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BANK AMERICA DL 0,01BANK AMERICA DL 0,01BANK AMERICA DL 0,01

BANK AMERICA DL 0,01

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BAC, Massive Descending-Triangle, Huge BREAKOUT-Expansion Setup!Hello There! Welcome to my new analysis of BAC. In recent times I have spotted important value stocks within the market that have the potential to emerge with a major transformational reversal and indicate main expansional determinations once the appropriate confirmations have emerged. In this case, I have analyzed BAC with the current ongoing underlying dynamics and with BAC it has to be mentioned BAC is a stock that can unfold its full potential with the current Consumer Demand to appropriately increase further. More and more supply-chain disruptions are repaired again and are recovering now, if this dynamic holds on it sets up a crucial turning in the overall market sentiment. Also, the fact that CPI has declined now for a consecutive period of time builds an important market from where stocks as I have spotted in my watchlist confirm the necessary opportunities. When looking at my chart now, BAC is forming an important structure, firstly as it is still trading within this gigantic ascending-trend channel in which it has the main supports within the lower boundary and already bounced several times within there. Secondly, BAC has the main support levels between the 26.3 and 27.5 area as it is marked in my chart. Thirdly, BAC is already bouncing within the areas and is forming an important support base within this zone. All these factors are building a determined support base from where BAC has considerable potential to emerge with a major reversal and increase the bullish edge. The most important formation in this whole structure is the momentous descending triangle formation. Such a formation is leading to a meaningful substantial expansion reversal in almost all of the cases. Once the whole descending triangle formation has been completed with the final setup as it is marked in my chart this is going to activate the further bullish price-action accelerations into the upper directions and emerge with the major wave-C within the whole wave-count that BAC already established. The completion of the formation also means BAC is going to activate the target zones within the 62.50 and 66.50 areas. Now, taking all these factors into consideration, currently, there is a massive digitization boom with digitization developments increasing within recent times and the growth rates in this sector are accelerating. When BAC moves further with the potential to transform their holdings into the newly developed ecosystem this is going to have a tremendous effect on the actual bullish sentiment prevailing here, especially in combination with the fact that the Consumer Expenditures in this sector are increasing. With such a backing and increase of these factors, this is actually indicating that the price-action accelerates in pace. Once the formation has been completed the next phase targets mentioned will be active. Thank you everybody for watching my idea about BAC. Support from your side is greatly appreciated. "With a prime perspective on stocks history, we can have a better understanding of the past and present, and thus a clear vision of the stocks future." VP
NYSE:BAC
by VincePrince
6
Cyclical price Patterns!If a price pattern moves in a sine wave pattern, we can say that it is cyclical. A sine wave is a repetitive pattern of peaks and troughs, which is the definition of a cycle. However, it is important to note that not all sine wave patterns are created equal. Some sine wave patterns may be more cyclical than others. For example, a sine wave pattern with a very short period may be more likely to be caused by random noise than by a true cyclical process. To determine whether a sine wave pattern is truly cyclical, traders and analysts often look at the following factors: Period: The period of a sine wave is the amount of time it takes for one complete cycle to occur. Cyclical price patterns tend to have periods that are relatively consistent over time. Amplitude: The amplitude of a sine wave is the distance between the peak and trough of the wave. Cyclical price patterns tend to have amplitudes that are also relatively consistent over time. Correlation: Cyclical price patterns are often correlated with other economic or market data. For example, the price of oil may be cyclically correlated with the global economic growth rate. If a sine wave pattern has a consistent period, amplitude, and correlation with other economic or market data, then it is more likely to be a true cyclical pattern. Here are some examples of cyclical price patterns that can be represented by sine waves: Business cycles: Business cycles are typically characterized by alternating periods of economic expansion and contraction. These cycles can be represented by sine waves with periods of several years. Seasonal patterns: Seasonal patterns are price movements that are repeated on a regular basis, such as the seasonal rise in the price of heating oil in the winter. These patterns can be represented by sine waves with periods of several months or weeks. Overall, if a price pattern moves in a sine wave pattern, it is likely to be cyclical. However, traders and analysts should carefully evaluate the period, amplitude, and correlation of the pattern before making any trading decisions.
NYSE:BACEducation
by Moshkelgosha
4
Distribution Curves and Investing I recently released an indicator called the Cumulative Distribution Density of Dataset indicator. One of the main highlights of this indicator is its, at the time of writing, the only indicator available on Tradingview/Pinescript that assesses the degree of normality as well as the type of distribution of a ticker, index or economic variable. Before this, you would need to export data into a statistical package such as Excel, SPSS, R or SAS to perform such an analysis. So I figured its probably time to talk about the bell curve again. Some of you may remember, I released an educational video called “Trading Using Bell Curves”: In this video, I discuss the implications of using bell-curves for trading. However, I want to reel it back and talk more specifically about distributions and trading, and why you, the investor and/or trader, should be paying attention to them. This is something I honestly have never seen talked about and really, you are doing yourself a huge disservice as a trader AND an investor for ignoring it. so let’s get into it! But before we start, I won't review the basics of the bell curve, but if you are interested, consider watching the video above. Alright, now on with the math! Understanding stock distributions, which come in various forms such as leptokurtic, platykurtic, and more, will provide you with valuable insights into market behavior and risk management. Did you know that certain distribution types can alert you that a stock generally has an unstable trajectory? And by looking at the distributions, you can also tell which stocks are more prone to aggressive crashes and which are more stable? Well you can, and I am going to teach you how! So let’s go over the main types of distributions in stocks and their implications for you as a trader. Types of Stock Distributions Normal Distribution This is probably the one you hear talked about a lot. A normal distribution, also known as Gaussian distribution or bell curve, is characterized by its symmetrical shape. In a normal distribution, the mean, median, and mode are all equal, and data points are evenly distributed around the central value. This distribution tends to be common in nature but tends to be not all that common in long term stocks. There are some exceptions; however. For example, NYSE:BAC (Bank of America) actually has a normally distributed dataset from initial listing to now: When stock returns follow a normal distribution, it becomes easier to predict future price movements and assess risk more easily. One way to do this is by using the cumulative distribution function (or CDF). Which is a mathematical function that provides the probability that a random variable takes on a value less than or equal to a specific value. For example, if we have 10 students with various test scores, we can plot all test scores using CDF and determine what the probability is that a random student’s test score will be above 90% or below 20%. We can visualize this on NYSE:BAC by having the indicator plot the CDF for NYSE:BAC : The image above plots the CDF distribution for NYSE:BAC on the monthly timeframe since its IPO. Because BAC is normally distributed, we can place a high level of confidence in the results of the CDF. We can also use the CDF to our advantage. How? By planning where we could buy. We should buy when the price is at a level where 50% to 60% or more of the time the price will fall above. Turning back to our BAC example, we can display this with a simple trendline: We can also operate on the assumption that NYSE:BAC is likely to go lower from here. Why? Because the normal distribution is not yet invalidated. As of right now, BAC retains a normal distribution. Thus, we can expect BAC to cycle back down to bring its CDF back towards 50% and 60%. We can see another example below, AMEX:XLE : Key Points for Tickers that are Normally Distributed: They tend to be more cyclical, having periods of sustained decline, followed by periods of sustained rise. They are the most stable and predictable type of ticker to invest or trade in, but tend to be general underperformers (because of their cyclical behaviour of decline and then rise). However, this is not always the rule, the advantage to a normally distributed ticker is you can calculate your likely returns to a high degree of accuracy! Some examples of stocks that have a normally distributed history are NYSE:T (AT&T), NYSE:BAC (Bank of America), AMEX:XLE (Energy ETF), T-Mobile and $BABA. You will generally notice that, if a ticker in one industry is normally distributed, chances are other tickers in the same sector is as well, even international tickers in the identical sector. For example, T-Mobile (TMUS), T (AT&T) and TSX:T (Telus) all are telecommunication providers and all have normally distributed data. They respond very well to log-linear and linear regression methods. But what about other distributions? Let’s talk about them. Leptokurtic Distribution A leptokurtic distribution is characterized by a higher peak and fatter tails than a normal distribution. In this distribution, extreme events, such as market crashes or rapid price spikes, are more likely to occur compared to a normal distribution. From my experience, most stocks fit this description, but one of major note is NYSE:BA : Leptokurtic distributions indicate higher volatility and a higher likelihood of extreme price movements. In general, you need to be more cautious with leptokurtic distributions because there is generally heightened volatility. A CDF on a leptokurtic distribution is not as clean, as we can see from plotting BA’s CDF: Because BA’s distribution is not normal, the CDF becomes slightly unreliable and we cannot employ the 60% rule. So can we still use the distribution to help us gauge entries? Yes! We can! However, it’s a bit more nuanced with leptokurtic distributions. The first thing to remember with leptokurtic distributions is… they crash… a lot. We can see this with BA: The flags in this chart represent areas BA has crashed. Crashes in leptokurtic distributions are usually characterized by a drop on the CDF of the probability a stock will go lower to around 75% to 85%. We can see this if we overlay the CDF for BA with the chart: These are the dips you would want to buy in a leptokurtic distribution. If we take a look at another example, AMD: Key Takeaways from Leptokurtic Tickers: They are among the most unstable tickers and experience among the most crashes. Your risk as an investor is heightened on any ticker that is leptokurtic. They do not respond well at all to log-linear or linear regression methods. Unlike normal distributions, leptokurtic distributions don’t generally follow sectors and they tend to be company specific tickers (which explains their proneness to crashing and volatility). Some major examples of leptokurtic distributions are NYSE:BA , NASDAQ:AMD , NASDAQ:MSFT , Platykurtic Distribution A platykurtic distribution has a flatter and wider shape compared to a normal distribution. In this case, the data points are more spread out, and extreme events are less likely to occur. As such, platykurtic distributions suggest lower volatility and a more stable market environment. However, it is important to know that prolonged periods of low volatility can be followed by sudden spikes, leading to unexpected market movements. These are extremely rare distributions that I have not observed in any of the tickers I have traded. However, theoretically, platykurtic distributions would come in smooth waves up and down. We can visualize this if we look at SPY’s January 2022 highs till its October lows. This was a platykurtic, negative distribution (indicating a stable downtrend): Because platykurtic distributions are cyclical, you long on the bullish peaks when the probability of higher prices is >= 90% and short on the bearish peaks when the probability of downside is >= 90%: However, this is not at all prevalent or observed in stocks ever, so you would be lucky to find a platykurtic distribution! Key take aways from Platykurtic distributions: Playkurtic distributions, theoretically, are cyclical like the normal distribution, which make them more stable. They would be similar to normally distributed tickers in their under-performance, but superior in their ability to not generally experience equal rises and declines. Skewed Distribution A skewed distribution is asymmetric, with a longer tail on one side. Positive skewness means the tail is on the right (indicating more extreme positive values), while negative skewness implies a left tail (indicating more extreme negative values). Skewed distributions can signal a bias in market sentiment. For example, positive skewness may indicate a bullish bias, while negative skewness may suggest a bearish bias. While many people look to EMAs or trendlines to identify long-standing bull or bear markets, its actually not necessary, you can ascertain this simply from the distribution. If we take a look at SPY: This is SPY’s distribution since the IPO. We can see that it has a positive skewness (right tail), with extreme outliers. This signals to us, the investors, that SPY has been in a bull run since its IPO. Despite multiple corrections and bear markets, SPY retains the distribution characteristic of a bullish stock. In fact, SPY frequently experiences extremely positive outliars (outliars to the upside) more often than extremely negative outliars (crashes to the downside). This is observed with its positive skewedness. Planning entries on a positively or negatively skewed ticker is a bit more difficult. Crashes substantial enough to bring the probability of going higher to 50% or more tend to be rare (see image below): So when you are dealing with a positively skewed stock, its best to apply alternative, complementary strategies to determine entries, such as using regression channels, longer running EMAs or time series modelling. You can still use CDFs, but you will need to focus on a narrower timeframe. For example, if we plot SPY from its January high to the current day, we can see the data is normally distributed and thus can refer to our parameters for entry on a normal distribution: Key Takeaways from the Skewed Distribution: The Skewed distribution are going to net you your returns (assuming, of course, the ticker is POSITIVELY skewed). These are the tickers that tend to experience exponential growth and returns. Skewed distributions tend to outperform other distribution types, but not without risk. Skewed distributions have an inherent tendency to see dramatic outliars either up or down. Unlike a leptokurtic distribution which is more prone to crashes, a positively skewed distribution is more likely to experience extreme outliars to the upside (meaning bull runs) than to the downside. However, a negatively skewed distribution is more likely to experience more frequent and dramatic drops to the downside than to the upside. So pay attention to the skewness! If it is negative, the risk of a downturn is greatly augmented. Skewed distributions respond reasonably well to log-linear and linear regression methods. Famous example is AMEX:SPY of course! Conclusion While I didn’t cover all possible distributions, I did cover the main ones to pay attention to. However, I hope you now have a better understanding and appreciation for the importance of paying attention to the distributions of stocks. The importance of this is often underestimated but it is, in fact, a crucial aspect of successful investing and trading. Various distribution types, such as normal, leptokurtic, platykurtic, and skewed, provide valuable insights into market behavior and risk assessment. Investors and traders who take the time to understand these distributions can make more informed decisions, manage risk effectively, and enhance their overall success. Thanks so much for reading and hopefully you learned something! Safe trades and, as always, feel free to share your questions and comments below :-). By the way, the indicator is linked below if you would like!
NYSE:BACEducation
by Steversteves
4
Bank of America vs oil (impacts on silver)MAXIMIUM COMPLACENCY. WHO ELSE IS WATCHING THIS? Bank of America CONTINUING downfall versus energy! Silver gained over 1700% and 550% previous times this happened. Technical notes: 1) 1970s this was a gold, silver & crude oil BULL ERA 2) 2000s this was a gold, silver & crude oil BULL ERA 3) COMPLACENY AT THE WORST POSSIBLE TIME 4) Silver's Performance Tracks Capital Flow Rotation into Oil 5) Energy putting lots of pressure right now. #gold #silver #crudeoil #energy #banks #inflation
NYSE:BACShort
by Badcharts
1
BAC is a good buy due to termoil in the markets. At the price of <$30 per share, this is a good opportunity to buy some Bank of America stock. There are major issues with the banking system, mortgage market and bond market. Due to the system we live in, this will lead to destruction of small banks, and the capital will flow into the large established banks. GS is too expensive so BAC offers some more potential. I also prefer BAC because they are a little bit more open to Bitcoin.
NYSE:BACLong
by josip
BANK OF AMERICA Stock Chart Fibonacci Analysis 091223 Trading Idea 1) Find a FIBO slingshot 2) Check FIBO 61.80% level 3) Entry Point > 29.2/61.80% Chart time frame : B A) 15 min(1W-3M) B) 1 hr(3M-6M) C) 4 hr(6M-1year) D) 1 day(1-3years) Stock progress : B A) Keep rising over 61.80% resistance B) 61.80% resistance C) Hit the bottom D) Hit the top Stocks rise as they rise from support and fall from resistance. Our goal is to find a low support point and enter. It can be referred to as buying at the pullback point. The pullback point can be found with a Fibonacci extension of 61.80%. This is a step to find entry level. 1) Find a triangle (Fibonacci Speed Fan Line) that connects the high (resistance) and low (support) points of the stock in progress, where it is continuously expressed as a Slingshot, 2) and create a Fibonacci extension level for the first rising wave from the start point of slingshot pattern. When the current price goes over 61.80% level , that can be a good entry point, especially if the SMA 100 and 200 curves are gathered together at 61.80%, it is a very good entry point. As a great help, tradingview provide these Fibonacci speed fan lines and extension levels with ease. So if you use the Fibonacci fan line, the extension level, and the SMA 100/200 curve well, you can find an entry point for the stock market. At least you have to enter at this low point to avoid trading failure, and if you are skilled at entering this low point, with fibonacci6180 technique, your reading skill to chart will be greatly improved. If you want to do day trading, please set the time frame to 5 minutes or 15 minutes, and you will see many of the low point of rising stocks. If want to prefer long term range trading, you can set the time frame to 1 hr or 1 day.
NYSE:BAC
by fibonacci6180
Burton Malkiel should Reconsider Market Randomness..!last night I published 3 analyses on 20 tickers and shared the links with my followers. (private posts) 20 out of 20 tickers behave as predicted today..! The odds of being correct is 1 in 1,048,576..! Banks: BAC,WFC,JPM,C,MS,and GS They are all down but they are near their support levels. they could experience a rally before their earnings! Keep your eyes on them for a possible 10% rally. Airlines: AAL, DAL,UAL,LUV,ALK, and SAVE They all made bearish engulfing (except SAVE), in the weekly chart near the upper border of a bearish channel! Lower prices in the coming weeks are the strongest possibility! EV makers: TSLA, LCID, RIVN, FSR, NIO, XPEV,Li, and WKHS All of them rejected their resistance and could experience lower prices, but the exception is LCID which is already at its support! LCID could be the only one to experience higher prices..! Have it on your watchlist! Today's charts: Banks: 6 out of 6 positive..! Airlines: 6 out of 6 negative! EV makers: 8 out of 8 negative: Dear Mr. Malkiel, You need to Update the book you have written in 1973, 5 years before computers became available to public..! Best, Dr. Moshkelgosha M.D DISCLAIMER I’m not a certified financial planner/advisor, a certified financial analyst, an economist, a CPA, an accountant, or a lawyer. I’m not a finance professional through formal education. The contents on this site are for informational purposes only and do not constitute financial, accounting, or legal advice. I can’t promise that the information shared on my posts is appropriate for you or anyone else. By using this site, you agree to hold me harmless from any ramifications, financial or otherwise, that occur to you as a result of acting on information found on this site.
NYSE:BACLong
by Moshkelgosha
2