This Is The Simplest Technique For Astronomical Returns (Explained With Bitcoin And Tesla)

Lesson 7: Trading Momentum | The Absolute Beginner’s Guide

The standard advice for investing and trading is “buy low and sell high.” But there’s a much better idea out there. Hang with me on this one, since I’m going to show you what it is with ordinary stocks first.

Back in 2018, I developed a counter-intuitive trading strategy that used a different idea:

“Buy high, sell higher.”

Being an academic, I had researched the heck out of it, back-tested it and published the results on SeekingAlpha–a speciality website for finance nerds–in 2019. It did astronomically well. 

After a two-year real-life test through the COVID crash, it’s done even better. Here’s a general image, with the strategy’s return in red and the stock market in blue.

Remember, these are ordinary stocks. That 20% annual return is better than Warren Buffett’s done and it has a lower draw-down than the stock market itself. That means it has both greater returns and lower risk.

Here’s its performance over the past 22 years, including 2020, where it got more than a 61% return.

How good is that? It’s so good that it put me in the top five of all major hedge-funds of 2020, beating the pants off quant hedge funds like Renaissance, Ray Dalio’s Bridgwater, and of course Warren Buffett’s Berkshire Hathaway.

The idea behind it is just this: the trend is your friend. When an asset is going up in price, it tends to continue upwards. When it’s going down, it tends to go down more.

Now, I was applying this idea to buying individual stocks that would be usable in a retirement account. But what if you transferred that idea to cryptocurrencies and other “bubbly” sectors like green energy and cannabis?

Well, that’s the strategy behind The Art of The Bubble. And since very late April of 2020, my bubble trades have yielded right around 700%. 

The purpose of this lesson is to teach you how to use this strategy for yourself.

Of course, I have a paid subscription. There are details that I can’t explain in one lesson, so for $9 per month, I’ll provide you with more of those details on a weekly basis. For less than $1 a day, if you buy the annual subscription, you can follow my trades on a daily basis to learn exactly what I’m doing and why.

I dare you to find a university course that will come close to those prices. (You also won’t find this taught in universities.)

Anyway, since I’ve explained the basic idea behind momentum trading, here are the main topics that I’m going to review today.

  • How to Find A Moving Average
  • Evidence For Its Effectiveness
  • Some Bad Ways To Use Momentum
  • Combining Momentum With Value Investing Through Economic Signals

As a reminder, any terms you need to look up can be found in my terminology guide here (use CTRL  + F to find a specific word).

Let’s start with the basics. You measure momentum by using moving averages, so you’ll need to start with finding and using moving averages on freely available sites yourself.

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How To Find A Moving Average

Just go to a website like Then type in the ticker of what stock you’re looking for. If you don’t know the ticker symbol, then you can usually just type the company name and you’ll get a list of options.

Tesla is TLSA, while Bitcoin is BTC-USD. Cryptocurrencies are a little weird because you are measuring the price of Bitcoin in terms of US Dollars, hence BTC-USD. When I get to the page for my stock or cryptocurrency, I click the chart tab and usually go full screen.

When you are looking at your chart, the first thing you’ll want to do is change the line graph to something where you can see individual movements for each day. I like the colored bars more than the candles because it’s easier to see what’s going on.

For each bar, the opening price is the tick to the left. The closing price is the tick to the right. The bar goes up and down to mark the high and low prices of the day. If the bar is red, that means the stock went down for the day. If it’s green, it went up for the day.

After you change to the bar chart, you’ll want to add in your moving averages by clicking on the “indicators” tab and choosing moving average.

You’ll be asked how long a span of time you want. This is crucial. For stocks, the number of days you choose will be trading days, so if you pick 200, then that’s about 10 months since the stock market isn’t open on weekends or holidays.

I often use 160 days, which is about 8 months worth, though Yahoo will default you to the 50-day average. Some people prefer even shorter times like 20 days. I don’t like doing that for reasons I’ll explain below.

You’ll also be given the option of choosing a simple or exponential moving average. Choose the simple moving average, since the exponential gives you a faster response to various events and that will introduce false signals.

Finally, pick a line color that you can see. I usually just pick black. That line represents the average price over the period of time you picked. Your screen should look like this (if you’re using TSLA).

You’ll notice that if you had decided to buy TSLA as soon as its price was above the 160-day simple moving average (SMA 160), you would have stayed in the stock and not traded anything for more than a year.

That’s the kind of “traditional bubble” that is the focus of my trading approach (other bubbles explained here). This is NOT day trading. And that’s a good thing.

If you do this right, then you should be able to live your life like normal. You’ll get up in the morning and check a few of your indicators, then go about your day. Repeat for the next morning. It should take no more than 5 minutes. (You don’t want to actually work for your money do you?!)

Evidence For Its Effectiveness

Now, I’ve already shown you evidence for the effectiveness of momentum trading using the triple momentum strategy above. That combines three kinds of momentum to achieve its results. The bubble trading strategy I use here combines momentum with macro-economic conditions for its effectiveness.

These are more complicated strategies, then, but even this simple approach does well. I want to review, very briefly, some of the academic articles in support of it.

Robert A. Levy, in 1967, used the computers of his time to analyze the results of the basic idea: the trend is your friend. He called his approach “relative strength,” but it later became known as “momentum” trading.

In 1968, he developed his initial study to cover 625 New York Stock Exchange stocks, using a 26-week time frame, and found that momentum outperformed significantly.

Later, Akemann and Keller (1977) demonstrated the superiority of the approach even including transaction costs. Those are the costs of buying and selling stock. With Robinhood, or most brokers now, you don’t have transaction costs, but with any cryptocurrency broker, you still do.

Transaction costs are a key reason why faster strategies–one’s with shorter time frames like 50 or 20 days–don’t work well. Not only do you have to buy and sell a lot, but you also spend a lot of money paying transaction fees. And those fees add up.

Robert Shiller, the Nobel laureate, wrote a paper in 1981, titled “Do Stock Prices Move Too Much to Be Justified by Subsequent Changes in Dividends?” which showed that stocks do not trade on their intrinsic value. He was joined later by De Bondt and Thaler (1985) and together they paved the space for the conceptual adequacy of momentum trading, rather than Warren Buffett style value investing.

With the additional advances of computer technology, the 1990s proved a watershed for this approach. Jegadeesh and Titman (1993) showed the superiority of momentum trading for the time period between 1965 and 1989. They used 6 and 12 month time frames.

Since that period, momentum trading exploded with more than 300 academic articles supporting its superiority to buying and holding the market itself. Those studies went on to show how general the persistence of the momentum trading advantage was across any time frame with basically any asset, from stocks to bonds to derivatives … to Bitcoin.

Chabot, Ghysels, and Jagannathan (2009) demonstrated the superior performance of momentum trading in UK equities all the way back to the Victorian Age. Geczy and Samonov (2012) showed that momentum trading was successful in the US all the way back to 1801!

The lesson? The outperformance of momentum trading is a robustly supported phenomenon by academic research.

There are some bad ways to use it though.

Some Bad Ways To use Momentum

I noted above that having a short time-frame increases transaction costs that make momentum trading less profitable, especially for cryptocurrencies.

An additional problem with short time-frames is that you will have slippage. If you used the SMA 160 as your indicator for Bitcoin in 2020, then your signal would have told you to buy Bitcoin the day after 4/28/2020.

You would do that because you check to see if the closing price of Bitcoin is higher than the average. But the next day, Bitcoin traded above $9300 and below $8600. Where exactly would you have bought in?

It’s hard to tell. But a starting price at $9300 is definitely worse than a starting price at $8595. That difference is slippage.

You almost never get exactly what an historical analysis suggests, then, because you encounter transaction costs and slippage. Both of those costs increase dramatically—so that the accuracy of your expected returns decreases proportionally—if you have a fast timer (using a 20-day or 50-day moving average).

Another trap is to overfit your moving averages. What if you changed your moving average to the faster exponential moving average (EMA) and moved its day tracking count to 140 days? Well, you’d get this signal.

Using closing prices as indicators, you would have purchased Bitcoin right around $7807. That’s much better than the best price of the 160-day SMA of $8595.

The thing is, you can only know that now.

A 140 EMA is a weird length of time and it’s hard to know why you would have been following that signal in the first place. As a result, there’s no reason to think that it would return especially better going forward. Following it in the future is pure hindsight bias. So, be wary of overfitting your moving averages.

The Lessons:

  • Fast-moving averages increase transaction costs.
  • Fast-moving averages increase slippage.
  • Quirky moving average times are subject to hindsight bias.

Now, let’s conclude with a real way to improve performance.

Combining Momentum With Value Investing Through Economic Signals

A better way to get a better signal is not to mess with your momentum indicators. It’s better to stick with conventional long-term indicators, like the 200 or 160-day averages, and then use something with a completely different information source.

That’s why I combine value-based indicators with momentum indicators. The way I do that is by having a really good macro-economic indicator (which I explain here), and when that is good, I look to the industry-specific momentum indicators.

This combined approach also has strong empirical support for outperforming on a risk-adjusted basis for a whole range of assets. And it makes sense why. When you do this, you will be using different sorts of information as inputs for your strategy. Because they are conceptually different kinds of information, you are more likely to avoid trading on the wrong signals.

Of course, paid subscribers receive access to the basic economic cycle information that my algorithms track.

Yet, even if you are not among that group, you’ll have learned enough with just the above about momentum trading to pick out strong trends when you see them. 

You know:

  • How to find your moving averages.
  • That this approach has strong support in academic research.
  • What the pitfalls of picking your moving averages are.
  • Why you might want to combine it with other approaches.

That’s it for this week! Happy trading!

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Akemann, Charles A. and Werner E. Keller (1997), “Relative Strength Does Persist!” Journal of Portfolio Management 4(1), 38-45.

Antonacci, Gary (2011), “Optimal Momentum: A Global Cross Asset Approach,” Portfolio Management Consultants.

Antonacci, Gary (2012), “Risk Premia Harvesting Through Dual Momentum,” Portfolio Management Consultants.

Antonacci, Gary (2013), “Absolute Momentum: A Universal Trend-Following Overlay,” Portfolio Management Consultants.

Antonacci, Gary (2015), Dual Momentum Investing: An Innovative Strategy for Higher Returns with Lower Risk, New York: McGraw Hill.

Brush, John S. and Keith E. Boles (1982), “The Predictive Power in Relative Strength and CAPM,” Journal of Portfolio Management 9 (4), 20-23.

Chabot, Benjamin R., Eric Ghysels, and Ravi Jagannathan (2009), “Price Momentum in Stocks: Insights from Victorian Age Data,” National Bureau of Economic Research Working Paper no 14500.

Geczy, Christopher and Mikhail Samonov (2016), “Two Centuries of Price Momentum (The World’s Longest Backtest 2018-2012), Financial Analysts Journal 72 (5).

Jegadeesh, Narasimhan, and Sheridan Titman (1993), “Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency,” Journal of Finance 48 (1), 65-91.

Jegadeesh, Narasimhan, and Sheridan Titman (2001), “ Profitability of Momentum Strategies: An Evolution of Alternative Explanations,” Journal of Finance 56 (2), 699-720.

Notes & Disclosures

General financial disclaimer: I am not giving you financial advice and I am not a financial advisor. I am only explaining how I think about things and imply no expected results from my statements. As they say in the news industry, this is provided for entertainment purposes only. Do your own due diligence before investing.

Specific disclaimer: I own a variety of cryptocurrencies, including Bitcoin and Ethereum. I might also own some of the stocks discussed in these essays. In general, I trade these, so by the time you read this, I may not still own them.

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This piece of content is provided for educational and entertainment purposes only. Robin Technologies and Analytics LLC is the firm that distributes 1.2 Labs products. The firm does not provide individually tailored investment advice and does not take a subscriber’s or anyone’s personal circumstances into consideration when discussing investments; nor is Robin Technologies and Analytics LLC registered as an investment adviser or broker-dealer in any jurisdiction.

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