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Oil Gains Strength After API DrawOil has regained strength, rebounding nearly to highs again. We have blasted through all levels of resistance in the 82's and 83's, and are currently just below the high at 85.55. We seem to be hovering around 84.75, which has been tested and rejected before, therefore we are adding it as a new technical level. Oil appears to be in a sideways corrective pattern for the longer term, ranging from the high 70's to 84.75. If we are able to break out then 87.21 is the next target.
NYMEX:CL1!
by quantguy
1
Twitter bull & bust Casetwitter has recently made a sharp downside move towards 100EMA on the weekly chart which looks like will hold for a couple of upside sessions as of now as the market is over all in strong upside momentum with daily record close across the board. That being said, the over all picture for the company and stock looks a bit shaky with sharp sell off from the past week. if the stock does manage to climb back towards the 70/72ish area i believe it can still dive down towards the 2019 Area all over again. which will be an Ideal place to go long on this stock. but for Day traders this stock can be a real treat as the daily volume is high so the volatility is there to make money both ways. Personally i would go long with $48 as the stop loss area and $70 as Take profit and would be a seller at $70 with stop loss at $76 and all the way towards $30/35$ take profit.
NYSE:TWTRLong
by shaxrashid
Inside Bars On Bitcoin Suggest A BreakoutWe see that bitcoin slowed down recently with an inside bars pattern, but above the falling trendline which is actually trying to act as support now. An inside bar formation can bring the volatility back, but after a daily close above the Mother bar. This can then cause a continuation back to the highs On the other-side drop below the Mother bar, may suggest that weakness from Oct 20 will be more complex and tricky with potentially lower prices ahead.
BINANCE:BTCUSD
by ew-forecast
1
#WYNN MACAU LONGthis stock has absolutely been beaten down towards the all time lows and taking a position in this company at this level can give some good rewards. The parent company WYNN RESORTS has a household name in the US in Casino and resorts segment. the company has gone through some rough patch in the last 2 3 years primarily due to Covid restrictions around the world. the casinos and resorts were shut down due to which the business had faced significant losses. that being said, it is not a bad time to take a position at this point as the casino/gambling business is coming back on air with covid restrictions being lifted all over the world now.
OTC:WYNMFLong
by shaxrashid
Education Excerpt: Simple Moving AverageSimple Moving Average The origin of inventing the Simple Moving Average (MA) is not clear. Although, some of the first documented cases of its use date as far back as the early 20th century. Implementation of moving averages in technical analysis is one of the most successful methods of identifying trends. Moving averages are simply constant period averages - usually of prices, that are calculated for each successive period interval. The result of calculation is then plotted on the chart as a smooth line that represents successive average prices. Thus, the calculation of the moving average dampens fluctuations of price of an asset, making it easier to spot an underlying trend. Though use of the moving average goes beyond identifying trends. Support, resistance and price extremes can be anticipated by correct interpretation of the moving average. Crossover Generally, when the moving average with a lower period interval crosses above the moving average with a higher period interval it is considered a bullish signal. On the other hand, when the moving average with a longer period interval crosses above the moving average with a lower period interval it is considered a bearish signal. These crossovers can serve as specific buy and sell signals in markets that are trending. However, moving average crossovers tend to produce many false signals in non-trending markets. Furthermore, these same crossovers can act as support or resistance levels. Illustration 1.01 Picture above depicts daily graph of PepsiCo (Ticker:PEP) with 20-day SMA (blue) and 35-day SMA (red). With implementation of these two moving averages it is easily observable that prevailing trend is bullish. Crossovers between these two simple moving averages reveal where trend began (10th February 2017) and where it ended (7th July 2017). In addition to that analyst can identify price extremes when price deviates too far from its 20-day SMA. Length of the period Different lengths of moving average directly translate to the amount of data used in the calculation. Including more data in the calculation of the moving average makes each data per time interval relatively less important. Therefore, a large change in one particular data would not have as large an impact on the overall result of the calculation in comparison to if the moving average with a shorter period was employed. Hence, the longer moving average produces less false signals at the cost of revealing underlying trend sooner rather than later. Usually, the use of two moving averages with different period intervals is encouraged as opposed to use of a single moving average. This comes from the premise that when two moving averages with different period intervals are plotted on a chart, they tend to show two separate lines converging and diverging. Illustration 1.02 Picture above depicts daily graph of XAUUSD with 3-day SMA (blue) and 6-day SMA (red). Viewer can see that 3-day SMA copies price move more agressively than 6-day SMA. Illustration 1.03 Picture above depicts exactly same graph as is showed in Illustration 1.02. However, length of SMAs differs. Blue line represents 10-day SMA while red line represents 20-day SMA. It is clear that when length of SMAs was extended then SMAs produced less mechanical signals (crossovers) as opposed to SMAs used in Illustration 1.02. Calculation The calculation of the moving average usually involves use of the close price. Normally, 10, 20, 50, 100 or 200 periods are used and the calculation is conducted by creating the arithmetic mean of a dataset. SMA = (A1 + A2 + An) : n A = average in period n n = number of time periods Illustration 1.04 Picture above shows daily graph of Coca Cola (Ticker:KO). In this particular example trend was neutral and it is visible that crossovers between two simple averages produced many false signals. Disclaimer: This content is just excerpt from full paper that will be published later. It serves educational purpose only.
OANDA:XAUUSDEducation
by Tradersweekly
60
Education Excerpt: SMA, LWMA, GMA, TMA, EMAWe decided to publish second part of the paper on moving averages. The first part detailed Simple Moving Average. In the second part we decided to present: linearly weighted moving average (LWMA), geometric moving average (GMA), triangular moving average (TMA) and exponentially smoothed moving average (EMA). The first part can be read by clicking on chart below: Possible uses of the moving average • Identification of trends • Identification of price extremes • Identification of support and resistance levels • Identification of signals Identification of trend The moving average can be used as simple tool to determine prevailing trend. Simplest way to determine current trend using moving average is to compare current value of security to current value of moving average. If value of moving average is below price of the security, then trend is considered to be upward. Contrary to that when value of moving average is above price of the security then trend is considered to be downward. Another method of determining trend is to use two same moving averages but with different length (different number of hours or days, etc.). These two moving averages would be then plotted on graph as two simple lines occasionally crossing. Trend would be considered upward when shorter moving average would be above longer moving average. Opposite to that, if shorter moving average would be below longer moving average then trend would be regarded to be down. Illustration 1.01 Picture above depicts daily chart of XAUUSD. It is observable that price continued to rise most of the time when it was above 10-day SMA. It is also observable that when price dropped below 10-day SMA then it continued to decline further. Identification of price extremes Analyst can find another utilization of moving average in finding the price extremes. This is possible due to natural tendency of price to move back towards its moving average after it deviated too far from it. Illustration 1.02 Graph above depicts General Motors on daily time frame. It is visible that when price deviated too far from its 10-day SMA then retracement followed. However, it is not a rule that price will retrace full length back to moving average once it deviated too far from it. Identification of support and resistance levels Another possible use of moving averages lies in using them as specific support and resistance levels. In rising markets price has tendency to correct towards moving average before continuing to rise further. Similarly, in declining markets price tends to suddenly increase towards moving average and then drop and continue lower. Identification of signals Generally, when moving average with lower period interval crosses above moving average with longer period interval it is considered bullish signal. On the other hand, when moving average with longer period interval crosses above moving average with lower period interval it is considered bearish signal. These crossovers can serve as specific buy and sell signals in markets that are trending. Illustration 1.03 Picture above shows same graph of General motors as is depicted in Illustration 1.02. However, instead of one 10-day SMA this graph also includes 20-day SMA. It is easily identifiable where these two moving averages cross each other and by doing so generate specific buy and sell signals. However, we have to note that in non-trending markets this method lacks utility since moving averages tend to produce a lot of false signals. The Linearly Weighted Moving Average (LWMA) The Linearly Weighted Moving Average (LWMA) is very similar to the Simple Moving Average (SMA) we introduced in our previous education excerpt. But while SMA gives each time period involved in the calculation same weight LWMA differentiates between the weight linked to each time interval. Normally, 10-day SMA calculation would be conducted by summing up each value per time period and then dividing this result by total number of time intervals (which would be 10 in this particular example). In this calculation each time period (each day) would have 10% weight. However, as mentioned before, LWMA gives each time interval different weight. This unequal redistribution of weight can be achieved in two simple steps. In the first step analyst multiplies each day's value and sums up resulting values together. Then in the second step analyst divides resulting value (from the first step) by the sum of all multipliers. For example, in 10-day LWMA first day's value would be multiplied by 10. Then second day's value would be multiplied by 9; and third day's value would be multiplied by 8 (continuing up to 10 days where last day's value would be multiplied by 1). Resulting value for each time interval would be then summed up and divided by 55 (multipliers: 10+9+8+7+6+5+4+3+2+1 = 55). This simple change in formula would result in giving 10th (most recent day) day in the calculation twice the weight of 5th day and ten times the weight of the 1st day. Calculation of 10-day LWMA for 11th day would then involve weighting data from 2nd day up to 11th day while dropping the 1st day's value from data set being used in the calculation. Assigning different weight to each time interval helps to give more relevance to the most recent days as opposed to giving less importance to days before that. Formula LWMA = / summation of W P = price for the period n = period W = the assigned weight to each period (highest weight goes first and then it linearly declines) Illustration 1.04 Chart above depicts two different moving averages. First is 10-day SMA (blue) and second is 10-day LWMA (yellow). While these two moving averages have same length they are different in shape. This is because of unequal redistribution of weight. This allows LWMA to act in advance of SMA. Geometric Moving Average (GMA) The Geometric Moving Average (GMA) is another form of moving average. But rather than using price in its calculation GMA uses percentage changes between the previous time period and the current time period. This type of moving average distributes weight equally as SMA. In addition to that it suffers from lag. When SMA and GMA (with same length) are plotted on same graph they are not different in shape or dimensions. Therefore they would overlay each other. The Triangular Moving Average (TMA) The Triangular Moving Average (TMA) is another type of moving average that is different from previous types of moving averages in that it is double smoothed. Its calculation begins with taking SMA with predetermined number of bars. After that these results are being used to take SMA of former SMA. However, length of second SMA is only half of that used in calculation of original SMA. For example, 20-day SMA would be smoothed through calculation of 10-day SMA that would use data from 20-day SMA. The result can be then plotted on graph and it is depicted as smoothed line. TMA represents the trend better since it is double smoothed, however, at cost of sensitivity to trend changes. When TMA and SMA (with same length) are plotted on same graph they are different in shape and dimensions. Illustration 1.05 Picture above shows daily graph of PEP. Three moving averages are depicted: SMA, LWMA, TMA. They all observe same 10-days, however, each acts differently. The Exponentially Smoothed Moving Average (EMA) The Exponentially Smoothed Moving Average (EMA) is type of moving average that weights importance on the most recent data. Decrease in weight from one time interval (one day) to another is exponential; and unlike SMA and LWMA exponential moving average has ability to use information outside the length of the moving average. Result from calculation of EMA can be then plotted on graph similarly like result from SMA, LWMA or any other moving average. EMA is considered to be more responsive to trend changes and it can be used when analyst is concerned with effect of lag (which is stronger in SMA and LWMA). Formula EMA = Pricet x k + SMAy x (1-k) t = today k (multiplier) = 2/(number of days in period +1) SMA = simple moving average of closing price y = yesterday Illustration 1.06 Picture above depicts daily graph of Raytheon. It also depicts 10-day SMA and 20-day EMA. It is visible that many fake signals took place once market started to trade sideways. Disclaimer: This content is purely educational.
NASDAQ:AAPLEducation
by Tradersweekly
47