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TQQQ trade ideas

UltraPro QQQ. Trump-a-rally gives no light for leveraged betsIt's gone 10 days or so, since Mr. Trump has secured a win over his Democrat-rival Kamala Harris in the 2024 U.S. presidential election, as it declared by the Associated Press. Since that, a lot of stocks soared in a meme-style mode, while Bitcoin clears $93,000 and Dogecoin soared amid Trump-fueled crypto rally. The main graph is for UltraPro QQQ NASDAQ:TQQQ and it indicates, that major 82-Dollars resistance for leveraged bets on Tech sector has not been broken yet. 👉 NASDAQ:QQQ is a traditional ETF that tracks the Nasdaq-100 Index, suitable for long-term investors seeking broad exposure to tech-focused stocks. 👉 NASDAQ:TQQQ is a leveraged ETF that aims to deliver triple (3x) the daily returns of the Nasdaq-100 Index, making it only suitable for short-term traders. Since US dollar interest rates are still near multi year highs and Powell says the Fed is in no hurry to cut interest rates.. all of that means Trump-a-rally gives no light for leveraged bets (yet). Potentially everything can be clear in January, 2025 only. GL y'all. Cheers, @Pandorra 😎
NASDAQ:TQQQ
by Pandorra
TQQQ WEEK OF 11/18/24TQQQ WEEK OF 11/18/24 To maintain simplicity, once the price moves beyond the WHITE range, monitor for a potential retest of the breached level. Be prepared to initiate long or short positions targeting the YELLOW ranges. While prices may surpass the YELLOW range targets, these levels provide a robust framework for securing profits. 🎯🫡 *These levels are derived from comprehensive backtesting and research, demonstrating over 90% accuracy. This statistical foundation suggests that price movements are likely to exceed initial estimates.*
NASDAQ:TQQQ
by J3Trad3s
Opening (IRA): TQQQ January 17th 65 Covered Call... for a 61.79 debit. Comments: Adding a "rung" at a break even better than what I currently have on, selling the -75 delta call against stock to emulate the delta metrics of a 25 delta short put, but with the built-in defense of the short call. Metrics: Buying Power Effect/Break Even: 61.79/share Max Profit: 3.21 ($321) ROC at Max: 5.20% 50% Max: 1.62 ($162) ROC at 50% Max: 2.60% Will generally look to take profit at 50% max, roll out short call on take profit test (which is at 63.40).
NASDAQ:TQQQLong
by NaughtyPines
Opening (IRA): TQQQ December 20th 74 Covered Call... for a 71.29 debit. Comments: Selling the -75 delta call against stock to emulate the delta metrics of a 25 delta short put, but with built-in short call defense. I'm looking at this as a starter position because I kind of have a feeling that I'm going to regret not putting this on in weakness/higher IV and will be adding at better strikes/break evens (only time will tell). Metrics: Buying Power Effect/Break Even: 71.29/share Max Profit: 2.71 ROC at Max: 3.80% 50% Max: 1.36 ROC at 50% Max: 1.90%
NASDAQ:TQQQLong
by NaughtyPines
Opening (IRA): TQQQ August 16th 73 Monied Covered Call... for a 70.80 debit. Comments: High IV at 55%. Buying a one lot and selling a -75 call against in the August 16th monthly to emulate the delta metrics of a 25 delta short put, but with built-in short call defense. I looked at doing something in the 45 DTE wheelhouse, which would be the August 30th expiry, but it was less liquid than the monthly, so opted to go shorter duration, with the plan being to roll out to the September monthly should we get further weakness and/or a test of 73. Otherwise, I'll look to just take profit at my standard 50% max. Metrics: Buying Power Effect/Break Even: 70.80 Max Profit: 2.20 ROC at Max: 3.11% 50% Max: 1.10 ROC at 50% Max: 1.55%
NASDAQ:TQQQLong
by NaughtyPines
Opening (IRA): TQQQ Sept 20th 63 Monied Covered Call... for a 59.60 debit. Comments: High IVR/high IV at 68.7/63.1%. Selling the -75 delta call against stock to emulate the delta metrics of a 25 delta short put, but with the built-in defense of the short call. Additionally, adding at strikes/break evens better than what I've current got on (the August 16th 73 covered calls; break even 70.80). Metrics: Buying Power Effect/Break Even: 59.60 Max Profit: 3.40 ROC at 50% Max: 5.70% 50% Max: 1.70 ROC at 50% Max: 2.85% Will generally look to take profit at 50% max; roll the short call out for duration on side test.
NASDAQ:TQQQLong
by NaughtyPines
Opening (IRA): TQQQ Oct 18th 59 Monied Covered Call... for a 56.10 debit. Comments: This isn't at a lower strike than what I currently have on, but I'm (somewhat) fine with it, since I'm still below what I have in mind to be maximally deployed in this instrument. Selling the -75 delta call against stock to emulate the delta metrics of a 25 delta short put, but with the built in defense of the short call. Metrics: Buying Power Effect/Break Even: 56.10 Max Profit: 2.90 ROC at Max: 5.17% 50% Max: 1.45 ROC at 50% Max: 2.58% Will generally look to take profit at 50% max, roll out in-profit short call for duration and credit.
NASDAQ:TQQQLong
by NaughtyPines
TQQQ I Next long opportunity - following correction from 74.87Welcome back! Let me know your thoughts in the comments! ** TQQQ Analysis - Listen to video! We recommend that you keep this pair on your watchlist and enter when the entry criteria of your strategy is met. Please support this idea with a LIKE and COMMENT if you find it useful and Click "Follow" on our profile if you'd like these trade ideas delivered straight to your email in the future. Thanks for your continued support!Welcome back! Let me know your thoughts in the comments!
NASDAQ:TQQQLong
by BKTradingAcademy
What happened today in the market?What happened today in the market? At the beginning of the week, we knew that Friday would be one of the most important days of the month, as the unemployment rate report would be published, among other indicators. The result was favorable; the current rate stands at 4.1%, better than the forecast of 4.2% and the previous figure also at 4.2%. Additionally, news regarding the possibility of an agreement on the strikes at the most important ports in the U.S. contributed to an optimistic atmosphere. Before the market opened, the indices surged upward. However, in the pre-market, we observed a particular behavior in the main ETFs, such as AMEX:SPY and $QQQ. In the case of NASDAQ:TQQQ , there was a price interruption of approximately $6 in $QQQ. This type of movement can leave many investors trapped due to the previous session's closures since they had open positions. Despite the positive report and the overall optimistic sentiment, at the market's opening, the price remained sideways for about 25 minutes, during which many investors decided to take profits. This resulted in a pullback that wiped out the pre-market gains. However, after that drop, the chart showed notable respect for the 61.8% Fibonacci retracement level, considered one of the most relevant levels. By respecting this level, the market experienced a new bullish impulse, better assimilating the profit-taking.
NASDAQ:TQQQ
by Mariofxtr
Weekly Seasonality Dynamic Kelly (Indicator Discussion)# Enhancing Trading Strategies with the Weekly Seasonality Dynamic Kelly Criterion Indicator Amidst this pursuit to chase price, a common pitfall emerges: an overemphasis on price movements without adequate attention to risk management, probabilistic analysis, and strategic position sizing. To address these challenges, I developed the **Weekly Seasonality with Dynamic Kelly Criterion Indicator**. It is designed to refocus traders on essential aspects of trading, such as risk management and probabilistic returns, thereby catering to both short-term swing traders and long-term investors aiming for tax-efficient positions. ## The Motivation Behind the Indicator ### Overemphasis on Price: A Common Trading Pitfall Many traders concentrate heavily on price charts and technical indicators, often neglecting the underlying principles of risk management and probabilistic analysis. This overemphasis on price can lead to: - **Overtrading:** Making frequent trades based solely on price movements without considering the associated risks. - **Poor Risk Management:** Failing to set appropriate stop-loss levels or position sizes, increasing the potential for significant losses. - **Emotional Trading:** Letting emotions drive trading decisions rather than objective analysis, which can result in impulsive and irrational trades. ### The Need for Balanced Focus To achieve sustained trading success, it is crucial to balance price analysis with robust risk management and probabilistic strategies. Key areas of focus include: 1. **Risk Management:** Implementing strategies to protect capital, such as setting stop-loss orders and determining appropriate position sizes based on risk tolerance. 2. **Probabilistic Analysis:** Assessing the likelihood of various market outcomes to make informed trading decisions. 3. **Swing Trading Percent Returns:** Capitalizing on short- to medium-term price movements by buying assets below their average return and selling them above. ## Introducing the Weekly Seasonality with Dynamic Kelly Criterion Indicator The **Weekly Seasonality with Dynamic Kelly Criterion Indicator** is designed to integrate these essential elements into a comprehensive tool that aids traders in making informed, risk-aware decisions. Below, we explore the key components and functionalities of this indicator. ### Key Components of the Indicator 1. **Average Return (%)** - **Definition:** The mean percentage return for each week across multiple years. - **Purpose:** Serves as a benchmark to identify weeks with above or below-average performance, guiding buy and sell decisions. 2. **Positive Percentage (%)** - **Definition:** The proportion of weeks that yielded positive returns. - **Purpose:** Indicates the consistency of positive returns, helping traders gauge the reliability of certain weeks for trading. 3. **Volatility (%)** - **Definition:** The standard deviation of weekly returns. - **Purpose:** Measures the variability of returns, providing insights into the risk associated with trading during specific weeks. 4. **Kelly Ratio** - **Definition:** A mathematical formula used to determine the optimal size of a series of bets to maximize the logarithmic growth of capital. - **Purpose:** Balances potential returns against risks, guiding traders on the appropriate position size to take. 5. **Adjusted Kelly Fraction** - **Definition:** The Kelly Ratio adjusted based on user-defined risk tolerance and external factors like Federal Reserve (Fed) stance. - **Purpose:** Personalizes the Kelly Criterion to align with individual risk preferences and market conditions, enhancing risk management. 6. **Position Size ($)** - **Definition:** The calculated amount to invest based on the Adjusted Kelly Fraction. - **Purpose:** Ensures that position sizes are aligned with risk management strategies, preventing overexposure to any single trade. 7. **Max Drawdown (%)** - **Definition:** The maximum observed loss from a peak to a trough of a portfolio, before a new peak is attained. - **Purpose:** Assesses the worst-case scenario for losses, crucial for understanding potential capital erosion. ### Functionality and Benefits - **Weekly Data Aggregation:** Aggregates weekly returns across multiple years to provide a robust statistical foundation for decision-making. - **Quarterly Filtering:** Allows users to filter weeks based on quarters, enabling seasonality analysis and tailored strategies aligned with specific timeframes. - **Dynamic Risk Adjustment:** Incorporates the Dynamic Kelly Criterion to adjust position sizes in real-time based on changing risk profiles and market conditions. - **User-Friendly Visualization:** Presents all essential metrics in an organized Summary Table, facilitating quick and informed decision-making. ## The Origin of the Kelly Criterion and Addressing Its Limitations ### Understanding the Kelly Criterion The Kelly Criterion, developed by John L. Kelly Jr. in 1956, is a formula used to determine the optimal size of a series of bets to maximize the long-term growth of capital. The formula considers both the probability of winning and the payout ratio, balancing potential returns against the risk of loss. **Kelly Formula:** \ Where: - \( b \) = the net odds received on the wager ("b to 1") - \( p \) = probability of winning - \( q \) = probability of losing ( \( q = 1 - p \) ) ### The Risk of Ruin While the Kelly Criterion is effective in optimizing growth, it carries inherent risks: - **Overbetting:** If the input probabilities or payout ratios are misestimated, the Kelly Criterion can suggest overly aggressive position sizes, leading to significant losses. - **Assumption of Constant Probabilities:** The criterion assumes that probabilities remain constant, which is rarely the case in dynamic markets. - **Ignoring External Factors:** Traditional Kelly implementations do not account for external factors such as Federal Reserve rates, margin requirements, or market volatility, which can impact risk and returns. ### Addressing Traditional Limitations Recognizing these limitations, the **Weekly Seasonality with Dynamic Kelly Criterion Indicator** introduces enhancements to the traditional Kelly approach: - **Incorporation of Fed Stance:** Adjusts the Kelly Fraction based on the current stance of the Federal Reserve (neutral, dovish, or hawkish), reflecting broader economic conditions that influence market behavior. - **Margin and Leverage Considerations:** Accounts for margin rates and leverage, ensuring that position sizes remain within manageable risk parameters. - **Dynamic Adjustments:** Continuously updates position sizes based on real-time risk assessments and probabilistic analyses, mitigating the risk of ruin associated with static Kelly implementations. ## How the Indicator Aids Traders ### For Short-Term Swing Traders Short-term swing traders thrive on capitalizing over weekly price movements. The indicator aids them by: - **Identifying Favorable Weeks:** Highlights weeks with above-average returns and favorable volatility, guiding entry and exit points. - **Optimal Position Sizing:** Utilizes the Adjusted Kelly Fraction to determine the optimal amount to invest, balancing potential returns with risk exposure. - **Probabilistic Insights:** Provides metrics like Positive Percentage (%) and Kelly Ratio to assess the likelihood of favorable outcomes, enhancing decision-making. ### For Long-Term Tax-Free Investors This is effectively a drop-in replacement for DCA which uses fixed position size that doesn't change based on market conditions, as a result, it's like catching multiple falling knifes by the blade and smiling with blood on your hand... I don't know about you, but I'd rather juggle by the hilt and look like an actual professional... Long-term investors, especially those seeking tax-free positions (e.g., through retirement accounts), benefit from: - **Consistent Risk Management:** Ensures that position sizes are aligned with long-term capital preservation strategies. - **Seasonality Analysis:** Allows for strategic positioning based on historical performance trends across different weeks and quarters. - **Dynamic Adjustments:** Adapts to changing market conditions, maintaining optimal risk profiles over extended investment horizons. ### Developers Please double check the logic and functionality because I think there are a few issue and I need to crowd source solutions and be responsible about the code I publish. If you have corrections, please DM me or leave a respectful comment. I want to publish this by the end of the year and include other things like highlighting triple witching weeks, adding columns for volume % stats, VaR and CVaR, alpha, beta (to see the seasonal alpha and beta based off a benchmark ticker and risk free rate ticker and other little goodies. //@version=5 indicator("Weekly Seasonality with Dynamic Kelly Criterion", overlay=false, max_labels_count=500) //////////// // Inputs // //////////// // User Inputs int startYearInput = input.int(2015, "Starting Year for Analysis", minval=1900) float capitalInput = input.float(10000, "Available Capital ($)", minval=0) // User-defined inputs float riskToleranceInput = input.float(1.0, "Risk Tolerance (0-1)", minval=0, maxval=1) float maxDrawdownInput = input.float(20.0, "Maximum Drawdown (%)", minval=0) float marginRateInput = input.float(0.5, "Margin Rate (0-1)", minval=0.01, maxval=1) float maintenanceRateInput = input.float(0.25, "Maintenance Rate (0-1)", minval=0, maxval=1) string fedStanceInput = input.string("neutral", "Fed Stance", options= ) // Options to display the results bool showTableInput = input.bool(true, "Show Weekly Summary Table") // Quarter Inputs // Options to filter returns by specific quarters bool q1Input = input.bool(true, "Q1") bool q2Input = input.bool(false, "Q2") bool q3Input = input.bool(false, "Q3") bool q4Input = input.bool(false, "Q4") ///////////////////////// // Function Definitions // ///////////////////////// // Function to calculate adjusted Kelly Fraction based on Kelly Ratio, risk tolerance, and Fed stance calculateAdjustedKellyFraction(float kellyRatio, float riskTolerance, string fedStance) => // Adjust Kelly Fraction based on risk tolerance float adjustedKellyFraction = kellyRatio * riskTolerance // Adjust based on Fed stance float kellyAdjustment = fedStance == "dovish" ? 0.1 : fedStance == "hawkish" ? -0.1 : 0.0 adjustedKellyFraction := adjustedKellyFraction + kellyAdjustment // Ensure Kelly fraction is within bounds adjustedKellyFraction := math.max(math.min(adjustedKellyFraction, 1), -1) adjustedKellyFraction // Custom function to sum over an array sumArray(float arr) => float total = 0.0 for i = 0 to array.size(arr) - 1 total += array.get(arr, i) total // Function to calculate standard deviation for returns calculateStdDev(float returns) => int count = array.size(returns) if count > 1 float avg = sumArray(returns) / count float sumSqDiffs = 0.0 for i = 0 to count - 1 float val = array.get(returns, i) float diff = val - avg sumSqDiffs += diff * diff float variance = sumSqDiffs / (count - 1) math.sqrt(variance) else 0.0 // Function to calculate Kelly Ratio calculateKellyRatio(float returns) => float winCount = 0.0 float lossCount = 0.0 float totalWinReturn = 0.0 float totalLossReturn = 0.0 int n = array.size(returns) for i = 0 to n - 1 float ret = array.get(returns, i) if ret > 0 winCount += 1 totalWinReturn += ret else if ret < 0 lossCount += 1 totalLossReturn += math.abs(ret) float winProb = (winCount + lossCount) > 0 ? winCount / (winCount + lossCount) : 0.0 float lossProb = 1 - winProb float avgWinReturn = winCount > 0 ? totalWinReturn / winCount : 0.0 float avgLossReturn = lossCount > 0 ? totalLossReturn / lossCount : 0.0 float kellyRatio = 0.0 if avgLossReturn != 0 and avgWinReturn != 0 float odds = avgWinReturn / avgLossReturn if odds != 0 kellyRatio := winProb - (lossProb / odds) kellyRatio // Function to calculate Max Drawdown calculateMaxDrawdown(float returns) => if array.size(returns) == 0 0.0 else float peak = array.get(returns, 0) // Updated to use array.get float maxDD = 0.0 float cumulativeReturn = 0.0 for i = 0 to array.size(returns) - 1 cumulativeReturn += array.get(returns, i) // Updated to use array.get if cumulativeReturn > peak peak := cumulativeReturn float drawdown = peak - cumulativeReturn if drawdown > maxDD maxDD := drawdown maxDDPercent = peak != 0 ? (maxDD / peak) * 100 : 0.0 maxDDPercent ///////////////////////////////// // Data Retrieval and Processing // ///////////////////////////////// // Initialize variables var float weekReturnSums = array.new_float(52, 0.0) var int weekReturnCounts = array.new_int(52, 0) var int weekPositiveCounts = array.new_int(52, 0) var float weekReturnSquares = array.new_float(52, 0.0) var float allWeekReturns = array.new_float() var int allWeekNums = array.new_int() // Get weekly data using request.security = request.security(syminfo.tickerid, 'W', ) // Detect new week isNewWeek = ta.change(weeklyTime) if isNewWeek int weekNum = weekofyear(weeklyTime ) // Previous week number int yearNum = year(weeklyTime ) if yearNum >= startYearInput float prevWeeklyClose = weeklyClose float currWeeklyClose = weeklyClose float ret = (currWeeklyClose - prevWeeklyClose) / prevWeeklyClose * 100 // Update the arrays int idx = weekNum - 1 // index in arrays if idx >= 0 and idx < 52 // Sum of returns sum = array.get(weekReturnSums, idx) + ret array.set(weekReturnSums, idx, sum) // Count of returns count = array.get(weekReturnCounts, idx) + 1 array.set(weekReturnCounts, idx, count) // Sum of squared returns sumSq = array.get(weekReturnSquares, idx) + ret * ret array.set(weekReturnSquares, idx, sumSq) // Positive counts posCount = array.get(weekPositiveCounts, idx) if ret > 0 posCount += 1 array.set(weekPositiveCounts, idx, posCount) // Store all returns and week numbers array.push(allWeekReturns, ret) array.push(allWeekNums, weekNum) //////////////// // Visualization // //////////////// if showTableInput and barstate.islast // Prepare data for the table var int selectedWeekNumbers = array.new_int() var float avgReturnsArray = array.new_float() var float stdDevArray = array.new_float() var float posPercentsArray = array.new_float() var float kellyRatiosArray = array.new_float() var float adjustedKellyFractionsArray = array.new_float() var float positionSizesArray = array.new_float() var float maxDrawdownsArray = array.new_float() for idx = 0 to 51 int weekNum = idx + 1 // Check if the week number is in the specified quarters bool isInQuarter = (weekNum >= 1 and weekNum <= 13 and q1Input) or (weekNum >= 14 and weekNum <= 26 and q2Input) or (weekNum >= 27 and weekNum <= 39 and q3Input) or (weekNum >= 40 and weekNum <= 52 and q4Input) if isInQuarter int count = array.get(weekReturnCounts, idx) if count > 0 array.push(selectedWeekNumbers, weekNum) float sum = array.get(weekReturnSums, idx) float avgRet = sum / count array.push(avgReturnsArray, avgRet) float sumSq = array.get(weekReturnSquares, idx) float variance = (sumSq - (sum * sum) / count) / (count - 1) float stdDev = variance > 0 ? math.sqrt(variance) : 0.0 array.push(stdDevArray, stdDev) // Positive percentage int posCount = array.get(weekPositiveCounts, idx) float posPercent = posCount / count * 100 array.push(posPercentsArray, posPercent) // Extract returns for this week float returnsArray = array.new_float() for i = 0 to array.size(allWeekReturns) - 1 if array.get(allWeekNums, i) == weekNum array.push(returnsArray, array.get(allWeekReturns, i)) // Kelly Ratio float kellyRatio = calculateKellyRatio(returnsArray) array.push(kellyRatiosArray, kellyRatio) // Adjusted Kelly Fraction float adjustedKellyFraction = calculateAdjustedKellyFraction(kellyRatio, riskToleranceInput, fedStanceInput) array.push(adjustedKellyFractionsArray, adjustedKellyFraction) // Leverage factor float leverageFactor = marginRateInput > 0 ? 1 / marginRateInput : 1.0 // Max position size float maxPositionSize = capitalInput * leverageFactor // Position size float positionSize = maxPositionSize * adjustedKellyFraction // Ensure position size does not exceed max position size positionSize := math.min(positionSize, maxPositionSize) // Max Drawdown float maxDD = calculateMaxDrawdown(returnsArray) // Adjust position size if max drawdown exceeds maxDrawdownInput if maxDD > maxDrawdownInput and maxDD != 0 positionSize := positionSize * (maxDrawdownInput / maxDD) array.push(positionSizesArray, positionSize) array.push(maxDrawdownsArray, maxDD) // Limit the number of rows to fit within the table size limit int maxRows = 49 // 49 data rows + 1 header row = 50 total rows int totalRows = array.size(selectedWeekNumbers) int numRows = math.min(totalRows, maxRows) int startIndex = 0 // Display from the first selected week // Create table var table returnsTable = table.new(position.top_right, 8, numRows + 1, border_width=1) // Set table headers table.cell(returnsTable, 0, 0, "Week", bgcolor=color.gray, text_color=color.white) table.cell(returnsTable, 1, 0, "Avg Return (%)", bgcolor=color.gray, text_color=color.white) table.cell(returnsTable, 2, 0, "Volatility (%)", bgcolor=color.gray, text_color=color.white) table.cell(returnsTable, 3, 0, "Pos %", bgcolor=color.gray, text_color=color.white) table.cell(returnsTable, 4, 0, "Kelly Ratio", bgcolor=color.gray, text_color=color.white) table.cell(returnsTable, 5, 0, "Adj Kelly Frac", bgcolor=color.gray, text_color=color.white) table.cell(returnsTable, 6, 0, "Position Size ($)", bgcolor=color.gray, text_color=color.white) table.cell(returnsTable, 7, 0, "Max Drawdown (%)", bgcolor=color.gray, text_color=color.white) // Populate table for idx = startIndex to numRows - 1 int row = idx + 1 // Adjust row index for the table int weekNum = array.get(selectedWeekNumbers, idx) float avgRet = array.get(avgReturnsArray, idx) float stdDev = array.get(stdDevArray, idx) float posPercent = array.get(posPercentsArray, idx) float kellyRatio = array.get(kellyRatiosArray, idx) float adjustedKellyFraction = array.get(adjustedKellyFractionsArray, idx) float posSize = array.get(positionSizesArray, idx) float maxDD = array.get(maxDrawdownsArray, idx) // Week Number table.cell(returnsTable, 0, row, str.tostring(weekNum)) // Avg Return (%) table.cell(returnsTable, 1, row, str.format("{0,number,#.##}", avgRet)) // Volatility (%) table.cell(returnsTable, 2, row, str.format("{0,number,#.##}", stdDev)) // Pos % table.cell(returnsTable, 3, row, str.format("{0,number,#.##}", posPercent)) // Kelly Ratio table.cell(returnsTable, 4, row, str.format("{0,number,#.####}", kellyRatio)) // Adj Kelly Frac table.cell(returnsTable, 5, row, str.format("{0,number,#.####}", adjustedKellyFraction)) // Position Size ($) table.cell(returnsTable, 6, row, str.format("{0,number,#.##}", posSize)) // Max Drawdown (%) table.cell(returnsTable, 7, row, str.format("{0,number,#.##}", maxDD))
NASDAQ:TQQQEducation
by livingdracula
Are we seeing a repeat of past situations, or has the market truIf we look at ETFs like QQQ, we can notice price breaks between sessions, let's see where these interruptions occur. First interruption this is a gap of $1.50, which represents an increase of approximately 2.6%, from $59 to $60. Second interruption occurs between $63 and $66, which implies an increase of 3.5%. On the chart, we can also observe a bullish channel that, due to the strength of the buyers, has not been respected, so far we could consider the price to be in a phase where it could take a break or pull back, which would be healthy for $QQQ. We have seen similar behavior in stocks with low market caps, which experience a phenomenon known as "pump and dump", where they rise non stop and then fall, therefore, a break in the price is considered healthy. Continuing with the analysis, in the past, the QQQ has shown patterns that are usually respected, although these can take days, weeks or even months to complete, for example, from July 16 to 17, there was a strong bearish opening that left a gap of $2.50 this pattern was repeated from July 23 to 24, and currently, the price has already completed half of that gap. Based on previous patterns, we can assume that a bullish move in QQQ of approximately 2% is expected, around $1.50 upwards, considering this, it might be prudent to wait for that move to complete to enter a bearish position and take advantage of the pullback, especially since the price is at resistance at $72. normally, when price first hits resistance, it tends to pull back as buyers decide to take profits. The Goldman Sachs Bull/Bear indicator is a cool way to check the market sentiment Currently, it is at 70% levels, indicating bullish sentiment and widespread optimism among investors However, this level can also be a cause for concern, as such a high indicator could signal a possible correction in the market. Although it is not an exact science and it is difficult to predict when a correction will occur, we can analyze historical examples to better understand its behavior November 2007 during this period, there was a real estate boom and a strong economy, a context reflected in the film "The Big Short" at that time, the goldman sachs indicator was close to 70%, and shortly after, the market began to correct, kicking off the 2008 global financial crisis. January 2020 the market was experiencing a continued rally due to optimism around monetary policy and widespread positive sentiment, however in February 2020, a sharp decline began due to the onset of the COVID-19 pandemic. December 1999 the rise of technology companies during the dot-com bubble led to extremely optimistic sentiment in march 2000, the bubble popped, resulting in a prolonged decline in this case, the Goldman Sachs indicator was also close to 70%. In my point of view, the market can rise a little more but it should have a final bullish impulse and be able to begin to retreat, historically september has been one of the weakest months of the year.
NASDAQ:TQQQ
by Mariofxtr
TQQQ and Tech Stock Intraday SetupI noticed today that there was a solid intraday trade opportunity across many tech stocks. It had to be well-timed, but it wasn't insignificant. While the large sell-off in the news is true, the data was a bit lagged in terms of a "today" trading opportunity that presented itself this morning on AMZN, TSLA, TQQQ, and several others for at least 1-2% upside with a reasonable risk/reward of 1:2. I wonder if anyone other than myself took advantage of this setup today?
NASDAQ:TQQQLong
by XchangeSignals
Opening (IRA): TQQQ August 16th 71 Covered Call... for a 68.76 debit. Comments: Buying stock and selling a -75 delta call against to emulate the delta metrics of a 25 delta short put with the built-in defense of the short call. As usual, this only makes sense in a cash secured environment where you don't get much BP relief going with the short put over the covered call; on margin -- stick with the short put. Metrics: Buying Power Effect/Break Even: 68.76/share Max Profit: 2.24/contract ROC at Max: 3.26% 50% Max: 1.12/contract ROC at 50% Max: 1.63% Will generally look to take profit at 50%, roll out the short call on side or break even test. (It's entirely possible that I will regret this trade later).
NASDAQ:TQQQLong
by NaughtyPines
1