Ebook on Moving Average.

Investors and traders are in constant search of tools they can use to gain any possible advantages from shifting markets.Technical indicators are especially vital parts of any trader's kit, and few indicators are as consistent (and dependable) as moving averages.

Traders use technical indicators like Moving Averages to make predictions about future prices. They verify how well a specific indicator works for a particular security. The calculation of odds of success under similar market conditions is the best way to go.

Always remember that there is no single indicator that works well for all securities.

Moving averages are a reliable way for traders to tune out the day-to-day noise of a financial asset by calculating and regularly updating the average price of that asset. By smoothing out day-to-day price fluctuations, traders are better able to identify trends that may otherwise be obscured. When combined with other types of analysis, and a little practice, traders can use moving averages to confidently make advantageous, market-beating moves.

Most institutional investors have access to sophisticated algorithms that can help them identify moving averages for just about any security they desire. For retail investors, the tools are not as readily accessible. In fact, for many retail investors, the technology and tools are not accessible at all.

But Tickeron is innovating to change this dynamic.

With the creation of A.I.dvisor, Tickeron is providing retail investors with Artificial Intelligence-driven trade ideas. With respect to moving averages, A.I.dvisor is programmed to scan thousands of securities in the marketplace, looking for moving average trends and indicators that may result in an actionable trade idea. Once A.I.dvisor finds a technical trade idea, it automatically generates news about the security so that the investors can take action.

Identifying trends using Moving Averages and other statistics is one thing - knowing how and when to trade the opportunity is quite another.

No one can ever know with 100% certainty what a security's price will do from one moment to the next.

Often times, investors and traders spend lots of time accumulating fundamental and technical data about a trade, only to find themselves unsure if the trade is likely to be successful. In short, traders often have lots of great data but don't know their odds of success.

Statistics can help change this outcome. Statistics are useful in helping determine probabilities of success, by using patterns and historical outcomes (backtesting) to make projections about the future. With the help of Artificial Intelligence, investors have the ability to analyze more data more quickly, resulting in data-rich statistics that may better help determine the odds of success in each trade.

Tickeron's Artificial Intelligence, known as A.I.dvisor, backtests every trade idea to give the user their " Odds of Success" for each potential trade opportunity. In this eBook, we'll show you how it works.

For Tickeron users, finding news -- and potential trade ideas -- regarding the moving average indicator is easy. A.I.dvisor will file all moving average-related news under #MovingAverages, so an investor can go straight there to find the latest trade ideas and technical indicators.

In the chapters that follow, we'll give you real examples of A.I.-generated news and trade ideas with regards to moving averages, and we'll also dig much deeper into what moving averages are and how to use them.

Tickeron's Artificial Intelligence, known as A.I.dvisor, is programmed to scan the stock, ETF, cryptocurrency, and FOREX markets in search of technical trading patterns. Once it discovers a pattern or a trend, it delivers it to users in the form of news, which includes trade ideas and odds of success. In this chapter, we take a look at news generated for Moving Averages.

Below you'll see an example of a technical analysis discovery made by A.I.dvisor. As you can see, the stock of the company Myomo (ticker: MYO) dropped below its 50-day moving average (yellow dot on the chart) on February 8, 2019. The A.I. immediately publishes the news once it makes the discovery, which is what you would see on Tickeron.com under #MovingAverage:

In the above case, a trader may notice that MYO started a slight uptrend in January, but on February 8 the stock crossed back below it's 50-day moving average (yellow dot on the chart). This may be a bearish sign for the stock, indicating that it's headed for another downleg.

A trader may now wonder: what are my odds of making a successful trade here?

Here's a second example of a discovery made by A.I.dvisor. Shenandoah Telecommunications (ticker: SHEN) stock price moved above its 50-day moving average on February 7, 2019 (yellow dot on the chart), and A.I.dvisor generated news to alert Tickeron's users of the move:

In this case, the trader may notice that in past instances when SHEN crossed above its 50-day moving averages (see November and late December on the chart), the stock tended to bounce higher. Since A.I.dvisor discovered that the stock yet again crossed above its 50-day moving average, it could mean SHEN has some upside runway ahead. A trader may consider going long the stock or exploring call options.

Again, a trader may wonder their odds of success in trading the indicator in this case. A.I.dvisor furnishes data to give the trader an idea: it found 36 similar cases when SHEN crossed above its 50-day moving average, and 25 of those cases led to a successful outcome. This data suggests the odds of success here are 69%.

This indicator can actually produce bullish and bearish outcomes. On the bullish side, when a 50-day moving average crosses above the 100-day moving average, it is known as the Golden Cross. It's a sign that the security may have the potential to break out to the upside. On the other hand, when a 50-day moving average crosses below the 100-day moving average, it is known as a Death Cross and may signal more downside ahead. Traders may decide to respond accordingly!

Here are two examples of A.I.-generated news for both the Golden Cross and the Death Cross. The first example below shows a Golden Cross when the stock NVE's 50-day moving average (blue line) crosses above its 100-day moving average (see yellow dot on the chart):

Traders may view this crossover as an indication of the stock being ready to breakout to the upside, which could mean going long the stock or exploring call options. A.I.dvisor helps the investor decide whether to engage in the trade by providing more data: in the 6 previous cases where the A.I. found a Golden Cross for NVE, 4 of them led to successful outcomes. Based on this data, the odds of success are 67%.

Here's an example of the Death Cross, when a security's 50-day moving average crosses below its 100-day moving average (yellow dot on the chart):

In this case, A.I.dvisor discovered that Hurn Consulting Group's (ticker: HURN) 50-day moving average cross below its 100- day moving average, which may be an indication that the stock is confirmed a downtrend. Traders may consider selling the stock, exploring put options, or even shorting the stock.

A.I.dvisor looked for past instances when HURD's price movements formed a Death Cross, and found 7 cases -- 4 of which resulted in successful outcomes. Based on this data, the A.I. projects the odds of success for the trade to be 57%.

For investors and traders interested in seeing current # MovingAverages trade opportunities and who want to learn more about A.I.-generated trade ideas, visiting Tickeron.com is the easiest way to get started.

For those interested in learning more about Moving Averages as a technical indicator, continue reading! In the next chapters, we will give you everything you need to know about Moving Averages, trade signals, and trade strategies.

Moving averages help mitigate noise around a financial asset, making them extremely useful for identifying trends that impact trading behavior. Two basic crossover signals – the Golden Cross and Death Cross – have strong predictive track records.

The Golden Cross is a breakout candlestick pattern formed when the short term 50-day moving average for a security exceeds its long term 200-day average, backed by high trading volumes. Investors typically interpret this crossover as a harbinger of a bull market, and its impact can reverberate throughout index sectors.

Golden cross patterns occur in three stages: stage one occurs at the end of a security's downturn, signaling the completion of a sell cycle. Stage two is the crossover of the short- and long-term moving averages, followed by a trading breakout. The final stage sees the security continue its upwards trajectory, with accompanying price increases. Eventually, a pullback occurs, leading to a golden cross' inverse – the Death Cross.

Golden crosses can be composed of variable moving averages, from minutes- to months long. Longer time periods typically equate to more sustained breakouts. 50-day short term and 200-day long term periods are most common for their ability to remove daily volatility from the equation, painting a more accurate picture of a security's trajectory.

Day traders will use shorter windows of time, evaluating moving averages throughout a trading day to identify and trade golden crosses – 5-period and 15-period moving averages are common for their purposes. Traders use momentum indicators like moving average convergence divergence (MACD) and relative strength index (RSI) in conjunction with golden crosses to gauge ideal times to buy in or sell off security.

For all their popularity, no golden cross is 100 percent reliable – a golden cross in unstable market conditions will not always result in a bull market, especially for single equities. Research indicates short-term gains may emerge but can be less substantial – around 1.31 percent for the 30-day period post-golden cross. 12-month average gains, however, were found to be around 11 percent.

The Golden Cross' inverse is the Death Cross: a chart pattern occurring when a security's short-term moving average crosses underneath its long-term counterpart, typically followed by an increase in trading volume. A death cross, which like a golden cross most commonly uses long-term 50-day and 200-day moving averages to detect the pattern, usually signifies an incoming bear market to traders.

The death cross accurately predicted 1929 and 2008's devastating bear markets, and savvy traders will treat them as a sign to lock in long-term gains in advance of a downturn. But death crosses do not always signal impending doom – 2018 alone saw stocks, indexes, and assets like Facebook, gold, Alphabet, Netflix, crude oil, the S&P 500, and more form the pattern, with variable results:

Death crosses are most accurate when short- and long-term moving averages are trending downwards – there is less cause for concern if one line is falling while the other is rising. Research indicates that death crosses signal lasting declines once a market has lost 20 percent of its value. A death cross best serves as an alarm of a slowdown, not a death knell, for a bull market. Savvy traders can even use a death cross as a signal to buy on the dip (or purchase stock after a price decline).

There is no single way to use a moving average – traders adopt different strategies based on need and comfort level. But these basic tools can provide traders with helpful cues about market behavior, especially when used in conjunction with other technical indicators.

If there was a Signals 101 class, crossovers would be prominently featured. Renowned for their objectivity, crossovers at their most basic indicate momentum shifts, specifically when a price transitions from one side of a moving average to another – a crossover above the MA may signal a positive trend; below the MA, a downtrend.

Crossovers can also occur between short- and longterm moving averages: short-term crossing above longterm indicates a good time to buy, while short-term crossing below long-term is a sell signal. The previouslymentioned golden crosses and death crosses are two crossover signals utilizing this principle.

If two moving averages in a single chart can provide traders with myriad useful insights, is it possible that additional moving averages would allow for greater certainty about long-term market behavior? Some traders say yes, advocating for a strategy called amoving average ribbon. This technique stacks three or more moving averages of different lengths on top of each other in an effort to reduce false signals and strengthen the evidence of an upwards or downwards trend. Six-to-eight moving averages, each in 10-day increments, make up a typical moving average ribbon; shorter timeframes mean a more dynamic response to price changes.

The wider the lines of a moving average ribbon, the greater the strength of a trend; narrower lines mean weakening momentum:

Technical analysts have long espoused the virtues of envelopes to identify extremes in trading conditions. Applying an envelope to a moving average allows a trader to minimize the potential false signals presented by rapidly shifting markets.

Envelopes are applied at an arbitrary percentage above and below the moving average line, theoretically permitting a trend to establish itself before showing buy or sell signals. A five percent envelope on a 25-day moving average would look like this:

In most cases, the price reverses when it hits the envelope, signaling that a trader should buy or sell an asset. Movement beyond that threshold typically leads to a return towards the average, within the lines of support and resistance.

A moving average (MA) is an indicator calculated by averaging a variable amount of historical data points for an asset. Once plotted on a chart, that measurement shows the average value of a security's price over a predetermined amount of time while minimizing the disruptive peaks and valleys of daily volatility.

There are five basic kinds of moving averages: simple (SMA); exponential (EMA); triangular (TMA); variable (VMA); and weighted (WMA). While each type has distinct applications and advantages, they are not entirely dissimilar – the main difference is the credence each gives to current price data. Simple moving averages, for example, apply equal weight to all prices; exponential and weighted moving averages apply more weight to recent prices for the security in question; triangular moving averages weight the middle of a chosen time period more heavily; variable moving averages shift the weight based on price volatility

Part of the appeal of moving averages is that they can be calculated using any time frame, depending on a trader's strategy. They are most commonly determined, however, using certain lengths, each with its own pros and cons. Traders evaluating long-term trends will be interested in 50-, 100-, and 200-day moving averages, which are less prone to temporary blips in trajectory and thus valued for their reliability. Stock traders are especially interested in 200-day averages – traders consider it a bull market period if a 50-day MA exceeds the 200-day. Inversely, a 50-day MA crossing under its 200-day counterpart usually signals a bear market.

Shorter moving averages – 5-, 10-, 20-, 30-, and, depending on context, 50-day – are valuable indicators of near-term trends. 10-day MAs, for example, are commonly used to guide intraday trading when plotted hourly. Similar to their long-term counterparts, crossover behavior of 10- and 20-day short-term MAs with a 50-day MA can also act as a harbinger of longer-term bull or bear performance.

Some traders even use Fibonacci numbers – 5, 8, 13, 21, etc. – to calculate moving averages. They believe those numbers can smooth out false signals, but there is little statistical evidence to back up the practice.

• Very Short Term: 5-13 days
• Short Term: 14-25 days
• Minor Intermediate: 26-49 days
• Intermediate: 50-100 days
• Long Term: 100-200 days

Moving averages are renowned for their malleability. This means there are plentiful types, each best applied to specific situations. We will focus on five common types of moving averages – simple; exponential; triangular; variable; and weighted – evaluating each pattern's strengths and weaknesses, how they are calculated, and what they look like when charted.

The most basic (and common) kind of moving average is, fittingly, called a simple moving average (SMA). A SMA is calculated by adding together closing prices for x number of time periods, then dividing the total by that same number of periods.

A 5-day moving average, for example, would add up an asset's closing prices for five trading days, then divide the sum by five. This number would evolve with the market, as indicated by the example below:

After the first day, each calculation drops the first data point from the previous day and adds the most recent data point in its place. This is the 'moving' part of the moving average – each new data point is an adjustment based on the most current data. A 50-day moving average would follow the above pattern but average out the closing prices for the first 50 consecutive days as the first data point; a 200-day moving average would do the same for the first 200 days, then continue as a rolling calculation thereafter.

The shorter a SMA's time frame, the quicker the response to price shifts. Longer time frames react more slowly to fluctuations, delivering a flatter reading. This makes long-term SMAs strong baseline by which to measure short-term behavior, smoothing out daily ups and downs.

A single simple moving average can quickly identify the upwards or downwards trajectory of a specific asset. Combining two moving averages of different lengths, as displayed on the above chart, allows traders to view crossovers for an asset – we will further discuss how those crossover points influence trading behavior later in this ebook.

Simple moving averages are effective in their simplicity, but their efficacy is most closely tied to how they are used. By giving equal weight to each data point, SMAs can limit bias towards any specific point in a specific time period. But some traders argue that this is also a negative; equal reliance on data from all points in time means an SMA does a poor job of truly reflecting a security's most-current behavior, thus limiting its predictive potential.

Also common is the exponential moving average (EMA). The key difference between an exponential moving average and a simple moving average is the weight applied to recent price data – an EMA places greater emphasis on current data than an SMA, meaning exponential moving averages are more responsive to price fluctuations than SMAs.

Simple moving averages and exponential moving averages remain closely linked – so much so that the first step to calculating an EMA for an asset is to determine its SMA. The next step is to calculate the multiplier used to weigh the EMA. Once both are established, the closing price-EMA from the previous day is multiplied by the weighted multiplier; the previous day's EMA is subsequently added to the result. The formula for calculating a 10-day EMA looks like this:

Weightings increase or decrease relative to the length of time being measured – shorter EMAs will use a larger multiplier; longer EMAs, a smaller one. In fact, this relationship is proportional: the weighting multiplier will halve as the time period doubles.

Because EMAs rely heavily on past data, they become more accurate as they incorporate more data points into their calculation. They also become less agile over longer time periods, slowing under the bulk of the data used to make them. That lag is illustrated here, with the 10-day EMA closely following the price and longer-term SMAs behaving more variably:

SMAs and EMAs are use case-specific – neither is objectively better than its counterpart. Simple moving averages limit bias within a specific time period, while exponential moving averages have more sensitivity to recent prices; the time period being measured plays a significant part.

The triangular moving average (TMA) is, at its basic level, a simple moving average, with one key difference – it is averaged twice. This more heavily weights the middle of a chosen time period and creates a lag in data movement.

A TMA value is first calculated by determining the SMA for an asset, then averaging the SMAs for multiple values:

The double average of a TMA makes for a line that reacts more slowly to the ups and downs of a market. That more gradual movement can keep traders in a trend longer (with potentially profitable results), as it would require a truly continuous movement in direction to see a TMA change trajectory.

That steadiness is less than ideal, however, if circumstances call for a more responsive moving average, making situational use key to the TMA's efficacy. The triangular moving average is not necessarily a strong trade signal, for example – traders would be better served by an exponential moving average's greater sensitivity to day-to-day price fluctuations.

The variable moving average (VMA) is a variation of the exponential moving average, created by esteemed futures trader and technical indicator developer Tushar Chande. Chande believed that an EMA could better capture (and respond to) shifting market conditions, slowing down when prices experienced a logjam but speeding back up during positive trends.

The desired response is accomplished by incorporating a volatility index into an EMA. The smoothing percentage automatically reacts in concert to volatility in a data series, shifting weight to current data in volatile situations.

Which volatility index to use is a matter of debate. Chande recommends his own Chande Momentum Oscillator (CMO); traders can also use an efficiency ratio, which is very similar to a CMO.

In this example, a 10-day VMA is calculated using a 50-day efficiency ratio as the volatility index, then plotted on a chart with a 50-day EMA:

The VMA performs more accurately in the back-and-forth highs and lows of trading ranges while also reacting more quickly in trending markets, where a long-term MA is slower to respond. The benefit is a moving average better equipped to avoid false signals.

The weighted moving average (WMA) is the sum of a flexible number of weighted values, each applying steadily more weight to the security in question's most recent prices (and, if desired, though far less common, the inverse). The WMA is similar in this way to an EMA, but it is calculated using a different formula:

The weighting factor is variable, but the dividing value is always the sum of the number of days measured. For example, a 5-day WMA would use 15 for the sum of days: 1+2+3+4+5 = 15. When calculated, a 5-day WMA would break down as the following:

Like an EMA, weighting recent values more heavily means a WMA hews closer to an asset's prices than an SMA:

Traders can assign different weights to different periods, making a weighted moving average extremely customizable. But that very flexibility also offers downsides – different weightings may obscure or distort the effects of market cycles in less-thanideal ways, making the WMA a less popular option than its EMA counterpart.

Moving averages are flexible, proven tools for identifying asset trajectories and possible behaviors. Whether mitigating risk or providing evidence in support of (or opposition to) a trading decision, moving averages can help traders navigate the ups and downs of the market and maximize their results. They may not be perfect – no tool is – but moving averages are an important part of any informed trader's bag of tricks.

Sergey Savastiouk, Ph.D. has a degree in Applied Mathematics from Moscow University and has a long track record as an entrepreneur, investor, manager, and mathematician. His professional expertise is in applied mathematics, mathematical modeling, system and pattern analysis, and software and hardware system integration. He has served as CEO of several hi-tech start-up companies and nonprofit organizations, which has given him proven capabilities in business strategy for high-tech start-up companies, market assessment, company formation, team building, product development, marketing, and sales. He has published numerous articles in journals and magazines in related fields. As a retail investor, he spent 15 years in the development of his proprietary trading and quantitative algorithms (now Tickeron's A.I.), which brought him significant returns in trading the stock market. His current work and goal in founding Tickeron is to bring professional, sophisticated stock market analysis capabilities to retail investors with an easy-to-use interface.

The A.I. identifies patterns by their key geometrical elements, formed by changing stock prices when plotted on a chart. The A.I. does not take into consideration stock fundamentals, expert recommendations, market conditions, and so forth. Trading in stocks and other securities involves the risk of loss.

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