Выбор редакцииOPEN-SOURCE SCRIPTMoving Regression
Обновлено
Moving Regression is a generalization of moving average and polynomial regression.
The procedure approximates a specified number of prior data points with a polynomial function of a user-defined degree. Then, polynomial interpolation of the last data point is used to construct a Moving Regression time series.
Application:
Moving Regression allows one to smooth noise on the analyzed chart, assess momentum, confirm trends, and establish areas of support and resistance.
In addition, it can be used as a simple stand-alone forecasting method to identify trend direction and trend reversal points. When the local polynomial is predicted to move up in the next time step, the color of the Moving Regression curve will be green. Otherwise, the color of the curve is red. This function is (de)activated using the Predict Trend Direction flag.
Selecting the model parameters:
The effects of the moving window Length and the Local Polynomial Degree are confounded. This allows for finding the optimal trade-off between noise (variance) and lag (bias). Higher Length and lower Polynomial Degree (such as 1, i.e. linear), will result in "smoother" time series but at the cost of greater lag. Increasing the Polynomial Degree to, for example, 2 (squared) while maintaining the Length will diminish the lag and thus compromise the noise-lag tradeoff.
Relation to other methods:
When the degree of the local polynomial is set to 0 (i.e., fitting data to a constant level), the Moving Regression time series exactly matches the Simple Moving Average of the same length.
The procedure approximates a specified number of prior data points with a polynomial function of a user-defined degree. Then, polynomial interpolation of the last data point is used to construct a Moving Regression time series.
Application:
Moving Regression allows one to smooth noise on the analyzed chart, assess momentum, confirm trends, and establish areas of support and resistance.
In addition, it can be used as a simple stand-alone forecasting method to identify trend direction and trend reversal points. When the local polynomial is predicted to move up in the next time step, the color of the Moving Regression curve will be green. Otherwise, the color of the curve is red. This function is (de)activated using the Predict Trend Direction flag.
Selecting the model parameters:
The effects of the moving window Length and the Local Polynomial Degree are confounded. This allows for finding the optimal trade-off between noise (variance) and lag (bias). Higher Length and lower Polynomial Degree (such as 1, i.e. linear), will result in "smoother" time series but at the cost of greater lag. Increasing the Polynomial Degree to, for example, 2 (squared) while maintaining the Length will diminish the lag and thus compromise the noise-lag tradeoff.
Relation to other methods:
When the degree of the local polynomial is set to 0 (i.e., fitting data to a constant level), the Moving Regression time series exactly matches the Simple Moving Average of the same length.
Информация о релизе
minor correctionsСкрипт с открытым кодом
В истинном духе TradingView автор этого скрипта опубликовал его с открытым исходным кодом, чтобы трейдеры могли понять, как он работает, и проверить на практике. Вы можете воспользоваться им бесплатно, но повторное использование этого кода в публикации регулируется Правилами поведения. Вы можете добавить этот скрипт в избранное и использовать его на графике.
DISCLAIMER: I am not a financial advisor, and my scripts are for educational purposes only. Any trades you make are at your own risk.
Отказ от ответственности
Все виды контента, которые вы можете увидеть на TradingView, не являются финансовыми, инвестиционными, торговыми или любыми другими рекомендациями. Мы не предоставляем советы по покупке и продаже активов. Подробнее — в Условиях использования TradingView.