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#TAOUSDT #4h (ByBit) Ascending trendline near breakdownBittensor printed an evening doji star deviation, a retracement down to 200 MA support seems next. ⚡️⚡️ #TAO/USDT ⚡️⚡️ Exchanges: ByBit USDT Signal Type: Regular (Short) Leverage: Isolated (5.0X) Amount: 5.0% Entry Targets: 1) 430.38 Take-Profit Targets: 1) 362.01 Stop Targets: 1) 464.64 Published By: @Zblaba GETTEX:TAO BYBIT:TAOUSDT.P #4h #Bittensor #AI #DePIN bittensor.com Risk/Reward= 1:2.0 Expected Profit= +79.4% Possible Loss= -39.8% Estimated Gaintime= 1 week
Short
by TradingOn
1
LCTITAN - The LOW is getting HIGHER !LCTITAN - CURRENT PRICE : RM0.470 The stock made a HIGHER LOW recently - this may consider as a half portion of new uptrend is completed. A breakout above RM0.630 will form the higher high structure - which confirm the new uptrend phase. For short term trading purposes, short term traders may anticipate to buy if the stock breakout nearest resistance of RM0.520 - which will give an entry point at RM0.525. So this is a pending breakout stock. Nearest target will be RM0.580 and RM0.620. Take note that when the price breakout RM0.520 , it will also be above EMA 50 and ICHIMOKU CLOUD - which strengthening the bullish outlook. ENTRY PRICE : RM0.525 TARGET : RM0.580 and RM0.620 SUPPORT : EMA 50 Notes : The higher low structure of LCTITAN looks like DRBHCOM - I share the link here for reading purposes.
MYX:LCTITANLong
by IMRAN_96
Pi Network Faces Scrutiny as Market Cap Hits $3.4BPi Network, the mobile-first crypto project that attracted millions of users with its "mine-on-your-phone" concept, is facing growing scrutiny as its market capitalization has surged to $3.4 billion, despite lingering concerns over its actual utility and use cases. As hype continues to propel its valuation, the community and broader crypto market are demanding answers: What can Pi really do? Background and Growth Launched in 2019 by a team of Stanford graduates, Pi Network aimed to democratize cryptocurrency mining by enabling users to earn tokens on their smartphones without expensive hardware. Through aggressive referral-based marketing and a user-friendly interface, it reportedly attracted over 50 million users globally. However, after years in beta and a still-limited mainnet release, the Pi token is not yet fully tradable on major exchanges, and its price remains speculative. Despite that, Pi's market cap is estimated at $3.4 billion based on limited OTC (over-the-counter) trading and projected supply metrics. Utility Concerns Intensify With a large user base and growing token valuation, the pressure is on for Pi Network to deliver tangible value. Key concerns include: Lack of utility – Most users cannot use Pi to pay for goods or services in real-world scenarios. No open mainnet – While a “closed mainnet” has launched, the network still lacks full public blockchain access, hampering transparency and developer participation. Centralization – Pi’s governance and decision-making remain opaque, raising questions about decentralization and long-term sustainability. No listings on major exchanges – Without real-time market discovery, token valuation remains speculative and potentially misleading. Community Reaction Many early adopters continue to support Pi, viewing it as a long-term project that needs time to mature. Some local communities even organize Pi-based bartering systems. However, critics argue that without real-world use cases, Pi remains a glorified points system rather than a functional cryptocurrency. The disconnect between market cap and utility has fueled concerns of a speculative bubble. In fact, some compare Pi to past projects like BitConnect, which promised much but delivered little. The Road Ahead To maintain its momentum and justify its valuation, Pi Network must: Open its mainnet to public interaction and developer contributions. Secure listings on reputable exchanges to enable transparent price discovery. Establish real-world utility, such as integration with e-commerce, payments, or DeFi applications. Improve transparency around tokenomics, governance, and long-term roadmap. Failure to address these areas could result in an erosion of community trust and capital flight to more proven projects. Conclusion Pi Network’s $3.4 billion market cap is both impressive and problematic. It highlights the power of community-driven growth but also underscores the risks of hype-driven valuations. As the crypto market matures, utility—not just marketing—is what will determine long-term success. For Pi, the clock is ticking to move from potential to performance.
ELong
by CryptoLifeone
Jack in the Box | JACK | Long at $18.48Jack in the Box NASDAQ:JACK has taken a massive hit to its stock price since its peak in 2024 at just over $124 a share. It's currently trading around $18 and has entered my "crash" simple moving average zone. More often than not, this area signifies a bottom (or future bounce), but I view it more as a consolidation area to accumulate shares. Float = 18M; short interest = 19%... Looking at NASDAQ:JACK fundamentally, this isn't the healthiest of restaurant companies. It is using a high level of debt to finance its operations and a high dividend yield of 9.28%. The company's revenue and profits have been slowly declining since 2023, as well. However, after 2025, the company anticipates a slow turnaround to begin. It will be closing 80-120 restaurants across the U.S. in 2025, which is a positive to help the company moving forward. NASDAQ:JACK also just got a new CFO and they are (at least from an outsider's view) attempting to change to generate share value. At this share price, I believe the company is in dire straits to get some investor confidence back. It's a strong name with long history. While the stock price may hit true resistance at just under $17, NASDAQ:JACK is in a personal buy zone at $18.48. Targets are set low due to economic uncertainty. Targets: $23.00 (+24.5%) $25.00 (+35.3%)
NASDAQ:JACKLong
by WorthlessViews
7
Natwest Group Quote | Chart & Forecast SummaryKey Indicators On Trade Set Up In General 1. Push Set Up 2. Range Set Up 3. Break & Retest Set Up Notes On Session # Natwest Group Quote - Double Formation * (Diagonal Shift)) - *A+ | Completed Survey * (2nd Entry Area)) | Subdivision 1 - Triple Formation * (P1)) / (P2)) & (P3)) | Subdivision 2 * (TP1) = a / Long Consecutive Range * (TP2) = b / Short Consecutive Pullback | Subdivision 3 * Daily Time Frame | Trend Settings Condition - (Hypothesis On Entry Bias)) | Indexed To 100 - Position On A 1.5RR * Stop Loss At 150.00 GBP * Entry At 156.00 GBP * Take Profit At 167.00 GBP * (Uptrend Argument)) & No Pattern Confirmation - Continuation Pattern | Not Valid - Reversal Pattern | Not Valid * Ongoing Entry & (Neutral Area)) Active Sessions On Relevant Range & Elemented Probabilities; European-Session(Upwards) - East Coast-Session(Downwards) - Asian-Session(Ranging) Conclusion | Trade Plan Execution & Risk Management On Demand; Overall Consensus | Buy
PEPPERSTONE:NWG.GBLong
by TradePolitics
Delta Airlines: Where’s the Pullback?Delta Airlines rallied two weeks ago on strong earnings, and some traders may see further upside. The first pattern on today’s chart is the tight consolidation pattern since July 10. The lack of pullback could reflect a lack of selling pressure in the transport stock. Second, DAL has remained above the March 6 closing price of $54.96 and its 200-day simple moving average. Has new support been established above the pre-earnings highs? Third, the 8-day exponential moving average (EMA) is above the 21-day EMA. MACD is also rising. Those signals may be consistent with short-term bullishness. TradeStation has, for decades, advanced the trading industry, providing access to stocks, options and futures. If you're born to trade, we could be for you. See our Overview for more. Past performance, whether actual or indicated by historical tests of strategies, is no guarantee of future performance or success. There is a possibility that you may sustain a loss equal to or greater than your entire investment regardless of which asset class you trade (equities, options or futures); therefore, you should not invest or risk money that you cannot afford to lose. Online trading is not suitable for all investors. View the document titled Characteristics and Risks of Standardized Options at www.TradeStation.com . Before trading any asset class, customers must read the relevant risk disclosure statements on www.TradeStation.com . System access and trade placement and execution may be delayed or fail due to market volatility and volume, quote delays, system and software errors, Internet traffic, outages and other factors. Securities and futures trading is offered to self-directed customers by TradeStation Securities, Inc., a broker-dealer registered with the Securities and Exchange Commission and a futures commission merchant licensed with the Commodity Futures Trading Commission). TradeStation Securities is a member of the Financial Industry Regulatory Authority, the National Futures Association, and a number of exchanges. TradeStation Securities, Inc. and TradeStation Technologies, Inc. are each wholly owned subsidiaries of TradeStation Group, Inc., both operating, and providing products and services, under the TradeStation brand and trademark. When applying for, or purchasing, accounts, subscriptions, products and services, it is important that you know which company you will be dealing with. Visit www.TradeStation.com for further important information explaining what this means.
NYSE:DAL
by TradeStation
Krispy Kreme | DNUT | Long at $3.04Krispy Kreme NASDAQ:DNUT has entered and slightly exited my simple moving average "crash" indicator. More often than not, this zone (currently between $1.88 and $2.88) is a bottom indicator. However, this means there is a still a probability that stock could drop to a value less than $2 in the near future (and I wouldn't be surprised if it gets there). But predicting a "true" bottom is a fool's game. Building a position using data-driven decisions is the best strategy. Fundamentally, NASDAQ:DNUT has many economic headwinds and a relatively high probably of bankruptcy in the next 24 months. It brought in $1.6 billion in revenue in 2024 and is a great target for takeover, but that requires a crystal ball. Earnings are anticipated to become positive by 2026, but growth is slow. The dividend yield, currently around 4.8%, may get cut in the near-term to help the company financially (again, crystal ball needed). Short interest is 30% with a float of 78 million... Debt to Equity = 0.90x. Essentially, if it can get through 2025, the company predicts it can turn things around into 2026 and beyond. So, is this a good investment? Fundamentally, many better options are out there. From a technical analysis perspective, a potential opportunity. Thus, a gamble. I'm not betting the farm here, but at $3.04, NASDAQ:DNUT is in a buy zone - with a warning for potentially better entries to emerge in the near future in the $1-$2 range. Targets: $3.60 $4.20
NASDAQ:DNUTLong
by WorthlessViews
GoHealth | GOCO | Long at $6.05GoHealth NASDAQ:GOCO is a health insurance marketplace and Medicare-focused digital health company that uses a technology platform with machine-learning algorithms to match consumers with Medicare plans (Advantage, Supplement, Part D) and individual health insurance. Understandably, a lot of investors aren't bullish on this stock given all of the healthcare provider and services headwinds. However, if the company can overcome some of their financial issues and bankruptcy risk (debt-to-equity: 1.6x; quick ration of 1.1x, Altman's Z score of .3x), it may dominate the health insurance marketplace (but do not hold my word to that...). This is a purely speculative play at this point - those who are risk averse should absolutely stay away. What truly caught my eye with this stock is that it is consolidating nicely within my historical simple moving average area. Often, but not always, this leads to a future change in momentum and propels the stock higher. It doesn't signal a bottom and there may be more room for it to plummet, but it is a bullish (overall) sign that shares are likely being accumulated by investors. Given the need for health insurance, particularly Medicare as the US / baby boom population ages, this is a company that may prosper IF it can get its financials in order. Thus, at $6.05, NASDAQ:GOCO is in a personal buy zone (but very risky). Further declines may be ahead before a stronger move up. Targets into 2028: $10.00 (+64.5%) $12.00 (+97.4%)
NASDAQ:GOCOLong
by WorthlessViews
The Empirical Validity of Technical Indicators and StrategiesThis article critically examines the empirical evidence concerning the effectiveness of technical indicators and trading strategies. While traditional finance theory, notably the Efficient Market Hypothesis (EMH), has long argued that technical analysis should be futile, a large body of academic research both historical and contemporary presents a more nuanced view. We explore key findings, address methodological limitations, assess institutional use cases, and discuss the impact of transaction costs, market efficiency, and adaptive behavior in financial markets. 1. Introduction Technical analysis (TA) remains one of the most controversial subjects in financial economics. Defined as the study of past market prices and volumes to forecast future price movements, TA is used by a wide spectrum of market participants, from individual retail traders to institutional investors. According to the EMH (Fama, 1970), asset prices reflect all available information, and hence, any predictable pattern should be arbitraged away instantly. Nonetheless, technical analysis remains in widespread use, and empirical evidence suggests that it may offer predictive value under certain conditions. 2. Early Empirical Evidence The foundational work by Brock, Lakonishok, and LeBaron (1992) demonstrated that simple trading rules such as moving average crossovers could yield statistically significant profits using historical DJIA data spanning from 1897 to 1986. Importantly, the authors employed bootstrapping methods to validate their findings against the null of no serial correlation, thus countering the argument of data mining. Gencay (1998) employed non-linear models to analyze the forecasting power of technical rules and confirmed that short-term predictive signals exist, particularly in high-frequency data. However, these early works often omitted transaction costs, thus overestimating potential returns. 3. Momentum and Mean Reversion Strategies Momentum strategies, as formalized by Jegadeesh and Titman (1993), have shown persistent profitability across time and geographies. Their approach—buying stocks that have outperformed in the past 3–12 months and shorting underperformers—challenges the EMH by exploiting behavioral biases and investor herding. Rouwenhorst (1998) confirmed that momentum exists even in emerging markets, suggesting a global phenomenon. Conversely, mean reversion strategies, including RSI-based systems and Bollinger Bands, often exploit temporary price dislocations. Short-horizon contrarian strategies have been analyzed by Chan et al. (1996), but their profitability is inconsistent and highly sensitive to costs, timing, and liquidity. 4. Institutional Use of Technical Analysis Contrary to the belief that TA is primarily a retail tool, it is also utilized—though selectively—by institutional investors: Hedge Funds: Many quantitative hedge funds incorporate technical indicators within multi-factor models or machine learning algorithms. According to research by Neely et al. (2014), trend-following strategies remain a staple among CTAs (Commodity Trading Advisors), particularly in futures markets. These strategies often rely on moving averages, breakout signals, and momentum filters. Market Makers: Although market makers are primarily driven by order flow and arbitrage opportunities, they may use TA to model liquidity zones and anticipate stop-hunting behavior. Order book analytics and technical levels (e.g., pivot points, Fibonacci retracements) can inform automated liquidity provision. Pension Funds and Asset Managers: While these institutions rarely rely on TA alone, they may use it as part of tactical asset allocation. For instance, TA may serve as a signal overlay in timing equity exposure or in identifying risk-off regimes. According to a CFA Institute survey (2016), over 20% of institutional investors incorporate some form of technical analysis in their decision-making process. 5. Adaptive Markets and Conditional Validity Lo (2004) introduced the Adaptive Markets Hypothesis (AMH), arguing that market efficiency is not a binary state but evolves with the learning behavior of market participants. In this framework, technical strategies may work intermittently, depending on the ecological dynamics of the market. Neely, Weller, and Ulrich (2009) found technical rules in the FX market to be periodically profitable, especially during central bank interventions or volatility spikes—conditions under which behavioral biases and structural inefficiencies tend to rise. More recent studies (e.g., Moskowitz et al., 2012; Baltas & Kosowski, 2020) show that momentum and trend-following strategies continue to deliver long-term Sharpe ratios above 1 in diversified portfolios, particularly when combined with risk-adjusted scaling techniques. 6. The Role of Transaction Costs Transaction costs represent a critical variable that substantially alters the net profitability of technical strategies. These include: Explicit Costs: Commissions, fees, and spreads. Implicit Costs: Market impact, slippage, and opportunity cost. While early studies often neglected these elements, modern research integrates them through realistic backtesting frameworks. For example, De Prado (2018) emphasizes that naive backtesting without cost modeling and slippage assumptions leads to a high incidence of false positives. Baltas and Kosowski (2020) show that even after accounting for bid-ask spreads and market impact models, trend-following strategies remain profitable, particularly in futures and FX markets where costs are lower. Conversely, high-frequency mean-reversion strategies often become unprofitable once these frictions are accounted for. The impact of transaction costs also differs by asset class: Equities: Higher costs due to wider spreads, especially in small caps. Futures: Lower costs and higher leverage make them more suitable for technical strategies. FX: Extremely low spreads, but high competition and adverse selection risks. 7. Meta-Analyses and Recent Surveys Park and Irwin’s (2007) meta-analysis of 95 studies found that 56% reported significant profitability from technical analysis. However, profitability rates dropped when transaction costs were included. More recent work by Han, Yang, and Zhou (2021) extended this review with data up to 2020 and found that profitability was regime-dependent: TA performed better in volatile or trending environments and worse in stable, low-volatility markets. Other contributions include behavioral explanations. Barberis and Thaler (2003) suggest that TA may capture collective investor behavior, such as overreaction and underreaction, thereby acting as a proxy for sentiment. 8. Limitations and Challenges Several methodological issues plague empirical research in technical analysis: Overfitting: Using too many parameters increases the likelihood of in-sample success but out-of-sample failure. Survivorship Bias: Excluding delisted or bankrupt stocks leads to inflated backtest performance. Look-Ahead Bias: Using information not available at the time of trade leads to unrealistic results. Robust strategy development now mandates walk-forward testing, Monte Carlo simulations, and realistic assumptions on order execution. The growing field of machine learning in finance has heightened these risks, as complex models are more prone to fitting noise rather than signal (Bailey et al., 2014). 9. Conclusion Technical analysis occupies a contested but persistent role in finance. The empirical evidence is mixed but suggests that technical strategies can be profitable under certain market conditions and when costs are minimized. Institutional investors have increasingly integrated TA within quantitative and hybrid frameworks, reflecting its conditional usefulness. While TA does not provide a universal arbitrage opportunity, it can serve as a valuable tool when applied adaptively, with sound risk management and rigorous testing. Its success ultimately depends on context, execution discipline, and integration within a broader investment philosophy. References Bailey, D. H., Borwein, J. M., Lopez de Prado, M., & Zhu, Q. J. (2014). "The Probability of Backtest Overfitting." *Journal of Computational Finance*, 20(4), 39–69. Baltas, N., & Kosowski, R. (2020). "Trend-Following, Risk-Parity and the Influence of Correlations." *Journal of Financial Economics*, 138(2), 349–368. Barberis, N., & Thaler, R. (2003). "A Survey of Behavioral Finance." *Handbook of the Economics of Finance*, 1, 1053–1128. Brock, W., Lakonishok, J., & LeBaron, B. (1992). "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns." Journal of Finance, 47(5), 1731–1764. Chan, L. K. C., Jegadeesh, N., & Lakonishok, J. (1996). "Momentum Strategies." Journal of Finance, 51(5), 1681–1713. De Prado, M. L. (2018). Advances in Financial Machine Learning, Wiley. Fama, E. F. (1970). "Efficient Capital Markets: A Review of Theory and Empirical Work." Journal of Finance, 25(2), 383–417. Gencay, R. (1998). "The Predictability of Security Returns with Simple Technical Trading Rules." Journal of Empirical Finance, 5(4), 347–359. Han, Y., Yang, K., & Zhou, G. (2021). "Technical Analysis in the Era of Big Data." *Review of Financial Studies*, 34(9), 4354–4397. Jegadeesh, N., & Titman, S. (1993). "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency." *Journal of Finance*, 48(1), 65–91. Lo, A. W. (2004). "The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective." *Journal of Portfolio Management*, 30(5), 15–29. Moskowitz, T. J., Ooi, Y. H., & Pedersen, L. H. (2012). "Time Series Momentum." *Journal of Financial Economics*, 104(2), 228–250. Neely, C. J., Weller, P. A., & Ulrich, J. M. (2009). "The Adaptive Markets Hypothesis: Evidence from the Foreign Exchange Market." *Journal of Financial and Quantitative Analysis*, 44(2), 467–488. Neely, C. J., Rapach, D. E., Tu, J., & Zhou, G. (2014). "Forecasting the Equity Risk Premium: The Role of Technical Indicators." *Management Science*, 60(7), 1772–1791. Park, C. H., & Irwin, S. H. (2007). "What Do We Know About the Profitability of Technical Analysis?" *Journal of Economic Surveys*, 21(4), 786–826. Rouwenhorst, K. G. (1998). "International Momentum Strategies." *Journal of Finance*, 53(1), 267–284. Zhu, Y., & Zhou, G. (2009). "Technical Analysis: An Asset Allocation Perspective on the Use of Moving Averages." *Journal of Financial Economics*, 92(3), 519–544.
SP:SPXEducation
by EdgeTools
Molina Healthcare | MOH | Long at $181.69Healthcare providers and services are at a major discount right now: and may be discounted even more this year. I am personally buying and long-term holding the fear, knowing the baby boom generation is going to utilize our healthcare system at a rate unseen in modern times. While the price discounts are valid "right now" given the current political administration's cuts, long-term it is far from valid... The strategy I am using with healthcare stocks ( NYSE:MOH , NYSE:CNC , NYSE:UNH , NYSE:ELV , etc) is cost averaging: not buying one single large position in an effort to predict bottom but buying smaller positions over time to create a cost average "near" bottom. If you are a day trader or want a quick swing in healthcare, I don't think it's going to happen for a bit. But those not entering in the coming months / year will likely miss out on a very large healthcare boom - especially when AI truly enters the picture in this sector... Fundamentally, Molina Healthcare NYSE:MOH is a very strong company. Low debt-to-equity (.9x), P/E of 8.8x, quick ratio of 1.7x, $41 billion in revenue in 2024. Yes, there will be issues in the near-term due to Medicaid and other funding cuts. But long-term, this sector is primed to benefit from an aging population. So, while NYSE:MOH is in a personal buy zone at $181.69, I don't think this is necessarily bottom. I anticipate this stock to drop even further, eventually closing the daily price gap at $135.00. My next buys are in the $150's and $130's, thus cost averaging into a larger position. For true value investors, those prices and anything below is a steal. Today's negative healthcare sector noise is loud, but it does not represent the future. Targets into 2028: $226.00 (+24.3%) $290.00 (+59.6%)
NYSE:MOHLong
by WorthlessViews
Ford Motor Pulls BackFord Motor began the month with a rally, and now it’s pulled back. The first pattern on today’s chart is the July 1 surge following a strong monthly sales report. That may reflect healthy fundamentals. Second is $11.27, a weekly close from last August. The automaker is back around that level, which could mean old resistance is becoming new support. Third, the 50-day simple moving average (SMA) had a “golden cross” above the 200-day SMA in late June. That may suggest F’s long-term trend has gotten more bullish. Next, the stock recently neared a bearish gap from July 2024. Could prices fill that gap? Finally, F is an active underlier in the options market. (It averaged about 90,000 contracts per session in the last month, according to TradeStation data.) That could help investors take positions with calls and puts. TradeStation has, for decades, advanced the trading industry, providing access to stocks, options and futures. If you're born to trade, we could be for you. See our Overview for more. Past performance, whether actual or indicated by historical tests of strategies, is no guarantee of future performance or success. There is a possibility that you may sustain a loss equal to or greater than your entire investment regardless of which asset class you trade (equities, options or futures); therefore, you should not invest or risk money that you cannot afford to lose. Online trading is not suitable for all investors. View the document titled Characteristics and Risks of Standardized Options at www.TradeStation.com . Before trading any asset class, customers must read the relevant risk disclosure statements on www.TradeStation.com . System access and trade placement and execution may be delayed or fail due to market volatility and volume, quote delays, system and software errors, Internet traffic, outages and other factors. Securities and futures trading is offered to self-directed customers by TradeStation Securities, Inc., a broker-dealer registered with the Securities and Exchange Commission and a futures commission merchant licensed with the Commodity Futures Trading Commission). TradeStation Securities is a member of the Financial Industry Regulatory Authority, the National Futures Association, and a number of exchanges. Options trading is not suitable for all investors. Your TradeStation Securities’ account application to trade options will be considered and approved or disapproved based on all relevant factors, including your trading experience. See www.TradeStation.com . Visit www.TradeStation.com for full details on the costs and fees associated with options. Margin trading involves risks, and it is important that you fully understand those risks before trading on margin. The Margin Disclosure Statement outlines many of those risks, including that you can lose more funds than you deposit in your margin account; your brokerage firm can force the sale of securities in your account; your brokerage firm can sell your securities without contacting you; and you are not entitled to an extension of time on a margin call. Review the Margin Disclosure Statement at www.TradeStation.com . TradeStation Securities, Inc. and TradeStation Technologies, Inc. are each wholly owned subsidiaries of TradeStation Group, Inc., both operating, and providing products and services, under the TradeStation brand and trademark. When applying for, or purchasing, accounts, subscriptions, products and services, it is important that you know which company you will be dealing with. Visit www.TradeStation.com for further important information explaining what this means.
NYSE:F
by TradeStation
#PENGUUSDT #2h (ByBit) Broadening wedge breakdownPudgy Penguins printed an evening star then lost 50MA, seems to be heading towards 200MA support next. ⚡️⚡️ #PENGU/USDT ⚡️⚡️ Exchanges: ByBit USDT Signal Type: Regular (Short) Leverage: Isolated (2.5X) Amount: 5.0% Entry Targets: 1) 0.030830 Take-Profit Targets: 1) 0.021034 Stop Targets: 1) 0.035740 Published By: @Zblaba CSECY:PENGU BYBIT:PENGUUSDT.P #4h #PludgyPenguins #Meme pudgypenguins.com Risk/Reward= 1:2.0 Expected Profit= +79.4% Possible Loss= -39.8%
Short
by TradingOn
1