Bootcamp will transform your approach to the markets. Watch the video to find out how.

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Trading is hard if you don’t have a plan

Most traders don’t take the problem seriously enough. They jump from idea to idea. They pass over high-probability edges in favor of heroic ideas that are unlikely to pay off.

This course is for the trader, ready to get serious about realistic, high-probability trading approaches.

Getting serious about trading involves two main things:

Market Intuition – understanding the market, its players and the kind of things that make money.

Data Analysis and Portfolio Construction Skills – being able to create and test market hypotheses using simple data analysis techniques.

The course runs in two parts:

The first, Trade like a Quant, is all about gaining market intuition.

Through explicit examples of real systematic trading strategies, you’ll learn the fundamentals of extracting edge from the market.

The second, Quant like a Trader, is an introduction to the techniques you need.

The simple data analysis and portfolio construction tools you can use to discover market inefficiencies, test trading ideas and manage trading.

By the end of the course, you’ll have:

A realistic understanding of the market (a highly efficient machine, not a casino)

A business plan for exploiting it profitably, based on selecting the least competitive games

Many examples of specific systematic trading strategies spanning multiple asset classes

A set of tools and techniques to manage your trading and do effective research and strategy development.

In this course, you’ll learn explicit examples of real, effective systematic trading strategies, including:

Risk premia harvesting in equity indexes and government bonds, diversified with alternative assets.

Crypto carry trades, which exploit the supply/demand dynamics of leveraged crypto futures.

Turn-of-the-month “window dressing” effects in treasury bonds

Trading large rebalance flows in stocks and bond indexes

Exploiting lopsided positioning in VIX derivatives

Trading NAV discounts in close-end funds and ETFs in times of severe market stress

Trading structural supply/demand imbalances in crypto futures

Trend and seasonality effects in cryptocurrencies

These are simple uncompetitive strategies that you could trade as a part-time trader.

You’ll learn about the following strategies in less detail (you won’t be able to trade most of these, but they are excellent case studies):

Market Making

ETF Arbitrage

Business-hour seasonality in foreign exchange

Post-earnings announcement drift in stocks

ADR pairs trading and futures spreading in severe market stress

Trading the FTX leveraged token rebalance

Trading brand-new products: example of the FTX MOVE contract

Buying liquidations in crypto futures

Cross-exchange spread trading in crypto

Commodity Carry (in the past)

Equity Pairs Trading and modern statistical arbitrage

You’ll also learn effective, repeatable techniques that you can apply to your trading, including:

How to think about market structure and the price discovery process

How to put together a realistic business strategy for success as a part-time trader

How to align your trading with large sources of drift, so you are likely to “be wrong and still make money”

A process for thinking about and defining market edge – “why will somebody trade at bad prices, and why do you get to trade with them?”

Minimizing costs in your trading and navigating the uncertain return / certain cost trade-off.

How to safely navigate periods of extreme market stress: playing defense, then offense.

Portfolio Construction: How to size positions, manage risk, hedge tails, and construct an effective trading portfolio.

We will teach you how to investigate market edges using simple data analysis tools, including:

How to uncover assumptions and create testable hypotheses

Using data in the most efficient way possible

Minimizing and accepting sources of bias in your data

Pulling data from APIs

Data munging: changing the shape of your data

Factor analysis

Event Studies and markout analysis

Vector- and event-based simulation

Weighing evidence and dealing with uncertainty.

Robot Wealth Bootcamps have been tried and tested by over 1,200 traders.

What do I get if I sign up for Bootcamp?

Bootcamp is an interactive learning experience, with live support from the RW team and real-time connection with your peers. You can do it at your own pace, and discuss in real-time with us too.

Video lessons, slide-based lectures and written content.

Training material is released immediately upon enrolment. You can go through it whenever you like, and it’ll always be available.

Some of it is pre-recorded from the last times we ran the course. Some of it is created as we go along, based on your questions and challenges and new things we find interesting.

Interactive live Q&A webinars with Kris.

Q&A webinars are held on Zoom to discuss the course content (during interactive course runs only).

We alternate the times of the webinar by approx. 12 hours, to ensure everyone can attend sometimes.

All webinars are recorded so that you can catch up on any you miss or re-watch at your own pace.

Exclusive Discord server where you can connect in real-time with your instructors and Bootcamp peers.

This is the main place where we talk and respond to questions.

A supportive community of part-time traders. Connect, collaborate and troubleshoot with your trading peers from across the globe.

Custom learning tools and clean data that’s ready to use, with comprehensive instructions on how to use it.

Lifetime access to all course material so you’ll never fall behind. (Compatible on desktop, mobile and tablet devices.)

You get lifetime access to all Bootcamp training material.

As part of your enrollment you get lifetime access to all the course materials so you can take it entirely at your own pace if our schedule doesn’t quite work for you.

Here’s what you’ll learn:

The Complete Curriculum you’ll work through.

Module 0

Enrolment & Initiation

You will get immediate access to the Discord community from the time you sign up. So introduce yourself, say hello, and spark up any discussion with us and the team.

Module 0 contains logistical information to get you oriented.

Module 1

Seeing the Market for What It Really Is

In this module, you’ll build up a mental model of the market as it really is (rather than what you’d like it to be!) and start to put together a business plan for making money as a part-time trader.

Buy too cheap / Sell too rich / Don’t get rekt

We start with the simple observation that successful trading requires you to:

buy things that are too cheap
sell things that are too rich
avoid unnecessary risks that lead to you losing lots of money.

We explore how trades would play out if you were the perfect trader, acting with perfect information. And we find that, even in those circumstances, luck or randomness plays a significant role in trading outcomes.

A good trade isn’t one that makes money, it’s one with positive expectation.

Market Structure and Price Discovery

Next, we confront reality.

You’ll learn that the goal of the market mechanism is to find the price that is the balancing point of supply and demand where the most trading happens.

Through many examples, you’ll see how this plays out in:

batch auctions, such as the closing auction in stocks
the central limit order book
automated market makers in crypto swaps

A Realistic Model of the Market

Many traders start off on the wrong foot because they think of the market as a big casino. They think it’s an easily exploitable game driven by fear, greed, and emotions. They see people throwing money around in unsophisticated ways and assume the market must be highly inefficient.

Running through these examples, you’ll begin to understand why the market is highly efficient, despite the presence of lots of gambling maniacs.

And why trading is so hard.

And that’s because we’re trading with many other people. And, although there might be plenty of people willing to trade at bad prices, there’s an extremely competitive race to get to trade with those people.

Just because there’s “dumb money” in the market, doesn’t mean you get to trade with it!

Appreciation of the true nature of the highly competitive game we are playing leads to two important realizations:

First, it’s quite hard to lose money consistently without doing something dumb. We call these dumb things “The Mortal Sins”, and they include:

Trading Too Much
Trading Too Big
Trying To Be a Hero.

These behaviors are sure to lose you money in the long run.

Avoiding these is your first and most important mission as a trader.

Second, as a small part-time trader, you can’t outcompete the best in the business. You need to search out the easiest, least-competitive games to build your business plan around.

And that’s what we start to do in Module 1.

You’ll start to outline a core strategy to exploit market returns, which we visualize as a pyramid. In the pyramid:

We need at least one “stonkingly-obvious high-probability edge” to build our trading upon. And it should be as “win-win” and uncompetitive as possible. You want at least one thing you can be very certain about.
As long as we avoid the Mortal Sins, the market doesn’t punish us that much for being wrong. This gives us the confidence to go after slightly more competitive active edges.
So we look to “Trade More Sh*t” and add simple systematic strategies that exploit market inefficiencies. We look at strategies that exploit large rebalance flows and misaligned trader objectives. These are noisy edges that are less attractive to big players.
When we combine noisy edges together, the whole is greater than the sum of the parts. Diversifying in this way is a good idea because it spreads our bets and tends to reduce the variance of our trading portfolio.

By the end of module 1, you’ll start seeing the market realistically, rather than hopefully. And you’ll start building a realistic strategy for exploiting it.

Module 2

Stonkingly Obvious High-Probability Edges

If you start a business venture, it’s clear you need an obvious, reliable way to make money. You wouldn’t try to blag it.

“I’m smart and hard-working” is not a business case.

You need a stonkingly obvious way to get paid.

Drawing lines on a chart and hoping you can figure out when to buy and sell is NOT a stonkingly obvious way to get paid. It depends on your discretion and skill. It’s a lousy business case. I wouldn’t lend you money to do that.

Throwing features into a machine learning algorithm is a lousy business case too. There’s no reason to think you’d get paid for that. I wouldn’t lend you money to do that either.

In this module, you’ll learn about the following systematic strategies which have historically been very effective:

market-making
ETF arbitrage
commodity carry and trend-following
equity pairs trading
risk premia harvesting
crypto basis effects
VIX futures basis effects.

You’ll discover they have a few features in common.

And the big one is that the reason these trades “worked” is that you were being paid for:

doing something useful (though this may not be immediately obvious)
taking on risk or “unattractive” work (otherwise everyone would do it)
doing it well

If module 1 was about putting together a high-level business strategy, this is about making sure the business case stacks up.

Every business needs a business case that makes sense.

So we look to build your portfolio around at least one specific “stonkingly obvious edge”.

We pick a “stonkingly obvious edge” and we’ll go in-depth, helping you to specify fully systematic trading rules and processes to trade it.

Risk Premia Harvesting in stock indexes and government bonds – diversified with alternative assets. We look to exploit the long-run positive drift of risk assets in a diversified, risk-managed way.

And we’ll close out the week with a discussion on how not to screw up the best edges by overcomplicating them.

Module 3

Inefficiencies and Where to Find Them

In module 3, you’re going to learn about Finding and Exploiting Inefficiencies.

Now we’ve looked at “stonkingly obvious high-probability” edges, we can start looking at slightly more competitive games which exploit market inefficiencies.

Strategies like risk premia harvesting are “win-win”. Nobody needs to “lose out” for you to harness excess returns there. That’s why we can easily believe in it persisting.

Exploiting market inefficiencies is “win-lose”, at least if you think about expected returns. Your excess returns come from “buying from someone too cheap”. Or “selling to someone too expensive”. Someone loses out.

So how do we find these opportunities?

First, we find other traders who are prepared to sell to us too cheap or buy from us too rich.

We’ll return to our model of market structure and price discovery and show how tradeable inefficiencies can arise due to:

market microstructure (stale orders or AMM prices)
conditional risk premia, or a desire for certain “fashionable” exposures
lumpy trading flow – due to random large trades or forced trading
under-reaction (trend)
over-reaction (reversion)

But maybe even more importantly, we need inefficiencies that aren’t being completely “gobbled up” by the bigger aggressive players.

And how do we make sure we are the ones that get the opportunity to trade these inefficiencies, given there are more sophisticated, faster players trying to do the same thing?

These are the questions we’ll explore together in this module.

You’ll discover that you need two things to be true:

First, you need to find a time when a large group is willing or forced to trade at inopportune prices. You’ll need to understand the constraints and incentives of the big “end users” in the market.

Second, you need inefficiencies that aren’t completely “gobbled up” by the bigger “aggressive” players.

There is a reason trading firms buy order flow from Robinhood. They can eat all the edge there. But not everything is so neatly contained.

Inefficiencies can “leak out” because:

the inefficiency is too big to be fully absorbed by the market
the flows that generate it are too noisy or unpredictable to be fully absorbed by the market
the opportunity is too small, too noisy, too capital intensive, or too awkward to be worth bigger players getting out of bed for.

Understanding this leads to us being able to identify inefficiencies that you can exploit.

To help you through this process, you’ll learn how to make simple testable “elevator pitches” for the inefficiencies we’ll look at.

These “elevator pitches” are simple statements of:

why you think the inefficiency exists
why you think you can exploit it.

A 5-year-old should be able to understand why your edge makes money.

You’ll go through examples with the team. They’ll look like this:

What: Mechanical wealth management equity/bond rebalance flows are very large.
Why: Due to their size they might not be fully dispersed when performance differences between the asset classes (and, therefore, rebalance trades) are very large.
How: We might get paid for buying what they’re selling around month-end.

5yo test — *Click the twitter image to read the rest of the thread*…

If doing this sounds intimidating, it’s probably because it is, a little. You probably aren’t used to thinking like this. And maybe you don’t have the experience to trust your instincts yet. But, through lots of discussion, examples, and investigation, we will work with you to make sure you “get it”.

You’ll learn about 13 different inefficiencies across many different asset classes, and how they might be exploited with simple systematic trading rules:

Turn-of-the-month “window dressing” effects in treasury bonds
Business day seasonality effects in foreign exchange
Supplying liquidity to large rebalance flows
Post-earnings announcement drift in stocks
Taking advantage of lopsided positioning in VIX derivatives
Buying close-end funds or ETFs trading at discounts in market stress
ADR pairs trading or futures spreading in market stress
Trading leveraged token rebalances
Trading brand-new products: example of the FTX MOVE contract
Shorting crypto perpetual swaps with structural supply/demand imbalances
Trend and seasonality effects in crypto
Buying liquidations in crypto futures
Cross-exchange spread trading in crypto

Module 4

Working with Financial Data

So far in the course, we’ve concentrated on growing your market intuition and trading smarts. In Module 5, you’ll learn the basics of doing effective quant research.

This isn’t going to be super-complicated nerd stuff.

This is simple data analysis you can do in Excel or a statistical programming language like R or Python.

These are the simple basics you need to test out ideas effectively and model market phenomena.

You’ll start by learning how to uncover assumptions you make about the markets and form them into testable hypotheses.

For example, you might say that you use a stop loss on your trades to “control risk”.

Well, what is that risk? What is it not? What risk do you care about? What needs to be true about the markets for that rule to actually do what you want?

Much of this is a process of “calling bullshit” on yourself. Then transforming the assumptions you have made into something you can test out: something you can look for evidence of in market data.

Typically, this requires getting hold of some data. So you’ll learn how to do that, and how to validate, clean, and reshape your data if you need to.

Finally, we’ll give examples of working with price and return data, including various gotchas.

By the end of module 4, you should be confident with the basic nuts and bolts of working with financial data.

Module 5

Research Mindset and Data Analysis Techniques

Now we can think about data analysis and research.

First, we talk about the mindset required to do effective research, and the importance of moving fast, not being prissy, and iterating on the simplest reasonable thing to disprove your ideas.

I want to give you the confidence to get out there, make a mess, and learn some lessons.

You can always loop back.

And this must start with a discussion on bias. Everything we look at is going to be biased in some way. You have to accept this, but it’s important to understand how you can best control for it in your analysis.

Next we look at the basics of return prediction – which, after all, is what most of trading is about.

How do we look for factors that help us predict asset returns, or future returns relative to other assets?

We’ll cover the essentials of the following techniques:

Data munging: changing the shape of your data
Plotting histograms: visualizing the distribution of your data
Plotting time-series charts
Dealing with non-stationarity: normalizing data
Scatterplots: does x explain y?
Factor analysis
Event studies and markout analysis

Next, we’ll look at how simulation can help us answer questions, even if we’re really bad at maths.

You might not always be able to work all problems in closed form, as I often can’t, but we’ll show you how you can use simulation to answer questions.

Questions like:

what is the probability an effect I observe is due to random chance?
under what conditions might a stop loss or time stop rule be helpful?

Next, we’ll look at how we can design a set of processes to exploit the effects we observe in the market.

Here, we allow everything to be driven by the effect we think exists. The effect comes first, then the rules and processes to exploit it.

We’ll also look at backtesting trading strategies, which is something we like to do if I can do it easily, though we’ll happily trade things without a backtest if we’re confident enough in the idea.

By the end of Module 5, you should be confident in your skills to do exploratory data analysis and design simple systematic strategies.

Module 6

Simulation, Portfolio Construction, and the Fine Art of Sitting on your Hands

In the final module, you’ll learn how you can use simulation to help in portfolio decision-making, sizing, and risk management.

You’ll learn a simple quantitative approach to portfolio management.

You’ll learn how to put simple trading strategies together to create a high-performing portfolio.

You can create extraordinary results at the portfolio level from many noisy strategies using “ensemble” methods.

Or, as we like to say, “Trading More S**t”.

By the end of module 6 you’ll understand:

a system for thinking about portfolio construction, both practically and emotionally
how to set portfolio management objectives
how to identify risk factors
how to size and rebalance positions and strategy exposures
how to attribute pnl to return drivers in your portfolio
what to do when things get weird
how to chill out and “Trade More S**t”

You won’t be left with your hands waving in the air.

We’ll help you think about how to structure things to meet your unique objectives and risk tolerance. And you will know what you should do with the next addition to your systematic trading portfolio.

We finish the course with a discussion of what’s next, and the reassurance that “There Will Always Be More Trades“.

The trader needs to balance confidence and humility. You must never pretend that trading is not hard. You must never think you’ve got it all figured out. A certain amount of anxiety that you are losing your edge is appropriate.

But you must be confident in your ability to find new trades.

You are smart. You have good tools. You have solid strategies. You have a solid approach to the markets. You have examples of good, quick, pragmatic research. You have tried-and-tested strategy frameworks. You have realistic expectations about the market mayhem. You have a team of people who will help you and share ideas. There will always be new trades and new ideas.

You just need to stay relaxed, think clearly, don’t get greedy, stay true to the fundamentals, and generously give your time to others.

performance-tweet — *Click the tweet image to read the full thread*

Bonus Module

Options, Volatility, Tail Hedging, and Market Chaos

We’ve taken this module out of the main course, but I think some of you will still find it interesting, so it’s presented as an optional bonus module.

In this module we look at options and how you can use them to protect a portfolio against extreme, unpredictable events. We also discuss the dynamics of equity volatility and present a systematic strategy for trading VIX derivatives. We finish the module with a discussion of the unique opportunities that present themselves in times of extreme market stress.

One of the challenges of active trading is that:

the best opportunities tend to occur in the eye of the storm, when normal market relationships are stretched
but that’s usually after a significant drawdown in most effective strategies.

So, if you’re not careful, you can end up in a situation where you have the least buying power just when you want it the most.

Options have useful properties to help us try to navigate this. We might choose to “pay up” for protection against a market crash, to cushion drawdowns, and ensure that we have the capital we need to take advantage of opportunities in the chaos.

We’ll look at very simple tail hedging techniques.

Next, we look at a time-varying volatility risk premium harvesting strategy in VIX derivatives.

In module 2 you learned about Risk Premia Harvesting. You learned that you tended to make excess returns over the long haul for taking on certain risks.

We didn’t try to “time” our exposure to the “risk premia’ in module 2 because:

these premia appear to be very large
every time we are not exposed to them we “miss out” on the premia
there’s a lot of evidence that timing our allocation to risk assets is hard
we didn’t want to screw up a “stonkingly obvious high-probability edge” by overcomplicating it.

However, there is some evidence that some risks are not rewarded all the time. In fact, there’s evidence that taking on some risks can sometimes appear to be quite a bad idea. A good example of this is what we call the Volatility Risk Premium.

On average, equity index options look to be slightly too expensive. It is easy to understand why. You learned earlier that an effective way to “insure” a portfolio of risk assets is to buy options that pay off large in bad times.

“Selling volatility” (using equity index options or VIX products) tends to receive a risk premium because it tends to incur large losses in market crashes.

But there’s evidence that it’s a bad bet to take on that risk all the time. In fact, due to very lopsided customer positioning, going long volatility tactically can be an excellent trade at times.

We’ll walk through this dynamic and describe a simple trading strategy to exploit these effects using VIX ETPs or VIX futures.

We’ll investigate the time-series properties of VIX. And you’ll note that it’s easy to predict:

It tends to cluster (stay the same in the short term)
It tends to revert to its mean over the longer term
It is positively skewed
It tends to have a floor under which it won’t go lower
It tends to increase when the equity index declines
It tends to show conditional trend effects.

It is not surprising that VIX is predictable, because you can’t trade VIX. So there is no competitive mechanism to drive out inefficiency.

So we’ll look at VX futures and observe significant basis effects in the futures prices. If VIX is very low, the futures will tend to trade higher than the index. If VIX is very high, the futures will tend to trade lower than the index.

Why?

Because everyone knows volatility is likely to revert from extreme values.

So you won’t find anyone prepared to sell you VIX futures at 9% when VIX is at 9%. The sellers will demand a premium and the buyers will be happy to pay – because everyone knows VIX is more likely to go up.

The futures contracts tend to “price in” the obvious, predictable changes in volatility. You’ll see that trading VIX products is not as simple as “predicting VIX”.

You’ll model the “basis” as made up of two elements:

Predictable future expected changes in volatility.
The left-over stuff that’s not explained by that – which we call a “time-varying risk premium”.

This leads to a simple systematic strategy to attempt to exploit the time-varying volatility risk premium. This is the most untamed of the strategies we’re looking at. So we’ll have a robust discussion about skew, risk, and sizing.

Finally, we’ll look at strategies for taking advantage of market stress. Some of the very best opportunities present themselves in times of severe market stress, because:

leveraged traders are trading when they have to, not when they want to
arbitrage trading is more constrained than usual.

This means that some of the normal “arbitrage relationships” between instruments can become stretched. Sometimes you can buy $100 worth of assets for $90.

We look at simple trades you can use to take advantage of market stress in Close-End Funds, ETFs, ADRs, equity futures, and crypto futures.

Here’s what some of our past students have to say about their Robot Wealth Bootcamp experience…

7-Day Money-Back Guarantee

If you start the course and decide it’s not quite what you need at this point in your trading journey, we genuinely don’t want your money.

All Robot Wealth Bootcamps come with a 7-day money-back guarantee.

This means you have 7 days to dive in and experience the course — to watch the training videos, have a go at implementing the strategies, join the live webinar sessions, get support and make some genuine connections in the private Discord server. If the Bootcamp experience still isn’t hitting the mark for you, we will refund your full enrollment fee, no questions asked.

Enrollment info

$447 USD

One payment

ENROL NOW

Your enrollment includes:

Course training material including video lessons, written content and custom tools

LIVE Q&A Webinars

Private Discord server for real-time discussion with your instructors and peers

A quantitative approach to portfolio construction and management

Lifetime access to all training material

BONUS Embrace the Mayhem video course

BONUS Options, Volatility, Tail Hedging and Navigating Market Chaos Module

BONUS Previous Course Webinar Recordings

BONUS Market Basics

BONUS Doing Financial Data Analysis in R

BONUS Simple Cryptocurrency Research in R