The importance of a trading plan can’t be overstated, but yet the number of traders who don’t have one far outnumbers traders who do have a plan. Without a comprehensive plan of attack it is easy to get off course. Your plan doesn’t need to be overly detailed, a few pages or so will do. But the more detailed the better. The idea here is to systematize as much of your process as possible – make them repeatable. There are a few key components that should be included, but keep in mind there is plenty of room for flexibility for tailoring to your specific needs. As you go through and construct your plan keep this acronym in mind – K.I.S.S. (Keep It Simple Stupid).


In anything we set out to do, if we intend on it having a shot at success, don’t we plan ahead? Some type of plan? Trading is of course not different. Markets are too dynamic, full of too much uncertainty, to try and navigate them without a solid framework in place. A plan is imperative if you are to achieve consistent trading results. They are also excellent at helping you identify your strengths and weaknesses so you can gravitate towards doing more of what works and further away from what doesn’t.


As I stress in all my webinars, proper risk management is one of, if not the most critical aspects to successful trading. Trading psychology for the discretionary trader runs with risk management in importance. Without good risk management the rest of the trading plan will not won’t matter, regardless of how good it may be. This is the very first thing you need to get straight in your plan.

You need to know your risk tolerance and adopt a risk management strategy which fits you. Know how much risk-per-trade you will take and total account risk across several positions. What is the max number of positions you will hold at once? (Fewer are easier to manage.)

Have a max drawdown figure in place as a ‘kill switch’ when things aren’t going well. For example, if you experience a drawdown of 10% you will either take a break or at least reduce your trading size. Remember, job #1 as a trader is capital preservation. (For more details, check out this session on risk management.)


This is straight forward – what do you use to identify set-ups? It doesn’t matter so much what you use, just that it makes sense and is used consistently. It could be some combination of price support and resistance, trend-lines/slope analysis, chart patterns, Fibonacci levels, moving averages, Elliot Wave Principle (EWP), sentiment analysis, fundamentals, etc. Perhaps all together something else. Have a process in place, is the point. In the beginning this will be difficult to outline but with time things will start to come together. Even if you have been doing this for a while, a year or two, your plan is likely to continue to change, perhaps dramatically so, as you continue to mature as a trader.


Not every market moves the same. It’s a good idea to keep your universe relatively small as it helps not only keep things simple but allows you to learn the personalities of the markets in focus. You can take it a step further and focus on specific time-frames from each market type. For example, you may trade equity indices on a very short-term time horizon (days or less), but choose to trade FX from a swing-trader standpoint (several days to several weeks). You could also have dynamic exposure to one market over another, i.e. – 75% FX, 25% indices/commodities.


On average, what is the intended hold time for your trades? Are you a swing-trader, holding for several days to weeks using weekly/daily/4-hr charts, or do you focus on day-trading, with hold times of a few hours or less, thus using daily down to even a 1-minute chart? It could be some blend of the two.

For beginner traders interested in day trading, it is a good idea to focus on taking it slow with slower time-frames before moving towards the rapid pace of intra-day trading. It’s a good idea to focus on only a couple of time-frames at most, as trying to manage trades across several time-frames can be a difficult task even for seasoned traders.

Traits of Successful Traders
Get My Guide

What set-ups work best for you, those that resonate with you the most? It probably goes without saying, but these should be at the core of your trading. Set-ups are based on the alignment (confluence) of any number of factors which make for a high conviction trading opportunity. If you are new to trading, then this will take some time to figure out, so be patient in making progress towards understanding what works best for you.

A set-up is one thing, but how you execute it is another. There is the set-up, then the method by which you will take advantage of the set-up. For example, a group of ten traders could sit down at a computer with the same set of tools, but how the market is analyzed and then executed upon with vary from one to another. For example, a market could be ranging and one trader will buy immediately as the market breaks out of the range while another trader who has identified the same range breakout, might wait to trade the first pullback. Or some combination of the two entry strategies.


When you hit the inevitable drawdown, what will you do to make sure it doesn’t become damaging? You should reduce your trading size or stop trading altogether for a short period of time so you can alleviate stress and figure out what is going wrong. It is very important to have a plan for this before it happens.

It is also important to have a plan in place for when things are going well. Overconfidence can be a killer and lead to a drawdown if not correctly managed. While it is good to be more aggressive when market conditions are conducive and you’re doing well, but you need to do so responsibly. Increasing your risk by 50% isn’t out of control, but suddenly quadrupling your risk is, and is setting you up for a frustrating and potentially devastating outcome.


You should set aside time to reflect on the week’s events and how you traded. It’s a good idea to regularly review your trading plan and make tweaks if necessary. Periodic review of your history and keeping a trading journal are excellent ways to ensure you are following the process outlined in your plan, as well as identifying patterns in your trading that can lead to further tweaking of your plan. Save charts of trade set-ups so you can remind yourself on a regular basis what good and bad trades look like.

Throughout history there have been a number of extremely meaningful volatility spikes across major financial markets. Each had defining characteristics that made them similar, despite occurring in very different markets and for different reasons. The continuity seen across these volatility cycles is a good thing, because while it doesn’t necessarily make a major volatility spike predictable, historical precedence offers a blueprint for identifying conditions that are supportive for a potential vol-event to occur, and how they are likely to unfold once in motion. This can be of great help in guiding trading decisions, whether that is to steer clear of a potential vol blow-up or move towards it with the appropriate strategy that can take advantage of the outsized price swings that come with unusual levels of volatility. We will first discuss what a volatility event typically looks like in terms of the behavior of volatility itself, then take a close look at some of the largest spikes ever witnessed in major financial markets.
“In simple terms, volatility can be defined as the variations at which a market fluctuates. The more an asset’s price moves, the higher the volatility – the less the price moves, the lower the volatility.” – Paul Robinson, DailyFX In this piece we are looking at a short-term measure of volatility (two-week duration) called Realized Volatility, which is volatility as it has already occurred. It is also known as Historical Volatility.
In the lead-up to a volatility spike, there is often a build-up period where volatility rises gradually, indicating markets could be headed for significant dislocation and disruption. The period of subtle unrest is followed by a sudden, vertical move in volatility that reaches a climax before quickly reversing and normalizing through a gradual, but bumpy decline towards pre-event volatility levels. The graph below is a composite of several past volatility cycles, accounting for 100 days before and after the peak in volatility. Notice the build-up period, the volatility spike itself, and the normalization phase, as well as the asymmetry between the phases. Cycle of a volatility spike A volatility cycle visualized.
Source: DailyFX
Another aspect of market volatility to understand is that it doesn’t behave in the same way across all asset classes, nor necessarily even within the same asset class. For example, stock market volatility generally behaves differently than it does in currencies and commodities. Stocks have had an inherent long bias to them, as they are generally an asset of appreciating value over the long run. Market participants invest in company shares, making the stock market almost an exclusively long market with limited short interest. Because of this bias, volatility runs high in down markets when there is fear as a result of financial losses and selling, and low in up markets where fear is minimal. Occasionally, you will see stock market volatility rise in a bull market as participants collectively suffer from FOMO, but this isn’t the norm and only happens towards the end of long, powerful trends – a couple of which we will look at here shortly. VIX demonstrates that volatility tends to rise on selling, decline on buying VIX demonstrates that volatility rises on selling and declines on buying
Data Source: Bloomberg As shown in the graph above, volatility typically runs opposite of the S&P 500, especially when the market declines. Over the long-run, currencies and commodities don’t have a natural bias to them and tend to oscillate in large bull and bear cycles that end in minimal net change. Volatility can rise in either direction and isn’t consistent over time. In the case of commodities (i.e. gold), volatility can actually be more likely to rise with a price rise than during a decline. But again, this is not wholly consistent across a cycle. Gold vs two-week realized volatility Volatility of gold over a two-week period Data Source: Bloomberg In the graph above, the green boxes mark periods when volatility rose while price appreciated, and the red boxes mark periods when it rose while the price of gold depreciated. This highlights the non-directional bias that volatility can have in commodities – the same also holds true for currency volatility.
We’ll have a look at the some of the most significant volatility cycles that have happened in the major financial markets since 1929 and investigate their build-up, peak, normalization phase and after-effects.
At the end of the roaring ‘20s’ bull market, the crash of 1929 kicked off the Great Depression of the 1930s. The October 28-29 crash in 1929 is particularly noteworthy and resulted in a two-day loss of 24% in the Dow Jones Industrials Average, with two-week realized volatility rocketing to 127%. In the short-term aftermath, the Dow price spent the next two weeks closing 6% higher or lower from the prior day’s session. As was the case with the death of other major historical stock markets, the crash didn’t come from all-time highs (ATH), but after a period of weakness that caused volatility to rise ahead of the major spike. Heading into the late-October rout, the market was already off the ATH by 21% with short-term volatility rising from only 11% to 81%. After the initial episode of the 1929-1932 stock market decline, volatility initially normalized by falling from a two-week reading of 127% to under 10% in about five months’ time. Volatility would ramp up again later, but did not exceed 100% again until almost two years later, when the worst part of the bear market drew near its conclusion. Dow Jones Industrial Average: 1929-1931 Dow Jones volatility during the Great Depression Data Source: Bloomberg In the chart above, volatility spiked sharply (red) after weeks of rising in an unsteady market (orange), then dropped sharply (green) as market confidence firmed up in the wake of the two-day crash.
During the late 1970s/80s period, the Hunt brothers attempted to manipulate the price of silver in what was one of the most famous market ‘cornerings’ ever. It wasn’t just the brothers’ trading activity though: inflation was rapidly rising, and precious metal hedges were in high demand. Silver topped out at over $49 after trading at only $6 a year prior. During the spectacular price rise, volatility at times rose sharply with each major surge, including the final one that concluded in January 1980. Volatility declined during the initial portion of the sell-off before spiking to near record levels as the market panicked out of long positions during the spring of 1980. From there it was a bumpy ride, but the two-week realized volatility declined to only 12% a mere five months after super-spiking to 240%. Silver: 1979-1981 Two-week volatility of silver Data Source: Bloomberg In this chart, the green boxes highlight the volatility spikes during bullish phases and the red boxes when volatility spiked on selling. It is clear there was a larger tendency for volatility to rise with the price of silver versus when it fell.
The 1987 stock market crash in the United States was in large part blamed on ‘program trading’, the first technology/financial engineering-driven crash of its kind. However, massive speculative excesses were built up prior to the crash, unlike anything since the 1920s.This played a significant role in the decline of stock prices and the massive spike in volatility. The one-day, 20%+ decline in the major averages was of course a significant surprise – outside the possibility of projection – but as has been the case with most other major volatility cycles, it didn’t exactly occur out of the blue. During the last ~10% of the bull market, two-week realized volatility rose with the S&P 500 from 8% to 15%, highlighting growing instability in the uptrend. By the time Black Monday rolled around, the SPX had already declined from the high by 16% while volatility was materially higher with a short-term reading of 25%. Short-term volatility spiked to over 130% in the wake of the Monday collapse in stock prices before easing off and eventually dropping back to near 10% by the following March. S&P 500: 1987-88 The S&P 500 versus two-week realized volatility between April 1987 and March 1988 Data Source: Bloomberg Growing unrest (orange) shows volatility increasing as the market is still in a bullish phase. When Black Monday rolled around, volatility went spiraling higher (red) before dropping off after the market stabilized (green).
The Great Financial Crisis was driven by irresponsible banking practices on Wall St. that eventually came at the cost of Main St. The decline from 2007 to 2009 was the largest plunge in both stocks and the economy since the Great Depression, but it wasn’t without some type of warning that a major blow-up in volatility could be in the works. Just before things got really wild in the fall of 2008, two-week volatility was already at 41%. From there, the S&P 500 fell another 27% in about five weeks, which saw short-term volatility rocket to 97%. During that time, the widely-watched VIX index exploded from 36 to 80. In the year following, volatility normalized with two-week realized vol and the VIX hitting 20% and 23, respectively. But even going back to 2007 before the bear market began, like in so many other bull markets nearing their conclusion, volatility began creeping higher. Despite the S&P 500 having gained about 8% YTD up to the October 9, 2007 high, the VIX itself had also risen from around 12 to 16 – a 25% increase. The wheels on the bus were beginning to wobble despite all looking well on the surface. S&P 500: 2007-08 The S&P 500 versus two-week realized volatility between April 2007 and March 2009 Data Source: Bloomberg Looking at the chart above, one can see volatility was generally heading higher (orange) prior to the big spike in 2008. Once panic hit a zenith (red) and market confidence came back, volatility died down (green). AUD/USD: 2008-09 AUD/USD two-week realized volatility spiked, but not without warning first Data Source: Bloomberg Turning to currencies, one of the biggest casualties of the Great Financial Crisis was the Australian Dollar (AUD/USD), which plunged nearly 40% as two-week volatility spiked to 80% from just single digit levels a few months earlier. Australia’s strong export ties to China proved to be costly when the emerging economy’s growth rate took a serious hit during the global recession. The rout wasn’t a total surprise, as few are; volatility rose steadily in the months prior to the final collapse of Aussie. Short-term volatility climbed from a mere 5% in July 2008 to nearly 30% before the final spike to 80% occurred into October. Once AUD/USD bottomed there was a fairly sharp drop in volatility before it tapered off during the first few months of 2009. Other currency pairs were also hit in a big way, such as EUR/USD and USD/CAD, but volatility never escalated like it did in AUD/USD. Volatility in those pairs rose to ‘only’ 30-40%, which is still extremely high for currencies.
The first major flash-crash to speak of occurred on May 6, 2010, when the S&P 500 e-mini futures were rocked by over 6% in about seven minutes before erasing all losses in less than fifteen minutes. A London-based trader, Navinder Singh Sarao, was accused and found guilty of ‘spoofing’ – the placing of large orders which are cancelled just before getting filled. Now while this may have contributed to the decline, the market was already in a fragile state to begin with, as is typically the case when flash-crashes occur. To put volatility into perspective, the VIX had risen from 15 to 25 in the weeks prior, before rocketing past 40 on the day of the crash. Volatility actually didn’t finish rising until about three weeks later when the VIX hit 48. From there, volatility declined in typical fashion until early 2011 before popping again. S&P 500: 2010 The S&P 500 versus two-week realized volatility between January 2010 and June 2010 Data Source: Bloomberg The S&P 500 e-mini flash-crash showed a familiar theme: the orange box highlights a period where stocks were still generally heading higher but the unrest underneath the hood was becoming apparent via rising volatility. The spike and higher levels of volatility (red) followed suit along with the May 6 flash-crash.
Of the blow-ups in volatility, this was one of the more surprising. The Swiss National Bank (SNB) had a floor in the EUR/CHF exchange rate that caused wide-spread complacency in the market and fueled the ‘thinking’ that the central bank would keep the cross supported. As it turned out, this was not the case. When the SNB removed the floor, EUR/CHF collapsed from 1.20 – depending on the quote source – to as low as 0.68. Short-term volatility went from virtually zero to nearly 100% in a flash. It only took days to take back most of the spike, but vol spent the next three months slowly normalizing. EUR/CHF: 2014-15 EUR/CHF blow-up on SNB lifting floor Data Source: Bloomberg With the SNB floor in place volatility dropped to nearly zero (orange), but once the floor was lifted the market was caught off guard, causing volatility to rocket to over 100% (red) before backing off once the dust had settled (green).
The Brexit vote in June 2016 wasn’t expected, despite it being a possibility, as evidenced by the way markets were hammered when the vote came out in favor of the UK leaving the European Union. Sterling was in a near-term upswing right before the results were announced, but GBP/USD ended up closing down 8% on the day that the vote was finalized. Two-week realized vol exceeded 46% thereafter. This was a known event to take place, so there was no surprise to see volatility rise ahead of time in anticipation – nevertheless, volatility provided a warning that things could get dicey. In the month before the vote, two-week realized volatility rose from a mere 6% to over 16% as market participants weighed in on the potential outcome, one that the market wasn’t fully prepared to handle even with warning. Post-Brexit vote, volatility initially cratered from 46% back to 16% in only about a month before entering the typical post-event grind towards normalization of around 7% in six weeks’ time. A few months after that there was the Pound flash-crash in October that again saw volatility spiral higher momentarily. GBP/USD & Vol Chart GBP/USD versus two-week realized volatility between January 2016 and December 2016 Data Source: Bloomberg Above it can be seen that volatility rose in anticipation of the Brexit vote (orange), then rose sharply on the surprise Brexit outcome (red) to eventually fade in the aftermath (green).
The ‘Volpocalypse’ of February 2018, while nowhere near as dramatic and damaging as the ’87 crash, didn’t exactly happen out of the blue. In the final months of 2017, U.S. stocks accelerated higher in an unsustainable fashion, taking vol with it – two-week realized volatility rose from just 3% at the end of September to around 8% at its peak in January 2018. After stocks peaked in late January, the market began to decline for about a week before the indices plunged and volatility shot up. The Dow experienced a 4% flash-crash in the span of about ten minutes. The VIX, the most popular measure of broad stock market volatility, saw an extremely unusual spike as the market was caught betting heavily on low levels of volatility via futures, options, and ETFs aimed at direct bets on the level of the VIX. This caused an exaggerated move in the VIX that pushed it to an intra-day high of 50. Like most vol blow-ups, this one too spent several months normalizing to pre-event levels. S&P 500 and VIX: 2018 S&P 500 versus VIX volatility between November 2017 and May 2018 Data Source: Bloomberg Above, you can see that volatility began rising during the last stage of the blow-off rally as it became unstable (green) and rose further on price weakness before super-spiking on a sharp decline in stock prices (red), followed by a period of normalization (orange). VIX Hits 50 intra-day The VIX intra-day spike to 50 Data Source: Bloomberg The intra-day VIX spike was much larger than the actual stock market decline would have caused under ‘normal’ circumstances, but massive short VIX bets helped fuel it much higher.
Major volatility events have always been a part of financial markets and always will be. Understanding what they look like and having historical precedence to operate as blueprints offers traders a framework to operate within going forward.

Michael Boutros, Strategist

I’ve been trading the markets for nearly 20 years – There’s been ups and down. Along the way you try to find what style trading works best for your personality and goals. Sometimes this process can be orderly and structured- it wasn’t for me.


Developing your strategy comes at a cost –often a lot of time, money and frustration. I came to this realization in 2003 as USD/JPY was pulling back from multi-year highs. The ‘116 death trap,’ as I’ve dubbed it, was a seemingly rock solid zone of support. It had caught the lows since 2001 with numerous defenses suggesting a perfect opportunity for me to ‘catch the lows’ and cost average in. Even when price broke, my hope was that every rebound was the lowNot quite. In September of 2003, the death trap began with a drop of more than 12% over the next year, taking USD/JPY to lows not seen since the late-90s. Even a glimmer of hope offered by a multi-week rally in early 2004 reversed. With the pain finally too hard to tolerate, I exited at the lows. So much for cost averaging into a loser. That was my hard lesson learned.


Big Mistake Leads Technical Trader to Top Three Tools


Well, there was a lot, but for starters, emotions are not your friend. Most of all, my error was not having a plan and not recognizing market conditions. Knowing the style of trading that works best for your personality, risk tolerance and goals is critical and without a plan you are on a ship without a rudder.

Everyone comes to this realization differently. I learned through years of managing market flow for one of the first brokers to offer online retail FX trading. It was through close observations of retail market flow and tracking near-term price-action that Ideveloped three basic principles for my technical trading strategy. It boils down to assigning a value for price, time and momentum.

Price Value – Key reversals, Fibonacci, High/Low-day Closes

It’s all about the levels – identifying support / resistance is a critical part of being able to create actionable trade ideas targeting or countering key levels in price. These lateral levels can be identified using various methods. The three I rely on most are key reversals (outside-day reversals), high and low-day closes and Fibonacci.

The word “Fibonacci” often appears in trading and it is likely that you have already seen or heard of it – for good reason. It is a simple measurement based on the golden ratio of 61.8%. This ratio can be observed everywhere in nature from trees to galaxy formations and is also very relevant to buying and selling behavior in markets. It is a tool you can implement in both trending and range-bound markets to help identify areas of possible support / resistance

Time value indicator – Slope and Trend lines

One discipline I rely heavily on is pitchfork and median-line analysis. The goal of this style of analysis is to identify the slope or gradient of a given market in an attempt to highlight when a price level may be significant. It is this time value that adds another dimension to the lateral levels identified earlier – these “confluence zones” are often points in price and time that can offer major pivots in near-term price-action. Trading into / off these inflection points todayform the basis of my favored technical trade setups.

Momentum value – RSI

The speed of a market can tell The Relative Strength Index (RSI). The RSI is an oscillator that is used as a measure of momentum — the speed of the recent price movement in either direction. While all oscillators are inherently backward looking, they can still be of value when you are trying to asses when the market maybe overextended in either the overbought or oversold conditions. The ability to assess the inertia of a given market is critical for near-term swing and trend trading as it offers a window into the possible longevity and strength of a given move as well as ideas on when it may expire.


Having a multi-faceted approach that takes into account price value, momentum value and time value could help steer you not only to possible opportunities, but also help you avoid failure. Knowing the condition of the market using price, time and momentum has been a major philosophy of my trading.

—Written by Michael Boutros, Technical Strategist with DailyFX

– Reviewed by James Stanley, Nov. 24, 2021

The primitive forces of capitalism rule markets like the laws of gravity. Buyers and sellers provoke a battle to find a happy medium agreement in every financial market. As prices dance around on charts, traders are often looking for reasons to explain price movements however, the underlying source of price movement boils down to the relationship between supply and demand.

Generally, positive news means increased demand and lessened supply – equating to higher prices. Negative news usually spells lower demand and increased supply.

This article will outline the following foundational aspects of supply and demand:

  • What is supply and demand?
  • Supply and demand zones
  • Supply and demand in the forex market
  • How does supply and demand work?


Supply and demand is the relationship between buyers and sellers that is used as a measure for price determination in financial markets. The forces of supply and demand interact to affect an equilibrium price between buyers and sellers whereby the quantity of demand equals the quantity of supply.

What are the laws of supply and demand?

‘Supply’ is purely the amount available, while ‘demand’ is the amount that is desired. The graphs below indicate the visual aspect of supply, demand and equilibrium respectively.

Supply: the relationship between price and quantity

supply grpah

Demand: the relationship between price and quantity

demand graph

Equilibrium: the most efficient price at which quantity demanded equals the quantity supplied:

supply and demand equilibrium graph


Supply and demand zones allow traders to gain a perception into the current financial markets, and these are illustrated in the charts below.

It is noticeable that supply and demand zones cover a broader area as opposed to support and resistance levels. These broader zones provide more reliable price regions than a single line/level which can be a better gauge for future price movements.

The supply zone below shows an area clustered by sellers because price tends to ‘bounce’ lower off this demarcated zone. This quick price movement off these zones characterizes the features of supply and demand zones. The demand zone exhibits the same attributes as the supply zone in the opposing direction – demand zone mimics a broad area of support.

supply and demand zone in forex tradingsupply and demand zone in forex trading


Supply and demand within a simple vegetable market is not all too dissimilar from that which takes place every day in the forex market. In some cases, these forces are moving at such high velocity that new traders can have difficulty understanding the granularity of the details.

The forex market is the largest financial market in the world because of the heavy demand behind the traded assets. Currencies are the basis for the world’s economy and whenever one economy wants to trade with another economy (provided different currencies are used) an exchange will be required.


In a nutshell, supply and demand works by analysing the quantity of buyers and sellers within the forex market.

How do supply and demand influence market price?

Imagine that the South African Reserve Bank (SARB) enacts an interest rate change. An entire chain reaction will be set in motion due to the forces of supply and demand. When rates increase, forex rollover payments also increase.

This means that investors that are holding the trade open at the specified rollover time (varies from country to country) will receive a higher rate of interest than they would have previously – incentive has just increased.

All else being equal, more traders would want to buy; and fewer traders would want to sell as the opportunity cost of selling (the rollover payment) has just gotten more expensive.

Supply and demand forex – USD/ZAR daily chart:

supply and demand forex

As you can see, price aims to find a comfortable point and will increase until there are no more buyers willing to pay that price. At this point, sellers outnumber buyers, and price will respond by moving down.

After price has moved down far enough (red circle) traders will come back into the picture, remembering in the increased interest rate and the additional rollover payment that can be received from holding a long ZAR position, and this lower price presents a ‘perceived value.’

As additional buyers enter the picture, price will move up to reflect this increased demand.

This is the process of price attempting to find its fair value as it takes place on many different time frames in every market in the world.

For more information, read out in-depth guide to trading supply and demand.


The relationship between supply and demand along with support and resistance is important. This is because when price crosses key support and resistance levels, changes in supply and demand may occur within that currency pair.

Reviewed by Nick Cawley on December 23, 2021

Traders with a strong understanding of technical indicators are usually better equipped to navigate the financial markets than those that lack this knowledge. While personal investing goals, risk appetite and trading style will help to determine a strategy and trading plan, knowing what technical indicators to use in your approach can help to determine possible entry and exit points.

Hundreds of technical indicators exist, and clear signals can be identified using effective indicators as part of a strategy. This article will cover six of the most popular technical indicators for stock trading.


For traders looking for the most effective technical indicators, it is important to consider the objectives of the trading strategy as well as the current market condition. For individuals trading individual stocks, it is often beneficial to apply indicators to the stock index in which that share belongs to get a holistic view of the larger market as a whole.

Below are six of the most popular technical indicators to use when analyzing stocks:




Client Sentiment

Contrarian Indicator

  • Shows client positioning of the market
  • Indicates when markets are nearing extremes
  • Leading indicator
  • Useful in trending markets

Relative Strength Index (RSI)

Momentum Oscillator

  • Plotted between 0 – 100
  • Indicates when the market is overbought or oversold
  • Leading indicator
  • Useful in trending markets


Momentum Oscillator

  • Plotted between 0 – 100
  • Consists of two lines, %K and %D line
  • Indicates when the market is overbought or oversold
  • Leading indicator
  • Useful in rangebound markets

Simple Moving Average (SMA)

Trend following indicator

  • The SMA represents the average price of a security over a specified period of time
  • Equal weighting is given to all points in the data set
  • Used to confirm the direction of the current trend
  • Lagging indicator
  • Useful in trending markets

Exponential Moving Average (EMA)

Trend following indicator

  • The EMA represents the average price of a security over a specified period of time with a greater emphasis on recent prices
  • Higher weighting is given to recent points in the data set
  • Lagging indicator
  • Useful in trending markets

Moving Average Convergence Divergence (MACD)

Momentum oscillator

  • The MACD measures both momentum and the trend
  • Overbought and oversold signals occur above and below the zero-line
  • Lagging indicator
  • Useful in trending markets


Client sentiment data is derived from a brokerage’s execution desk data, measuring live retail client trades to determine possible directional biases in the market. When sentiment is approaching extreme levels, stock traders may begin to see a reversal as more likely which is why it is seen as both a contrarian indicator as well as potentially having a leading component.

Below is an example of the FBS Client Sentiment Index, FBS sentiment gauge derived from execution desk data, for the Dow Jones index (Ticker: Wall Street). Based on the data below, 64% of traders have short positions which means that majority of traders expect the price of Wall Street to drop. However, sentiment is seen to be bullish, meaning that based on this data the price of Wall Street may be expected to increase. Although it is not advisable to trade-off sentiment (or any individual indicator) alone, an individual who is trading a constituent of the DJIA could use this data as an informative tool before applying additional indicators.

Wall Street client sentiment

FLA provides client sentiment data which is derived from live FBS retail client trades for forex, commodities, cryptocurrencies and major stock indices. Stock sentiment analysis is also available for individual shares on the IG platform where applicable or available.


The relative strength index (RSI) is a momentum oscillator that measures the magnitude of price movements to determine whether a market is overbought or oversold. A market is seen to be oversold when the RSI is below 30 and is overbought when the RSI is above 70. These are key levels could indicate potential reversal, classifying the RSI as a leading indicator.

The chart below shows the RSI being applied to the daily chart for Uber Technologies (Ticker: UBER). The RSI trades between 30 and 70 for some time before falling below the 30 level. Below the 30 level, the first signal is a false signal because although it looks like the trend is going to reverse to the upside, the price continues to fall. However, the second signal is present when the RSI is below 30 and turns towards the upside. However, the RSI only confirms the reversal by crossing above the 30 line the next day.

Uber Tech price chart with RSI


The stochastic oscillator is another momentum indicator which is useto determine overbought and oversold conditions when trading stocks. Unlike the RSI which measures the speed of price movements, the stochastic measures current price in relation to its price range over a period of time.

The %K line (the black line) is calculated by using the latest closing price relative to the lowest low and highest high over a specified period of time and the %D line represents the simple moving average of the %K (three period Simple Moving Average is the most common).With stochastics, a bullish crossover occurs when the %K line (the black line) crosses over and above the %D line (the red dotted line). Likewise, a bearish signal occurs when the %K line crosses under and below the %D line. The strongest signals will often occur when there is a bullish crosscoupled with a move above 20 from below and a bearish signal coupled with a move below 80.

In the image below, the stochastic indicator is applied to the S&P 500 price chart (Ticker: US 500). As indicated on the chart, a bearish crossover occurs from above the 80 line, indicating that the trend may reverse to the downside. The reversal is then confirmed once the lines cross 80. Likewise, the bullish crossover occurs below 20 and the reversal is confirmed once the 20 line is crossed.

US 500 price chart with stochastic indicator


A simple moving average (SMA) is a lagging indicator which represents the average price of a security over a specified period of time. In a trending market, the moving average modulates short-term price fluctuations and allows stock traders to identify the trend in a simplistic way.

As depicted in the chart below, in a rangebound market, it is also possible to use a moving average to identify support and resistance levels. By applying the 50 day MA to the Boeing price chart, it is clear that the 50-day SMA can also be seen as potential support even as Boeing is trading in a ranging environment.

Boeing price chart with 50 day SMA


As with the SMA discussed above, the exponential moving average (EMA) is a lagging indicator which represents the average price of a security over a specified period of time. However, unlike the SMA which gives equal weighting to all data points in the series, the EMA gives more weight to recent prices, removing some of the lag found with a traditional SMA. This makes the EMA an optimal candidate for trend trading as it allows traders to get a holistic view of the market without missing out on opportunities with may be due to the lag of a simple moving average.


The MACD (moving average convergence/divergence) is a technical indicator that can be used to measure both momentum and the strength of the trend. The MACD displays a MACD line (blue), signal line (red) and a histogram (green) which shows the difference between the MACD line and the signal line.

The MACD line is the difference between two exponential moving averages (the 12 and 26 period moving averages using common default settings), whilst the signal line is generally a 9-period exponentially average of the MACD line. These lines waver in and around the zero line, giving the MACD the characteristics of an oscillator with overbought and oversold signals occurring above and below the zero-line respectively.

With reference to the chart below, featuring Apple, Inc. (Ticker: AAPL):

  • A bullish signal is present when the MACD line crosses ABOVE the signal line from BELOW the zero line.
  • A bearish signal is present when the MACD line crosses BELOW the signal line from ABOVE the zero line.

Apple Inc price chart with MACD cross


What is the difference between a leading and a lagging indicator?

Although leading and lagging indicators are both derived from historic price data, a leading indicator is used to indicate expected price movements in the market while lagging indicators are used to provide entry and exit signals once the trend has been identified.

Although similarities and differences exist between the two, both are equally important and it is often beneficial for traders to use both leading and lagging indicators simultaneously.


  • The various stock markets of the world have different trading hours which can impact global capital flow
  • Overlapping sessions can result in higher aggregate volume and/or smoother market conditions
  • Conversely, thin trading volume and holiday periods can urge volatility.
Due to technological advancements and global interconnectedness, traders have the ability to check the pulse of equity prices at almost any time of day through a variety of media. Still, not all stock markets can be traded at any time. Each stock market, whether it be the Dow JonesDAX 30 or Nikkei 225 has a strict schedule when shares can be traded by market participants according to exchange times. Knowing the trading hours of operations for each index, and thereby when the most heavily-traded markets overlap, can help contextualize market conditions and potential price reactions to breaking news.


Dow Jones, Nasdaq 100, S&P500 9:30AM to 4:00PM 2:30PM to 9PM
FTSE 100 3:00AM to 11:30AM 8:00AM to 4:30PM
DAX 30 3:00AM to 11:30AM 8:00AM to 4:30PM
CAC 40 3:00AM to 11:30AM 8:00AM to 4:30PM
Nikkei 225 8:00PM to 4:00AM Lunch 10:30PM to 11:30PM 1AM to 9:00AM Lunch 3:30AM to 4:30AM
Shanghai Composite 9:30PM to 3AM Lunch 11:30PM to 1:00AM 2:30AM to 8:00AM Lunch 3:30AM to 5:00AM
ASX 200 8:00PM to 2:00AM 1:00AM to 7:00AM


Given the dispersion of the major stock exchanges, trading overlap exists only at a few periods each day, typically as one region of the world winds down and passes the torch as the next region comes online for the day. As a result, global trading volume temporarily increases and liquidity is bolstered, usually fostering smooth price action in the event of scheduled and breaking news within the timeframe. GMT stock exchange opening and closing times Check out our market snapshot page which includes the opening and closing times for the major exchanges. The opposite also holds true. When the US trading session winds down and Asia is still offline, there is a period of usually thin liquidity than can see news events spark amplified price reactions like flash crashes or trading gaps. Each trading day has differences in volume and liquidity depending on the backdrop at the time, like whether there was a Central Bank announcement coming up on the economic calendar.


Holiday periods are one notable example of liquidity distortion, where many traders may have left their desk for vacation and the market is scheduled to close for a special event. In turn, the number of participants are reduced alongside the available exchange-based trading hours which can potentially stoke massive price moves if fundamental developments occur. Similarly, there are seasonal influences like the summer months whereby markets naturally experience lower volume compared to the other seasons, a phenomenon known as the summer doldrums.