy to abuse a backtest until it tells you what you want to hear. ● To suggest that trading is as simple as finding ‘the right style’ for you is delusional, ignorant, or both. ● Trading involves dealing with uncertainty. There are very few, if any, concrete answers. ● There are vested interests in spreading false assumptions about the simplicity of trading. Those interests have nothing to do with you making money in the markets.

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CHAPTER 2

WHAT DOES IT TAKE TO BE A TRADER?

Besides what you may be thinking by now, my intent isn’t to scare you away from trading so I can swim in a symbolic bathtub of ‘secret’ alpha! Rather, I want you to gain a realistic understanding of what day-to-day trading looks like, and what’s genuinely achievable. I also want to get you excited about how it feels to turn money into more money. Making money is a rush that never dulls. That way, once you’ve decided whether or not trading really is the life for you, you know how to walk out there and fight for it. That’s right, I said fight — the markets like to slap you in the face just when you think you’re becoming friends. There’s great, life-changing money to be made in the markets. Millions of dollars have exchanged hands since you started this very book, and to gain your tiny yet life-changing share is to stretch your arm out and dip a small cup into an endless, ferocious torrent speeding by. But even this requires a lot of calculated risk and ongoing effort. To make it work, you need passion, energy, and some amount of obsession for this neverending torrent that we call the markets. In fact, I’ve not met a single successful trader who ISN’T head-over-heels obsessed with the markets. Since you are still reading, you are probably wondering whether or not YOU have this obsession. Let’s answer a few important questions. Then, if you’re still excited about diving into this fascinating life-long journey, you likely have what it takes to make life-changing profits. You probably want answers to the following: 1. What does it take to be a successful trader? 2. What kind of money can you really make trading? 3. How do you go out and make it? Grab a strong coffee, maybe even a notepad and let’s dive in.

What does it take to be a successful trader? I’ve dealt with plenty of rich traders in my career, including some who’ve blasted past 8figures in wealth, and below I’ve boiled down what I believe are the most common traits shared between them. They’re also reflections from my experience of going from a total beginner to full-time trader over the last decade.

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You probably won’t have all these right now and that’s fine. If you plan to put in the effort and acquire them you should keep going. Let’s see what it takes to win in this game: ● Time ● Obsession ● Skills and knowledge ● Experience, humility, and comfort with uncertainty ● Capital

Time Despite the impression you get from articles like the ones I, uhm, critiqued in the previous chapter, trading is a big time commitment. Most aspiring traders have regular day jobs and other commitments, like family or higher education. Between keeping your boss happy and changing diapers, it’s hard to carve out time in your day for trading. That’s understandable. But, it doesn’t make it impossible. I haven’t always traded for a living. In fact, I was in the exact position I’ve just described, with a 60-hour per week engineering career and a small family to raise. Yet I and many others carved out the time from a busy schedule and made it work. If you can’t carve out time, make time. Get up earlier, it worked for me, and trading an hour of sleep per day in order to make a living from my passion was a choice I’d happily make again 1000 times over. Would you? Here are some tips and tricks that allowed me to eventually trade full-time: ● Consistency beats the sporadic marathon sessions It’s better to commit just twenty minutes per day than five hours every second Sunday. The former lets you maintain momentum, keeping new ideas fresh in your mind so you develop them when you’ve got a spare moment in the day. The latter is like starting on a clean slate every time - not a smart way to work. Be consistent. ● Discipline trumps motivation every day of the week Don’t rely on motivation. No matter how excited you might feel by visions of Lambos and Dom Perignon on tap, motivation will fail you right when you need it most. Instead, schedule a regular time to work on your trading business and stick to it. If that means getting up an hour earlier every day then that’s just what it takes.

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Invest some time in learning what sort of productivity tools and systems help you get more done in less time. Personally, I like Evernote, Dropbox, the Google suite of tools (Drive, Calendar, Gmail, etc), and to-do lists for today, this week, and this month. ● Prioritise the right things This is a big topic that I can’t do justice to in this short book, but the main idea is to take sensible shortcuts. For example, using a commercial backtester or open source software libraries is more sensible than coding everything from scratch. You also want a research approach that lets you test an idea quickly, so that you can discard less promising ideas as early as possible. We’re very big on this in the Robot Wealth community, and it’s something we’ll help you with on the inside. ● Be realistic about schedule restrictions from work, family and other commitments Having a day job likely means you won’t be able to trade that super short-term scalping strategy everyone’s talking about over at your favourite forex forum. Something like that almost certainly isn’t a good idea anyway - but it is definitely not a part-time gig. Whilst you have a day job, you’re probably limited to strategies that rebalance positions one or twice daily, at most. Trade within your means.

Obsession I don’t mean your favourite TV show type of obsession. I’m talking about the obsession that occupies your mind most minutes of the day regardless of what you’re doing — and let me tell you, that gets weird. In all seriousness, here are some examples of what that looks like: ● You read as much trading stuff as possible, almost to the exclusion of everything else. ● You think about the markets whenever you’re alone (my wife always comments about me taking ages in the shower, but I can’t help but think about trading when I’m standing under the water. I lose all track of time). ● You mull over ideas when you should be sleeping. ● At work, you nod and say “hmmm yeah uh-huh” while your boss is talking at you without hearing a word he or she is saying because you’re thinking about the markets. ● You spend the bulk of your spare time working on your trading operation, whether studying some topic or researching and developing new systems.

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Yeah - it’s not entirely healthy. But it does seem to be necessary. Let me point out that in this context trading is different from investing. By trading, I mean active speculation in the markets on a relatively short term basis, where holding time might be seconds to months. Investing refers to allocating capital to risky assets for the long term. Trading is a full-time job; investing can be managed in parallel with the constraints of a normal life. I don’t think you need to be obsessed with the markets to be a successful investor. Market obsession itself comes in one form that will make you money, and another which will not: When you think about the markets, do you dream of windfall profits from what might have been if only you’d bought that stock last week? Or do you think about solutions to fascinating problems and ideas for trading strategies?

The former approach won’t quite gel in the markets, while the latter is the sort of constructive thinking that facilitates success in the long term. The point is that instant gratification in the markets is completely unrealistic, and retrospective what if scenarios definitely won’t make you wealthy. Let’s shift your obsession from dreams of “if only I’d...” to the kind where smart problem solving will see you building trading strategies that make your money grow.

Still, if only we’d bought Bitcoin back in 2011… Or shorted the futures in Dec 2017...

Skills and knowledge Trading is a multi-disciplinary activity. You’ll need to sharpen your general knowledge of various worldly topics. The perks of this include more profitable trading, becoming the polymath amongst your social circle and kicking ass at the weekly pub quiz. ………………………………………………………………………….

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Kris goes to weird pubs.

Here are some of the knowledge and skills you’ll need to win in trading: ● Programming I’ve met traders who try to outsource programming requirements or use ‘drag and drop’ tools that circumvent the need for coding skills. I’m still to meet one that has done this successfully long term. The reality is, you need to know enough programming to translate your strategy ideas into computer code and test them, at a minimum. ● Data wrangling The perpetual thorn in the side of traders globally. Data underpins the majority of the research you’ll do as a trader, but it’s rarely clean and ready for immediate use. If you can gain the skills to obtain, clean and process data efficiently, your life will be much easier. Thankfully there are excellent data wrangling tools that do most of the heavy lifting for us, such as the dplyr package for R and the pandas package for Python. ● Math - but less than you think You don’t need to be a mathematician to succeed in the markets. But the broader your knowledge of math the better, as you can implement more sophisticated trading models. Notice I mentioned a ‘broad’ knowledge of math as opposed to a deep one. Broad knowledge implies you know a little about a lot of things. This means an appreciation of what tools and techniques exist, even if you can’t derive them or discuss the theory yourself. If you know what exists and what it’s useful for, you can learn what you need to know when an opportunity to use it presents itself.

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● Scientific method You don’t need a PhD in physics to win in the markets, but it’s vital to approach research as a scientist would. This means treating observations about the markets as hypotheses (as opposed to fact) and testing their validity. Be sceptical of ‘concrete’ answers or theory This applies as much to trading folklore (“don’t fight the trend”) as it does to concepts that appear to be more scientific in nature. For example, many classical statistical tests are not super useful in practical trading, despite being quantifiable and appearing to be very important. It is important you test your hypotheses. One of the most important hypotheses to test is your assumption about the profitability of a certain trading idea. Unfortunately you’ll rarely reach a binary conclusion regarding this hypothesis. (Strategies don’t tend to leap out and scream “trade me!”) Instead, you’ll have to weigh evidence and trade-offs and be forced into a decision in the face of uncertainty. Dealing with this is what I consider to be the art of scientific trading, and it gets easier with experience. ● Knowledge of financial markets This is obvious, but you’re not going anywhere without understanding the parochial details of the instruments you want to trade and the exchanges those instruments trade upon. ● Economics Again, you don’t need a PhD in economics, but you need an appreciation for key economic concepts, like the time value of money, the calculation and interpretation of asset returns, and the role of interest rates. ● Appreciation for uncertainty Likely the biggest source of fun in trading, it’s also the cause of sleepless nights. This is the bit most retail traders struggle with more than others. This requires internalising the concept that in the markets, the future may not be like the past. This is more important a realisation than it sounds, and internalising this requires humility, an open mind and thick skin. The ability to Embrace the Mayhem is a trait of all the successful traders I’ve met, so we’ll keep coming back to this throughout the book. If that list gives you mild feelings of existential dread please fear not - we’ll talk about the best way to pick up these skills. It won’t involve textbook after textbook. At Robot Wealth, we’re all about learning by doing.

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Let’s discover how…. What is the best way to learn these skills? Skilled practical application is usually more helpful for trading than deep theoretical knowledge. This means knowing just enough about: ● What tools are out there ● What problems they help you solve ● How to use them to solve those problems

The top-down approach In my opinion, this is the most efficient and sensible way to gain the skills and knowledge you need to succeed as a trader.

What is this top-down approach? Well, let’s talk about what it isn’t by looking at the bottom up approach. The bottom-up approach is the one often taught in school. In this approach, you first learn the theory before you learn to apply it. It’s difficult and slow for most of us. Worse, you typically forget what you’ve learned when it finally comes to use it in practice, and you end up having to re-learn it anyway (ironically, the re-learning at this point is much like the topdown approach).

The bottom-up approach is learning about grammar and memorising words of another language via a textbook in your home country, then switching back to English as soon as you step outside the class. The top-down approach is going to a foreign country and immersing yourself in the culture in order to learn the language, aided by constant contextual cues and challenges in real time, like trying to order coffee. In contrast, the top-down approach starts with an application or a real problem. You find a problem you don’t know or can’t solve with your current toolkit. At that point, and not before, you go and learn the tools, methods and approaches that’ll help you solve your problem. You learn the tools by applying them to your problem — you don’t spend months going deep on the theory. Of course, there’s a place for pure theory in all this. Learning it will probably make you a better trader all around, but it isn’t necessary for solving your most pressing problems early on. You can build that kind of understanding over time, and it works better through the lens of experience.

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Applying the top-down approach to trading The first obstacle with the top down approach is that you need to know broadly what tools and techniques are out there. Otherwise, you wouldn’t know where to start. This comes with experience, through talking with others, asking questions on forums like Stack Overflow and Cross Validated, and from talking to your fellow Robot Wealth members. What I do at my prop firm can be described as “applied data science”. The more I think about it, the more I think that’s what quant trading really is — data science applied to the problem of generating trading profits. That’s right, I’m in the science of making money. The other day, I was chatting to a colleague and I mentioned that there has never been a better time in history to be an applied data scientist (especially in a trading firm). He asked me why. I sipped my beer and thought about the dozen software programs I have running at any one time. You have a breathtaking suite of tools for tackling complex quantitative analysis. Many were developed by people who spent most of their career on a single topic. These tools let people like me, who have deep knowledge of a particular application (financial markets), but only a broad knowledge of the various quantitative disciplines, to leverage the skill of the expert towards my problem, making it a lot more approachable.

A while back, I was exploring Markowitz’s mean-variance optimisation framework in a trading algorithm. This can be solved using convex optimisation. There are numerical techniques for solving these problems. but I don’t know how to implement them. Luckily for me, there are a group of experts (the Stanford Convex Optimisation group, in this case) who not only wrote a 700-page textbook on the subject, which they make freely available, but who also developed open source convex optimisation software that makes solving the problem only as difficult as expressing it correctly in simple code.

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You’ll notice that acquiring the knowledge and skills for trading on the fly is closely related to another useful tool: experience and humility. This is especially true when you take a bottom-up approach to learning and a hypothesis-driven approach to research. So let’s take a look at what it means to have “experience” in trading.

Experience, humility, and dealing with uncertainty If you’re new to all this, you’re probably itching to get out there and get your hands dirty. But before you set about gaining some combat experience in the markets, let’s quickly look at why experience is so crucial to your success. Thinking about the markets is a different way of thinking altogether Imagine the markets are like a modern metropolis: they’re complex, dynamic and in a constant state of evolution. Your job in this metropolis is that of the weatherman, policeman and fireman — you’re forecasting the future environment, dealing with bad actors and putting out burning buildings…. all at the same time. If you think about it, an exchange exists to facilitate transactions between buyers and sellers. All of those buyers and sellers have: ● different reasons for participating in the market (like speculation, long-term investment, and hedging) ● access to different information, as well as potentially different interpretations of the same information ● different constraints around their participation in the market, such as capitalisation and thresholds on risk There are interesting behavioural and game theoretical effects going on too. An underlying assumption of the Efficient Markets Hypothesis is that participants make rational decisions (that is, optimal given all available information). While we can generally work out the most rational choice for ourselves in a given situation, in reality we can’t assume that everyone will likewise act rationally. If predicting the actions of others with all this in mind wasn’t hard enough, the markets are governed by second-order effects. That’s just a fancy way of saying that every action has a consequence, and every consequence has another consequence. Every change you make to the system can affect the path over which it evolves in the future. But, that doesn’t necessarily mean the markets are strictly deterministic.

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With each series of actions and consequences the markets change a little. What used to work no longer works as well, if at all. Rules change. Edges are lost. It’s a constantly moving puzzle and it’s one of the most fun parts of trading. The result of all of this second-guessing is that markets are extremely efficient. In traderspeak this simply means that it is hard to make money. Fluctuations in price are almost indistinguishable from noise. If you understand that point, you’ll understand that the markets require a different approach. They require you to be open-minded and humble enough to accept that you can’t possibly know everything, that your brilliant idea might be wrong, or that it might become wrong at some point in the future. You’ll also understand that there are no right answers, only evidence to be gathered, weighed and acted upon in the face of uncertainty. This can feel really uncomfortable, but it gets easier to swallow with experience. There’s only one way to gain this experience and intuition, and that’s by getting started. You can pick up useful skills by playing with financial data and paper trading, but if you’re serious about trading as a business and want to eventually make life-changing money, you need to be executing real trades with small amounts of money as soon as possible. Think of this as paying for any other educational course. It’s still an investment in order to learn, you’re just learning much more efficiently. Don’t wait until you’ve developed the ‘perfect’ system before really getting out there, either, because it doesn’t exist and you don’t need it anyway. We’ll show you an approach that will produce extraordinary results with some quite mediocre raw material.

Capital You need some money to make money. You probably want to see the exact dollar figure you need to say “okay, now I can start trading and expect x, y, z returns”. We can’t do that. Everyone’s circumstances are different — expendable income, financial ties, goals and so on. I know people who’ve started with accounts under $500, others at $500k+. The small account might want to eventually pay themselves a salary to replace their job when the bigger account wants to grow their capital for a nice comfy retirement in the south of France. While there’s no single dollar figure, one thing that can clarify capital needs and expectations based on YOUR situation is understanding the nature of trading profits.

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Trading profits are lumpy by nature. You’ll have good periods, and you’ll have bad periods, and you never know when those good and bad periods will come up. It’s true that we do spend a fair bit of time losing, but we spend more time winning. That’s how we get rich, and it can be a slow process. Short on capital? Let’s quickly touch on why that’s not as big a barrier as you may think.

Ways to bypass capital limitations What’s better than risking your own capital to generate a profit?

Risking other people’s capital to generate a profit. Sounds like the perfect plan. I mean, why risk your own hard-earned money when you can risk other people’s, often in much larger quantities, and make even more money in return? This is essentially what happens inside a hedge fund or a proprietary trading firm: - a hedge fund trades on behalf of outside investors - a proprietary trading firm trades the company’s money. These are potentially viable paths. In fact, I’ve earned my living in both types of organisations at one time or another, so I can tell you a little about the pros, cons and the realities of each: ● Both operations are highly competitive and results-driven — you’ll either love or hate this ● Some firms foster a collaborative environment; others are more siloed. The culture and “office vibes” vary in this respect ● They’re both very fast-moving, dynamic operations. Expect to work really hard, but to also be rewarded plentifully, not just with a salary, but an often bigger bonus Firms vary drastically and in a variety of ways. You’d be smart to not just chase the one with the biggest financial rewards, but the one that also suits your personality. It’s possible to join a firm that’s rewarding, sustainable and a huge pleasure to work inside. If you find the right team, you’re off to a brilliant start. Fortunately for me, I’ve been on both ends of the spectrum. I once had a hedge fund job where I was shoehorned into a suit every day against my will, despite working on the trading floor. While I didn’t care much for this, others in my team thrived on dressing in expensive clothes, watches and shoes. I understand, but can’t relate. On the flip side, I later worked at a prop trading firm in a scenic city office overlooking the bay, where I wore shorts and t-shirts every day. Every minute was spent focused on the markets. We worked on fascinating projects, collaborated in small teams and made a good

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chunk of money along the way. I loved this environment, but I also know that the singleminded focus and relentless work ethic would drive some people bonkers. If you want to trade other people’s money, you might get lucky and find somewhere that matches your personality to a T. Or you might need to make sacrifices in terms of the culture of your workplace, just like any regular job.

Opportunities outside the office? You really live in the best of times to be a quantitative trader. You have opportunities to trade on behalf of a fund without ever spending a day in their office. The attraction of these platforms is that anyone can submit an entry from the comfort of their lounge room. You can do it in your underpants while covered in crisps and no one will judge you. For instance: ● Quantopian provides a platform for traders to develop and execute automated trading strategies and allocates investor capital to the most promising; the trader earns a performance fee from the profits. ● Numerai runs data science competitions related to the stock market and pays data scientists who come up with good solutions for inclusion in their trading decisions. You can also trade on behalf of investors through social trading platforms such as Darwinex, Collective2 and others. These platforms enable investors to “follow” a trader’s strategy by automatically mirroring trades across connected brokerage accounts. Even with all these options for firms and ‘remotely-traded’ funds, I think that for the majority of people with full-time jobs, the most accessible and immediately applicable path to obtaining trading capital is to work hard and save money, and compound your capital through parttime trading.

So how can you actually get started trading? After such a long exposé about expectations, skills and resources, you must be thinking “Yeah, that’s fine Kris, but how do you actually get started? I’m prepared to meet the requirements, where do I begin?” There’s no perfect entry into trading. No one can tell you not to enter the markets. No one can stop you from building profitable trading strategies. The origins of the members in the Robot Wealth community are about as diverse as it gets. We have a postman who is now managing nearly a million dollars of capital, and we have industry veterans trading accounts many multiples bigger. Regardless of your history, there are definitely guideposts you can follow to steer you down a path towards success.

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In the next chapter, we’ll explore what success in trading looks like and how it came to be. We’ll look at examples of what isn’t very realistic, and how you can find life-changing success anyway. You’ll also see how James and I paved our way to trading victory, despite vastly different backgrounds. Throughout the next chapter, bear in mind that we haven’t yet talked about the specific approach, markets and strategies we use to trade. That’s totally fine, we’ll get to it right after. For now, we want to show you what is and isn’t possible so you can dive into our approach with a frame of reference, then we’ll show you how to get it. Let’s go!

Main points ● If you really want to trade, you’ll make the time. Otherwise, you’ll make excuses. ● All the wealthy traders I’ve met are obsessed with the markets; they look at the markets from the perspective of fascination and curiosity - not from instant gratification with windfall profits. ● Trading is multi-disciplinary, requiring broad knowledge and skills. ● Having a team is a great help in getting the right skills together in one place. ● You don’t need to be a mathematician to be a quant trader. ● In today’s world of open source software, a broad knowledge often serves practical applications (like trading) better than being an expert, as we can leverage the skills of multiple experts via the open source tools they develop. ● The top-down approach to learning is practical and highly applicable to trading. ● You’ll be forced into weighing evidence and making decisions in the face of uncertainty. Experience counts for a lot in weighing evidence, and in trading generally. ● Markets aren’t like the deterministic systems many of us are used to. They’re constantly evolving in ways we can’t understand. The ability to cope with this uncertainty may be the most significant determinant of a trader’s long-term success.

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CHAPTER 3

WHAT’S YOUR TRADING TRAJECTORY?

By now, you’re saddled with a more realistic understanding of trading. There’s a chance you’re a little tired of the lecturing, too... Yet you haven’t furiously dragged this book down to the ‘trash’ folder and trodden back to your favourite trading forum, which tells us you’re almost ready to grab the bull market by the horns. Before we get specific around execution and what you might want to do next, we’ve got one last reality to unravel. Don’t run, you’ll be eager to hear about it. This is a super short chapter where we simply must touch on this elephant in the room:

Can you get the lifestyle you want through trading? No discussion about the realities of trading would be complete without talking about what sort of money you can make. Like most topics in finance, there are so many variables that claiming to know a concrete answer is like claiming to be someone who can predict your future from the creases on your palm skin. You can make life-changing money, or you can make absolutely zero - or even lose a whole bunch. So what’s the deal? Before we talk about what success in trading looks like, let’s work backward from what isn’t very realistic. I get a ton of emails from excited Robot Wealth readers eager to bet their life’s savings/pension/mother’s house as if losing is a thing that never happens. These guys have been sold, through no fault of their own, into a vision which is unrealistic given their current skill level, free time or capital. Here are some of the more outlandish messages I’ve received on return expectations: ● “I want to put my retirement savings into a trading account. I’d be happy making around 30% per year, so long as my capital was protected.” ● “I’m aiming to make 6% per month with no more than 20% drawdown.” ● “I worked out that if I compound a return of 1% per day, I can retire in less than 2 years.” ● “I want a trading strategy that gives me a few percent every month, smooth equity curve and minimal drawdown.”

Right now, you’re either scoffing at the fact anyone could believe trading to be such a walk in the park. Or, you’re suddenly sitting a bit straighter, asking yourself whether everything you’ve been told about trading is a lie. Is it?

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Well, if 30% per year, every year with no risk was realistic, I’d gladly bet my savings, house and kidneys. But it isn’t, and yet it’s not the end of the world and definitely not a reason to ditch the idea of trading for a living. Not being able to retire in 2 years isn’t failure. You have time. You can make great money in trading. James and I have done well enough to afford a comfortable lifestyle for us and our families in two of the world’s most expensive cities. I’ve also met hundreds of others who are not only trading for a living but have upgraded their lifestyle by several tiers. So what separates the traders who make money from the rest? The biggest problem with the guys above is not unrealistically high return expectations, it’s the divorcing of reward and risk. The fundamental reality of trading is that in order to achieve a reward - a return above the risk-free interest rate - you have to take on risk. In order to participate in the opportunity to make money, a trader has to be prepared to lose money. When you hear a gloating trader say they made “consistent 20% returns”, what they’re saying is they’ve traded for many years, and have a whole bunch of annual returns (including negative ones), which compound to the equivalent of 20% per annum. It could have been a wild ride. But that’s not what a beginner tends to think when they hear it. They imagine 20% returns, consistently, every year. None of this means you can’t make money. Successful traders don’t just think of how much money they can make (though we love to daydream as much as the next dopamine-driven primate). Successful traders consider how much risk they have to put on to achieve a certain return. That risk doesn’t always work out, but that doesn’t stop them from believing another calculated risk may just pay off. The takeaway point is this: If you want trading profits, you need to accept trading losses.

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What success in trading looks like Well, what better way to find out than to quickly take you through my personal “success story”? I didn’t wake up one day and decide “that’s it, I’m going to be a trader!”. It took a while to earn even a basic measure of success. In the spirit of making life difficult, I was mostly trading overthe-counter foreign exchange products. After a couple of years of trying really hard, losing money, and blowing up the odd account, I finally developed a strategy that made money! From there, I continued to refine my approach, building other profitable strategies over a couple of years. One day I was chatting to a friend involved in capital introduction — connecting traders with people with lots of money. It turned out he had a client who tapped promising traders, but who could be engaged cheaply. Not only did I score a real trading account, but through this network I was introduced to a hedge fund interested in setting up a systematic trading desk to complement their discretionary approaches. I leaped at the chance to immerse myself in this new world of finance and money management. I left my ‘good’ yet fairly passionless engineering career behind. It was life-changing. Years later and after missing my young family for too long, my consulting company Quantify Partners was born. Consulting introduced me to a network of money managers and finance professionals throughout Australia and the Asia-Pacific, and before long I was working with some highly secretive institutions. Eventually, I was engaged by a small proprietary trading firm in Sydney and we hit it off so well they asked me to join them as a partner, shareholder, and quant researcher. This is the perfect environment to indulge my passion for technology and the markets in a laid back, yet focused and supportive environment. I also get to wear shorts, so it’s a dream come true. These days, the majority of my time is invested between my trading firm and Robot Wealth. Outside of that, I run a simple, low-maintenance FX strategy in my personal account. It exploits some simple edges (the well-known carry trade, and a lesser-known edge I won’t talk about outside the Robot Wealth community). At the time of writing, the strategy had been active for 24 months and returned just over 165%. It’s also had a 22% drawdown, a 16% drawdown, and a bunch of 2-7% drawdowns in that time.

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These are exciting returns for anyone with a decent chunk of capital to risk. Imagine doubling your account in 2 years. If you started with $500k, you’re now a millionaire. Not that I’d suggest putting all your eggs in one basket, particularly a leveraged foreign exchange basket, but that’s a compelling return in anyone’s language. In my opinion, this is a realistic return based on a sustainable level of risk. Meaning I would expect to trade this edge until it disappears, without blowing myself up or taking enough loss to drive me into a dark room with a few bottles of Cognac.

Based on my experience, here are some tips I wish somebody had told me at the start: ● Just start programming if you don’t know where to start. This isn’t the only prerequisite, but with it you can learn about statistics, finance and simulation in parallel. ● Put your trading ideas into code as soon as you have the skills. Run simulations and analyse the results. Don’t wait until you feel like an expert to start writing trading strategies. Just dive in and learn from your mistakes. You can’t lose money by running simulations, and you’ll learn more by doing than by just reading about it. ● Have fun with your trading journey. Diving into quantitative trading opens up new worlds of knowledge and challenges. If you can approach it with a sense of awe rather than a feeling of being overwhelmed, you can’t help but have fun. ● Be curious, critical, open-minded, and empirical. Rely on your own evidence rather than opinions or assumptions. Accept that if you want to go a long way, you’ll need to put in the hours.

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That’s my story, what does James credit for his success in the markets? In contrast to Kris, my career started in institutional finance. I traded my own account on the side during my career, and would later go on to set up a proprietary trading desk which traded systematic trading strategies across multiple asset classes, styles and timeframes. I credit two main things as critical to my success as a trader. The first is that I quickly learned to understand and Embrace the Mayhem. Through happy circumstance and proximity to finance professionals, I came quickly to understand “the market” to be a highly efficient machine. And I came to understand changes in asset prices to be mostly random fluctuations with little signal. My understanding of the best way to embrace this mayhem and profit from it came from: ● Exposure to Modern Portfolio Theory helped me to understand the value of taking on long term risks that tended to be rewarded over time, whilst reducing risks that I don’t get paid for. I bought stocks and I kept hold of them; I rode the carry on US Treasury futures for almost twenty years. ● Exposure to Commodity Trading Advisors (CTAs) helped me understand that market inefficiencies are blunt, noisy and time-varying. And the best way to harness this is not to try so damned hard, but trade in a simple, blunt, well-diversified manner. Trading a diverse range of simple, robust strategies has been fundamental to my trading. Second, I had access to great partners and team members. Successful trading is a multidisciplinary endeavour. I’ve managed to run successful trading operations despite being quite bad at many of the important skills and traits a trader needs. I'm a big picture guy, but I'm also a flake. I'm a brilliant starter and a terrible finisher. The details often bore me. Despite these weaknesses, I’ve done very well because I’ve shared my ideas and worked closely with others. I worked with other people who were more patient than me, more detailed, better coders, better system admins. I worked with people who had different experiences and different points of view. In the Robot Wealth community, we’re giving retail traders the same advantages I had when figuring this out alone, albeit much faster. We dispel the unrealistic approaches to trading and pass on the practical skills that are likely to lead to your success. Okay, it’s Kris again.

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As you can see, there’s no one or correct way to begin your trading journey. But one thing that should be clear is the need for others. It’s almost impossible to do this alone when the best minds in the business are doing this in well-funded teams, not only from a knowledge and skills perspective but down to the time-intensive technology setup and execution. What about other traders I’ve encountered over the years? What can we learn from other people and organisations who’ve done what you hope to do?

The independent trader Independent traders come in all shapes and sizes. There are exceptions, of course, but most successful independent traders I’ve met have a few traits in common: ● They tend to live modestly ● They tend to value their freedom above all ● They are obsessed with the markets ● They are prepared to take on short term risk for long term reward ● They don’t try to score home-runs, just regularly hit singles ● They let the power of compounding work its magic over many years.

The professional equities fund If you’re happy to kick around in suits each day, what can you expect if trading is your job? Rather than trying to trade independently from your personal account, you can get paid to turn up to an office and trade for someone else. Or you can get paid to develop trading strategies for third parties. Over the years I’ve been exposed to equities funds in a few different capacities: as a quantitative researcher, in my days as a consultant, and through friends working in that sphere. From what I’ve seen, salaries usually start at around $100k for entry-level positions, and senior people are paid in excess of $300k — not exactly spare change, and that’s only half the story. The real fun begins when you get your bonus each year. These usually form a big chunk of your takeaway pay, often over and above your salary amount. In other words, if you land one of these positions and your firm does well, you’ll be pretty much sorted financially, and drinks are definitely on you.

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The drawback? They don’t do as many boozy lunches as they used to back when James was kicking around these firms pre-GFC!

Proprietary trading Continuing with the theme of trading jobs, let’s not forget the proprietary trading world. In a proprietary trading (“prop”) firm, your job is to grow the company’s money, as opposed to outside investors’ money, or contribute to the development of strategies to trade it. Any serious prop firm will pay you, one of their traders, a salary. Prop salaries used to be lower than the salaries of fund traders (less than $100k for entrylevel positions), but there was more scope to receive big bonuses based on individual, team, and company performance. That seems to have changed in recent years, with competition heating up between firms to attract talent. Now, you can be paid more than $100k for an entry-level position in a top-tier prop firm, and more experienced traders, researchers and developers can expect $250k or more. Bonuses in good times are generally 50-100% of salary.

What does this all mean for you? If you plan to put active energy into this lifelong journey, there’s a chance you can do really well in the markets. Not only that, but there’s a good chance you can significantly impact the direction and quality of your life. I’m not just talking financially, but also in terms of passion, fulfilment and camaraderie with people who share your madness for the markets. I’ve spent 60% of my working life as an engineer and 40% in the markets, yet what I’ve earned from the markets accounts for 90% of my net worth, and I know plenty of traders who’ve done better than I have. Young, old, male, female, degree or no degree, workingclass background or wealthy parents — I’ve seen many walks of life win in the markets. As we wrap up this chapter and before we get more specific on the approach and strategies that work, keep these points in mind when you think about potential earnings: ● No trading strategy produces a “stable monthly income.” ● Short-term outcomes are largely random, even if your long-term expectancy is positive. ● A decent active retail trading strategy can have drawdowns lasting 100-300+ days ● You won’t return 100% every year, regardless of what you read on various FX websites. You might do it in one year, but the level of risk required is unsustainable.

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● Regardless, it is still possible to build a life-changing capital from a much smaller starting point with only modest annual rates of compounding. To cap off with some practical advice, I’d suggest not depending on trading profits to pay the bills and put food on the table. Accept that you’re going to need to get paid from a source other than trading profits so you can let your capital grow. That could mean not quitting your day job or selling your business, being ready to draw down on your appropriately-capitalised trading account or savings, or leaning on a very understanding spouse. I also recommend not drinking before midday, even on losing days.

Main Points ● It is naive to talk about how much can be made from the markets without also talking about how much risk must be taken. ● “Risk” means the possibility of losing money. ● Learning to navigate the markets is a huge investment in time and effort, but the process can be expedited by surrounding yourself with a good team. ● People of all socio-economic backgrounds can find success in trading. The markets don’t care. ● Independent trading is not the only way to make your living from the markets. ● It’s possible to grow your capital into a life changing amount, even with realistic return expectations. ● If you take the independent trading route, it’s is very sensible to not depend on your trading profits to pay your regular expenses.

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CHAPTER 4 TRADE WHAT WORKS, DITCH WHAT DOESN’T

We’ve talked about what it takes to succeed in the markets and the kind of rewards you can expect from them. If you’ve landed in this book hot from your favourite hyped-up trading forum, that’s probably not music to your ears. But, neither is losing your savings because a kid told you that his uncle got rich buying the dip. What’s the next logical step after all that? Well, how you actually go out and make money in the markets. Specifically, how to do it based on what works. In this chapter, we’ll finally explore the different edges and inefficiencies you can use to generate returns. I’ll lay out the important concepts of how successful traders really grow their wealth so you can walk in their footsteps, and importantly, I’ll tell you what they don’t do. Ironically, that often looks quite like what retail traders do most. To start, let me just snare the elephant in the room. I know this is going to come as a shock to anyone still catching their breath from the brutal honesty served earlier in this book:

Learning to trade is not learning technical analysis. That’s right. Everyone’s favourite pastime of drawing obscure lines on a chart is about as reliable for trading as sneezing on your monitor and connecting the dots of your own mucus. Chances are, you’ve spent a lot of time reading books about moving averages, relative strength indexes, MACDs (you’re not alone, I spent too much time on this stuff when starting out, even buying some downright embarrassing books). But that’s all weak sauce compared to what’s out there. If you’re deep in the trenches, maybe you even studied the more esoteric Gann lines, chart patterns, trend lines, and Elliot waves. Maybe you invested not only your time, but also your hard earned cash by paying someone to teach you or interpret all those lines for you. If that’s you, you’re probably pretty invested in technical analysis, and my words might feel like a slap to the face. Sorry to be so blunt about it, but it’s totally unequivocal: there is a huge difference between learning to trade effectively and learning technical analysis. The two aren’t even remotely the same thing, because only one of them makes money. The pros know it. Entire firms know it. From years of experience, I know it too. Now, I want you to know about it so you can save yourself from wasting endless hours when you could be making money. Taking a sip of my brandy, I want to play armchair psychologist again for a moment. People fall into the trap of thinking technical analysis (TA) equates to trading because it’s a comforting thing to believe. It replaces uncertainty, which isn’t so comfortable.

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It lets determinism creep in to your thinking1 (like believing the market goes up when the RSI is oversold). My experience in the markets has included working as a quant researcher in both hedge fund and proprietary trading settings. I’ve also consulted to fund managers around the AsiaPacific region, and even ventured as far as the US for a particular client. In my adventures, I’ve seen firms with statistical arbitrage trading desks, market-making operations, portfolios based on factor investing, and value funds. I’ve never encountered a technical analysis desk in any of these firms, even the most unsophisticated. Ever. If TA worked then sophisticated trading firms would have TA desks. No sophisticated trading firm has a TA desk.

Is technical analysis really 100% useless? If the serious trading firms aren’t creating trading operations based on technical analysis, is there ever a place for TA tools in a trader’s arsenal? Actually yes, but not in the way you expect. For the most part, successful traders aren’t using technical analysis as a predictor of future price movement. That’s because historical price patterns simply aren’t predictive of future price movement. It’s also subjective and unreliable, like estimating distances by ‘eyeballing it’. Here’s what I mean: Look at this chart of stock price movement over a period of about 20 years. I’ve drawn a few things a technical analyst might highlight. There’s plenty more you can highlight depending on what you know about TA — perhaps some Elliot waves or Gann lines?

1

The concept of “creeping determinism” actually isn’t one of my made-up armchair psychology terms. It was coined by Amos Tversky and Daniel Kahneman, two actual psychologists, the latter who won a Nobel Prize. "Creeping determinism" is the tendency to shift one's story to fit whatever just happened, for instance imposing false order on random events that explains some outcome. It’s the tendency to overstate the power of hindsight to see meaning in randomness, simply because that meaning provides a plausible story. As well as in historical technical analysis, we also see this in history textbooks where the historian explains how and why some event occured and it feels amazing that the people caught up in the narrative couldn't see what the historian so plainly sees. As Tversky and Kahneman put it, "He who sees the past as surprise-

free is bound to have a future full of surprises."

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This is all fairly typical of what technical analysis of a stock chart might throw up. But here’s the sneaky part: I generated the stock prices using random numbers. That means by definition the numbers are completely unrelated to one another, and any “pattern” we might perceive in those numbers is completely meaningless. If technical analysis identifies meaningless patterns, how useful can it be as a tool for finding meaningful patterns? How could it discern the meaningless from the meaningful?

“TA enthusiasts are going to tell you that you drew those lines wrong”

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Technical analysis is, at best, a sub-optimal tool for modeling the markets. In actual fact, I do know some traders who do okay with technical analysis. I know of one or two who do much better than okay. But these people are the exception and technical analysis isn’t the whole story. They also exploit well-defined and real inefficiencies and use well-implemented strategies to do so. Where TA is used, they also use quantitative methods and optimization to find sweet-spots in the parameter space that tend to “work”, at least for a limited period of time. These traders exploit rational, verifiable inefficiencies (or edges, or alphas, or whatever you want to call them). The edge is the strategy. Technical analysis is not. It’s merely a tool that helps exploit the edge, for instance by triggering an entry or exit signal.2 Still not convinced? Consider the moving average, one of the first TA “indicators” people encounter. A moving average is a tool for filtering a signal. It removes noise from a signal and smooths the output. In that sense, it “works”. But here’s where technical analysis makes an enormous leap: there’s an implicit assumption that the moving average is predictive! How can it be predictive? All it is doing is making a price series less wiggly by representing it as the average of its last several values. You could potentially use a moving average as part of a larger system to exploit some market inefficiency you’ve identified. You could use a moving average to quantify and compare momentum of different assets. Momentum has been a persistent market effect across time and asset classes, but we can’t directly observe it. Comparing moving averages of different price series could be a good place to start exploring the momentum effect. But it’s a fool’s errand to make the moving average itself the basis of the strategy. The technical analyst buys because price is above its moving average. The quantitative trader buys because the stock’s momentum is in the top 10% of all stocks in the universe, and in the past these stocks have outperformed (for example). The key point is, the moving average isn’t a predictive tool; it’s just a signal smoother. Thinking it explains the market is, to put it bluntly, crazy. 2

Far be it from me to decry the success of traders who’ve made money. But I do maintain that technical analysis as a tool for understanding the markets is, at best, far from optimal (in reality, it’s a lot worse than that). Traders who’ve made money with technical analysis were almost certainly exploiting genuine market inefficiencies through a sub-optimal and subjective lens, and could have done even better with the right tools.

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Enough TA bashing, let’s get to the good stuff.

What really generates profit in the market? It looks like I’ve just relieved some pent up frustration over technical analysis. That wasn’t entirely just for self-therapy — you’ll save a lot of time, money and headaches in knowing it’s not worth your attention. But if TA isn’t the right tool for modeling the markets, what is? What are these mysterious edges, inefficiencies and alphas we’ve been talking about? Broadly there are two ways of making money in the markets: ● Collecting risk premia ● Systematically exploiting market inefficiencies In other words, you get rewarded for either taking on risk that others don’t want, or you exploit genuine inefficiencies. Most of us who’ve been playing in the financial markets for a while will admit the markets are frighteningly efficient. But they aren’t completely efficient all the time. It’s finding these fleeting inefficiencies that most excites traders - but we should never forget that, more often than not, we are simply being paid to take on a risk that someone else doesn’t want. Let’s dig into these concepts a little deeper.

Risk premia Risk premia is long-term payment for accepting a short term risk. Check out this chart of long-term asset performance:

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Do you really want to try shorting this, big shot?! ● The blue line shows returns from US Stocks from 1900 to today. That’s a 48,000x increase in nominal value. ● The yellow line shows returns from US Bonds from 1900 to today. That’s a 300x increase in nominal value.

If you look at this chart in isolation, or if you’re 100 years old, you get the impression that investing is the easiest game in town. We buy stocks, we buy bonds, we retire and down Pina Coladas at the beach bar in Hawaiian shirts. Hold the cocktails, waiter, it’s not going to be that simple. Part of the deal of being human, with your bag of flesh and neurotransmitters, is normal human fears, feelings and lifestyle uncertainties. This means we can't discuss the rewards of buying stocks and bonds without including the emotional challenges and operational constraints. Take another peek at the chart above. Notice the logarithmic y-axis. Granted, that’s the best way to look at long term asset prices, but it tends to misrepresent what the experience of holding US stock exposure over that period would actually have been like. Overall? Great. Short to medium term? Best to buy a blood pressure monitor.

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Check out this chart, which takes the blip in the red square, corresponding to the GFC, and plots the S&P500 stock index in dollar terms:

That 50% decline looks benign in the long-term chart, but how would you feel if your million-dollar stock portfolio was suddenly worth $500k? Obviously, it’s not anyone’s favourite hobby to watch half of their asset value crumble in front of their eyes. Though admittedly, the result would probably be the same as if it had increased 100% — you’d get very drunk at the beach, uttering incoherent ramblings to the bartender. The reason stocks go up in the long term is that they tend to go down (sometimes violently) in the short and medium term. But it’s not just stocks. Any asset whose fundamental value is dependent on uncertain factors (risk) tends to increase more over the long term than the interest you would receive on the same money. We don’t say investors are compensated for investing in assets, we say that investors are compensated for taking on risk. Hence the concept of "risk premia." For the risk premia guys, investing becomes an exercise in risk management. Good risk management requires a decent understanding of the risks being taken, coupled with some intuition around why reward should flow to the investor for taking on a particular risk. This all probably sounds quite weird, but you play by the same rules.

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Any investment you make is you anticipating reward or payoff, knowing there's some level of risk involved. Say you purchase a government bond. In this case, you know with a high level of certainty what the reward will be at maturity. The risk you bear is the chance of the government defaulting, as well as the volatility in the price of the bond between the purchase time and maturity (risky in that if you needed to liquidate prior to maturity, volatility exposes you to the risk of making a loss on the sale). If instead you invested in a stock, you might be less certain of the expected reward. Plus, the risks associated with stock investing are usually greater than buying bonds - one look at the historical volatility of stock indexes compared with bond markets will tell you that. The different risk-reward profiles of these investments (including their uncertainty) should make the investor seriously contemplate their approach. Is one investment better than the other? Should you put all your eggs in one basket? Is there an optimal allocation into both investments? Is there a limit on how many rhetorical questions I can use in one paragraph? These questions are really the crux of risk premia investing, and an understanding of the risks associated with each investment is key to any investment decision in this realm. Risk premia is one of the edges we list in the next section. But it deserves its own section because it’s the best place for retail traders to start their journey. Not only has the harvesting of risk premia been the most pervasive and persistent edge in the markets for ages, but if you understand risk premia, you understand a lot of important concepts about the markets that will inform your more active trading. Plus, you can often trade more active edges with a slight “risk premia tailwind”. More on this shortly. A crucial part of this paradigm is that we only get paid over the long run by taking on risk that others find distasteful. The reward is a premium for taking on the risk and we can’t divorce those effects. In practical terms, this means that harvesting risk premia is a bumpy ride. An extremely simple implementation of a risk premia harvesting strategy is the standard 60/40 stock/bond portfolio. Holding bonds over the long term tends to be rewarded due to their value being at risk from changes in inflation and interest rates. Stocks tend to be rewarded over the long run due to their value being at risk from uncertainties around interest rates, inflation, economic growth, and political and liquidity conditions. Many academics and wealth managers will pontificate about the causes of this - but you need not participate in that. Just know that these risk premia provide you with significant tailwinds you’d be mad not to take advantage of: ● prefer to be on the long side of equities and bond trades ● prefer financial assets to real assets (such as commodities) over the long run ● prefer to sell insurance rather than buy it, if you can afford to.

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The very first strategy we developed at Robot Wealth was a simple but sophisticated longterm risk premia harvesting strategy. Over just 12 weeks, we researched and built this strategy in full view of our members. They literally saw every step of the process, including our trains of thought and the ideas that didn’t make it into the final strategy. We trade this strategy live as do many of our members. You can read all about our Bootcamp approach to teaching strategy development here.

Market edges Does everyone who makes money in the markets have some kind of secret sauce? No, in fact, I’ve barely met any traders who had anything that unique. Well-known and persistent edges have existed for a long time, meaning the sophistication of a trading operation is often not in the identification of an edge, but in its exploitation (the methods we use to trade it).

As James once put it:

“Stop looking for the perfect edge; trading is a business problem”.

Some new and mystical alpha sources are only known by a few people or firms at any given time. But you can’t bet your future on trapping these unicorn-like alpha sources. They are rare and almost impossible to catch. In my experience, the people making their living from the markets are all trading variations of the rather boring edges I’ll share with you now. Here’s a birds-eye view of the trading landscape that James put together. It shows the asset classes that are available to us, the edges and inefficiencies that have persisted over time, and the strategies that we can use to take advantage of them:

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Let’s work our way around this map of our trading territory. It’s going to look a bit like a shopping list, which is quite fitting actually, but we’ll make sense of it all afterward. Risk premia For fear of becoming an old man who repeats himself every 5 minutes, I’ll just reiterate that short-term risk is the price we pay for excess returns. We can get exposure to various risk premia by holding equities, bonds, and illiquid assets, and selling volatility. Before we move on, though, one point we should note is that if we understand risk premia, we can use it as a tailwind for our more active trading. Thanks to the inherent biases in the various assets (due to the different risk factors to which they’re exposed), we can get a boost by leaning long equities and bonds, and short volatility in our active trading. Aligning with these tailwinds means that we can make money even if our active views are wrong. This is smart trading. We wouldn’t not bet against these biases as a rule, especially in the short term, but knowing how the tailwinds tend to blow can come in handy if you can use it. Note that there is little significant drift or bias associated with FX or commodities.

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Instruments We have quite a broad and interesting universe of financial assets available to us: ● Stocks: Tend to have an upwards bias or drift and are fairly cheap to trade and accessible to retail traders. ● ETFs: A convenient way to access global markets and market segments, and even get exposure to active strategies and leverage in a non-margin account. ● FX: Very accessible to retail traders, can be traded with significant leverage, but is extremely efficient with no inherent bias. Largely traded over-the-counter (no central exchange.). Despite what you read online, it’s tough to make money in this market. ● Cryptocurrencies: Relatively new on the scene, and regardless of your convictions for the future of crypto, there are bound to be interesting opportunities for active trading. ● Futures: Interesting opportunities for retail traders with more capital. Convergence trades (spreads, statistical arbitrage and basket trading), carry trades, trend-following and higher frequency scalping or skew trades have potential. ● Options: Also tend to have a bias on the short side, especially for equity index options. Far out-of-the-money options tend to be overpriced on average.

Factor styles Factor styles are essentially ideas which have worked well across a number of asset classes and include: ● Short term mean reversion: Over short time scales, prices tend to overshoot, so betting on short term reversion can represent a good opportunity. In reality, it’s hard (not impossible) to make this work under retail costs, but this factor can often help your execution (entries and exits) of other slower strategies significantly. ● Momentum: Buying things that have gone up in the past, and/or selling things that have gone down (possibly relative to other assets). ● Carry: A yield-seeking strategy where you earn some (usually known) return when nothing changes. Examples: buying a high yielding currency and financing the purchase by borrowing in a lower yielding currency, and buying dividend stocks in a low rate environment.

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● Value: Buying cheap or undervalued assets. ● Low volatility: Buying low volatility assets within an asset class seems to outperform. People tend to overpay for ‘lottery ticket’ style assets with higher volatility, leading to the outperformance of lower volatility alternatives.

Other inefficiencies There are other inefficiencies that don’t fit into the more traditional factor styles. These include: ● Seasonality: Seasonal regularity in price and volatility patterns, both at the intra and inter-day time scales. ● Cointegration of assets: Assets exposed to similar risk factors can sometimes be combined to create synthetic assets with desirable properties that lead to profitable convergence (statistical arbitrage) trades. ● Conditional over/under reaction to certain news events: This is a classic of behavioural finance and has been remarkable in its persistence over the years. What’s interesting is the dynamics of this trade have changed dramatically, meaning you need to be nimble and reactive to take advantage of this inefficiency in the long term. So those are what you CAN trade to make money, but HOW do you go about it?

Strategies A shopping list is all well and good, but unlike your trip to pick up milk and eggs, you can’t just buy everything off the shelves and get the results you want. Next, let's talk about how we can actually take advantage of all these opportunities in a sensible, experience-backed way. You’ll hear strategies classified as “beta” and “alpha”. What’s the difference?

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Beta strategies3 take advantage of risk premia and factor styles. They’re fairly conventional since they are available to most market participants and aren’t particularly capacity constrained. For the most part, you don’t need to be worried about being beaten to the punch (front run) with these strategies. This is your school sports day where everyone gets a prize for participating. Beta strategies include: ● Diversified risk premia: Long stocks, long bonds, short volatility. This can be tactical (attempt to time the market) or you can adjust exposures more subtly. We do a bit of both in the Robot Wealth risk premia strategy, but more of the latter. ● Momentum or trend following ● Short-term mean reversion ● Carry trading ● Volatility selling ● Value investing ● Seasonality trades (these can be alpha strategies under some circumstances)

The main points to consider when trading beta strategies are: ● Prefer asset classes with a strong inherent structural bias (such as equities) over ones without (such as FX and commodities) if you can ● If you’re trading equities, you’ll want to lean long ● If you’re trading options, you’ll want to lean short, and be very very very careful ● If you’re trading futures or FX, you’ll want to lean on the side of the trade which rolls in your favour (“carry”) ● Diversification is a free lunch, up to a point ● Trade lots of beta sources if you can ● Trade lots of assets within an asset class, otherwise you’re taking on unrewarded asset-specific risk (the nerds call this “idiosyncratic risk”).

3

We’re using the term beta slightly differently here to how it’s used in academic finance papers.

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Alpha sources are those that we must “take” from less-fortunate participants - usually less sophisticated ones. The search for alpha is one of the most exciting areas of trading, but it’s a zero-sum game. Your winning trade is someone else’s losing trade…. ….I imagine some people actively take pleasure in knowing that fact. How do they work? Alpha strategies take advantage of short-term mispricings. A mispricing is a deviation from the Efficient Markets Hypothesis. That is, an instrument is priced differently to where it would be priced if all the available information was incorporated into the price. When such a deviation occurs, the market will typically adjust until the opportunity has dissipated. Profit flows from riding the wave of this adjustment. It sounds simple enough, but in reality, it’s quite complex and requires: ● The skillful and timely identification of the deviation ● The ability to execute trades quickly enough to capture the opportunity ● The ability to trade at a cost that doesn’t exceed the opportunity’s profit, or the judicial selection of trades on the basis of some profit forecast in line with your constraints. There are plenty of alpha strategies out there, but if we’re honest, many will be difficult to exploit under retail constraints. But that’s no reason to down tools and call it quits! So here are some alpha strategies that you can still make money from as retailers: ● Pairs, baskets, and other statistical arbitrage (convergence) trades ● Equity earnings/news events trading (especially within industry/theme ETF constituents) ● Options relative value (as long as you’re happy to take on some directional risk and some skew) ● Small cap price swings (including “pump and dumps” and other inefficient trading) ● Scalping and skew trades (certain commodities and cryptocurrencies have shown return skews that can be harvested systematically.)

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Finally, since we’re retail traders, we have no boss and aren’t restricted to conventional financial markets. There’s nothing stopping us from looking at other opportunities to profit. Some ideas include: ● Real estate: This asset class is exposed to various risks and has historically increased in value over the long term; you could, therefore, make the case that exposure to real estate has a place in diversified speculation. ● Betting markets: These offer interesting opportunities, but maybe not the ones you think. For instance, you don’t necessarily need to develop a model to predict the outcome of sporting events. You could instead seek inefficiencies in the movements of the odds themselves with the goal of laying the outcome (‘buying’ the bet) at low odds, and backing an outcome (‘selling’ the bet) at higher odds and thus locking in profit regardless of the outcome itself. ● P2P lending: This is essentially an illiquidity and credit risk premia play where you tie up money for a long time, and take on the risk of borrower default, in order to receive a high rate of interest. When thinking about exploiting market edges, it’s important to remember that there's no such thing as a free meal in trading. (Except boozy luncheons, on the broker’s tab, of course.) As a retail trader, you can’t hedge like a professional can. You’ll be forced to live with properties of your alpha strategies that funds and sophisticated firms wouldn’t bother with - which may be why they still “work” under retail constraints (sophisticated traders can trade more attractive risk/return profiles). Trading successfully is about exploiting an edge by getting exposure to a mispriced risk factor, or taking advantage of inefficiency in the market. However, none of these edges we’ve mentioned so far are particularly big edges. Trading any one of them alone on a handful of markets would inevitably lead to a disappointing equity curve. But guess what? None of these edges are actually your biggest edge. Your biggest edge comes from having the right approach, which can lead to extraordinary outcomes even from mediocre edges. What better time to indoctrinate you into the Robot Wealth philosophy:

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Trade small, trade broad, trade lots and trade humble I’ll explain what this means in the last chapter, but as an example of this approach in action, you can take a fairly mediocre strategy that produces an equity curve like this:

And turn it into something like this:

The charts above are something James has actually traded, and I’ll tell you how he did it. But before we do that, recall how I said that technical analysis for deciphering the markets is about as reliable as connecting the dots of your own sneeze on a chart? Well, it’s time to find out what you should be using instead.

Let’s dive in.

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Useful tools for trading If you’re going to learn anything, besides programming, I recommend diving into any of these concepts and tools: ● Time series analysis ● Correlation ● Momentum and mean reversion ● Statistics ● Regression ● Seasonal patterns ● Histograms, scatter plots ● Linear algebra ● Graphical models.

“That’s a nice list you have there, Kris, but what the heck do I do with it?”

Going into detail on how to apply all those concepts is well outside the scope of this book. You CAN read about them online, but if you’re interested in gaining the Robot Wealth approach to these concepts, we do offer in-depth training on them in the Robot Wealth algorithmic trading courses. You can read all about our courses here. If not, that’s totally fine, too. But our courses are free from the usual junk you’ll read about online and are written through the lens of experience (and success). The time and energy you can save with this approach will likely pay for the courses pretty swiftly. Shameless self-promotion aside, what do you notice about all of those tools listed above? They’re all quantitative in nature. That’s no coincidence or drunken tomfoolery. Ready for another bold claim? Quantitative trading works. And it is the only type of trading that works.

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Quantitative trading is based on the analysis of data - analysing what happened in the past and inferring what might happen in the future. That can only be done by comparing numbers. I have a buddy who works as a portfolio manager of a young but successful small cap fund. He travels a lot, meeting important people in suits; CEOs and boards of the companies he’s considering investing in. He does a lot of fundamental research at the company and industry level. He doesn’t consider himself a quantitative investor. I object. Here’s the thing. He has a process for making investment decisions. That process includes comparing the market valuation of a company to his own valuation, which he calculates through his own research. His method of valuation is proprietary, but that’s not the point. He justifies investment decisions by comparing these valuation numbers. The only way he can make a meaningful prediction is to quantify his valuation of a company. Without quantifying those valuations, he’d simply be guessing. That’s precisely the point. A prediction that isn’t based on quantitative analysis isn’t a prediction. It’s a guess. All sensible trading is quantitative in nature. If you can’t quantify something, you can’t make a meaningful prediction - you can only take a stab in the dark. Why would you want to trade the markets by guessing when there’s a better approach sitting in front of you? You wouldn’t, of course. Learn to use quantitative tools and make meaningful predictions that generate more profits than you lose, that’s more or less it.

What about training neural networks so you can set it and go to the beach? You’re probably puzzled as to why I didn’t include machine learning in the list of tools above. I get a lot of questions from people who want to build a machine learning prediction system based on historical market prices or technical indicators (not just newcomers - in my consulting days I was asked about this by several professional money managers who should have known better). Machine learning can be a useful quantitative tool, it’s just not the first tool you should reach for. Machine learning isn’t magic. It can’t make a prediction about the future when the data it has access to bears no relation to the future. It can, at best, only guess. That means successful machine learning is mostly about the data (or “features”) and the relationship they have to whatever you want to predict (the “target”).

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The key to smart trading is the intelligent application of quantitative techniques within the context of a sensible model of market behaviour. Machine learning cannot be "the strategy". You can't just throw price data at a neural network and expect it to predict future returns. Here’s an example of what you might do instead. 4 You could come up with a number of market features which you hypothesise have some weak predictive power (ideally, you would quantify that predictive power) and use machine learning techniques to create a combined signal which is greater than the sum of the parts. For example, you might find that the following features have some (low) predictive power over future equity index returns: ● The relative performance of stocks over long duration bonds over the past 12 months ● The ratio of the bond yield to stock dividend yield ● The steepness of the implied volatility forward curve ● Skew in options on the equity index ● The level of short selling taking place in dark pools. Armed with these features (for which you would quantify historical predictive power, correlation to one another, stationarity, and so on), you then might look at statistical techniques to combine them into a prediction with enough edge to be tradeable. Machine learning provides many such techniques under the umbrella of supervised learning. An alternative use of machine learning is to simplify the problem to make it more manageable using unsupervised learning techniques. An example would be to use clustering algorithms to simplify a universe of hundreds of stocks into a few clusters with similar characteristics. It's much easier to reason about (and calculate correlation matrices on) five clusters than 500 individual stocks. Regardless of the approach, this all boils down to this very important point:

Machine learning is not a trading strategy. Just as technical analysis is not a trading strategy.

4

This is a toy example that we dreamed up for the purposes of illustrating the point.

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So, let’s quickly run through what we’ve covered so far before we talk about your new, evidence-based approach to trading:

Main points ● Learning to trade is not learning technical analysis. The two aren’t even remotely the same thing. ● Money is made in the markets be either taking on risk that others shun, or exploiting real market inefficiencies. ● Useful tools for tackling the markets are universally quantitative in nature. ● Quantitative trading works. ● Quantitative trading is the only trading that works. ● Trading without quantitative tools is more commonly known as guessing. ● You don’t need “secret alpha” to do very well as a trader. Trading is primarily a business problem. Which is wonderful news, because solving business problems is far simpler than finding previously unknown alpha. ● There is an enormous range of markets and potential edges that we can seek to profit from. ● However, your biggest edge as a systematic trader isn’t any one of these market edges. Your biggest edge is your approach, and it enables you to achieve extraordinary outcomes from weak market edges. We’ll talk about this next.

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CHAPTER 5 HOW TO EMBRACE THE MAYHEM AND WIN

Want to know the exact approach used by James and myself, as well as countless other successful traders, to grow our capital into something truly life changing? Don’t worry, this isn’t a catalogue of our trading strategies! Instead, we’ll cover the principles that govern how we view the markets and approach our research and development, as well as a few things we don’t do. Enough waiting around, let’s get straight into it!

Leverage your greatest edge In the previous chapter, I shared a bunch of edges that have existed in the markets for some time. I also mentioned that none of them represent your biggest edge as an algorithmic trader, and that your biggest edge comes from having the right approach, which can lead to extraordinary outcomes from a bunch of mediocre edges. What exactly does this all mean? Well, if you type “equity curve” into Google, you can find lots of examples like this:

What’s this? The mythical, elusive equity curve that graces the dreams of traders the world over? You’re not imagining things. This is essentially a straight line from bottom left to top right with minimal drawdowns. What’s most troublesome is this example is supposed to be a single trading strategy on a single financial instrument - in this case, the EUR/USD futures contract.

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I don’t know who drew this chart, but they may as well have used a ruler on that equity curve. It’s a complete waste of your time, money and energy trying to perfect something like this. Successful traders know that: ● Asset returns are mostly random ● Any edge you trade could disappear at any time. So it makes no sense to try to perfect individual edges on individual instruments. Instead, it makes a lot of sense to trade a diverse range of simple strategies across a number of styles, instruments and timescales. Here’s a simple example: We know some markets have a tendency to trend over some timescales. Said differently, we know that returns over some timescales are weakly predictive of future returns. We don’t really know why this is, but we can hypothesise some behavioural reasons. We also don’t know precisely how to measure the effect, only that we can be fairly sure of its existence. Or, more accurately, that it used to exist. So how would you trade this effect? You could data mine historical price curves and find the perfect set of rules that performed best on that data. But this is a terrible approach. You end up discovering rules that fit the noise in the data quite well (remember, asset returns are mostly random, and so have a significant noise component). But trading past noise generates profits for no one other than your broker. This effect is crude and broad, we don’t know much about it other than it has been weakly predictive in the past. A better idea is to trade the effect across a broad range of instruments, and to combine several simple signals and data windows (say a moving average calculated over 20, 50, 100, and 200-day windows), that we think will give us exposure to this general effect.

Great, but what does this look like in practice?

Here’s a chart that you saw earlier. It’s the equity curve of one signal applied to a single market (again, the EUR/USD futures contract). The signal is to buy when the 100-day return is positive, and sell when the 100-day return is negative. The equity curve isn’t particularly special, compared to the example above, but it makes money:

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Most successful algorithmic traders’ equity curves for a single signal, single instrument strategy will look a lot more like this equity curve than the impossibly smooth one before it. That’s comforting, but we can do better. If we apply this same signal to 35 different futures contracts, we get this:

From combining a lot of unimpressive raw material together, we wind up with a very acceptable result. But we can go even further, by combining different strategies. Here’s an equity index options strategy that takes advantage of the tendency of implied volatility to trend over the short term and mean revert over the long term:

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Again, it looks pretty good, but not necessarily something you’d brag about too much. It’s a high return-high volatility strategy you wouldn’t want to trade with much size. This next strategy takes advantage of the carry on VX futures:

And finally, this one trades a carry strategy across a bunch of lower volatility futures contracts:

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What happens when you stack all these ingredients on top of one another like some beautiful financial lasagne and weight the components according to their volatility?

Now that’s what we’re talking about. There’s something you’d want to throw money at. This isn’t some untested theory, either. All the charts above are things James has personally traded over the years5. This approach makes money, and you have our permission to copy it! In any event, the general principle holds that combining multiple simple strategies together can yield excellent results at the portfolio level.

5

James no longer trades any of these strategies.

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That means we want to: 1. Identify broad inefficiencies we expect to persist. 2. Produce numerous simple, robust strategies to get exposure to them. 3. Diversify a lot across strategy, implementation, instrument and timescale. Now of course, it’s a little more complex for the retail trader. If we were an institutional fund we’d trade everything above super cheaply and hit the pub. But as retail traders we can trade fewer things and it costs more. That means the average retail trader couldn’t trade the strategies above in exactly the same way, since multi-asset strategies don’t scale down well to retail levels. So we have to downplay our weaknesses and play to our strengths. This means we need to be more targeted. We can’t afford to trade 35 different futures contracts and rebalance daily. But we could trade similar simple strategies a bit slower, maybe long only (or in the direction of carry), across a smaller range of instruments. A smart way to be more targeted is to align your alpha strategies with the direction of the bias in the beta strategies we described earlier. That’s downplaying our retail-level weakness, but what about our strengths? You really do have advantages as a retail trader, and it’s crucial you leverage these: ● We have no shareholders, investors, or investment mandate. We can trade anything we think has an edge, or nothing at all, since there’s no one to throw blunt office stationary at us for sitting on our hands. ● We can wield strategies that are unattractive to big money. For instance, earnings events and seasonality trades that pop up only rarely, or capital-constrained statistical arbitrage trades. We can even implement algos in cryptocurrency and sports betting markets. This leads us to the primary Robot Wealth mantra for retail traders: The biggest edge you have as a systematic trader is that you can trade lots of mediocre stuff together for extraordinary results.

We haven’t covered the technical details about that approach in this book. That’s not the intent here, and we’d rather save that for the Robot Wealth Courses and Bootcamps. We want to show you what successful retail trading does and does not look like. As you can see, it’s practical and not very academic.

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The markets can also be frustrating for those who have a deep desire to know how things really work. Especially for those who like rules and correct answers. If that’s you, this next concept will likely save you from quitting trading altogether.

Embrace the Mayhem! Get Comfortable with Uncertainty Let’s take a few steps back for a minute. If you look at it properly, what we see as “the market” is the aggregate effect of a bunch of transactions made by people, corporations, governments and funds, all with unique objectives. In aggregate it behaves in a highly efficient and highly random manner, as new information is made available. Now, I used to be an engineer, many of our Robot Wealth community members are also engineers. Our kink is analysing deterministic systems which can be well understood by natural laws and mathematical reality.

The market isn’t like this at all. It can’t be understood like that.

This sits about as well in our stomachs as off milk. But, if you want to win you must embrace it. When it comes to trading, there’s never a correct answer or a “right” way. Even more headsplitting is that what used to work can stop working at any point, with no advance warning. I get people asking for help, saying they’ve been trying for, in some cases, years to figure out the markets without getting anywhere. These guys can’t “crack” the markets, so they often quit. The thing is, you can’t crack the markets. But as a trader, you don’t need to.

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So how can the retail trader embrace the mayhem of the markets, in practical terms? ● Don’t fall in love with ideas. If you need help staying detached, know you’re not the first to think of whatever idea you’re working on. Be comfortable with not being original. ● Aim to make money, not to look intelligent to other people. ● Be nimble, adapt, keep an open mind, and be prepared to be wrong. Most strategies die out at some point, but this keeps it fun. Always look for new trades and ideas. ● Go broad. Trade a number of strategy styles, assets, and timescales. Don’t get stuck perfecting what’s already “good enough”. Know there’s less signal in your ideas than you think there is. ● Trade bluntly. Your strategy implementation needs to be simple and robust to most sensible implementations. ● Trade each of your edges small. ● Try to grasp the intuition. Go deep only when you have to. ● Be okay with not having all the answers. You don’t have time, and the answers don’t exist anyway. ● Treat strategy evaluation as evidence gathering and weighing, rather than seeking a binary outcome. You’ll find evidence for and against an idea, and you’ll have to weigh it in the face of uncertainty.

We can summarise all this into the second Robot Wealth mantra for retail traders:

Uncertainty is a fact of life. Rather than trying to figure out the markets, deal with uncertainty by trading simple, small and broad, being adaptable, weighing evidence rather than seeking binary answers, and being okay with being unoriginal.

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Your reason to trade Before you worry I’m about to derive the meaning of life from the markets, as fun as that would be, I’m talking about something a little more important to your survival in the trading game: The need for your trades to have a basis in rationality. Unlike life, thankfully, if there’s isn’t a reason for a trade to exist, it’s almost certainly not something to pursue. In other words, any strategy you consider needs to be based on a real market effect you can rationalise on a financial, economic, structural or behavioural basis. The 200-period EMA entering the chikou span while being eaten by a harmonic bat pattern most definitely does not constitute such a reason. We humans are pretty good at justifying things we want to believe, but if you have the confidence to justify that kind of trade then you can probably just convince random people on the street to hand you their money. Instead, we might hypothesise that stocks which outperformed over the last twelve months tend to outperform in the short term future. This is a hypothesis that captures the idea of stock momentum, and we can come up with some plausible-sounding behavioural justifications for this effect. Perhaps people tend to be slow to recognise when a stock is worth more than it’s currently being traded at. Meaning it tends to get bid up over time as people catch on, rather than being repriced to its fair value immediately. That may be complete and utter bollocks, but as a hypothesis it’s plausible. It certainly could be true. More importantly, we can test it. There’s no need for our hypothesis to be factual beyond doubt (it wouldn’t be a hypothesis otherwise). But it does need to be plausible. Any trading idea you can’t rationalise is best left alone. It’s too easy to find deceptive relationships, and with no means to tell whether a good result is due to randomness or a real effect, you’ll end up wasting a lot of time and losing money.6 Ideas with a rational basis tend to be far more rewarding and fruitful. It’s great having a reason for an idea, but what about testing it?

Robustness counts for more than backtest performance We’ve already mentioned that most market edges are small, simple and fleeting. Most people are okay with the small and fleeting ideas, but many get hung up on the idea that 6

Granted, it may be possible to data mine profitable trading rules and use sophisticated statistical tests to calculate the probability of a result being due to chance. We even provide a few lessons on this approach in our Advanced Algorithmic Trading course. But in our experience, it’s much easier and far more interesting to just focus on things that make sense.

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good trading is based on simple ideas. Simple ideas implemented with sophistication, but simple ideas nevertheless. Backtesting and empirical evidence are important. It’s harder to get excited about a trade which doesn’t backtest well - but the problem, even with a good backtest, is you can’t really believe it. Does that surprise you? I’ll explain. The purpose of backtesting is to validate a trading idea and get a feel for how it actually trades. Even this is a little dicey because there’s never any guarantee that an edge will carry on just like it did in the past (it probably won’t), but it’s better than not trying at all. Backtesting can tell us if something did work in the past. We at least want to see that backtested profits covered our costs of trading with a decent amount of fat left over. We then make a leap and assume it will persist, at least temporarily, out in the live markets. The problem is most people abuse their backtests. In particular, they use them to set performance expectations. Please, don’t do this. It’s a slippery slope when you start tweaking a parameter because it eked out a few percent extra in backtested annual average returns. Doing that is a fast track to overfitting and poor outof-sample performance. Overfitting is a destructive thing to do. It stems from the temptation to use your backtest for something it should never be used for — setting performance expectations. To avoid overfitting, the first thing we do is recognise that 80% of the alpha is in the core idea. It should therefore be robust to any sensible implementation. If it needs bells and whistles to make it work, it’s probably a bad idea. Bells and whistles increase the degrees of freedom of your trading strategy and open the door to overfitting. “Bells and whistles” refer to things like trade filters, highly-tuned stop and take profit limits, and other fancy entry and exit conditions. A good trading idea doesn’t need this stuff to produce a profitable backtest. All of this implies that when designing a trading strategy, we should default to the simplest thing that makes sense. This is hard for people to do in practice, despite the fact that simple ideas tend to be more robust than complex ideas. In our experience, a lot of aspiring traders default to complexity, because they assume that there is a direct relationship between the level of complexity and the validity of a trading strategy. This couldn’t be further from the truth, and it’s such a widespread phenomenon that it deserves a closer look.

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The Lure of Complexity Aspiring traders tend to get excited when talking about shiny, complicated stuff. Traders are, generally, a clever bunch who tend to be attracted to complex topics. But just because one can wrap their heads around complexity doesn’t mean you should throw complexity at all your problems. That goes for trading, especially. On that note, the same two topics crop up time and again: •

machine learning

•

options trading

In my experience, many options traders think they can somehow cheat probability. Likewise, many machine learning enthusiasts (particularly those without formal training in a quantitative discipline) mistakenly think complex statistical models can derive insight from randomness. That’s commendable bravery, yet not very effective. We can be brave, but also sensible. Let’s be clear. You can't cap your downside without reducing your expected trade return. You can’t indiscriminately feed price data to a neural network and expect it to find alpha hidden amongst the disorder. Concentrating on the complex is often just a feel-good distraction from taking a genuine risk. So why is simplicity better when it comes to trading strategies? Well, any edge we think we’ve found is a weak and changing signal in an ocean of noise. It is blunt and often ill-defined. It doesn’t make sense to attempt to precisely isolate that weak, poorly-defined signal. In fact, this is an exercise in futility. It makes much more sense to seek future robustness. And guess what? When it comes to the markets: •

simple ideas tend to be more robust

•

complex ideas tend to be more fragile

Focusing on simple trading ideas is also an act of kindness to your future trader self. We can readily understand and manipulate simple things. On the other hand, it can be difficult to get to the heart of complex matters. But why does that matter? Well, consider trading a strategy and going into a painful drawdown. Do you stick it out or cut your losses and ditch the strategy? If you know in broad and simple terms why the

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strategy works, you can reason about the likelihood of something having fundamentally changed, which is powerful evidence to act one way or the other. On the other hand, if the strategy is complex, it gets painfully difficult to find this insight. Taken to an extreme, say you are trading a strategy based on the predictions of a deep neural network. This model takes the data you feed it to transform and combine it in a limitless number of ways to produce predictions. Here, you have virtually no chance of understanding why the network makes these predictions. The only evidence you have as to whether it’s stopped working is the live performance itself. If the strategy had a typical retail Sharpe ratio, you’d have to wait for ages to make a decision on this basis alone, all the while going around in circles seeking clarity on a question that has no answer. We can all agree that’s not an enviable position to be in, particularly when you’re underwater. While we’re on deep neural networks, I have first-hand experience in this area. Remember when I showed you the performance of my FX account? Well, the first strategy I traded on that account, for about the first nine months of its life, was the very thing I just warned you about in the previous paragraph - a deep neural network trained on a bunch of inputs for predicting the direction of the next day’s return. Nobody’s perfect. The neural net strategy did OK. It made some money, with an equity peak in early July 2016. Then it entered a shallow but long drawdown period. The account didn’t make a new equity high until March 2017. During that drawdown period, I went through exactly what I described above - constant stress from wondering whether it had stopped working. I even wondered if it had ever worked at all, and maybe I’d just been extremely lucky (this is actually a real possibility). Here’s a zoomed in view of that part of the strategy’s performance:

….

….

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Not only did I go through this horribly draining merry-go-round of wondering about questions to which there were no answers, but I also invested a lot of time and effort in just keeping the strategy up and running. The neural net had a taste for good data, so I had all this hungry data infrastructure to maintain. It also needed to be re-trained at regular intervals, like an unruly teenager. It got so bad that in the end I quit trading the neural net strategy altogether. I replaced it with a much simpler strategy whose edge I can describe in two short sentences. You can see on the chart above the point at which I switched over; I’ve been trading this same strategy ever since (you can see how it’s performed over a longer time horizon if you look at the chart in Chapter 3). The relief (and result) was immense. I was now trading something I could genuinely understand, and not only did the new strategy cost me much less mental energy, but it also cost much less physical energy. I was suddenly free to explore other edges and other markets, and my trading took a significant leap forward. It also saved me a few years of accelerated aging. The stiff-backed, bloodshot-eyed manual traders reading this just jolted upright. But if complexity isn’t helpful in the core trading idea, is there a place for it at all in trading? The answer is of course yes. Complexity is justified at times, just not where most traders think. In systematic trading, the complexity is often in the execution of a simple idea. Remember how we implored you to optimise for future robustness? Well, it turns out that’s not quite as simple as our words might have implied. It requires giving yourself every chance of exploiting that weak, poorly-defined edge into the future.

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A sensible way to do that is to trade a bunch of different implementations of the same trading idea in parallel. Machine learning people call this approach an “ensemble.” It tends to be a useful way to build robustness into a strategy, but it also introduces a whole lot of complexity, for example, to name a few issues: ● Deciding which implementations to include in the ensemble. ● Deciding on a method of combining the predictions of the different components, when these predictions might be on vastly different scales. ● Solving the problem of holding conflicting positions that result from different ensemble components. Dealing with this complexity is beyond the scope of this book, but it’s something we tackle in our Bootcamps, and it has a huge impact on the future success of an otherwise-sound trading idea. We’d love to share this with you on the inside when you’re ready for the team. As we wrap this up I must re-assert the following: allow complexity to creep in only when it’s justified, and you understand exactly what it is you’re doing and why. Don’t add complexity simply because you’re enthusiastic about a particular technology, or even worse, want to appear clever. Focus on making money instead. The good news is that you don’t need to go chase complex trading ideas to succeed in the markets. In fact, the best ideas are simple.

Finding and Evaluating Trading Ideas Hopefully, we’ve convinced you that your greatest edge is having the right approach, and you now understand that this new approach can transform a bunch of mediocre edges into extraordinary results. You’ll find this both comforting and exciting because: ● You don’t need to unlock the secret key to the markets (if it even existed) to do well. ● Opportunities are out there, but they probably aren’t what you thought. They’re also not complex in the way you thought. ● Trading is a business problem first and foremost, and business problems are generally tractable. In spite of your excitement about all these realisations, you might still be wondering how this looks in practice. How do you actually find and evaluate a trading idea? Well, we’ll show actual strategies in-depth inside our Bootcamps, as well as build brand new ones, but here in this book we’ll give you a high-level overview. Here goes.

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First, you need to come up with an idea. This is your hypothesis for the existence of an edge, pricing inefficiency, alpha, or whatever you want to call it. It’s not important where it comes from, but some of the most fertile grounds for finding trading ideas include: ● Reading widely. The quant blogosphere is hit and miss. There’s definitely gold in those hills, there’s also a lot of useless information and separating the two comes with experience. Yet, you’ll find a wealth of academic literature on SSRN. Anything written by Euan Sinclair is worth reading, and Ernie Chan can inspire you with pretty well known, but well explained quant trading strategies. ● Observing the markets. There’s no greater inspiration for ideas than actually tapping into the pulse of the markets themselves. You can learn by not only observing, but also participating via a paper trading account or by executing real trades at small size. A cheap way to get started is via a subscription to TradingView, where for a few bucks per month you can get data and visualisation on hundreds of different global markets (just mind the technical analysis that this tool, like most retail trading tools, is rife with). ● Talking to other traders. Believe it or not, independent traders aren’t as secretive as you think. The best traders know their success is not dependent on having secret alpha so much as it is on having the right approach and good business practices. That means idea sharing happens much more than you imagine. Just make sure to bring some of your own to the table - it’s a two-way street. Be a light-footed scientist when looking for trading ideas. Prefer things you can directly test, or translate into something you can test, and falsify quickly. The faster you can move on from a bad idea, the faster you can get to the good ones that will make money, and surface for sunlight. You probably think I’m about to say that there’s still a place for ideas that aren’t easy to test and falsify, and you’re absolutely right. We aren’t the scientific/systematic police, and if you think about all the reasons you could be deluding yourself and can put those aside, you are well within your rights to pursue an idea you think has merit. Just know that if you can’t easily test and falsify the idea, you risk chewing up a lot of time and effort evaluating the idea. Experience counts for a lot in this case; beginners would be best served to stick to things that are easily testable. So how might you translate an idea into something you can test? Well, bearing in mind that good trading ideas are robust to pretty much any sensible implementation, and that simplicity trumps complexity, the best place to start is the simplest possible implementation that makes sense. For example, if your idea was based on say momentum or mean-reversion, you might start by looking at whether percent price change was a useful trading signal. If your idea had merit, you’d quickly find that this signal was profitable, at least across a range of its parameter space that was stable through time.

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Ummmm, what?!? What’s a “parameter space”, and what do I mean by “stable through time?” Simply, the parameter space is the range of values that can be taken by a parameter that defines the trading idea. In our example, the parameter is the period over which we calculate percent price change, and the parameter space is the range of values this parameter can take. For example, we might look at one-day, two-day, one-week, or twomonth percent price changes. We could look at percent price change over any time scale. “Stable through time” means the range of the parameter space that produced profitable trading signals didn’t change much over time. You’ll never see perfect stability through time, so use your judgment to decide whether your idea is legitimate, or whether any good performance is random. This weighing of evidence is the art of scientific trading, and it comes with experience. At this point you’re in a position to either discard the idea and move on, or if the idea has potential, to test it in more detail and finally move into implementation and trading.

Here’s the workflow we use inside our Algo Bootcamps A detailed evaluation will generally involve testing the strategy against a range of implementations and asset universes. You’ll also want to check that your assumptions can be realised in live trading. This might involve evaluating liquidity and bid-ask spreads at the strategy’s trading times, the speed at which you need to react to the signal in order to capitalise on it, or any other trading realities that you have dealt with by way of assumption to this point.

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There’s a lot to consider at the implementation phase as well, but a significant aspect is the idea of optimising for future robustness. We mentioned previously that embracing uncertainty is critical at this point, and a practical solution is the sensible use of ensembling techniques. That’s all we can say about detailed evaluation and implementation within the scope of this book, but we’d love to show you some real examples in one of our Bootcamps.

The Recipe for Trading Success At this point, you’re probably finding yourself saying OK, you’ve given me a high-level

overview of strategy generation and evaluation, can’t you just give me a recipe that I can follow myself? That’s a really fair question, but unfortunately the answer is a resounding no. The reality is that you can’t systematise this process. You can’t wrap it up into 7 steps to trading success. Lots of people will try to sell you something that does precisely that, but you can be certain that that’s little more than a marketing trick to appeal to your very human need for certainty. Don’t fall for it. Instead, recognise that successful trading is uncomfortable and requires reconciling yourself to uncertainty. It requires you to weigh the evidence you collect through your own ingenuity and insight, all the while anticipating that things can change and invalidate your conclusions. This is what we really mean by embracing the mayhem. There’s no process or recipe we can give you that charts the right course through the disorder. But there’s certainly some guiding principles and an approach that has proven successful that we’d love to see you adopt. Now, don’t be disheartened that there is no such thing as 7 steps to trading success. This is a good thing! If there were a simple process to finding and exploiting edges, you can be sure that the most well-resourced entity would eventually come to completely dominate the markets. The very fact that creativity, ingenuity, smart risk-taking, perseverance and experiential insight play such a significant role in trading success is precisely what provides the opportunity for people like you and me to extract life-changing profits from the market. Absent that, there would be little point or fun. I’m acutely aware that this chapter, in which we described an approach that works, is very different from what you’ll find in most retail trading literature. But it should be cause for great excitement. This approach is realistic, highly accessible, doesn’t require secret market

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knowledge or mathematical genius, and rewards creativity and perseverance, which no doubt you have in spades.

Main Points ● Most edges are small, fleeting and impossible to define precisely. Traded in isolation, their equity curves typically look unattractive. ● However, combining multiple simple strategies together can yield excellent results at the portfolio level. ● Your greatest edge as a systematic trader is not some amazing alpha signal. It’s your approach, in particular, your ability to trade multiple mediocre edges together to yield extraordinary results. ● Your ability to reconcile uncertainty - to embrace the mayhem - is the single biggest determinant of your success as a trader. ● As a retail trader, you need to play to your strengths and downplay your weaknesses. And you most definitely have both. ● Don’t get hung up on trying to crack the markets. Instead, deal with uncertainty by trading simple, small and broad, being adaptable, weighing evidence rather than seeking binary answers, and being okay with being unoriginal. ● Prefer simple trading ideas with a basis in rationality that can be easily tested and falsified. ● Robustness is much more important than finding the backtest with the best performance. ● The best trading ideas tend to be simple, not complex. However, complexity often arises in the implementation of a trading strategy. ● There is no well-defined recipe or process for finding and developing trading strategies. But that’s a very good thing, and in fact, is the reason people like you and I can do very well in the markets.

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CONCLUSION

My personal motivation for writing this book was actually somewhat selfish. We spend so much time resetting the expectations of new Robot Wealth members and prospective members, that I decided it was more efficient to simply write a book about it. Not that my ego wanted the honour of calling myself an author or anything (let me know if you want a signed copy. Digital, not print). Of course, the BIGGER goal here was to arm you with a realistic approach to trading that’s based on evidence and experience, as well as the innate pleasure of being in the markets over seeking quick riches. That way, you can skip all the bogus information, overpromising products and untested theory out there and begin realising your passion for trading on the right foot. Regardless, I can guess readers will respond in one of three ways to this book: 1. You’ll conclude there are easier ways to make money and you’ll run a million miles 2. You’re truly excited if somewhat daunted at the prospect of tackling the markets, yet you feel so much closer now that you know what does and doesn’t work 3. You’ll feel a strong, negative emotion towards what I’ve written - maybe you’re dismissive towards it. Either of the first two responses is hugely positive. If your response is the first, you’ve saved a ton of time and energy. You can really throw yourself at other ways to make money with some oomph. Best of luck. If your response is the second, that’s fantastic. If you feel excited now that you have realistic expectations and a solid approach, then you clearly have the passion and energy for the markets needed for succeeding long term. Congratulations! If your response is more like the third, let’s consider for a moment why you feel that way. At the time of writing, I’d personally dealt with more than 500 aspiring traders in our community. Some of the saddest outcomes I’ve experienced are when people have told me they’ve struggled in the markets for upwards of five years without any success. Yet when I lay out what they need to do, they walk away because it isn’t aligned with what they wanted or were expecting to hear. Just think about that for a moment. You’ve tried and failed at something for years, pouring time and money into it, and when someone with a history of success at that very thing gives you some guidance, you go back to what you were doing previously because you didn’t like the idea of it all. Is that sensible? Rational?

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It doesn’t matter; people do it anyway. Here’s why I think this is. People who are attracted to the markets generally have a history of success in other arenas. They’re usually intelligent and comfortable enough with their intelligence to try out the markets, where they’re competing with the planet’s most well-resourced companies and brightest minds. Finally, something that can truly challenge them. But it’s this very intelligence, or the unshakable belief in that intelligence, that makes those people blind to unexpected or uncomfortable ideas. As I mentioned earlier, humility, rather than believing you have the answers, is critical for being a successful trader. You can do really well in the markets even if you feel this way. Just stay open-minded. If that’s you, or if you find your leg bouncing enthusiastically as you reach for your next trading book, let’s talk some more. You sound like someone who would fit in perfectly with our fun and enthusiastic team, and we’d love to help you turn your passion into an income-generating reality. Our community is super welcoming, non-judgemental and incredibly collaborative. It’s a transformational place to be. The diverse skills of 350+ (as of writing) like-minded traders aside, just having people to talk to who share your passion is life-changing. Too many of us have no one to chat to about trading, no one who understands our obsession for this “weird hobby”. Now, the most active, passionate algo trading community online is literally at your fingertips. Along with the community, we also run Algo Bootcamps, exclusive to Robot Wealth. This is where James and myself team up with the entire community to brainstorm, build and trade live strategies from scratch in as little as eight weeks. Nothing is hidden, every step and nugget of intuition is documented and laid bare for you to collaborate on with your fellow teammates. We’d love to meet you there. It’s likely the best decision you’ll ever make in your trading career. Here’s how we help the Robot Wealth community succeed in retail trading: ● Sharing honest and transparent advice centred on our quantitative, data-driven, evidence-based approach to the markets. ● Running “Bootcamps” where we show our members this approach in action, as we research and develop trading strategies in full view of the community. ● Showing members what works, as well as what doesn’t. ● Helping members structure their trading business for success - whether that “business” is an actual company or an individual with a trading account.

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● Providing access to our trading tools and technologies, such as scalable data processing, analytics and execution that would be difficult or costly for the retail trader to acquire independently. ● Providing a platform for networking, knowledge sharing, and ideas exchange. ● Helping individuals develop realistic and honest expectations around what sort of money can be made in retail trading, as well as what it takes to make it. We are not a signal or strategy selling website in which you can live from a “set and forget” solution (this doesn’t exist, so by definition, we can’t sell it). Instead, we focus on teaching people how to create, build and use automated trading systems successfully. One of the massive benefits of being a Robot Wealth community member is that we get to work together. We share research and implementation work. We question each other's assumptions and keep each other humble. We share tools, software, IP on processes. We share data and access to market intelligence. We share "buying power" or "negotiation power" in the market (we recently negotiated a great deal on brokerage for our members). We share "insider knowledge" so you know what “finance people” are saying, even if you don't know anyone in finance or trading in real life. We get to have fun together. These benefits make the implementation of successful, semi-professional trading strategies realistic and enjoyable for Robot Wealth community members.

Want to find out if you’re a good match?

Check us out at https://robotwealth.com/bootcamp

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FINAL WORD Many people try to sell you confident answers to the trading problem. There’s a whole industry of technical analysis “experts” and gurus apparently living the “trader lifestyle” who sell confident, easy to swallow lies. That’s a fast track to nowhere. The reality is messier, more complex and actually much more exciting. There are no rules. There are no right answers. But there is a lot of money to be made for those who are openminded, pragmatic and prepared to adapt and move fast. If that’s you, go get it. https://robotwealth.com/bootcamp

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Appendix - Backtesting the RSI Strategy from Chapter 1 This Appendix provides detail on how we backtested the RSI strategy from Chapter1. Recall that this strategy was described by its author as follows: out:

“If the RSI is less than 30, it means that the market is oversold and that the price will probably eventually increase. Once the reversal is confirmed, a buy trade can be placed. Conversely, if the RSI is more than 70, it means that it's overbought and that the price will probably soon decline. After confirmation of the reversal, a sell trade can be placed. The 50 level is the midline that separates the upper (Bullish) and lower (Bearish) territories. In an uptrend, the RSI is usually above 50, while in a downtrend, it is below 50.”

In order to backtest the RSI strategy, we need to define how we “confirm a reversal.” Despite the implication that how we do this self-evident, we still need to make it explicit in order to put it into computer code. There are a lot of ways we could do this, but here we simply take confirmation of a long trade as a close above the high of the bar at which the RSI crossed under the oversold threshold. The short confirmation is the reverse of this. We test the strategy over the period 2010 to 2017 using midnight-aligned daily FX data (EUR/USD, GBP/USD, AUD/USD and USD/CAD). The original article was from an FX website, so we use FX data here, but you could equally use stocks or futures. With $10,000 of starting capital, we trade $1,000 worth of currency using 20x leverage for each trade (20x leverage is not unusual in retail FX trading - in fact, it’s on the smal side). Spreads and commissions typical of a retail account are included. Here’s some Zorro code for performing this simulation (Zorro is an excellent tool for coding and simulating trading strategies using a simple C-based scripting language. There’s a free version, and the full-featured version is great value for money):

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/* RSI Trading Strategy If RSI < 30, buy on confirmation of reversal. If RSI > 70, sell on confirmation of reversal. Confirmation of reversal is a higher or lower close than high/low of bar when RSI crosses threshold */ function run() { set(LOGFILE); StartDate = 2010; EndDate = 2017; BarPeriod = 1440; MaxLong = MaxShort = 1; Capital = 10000; while(asset(loop("EUR/USD", "GBP/USD", "AUD/USD", "USD/CAD"))) { Margin = 50; int period = 9; vars close = series(priceClose()); vars rsi = series(RSI(close, period)); static bool trade_long, trade_short; static var reference_price; if (crossUnder(rsi, 30)) { trade_long = TRUE; trade_short = FALSE; reference_price = priceHigh(); } if (crossOver(rsi, 70)) { trade_short = TRUE; trade_long = FALSE; reference_price = priceLow(); } if (trade_long && close[0] > reference_price) { enterLong(); } if (trade_short && close[0] < reference_price) { enterShort(); } set(PLOTNOW); plot("tradelong", trade_long, NEW|DOT, GREEN); plot("tradeshort", trade_short, 0|DOT, RED); plot("RSI", rsi, NEW, BLUE); plot("OS", 30, 0, BLACK); plot("OB", 70, 0, BLACK); } }

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The software performs an eight-year simulation on four different exchange rates in a matter of seconds. See Chapter 1 for the (disappointing) results of this backtest.

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