In this article will we look over some of the most important things for a forex trader – covering technical analysis, fundamental analysis, as well as some basic terms about the forex market as a whole.

Bollinger Bands

Bollinger Bands are technical indicator developed by John Bollinger during the 1980’s and most thoroughly explained in his 2001 book ‘Bollinger on Bollinger Bands’. Bollinger bands are volatility indicator similar and can be used to measure the ‘highness’ or ‘lowness’ of an instruments price relative to previous closes.

This particular indicator involves overlying three bands (lines) on top of either bar or candlestick chart. The central line or band can be either a simple moving average or alternatively an exponential moving average. However calculating the central band using a simple moving average tends to be the more popular choice among traders. The upper and lower bands are the running standard deviations from the central moving average. Due to the fact that standard deviations are themselves a measure of volatility, Bollinger bands are self-adjusting widening during periods of high volatility and contracting during periods of low market volatility. John Bollinger himself recommends using  10-day bands for short term trading, 20 days for intermediate-term trading and finally 50 day bands for long term trading. However, these quoted values are intended to apply to Stocks and bond, meaning that many currency and commodity traders may want to use shorter time frames.

When setting up Bollinger bands, traders are typically required two enter in two different parameters the time frame (as mentioned above) and the number of standard deviations to plot above and below the central Bollinger band. Statistically analysis suggests that around 95% of all daily closes will fall within three standard deviations of the mean. Typically traders opt with between 1.5 to 2.5 standard deviations of the central Bollinger band.

Bollinger bands can be given a variety of different interpretations, with there being no widely agreed upon way to use the indicator:

A Buy Signal, when the price touches the lower Bollinger band with a plan to exit when price touches the moving average in the center.

A Sell Signal, when the price touches the upper band with a plan to exit when the price touches the moving average the in the center.

As a Buy or Sell signal, when the price action breaks through the upper or lower Bollinger bands.

Bollinger bands have been used by option traders, who sell Options when Bollinger bands are historically wide apart and buy options when Bollinger Bands are historically close together.

Bollinger bands, however are rarely used on their own and are more commonly used alongside other indicators, wit Bollinger bands commonly being used alongside the RSI (Relative Strength Index). It is generally required to use Bollinger Bands alongside another technical indicator, as the ambiguity associated with interpretation of the indicator can lead to false signals. Thus Bollinger Bands shouldn’t be used on their own, as the use of other technical indicators is required to confirm price action.

Fibonacci Retracement

Who was Fibonacci?

Leonardo Pisano Bigollo better known as Leonardo Fibonacci was an Italian mathematician. Fibonacci is often considered to have been the most talented mathematician of the middle ages and is best known for a number sequence named after him.  The number sequence is begins with 0 followed by 1 and then continues by adding 0+1 to derive; 1 the third number in the sequence.   The sequence then continues by adding the second and third number together getting 2. The sequence continues to progress in the same way.

• The Fibonacci number sequence: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144….

Fibonacci Ratios

After the first few numbers in sequence, if you measure the ratio of any two succeeding numbers you arrive at the ratio of 0.618. For example if you divide 89 by 144, you arrive at a ratio of 0.618.

Again after the first few numbers in the sequence, if you measure the ratio between two alternate numbers you get a ratio of 0.382. You can try this with the above Fibonacci number sequence; for example if you divide 13 by 32 you arrive at 0.382.

These recurring ratios are known as the golden mean and it has been claimed that these ratios naturally occur in nature. Traders have made use of these Fibonacci ratios to create a number of Fibonacci studies, using a series of retracement and extension levels.

Need To Know Fibonacci Retracement and Extension Levels

• Retracement Levels: 0.236, 0.382, 0.500, 0.618, 0.764
• Extension Levels:  0, 0.382, 0.618, 1.00, 1.382, 1.618

You are probably never going to have calculate these levels yourself, as the overwhelming majority of charting software will do all the work for you. There are also a number of Fibonacci calculators available online.

These retracement and extension levels are used as potential support and resistance levels. It has been argued that Fibonacci levels are a self-fulfilling prophecy, due to the sheer number of traders using Fibonacci levels in their trading strategy.

Using Fibonacci Retracement and Extension Levels

Fibonacci Retracements

It’s important to note that Fibonacci tools are most effectively used when markets are trending. When markets are trending upwards traders go long (Buy) when the price hits a particular Fibonacci retracement. When markets are trending downwards traders go short (Sell) when the market price hits a particular Fibonacci retracement level.

Fibonacci Retracements: Uptrends  

To begin you have to draw a line from the start of the trend to the trends high point. If you use the Fibonacci retracement tool on MetaTrader the retracement levels will be automatically placed over the chart.

As you can see from the above chart, the retracement levels are set at 23.6%, 38.2%, 50.0% and at 61.8% of the trend. Many traders believe that if the trend begins to retrace from the trend high, the instrument will find support at one of Fibonacci levels. As it is thought that traders will place buy orders around at the various Fibonacci retracement levels.

 In the above example, you can see that the USD/CHF found brief support at the 23.6% retracement level. Before falling through to find stronger support around the 61.8% retracement level, where presumably traders buy orders kicked in. The instrument recovered to around the 38.2% retracement level before again falling towards the 61.8% retracement level, where the USD/CHF again found strong support.

 Fibonacci Retracements: Downtrends  

To begin you have draw a trend line from to the start of the trend to the trend low. Many traders believe that as the downward trend retraces on itself the instrument will find resistance at one of the Fibonacci retracement levels. As traders have placed Sell orders around the various Fibonacci levels.

In the above example, you can see that the EUR/USD began to retrace the previous downward trend pretty rapidly. But found significant resistance level around the 61.8% retracement level. Presumably traders have decided to place sell orders around the 61.8% mark, which means the EUR/USD faces significant resistance around the retracement level.

When Fibonacci Levels Fail

The above examples show instruments finding significant resistance around one or more of the Fibonacci retracement levels. The markets have tendency to break through resistance and support levels, often at a cost to those who have placed orders on the basis of Fibonacci studies.

While Fibonacci levels can be a useful tool traders have to realise that they have limitations. You don’t always know at which retracement level the instrument will find support or resistance, meaning that you can place an order only to see the market move against you.

It is for these reasons that Fibonacci studies are often used alongside other technical and charting techniques. Fibonacci levels can be a useful tool in a traders arsenal, though traders have to realise the limitations of the technique.

The parabolic SAR,

The parabolic SAR, was first developed by Welles Wilder is price-and-time based trading system. Wilder originally called the system he developed the ‘Parabolic Time/Price System’. The SAR in the name stands for ‘Stop and Reserve’, which is the indicator used in system. The indicator falls below price action when prices are rising and rises above price action when prices are falling. This is where the ‘SAR’ comes in, the indicator stops and reserves when price trends reserve and break through or below the indicator.

The system was introduced in his 1978 book New Concepts in Technical Trading Systems. Wilder introduced a number of other indicators in his book including the RSI, Average True Range (ATR), and the Average Directional Range (ADX). Despite the age of Wilder’s Indicators they have remained incredibly popular.  The parabolic SAR remains a very useful indicator for traders engaging technical analysis and can help a trader determine where to place trailing stops.

Calculation 

Calculating the Parabolic SAR is not particularly easy, thankfully the majority of trading and charting software will calculate the Parabolic SAR for you. This is partly their are different methods for calculating falling and rising parabolic SAR’s, this makes calculating Parabolic SAR using a Spreadsheet quite difficult.

Interpretation  

The Parabolic SAR, follows price action and should be considered a trend following indicator. Once a downtrend reverses and a new trend starts the Parabolic SAR follows the price upwards like a trailing stop. The SAR continues to rise as long as the upwards trend continues. The Parabolic SAR never decreases during an upwards trend and can help you continuously protect your profits. Thus the indicator can work to help you stop lowering your stop-losses, which is can be very tempting. When a upward trend ends and prices start falling the Parabolic SAR will remain above the price, again the indicator follows prices down like a trailing stop. Due to the fact that the Parabolic SAR never rises during a downtrend, thus it can help you protect your profits on short positions.

Adjusting Sensitivity: Step Increments/Acceleration Factor   

The sensitivity of the Parabolic SAR can be adjusted by altering the Acceleration Factor (AF). The AF is also referred to by some as the Step Increment. The Step or Acceleration Factor acts a multiplier which influences the rate of change of the indicator. The Step increases gradually as the trend extends until it hits it’s maximum value. The sensitivity of the Parabolic SAR can be decreased by decreasing the step. Lowering the Step will result in the SAR being further away from the price, which makes a reversal much more likely.

The sensitivity of the Parabolic SAR can be increased by increasing the Step or Acceleration Factor (AF), a higher Step will lead to the Parabolic SAR being closer to the price. However if you increase the sensitivity too much, the indicator will reverse very often leading to the indicator producing regular whipsaws.

Adjusting Sensitivity: Maximum Step 

The sensitivity of the Parabolic SAR can also be adjusted by changing the Maximum step. The Maximum Step affects the sensitivity of the indicator to a less degree than the Acceleration Factor. A lower maximum step will decrease the sensitivity of the indicator, meaning that price reversals will happen less frequently. While a higher maximum step will increase the sensitivity of the indicator leading more reversals. Again traders need to be careful not to increase the indicators sensitivity too much.

Concluding Thoughts 

The Parabolic SAR works best when instruments are trading. According Welles Wilder’s original research this occurs around 30% of the time. This means that traders need to be careful when using the indicator, as it is prone to produce whipsaws when the instrument in question is not trending. The Parabolic SAR is designed primarily to follow an overriding trend and act as a trailing stop. As with the majority of technical indicators they quality of the signals produced depends on the underlying market and the settings used. Traders have to be particularly careful when using the Parabolic SAR as an indicator, as the wrong settings can lead to frustration and heavy losses.  As with many other indicators the Parabolic SAR is often used in conjunction with other technical indicators, with the Parabolic SAR often being used in conjunction with Wilder’s Average Directional Index.

Candlestick charts were first developed in the 18^th Century by successful Japanese rice trader Munehisa Homma. It took a long while for candlestick charts to gain popularity in the Western world. It wasn’t until Steve Nison’s 1991 book Japanese Candlestick Charting Techniques that the charting style gained any real traction with western traders. Throughout the 90’s candlestick charting grew in popularity and is now used by the majority of currency traders.

Forex Candlesticks 

Candlestick charts are best explained with the aid of several diagrams. Candlesticks can be used in conjunction with any time frame, with the Candlestick providing the trader with a visual description of price action during the specified time frame.

Candlesticks are created using the open, high, low and close of the selected time period. The area between the close and open is known as the real body, price extensions above and below the real body are called shadows. With the wick (thin line) illustrating the high and low prices of the instrument in the given time period. If the instrument closed higher than it opened the real body will typically be unfilled or white. But if the instrument closes lower than it opened the real body will be filled or black, as it is the above diagram.

You should now understand that Candlestick charts display significantly more information about price action than standard line charts.

Body Sizes

If you open up a candlestick chart you will notice that the size of the real body will often vary significantly. Longer bodies suggest either strong buying or selling action, while shorter bodies suggest little selling or buying action, with the currency pairing trading in a narrow range.

You should have now worked out that a long white candle suggests that there is strong buying pressure.  The longer the candlestick the further the close is above the open pricing. Typically long white (unfilled) candlesticks suggest that lots of buyers were entering into the market forcing prices up.

Long black (filled) candlesticks suggest that there is strong selling pressure. The longer the candlestick the further the close is below the open. Typically long filled candlesticks suggest that lots of buyers were selling forcing prices down.

Shadows

The upper and lower shadows also impart important information about price action. The upper shadow signifies the high of the time period, while the low shadow signifies the low of the time period. Candlesticks with long shadows show that trading occurred well below and above the close and open, this pattern suggests that there was significant amount of volatility during the selected time period. Candlesticks with short shadows show that the trading was confined to a relatively restricted price range with the market displaying little volatility.

If a candlestick has a long upper shadow but little or no lower shadow, this shows that there was a lot of upward buying pressure. But for some reason or other a number of sellers came in to force prices back down.

Candlesticks with a long lower shadow and no or little upper shadow show that there was significant selling pressure, but for some reason a number of buyers came in provided support to ensure that the close remained close to the open.

Conclusion

Candlestick charts impart traders with significantly more information about price action than standard line charts do. Traders can use the information displayed in candlesticks to get a feel for price action. Many traders keep an eye out for certain candlestick patterns which can be taken as being bullish or bearish. Getting to grips with candlesticks is certainly worth the time, as Japanese candlesticks provide traders with greater insight into price action.

ADX

The Average Directional Index (ADX), as well as the related Minus (-DI) and Positive (+DI) Directional Indicator are a set of directional movement indicators that are part of a trading system designed by Welles Wilder. The Average Directional Index was designed by Wilder with commodities and currencies in mind. But the indicator can be applied to a range of other financial instruments. The ADX measures trend strength, but not does not measure trend direction. The other two indicators mentioned above, the -DI and +DI are used alongside the Average Directional Index to help identify trend direction. By using these indicators together traders can both identify trend direction and trend strength.

The indicator was heavily featured in J. Welles Wilder’s 1978 book, New Concepts In Technical Trading Systems. The book has become particularly well known due to the fact it featured information on a number of now popular technical indicators including the Average True Range (ATR), the use of Parabolic SAR’s and the Relative Strength Index. Even though these indicators were developed before the age of computerized trading, these indicators have stood the test of time and are used by many traders today.

Calculation

Calculating the Average Directional Index and its sister indicators the +DI and -DI is very complex. With it being a subject that deserves an article all of its own. The calculation of the ADX is made more complicated by the smoothing used by Wilder. Thankfully, the majority of trading and charting programs will calculate the indicators for you. The important thing to take away is that the ADX establishes whether an instrument is trending. While the +DI and -DI, indicate the direction of any trend.

Identifying Trend Strength 

The ADX can be easily used to identify whether an instrument is currently trending. In Wilder’s 1978 book, he suggested that an instrument is strongly trending when the Average Directional Index gives off reading greater than 25. While readings below 20 indicate that the instrument in question is not currently trending. So on Wilder’s advice there is a slight grey area between 20 and 25, typically such a value is interpreted as signalling a weak or burgeoning trend. Due to the smoothing techniques utilized by Wilder, the Average Directional Index tends to suffer from a fair bit of lag. Many technical analysts use 20 as the key level when utilizing the indicator.

DI Crossovers: A Complete Trading Strategy 

Using the ADX as well as the +DI and -DI indicators, Wilder proposed a simple trading system. Firstly Wilder required that the ADX value be greater than 25, meaning that prices are currently trending. Though it should be noted that many traders use 20 as the key level. According to Wilder a buy signal occurs when the +DI value rises above the -DI.  Again according to Wilder, the initial stop loss should be set at the current days low. Traders should wait for this low to be penetrated before closing the position and admitting that the signal was mistaken. A buy signal is strengthened when the ADX continues to increase. Once the position has become profitable traders will need to incorporate a new stop loss and if the trend is to continue further traders should adopt a trailing stop.

A Sell signal occurs when the -DI rises above the +DI, while the ADX is greater than 25. In this situation Wilder suggests that you use the days high as your initial stop loss. Again if the bearish trend continues traders will be forced to change their stop loss, possibly incorporating a trailing stop loss should the trend continue for some time.

Conclusion 

While calculating the ADX is particularly difficult, the interpretation of the indicator is pretty easy. The ADX can be used alone to determine either the strength of a trend or whether an instrument is trending. The ADX used alongside the -DI and +DI indicators can produce a simple to use crossover trading strategy. However, DI crossovers occur quite frequently and the lagging nature of the ADX indicator means that traders are often forced use to other indicators for confirmation purposes.

R. N. Elliot 

Ralph Nelson Elliot (1871-1948), the creator of Elliot Waves had a successful career as financial consultant to a number of railroad companies. A sector which was undergoing a resurgence during his early professional career, considered an expert in business planning Elliot’s services were in high demand. Which led to the US government sending Elliot to Nicaragua as an economic consultant where he was in part responsible for reorganizing the country’s finances. It is said that one of his greatest talents was a shrewd eye for detail.

Elliot, then turned his meticulous eye for detail on decades of market charts. It was then that Elliot felt he had discovered a persistent and recurring pattern, that occurred between market tops and bottoms. These patterns which he described as Waves, were a collective expression of investor sentiment and gave markets a very particular form of behavior. Elliot contended that through the use of measurements which he called wave counting, it would be possible for analyst or investor to predict turns in market prices with a high degree of accuracy.

Elliot then preceded to test his theory over the next four years, before organizing his research into the 1938 essay entitled ‘The Wave Principle’. Published with the assistance of Charles Collins then a respected newsletter publisher, who did much to help popularize Elliot’s Wave analysis in the early years.

By the early 1940’s, Elliot had fully developed his theory that the ebbs and flows of human emotions and activities follow a natural progression, being actively governed by certain laws of nature. He tied these collective patterns of human behavior both to Fibonacci numbers and the Golden Ratio, both mathematical phenomena which had been observed in nature for centuries.

In 1946, Elliot published his definitive work ‘Natures Law: The Secret of the Universe’. This rather grandly titled book contains almost every thought that Elliot ever had regarding his Wave principle, with the first 1000 copies of the book selling out to various individuals in the New York finance community. Elliot Waves and the Wave principle are something that is still actively used by traders today, being particularly popular with traders based in Eastern Bloc countries.

Wave Patterns

It was Elliot’s believe that one business cycle or trend consisted of five-wave motive component and a three wave corrective component. A motive or impulse pattern consists of a five waves representing a distinct trend in a particular direction. As illustrated in the diagram below:

The pattern always satisfies the following logical conditions: Wave 1 > Wave 2 < Wave 3 > Wave 4 < Wave 5

• Wave 1 is greater than Wave 2.
• Wave 2 is smaller than Wave 3.
• Wave 4 is both smaller than Wave 3 & 5.

A corrective pattern consists of three waves which represent a counter trend to the general preceding impulse pattern, as is shown below:

 Again the Corrective pattern is meant be logically constrained in the following way: Wave A > Wave B < Wave C

• Wave A is greater in size than Wave B.
• Wave B is smaller in size than Wave C.

Elliot went on to identify several other wave patterns (including the zigzag, triangles, flats etc.), however it is generally held that the corrective and impulse patterns are the most important when analyzing trends.

Herd Psychology: The Idea Behind the Waves

One of the main tenets of Wave theory is that certain herd psychology governs each Wave, which is what makes Wave theory a reliable indicator of price direction.

• Wave 1: The market takes an initial move upwards. This initial move is usually caused by a relatively small number of people who decide that the particular instrument is undervalued etc. For instance in the case of Stock trading a number of traders may have identified that the Stock is cheap.
• Wave 2: It is at this point that a number of people involved in the first Wave, feel that the instrument is becoming overvalued and thus take the profits they have made. This causes the instrument to fall in price, however the price will not fall to it’s previous lows before more traders decide to jump on the bandwagon.
• Wave 3: Is typically meant to the longest and strongest wave. With the performance of the particular instrument having caught the attention of the larger trader community, meaning that many traders and investors go out and buy up the instrument. This Wave typically exceeds the peak achieved in Wave 1.
• Wave 4: People again decide to take their profits again causing a fall in the instruments value. This Wave tends to be quite weak, as the performance of the instrument during the impulse trend has led to a large number of traders to remain bullish regarding the instruments prospects.
• Wave 5: This is the Wave most driven by hysteria. This is when the instrument typically becomes the most overpriced, often leading to a corrective pattern to follow soon after. When contrarian traders identify a good opportunity to short the particular instrument.

The motive or impulse phase contains five waves, if the general trend direction is upwards we have a bull market and if the instrument is trending downwards we have a bear market. The first wave as stated above is generally quite tentative with only a small number of traders participating. The rules laid out by Elliot state that the second wave must retrace the first wave by at least 20 percent, but the retracement is often between 30-80%. It is also claimed that volatility and volume generally decline during the second wave.

 Wave three begins slowly with light to low volume, and as it finally pushes towards the end of the first wave. Many traders have placed stops around this level, thinking that the move is unsustainable. However when the third wave approaches the peak of the first wave many of these stops will be taken out, creating a gap and an increase in volume. Some of the traders that were previously skeptical of the move, now join in taking long positions in the instrument.

 The fourth wave begins when traders who have profited from the powerful third wave start taking their profits. Meaning that the fourth wave exhibits an orderly profit taking decline. With the fifth wave beginning when traders who are not convinced that the trend has ended buy into the dip. This fifth wave rally is generally weaker and has significantly less volume than the third wave volume. As buying interest begins to fade, we are likely to witness a corrective phase.

Waves Within A Wave

Elliot himself proposed that Waves exist at many levels, meaning that it is possible for their to be shorter terms waves within a more general Wave. Thus bigger trends can be broken down into smaller wave trends and this idea is often used when Elliot Wave analysis is applied by Forex traders etc. The concept of waves within other waves can be traced back to Charles Dow, who believed their were three levels of Wave composition. The idea of a waves within a wave, illustrates how Elliot Waves can be used for different trading timescales meaning that they analysis can be used by both short term and longer term traders.

The Efficient Market Hypothesis

What is the Efficient Market Hypothesis?

The Efficient Market Hypothesis (EMH) states that financial markets are informationally efficient, which means that investors and traders will not be able to consistently make greater than market average returns. To put it simply, the EMH states that it is not possible to beat the market over the long run. Supporters of the theory hold that those who do in fact make more than average returns do so because they have access to inside information or alternatively have simply enjoyed a prolonged lucky streak. The modern form of the Efficient Market Hypothesis was developed Professor Eugene Fama of the University of Chicago during the mid 1960’s and was widely accepted within academia until the 1990’s when work in behavioural finance began to bring the hypothesis into question. Despite this many academics and some in finance hold the efficient market hypothesis to be true to this day.

Those who support the EMH typically layout their claims in one of three mains forms, with each form of the claim having slightly different implications.

• Weak Form Efficiency: In this formulation of the EMH, future market prices cannot be predicted by simply analysing past price performance. It is therefore impossible to beat the market in the long run by using investment or trading strategies which rely on historical data. While the use of technical analysis may not allow traders to beat the market in the long run, some forms of fundamental analysis may allow for market participants to beat the market. This form of the hypothesis, holds that future price movements are determined by information which is not contained in past and current market prices, essentially ruling out the use of technical analysis.
• Semi Strong Form Efficiency: This formulation of the EMH, goes quite a bit further than it’s Weak Form cousin. Holding that market prices rapidly adjust to any new and publicly available information, this rules out both technical and fundamental analysis. Only those with access to inside information would be able to beat the market in the long run.
• Strong Form Efficiency: Those who believe in the strongest form of the EMH believe that current market prices reflect all public and private information meaning that no one can beat the market, even those with insider information. It might seem that this version of the efficient market hypothesis can be easily refuted, as there are a considerable number of money managers who have been able to beat the market year after year. Those who support this hard line version of the EMH, often respond by pointing out that with the sheer number of people who actively trade the financial markets you will expect some to get lucky and make impressive returns year after year.

Criticism of the EMH and Behavioural finance

Investors and increasingly those in academia have been very critical of the Efficient Market Hypothesis, questioning the hypothesis on both theoretical and empirical grounds. Behavioural economists have pointed to numerous market inefficiencies, which can often be attributed to certain cognitive biases and predictable errors in human behaviour. The rise of algorithmic trading and quantitative finance hasn’t necessarily rid the financial markets from such cognitive biases, with the 2008 financial crisis demonstrating how cognitive biases can work there way into complicated quantitative models. In fact some have gone as far to suggest that the EMH was partly responsible for the 2007-2012 financial crisis, with the hypothesis causing financial and political leaders to have a “chronic underestimation of the dangers of asset bubbles breaking”. Much of the work of behavioural economists suggests that we have good reason to reject both the Strong and Semi-Strong versions of the efficient market hypothesis.

Are the Forex markets an example of an efficient market?

The majority of the research into the efficient market hypothesis has focused on Stock Markets, but there have been a number of researchers who have looked into whether the Forex markets are informationally efficient. A study published in 2008 by J.Nyugen of the University of Wollongong looking at 19 years of data found that it was possible to create trading rules which could deliver significant returns indicating that the FX markets may be inefficient. Though the study went onto say that the trading rules only delivered significant returns during the first five year period, suggesting that either the FX markets became more efficient during the time period or simply the trading rules created by the studies authors broke down. Another 2008 study, found that it was possible to predict movements in price using only statistical data, however it wouldn’t have been profitable to have traded the markets using the studies predictive model. These studies suggest that the FX markets are somewhat efficient but they certainly don’t demonstrate that it is impossible to consistently turn a profit trading Spot FX.

Conclusion

The Efficient Market Hypothesis (EMH) was extremely popular among those in academia during the late 20^th Century, however many of those active in finance were never convinced by the EMH. During the 90’s, the hypothesis began to lose credibility with many behavioural economists beginning to seriously undermine the hypothesis. When it comes to the question of whether the Spot FX markets are efficient as defined by one of the forms of the EMH, there is simply not enough research to make any sort of conclusive statement. The data shows that it is possible to create rules and trading strategies which allow one to predict market movements with a significant degree of accuracy. The strategies in these studies struggled in regards to profitability, but this may only hint at the FX markets displaying some weak form efficiency under certain circumstances.

Liquidity Risks in Forex

When traders are trading with a brokerage who executes client orders using a No-Dealing Desk model (STP/ECN brokers), retail traders are exposed to what is known as liquidity risk. Genuine, No-Dealing Desk brokerages simply act as an agent for their clients passing trades onto the firms liquidity providers. No-Dealing execution can lead to a decrease in counterparty risk and also removes any conflict of interest between the brokerage and it’s client base. However, the trader may be exposed to liquidity risk during periods of high volatility, which can make it difficult for traders to exit their positions and may lead to them incurring huge losses.

The Spot Forex market is one of the most liquid financial markets in the world, which means the majority of the time traders can enter and exit positions at a price relatively close to the last traded price. Most of the time volatility in the major FX pairings is pretty low, which allows Tier 1 liquidity providers to still operate profitably while offering tight spreads and prices close to the last traded price. However, during periods of massive uncertainty these liquidity providers look to protect themselves from this volatility. What this leads to is liquidity providers either withdrawing their liquidity completely or offering prices a significant distance away from the previous trading range. This can lead traders to be unable to exit their positions or alternatively may lead them to have their positions closed out at very unfavourable prices.

Understanding liquidity

Offering services to the retail market involves understanding a different mentality. Retail clients are primarily looking at the ‘top of the book’ while institutional clients care less about the price on EUR/USD at $250,000 and are far more interested in the average price they will get for a $1,000,000 or $5,000,000 order.

So when it comes to liquidity, it’s not just the amount that matters but also its depth.  Retail traders would not be content with Institutional liquidity pools because they service a different motive. An individual trading at an institutional venue such as FXAll or HotSpot would find high minimum trade requirements, lower leverage and an abundance of trading tools that are not applicable.
Vice versa, institutional traders trading at a retail venue are likely to see re-quotes, comparatively slower execution and ‘shallower’ liquidity. This would effectively rule out any price certainty and would prevent effective risk management.

When it comes to liquidity there are 3 main factors….

•  Depth and availability of liquidity
• Spreads that are offered
• Market volatility

All go hand-in-hand, and each factor applies to all market participants on both the buy and sell sides.
Low market volatility and low margins affect liquidity in the market. If volatility is too high, the amount of traders active in a market often falls — but in the retail space, volatility brings more traders into the game.

There isn’t a lack of liquidity but providers have to be much smarter when offering very competitive pricing because the room for getting it wrong is now minuscule and still falling. More competition is forcing all brokers to be more shrewd in how they price their services, which can sometimes mean marketing ECN/STP accounts as a means for retail traders to move into professional trading. In fact, this is rarely the case and is only a marketing gimmick.

It’s clear that while spreads and market volatility have broadly fallen, especially in the major currency pairs over the past 5 years, the availability and depth of liquidity have risen. In very few other industries would you expect to see narrowing margins and lower activity enticing greater amounts of market entrants.  This counter-intuitive trend can be partially attributed to technological progress, and the rapid growth of financial technology, aptly titled ‘Fintech’.

 Risk vs Reward

The underlying unchangeable fact in trading is that risk and reward are inextricably linked and are always proportional to one another.
If you seek more reward, you must take on more risk; and vice versa. In modern markets, brokers have felt the pinch of tigther spreads — but must still execute trades with the same potential loss. This effectively means that the risk brokers see is relatively higher today, than it was when spreads were much wider.
Spreads on EUR/USD in 2000’s were around 2 pips, compared to 0.2-0.5 today. That has mean brokers are now playing for a much smaller pie, but must still risk manage the same nominal amounts. It’s much harder to maintain revenues without taking on more risk.

Every provider has different priorities when they offer liquidity. For some, the primary objective is to revenue optimise which often means offering a standard feed with a mark-up. For others, it’s to hold the position for short periods until the spread is realised and then offset with another liquidity provider or broker. Others sit on a position and target long-term profits because they believe the market will eventually go in their direction and against the client. Providers such as large banks, often want to accept as much flow as possible and potentially suffer losses on some trades as long as they can offset the exposure with other asset classes.

The average amount of revenue generated from every $1,000,000 traded has fallen rapidly over the past 5 years. When trading activity declines due to falling market volatility and in tandem trading spreads decline due to competition, it puts additional pressure and challenges on all sell side market participants such as brokers, liquidity providers and market makers.

A prime example of this would be the extreme volatility in the Swiss Franc which occurred during early 2015. Without providing prior warning the Swiss central bank announced they would no longer be defending the Swiss Franc peg against the Euro. Many of the liquidity providers to retail FX brokerages completely withdraw the liquidity they were offering on CHF pairings, while others continued to offer prices but a significant distance away the previous trading range of the CHF. This lead to a large number of traders having their entire account balance wiped out, with some traders running up massive negative balances. Another recent example would be the significant volatility in the Russian Rouble which lead to brokerages to close out positions and stop trading in Rouble currency pairings as they were worried that their would not be the liquidity to close out positions. Though in this particular case traders themselves were not to badly effected.

Mitigating Liquidity Risk

In theory retail FX traders who are trading with a brokerage with a variety of liquidity providers should face lower levels of liquidity risk, than those trading with a brokerage who relies on a sole liquidity provider. However, in the case of ‘Black Swan’ events the majority of liquidity providers will react in the same way and withdraw their liquidity from the market place. This can lead to stop and limit orders being fulfilled at very unfavourable prices for traders. There is little that traders can do about such risk. One way to limit liquidity risk, is simply to limit the leverage used with many of the worst effected during the CHF volatility of 2015, being those who were using large amounts of leverage. However, many traders are reluctant to cut back on the use of leverage as it allows them to maximise profits when markets move in their favour. A certain degree of liquidity risk is part and parcel of trading with a brokerage who operates an agency model (ECN/STP execution).

What is DOM in Forex?

Depth of Market (DOM) provides traders with a measure of the number of pending buy and sell orders for a currency pairing at a range of different market prices. Depth of Market provides traders with information regarding of the amount of liquidity available at different market prices. The larger the volume of buy and sell orders at each price, the greater depth the market is said to have. Depth of Market is often referred to as the order book, due to the fact Depth of Market data shows the current pending orders for a currency or security. Depth of Market data is usually available from exchange for a fixed fee; however those trading Forex may be able to make use of Tier II Depth of Market data straight from their brokerage.

The Uses of Depth of Market Data

Scalping: Some traders who use scalping strategies use Depth of Market data to help them determine when they should enter in and out of positions. Depth of Market data is particularly useful to those who scalp as technical indicators and candlestick charts tend to be less reliable over shorter time frames. Very few traders base their short term trading decisions solely on Depth Market data, and instead use depth of market data alongside technical analysis and other trading tools. FXTM have produced a very good video outlining how Depth of Market data can be used by scalpers and short term traders.

Feel out the Market: Seeing Market Depth allows the trader to see order flow from the brokerages perspective, which offers traders with a unique look at the markets directional bias. Traders can keep an eye on order flow and begin to get a general feel of where the market might be headed.

Large Ticket Traders: Depth of Market data is also useful for those who are trading larger tickets (greater volume) as it allows them to see how much liquidity there is at each price level. VWAP (Volume Weighted Average Price) depth of market functionality is particularly useful for those who are placing very large trades as it allows them to see expected entry price instead of the quoted spot price. The majority of retail traders will find enough liquidity for their needs at every price level, but being able to see liquidity levels is still useful.

Depth of Market Functionality

Many STP/ECN brokerages will give their trader’s access to Tier II Depth of Market data for free. Some of the older trading platforms such as MetaTrader 4 do not have Depth of Market functionality built into them, meaning that brokerages using the platform have created separate programs which allow their traders to access Depth of Market data. When picking a STP/ECN brokerage it may be worth asking whether your brokerage can offer you access to Depth of Market data.

Newer platforms such as cTrader and MetaTrader 5 have Depth of Market functionality built into them, making it easier to access tier to Depth of Market data. cTrader from Spotware offers particularly impressive Depth of Market functionality with the platform displaying depth of market data in three different formats, depending on the traders particular needs.

Conclusion

Does it matter whether your brokerage gives you access to depth of market data? Well that depends, large ticket and short term traders will certainly benefit from having access to depth of market data. Smaller traders who take a more long term view will not find as much value in having access to depth of market data, but this is not to say that depth of market functionality wouldn’t be useful. An increasing number of brokerages are giving their traders access to Tier II DOM data, though many older platforms including the ever popular MetaTrader 4 do not have depth of market functionality built in. When picking a brokerage it is certainly worth asking whether they allow traders access to DOM data.

The Commodity Channel Index

The Commodity Channel Index, is a technical indicator which was developed by Donald Lambert and first featured in Commodities magazine during 1980. The Commodity Channel Index or CCI for short, is a very versatile indicator that can be used to both identify a new trend or warn a trader of extreme trading conditions. The CCI was originally developed in order to identify cyclical turns in commodities, but it was soon realized that the indicator could be applied to a far broader range of securities. The Commodity Channel Index, works by measuring the current price level relative to average price over a selected period of time. The Index gives out a high reading when prices are far above their average and a low reading when prices are far below their average. This is why the Commodity Channel Index is often used to identify overbought and oversold conditions.

Calculation 

Calculating the Commodity Channel Index is rather complicated, however the vast majority of trading and charting programs will work out the CCI for you. Below I’m going to outline how to calculate a standard 20-day CCI Indicator:

CCI = (Typical Price – 20-day SMA of Typical Price) / (0.15 x Mean Deviation)

Typical Price= (High + Low + Close) /3

As calculating the mean deviation is a four step process in-itself many traders rely on automated programs to calculate the CCI for them. Donald Lambert set the constant at 0.15, to ensure that between 70-80% of CCI values would fall in-between -100 and +100.  The percentage of CCI values falling between -100 and +100, is also partly dependent on the period selected. Longer look back periods will see a higher percentage of CCI values falling in the -100 to +100 range. It is important to take note of this fact when using the CCI, as it will play an important role in interpreting the indicator.

Interpretation 

As the CCI is designed to measure the difference between a instruments price change and it’s average price change. Traders usually take high positive readings as indicating the prices are trading well above their average range, which is generally considered to be a show of strength. While low negative readings indicate that prices are trading well below their average range, this is generally considered a show of weakness.

However how to interpret the CCI can be quite controversial, as the Index can be used as both a leading or coincident indicator. Those who use the Commodity Channel Index, as leading indicator are generally on the look out for overbought and oversold conditions which may signal a potential price reversal. Those who use the CCI as a coincident indicator take surges above +100 to reflect strong upwards price action which may signal the start of a new trend. While the value CCI dropping below -100 is taken to signal strong negative price action which may signal the start of a new downwards trend.

Identifying Overbought and Oversold Levels 

Identifying overbought and oversold levels can be a bit more tricky than with some of the other indicators typically used to determine overbought/oversold levels (RSI and %R). This is because the Commodity Channel Index, is an unbound oscillator which means that theoretically there are no upside or downside limits. This makes determining whether an instrument is overbought or oversold a subjective matter. As with all overbought and oversold indicators traders need to be careful as securities can continue to move higher or lower, even after the indicator suggests that a particular instrument is overbought or oversold. This means that overbought and oversold indicators tend not to perform very when markets are trending strongly.

The definition of overbought/oversold varies when using the Commodity Channel Index. During periods where an instrument is trading in a particularly narrow trading range having +/- 100 as an overbought/oversold level might be quite effective. However during other trading environments more extreme levels might be needed, it is not unusual for traders to use +/- 150 or 200, as indicating overbought or oversold levels. The selection of the overbought and oversold indicators can also vary depending on the volatility of the underlying instrument.

Identifying Emerging Trends  

In the interpretation paragraph I briefly discussed how the Commodity Channel Index can be used to help identify new trends. The indicator was designed so that around 70-80% all prices would fall within the -100 and +100 range.  Using the 100 -/+ range as providing indication of a new trend can lead to a number of whipsaws (were the indicator doesn’t provide the kind of confirmation you are looking for).  This generally means traders don’t use the CCI on its own, with traders using other charting techniques or technical indicators to give confirmation to the signals produced by the Commodity Channel Index.

Concluding Thoughts 

While the Commodity Channel Index is a particularly versatile indicator which can be used to identify overbought/oversold levels, as well being useful in identifying new emerging trends. There are a number of things that a trader should be aware of. The indicator produces overbought/oversold signals when prices reach a relative extreme, the particular extreme is dependent on both the underlying trading conditions as well as the volatility of the security in question. It is also possible to use the indicator to identify new emerging trends, to do this traders often use a preset level to identify such emerging trends, however it is important to use the indicator alongside other tools. It is often suggest that the On Balance Volume and Accumulation Distribution Line, can be used alongside the Commodity Channel Index.

Simple and Moving Averages

Moving averages are used to smooth price data in order to create a trend following technical indicator. Moving averages are not used to predict price movement, but are instead used to help confirm the current trend direction. Moving averages lag due to the fact the indicator is based on past prices. In despite of this lag moving averages are deemed useful as they both help smooth price action and filter  out market noise. Moving averages also play an important role in the construction of a number of other technical indicators, such as Bollinger Bands, MACD and the McClellan Oscillator. The majority of traders either use Simple Moving Averages (SMA) or Exponential Moving Averages, a smaller minority of traders use Weighted Moving Averages but these won’t be addressed in this article.

Calculating Moving Averages: Simple Moving Averages and Exponential Moving Averages 

A Simple Moving Average is calculated by working out the average price of an instrument over a specific time period, with the majority of moving averages being based on closing prices. For instance a 10 day Moving average is arrived at by adding up the closing prices of the previous 10 days, before dividing the total by 10.  Old data is dropped from the moving average once new data becomes available, for instance at the end of the current days trading the oldest data point from our 10 day moving average will be replaced.

The calculation of Exponential Moving averages is more complicated and involves first calculating a simple moving average and then calculating the weighting multiplier, which then allows you to calculate the exponential moving average. Thankfully, the majority of trading programs will calculate both Simple Moving Averages and Exponential Moving averages for you, meaning that you don’t need to mess around with calculating moving averages unless you want too. Investopedia has a very good short video on the topic of Simple and Exponential Moving Averages which is worth a watch.

Moving Averages and Lag

The longer the time period used, the more lag you can expect from a moving average. While a 10 day exponential moving average is likely to hug price action closely, longer term moving averages are likely to experience some lag between shifts in price and significant changes in the moving average. It takes much longer for a 100 day moving average to change course or direction, due to the fact that the average contains much more data.

Picking a Time-frame 

What time-frame to pick when using moving averages depends on your objectives. Short term moving averages are best suited to those taking a short term outlook in regards to a particular instrument, this is why the majority of retail Forex traders opt for short term moving averages. Those interested in medium-term trends typically tend to use 20-60 day moving averages, while long term investors will use moving averages of and in excess of a 100 days.

Different time periods are more popular than others, with long term stock traders the 200 day moving average is perhaps the most popular. The 50 day moving average is very popular among traders adopting a medium term outlook, with many traders using a 50 and 200 day medium average in conjunction with one another. In the past a 10 day moving average was very popular with short term traders due to the fact that it was very easy to calculate, however with the rise of computer trading short term traders now use a variety of different time-frames to trade.

Using Moving Averages to Identify Trend Direction 

The direction of a moving average displays important information about prices, albeit with some lag. A rising moving average shows that prices are generally rising, while a falling moving average shows that prices are generally falling. Meaning that a rising moving average reflects a general uptrend and falling moving average reflects a downtrend.

Crossover Signals  

Two or more moving averages can be used to generate crossover signals. The most popular crossover signals are double and triple crossover signals. Here we are going to focus on double crossovers, this signal involves using two moving averages. One relatively short term moving average and one longer term moving average. The general length of the moving averages determines the time frame of the system.

A bullish or golden cross occurs when the shorter term moving average crosses above the longer term moving average. Conversely a bearish or dead cross occurs when the shorter term moving average moves belong the longer term moving average. While crossovers are very easy signal to use, it is generally quite a late or lagging indicator. This is due to the fact that the two moving averages the signal uses themselves lag slightly behind price action.

Price Crossovers 

Moving averages can also be used to create simple price crossover signals. Again these signals are very easy to use, a bullish signal occurs when the price moves above the moving average. A bearish signal occurs when prices move below the moving average. Again price crossovers tend to be a late indicator due to the fact that they use lagging moving averages.

Support and Resistance 

Moving averages are occasionally used to produce support signals in an uptrend and resistance signals in a downtrend. For instance a short term upwards trend might find support close to the 20 day moving average.  A longer term uptrend could for instance find support from the 200 day moving average. While this is one possible use for moving averages, the majority of traders tend not to use Moving averages to produce support and resistance signals.

Conclusion 

As with all indicators there are pros and cons when it comes to using moving averages to generate trading signals. As moving averages are trend following they tend to lag behind price action, meaning such indicators will always be one step behind. This isn’t necessarily a bad thing, as many traders simply look to ride the dominant trend with moving averages give you a clear indication of the current trend. More problematic is the fact that moving averages tend to give you late signals when it comes to trend breaking down meaning that you may not exit positions as quickly as you would wish. You can’t expect to be able to buy at the very bottom and sell at the very top using moving averages, however you can still capture a large chunk of a trend. As with other technical indicators its a good idea to use moving averages alongside another technical indicator, with many traders using moving averages alongside the Relative Strength Index in order to determine overbought and oversold levels.

The Williams Percent Range (%R)

Developed by successful commodity trader Larry R. Williams, the Williams Percentage range or %R is a technical indicator which is the inverse of the fast Stochastic Oscillator. The Williams Percentage range measures the current close in relation to the highest high of the selected period. This is to be contrasted with the standard Stochastic oscillator which gives a gauge of the current close in relation to the lowest low of the selected period. This means that the fast Stochastic oscillator and Williams Percent Range give exactly the same lines, with only the scaling being different. It should thus be unsurprising that signals derived from the use of the fast Stochastic Oscillator can also be used with the Williams Percent Range.

The Williams Percent Range is calculated in the following way:

%R= (Highest High – Close) / (Highest High – Lowest Low) * -100

• Lowest Low, the lowest close during the selected period.
• Highest High, the highest close during the selected period.

Thankfully the majority of trading programs will calculate the Williams Percent Range for you, but the above formula should demonstrate how the indicator is the inverse of the fast Stochastic Oscillator. The Williams Percentage Range always oscillates between 0 and -100.

Interpretation

As an oscillator the Williams Percent Range can be useful for determining overbought and oversold levels. The oscillator always ranges between o and -100. regardless of the volatility of the particular instrument. Traditionally, technical analysts take -20  reading as indicating that an instrument is overbought and -80 to show that the instrument is oversold. For instance readings above -20 would show that the particular instrument in question was trading near the highest high of the selected period. While readings below -80 would show that the instrument was trading near the lowest low of the selected time period.

It is always important to realize that overbought and oversold indicators are not necessarily bearish/bullish, as with other overbought/oversold (such as the RSI and the Stochastic Oscillator) indicators the Williams Percent Range has the ability to mislead a trader. The indicator tends to breakdown during periods when the market is trending strongly, it is possible for instance for a instrument to become overbought and remain so during a period of trending conditions. This is why overbought/oversold indicators tend to be used alongside other technical indicators in order to provide confirmation that an instrument is indeed overbought/oversold.

Momentum Failure 

Apart from being an overbought and oversold signal, the Williams Percent Range can be used to identify a change in momentum which may foreshadow a significant reversal. Typically continued readings above the -20 level would indicate that the instrument is trending upwards. This is due to the fact that it takes considerable buying pressure to push the indicator above -20 level. If an instrument has consistently shown strength by moving into the -20 or above range, a subsequent failure to break this barrier may foreshadow a price reversal or a beginning of a new trend.

The Williams Percent Range can be a useful indicator when used correctly, but one has to be wary when using it on it’s own as overbought and oversold indicators are notoriously unreliable during periods when an instrument is trending strongly in one direction. More sophisticated traders may also be able to use the indicator to determine when momentum is shifting allowing traders to get in on some significant price reversals and momentum shifts. As the Williams Percent Range is simply the inverse of the fast Stochastic Oscillator there is no benefit in using one indicator over the other, with it just coming down to personal preference.

Gann Angles

William Delbert Gann (1878-1955), built a reputation as one of the most successful stock and commodities traders of his era. He has also gained a legendary status with many being impressed not only by Gann’s legendary trading abilities, but also with his spectacular record when it came to making financial market forecasts. Gann started trading Stocks in 1902 before moving to New York in 1908 where he promptly opened up his own brokerage firm. However his early trading career was far from successful leading to Gann going bust more than once. Undeterred by these early failures, Gann carried on studying the markets and his research led to him making some startling conclusions. Their to this day remains great controversy regarding the extent of his trading successes and whether he did fully reveal his trading secrets before his death.

Gann reportedly made a total of $50 million dollars from his market forecasting and trading activities, at the time this was a very significant fortune. If we were to factor in inflation Gann’s 1950 fortune is equivalent to having to a $482 million dollar fortune today. After having traded the markets successfully for 50 years Gann retired to Florida where he wrote, published and taught on the subject of financial markets until is his death in 1955.

Basic Assumptions Made By Gann 

Gann based his theory of price prediction on  the following three assumptions:

1. Price, time frame and range are the only relevant factors one needs to consider.
2. Markets are cyclical in nature.
3. Financial Markets are geometric in design and function.

According to Gann, by studying the past we are able to predict the future. It is also important to note that Gann though human nature was unchanging over time and therefore showed up in repetitive price patterns. Those of you who have studied Elliot Wave Theory, should not that Elliot Wave Theory makes the same assumption regarding the idea that human nature is fixed and thus we can establish and trade based on certain repeating Wave patterns. If assumptions like this are in fact true, it should be possible to identify repetitive price patterns and act upon them making significant profits. Some of the assumptions that underpin Gann’s theory have been challenged in recent times and thus their is an active debate regarding the usefulness of Gann Angles.

Geometric Angles

Central to Gann’s charting studies was the concept of geometric angles in conjunction with price and time. It was Gann’s believe that certain geometric patterns and angles had unique characteristics which could be used predict price action. Therefore all of Gann’s techniques require equal price and time intervals are used, to ensure that a rise of 1×1 will always equal a 45 degree geometric angle.  In MetaTrader this can be achieved changing the scale ratio 1 to 1, or you alternatively use the Gann line, fan and grid tools built into the program. Which are added to charts and can be found under the insert tab.

Interpretation 

Gann believed that the ideal balance between price and time exists when price either rises or falls at 45 degree angle, relative to the time access. Again to get accurate Gann angles you have to have charts setup to a 1 to 1 ratio. In the literature surrounding Gann Angles this magic 45 degree angle is often referred to 1×1 angle, this is due to the fact the price rises one unit for each time unit.

Gann angles are to be drawn between a significant bottom and top, or vice versa. 1×1 angle which was deemed must important by Gann signifies a bull market if prices are above the 45 degree line and bear market if prices are below. Gann also felt that the 1×1 trend line provided significant support during an uptrend and when the trend line is broken signifies a major reversal in the trend. Gann identified a total of nine important geometric angles, however he always held the 1×1 angle to be the most important.

Gann’s observations led him to conclude that each of the significant Gann angles can provide support or resistance depending on the trend. During a strong uptrend it is the 1×1 angle that tends to provide major support, with a major reversal being signaled when the price falls through the 1×1 trend line. Gann believed that prices would then fall to the next trend line, so if for instance the 1×1 trend was broken prices would fall down in line with the 2×1 angle. As one angle is broken through we can expect prices to consolidate around the next significant Gann angle. Gann developed several techniques for studying such market action, including Gann fans, Gann grids and cardinal squares.

The Importance of Time 

Gann based his trading and forecasting methods on time used alongside price action, stressing the importance of determining not only the price which would signify a turning point but also the time at which it would happen. Being able to forecast both the time and price points at which reversals and trend changes will happen is the ultimate goal of technical analysis. It is debatable whether Gann revealed his real techniques in regards to identifying the time at which reversals and trend changes happen, however it is sometimes considered that his book Tunnel Through the Air  contained his techniques in a concealed form. Some of Gann’s public statements regarding his methods can be summarized thus:

• According to Gann, time and price are the same. Hence the use of Gann angles to determine reversals and changes in trend.
• Time is the most important factor. Gann suggested that traders do day/week counts of 30, 45, 60, 90 and 180.
• Resistance levels in both time and price are 25, 33, 50, 66 and 75 percent of the previous range. These can also be extroplated out to 100, 150, 200.
• Trend lines work.

The Use of Gann Angles

Gann Angles aren’t used by many traders with opinions sharply divided about the relevance of his work. The most common use for Gann Angles these days is to indicate support and resistance levels. As there are many different methods to use to identify support and resistance levels, why use Gann Angles?  Gann Angles add a new dimension to support and resistance studies, as both resistance and support levels can be diagonal.