Australian (ASX) Stock Market Forum

# Basic chart analysis/chart reading 101 for beginners

#### over9k

##### So I didn't tell my wife, but I...
Ok so the purpose of this thread is to enable any total newbie to stock trading to understand the basic information being communicated in the most common stock charts used/seen in the business and then also read and understand (or, make sense of) the information being communicated in them and the jargon used to refer to that information.

To put things another way, stock charts are simply a graphical version of a verbal statement/communication and vice-versa (read that again, make sure you understand that I am saying that you can communicate the exact same information in a graphical form as you can verbally) and by the end of this guide I hope to have taught you how to translate between the two.

Before I begin doing that however there are three golden rules to chart analysis/mathematical trading that you MUST keep in mind, always. NEVER forget these three things:

1. Mathematical/algorithmic/quantitative trading is NOT valid outside of normal market conditions.

This is not to say that quantitative analysis cannot be of any assistance when markets are in unusual conditions, but in order for a mathematical model to work there are certain conditions which have to remain the same between its model of the past and its projection into the future.

The more those conditions change (the more unusual the conditions are), the more invalid/less useful the modelling becomes.

If things change enough, the entire model is completely invalidated, and I can prove it to you.

I wrote this following piece for a systems theory unit I did at uni a few years back. It’s a bit long, but it’s important that you read it because this is a point that cannot be overstated.

I am drumming this into you for a reason.

"In 1970s academia, mathematicians and economists were looking for a way to try to quantify financial events, i.e markets. Their physicist cousins knew that particles in liquids and gases move or shake without any apparent cause, and this was a sign of the existence of molecules and atoms whose erratic movements caused the motion – i.e particles bounce around and into one another.

In the 1900s, physicists like Einstein, with his general theory of relativity, had explained how this motion worked. They found that particles moved randomly, but they could model the most likely paths that something which collided with these randomly moving particles would follow.

So mathematicians and economists attempted to apply this formula and adopted a “naturalistic” view of the stock market. Roger Lowenstein (2011) has even stated that he and other students would “go to the physics library looking for concepts we could jam into finance”.

In its first iteration, Black and Scholes developed a formula for pricing options based on the Capital Asset Pricing Model, but its use was limited as it only applied for a given moment in time. They were still left with the dilemma of developing a formula that worked over continuing time.

It fell to a young PhD candidate at MIT by the name of Robert Merton to figure that out. Merton took a formula from rocket science that calculated launch trajectories and their change with time and then integrated this with Black & Scholes’ formula. This alternative approach showed that the option prices derived by Black and Scholes held up under considerably more robust assumptions than those in their original work because it could factor in time being a continuing thing rather than just at a fixed point.

The website “Priceonomics” explains how the formula works:

“If movements in the stock market are random, they can be modeled and managed. This is what Black-Scholes does. The formula does not try to predict how stock prices will change. Instead it assumes the future path of stocks’ prices will—just like the dust mite buffeted by atoms and molecules—follow a normal distribution, which means that small movements are much more likely than extreme movements”.

So with this formula, the only variable needed to understand the normal distribution (the probability that a stock’s price will change by such and such an amount) is the stock’s volatility. By looking at the stock’s volatility in the past, the Black-Scholes formula can model how a stock price may change and determine the right price for an option.

(For the more advanced reading this, it is “simply” the formula used to derive the options greeks)

This was actually initially rejected by the finance industry when it was developed in 1973. Traders at the time were very skeptical about the notion of completely quantifying the billions of separate interactions that made up an option, not to mention how absurdly difficult the calculations actually are - there is a reason investment firms have to hire rocket scientists to actually do them.

So the “quants” (as they became known) like Salomon Brothers’ John Meriwether that could actually do the calculations were almost exclusively given research positions within investment firms, and kept out of harm’s way.

But after 20 years of resistance and caution, the incredible predicting power of the formula and its ability to make money became undeniable, and in 1994, “the quants” got their own hedge fund.

In 1994, Meriwether started the now infamous hedge fund “Long-Term Capital Management”, and many of his colleagues from Salomon Brothers left with him.

He was “staggeringly ambitious” (Lowenstein, 2001). He demanded investors commit their money and be unable to withdraw it, no matter the losses, for 3 years. With fees of 25% of profits (in addition to fees of 2% of the assets under management) compared to the industry’s normal 20%. And wanted the original developers of the formula to join him at the firm.

He also wanted a total of two and a half billion dollars from his investors.

And he actually got most of it. Robert Merton and Myron Scholes, whose work on the Black-Scholes formula made them both rumored candidates for the Nobel Prize, joined as advisors. He got 1.5 of the 2.5 billion dollars he wanted, and he got it under the terms he demanded.

“The nerds’ day had come”. (Lewis, 1999)

You can see the appeal of this – the developers of the formula itself collaborating with those who had, so far, very successfully put it into practice. One of the investors is on record describing Myron’s grasp of financial mathematics as what was used to “Blow investors away”. (Lowenstein, 2001)

This was the ultimate test, and it was tremendously successful.

In 1994, its first year, Meriwether and his traders yielded 28%. In the next year it was a yield of 59%. In the third it was 57%, and Scholes and Merton were subsequently awarded the Nobel Prize in Mathematics. These were numbers unheard of in investing.

And everyone else quickly realized this. Other firms very quickly started raiding everywhere they could think of - academia, NASA, everywhere they thought they could find someone smart enough to perform the immensely difficult calculations necessary to accurately model financial markets with this formula, and paid the maths geniuses or the rocket scientists or whoever they managed to get huge salaries to perform these financial calculations for them. The speed of adoption was unparalleled. Texas instruments even created a specialized hand-held calculator precisely for using the formula.

Meriweather negotiated access to big banks’ capital on unprecedentedly friendly terms. Everyone wanted a slice of the action. “It was generally believed that Long-Term had the benefit of superior, virtually fail-safe technology” (Lowenstein, 2001).

But there is one fatal flaw with this – the formula only works under normal market conditions. Like the rocket taking off, you cannot calculate a trajectory when some other factor influences it. A calculation of a launch trajectory would become completely invalid if part of the rocket fell off or something for example. And because this is unforeseeable, it cannot be factored in – if it could be, it would have to first be foreseeable.

So everything was going gangbusters and the firm was levered up to the hilt and making a fortune from it, which only allowed them to borrow more. And with them ploughing their previous profits back into the firm as well, this simply ensured that if they fell, they would fall hard.

Of course, with the success of the formula so far you can forgive them for what they were doing – it was as if it had given them the ability to turn the market transparent.

And then the Asian crisis hit.

“The firm had bet that volatility would be low and that the premiums for low-risk assets (like brand new Treasury bonds) would lessen. Instead the opposite happened: Investors worried about a global downturn fled to safety. In some months, the firm only broke even, and Long-Term Capital had its lowest returns yet on the year.

Yet Meriwether and co. did not panic. While other investors got out of their positions, Long-Term Capital doubled down. The traders believed the Asian Crisis was an opportunity, just like Black Monday in 1987. They expected the market would calm eventually; in the meantime, investors' flight to safety was the exact type of inefficiency and stupidity they exploited.

But the Asian Crisis kept getting worse, and the market kept getting more erratic and volatile. Unprecedented events kept occurring: It was an article of faith among investors that Russia, a nuclear superpower, would not default. But then the Russian government decided that it preferred to pay Russian workers rather than bondholders, defaulted, and the International Monetary Fund decided not to come to the rescue.

The markets panicked. As investors bought safe assets, the fund’s trades looked worse and worse. In one day, the firm lost \$553 million”. (Lewis, 1999)

By the time the dust settled, the firm had been completely wiped out, and they weren’t the only ones – as I showed earlier, everyone else in the business had essentially seen their success and just copied them by this point, so the entire market was levered up to the hilt. In five weeks, over a trillion dollars of losses had accumulated across the industry, all because they stuck with using the formula as if it was a law (like part of it is in physics) instead of an imperfect predictor, and ignored what their intuition was telling them.

Traders learned their lesson in trying to make an absolute quantification of human activity, i.e a predictive law rather than likelihood. Nowadays, traders see the formula as just one of many tools to use in a toolkit for risk appraisal, one which includes their own intuition about what’s going on in the market and whether things are “normal”.

The abstract of Ogilvy’s
Systems Theory: Arrogant and Humble states the following:

“Arrogant systems thinkers aspire to a totalizing grasp of the whole. Humble systems thinkers start from the same premise of interconnectedness, but recoil from totalization”.

It is clear that if we are going to try & take an approach from the natural sciences where formulas of systems are developed as laws, and apply it elsewhere, then we absolutely need to take a humble systems approach and use it as just one tool of a multi-faceted approach to whatever we are doing, not a revolutionary paradigm shift.

A trillion dollars tells us why".

For a more modern example I can demonstrate, we need look no further than the coronavirus pandemic because we actually now have cold hard data on how retail traders (which aren’t rocket scientists capable of doing absurdly complex financial mathematics) have fared vs the hedge funds, and it’s not even a comparison:

However, this is data just for the hedge funds as a whole. Why don’t we take a look at the quantitative funds specifically:

As you can see, the quantitative funds have not even matched the market throughout this period. They have actually lost money in a bull (increasing) market.

That’s right, they have made less money than if you had simply parked all of your money in an index-tracking ETF like SPY, walked away, and not made a single trade.

But the news for the quantitative funds actually gets even worse. Several of them are on the brink of bankruptcy due to their dogged refusal to admit that their trading method simply isn’t valid under these conditions:

Mathematical trading methods are not valid outside of normal market conditions.

2. Chart reading is not an exact science

All chart reading, mathematical modelling, quantitative trading and so forth is an approximation. Even objective, precise calculations like correlations do not imply any link between to sets of data (or data points) whatsoever. Chart reading is simply the art (and it is an art) of identifying how a stock is behaving and estimating when to buy and/or sell it.

There is a saying in medicine that goes “If you hear hooves, you think horses, not zebras”. What this statement is implying is that if you are looking for a reason to believe something, you will be able to find one. If you are looking for evidence of there being a zebra in the vicinity and you hear hooves, then you will have your “proof” when the reality is that outside of a zoo or actual zebra habitat, it is about a billion times more likely to be a horse that you are hearing, not a zebra.

In chart reading, this takes the form of false positives. I.e seeing something you want to see. This is also known as confirmation bias.

I guarantee you that if you look at the chart of almost any stock, at any time, for long enough, you will be able to see some kind of pattern. This does not necessarily mean you have actually identified some kind of trend.

It is far, far, far more likely that the movements of the stock have simply coincided with something that you are TRYING to see (a pattern) rather than any kind of actual trend developing. In other words, if you are TRYING to find a pattern, you will probably find one, but that doesn’t mean you’ve actually identified anything at all.

The first thing you must do when you think you have identified some kind of trend is to ask yourself why the stock might (because you aren’t sure it’s following any kind of trend at all yet) be following the trend that you think you have identified and then work your way out from there.

You won’t have to lose your money many times before you start getting very honest with yourself very quickly as to whether you’re actually identifying trends vs just seeing a trend (money making opportunity) when you want to see one.

This becomes particularly so when your first couple of trades make you money and your next ten lose it. Did you actually identify a trend in the first two trades or did you just get lucky?

There is nothing more dangerous than overconfidence.

So with all of that said, let's begin.

Part 1: What is a candle graph and why use it?

The following is what’s known in the business as a candle graph and it is the overwhelmingly most commonly used way to chart a stock/look at what a stock is doing because it shows the three most fundamental things you need to know about a stock when analyzing it:

Its price, its volume, and its volatility.

It’s called a candle graph because it shows its data by using indicators that look like little candles:

Now there’s actually a few different types of candle graphs but we’re going to use what are called “heikin ashi” candles (or just “ashi candles” for short) in this example and to those who don’t know any better it all looks absolutely terrifying, but it’s actually really easy.

What candle graphs show is a combination of both the path a stock is following (so its actual price) as well as the volatility (how much a stock is bouncing around in price) of a stock within a given time period, and then the volume of the stock traded within that time period is overlaid as a bar graph at the bottom of the chart.

As you can see, the price and volume indicators in the graph look like little double-ended candles with a wick on each end, and it is actually both the “wick” section as well as the “wax” section that communicate that volatility information to us.

What the ends of the “wax” sections of the candles show is the open and close price of the stock in the given time period. So in this case, if you look to the top left of the graph you’ll see a little “1m”, which means I have set the graph to show the candles in one minute time increments, but the cool thing is that you can set them to whatever you want – if you wanted to set the graph to five minute increments then you can do that. If you wanted to set it to ten minute increments then you can do that too – and so on and so forth.

So what the wax sections of these little candles show is what the stock price was at the open and close (so the beginning and the end) of each of these one minute time periods. Make sense?

What the “wick” sections of the candles show is the maximum and minimum prices the stock traded at within this time period.

Now a stock might have been at its lowest or highest at the open and/or the close of the time period, which means that there won’t be any wick section to show for that particular candle, so just remember, if you see a candle that’s missing a wick (or both wicks) that’s not an error, that’s just a candle where the open and/or the close number was the maximum or minimum in that time period.

The other thing that you’ve probably noticed about this graph is that the candles are coloured. Again, this is not an accident or error because the colours also tell us information.

The way a heikin ashi graph differs to a normal candle graph is that while a normal candle graph simply compares the close & the open as it moves from one time period to the next, a heikin ashi graph uses the average of the stock price within that time period to do so and then colours the candle either red or green to indicate whether a stock went up or down respectively on average compared to the previous time period.

So if the stock on average went up, then the candle for that time period will be green, and if it on average went down, then the candle for that time period will be red.

The reason why it’s called a heikin ashi graph is because this type of graph was developed in the 1700’s by a Japanese chap called Munehisa Homma and heikin ashi means “average bar” in Japanese.

And yes, if you want to know what a simple line graph would look like you can plot (overlay) that too and your graph will look like this:

But in my opinion, this isn’t necessary.

As for the bars at the bottom of the chart, these indicate the volume (so the amount of stock) traded over whatever time period we have set (so remember our settings before, we are currently using a graph that is set in one minute time increments) and there are again several different types of volume graphs but we are going to use a basic one here because it really tells us everything we need to know at this level of analysis.

So a volume graph is, quite literally, a bar graph – the higher the bar, the more stock was traded in that time period and vice-versa. And, like the candles, you’ll see that the bars are also colour coded, but, and this is critical, they are coded according to the price change of the stock, not the volume change. So the colour of the bar goes green or red when the average price of the stock goes up or down, not the volume. A green bar which is smaller indicates lower volume but a higher price. A red bar which is bigger indicates higher volume but lower price.

So it is the height of the bars which changes with volume, not the colour. Colour changes with price. Remember, this type of bar graph does not tell you how much the price of the stock went up or down in our set time period, it tells you how much the volume did.

So, now that we know how to read everything going on, let’s put it to the test shall we?

Let’s take a look at the time period of 15.44 (so 3.44pm) as indicated here:

As we can see it’s a green candle with both a top and a bottom wick so the price should have on average gone up in this period and had a high and a low that were both higher than the close and lower than the open, right?

Well:

Yes, actually! If we simply hover our mouse over this point we can see that we had an open of \$30.23, a high of \$30.27, a low of \$30.21, a close of \$30.24, and we on average went up by \$0.02 or 0.06% from the previous minute.

We also had 1300 shares bought & sold in this time period, with a larger green bar telling us that there were MORE shares bought & sold at a HIGHER average price than the previous minute.

So now that you know how to read one of these charts, why is the data in it important?

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Part 2: What do these things mean?

The price of a stock I should hope to be self-explanatory. It’s how much the stock sold for at its last trade. But why volume and volatility matter are far less obvious.

I’ll deal with volume first:

Think of volume (amount) of stock bought & sold as a measure of the market’s interest in a stock. Not interest like the rate you would get on a loan but rather, how interested the market is in buying and/or selling it.

Now remember, interest is a neutral term. The market could be interested in a stock for a bad reason just as much as it might be interested in it for a good reason. I might be very interested in SELLING a stock due to some bad news for example, same as I might be very interested in buying it because of some good news. When volume increases it almost always comes with a corresponding big movement (so increase the the volatility) in the stock and this will tell you why the market’s interest in it just shot up – it will have either gotten very interested in selling it, or very interested in buying it.

There’s a lot of reasons why the market will/won’t be interested in a stock, arguably an infinite number as every single buyer/seller will have their own personal reasons for wanting to buy or sell, and this is what you as the budding new chart analyst in this thread need to think about when you look at changes in a stock’s volume bought/sold:

Why is the market interested in this stock? Was there good news? Was there bad? Are they anticipating good news? Are they anticipating bad? Did something spook them? Are they running scared from something else risky and just looking for a safe place to park their cash for the time being? WHY are people buying/selling this?

Volatility, however, is a whole different kettle of fish. Volatility is an indication of the market’s confidence of its valuation.

Think about this logically – if a stock only ever traded in a very narrow price window (or, band), say only ever between \$999 & \$1001, then the market is, as a whole, very confident in its valuation. To put it another way, there’s a strong market consensus of the value of the stock being right around the \$1000 mark.

But imagine if the stock regularly traded between \$500 and \$1500. That’s a tripling one way or a 67% drop depending on how you look at it. That’s a WILD fluctuation in price which tells us that the market really has no idea how much this thing is actually worth aside from being somewhere between \$500 and \$1500, which is an enormous range to be bouncing around in (very deliberate use of term - more on range later).

This volatility is so important that an entire index called the “vix” (which is literally Volatility IndeX) was created to use as a trading tool and you can see on this graph just how closely market movements follow it:

The vix is also referred to as a “fear index” which is quite an accurate descriptor in my opinion – you’ll see the vix spike when the markets plummet and then drop when they return to more normal movements.

There is also a vix for every individual stock and it is called “implied volatility” but that is beyond the scope of this beginners thread – I or someone else will cover it in the intermediate part of this guide.

All you as a beginner need to know is that the longer the candles are, the more volatile the stock is.

Part 3: Putting it all together

Ok so now that you know what these metrics are, it’s time to learn how to use that knowledge to actually infer something from a chart.

So if someone was to ask me what chart reading is, I would tell them that it’s the graphical (or, chart) version of a verbal statement.

So, let’s take our three main datasets (price, volume, and volatility) and ask ourselves what these might sound like as a verbal statement.

So if I said to you that a stock was “going up”, what would the graph look like in your mind?

I bet it’s something like this, right?

Well, actually, yes! This is what we call a trend.

But that wasn’t too difficult was it, so let’s get a bit more complex. What if you asked me how much I think a stock is worth and I said to you that I reckon a stock is worth “\$15 NOW, but it’s going up by \$10 a week”. What would that look like?

Hopefully, you’ve thought of something like this:

See how we’ve added some specifics here – we’ve added a starting point, a rate of increase, and now have a scale for our time increments and putting this all together we have a trend.

So let’s take another statement. Let’s say you asked me how much you reckon I think that the EURO is going to be worth in Japanese YEN for the next few days and I said “I reckon it will remain somewhere between 140.6 and 141.5”.

This is where we get to introduce the second of our key datasets aside from just price – we get to introduce some volatility with this statement because we’re implying that the price will be at an unfixed point (i.e it will change).

However, you will also note that I’ve said that I think it will be between two different numbers – I have given you a range that I think it will be in. Now let’s think about this logically: The greater the range that the stock will bounce around in the more volatile it is because it is moving more, or the more volatile it is the larger the range will be (so vice-versa).

Now when I talk about this 140.6 to 141.5 range what I’ve just given you is a floor (minimum) and ceiling (maximum) price I think the stock will be. These are known in trading as “support” (the floor) and “resistance” (the ceiling). These terms obviously mean the same thing and you could consult a thesaurus and use bottom or base or top or peak for more synonyms, but just remember that the particular terms the industry has chosen to use to refer to the bottom and the top of the range are support and resistance.

So again, think in your head what a graph with a “support” of 140.6 and a “resistance” of 141.5 would look like. If I’ve made sense so far, hopefully it looks something like this:

And this gives us our first bit of actual real chart analysis. If we can use a chart to identify a range then we can use that range to give us our buying and selling points. Again, if I’ve made sense then when you think in your head where those points should be you should have thought of something like this:

So, now that we’ve understood what a stock looks like when it has a trend and a range, let’s put the two together. What if you asked me how much I think a stock was worth and I said to you “Ah well probably somewhere between about \$150 and \$120 at the moment but I reckon it’ll go up by about \$1.25 a day or so”.

Again, think about it in your head. You have both a trend and a range now. What would that look like?

Hopefully, like this:

So now that you know what you’re looking at, a stock which moves with both a trend and a range like this is referred to as trading in a channel.

Now the cool thing with apps like the one I use called “tradingview” is that it has inbuilt little tools that we can use to test our assumptions and draw a point from the middle of our channel at the start of the chart to the middle of our channel and see how we did:

As you can see, that’s a movement of about \$75 over a period of 60 days so that’s almost bang on \$1.25 a day. Cool huh!

So, this has been pretty good so far, but as I’m sure you’ve guessed, perfect little parallel channels like I’ve shown above are not the only way a stock can trade. What if the support & resistance lines were at different angles to each other and the pattern was more of a triangle than a rectangle?

Well, actually, yes, there is a name for that: It’s called a wedge pattern.

Now those of you who have followed everything closely and understood things well should be able to deduce that a wedge pattern is when a stock has a trend and range but its volatility (and therefore its range) is decreasing. This means that your opportunities to get in & out at support & resistance get smaller and smaller as time goes on, but your overall trend becomes more and more certain.

Incidentally, there are also patterns referred to as broadening wedges where the volatility is increasing (and thus the trend is becoming less and less certain) which work in the opposite way:

But it is my very firm opinion that trying to trade these is playing with fire and thus should be avoided.

So, now that you know the basic patterns and forms a chart can take, I’m sure you’re wondering about what happens when a stock is no longer in its range or channel.

“Ok Mr Over9k, this is great and all and I was identifying ranges and channels and buying at support and selling at resistance just like you showed me, but the stock’s no longer within its channel or range, what do I do now?”

It depends.

When a stock busts its support/resistance or channel, what this usually means is quite simple:

Something has changed.

Now it might be really really obvious. It could be all over the news. Or it could be very difficult to figure out. The challenge for you is to figure out first what it is and second, whether you think the market has reacted appropriately to it. The market could be underreacting, overreacting, or even incorrectly (moving in the wrong direction entirely) reacting.

Only once you have figured that out can you start to think about what to do next (you would, for example, hold onto the stock if you think it’s dropped when it shouldn’t have same as you would sell it if you think it’s bounced when it shouldn’t have, or even just bounced too much).

But, and it’s a big BUT, the inverse is also true too. Something might change (and it could again be something really big and all over the news) and you then need to figure out how it’s going to effect your stock’s range and/or channel. It could shift the whole thing in a particular direction, it could only move the support side, it could only move the resistance side, who knows.

So now that we all have a basic understanding of chart analysis, let’s put it to use with a real world example and like I explained waaay back at the start of this guide, convert a verbal statement into graphical form.

There is a solar (renewable) energy etf I hold called “TAN”. It’s been an absolute ripper since the march lows of 2020 and made me an amazing return since I bought it. However, being a renewable energy stock, it is extremely sensitive to changes in the political environment, i.e it carries a significant amount of political risk.

One of the times it absolutely surged in price was in response to the two runoff U.S senate elections held on the 5th of January 2020.

At the time, Joe Biden had just won the presidential election two months prior and the democrats had the house, but the republicans held the senate 52/48. Now the way the U.S system works is that in the event of a 50/50 vote in the senate, the vice president becomes the 101st vote/the tiebreaker, meaning that if the senate is 50/50, it is de facto in the hands of the party of the vice president.

This meant that if the democrats could pick up those two seats they would effectively flip the senate and in doing so have control of the presidency, the senate, and the house (and thus be able to ram through virtually anything they wanted without the republicans able to do a damn thing to stop them) at the same time.

Problem was, nobody was expecting them to do it. One of them maybe, but certainly not two.

And then they did.

Now markets everywhere went nuts, but the stocks which went the craziest were those most exposed to political risk, and of those, those benefited the MOST were renewable energy stocks on account of the democrats being relatively pro-green-energy compared to the republicans and they now had total control of the entire U.S political system aside from the supreme court.

So with all of this in mind, let’s put our new chart analyst hats on and think about how this both might and will effect the price of TAN, the solar energy etf that I held at the time.

What we can absolutely say is that this is overwhelmingly good for renewable energy stocks, which means they are going to go up. Ok, but how much? Well we don’t know how much, we’re now in a totally new political environment. We have NO data to work with.

But the one thing we can say is that they certainly aren’t worth any less.

Now, that wasn’t an accidental use of term – whilst we have no idea how much more the stocks are worth now we can be certain that they are not worth any less. So let’s think about that for a second – what would a certainty that the price can’t be any lower look like graphically?

See how our previous close price, the last price the stock traded at (meaning the price that the market agreed to be the fair valuation) before the election now becomes the support or minimum price after it?

Now it’s possible that it might have formed a new support level above this point but like a new resistance level, what that point would be we simply couldn’t know. What we could, however, be absolutely sure (certain) of is that the stock could not go below this point because it certainly isn’t worth any less now.

So this meant that the way to play this (assuming nothing else changed) was to put a big buy order in if the stock happened to get back to its prior-to-the-election close price because we know (we are certain) that it will not go any lower.

The only way it could go below this point again would be if something else significant was to change, or the market had mispriced the stock too high beforehand.

However, when we zoom the chart out we can see that TAN was already on a major uptrend completely irrespective of the election anyways:

And so this can give us some serious, serious confidence that we are not going back to anything below where we were before.

You will also note that I’ve marked what are called “consolidation points” on the chart – these are points at which a stock’s volatility and trend essentially just vanish. See how the movements in the stock have diminished enormously (so the stock has flatlined) while the candles (so the volatility) have gotten much shorter as well?

This means that this is the point that the market has basically just agreed to be “about right” until something else changes to set it off (in either direction) again.

You will also note that I have NOT drawn any trends or channels (or indeed, anything) that continues from the prior-to-election area into the post-election area because a major change like a total democrat sweep of the entire political system changes the market conditions entirely and thus all of your previous data/analysis/calculations are completely invalidated, so consider this me showing you me practicing what I have preached.

So there you have it, that’s basic chart analysis/chart reading 101 for beginners! If you’ve made it this far, you can now wear the honorary title of “No longer a COMPLETE newbie”

Godspeed!

Ok so the purpose of this thread is to enable any total newbie to stock trading to understand the basic information being communicated in the most common stock charts used/seen in the business and then also read and understand (or, make sense of) the information being communicated in them and the jargon used to refer to that information.

To put things another way, stock charts are simply a graphical version of a verbal statement/communication and vice-versa (read that again, make sure you understand that I am saying that you can communicate the exact same information in a graphical form as you can verbally) and by the end of this guide I hope to have taught you how to translate between the two.

Before I begin doing that however there are three golden rules to chart analysis/mathematical trading that you MUST keep in mind, always. NEVER forget these three things:

1. Mathematical/algorithmic/quantitative trading is NOT valid outside of normal market conditions.

This is not to say that quantitative analysis cannot be of any assistance when markets are in unusual conditions, but in order for a mathematical model to work there are certain conditions which have to remain the same between its model of the past and its projection into the future.

The more those conditions change (the more unusual the conditions are), the more invalid/less useful the modelling becomes.

If things change enough, the entire model is completely invalidated, and I can prove it to you.

I wrote this following piece for a systems theory unit I did at uni a few years back. It’s a bit long, but it’s important that you read it because this is a point that cannot be overstated.

I am drumming this into you for a reason.

"In 1970s academia, mathematicians and economists were looking for a way to try to quantify financial events, i.e markets. Their physicist cousins knew that particles in liquids and gases move or shake without any apparent cause, and this was a sign of the existence of molecules and atoms whose erratic movements caused the motion – i.e particles bounce around and into one another.

In the 1900s, physicists like Einstein, with his general theory of relativity, had explained how this motion worked. They found that particles moved randomly, but they could model the most likely paths that something which collided with these randomly moving particles would follow.

So mathematicians and economists attempted to apply this formula and adopted a “naturalistic” view of the stock market. Roger Lowenstein (2011) has even stated that he and other students would “go to the physics library looking for concepts we could jam into finance”.

In its first iteration, Black and Scholes developed a formula for pricing options based on the Capital Asset Pricing Model, but its use was limited as it only applied for a given moment in time. They were still left with the dilemma of developing a formula that worked over continuing time.

It fell to a young PhD candidate at MIT by the name of Robert Merton to figure that out. Merton took a formula from rocket science that calculated launch trajectories and their change with time and then integrated this with Black & Scholes’ formula. This alternative approach showed that the option prices derived by Black and Scholes held up under considerably more robust assumptions than those in their original work because it could factor in time being a continuing thing rather than just at a fixed point.

The website “Priceonomics” explains how the formula works:

“If movements in the stock market are random, they can be modeled and managed. This is what Black-Scholes does. The formula does not try to predict how stock prices will change. Instead it assumes the future path of stocks’ prices will—just like the dust mite buffeted by atoms and molecules—follow a normal distribution, which means that small movements are much more likely than extreme movements”.

So with this formula, the only variable needed to understand the normal distribution (the probability that a stock’s price will change by such and such an amount) is the stock’s volatility. By looking at the stock’s volatility in the past, the Black-Scholes formula can model how a stock price may change and determine the right price for an option.

(For the more advanced reading this, it is “simply” the formula used to derive the options greeks)

This was actually initially rejected by the finance industry when it was developed in 1973. Traders at the time were very skeptical about the notion of completely quantifying the billions of separate interactions that made up an option, not to mention how absurdly difficult the calculations actually are - there is a reason investment firms have to hire rocket scientists to actually do them.

So the “quants” (as they became known) like Salomon Brothers’ John Meriwether that could actually do the calculations were almost exclusively given research positions within investment firms, and kept out of harm’s way.

But after 20 years of resistance and caution, the incredible predicting power of the formula and its ability to make money became undeniable, and in 1994, “the quants” got their own hedge fund.

In 1994, Meriwether started the now infamous hedge fund “Long-Term Capital Management”, and many of his colleagues from Salomon Brothers left with him.

He was “staggeringly ambitious” (Lowenstein, 2001). He demanded investors commit their money and be unable to withdraw it, no matter the losses, for 3 years. With fees of 25% of profits (in addition to fees of 2% of the assets under management) compared to the industry’s normal 20%. And wanted the original developers of the formula to join him at the firm.

He also wanted a total of two and a half billion dollars from his investors.

And he actually got most of it. Robert Merton and Myron Scholes, whose work on the Black-Scholes formula made them both rumored candidates for the Nobel Prize, joined as advisors. He got 1.5 of the 2.5 billion dollars he wanted, and he got it under the terms he demanded.

“The nerds’ day had come”. (Lewis, 1999)

You can see the appeal of this – the developers of the formula itself collaborating with those who had, so far, very successfully put it into practice. One of the investors is on record describing Myron’s grasp of financial mathematics as what was used to “Blow investors away”. (Lowenstein, 2001)

This was the ultimate test, and it was tremendously successful.

In 1994, its first year, Meriwether and his traders yielded 28%. In the next year it was a yield of 59%. In the third it was 57%, and Scholes and Merton were subsequently awarded the Nobel Prize in Mathematics. These were numbers unheard of in investing.

And everyone else quickly realized this. Other firms very quickly started raiding everywhere they could think of - academia, NASA, everywhere they thought they could find someone smart enough to perform the immensely difficult calculations necessary to accurately model financial markets with this formula, and paid the maths geniuses or the rocket scientists or whoever they managed to get huge salaries to perform these financial calculations for them. The speed of adoption was unparalleled. Texas instruments even created a specialized hand-held calculator precisely for using the formula.

Meriweather negotiated access to big banks’ capital on unprecedentedly friendly terms. Everyone wanted a slice of the action. “It was generally believed that Long-Term had the benefit of superior, virtually fail-safe technology” (Lowenstein, 2001).

But there is one fatal flaw with this – the formula only works under normal market conditions. Like the rocket taking off, you cannot calculate a trajectory when some other factor influences it. A calculation of a launch trajectory would become completely invalid if part of the rocket fell off or something for example. And because this is unforeseeable, it cannot be factored in – if it could be, it would have to first be foreseeable.

So everything was going gangbusters and the firm was levered up to the hilt and making a fortune from it, which only allowed them to borrow more. And with them ploughing their previous profits back into the firm as well, this simply ensured that if they fell, they would fall hard.

Of course, with the success of the formula so far you can forgive them for what they were doing – it was as if it had given them the ability to turn the market transparent.

And then the Asian crisis hit.

“The firm had bet that volatility would be low and that the premiums for low-risk assets (like brand new Treasury bonds) would lessen. Instead the opposite happened: Investors worried about a global downturn fled to safety. In some months, the firm only broke even, and Long-Term Capital had its lowest returns yet on the year.

Yet Meriwether and co. did not panic. While other investors got out of their positions, Long-Term Capital doubled down. The traders believed the Asian Crisis was an opportunity, just like Black Monday in 1987. They expected the market would calm eventually; in the meantime, investors' flight to safety was the exact type of inefficiency and stupidity they exploited.

But the Asian Crisis kept getting worse, and the market kept getting more erratic and volatile. Unprecedented events kept occurring: It was an article of faith among investors that Russia, a nuclear superpower, would not default. But then the Russian government decided that it preferred to pay Russian workers rather than bondholders, defaulted, and the International Monetary Fund decided not to come to the rescue.

The markets panicked. As investors bought safe assets, the fund’s trades looked worse and worse. In one day, the firm lost \$553 million”. (Lewis, 1999)

By the time the dust settled, the firm had been completely wiped out, and they weren’t the only ones – as I showed earlier, everyone else in the business had essentially seen their success and just copied them by this point, so the entire market was levered up to the hilt. In five weeks, over a trillion dollars of losses had accumulated across the industry, all because they stuck with using the formula as if it was a law (like part of it is in physics) instead of an imperfect predictor, and ignored what their intuition was telling them.

Traders learned their lesson in trying to make an absolute quantification of human activity, i.e a predictive law rather than likelihood. Nowadays, traders see the formula as just one of many tools to use in a toolkit for risk appraisal, one which includes their own intuition about what’s going on in the market and whether things are “normal”.

The abstract of Ogilvy’s
Systems Theory: Arrogant and Humble states the following:

“Arrogant systems thinkers aspire to a totalizing grasp of the whole. Humble systems thinkers start from the same premise of interconnectedness, but recoil from totalization”.

It is clear that if we are going to try & take an approach from the natural sciences where formulas of systems are developed as laws, and apply it elsewhere, then we absolutely need to take a humble systems approach and use it as just one tool of a multi-faceted approach to whatever we are doing, not a revolutionary paradigm shift.

A trillion dollars tells us why".

For a more modern example I can demonstrate, we need look no further than the coronavirus pandemic because we actually now have cold hard data on how retail traders (which aren’t rocket scientists capable of doing absurdly complex financial mathematics) have fared vs the hedge funds, and it’s not even a comparison:

View attachment 121878

However, this is data just for the hedge funds as a whole. Why don’t we take a look at the quantitative funds specifically:

View attachment 121879

As you can see, the quantitative funds have not even matched the market throughout this period. They have actually lost money in a bull (increasing) market.

That’s right, they have made less money than if you had simply parked all of your money in an index-tracking ETF like SPY, walked away, and not made a single trade.

But the news for the quantitative funds actually gets even worse. Several of them are on the brink of bankruptcy due to their dogged refusal to admit that their trading method simply isn’t valid under these conditions:

View attachment 121880

View attachment 121881

View attachment 121882

Mathematical trading methods are not valid outside of normal market conditions.

2. Chart reading is not an exact science

All chart reading, mathematical modelling, quantitative trading and so forth is an approximation. Even objective, precise calculations like correlations do not imply any link between to sets of data (or data points) whatsoever. Chart reading is simply the art (and it is an art) of identifying how a stock is behaving and estimating when to buy and/or sell it.

There is a saying in medicine that goes “If you hear hooves, you think horses, not zebras”. What this statement is implying is that if you are looking for a reason to believe something, you will be able to find one. If you are looking for evidence of there being a zebra in the vicinity and you hear hooves, then you will have your “proof” when the reality is that outside of a zoo or actual zebra habitat, it is about a billion times more likely to be a horse that you are hearing, not a zebra.

In chart reading, this takes the form of false positives. I.e seeing something you want to see. This is also known as confirmation bias.

I guarantee you that if you look at the chart of almost any stock, at any time, for long enough, you will be able to see some kind of pattern. This does not necessarily mean you have actually identified some kind of trend.

It is far, far, far more likely that the movements of the stock have simply coincided with something that you are TRYING to see (a pattern) rather than any kind of actual trend developing. In other words, if you are TRYING to find a pattern, you will probably find one, but that doesn’t mean you’ve actually identified anything at all.

The first thing you must do when you think you have identified some kind of trend is to ask yourself why the stock might (because you aren’t sure it’s following any kind of trend at all yet) be following the trend that you think you have identified and then work your way out from there.

You won’t have to lose your money many times before you start getting very honest with yourself very quickly as to whether you’re actually identifying trends vs just seeing a trend (money making opportunity) when you want to see one.

This becomes particularly so when your first couple of trades make you money and your next ten lose it. Did you actually identify a trend in the first two trades or did you just get lucky?

There is nothing more dangerous than overconfidence.

So with all of that said, let's begin.
OK, got the message, thanks.

No longer a complete Newbie, thanks. Wonderful intro, had figured out most of the basics over the last couple of weeks, but trend, support etc. still had me a little confused. Sure it is basics, but so well explained with all the warnings and stories to go with it. Much appreciated, you have done this forum proud with the considerable time you put in to help others. I am finding that there are many others who are helping too so thanks to all.

Over 9 k

Great work some excellent information.

My 101 will be quite different
More visual with practical application to the discretionary trading method shown
I’ll be using Bar charts

Due to time restrictions will come in smaller “ bites”

Lot of work there

?

Great post for a newbie who has had a crash course on stock trading And/or investing over the last few weeks.. it's really clarified a lot of the terms I was unsure about and made a lot more sense about the charts I have been looking at.

Thank you for putting this guide together.

No problem at all - feel free to share it around

I started buying shares in 1993 before the internet provided so much information.
In the shares I invested in I got their annual reports , P&L, BS (Balance sheets) and just read, everything related to investing. I just set about educating myself and invested in that information.
At that time there were ‘chartists’. It was new, different. I tried to read and learn from them. It was interesting but a little bit of snake oil.
All of us here (ASF) are trying to expand our knowledge and get an edge.
Over the past almost 30 years I have gone through company financial modelling (DDM), mutual funds, and ETF’s. I have now come back to TA and charting. I remember a lot.

I find it interesting. Intriguing. ..... and fun.

A great introduction and explanation and something I plan to get reacquainted to for my swing trades.

Gunnerguy

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