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Gaussianfilter

Bullrun📈 : Gaussian Channels + HalvingGaussian Channels: Defining Price Ranges Gaussian Channels, a technical analysis tool, provide traders with a visual representation of price movement within specific boundaries. These channels help us understand Bitcoin's typical price oscillations and set the stage for potential breakout points. The Halving Effect: Supply Reduction and Bullish Sentiment Bitcoin halving events occur approximately every four years, reducing the rewards for miners by half. Halving results in a decrease in the rate at which new Bitcoin is created, promoting scarcity and potentially driving up its value. Historically, each halving has been followed by a bullish market, indicating the correlation between these events and price surges. Breaking Bearish Structures: Shifting Market Sentiment Observing the recent break of bearish patterns is a positive sign. When a market transitions from bearish to neutral or bullish, it opens up new opportunities for traders and investors. Combined Signals: Predicting the Next Bull Run The combined signals from Gaussian Channels, Bitcoin's halving history, and a shift in bearish structures suggest that the next bull run might be approaching. Traders and investors can use these indicators to make informed decisions, prepare for potential volatility, and position themselves strategically in the market. Risk Management: A Crucial Aspect As always, risk management remains a vital component of trading and investing. The crypto market is known for its unpredictability, so it's essential to have a well-thought-out strategy and use appropriate risk mitigation techniques. Conclusion: Navigating Bitcoin's Journey The confluence of Gaussian Channels, Bitcoin's halving cycle, and the shift in bearish patterns paints an intriguing picture of what might lie ahead for the cryptocurrency. While no prediction is foolproof in the world of crypto, these indicators provide a roadmap for traders and investors. As we look forward to the potential onset of a new bull run, remember that staying informed, adapting your strategy, and managing risk are the keys to success in the ever-evolving crypto landscape. 🚀📊🌐 ❗See related ideas below❗ Don't forget to like, share, and leave your thoughts in the comments! 💚🚀💚
INDEX:BTCUSDLong
by Yaroslav_Krasko
2
Why A Cascading SMA Approximate A Gaussian Filter ?Introduction The gaussian filter don't see many uses in technical analysis and financial data smoothing in general, however it possess really interesting properties and a really close relationship with the simple moving average. The gaussian filter is a filter which possess a function approximately gaussian (bell shaped curve) as : impulse response, step response and frequency response. This characteristic is pretty cool actually, the gaussian function is always mysterious. Now why do I talk about sma and estimation ? Well it is true, you can estimate a gaussian filter by applying an sma to another sma and so on such as : sma(...sma()) But why ? Just why is that so ? Well there are a lot of explanations, some of them involving the central limit theorem which would lead to a statistical explanation but I'll give a simpler explanation of this case by using signal processing. Understanding Impulses Responses The impulse response of a filter is the filter output using an impulse function as input or more simply : filter(impulse) The impulse function is a simple function equal to 1 at a certain point in time, for example we can use : impulse = 1 if t = 10 else 0, where t = 1,2,3...inf The impulse response of a filter tell us how to actually make the filter, for example : a = filter(impulse) b = sum(input*a) = filter(input) This process is called convolution, and is simply the sum of the product of two functions, the input function and the kernel function, a kernel is just a way to say filter coefficients. The Explanation Now that you know that, let's explain why sma(...sma()) approximate a gaussian filter. To do so let's take an impulse function and let's start applying an sma to it such as sma(impulse) (the sma period doesn't matter here) Only one sma give a constant, let's use two sma's such as sma(sma(impulse)) This give us a triangular function, this is why sma(sma()) is often called triangular moving average, now let's repeat the process and add more sma's. Do you see ? We are approximating a gaussian curve, if we do it many times the approximation will be even more correct. Now let's recall : The impulse response of a gaussian filter is a gaussian function f The impulse response of many sma's give a function f' who approximate a gaussian function, therefore f ≈ f' So sum(input*f') ≈ sum(input*f) and therefore sma(...sma(input)) ≈ gaussfilter(input) Note : the process of applying a filter several time is called cascading Conclusion Simple isn't it ? The simple moving average is always fun to use and posses many properties, now you don't want to use such method because it's mega inefficient. But maybe that you want to know about an efficient gaussian filter implementation ? I can work on it. Thanks for reading !
Education
by alexgrover
14