Here’s a bit of a stream of consciousness post in response
to today’s (5/13) market movements.
First, these types of moves are often called ‘risk off’
moves because we see nearly one-sided trading in different assets—people rush
to sell assets that are perceived as risky (stocks are the standard example,
with smaller cap and emerging market stocks usually rating higher on the risk
scale); they rush to buy assets that are considered “safer”. Fixed income
instruments and gold are the classic examples here.
There are other examples and many nuances to these “buckets
of risk”, but the concept is valid. Let me share a few thoughts about these
environments, and what usually comes next:
almost always a catalyst, but the reaction takes on a life of its own.
Typically, we see feedback loops where selling leads to more selling (mechanically,
just imagine stops being hit and that selling driving the market down furthers),
and the resulting move is often far out of proportion to the catalyst. That’s
because the move was not about the catalyst—it is about psychology.
usually a “perfect storm” alignment of other variables, ranging from
economic, macro, geopolitical, to previous market moves. A corollary to this is
that these moves usually don’t come
out of nowhere. They can, but it’s more common to see them follow earlier signs
of market stress.
patterns often don’t work as expected… these moves are largely
unforecastable, though, again, they usually come from periods of stress. If you’re
honest about it, you will see that most of these events sort of break whatever
patterns or analytical structure you were using, or at least push it to
Volatility increases, often dramatically.
Markets overshoot all reasonable limits.
Whatever levels define normal and extreme, expect markets will move to “next level
extreme” or even beyond. Someone, after the fact, will show you an exact level
that worked, but you won’t find it in real time.
Correlations increase. In extreme situations, such
as we haven’t seen for 10 years, nearly everything moves together in the short
term. You might scratch your head and say “why are they selling bonds, cocoa
futures, and tech stocks at the same time?” There’s no good answer, but it
rarely one-time events. There are aftershocks, but here’s the good news: a
disciplined technical process will usually handle these aftershocks pretty
well. They tend to be more predictable, and more tradable, than the event
There are two broad ways these things play out:
the usual is for a quick recovery and everything is forgotten—whether in a week
or a few months—and to see the technical damage absorbed easily. Though that’s
the normal expectation, there are cases where these events lead to a cascade of
sustained selling, and that’s what we call a bear market.
likely be emotional. This environment is driven by human emotion, and
anyone actively participating will at least feel some emotional contamination.
in these environments is to follow your plan and to try to not make mistakes.
Since the beginning of 2018, we’ve had a handful of these types of events. There will be more in the future, and we will eventually enter an environment where this type of stress lasts for months on end. Even though these are extreme and abnormal conditions, they are still part of the normal market cycle.
These thoughts will likely be more relevant soon, and the
stress we’ve felt today will be more commonplace. Monitoring yourself—both your
emotional state and your actions—because it’s so important I’ll repeat that:
monitoring both what you feel and
what you do, becomes very important.
If you find yourself struggling, then there’s a simple
solution: get smaller. Reduce your trading size. Exit some trades. Focus on
only taking the best possible trades as new trades. All of these are ways to
reduce your size and your exposure.
Get smaller. As your positions get smaller, so do your
problems. Managing both becomes a lot easier.
This might be a more challenging blog post than most, but if
you’re interested in really understanding price action I think it’s very
Over the past several weeks I’ve been doing some deep dive
coding and quant work. (Stuff like looking at correlation and cointegration in
the higher moments of distributions… and using tools like Granger causality to
see if there’s predictive potential there. For a concrete example, what if one
market starts showing more “extreme” values (kurtosis increasing)… might there
be points where this type of action points to a change in the volatility of
It’s fascinating stuff, but it also sent me back to a different
place too. Many quants disdain charts, saying that all efforts at chartreading
are doomed to failure. (To be fair, most quants are highly educated in mathematics,
but very few of them have spent any time at all learning to read a chart
correctly. When they do investigate the discipline, they usually run down the
route of traditional technical analysis and (correctly, I believe) find that
most approaches there are too subjective and lack any quantifiable edge.)
However, I was driven in a different direction over the past
few weeks: to investigate implicit learning that hovers at the edge of what is
knowable and quantifiable—to think about those, potentially very valid, aspects
of discretionary trading that we can’t really express in numbers and code… and
then, to think about how I might share those thoughts with you, my reader.
Here’s one way to think about it. We are all familiar with
the heavy-handed books that show random price charts and say something like, “look!
You can see the same chart patterns on random charts as in the real market.
Therefore, patterns are random and useless. Therefore, you should just put
money in the index product I’m marketing…” (Most people don’t realize how
effectively Wall Street has hijacked mathematics for marketing purposes!)
Now, there’s absolutely a huge grain of truth in that
argument. We cannot make money with something that is random. To say the same
thing another way, we must have something that is non-random to create a
trading edge. So, comparing things to randomness is actually a very valid
concept—if we can’t say something is “not random” then we probably shouldn’t
build a trading system around it. This concept is the core of significance
testing, but let’s not go there.
So while many books present a facile dismissal of
chartreading by comparing price charts to random data, there’s much more to the
We can tell the difference
There’s a pretty significant problem with the argument I just presented: humans can tell the difference between random data and actual market data. Andrew Lo ran an online experiment called Arora that showed humans could very quickly learn to tell the difference between real market data and randomly generated fluctuations. Antoine Bruguier took the experiment a step further and found that even completely untrained traders could actually tell the difference when they were operating in a market that was being manipulated by informed traders or not. (The actual language from the abstract begins “experimental evidence has consistently confirmed the ability of… even novices to infer information from the trading process.”)
The two examples above presented data in ‘real time’, as it
developed, to traders, but, over the years, I’ve run variations of this experiment
with static, printed charts. I found the same thing: humans do a pretty good
job of telling the difference between real and random charts. Not 100%, and
some random charts “look pretty real”, while some real charts “look pretty
random”, but, overall, there’s a very good chance you’d score higher than 50%
if we played the ‘real or random’ game.
Despite the examples in many books, the people who say you
can’t tell the difference between real and random charts are wrong.
How we can tell the difference
Here’s the question that grabbed my attention over the past few
weeks: how can we tell the difference between randomly-generated and real
price charts? That question—the how—is a question of profound importance.
Why? Because it points us toward a homespun type of
significance test. If we can isolate the elements of a chart that help us, with
some degree of reliability, to pull out the real from the random price charts,
we might have isolated valid aspects of price action. If we can isolate valid,
non-random aspects of price action, we might be able to find profitable trading
So I spent a lot of time staring into the abyss of
randomness (and yes, in deference to Nietzsche I suppose we should acknowledge
that said abyss probably stares back at us), and here are some things I believe
are true. These are aspects of real price charts that probably offer trading
Volatility tends to be more or less consistent.
It’s really weird to see giant bars coexisting alongside (i.e., close in time)
to tiny bars. Typically, markets move at more-or-less one level of volatility,
until a volatility spike hits the market. When that happens, bars usually stay
big for a while.
Specific candlestick patterns showing thrust
(full body, no shadow) or exhaustion (long shadows with wide range) usually
happen near one end of a price move.
Thrust bars (full body) typically have some
Big gaps usually lead to some motion, either in
followthrough or snapback.
There are specific patterns that tend to occur
at turning points of larger swings, and not in the middle.
Armed with those ideas, take a look at the chart below,
which is randomly-generated (by shuffling price data from an active market):
I highlighted some points on the chart that I thought showed
departures from real market data, and that would have caused me to ask if this
was a real chart. Here are my quick notes:
Strange to see this large bar here. Would expect
after larger upswing.
Weird tiny dojis alternating with more normal
bars. Also weird to see advance happen on black bodies.
Very strange large bar out of nowhere. Also odd
to see tiny bodies just following the big bar, and for all price action to have
remained in the bar for 8-9 bars after.
Weird gaps and tiny bodies.
I don’t know why this comes after 6. It was late
at night, I guess. Strange to see no followthrough after the failure of such an
optimistic gap opening.
Another weird tiny bar following big bar.
More tiny bar weirdness
Unheard of? No, but it is a slightly odd
location for this big bar… and no real followthrough.
Strange overlapping formation
I circled this at the time, maybe not so strange
in retrospect. This could be real.
My thoughts won’t really fit in a bullet point…
This is weird alternation of small and big bars.
You should also look at what things are on this chart that
do “look pretty real”. We could make a long list, but I see alternation of
trading range and trend. (I do not see volatility correlating with moves out of
ranges, however.) I see pullback in trends. I see support and resistance being
respected and tested. (I do not see evidence of pressure around S/R, however.)
I see some gaps that appear to come in the “right” places in trends—exhaustion,
Now, you may disagree with some of those points, and that’s
ok. This is a highly subjective
exercise, but I’m just trying to illustrate some of the important points.
Nearly all of the authors who would like us to believe you
can’t tell the difference between real and random charts use line charts in
their illustrations! Of course, a line chart presents us with only the barest
aspects of market movement. If allowed to stack the deck like this, yes, I
would admit we probably could not tell the difference. But give us more
information—give us open, high, low, and close data—or give us tick charts that
are generated in real time (so we have some understanding of pace), and then
things become a bit more interesting.
I’ve just written 1400ish words on this subject, and, to be honest, I don’t think I’ve communicated the point as clearly as I wish. Take a look at the charts below, and play the same game on them. I’ll revisit this topic soon, because I think the entire essence of what is knowable about charts and price action might be hidden here!
Here’s a quick look at two concepts that should be a part of
your chartreading: ranges with pressure, and measured moves.
There are two common approaches to chartreading, and I think
both can be improved upon. One is to look at “big” patterns spanning many bars
(think heads and shoulders, or wedges) and the other is to look at 1-3 bar
patterns (think candlestick patterns). The problem with these approaches is
that they teach you to see patterns, but they don’t teach you to think about
Take a look at the 15 minute chart of the S&P 500
I’ve marked two separate concepts. The first is the
rectangular range highlighted by the transparent blue overlay. This is a good
example of the type of range that we could expect to break to the upside
because it shows buying pressure.
Why buying pressure? This is a good question, because a
range shows a temporary area of agreement—an area in which buying and selling
pressure have found temporary balance. But the location of the range, near the
high of the previous thrust, shows that, overall, the bulls are winning this
fight. Selling pressure has been unable to overcome the advance buyers made
All other things being equal, this type of range will break
to the upside. This is a specific example from an intraday chart, but you can
easily translate it to any timeframe or any market.
The second concept is the measured move. There’s no magic
here, but markets tend to trade with more or less constant volatility unless
some big, new piece of information comes into the market. In the absence of
that new information and a corresponding “volatility shock”, your best bet for
future volatility is that it will look a lot like past volatility.
This means that we can reasonably expect a market to move “like”
it has in the past in the future: about as far, about as fast, and with similar
character. This is why measured moves work. If we look at the swing that came
before the range, a reasonable expectation is that prices will move that far
out of the range. (The arrows on the chart show this concept.)
This is a simple chart concept—almost too simple to call a
pattern—but it is powerful. Look at your own trades and analysis and see how an
awareness of these concepts could make you a better trader tomorrow than you
It’s great when all stars align and every signal tells you
to do the same thing. That’s the idea behind multiple timeframe analysis
(which, by the way, is a topic that should be more controversial!)… imagine the
profits that can result when forces on different timeframes roll in the same
direction, but what do we do when there’s conflict? What do we do when the signs
I want to share an example from a recent trade I got wrong.
There are two levels of lessons here: first, some thoughts on multiple timeframe
analysis. Second… and maybe this is where the real treasure is… thoughts on
professional trade management and selection.
Multiple Timeframe Analysis
There are many ways to do multiple timeframe analysis.
Perhaps the most common is look for a case where a trend is clear on the higher
timeframe (e.g., weekly chart, if you trade daily) and then to only trade in
the direction of that trend. While this has the advantage of being very
logical, I don’t think it can be applied in a vacuum. If you use any of the
common trend definition techniques (e.g., ADX, confluence of multiple moving
averages, slopes of moving averages) you’ll find they lag so much that they
actually put you on the wrong side of
the market. This is one the less-known and dirtiest secrets of technical
analysis: by the time your trend indicator flashes an “all clear”, you’re on
the wrong side of the trend.
Saying this puts me in conflict with some famous books and gurus, but it won’t hurt my feelings if you disagree with this. You’re not disagreeing with me, you’re disagreeing with the data. Do your own work and see what the data actually has to say. (This blog post is a good place to start if you want to do your own work.)
A better way to use multiple timeframe analysis is to find
patterns on the higher timeframe that have a statistical edge, and then to respect
those patterns. A bull flag is a good example; this is a pattern that says,
very simply, the market is a little bit more likely to go up than down. Check
out this bull flag on the weekly chart of gold futures. (All charts in this
blog post are from a few days ago.)
When we see a pattern like that, I know that we will, on
average, be on the right side of the market if we buy—this pattern has one of
the strongest statistical edges of any price pattern. So, what happens when we
see a pattern like this on the daily chart? A bounce that might be setting up a
From analysis to trading
Well, what happened in this case was that Tom pointed out the
potential short in gold, and I immediately dismissed it because of the higher
timeframe conflict. (Stick with me here, I had to eat my words in a few days!)
This is one of the more effective ways I’ve found to use multiple timeframe
information: when you see a trade that clearly conflicts with a higher
timeframe tendency, don’t do that trade.
It’s the opposite of waiting for alignment—use conflict to
show you which trades to avoid, rather than using alignment to show you which
trades to take. This works better because it avoids the issue of lag; by the
time you have alignment, the trend may be ready to turn. Alignment requires a
zone, while conflict is often more of a point in time, and that point in time is
So, I used a proven technique to skip a trade, but a few
days later, we found ourselves short, we had published the trade for our MarketLife
and Talon Advisors clients. The trade worked out very well and melted through
the first profit target on the short, and a few days later we’ve tightened the
stop below the entry price, essentially locking in a risk-free trade. Here’s
the chart of gold futures today:
So why and how did this happen? First, from a technical perspective, chart patterns fail, even high-probability chart patterns. The bull flag on the weekly was not set in stone, and I’m aware of three broad ways flags fail. (See my first book for details, but those three ways are 1) strong momentum against, 2) failure at the previous pivot, or 3) failure of momentum.) The action over the next two days suggested the emergence of downside momentum on the daily, and that’s one of the ways a higher timeframe pattern starts to crack.
There’s also a behavioral aspect to this, and a delicate balance: once I decided it was not a short, I kept looking. I kept taking in new information, and adding it to the analysis. Though I had rejected the short with complete confidence, conditions had shifted enough two days later to justify the short entry.
That type of balancing act is hard to do, and it took me a long time to learn it. We can err on either side–revising our view constantly or being stuck and inflexible. One thing I can tell you with certainty is that emotional balance is critical for this type of analysis. If you find yourself having emotional reactions to market moves, you can’t see or think clearly. Managing those emotions, finding that emotional balance so we can have the intellectual clarity–those are some of the main tasks of trading.
So, I had to apologize to Tom for “pooping” on his short idea. (Though his reply was “what are you talking about? We’re short, aren’t we!?”) I think there are some important lessons here:
The idea of using multiple timeframe conflict to filter trades, rather than confluence to confirm
Being open to new information and revising your read of a market
Balancing all of this in context of trade entry and trade management
A few days ago, I published a series of charts on Twitter throughout the day. (You should already be following me on Twitter!) These were intended to be quick-hit, one-off lessons contained in a single chart, and I thought it might be useful to republish them here so you can find them easier.
Just a quick end-of-week update on the Options Course. We had the first session two weeks ago (replayhere) in which I introduced the basics of options and outlined the course.
I’ll be doing the second module live this Sunday late morning (New York time). You can register to attend here, and a replay will be available. (We’re exploring different times for people in different parts of the globe!)
This session will focus on options pricing. I realize that’s
not the most riveting topic; it can easily become purely academic, or just irrelevant.
I’ll show a different way to think about the pricing problem—we’ll
go back to the early days (when trading was done—gasp—on the phone!) and see
why it’s hard to figure out what an options contract should be worth. Once we
reason through the problem from those first principles, a lot of things that
might have been murky or confusion about trading options will almost magically
clear up for you!
To get the most out of this module, you’ll need to know what
a put is and what a call is. We’re not starting at the very beginning, but 5
minutes work, if you’re a complete beginner, will get you where you need to be!
Creativity is, for me, right at the heart of our growth as humans. In this post, I’ll share some ideas for connecting with your own creativity and bringing the power of a new perspective to problem solving.
Right aware, we run into a problem. “Creativity” is a
problematic word. Given my background (formal training and first career as a
professional musician/composer), creativity means, to me, creating something
new. Maybe it’s a significant variation on something that has been created
before, or maybe it’s something the world has never seen before. This is
certainly one aspect of creativity.
When I did my MBA, I connected with the business-centric
perspective on creativity. I have to admit, I was disappointed at first. I saw
nothing in all the books, writing, thinking, and coursework on creativity that
really connected with my experience as a composer of music. (Which was,
basically, you sit down with pencil and paper and make something.)
However, over time I began to understand what I did as a musician
in a different light; I began to understand how to apply the same tools I
applied in a creative context to other kinds of problem solving, and that
certain ways of thinking were intuitive to me, because of many years of musical
training, that were foreign to many other people.
Ironically, once I understood the problem better, I saw
there was no huge divide: the same tools that allow someone to create something
Just get started. You can even play games with this. If you’re stuck, “bargain” with yourself that you’ll “just spend 5 minutes on this…” and then do so. Over and over. This is a little trick that will help engage some of these other tips.
Understand what’s already been done. You really can’t be creative without a pretty good understanding of what other people have done before you. In the arts, this drives to vocabulary, technique, and style. In other types of problem solving, previous solutions and attempts to similar problems can spark new solutions, but only if we know about them! Spend a lot of time becoming a deep expert on your subject matter. You can’t “hack” this.
Steal from others. That’s not advice you’ll hear often, but it’s good advice. First, you can’t be someone else and you can’t replicate someone else’s work exactly. If you find the perfect solution that works, then your job is done and you’ve just saved time. What’s far more likely is that, in trying to steal someone’s solution, you’ll find connections and motivations to create your own.
Create a ritual. I’ve told the story before that one of my composition rituals was to sharpen three #3 pencils and start writing. When the last pencil died, I was done for the session. Find a place and a time that you will return to do your work. A ritual can be as simple as closing your eyes and taking two deep breaths before you begin, or it can be much more complicated. Over time, you’ll create cues that tell your brain it’s time to slip into another way of thinking.
Be alone. I know this flies in the face of the current fad of collaborative working, but true creativity, deep creativity, is a solitary experience. It won’t happen brainstorming in front of a peer group. Other people absolutely have something valuable to offer, but they belong in a later part of the process. As part of this, get rid of outside distractions. You can’t do deep work if you’re checking Facebook every 3 minutes…
Vomit ideas on the page. Now that’s a colorful statement isn’t it? When I was composing actively, I’d carry a notebook and write down ideas as they came to me. You can do the same, with whatever problem you are trying to solve. Just write down ideas with no critical perspective at all. Become a free conduit for ideas. Know that the vast majority of these ideas will be terrible; maybe they are all terrible! But the time for evaluating them comes later. Right now, just get ideas out of your head. The process of writing them down already creates something where nothing existed before. This is part of the process of creativity. (I suppose I could have called this bullet “brainstorm”, but I bet you’ll remember “vomit” longer…)
Ask the cards. Ok, now this is going to get weird. What you want to do is to incorporate something completely out of left field. You can draw a card from a deck like this. Open a book to a random page and read a few words. If you know something about a school of numerology, roll a die and take associations from the numbers. Take a random line from a poem. Turn on the TV and take the first phrase you hear. Take whatever random input you are using, and then try very hard to relate it to your problem or to incorporate it into whatever you are working on—pretend that it is a message full of insight and wisdom, and then figure out how to apply it to your problem.
Sleep on it. You’re not being lazy; sleep can supercharge your creativity. You can sleep overnight, you can nap, or you can take a tiny “micronap”. Salvador Dali was a big fan of this: he’d fall asleep in a chair, holding a key over a plate. As he fell asleep he would drop the key, wake up, and return to work. That little dip into the half sleep state was a wellspring of creativity for him. If you think that’s crazy, Thomas Edison, Einstein, and Beethoven, among others, used a similar process!
Get a change of scenery. Change your perspective by going for a walk or talking a friend. Many creative geniuses developed less-than-optimal relationships with certain substances; this was often to facilitate a shift into another mode of thinking and problem solving. If you’re trying to solve a business problem, a short walk might be better than a bottle of Absinthe (which can lead to you arguing with the door of a donut shop at 2 AM because the shop is closed… or so I am told…)
Throw it all out. If you have the luxury of time, a good working process is to create a first draft, throw it out, and start over. Not very efficient, and certainly not appropriate for all tasks. But it is one nearly-magical process to get the best out of your creative self.
Manipulate the idea. This will mean different things depending on what you’re doing. You might try to see an argument from another side. You might try to create bad solutions. You might try to solve a similar problem. You might imagine conditions were different. You might take some small part of the problem (or solution) and twist it, expand it, transform it into something different.
There’s a time for editing and refining, but that work can
often interfere with the first sparks of the creative process. Take these ideas
and see if they can help transform your approach to problem solving.
Sometimes we can take tools we know and look at them in new
ways. Doing this can give a surprising new perspective on how markets move, and
can often suggest new places to look for edges.
Here’s an example. I use Keltner Channels on all my charts,
and have for nearly 20 years. (You can do the same thing with Bollinger Bands
or any similar tool.) There are many ways to use bands, but one common use is
to assume that a market touching a band might mean both that the market is
showing momentum and maybe that it’s overextended and due for mean reversion.
To do this, most bands are created so that the bulk of the
market data is within the bands.
What happens when we focus on the unusual? Take a look at the chart below, which shows only the times the S&P futures went outside of the channels (using day session-only futures data.)
I’ll leave you to draw your own ideas from this, but here are a few points to get you started:
As expected, seeing the market come outside the bands is a little unusual.
What happens after the market goes outside the band? Would it be different itwe loooked at more data?
Does the slope of the moving average (i.e., the overall trend of the market) matter?
How might this tool be different on daily charts (as opposed to intraday)?
Think about that other tools you are using that you might be able to tweak and twist in a different way. (For instance, I wonder if the MACD evolved out of a similar process!)
Sometimes, using simple tools in novel ways can show us entirely new ways to see the market.
One of the very positive advances over the past decade is
the amount of attention focused on cognitive biases. We are, through the work
of some gifted authors and speakers, becoming aware of how easily our brains
misfire and cause us to make mistakes.
The list of cognitive biases is long, and many of those will
hurt us in the market. Today, I want to write about one of the worst, and also
one of the most-ignored. This might be the cognitive bias you’ve heard to least
about, and it might be the one that hurts you most: the law of small numbers.
Law of large numbers
The law of large numbers
is well-known, stats 101 territory: the proportion of results will tend toward
an expected value as the number of trials increases. With large numbers, we can
expect that proportion to be very close to the expected value of the process.
(In plain English: if you flip a fair coin a bunch of times, you’ll see roughly
as many heads as tails. If you take a deck of cards, pick a card at random,
record the suit, return the card to the deck and choose another… over a large
number of draws you’ll get about ¼ hearts (assuming no jokers.)
So that’s the law of large numbers, and it’s basically what
makes much of the field of statistics (and, by extension, science) work.
Law of small numbers
The law of small numbers is a mistake in thinking. We might
take a small sample and assume that what we observe there is true for the broad
This cognitive fallacy goes by other names such as
“generalization from the particular”, or just “leaping to a conclusion”, and
traders do it all the time. I’ve done it, and, chances are, you have too.
The key is to recognize it and to stop doing it!
The law of small numbers drives the gambler’s fallacy. (This
blog actually started as a rant on that topic… I’ll come back to that later!)
If we flip many heads in a row flipping that fair coin, we’re likely to think
it must somehow “even out” because our little sample of a few flips must look like the expectation for that
coin. We know that, over very many flips, we’re likely to get about 50% heads,
so we think this should be true in smaller samples also.
But in trading, it’s not an abstract coin—it’s blood, sweat,
tears, and money. We do a lot of work and then put ourselves on the line. If
we’re not careful, we do that in every way: financially, mentally, emotionally,
and even tie up our self-worth in the outcome of the market event. (This is one
reason why it’s important to untangle the Gordian Knot and remove whatever we
can from the trading equiation.)
The market event, the outcome of our trade, is liable to be
an emotionally-charged learning event. Will we remember that we made the trade
when a candlestick pattern broke a moving average? Will we remember we made a
lot of money when we took “tips” from a guy named BigzMoneyNoWammiez in a
Facebook group? Will we assume that someone’s short-term run of wins means they
will keep winning in the market?
How about losses? What if we do exactly the right thing, and
lose? What if we then do traditional trade review and come up with any number
of factors that might have influenced our outcome? Might we think that we can’t
have oatmeal for breakfast if we are going to trade well that day? (incorrect
trade review is one way developing traders hurt themselves badly, and it’s
always well-intentioned, focused work.)
What to do?
As with all cognitive biases, you can’t “fix” this—it’s a
fundamental part of how humans think and process information. (And, from an
evolutionary perspective, there was probably a benefit.) What you need to do is
to be aware of it. Think about how this bias might impact your perception of
data, and then work to counteract it.
Some suggestions for this:
Cultivate the skill of thinking in large sample
sizes. Don’t pay too much attention to the outcome of any one trade.
Be aware of how emotional flags can (often,
falsely) highlight certain events. For instance, if you’re thinking about your
last 30 trades, you might find yourself focusing in on 2 or 3 that were quick
losses wondering what you could have done better in those trades. Sometimes,
this is justified, but often it is just noise in the data.
Always ask “am I sure” and “what am I missing?”
Become obsessive with your focus on these questions and honest with your
answers. (Hint: the right answers to those questions are almost always “no” and
“I might not know what I’m missing”!) Simply thinking around the corners like
this will separate you from the mass of struggling traders who never dare face
such hard questions.
This is a continuation of the little mini-series on getting
started in technical analysis. Today I want to talk about the mindset and goals
of a successful trader. This is something I wished I had known much earlier
when I started trading.
Where we start
Most of us get started with the idea that we’re going to
find the secret key to the markets. Because we come from diverse backgrounds,
this might mean different things to different people, but some common threads
We might think we will find a pattern that shows
us what is going to happen in the future. This pattern might be a shape on a
chart, some secret ratio, or some combination of other factors.
We might think that we are going to find someone
with good tips and ideas (recent examples: buy cannabis stocks and bitcoin) and
ride their recommendations to profits.
We might think we are going to solve the
fundamental puzzle and figure out some way to know what a company will really
be worth in the future.
We might think that we are going to use heavy
duty programming and machine learning to find some quantitative system that
will pull consistent profits from the markets.
I’m guessing some variation of the above probably resonates
with each of my readers (unless, for instance, you were very lucky to come from
a family background where someone taught you how markets worked from a young
One other thing: we often expect it’s going to be pretty
fast and easy. We might be able to take a few thousand dollars and roll it into
enough trading profits we can retire in a few years and live on an island of
our choice, that we will buy.
…And then it gets hard
Nearly all of us come to the market with a dream like that,
but it’s quickly frustrated. We learn that our secret patterns aren’t as good
as we thought. (Good news lurks here: those secret patterns aren’t so important
after all.) We see that we keep making the same stupid mistakes and sabotaging
ourselves. (Another lesson here: we are one of the most important parts of our
own success. We’ll need to spend some time working on ourselves.) And we see
that we probably aren’t going to make a lot of money from a little money
quickly—that might happen, but we’ll also discover we lose more than we expect
quicker than we expect. (Alas, another set of lessons: risk management is
really important because it’s how we protect us from ourselves and from the
unexpected. On the other hand, compound interest is a powerful force and a
successful trader can indeed generate an impressive return.)
Now, most people who come to trading are going to spend some
time in some trading groups, lose some money, and go away. Some will be very
jaded (you should see the emails I get!), some will be angry for the rest of
their lives, and some will have just had a fun experience. But the common thread
is that almost everyone who comes to the market with a living color dream will
go away with a less money and with that dream shattered.
Many of those people could have been helped if they knew a
few truths about the market and trading.
Where we end up
It’s hard to give lessons that fit every trading and every
situation. There are many ways to make money in the markets, and many of those
involve doing almost exactly opposite things! (For instance, one trader might
go long on a move while another might initiate a short at the same spot… and,
over time, both could be profitable. If you understand what things could make that
statement true, you’re on your way…)
However, there are a few common threads that I think every
trader learns. Most of those traders who quit were taught these lessons in
painful ways. Many of them gave up because they weren’t ready to absorb the
lessons. Learn them before the market beats you up with them!
Trading successfully, with any degree of
consistency, is very hard. If you think you have some easy way to trade, you’re
wrong and you won’t be successful in the long run.
You are a big part of your eventual success. Your
mental state, attitude toward risk, emotional control, focus, ability to do the
work, intellect, personality, etc.—all of this must create a coherent whole
aligned to support your trading. Without this, you won’t be able to make and
Trading is really about having a small edge, and
those edges are smaller than what you think at first. Ironically, the idea of
having a magic pattern that shows you the future has a seed of truth in it, but
it’s just that your pattern will work more like 55/100 times than 90/100. You’re
going to be wrong a lot.
I should qualify that previous statement. You
get a choice: you can be wrong a lot, or you can take very large losses
(relative to the size of your winners.) Everyone talking about “high
probability trading” or “high risk/reward ratio” (they usually say it backwards
like this, for whatever reason) trades is only seeing part of the picture. Both
are important and a big part of finding your trading style (ahem, one that is
aligned with who you are in point #2) is dialing in that combination of how
often you want to win vs how big you want your losses to be.
Math matters, but it’s more street smarts,
common-sense, gambler’s math than calculus. (For some styles of trading, you
may need more hard math tools, but then we’re also back to finding what works
for you in point #2.)
You absolutely can make enough money in the market
to change your life, but it’s going to take you longer than you think to be
able to do it. The learning curve is at least 3-5 years. You’ll likely have
success before that, but you’ll also probably have trouble holding on to money
Stay small while you’re learning. You’re going
to lose, and you’re going to make mistakes even when you think you are immune.
Let’s keep those mistakes as cheap as we can—bigger trading = bigger mistakes. And
bigger mistakes = a bigger chance of us becoming one of those traders who
If you have your mindset shaped like this from the
beginning, you can avoid a lot of the hype and misinformation out there, and
set yourself up for long-term success.