FandingMarco has covered a few of Cryptocurrency technical analysis strategies that you are likely to encounter, let’s take a look at some of the most common patterns you will run into as you learn to trade cryptocurrency.
Fundamental analysts look at and think about price charts in a wholly different and far more simplistic way than technical traders. The charts that you most often see in the newspaper, investing magazines and on financial news channels plot price on the vertical Y-axis and time (in various intervals) on the horizontal X-axis.
These price charts usually show relatively smooth curvy lines rendered by connecting the data points. Such graphs are not entirely useless for technical analysis, but most serious traders prefer a different type of chart that’s loaded with a lot more data..
A candlestick chart encapsulates one day’s worth of trading activity. Each candlestick shows the open, high, low and close. The high price for the day forms the top point of a line segment; the low price during the session is the bottom.
The close and open are marked onto this line by horizontal dashes, which form an elongated rectangle called the body, the candle or candlestick. The segment reaching up from the body is called the upper shadow; the segment below is referred to as the lower shadow (or wick or tail). Since it is impossible to tell which hash mark is the open and which is the close, the box is typically color-coded:
If the open is higher than the close, the body is usually black or red. If the close is above the open, the graph usually will display a green or a hollow/uncolored/white candlestick is used. The body of the candlestick is described as long, normal or short relative to its proportion to the lines above or below.
Just in case you’re ever on Jeopardy: Some postulate this type of chart came from a well known rice trader in 1700s Japan and, in homage, a few purists insist on calling them “Japanese candlesticks.” Candlesticks create more than 40 recognized patterns, some of which we will cover in a later chapter.
1 The Efficient Market Hypothesis
Before opening an account or even clicking a mouse, traders need to do some thinking about a little morsel of Economics 101 that most of us probably haven’t thought of since called the Efficient Market Hypothesis. The Big Idea is that the participants in any market price in all known information about the security being traded.
It sure sounds pretty good. History seems to bear it out, too: One need look no further than the (often) significant price swings that occur when new information, such as an earnings report, is released. Chipotle battles an E. coli outbreak and its share price plummets. Another big company might announce layoffs that investors cheer by bidding up the price on the notion that cost savings will increase profitability. And so on. But there is, to be sure, a limit to how far the Efficient Market Hypothesis can go.
Assume Company A and Company B both have equal revenue and earnings. They might even pay the same dividend and have similar balance sheets. But the value of these companies will not only change, it can vary hugely, because no two companies are exactly the same so straight apples-to-apples comparisons are simply impossible. The best we can say is that they are pretty close, which, in reality, might not be close at all.
Another part of the pricing difference is simply chronology — time. As time passes, more is revealed about a company, its results and other industry dynamics. But a larger element of that pricing difference will stem from the fact that investors do not always act in their ultimate best interest.
To put it another way, investors sometimes make irrational decisions based on impulse, instinct or emotion. The Efficient Market Hypothesis is, thus, mortally flawed. If it were true, there would be no need for exchanges — buyers and sellers wouldn’t set prices, prices would instead be determined by algorithm, not double-blind auction haggling.
The point bears repeating: Investors do not always act in their own best interest. Even if they did, that interest and the factors that comprise it change so often that said interest is rendered moot. If the Efficient Market Hypothesis is correct, it is only correct in the long term. In the short term, traders tend to overreact. No one – well, very few, anyway – ever went broke underestimating the myopia of the short-term market. It is those fluctuations that Mr. Morgan noted that traders are betting on.
The question becomes, then, what approach is best used in the trading of cryptocurrency? Most investors start with fundamental analysis — the deliberate process of studying and comparing a company’s financial underpinnings. This is counter to the Efficient Market perspective: Investors are studying known information that all market participants have immediate access to.
In the information age, the idea that one lone analyst could stumble upon some heretofore unconsidered or unrealized fact is outright silly. But for the sake of argument let us assume that fundamental analysis, which wisely rejects the Efficient Market Hypothesis, is as sacrosanct as holy scripture. The trouble is, there is no fundamental analysis of cryptocurrency. There are precisely zero fundamentals to assess.
Market participants only know how many bitcoins there are and what the market says they are worth at any given instant. That leaves traders with only one source of hard data to go on, bitcoin pricing. The only logical approach to bitcoin trading, thus, is technical analysis, which eschews all information about a company except for price. It is pure trading.
Okay, so that was kind of a lecture. Let’s get down to brass tacks. The market is always right, but not always in the short term. It tends to overreact. In the short term, it’s a ballot box – prices swing like a politician’s approval numbers – over the long term, though, it’s a scale that weighs results. In the meantime? Utter pandemonium. A perfect place to trade.
2 Technical Trading’s Potential
Technical trading not only offers a simplified approach to investing, it also has far greater return potential than simply buying and holding. Now, to be sure, the buy-and-hold strategy does work. Over time, the potential for capturing strong gains on your money using this method is, in fact, not only very strong but statistically likely.
A dispassionate assessment of the U.S. stock market, for example, shows that from 1965 through 2015, the Standard & Poor’s 500 Index returned an average annual gain of 11.1%. This equates to a compound annual growth rate of 9.5%, which includes the reinvestment of all dividends.
At this rate of growth, an investor’s value doubles roughly every 7.5 years. This is easy to calculate: If you divide any rate of return into the number 72, the result will be the number of years it takes for the investment’s principal to double. This is known as the Rule of 72.
That’s good. But the good is always the enemy of the great. So while a 9.5% compound annual return is pretty good, there’s no reason to settle for that when outright great is within reach. Consider: If you execute 26 trades in one year that return an average of just 1.25%, you will achieve a nearly fourfold rate of return. That’s because the math doesn’t work out to 1.25% times 26 times initial principal. Rather, it works out to 38.5% a year – nearly FOUR times what can rationally be expected of the market. That’s a great return. Even assuming one could capture (only) a gain 25% is still a reasonable annual, the result. After five years, it’s truly remarkable: The stock market as measured by the S & P 500 Index likely would earn what it has always earned, about 9.5%. In five years, $10,000 would grow to roughly $15,000. But even modestly successful trading turns that same ten grand into more than $30,000!
It’s no sin to play with the numbers from there. For instance, merely upping average gain to 1.30% moves the needle to a 40% annual return. And achieving that only requires that a trader achieves a net success rate of 1.25% every two weeks. Is this possible? Absolutely. Just ask the trading desks at the major financial houses like Goldman Sachs and Renaissance Technologies.
At the end of the day, it really doesn’t matter at all what is being bought and sold by whom or for what reason. That’s for the fundamental investors, who prefer the long-term buy-and-hold approach. All that matters to a technical investor is there is trading. Once there’s a chart, there’s a trade to be made.
3. Introduction to Short Selling
The first step to understanding pure trading is to wrap your arms around a concept you might have heard before known as “short selling.”
For most investors — for lack of a better term the “buy and hold” stock-market crowd — investing means buying something. The idea is easy enough to understand: Bob buys 100 shares of General Electric because he thinks the price of the company’s shares will rise, for whatever reason. In the best-case scenario, Bob buys his GE shares for a lower price than he later sells.
The difference between the price he paid and the price he sold for is profit. Buy low, sell high. We’ve all heard it before. To be clear, it is a failsafe way to make money in the market. But — and this is a biggie — there is no rule on this good earth that says an investor has to take those two steps in any particular order.
What in the world does that mean? To be sure, it can be a little counterintuitive until you get the gist. But stick with it, because this is a vital concept for ALL successful traders.
If the way an investor “bets” on a price increase is to buy low and sell high, then it follows logically that the way an investor bets on a price DECREASE is the opposite, to sell high and buy low — in other words, to take the steps backward.
If one wants to bet on IBM rising, one might elect to buy the shares to attempt to capture a profit. If one wants to bet on the price falling, then one has to sell. The obvious question is how an investor can legitimately sell what he does not own. And the answer is easy — the investor simply borrows it.
To bet on a price drop, that is, to “short” a security, an investor borrows a quantity and immediately resells it. If Frank thinks 3M is going to fall, he shorts the shares. Say they are at $150. Frank borrows 100 shares and sells them at that price. His account shows a credit balance of $15,000. How much does Frank owe his broker?
If you said $15,000, you’re wrong. Frank did not borrow money. He borrowed shares, and it is shares that he must return. (This the adage: He that sells what isn’t his’n/ buys it back, or goes to prison.”
At this point two things can happen. Either the stock can rise, forcing Frank to buy it back for more money than he paid, or the price falls, and he can buy it for less. If the price drops to $140, Frank the seller will clear $10 a share from the decline, just as a buyer would pocket the same sawbuck if the price were to rise the same amount. Frank sold high and bought low. No one ever went broke making that trade. (Certainly not the brokerage, which gets a nice fee.)
Short selling assures market liquidity. Liquidity is vital to any market, which must have both buyers and sellers to function. The broader point, of course, is that a savvy trader can make money in any market as long as there is immediate transparent pricing. Here’s the key takeaway once you understand and embrace shorting: Prices don’t need to trend up or down for traders to make money, prices just have to move.
4. Support and Resistance
Charts and the patterns that you can discern will put all buying and selling into perspective by merging the forces of supply and demand into a simple line on a graph. Patterns provide a framework to analyze the constant tug of war between the bulls and the bears.
The first concept is the twofold idea of “support” and “resistance.” Support is the basement price that’s so low it attracts buying. As more buy, demand rises and so does price. A jillion factors might go into the reasoning behind these trades, but all technical traders are interested in and focused on is the price.
Resistance is the opposite. It’s the price that investors just won’t pay. Lilly might be willing to pay $49.95 for a share of Facebook, but she just won’t pay $50. Again, there’s no reason to speculate as to the reason.
The point is simply to realize that everything can become too expensive, at which point other owners might decide to sell and take their profits. When this happens in sufficient quantity, the additional supply has the opposite effect of demand and lowers the price. As a technical trader, you need to know only that prices tend to float between their support and resistance levels.
5. Trend lines
Chart pattern analysis can be used to make short-term or long-term forecasts. The data can be intraday, daily, weekly or monthly and the patterns can be as short as one day or as long as many years. Gaps and outside reversals may form in one trading session, while broadening tops and dormant bottoms may require many months to form.
Technical trading entered the investment conscience in the early part of the 1930s, which was a period of massive legal transition on Wall Street. The 1929 Crash had not only caused the Great Depression, it had also ushered in a new era of securities laws designed to foster fair markets.
Believe it or not, most of the rules that your broker and Wall Street have to follow were devised before most of them were even born. In 1932, Richard Schabacker published Technical Analysis and Stock Market Profits, which established much of the foundations for pattern analysis as we know it today. In the book, Schabacker is quick to issue a warning:
The science of chart reading, however, is not as easy as the mere memorizing of certain patterns and pictures and recalling what they generally forecast. Any general stock chart is a combination of countless different patterns and its accurate analysis depends upon constant study, long experience and knowledge of all the fine points, both technical and fundamental, and, above all, the ability to weigh opposing indications against each other, to appraise the entire picture in the light of its most minute and composite details as well as in the recognition of any certain and memorized formula.
Schabacker unambiguously characterizes chart reading as science. But there is also a degree of artistry to it. One must also acknowledge that pattern recognition is open to interpretation and thus subject to personal bias. Defending against this means employing other aspects of technical analysis. Think of it as the Island Rule: No number stands alone. Any price, ratio or other metric is useful only in the context of rational comparison. No number on Wall Street can be taken purely at face value. It must be contextualized.
No information, even price, exists wholly in a vacuum. With a nod to the Efficient Market Hypothesis, which, though flawed, can be instructive. The trick is to assess all available data to verify or refute the investment case being considered. Are the conclusions accurate? Check and double check.
No one can see every pattern, no one can predict the future precisely. So bear in mind that while many patterns may seem similar, none are exactly alike. False breakouts, inaccurate reads and, to be sure, exceptions to the rule are all part of technical trading.
6. Thinking Like a Chartist
Imagine a line on a graph tilted upward fairly steeply, say 45%. This is the (hypothetical) price chart for Wimpy’s Hamburgers. Let’s consider how a normal fundamental market analyst would approach this image to make a buy/sell decision.
The uninterpreted upward slope of the price chart betokens a very successful and rapidly expanding enterprise: Quarterly results continue to impress. The growth strategy is strong, the market is deep, and the company’s outstanding products give it a sustainable competitive advantage over its rivals. A fundamental analyst is likely to issue a “buy” recommendation, and perhaps even a strong buy recommendation, based on the company’s past performance. You don’t need a Ph.D from the Wharton School of Business to get the concept here:
The analyst simply looks at the past results, determines their compound growth rate, which she will discount a little in the name of prudence and to make up for real-w unknowable things like supply-chain interruption or currency fluctuation. The result of this analysis is nothing more complicated than a line. The thinking is simple: If the price is X today and one can rationally count on 8% annual earnings growth, then future price is the solvable variable in a fairly simple equation.
“If the price is $40 today,” the analyst would say, “It is reasonable to assume, given current industry trends and the company’s history of financial performance, that the shares should reach $52.50 in the next 12 months, exceeding the expected likely return of the broader market.”
To be sure, it is a defensible position. And one to which a chartist would reply, “You sir, are completely out of your head.” How can our polite chartist assert this bold claim? Math.
The same arithmetic that the analyst used to predict a future price can also point to a different conclusion. While the line on the chart represents price, the bottom axis of the chart shows a far more important variable — the passage of time.
Newton’s law of motion states that an object in motion tends to remain in motion unless acted on by an outside force. In other words, a thing will move at a certain rate indefinitely unless Something Happens. And this is where the Island Rule comes into play. The price chart over any period of time must be compared to another price chart for the same period.
And while our theoretical Wimpy’s Hamburgers saw its example price only rise, this phenomenon has not occurred in real life. Prices might move in an upward direction during a period, but they will decline along the way as well. Our chartist is merely employing some simple but very sound coin-flip statistics: The penny will land on heads eventually. As the sample size increases, the data will show the odds of heads and tails are perfectly even. That’s the math when there are two outcomes.
In the market, of course, there are an infinite number of outcomes. A wiseacre might say that all probabilities are 50-50 — either a thing will happen or it won’t. A chartist will take exception with that line of thinking. He will endeavor to use math to process past results to narrow the odds. Assume instead of a wager over two outcomes, heads or tails, two parties agree to bet on the outcome of the Standard & Poor’s 500 Index — whether it will exceed its average annual return for the next year. Each party has only a list of the index’s past performance and no other information.
The wiseacre assesses these odds at 50-50. His prospect is for a gain or loss of 100% — just another pure risk game. He’s not looking at his real chances. In fact, the only actually reliable predictor of his choice is his last bet.
The chartist takes a more prudent approach. He first determines the market’s average annual return — the arithmetic mean. That’s easy enough: Add the values and divide by their total number. The more values, the better (that is, the more accurate) the average. This offers a rational point of comparison — par, to borrow a term from golf. Those values, of course, will vary, in this case from year to year rather than from hole to hole.
With that variance from the norm in mind, the chartist literally measures by exactly how much they vary each year. It turns out this makes even random events relatively predictable. Statistics show that about two-thirds of all outcomes in a random series occur within a certain range of the average.
This is called standard deviation, or “sigma,” and it is expressed as a unit of measure, as though on a graph. So instead of saying the odds are 50-50, the chartist can now go a little further and say that in nearly seven years in 10, the S & P will return within one sigma of the median return.
That average, incidentally, is 11.1% (using data from 1965 to 2015). The crux of the decision comes in determining whether it is likely that the index will exceed its average. Here again, the chartist will refer to the historical data:
For instance, if the previous two years were “up” years for the market, during which they exceeded their historical average return, the chartist would then make a list of what happened in the third year.
If it can be said that in every instance after which the market rallied for two years it failed to meet its historical average, the odds of a correct prediction have increased significantly, and the only question left for the respective bettors is how much they are willing to risk given the odds they have calculated for their potential success.
Regression analysis requires serious computation but the takeaway is simply this: Most outcomes will be average. So by looking at what happened last time, it is possible to gain a significant advantage in predicting what’s going to happen next time. It’s not infallible, but you can use the power of pattern recognition and statistical analysis to stack the odds in your favor.
The fundamental analyst sees this as a clear buy. The chartist could not find a stronger sell signal. They both can be successful — it all depends on the time frame of the trade. It may well be that the long-term holder achieves a favorable or in fact even an equal result.
Fundamental versus technical is simply a matter of intellectual perspective, a question of methodology. Though Benjamin Graham, the father of fundamental analysis and mentor to Warren Buffett, sniffed at trading as more speculation and somehow inferior to his incessant prattle about ratios and percentages. It isn’t. There is nothing wrong with trading. If you wish to denote a difference between trading (or, scandalously, “speculating”) and investing, that’s up to you.
7. Restate and Review
7.1. Begin with an examination of long-term charts
Don’t listen to anyone else, don’t accept another’s chart without doing your own. Start your analysis with monthly and weekly charts, and keep the long perspective – years. Your GPS shows you the next street, sure, but you have to keep the overall map in mind. Overall, the market extends you more visibility with the longer-term perspective you embrace. After you feel comfortable with the macro, then you’re ready to begin trading in the micro. Daily and intraday charts come into play here. Remember, the short-term view can be deceptive.
7.2. Follow the trend
Maybe it’s a long-term trend, it might well be a very short term trend. Determine which you wish to trade and equip yourself with the right chart to guide prudent decisions. As an example, buy dips if the trend is up. Sell rallies if the trend is negative. Intermediate trends mandate daily and weekly charts. Daily trades use daily or intraday charts. Either way, don’t lose sight of the long-term trend you identify. Don’t forget. Don’t fight it, and don’t force it.
7.3. No matter which time frame you select, determine support and resistance
This is common sense and a little counterintuitive at the same time – the best plan is to buy at support and sell near resistance. BUT – please, please remember this – NO ONE, NO ONE, NO ONE can buy at every bottom and sell at every top. Remember the old Wall Street saying: Bulls make money, bears make money – and pigs get slaughtered.
7.4. Draw trend lines on your chart
They’re simple but effective, and all that’s needed are a ruler and two points on a chart. “Up” trend lines are drawn along two successive lows. “Down” trend lines are drawn along two successive highs. Prices often tend to pull back to the trend line before resuming their trend. Breaking trend lines often typify a trend change. The longer a trend line has existed, the more ties it has been tested and the more critical it becomes as a predictive tool.
7.5. Moving averages are your friend
They can provide objective buy/sell signals. They can show you if the trend you perceived is still in play. That’s the good news. The bad is they can’t tell you in advance if a trend is about to change. Here again we run into the context problem and need to seek out a little more data to make the picture more clear: A combo-pinch of two moving averages is a popular way to ascertain trading signals. Try looking at the 4- and 9-day moving averages, the 9- and 18-day, and the 5- and 20-day. How to spot this signal? It’s when the shorter average line crosses the longer. These so-called “price crossings” – either above or below a 40-day moving average – are a strong trading signal.
7.6. Never ignore volume – it’s a strong confirming indicator
Volume trumps price. During an uptrend, expect to see strong volume on up days. Increasingly strong volume double checks new money is supporting the prevailing trend. A falling column can indicate a trend is at its ebb. Takeway: A solid price uptrend should always be accompanied by rising volume.
8 How To Read a Chart Chart Identification
Every chart will have one, and you want to make sure you know exactly what you are looking at. For this you will need to not only need to know the name of what you are looking at but you should double-check that the ticker symbol, which most charts rely on to some degree, is both accurate and what you are looking for.
This might seem obvious, but everyone has made a trade based on the wrong chart – it just happens, and the results can be an unwelcome gut punch. Time Period. It is ALWAYS the x-axis. For those of you who haven’t thought of the classic mathematical quadrant since Adlai Stevenson was running for president, that’s the horizontal line on the chart.
Again, this might seem like a dumb thing to point out, but the fact is a trader might look at 25 charts of the same underlying security and the time period is the most important immediate contextualization factor. Always look. Remember the old newspaper editor’s motto:
“If your mother says she loves you, check it out.” Don’t assume anything and take nothing for granted. Double and triple-check with your eyes wide open. Daily Trade Range. The price charts you are probably used to are lines made up of connected dots that generally represent the closing or intraday prices of whatever is being traded. Technical traders demand more data than that. In lieu of a dot, they use a series of vertical lines called Candlesticks, which we mentioned earlier.
Again, the top point of the line is the high price the security reached that day. The bottom point is the low, and the open and close are hash marks on the line somewhere between. You will want to carefully review how your trading platform displays these and what the color scheme represents. Volume. This one comes last but it is really not least, because volume – the number of units being traded – is of prime importance to the technical trader.
Volume is displayed below the chart as a bar graph, the higher the bar, the greater the volume. Sometimes the bars are color-coded, with red indicating a down close from the previous. Generally a red line runs through the bar graph showing an average figure.
9 Common Chart Lines
If you’ve been trading stocks, you’ve likely been primarily considered with only one real line on the chart – the price. But there can be many others, and they all offer their own insights. While professional trading software can have hundreds of graphical options, we’ll get you started with the ones that are readily available, and free, at Google Finance.
Be sure you are using the most current version of your browser, with Flash and Java enabled and updated. Note: These charts will literally just stop working and not show up on your screen if you don’t do this. Setting your operating system to automatically search for and install these crucial software upgrades is a prudent move. And it bears repeating that you want to be running the most robust anti-virus software available.
9.1. Simple Moving Average
An arithmetic moving average is computed by adding the daily closing price for a number of time periods, then dividing that figure by the number of time periods. A 10-day moving average would show the 10 most recent daily closes.
After 10 days, that is, on the eleventh day, the first closing price in the series is deleted, No. 2 slides up to the No. 1 spot and the new close is added in the tenth place to recalculate the “running average.” Critical takeaway: Moving averages smooth out daily volatility.
9.2. Exponential Moving Average
It’s like a simple moving average, but it puts greater weight on the most recent days. That’s the concept – don’t overthink it. And frankly, the math here, while not difficult, looks like something that they dug out of the wreckage at Area 51. Just for fun, here’s the Formula:
EMA [today’s] = (Price [today] times K) divided by (EMA [yesterday’s] times (1 – K))
K = 2 ÷ (N + 1)
N = the length in days of the EMA
Price [today] = the current close
EMA [yesterday] = the previous EMA figure
EMA [today] = the current EMA figure
Begin by creating either a simple average of the first fixed number (that is, “N”) of periods and use that value to initiate the EMA calculation, OR use the first data point (the closing price) as the seed and calculate EMA from that point forward. Or for God’s sake let the microchips do their thing!
Critical takeaway: The idea behind “EMA” is to make sure one-day events don’t skew the average and point to a trend that isn’t really occurring.
9.3. Moving Average Convergence/Divergence
Developed in the 1960s by Gerald Appel, MACD monitors trend momentum by comparing two moving price averages. It is computing by subtracting the 26-day exponential moving average from the 12-day EMA.
A third element, the 9-day EMA of the MACD, known as the signal line, is drawn atop the MACD. Its reading is used to discern buy/sell signals. When a short-term average crosses ABOVE a longer one, traders perceive increasing upward price momentum.
It is a buy signal, one that shows, from the data, that prices are increasing at a faster rate now than they have in the recent past. But remember our wise mathematican friend Carl Jacobi, who tells us: “Invert! Always invert!” And in this case, happily, that works.
When a short-term moving average crosses BELOW the longer-term average, the opposite is true and prices have begun to move downward at a faster rate in the now than in the recent past – a classic sell signal.
Again, you don’t have to do this math. The chart will do it for you. Critical Takeaway: This is used for short-term holding periods.
9.4. KDJ Indicator
Here’s something to love about financial writing. Be prepared, it’s a grabber. “The KDJ indicator is actually a derived form of the Stochastic with the only difference being an extra line called the J line. The J line represents the divergence of the %D value from the %K.”
So: Yikes. That is a mouthful. Learning any new language is difficult, so let’s break this down one morsel at a time so we don’t choke on the geekiness.
Stochastic describes the random-yet-predictable nature of the stock market. On any given day, or at any given second, anyone who tells you he can predict anything in the immediate short term is likely full of balloon juice and/or trying to sell you something of dubious value.
That said, using the immutable laws of statistics – the arithmetic mean, standard error and standard deviation, all covered in the first week of the intro freshman course – we can say that in roughly two-thirds of all outcomes the annual performance of the S&P 500 will be within one sigma of the historical average annual return (1965- 2015) of 11.1%. If you know the value of sigma, the greek for standard deviation, then you can give a reasonable prediction of the market’s likely range in fully two-thirds of all years. So stochastic means simultaneously random but also statistically predictable.
Divergent just means separated in two directions. How far they diverge is what is being measured by the J line, which compares the distance of the %D value from the %K.
%D Value is the Moving Average times the %K Value.
%K Value is 100 times (Close-Lowest Low [of last “n” periods]) / (Highest High [of last “n” periods] – LowestLow [of last “n” periods])
Here’s how it works In Real Life: When the oscillator exceeds 80, the security (in this case the currency) is considered overbought. When it goes down to 20, the security is said to be oversold. So the “buy” signal flashes when the J line goes under 0 when K and J are in oversold territory. The “sell” signal goes on when the J line goes above 100 when K and J are overbought.
Critical Takeaway: Most of the time what you’re going to see is a value between 20 and 80 which indicates the market conditions are effectively neutral. This indicator moves pretty slowly and as such is sometimes thought of as a lagging indicator.
9.5. Bollinger Bands Bollinger
Bands are a visual representation of price volatility, and this is a nifty indicator for newbies to embrace. The bands are drawn above and below a 20-day simple moving average. They constantly measure standard deviation, which moves as volatility rises or falls. The outer bands are set two sigmas — or standard deviations — above and below the middle band. They widen as volatility increases and narrow when volatility goes down. John Bollinger contends his namesake bands should contain 88% to 89% of overall price action, so a move OUTSIDE these bands is meaningful — but still far from guaranteed!
Critical takeaway: With Bollinger bands, prices are thought to be relatively high when they rise above the upper band. On the flip side, prices are thought to be relatively low when they fall below the lower band.
9.6. Relative Strength Index
In his 1978 book New Concepts for Technical Trading Systems, Welles Wilder introduced a momentum oscillator that measures the velocity and change of prices. The RSI is a scale between 0 and 100. When RSI exceeds 70, the security is deemed overbought (and it’s time to sell). If RSI drops below 30, the security is thought to be oversold, which means it’s time to buy. It’s a simple scale that’s easy to use and is very popular with trading newcomers and pros.
The formula for this one is easy:
RSI = 100 – (100 / 1+ RS) when RS = Average Gain / Average Loss
The first calculations for average gain and average loss are simple 14 period averages. That just means you add up all the gains for the past 14 days and average them, and then you do the same with the total losses for the past 14 days. Then divide to determine RS. A cakewalk. Especially when you let the computer do it, even though now you know how…
Critical takeaway: This is a good indicator to watch on its own – but try using it with other lines to double-check a buy/sell signal.
9.7. Bias Ratio
This ratio is a relative newcomer to the technical space that dates to 2001, though it wasn’t publicly discussed until 2004 and wasn’t published until 2006. It detects, well, bias, in the sense of price manipulation. It’s a concrete metric that measures abnormalities in the distribution of returns that illuminates the presence of bias in pricing. It works by comparing how far the returns are from an unbiased distribution.
Critical takeaway: Though you should be aware of it, this is not a tool that’s helpful with digital currency trading. Since you had to read this, you’re entitled to a Fun Fact. This was how they got Bernie Madoff.
9.8. Williams % R
Properly said as “Williams Percent Range,” this is an oscillator that seeks to determine if a security is overbought or oversold. Use the Williams Percent Range to set entry and exit points (that is, prices) as you trade. The range works by comparing a closing price to the high-low range during a 14-day period. It was invented by a trading expert named Larry Williams.
The formula: %R = (Highest High – Closing Price) / (Highest High – Lowest Low) x – 100
Critical takeaway: Best used as a predictive indicator to signal an imminent market reversal at least a day or two in the future.
9.9. Fast & Slow Stochastic Oscillator
George Lane came up with the stochastic oscillator in the 1950s. It shows the location of the closing price relative to the high and low price range for a 14-day period. Lane’s contention was that – all other things being equal – the speed of a price movement changes before the actual price does. The oscillator’s sensitivity can be turned up or down by adjusting the time period OR by using a moving average of its reading.
The Formula: %K = 100(C – L14)/(H14 – L14)
Where C = the most recent closing price,
L14 = the low of the 14 previous trading sessions,
H14 = the highest price traded during the same 14-day period,
%K= the current market rate for the currency pair, and
%D = 3-period moving average of %K
Critical Takeaway: Readings above 80 indicate the security is at or near the top of its high-low range. Readings below 20 are thought to signal the security is trading near the bottom of its high-low range.
9.10. Volume Moving Average
Self-explanatory: A moving average to assess volume.
That concludes the major charting functions you will find on Google Finance. You have certainly earned a break! If you are feeling lost or overwhelmed right now, don’t worry. That is perfectly normal. Technical trading is complicated and, initially, isn’t always intuitive, especially to non-math folks. But it’s a skill that CAN be learned and honed. Give yourself time.
There’s only so much you can glean by reading this or even memorizing it word for word – it’s something that you will have to use and use a lot to become familiar with the nuances of each indicator. The best advice is to practice, practice, practice. Use these tools until they become intuitive. No one starts out understanding how all of this comes together. It is a multi-event mental gymnastics meet.
Don’t try to learn it all in one day. Be patient and keep studying. Grow comfortable using each of these indicators on the target range before using live ammo.
Take a close look at the data and analytic tools available on the various trading platforms before you commit to opening an account. Many will also offer additional learning opportunities, such as video tutorials, which beginners simply cannot get enough of.
Now that we’ve covered the chart basics and a few of the analytical tools you are likely to encounter, let’s take a look at some of the most common patterns you will run into as you learn to trade cryptocurrency.