smolka Bayesian Volatile ChannelDescription in English and Russian.
Bayesian Volatile Channel
The script is a loose interpretation of Bayes' theorem, which allows calculating the probability of events given that another event related to it has occurred, the script analyzes volatility and detects anomalies in price charts using a Bayesian approach, updating the model parameters to accurately estimate market fluctuations and detect changes in trends.
How does it work?
1. The script sets the initial parameters (mean price and standard deviation), creating a "hypothesis" about the market behavior.
2. When a new price appears, the script calculates the probability of its compliance with previous expectations. If the new price differs from the forecast, the model parameters (mean and standard deviation) are updated.
3. After updating the model, the probability that the current price and volatility correspond to a normal distribution is calculated.
4. Based on the updated model, volatility channels are built (mean price ± two standard deviations). If the price goes beyond these limits, this signals a possible anomaly indicating changes in the market.
5. The moving averages in the script act as data smoothing and trend analysis, helping to identify the market direction and minimize the impact of random fluctuations. The script uses moving averages to identify uptrends and downtrends, and calculates the average between them to display the overall market balance. These moving averages make market analysis clearer and more resistant to short-term fluctuations.
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Описание на английском и русском языках.
Байесовский волатильный канал
Скрипт является вольной интерпретацией теоремы Байеса, которая позволяет расчитать вероятность событий при условии, что произошло связанное с ним другое событие, скрипт анализирует волатильность и обнаруживает аномалии в графиках цен, используя байесовский подход, обновляя параметры модели для точной оценки рыночных колебаний и обнаружения изменений в тенденциях.
Как это работает?
1. Скрипт устанавливает начальные параметры (среднюю цену и стандартное отклонение), создавая "гипотезу" о поведении рынка.
2. При появлении новой цены скрипт вычисляет вероятность её соответствия предыдущим ожиданиям. Если новая цена отличается от прогноза, параметры модели (среднее и стандартное отклонение) обновляются.
3. После обновления модели рассчитывается вероятность того, что текущая цена и волатильность соответствуют нормальному распределению.
4. На основе обновлённой модели строятся каналы волатильности (средняя цена ± два стандартных отклонения). Если цена выходит за эти пределы, это сигнализирует о возможной аномалии, указывающей на изменения на рынке.
5. Средние скользящие в скрипте выполняют роль сглаживания данных и анализа трендов, помогая выявить направление рынка и минимизировать влияние случайных колебаний. Скрипт использует скользящие средние для определения восходящего и нисходящего трендов, а также рассчитывает среднее значение между ними для отображения общего баланса рынка. Эти скользящие средние делают анализ рынка более чётким и устойчивым к краткосрочным флуктуациям.
Bayes
Bayesian Bias OscillatorWhat is a Bayes Estimator?
Bayesian estimation, or Bayesian inference, is a statistical method for estimating unknown parameters of a probability distribution based on observed data and prior knowledge about those parameters. At first , you will need a prior probability distribution, which is a prior belief about the distribution of the parameter that you are interested in estimating. This distribution represents your initial beliefs or knowledge about the parameter value before observing any data. Second , you need a likelihood function, which represents the probability of observing the data given different values of the parameter. This function quantifies how well different parameter values explain the observed data. Then , you will need a posterior probability distribution by combining the prior distribution and the likelihood function to obtain the posterior distribution of the parameter. The posterior distribution represents the updated belief about the parameter value after observing the data.
Bayesian Bias Oscillator
This tool calculates the Bayes bias of returns, which are directional probabilities that provide insight on the "trend" of the market or the directional bias of returns. It comes with two outputs: the default one, which is the Z-Score of the Bayes Bias, and the regular raw probability, which can be switched on in the settings of the indicator.
The Z-Score output value doesn't tell you the probability, but it does tell you how much of a standard deviation the value is from the mean. It uses both probabilities, the probability of a positive return and the probability of a negative return, which is just (1 - probability of a positive return).
The probability output value shows you the raw probability of a positive return vs. the probability of a negative return. The probability is the value of each line plotted (blue is the probability of a positive return, and purple is the probability of a negative return).
Bayesian BBSMA + nQQE Oscillator + Bank funds (whales detector)Three trend indicators in one. Fork of Gunslinger2005 indicator, with a fix to display the nQQE oscillator correctly and clearly, and converted to pinescript v5 (allowing to set a different timeframe and gaps).
How to use: Essentially, nQQE is a long term trend indicator which is more adequate in daily or weekly timeframe to indicate the current market cycle. Banker Fund seems better suited to indicate current local trend, although it is sensitive to relief rallies. Bayesian BBSMA is an awesome tool to visualize the buildup in bullish/bearish sentiment, and when it is more likely to get released, however it is unreliable, so it needs to be combined with other indicators.
Please show the original indicators some love:
Bayesian BBSMA:
nQQE:
L3 Banker Fund Flow Trend:
Originally mixed together by Gunslinger2005:
