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SQD trade ideas

Square(SQ) Exceeds Earnings Expectations After a decent decline towards the end of October, Square had been on the downtrend until a trading range channel evolved during the last days of October and first week of November. We see a slight Double bottom with a breakout up( purple squares). This led to a slight throwback but confirmed the breakout around market close on November 3rd. there are multiple gaps that confirm the breakout and the uptrend. The first ellipse is seen, which is the breakaway gap occurring at the beginning of the trend. Following throughout the day as earnings were to be announced post-market, there was a runaway gap(second ellipse) which strengthened the trend when approaching close and earnings news. Post market is settled at around 4.66% in the green for SQ at a price of $183.40 USD. Once earnings were announced, Square exceeded estimates and grew their cash app gross profit increased 212% YoY, as well as gross profit of $794 million which is 59% increase YoY. After a net loss of $11 million last quarter, this quarter was more profitable with a positive net income of $37 million. Outside US market users grew by 46% year over year. With the big increase in Cash App Gross Profit we can see that it is becoming Square's cash cow when dealing with income earned from their operations. My investment strategy pertaining to SQ is a long play, as payment processing solutions are expected to grow 62% from 2020 to 2025. This increase can be caused by more eCommerce business as well as more cashless banking, whether it be deposits, loans, both personal and for small businesses! Let me know your thoughts! I'll be happy to hear your opinions on my analysis.
NYSE:SQLong
by aidanstrasser98
$SQ Square pops On Earnings Beat Potential Long entry on break above previous high @ $194.00 targets as per chart to Fibonacci levels of confluence. Much depends on the market remaining strong Earnings Details Square (NYSE:SQ) Q3 adjusted EPS of 34 cents beats the average analyst estimate of 16 cents and increased from 25 cents in the year-ago quarter. Q3 total net revenue of $3.03B and far surpassed the $2.08B consensus; surged from $1.92B in Q2 and $1.27B in the year-ago quarter. Square surges 7.7% in after-hours trading. Q3 adjusted EBITDA of $181M increased from $98M in Q2 and $131M in Q3 2019. Q3 Cash App ecosystem gross profit jumped 212% Y/Y for $385M; Cash App revenue of $2.07B rose 574% Y/Y; excluding bitcoin, Cash App revenue was $435M, up 174% Y/Y. Q3 Seller gross profit rose 12% Y/Y. Seller gross payment volume of $31.7B increased from $22.8B in Q2 and $28.2B in the year-ago quarter. In markets outside the U.S. the Seller gross payment volume increased 46% Y/Y and represented 11% of total Seller GPV. Looking at its operations in October, Square reports Seller exhibited positive revenue and gross profit growth Y/Y; Seller GPV was up 8% Y/Y, improving modestly from Q3 Y/Y growth. Cash App revenue and gross profit showed strong growth in October, but gross profit growth in the month moderated vs. Q33 growth, driven by a decrease in transaction volume per active customer.
NYSE:SQ
by Bullishcharts
Technical Indicators - What's Useful & What Isn'tThis post discusses the usefulness of technical indicators. We can conclude that the question of whether or not technical indicators are useful depends on many aspects, such as indicator settings, interpretation methodology (contrarian/trend following) etc. 1. Introduction Since the introduction of technical indicators, academics have produced a significant amount of research in order to know if whether or not technical indicators are useful to trade profitably and results show mixed conclusions. Some studies support the effectiveness of technical indicators, while others reject this idea. The attitude of academics toward technical analysis has drastically changed over time, in the past this attitude was often negative, with Malkiel (1981) often being quoted: "Obviously, I am biased against the chartist. This is not only a personal predilection, but a professional one as well. Technical analysis is anathema to, the academic world. We love to pick on it. Our bullying tactics' are prompted by two considerations: (1) the method is patently false; and (2) it's easy to pick on. And while it may seem a bit unfair to pick on such a sorry target, just remember': His your money we are trying to save." This skepticism is due to the belief in the efficient market hypothesis (EMH), and it is true that any method relying on the study/processing of past prices is subject to ineffectiveness, which would imply that technical analysts are irrational, however, the EMH has been subject to heated debates over the years. Studies have found inefficiencies and memory in market prices, which would encourage the usage of technical analysis. Newcomers to trading can easily be attracted by technical indicators, this is due to their simplicity and popularity, and even if they are criticized, they are often highlighted in educational posts by brokers, and are commonly available in most trading platforms. As such, it can be difficult to not use them, since they are the primarily tools available to traders. 2. Evolution Of Technical Indicators Historical prices describe a time-series/digital signal, as such, tools designed for time-series analysis and digital signal processing could be used to process historical prices. Most technical indicators are simple rolling statistics, such as moving averages, rolling variance/standard deviation, momentum oscillator, etc... With the advance of computing technology and the ability for any user to access historical prices more easily, the creation and usage of technical indicators has become more simple. Trading platforms began including simple technical indicators, thus democratizing their availability to traders. The introduction of programming languages specifically designed for the creation of technical trading tools (such as Pinescript) mark an important turning point for technical indicators Any trader has the ability to create their own technical indicators more easily, as well as being able to share them with the trading community. At this point in time, we can see the appearance of more complex technical indicators making use of more complex calculations and graphical elements. 3. Technical Indicators Performances There currently exists a high number of technical indicators. This shows that the creation of new ones is a very active practice but it also shows that there isn't a pre-determined set of technical indicators with proven performances. The most common ones used by traders remain old indicators such as simple/exponential moving averages, momentum oscillators, stochastics, the relative strength index, Bollinger Bands, etc... Most of these technical indicators do not account for the ever changing conditions in market prices which introduced the creation of adaptive indicators such as the Kaufman adaptive moving average ( KAMA (1)), fractal adaptive moving average ( FRAMA (2)) etc... Yet adaptivity does not seem to introduce significant profitability improvement over fixed length moving averages (3). One explanation could be that adaptive moving averages still require one user setting. The user settings used by technical indicators remain a huge problem as they require optimization and since past results are not indicative of future results; optimal settings can change over time. As such we can say that a technical indicator needs to produce positive results by using a wide variety of setting combinations to be considered optimal; which is rarely the case. Another interesting aspect of user settings is that two different indicators could potentially give the same or similar signals using different settings, which would make certain indicators redundant. As such it is extremely difficult, maybe impossible, to answer the question "Is there a profitable technical indicator" taking into account the ever changing market conditions. A more realistic question, yet still complex is "What is the best technical indicator under certain market conditions?". 4. Are Popular Indicators Better Than Less Popular Ones? Is the popularity of a technical indicator a good indication of its usefulness? A logical answer could be yes, but we can see that this is not always the case, and that popularity can be determined by many factors. The popularity of any tool can be determined by external factors such as marketing, author popularity, and by consumer/user behavior, etc... One interesting point is the visual aspect of technical indicators, as technical indicators are fully digital and rely on their visual aspect to appeal to users. The psychology of color has been studied to see how consumer behaviors can react to certain colors, and it has been shown that colors play an important role when it comes to people's judgement regarding certain products. More colorful products might be seen as more complex, while visual complexity can induce a user to think a higher quantity of information will be displayed, thus increasing their chances to trade better. Considering that technical indicators can also be "products" it can be more convenient for any author/vendor to focus on technically simple indicators with more visually attractive features instead of focusing on performances in order to boost their popularity. While advertising is also a successful option, as such indicators highlighted by well known publishers/journals will not necessarily posses positive performances but will still have an impact in the trading community. 5. Redundant Information The goal of any good technical indicator is to give as much useful, easy to read/access, non redundant information to the user while minimizing interaction with the indicator settings. In theory, both should be correlated with more information requiring more interaction (using toggles/ drop down menus etc). The redundancy problem is a major one since it affects all the previously described goals of a good indicator. Let's take the momentum oscillator as an example: This oscillator has many interesting properties, it can determine the current trend, show divergences, but can also determine the sign of the changes in a simple moving average of the same period. Based on this an indicator showing the sign of the changes of a simple moving average will be less attractive than a momentum oscillator, which returns more information and is faster to compute. There are a lot of existing indicators with the potential of being redundant. Ribbons are good examples of indicators that often return excessive redundant information. Ribbons consist of multiple plots of moving averages using different periods. Depending on how the type of moving averages and their periods are chosen, the information returned by the ribbon might be redundant and hard to analyze. 6. Repainting And Non Causality We earlier mentioned that the visual aspect of an indicator can be a determinant factor toward its popularity, while attractive indicators might usually receive significant interests from user, it is still indicators appearing to generate excellent entry points that will generate the most interests from traders. Along the years, a particular set of technical indicators has been known to show extremely attractive results, those being "repainting" indicators. Repainting indicators refer to indicators who's past values are subject to change over time, repainting can be caused when an indicator is using future price data as an input or when historical data is removed which makes the indicator recalculate and thus potentially change. Repainting indicators are only useful for non real-time applications, most of them only track the price or could show a signal after a certain amount of time at a past location, as such, repainting indicators are rarely useful when it comes to determining entry points and are usually subject to delayed decision timing. This makes them as useful as any other indicator with lag (this is the case for a lot of horizontal support and resistances indicators relying on pivots). 7. Conclusions It can be difficult to support the profitability of automated technical indicator based strategies. Trend following strategies will benefit from clean trends while contrarian strategies will have better performances when price is stationary, considering that price tends to switch between these two conditions it is easy to see where indicators might encounter problems being profitable with consistency. As such the worth of a technical indicator will hardly be found in its ability to make you money by itself. The complexity of market price variations makes this way too difficult and a technical indicator is not smart nor adaptable enough to overcome such extreme conditions, however, this is not generally the case for an experienced trader who could use the indicator as a supportive tool for decision making. Considering the role of a trader, we can conclude that the usefulness of an indicator is determined by the quantity of non redundant and useful information it outputs, and not necessarily by its ability to provide early and accurate entry points which is harder to achieve. Achieving an indicator able to deliver this amount of information would still remain challenging considering the amount of methodologies used by traders, such "universal" indicator would require some user interaction, making it as easy as possible to use is what will play a role in the overall indicator usefulness. 8. Code Used In Snapshots //@version=4 study("Ribbon",overlay=true) ma1 = ema(close,20) ma2 = ema(close,40) ma3 = ema(close,60) ma4 = ema(close,80) ma5 = ema(close,100) ma6 = ema(close,120) ma7 = ema(close,140) ma8 = ema(close,160) a = plot(ma1,transp=100) b = plot(ma2,transp=100) c = plot(ma3,transp=100) d = plot(ma4,transp=100) e = plot(ma5,transp=100) f = plot(ma6,transp=100) g = plot(ma7,transp=100) h = plot(ma8,transp=100) F(x)=>x>ma8?#0cb51a:#ff1100 fill(a,b,F(ma1),transp=80) fill(b,c,F(ma2),transp=70) fill(c,d,F(ma3),transp=60) fill(d,e,F(ma4),transp=50) fill(e,f,F(ma5),transp=40) fill(f,g,F(ma6),transp=30) fill(g,h,F(ma7),transp=20) //@version=4 study("Pivots S/R (negative offset)",overlay=true) length = input(14) //---- ph = fixnan(pivothigh(length,length)) pl = fixnan(pivotlow(length,length)) plot(ph,"Plot",change(ph) ? na : color.black,2,offset=-length) plot(pl,"Plot",change(pl) ? na : color.black,2,offset=-length) 9. References (1) Kaufman, P.J., 1995. Smarter Trading. McGraw-Hill, New York. (2) Ehlers , John. "FRAMA–Fractal Adaptive Moving Average ." Technical Analysis of Stocks & Commodities (2005). (3) Ellis, Craig A., and Simon A. Parbery. "Is smarter better? A comparison of adaptive, and simple moving average trading strategies." Research in International Business and Finance 19.3 (2005): 399-411.
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NYSE:SQEducation
by LuxAlgo
108