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BOLLINGER BANDS.

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MARKET INSIGHTS!

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28,120 次观看 • 7 个月前 •via X (Twitter)

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🚨 A HUGE BITCOIN MOVE COULD HAPPEN AT ANY MOMENT. The $BTC Bollinger Bands are extremely tight. The Bollinger Band Width has dropped to one of its lowest levels. Similar readings previously marked the start of massive Bitcoin moves. Volatility is coming. Be prepared.
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HOW I USE BOLLINGER BANDS Bollinger Bands are one of my main indicators for intraday and daily charts. I do not use them as exact price targets or levels but as a tool to measure overextension, mean reversion, and volatility. They help me spot stretched moves, identify capitulation opportunities, and gauge periods of low volatility, giving a clearer picture of price action and market context. Full video @
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Jane Street was just accused of crashing Luna on purpose in 2022. Now in 2026, the same pattern seems to be playing out. Retail is selling at record losses, while whales are buying everything. The Bitcoin Bollinger Bands have never been this compressed. PMI expansion hasn't even begun this cycle. The real bull market is still ahead.
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$META pullback expected! Meta is in a bullish trading cycle and has reached the 161.8% Fib extension level based on the Oct 2022 lows and Feb 2024 consolidation pattern. This is in tandem with 2 standard deviations as depicted by Bollinger Bands. This continuous pattern suggests a pullback to the 13-weekly (currently $641, this is a moving target, additional closes above this level will raise support) over the next 4-6 weeks. 📈Resistance: $740 (161.8%) This is a natural stopping point. I expect consolidation prior to a move higher. This is confirmed with a trend follow through in RSI 📉Support: $641 (13-weekly moving average) if this breaks, the bullish trading cycle will be at risk

Jessica Inskip

22,736 次观看 • 1 年前

There's a famous technical study called Bollinger Bands. It covers trend, momentum, and volatility all in one, because that band automatically adjusts itself." "Out of 750 stocks, I only want stocks closing beyond 3.7 standard deviations. Normal setting is 2 standard deviations, that already covers 95% of all price points. At 3.7, you're going up to 99%. You're only picking that top 0.5-1% of stocks." "Why 3.7? I used this in prop trading first, then rolled it out on smallcase in 2023. If the price closes beyond 3.7 standard deviations on a Friday, you buy at Monday's opening price. That's it." In an interaction with Sanjay Kathuria Rakesh Pujara (Founder, Compounding Wealth Advisors LLP) has revealed complete strategy with detailed rules Do watch : 👇👇 Watch Full Podcast : Discl: Strategies/stocks discussed are not a recommendation. Please consult a SEBI/SEC regd. advisor.
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There's a famous technical study called Bollinger Bands. It covers trend, momentum, and volatility all in one, because that band automatically adjusts itself." "Out of 750 stocks, I only want stocks closing beyond 3.7 standard deviations. Normal setting is 2 standard deviations, that already covers 95% of all price points. At 3.7, you're going up to 99%. You're only picking that top 0.5-1% of stocks." "Why 3.7? I used this in prop trading first, then rolled it out on smallcase in 2023. If the price closes beyond 3.7 standard deviations on a Friday, you buy at Monday's opening price. That's it." In an interaction with Sanjay Kathuria Rakesh Pujara (Founder, Compounding Wealth Advisors LLP) has revealed complete strategy with detailed rules Do watch : 👇👇 Watch Full Podcast : Discl: Strategies/stocks discussed are not a recommendation. Please consult a SEBI/SEC regd. advisor.

Rakesh Pujara

219,581 次观看 • 1 个月前

claude shannon was trying to fix phone calls when he accidentally solved market prediction bell labs published the paper in 1948. two sigma found it 50 years later, built a $60 billion fund around it, never mentioned it to retail the idea is almost insultingly simple: every price series is signal buried in noise shannon proved you can measure *how much signal exists* at any moment - not predict direction, just quantify structure vs randomness the formula is entropy: H = -sum(p * log2(p)) run it on rolling return distributions. outputs a number between 0 and 1 H above 0.7 = noise. random walk. stay out H below 0.35 = structure detected. something non-random is happening in this market right now that's the filter. not RSI, not bollinger bands - entropy backtested SPY, 2003-2023: > entries in low-entropy windows: 61% win rate > same entries in high-entropy windows: 49% one formula, 12% win rate difference, zero change to the underlying signal shannon published this math 77 years ago. it's chapter 2 of every information theory course on the planet data to build it is free. the python is 40 lines Bookmark this before you scroll past they spent decades keeping you staring at lagging indicators while they were measuring the information content of the exact same price data you've always had
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claude shannon was trying to fix phone calls when he accidentally solved market prediction bell labs published the paper in 1948. two sigma found it 50 years later, built a $60 billion fund around it, never mentioned it to retail the idea is almost insultingly simple: every price series is signal buried in noise shannon proved you can measure *how much signal exists* at any moment - not predict direction, just quantify structure vs randomness the formula is entropy: H = -sum(p * log2(p)) run it on rolling return distributions. outputs a number between 0 and 1 H above 0.7 = noise. random walk. stay out H below 0.35 = structure detected. something non-random is happening in this market right now that's the filter. not RSI, not bollinger bands - entropy backtested SPY, 2003-2023: > entries in low-entropy windows: 61% win rate > same entries in high-entropy windows: 49% one formula, 12% win rate difference, zero change to the underlying signal shannon published this math 77 years ago. it's chapter 2 of every information theory course on the planet data to build it is free. the python is 40 lines Bookmark this before you scroll past they spent decades keeping you staring at lagging indicators while they were measuring the information content of the exact same price data you've always had

Livsun

111,839 次观看 • 1 个月前

the entire math roadmap for quant trading fits on a single page and this video breaks it down in 60 seconds probability. linear algebra. calculus. statistics four subjects. every quant strategy ever built sits on top of these four probability comes first because everything else depends on it conditional probability, Bayes' theorem, expected value this is the actual language trading models are written in linear algebra is how you stop thinking about one stock and start thinking about 5,000 at once factor models, PCA, portfolio optimization Bridgewater, AQR, Two Sigma all run on matrix math calculus is derivatives pricing. Black-Scholes is a PDE if you trade options without understanding the equation underneath them, you're reading the answer key without knowing the question statistics is the filter regression, hypothesis testing, distribution fitting this is how Renaissance decides if a signal is real or if a backtest got lucky here's what nobody tells retail: every indicator you've ever used is a dumbed-down version of one of these four subjects RSI is momentum statistics. Bollinger Bands are standard deviation. MACD is signal processing they took the math, removed the part that makes it useful, and sold you the wrapper > this roadmap: free, 60 seconds, in this video > same four subjects tested at every quant interview since the 1990s > time to learn properly: 6-8 months with free resources > what they unlock: the actual language Wall Street runs on the information was never gated it was just never packaged for the people who needed it most full breakdown in the video below
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the entire math roadmap for quant trading fits on a single page and this video breaks it down in 60 seconds probability. linear algebra. calculus. statistics four subjects. every quant strategy ever built sits on top of these four probability comes first because everything else depends on it conditional probability, Bayes' theorem, expected value this is the actual language trading models are written in linear algebra is how you stop thinking about one stock and start thinking about 5,000 at once factor models, PCA, portfolio optimization Bridgewater, AQR, Two Sigma all run on matrix math calculus is derivatives pricing. Black-Scholes is a PDE if you trade options without understanding the equation underneath them, you're reading the answer key without knowing the question statistics is the filter regression, hypothesis testing, distribution fitting this is how Renaissance decides if a signal is real or if a backtest got lucky here's what nobody tells retail: every indicator you've ever used is a dumbed-down version of one of these four subjects RSI is momentum statistics. Bollinger Bands are standard deviation. MACD is signal processing they took the math, removed the part that makes it useful, and sold you the wrapper > this roadmap: free, 60 seconds, in this video > same four subjects tested at every quant interview since the 1990s > time to learn properly: 6-8 months with free resources > what they unlock: the actual language Wall Street runs on the information was never gated it was just never packaged for the people who needed it most full breakdown in the video below

delost

34,884 次观看 • 3 个月前