This script is for a triple moving average strategy where the user can select from different types of moving averages, price sources, lookback periods and resolutions.
Features:
- 3 Moving Averages with variable MA types, periods, price sources, resolutions and the ability to disable each individually
- Crossovers are plotted on the chart with detailed...
This script is for a triple moving average indicator where the user can select from different types of moving averages, price sources, lookback periods and resolutions.
Features:
- 3 Moving Averages with variable MA types, periods, price sources, resolutions and the ability to disable each individually
- Crossovers are plotted on the chart with detailed...
This script is for a triple moving average indicator where the user can select from different types of moving averages, price sources and lookback periods.
Features:
- 3 Moving Averages with variable MA types, periods, price sources and ability to disable each individually
- Crossovers are plotted on the chart with detailed information regarding the crossover...
This script is written totally thanks to Alex Grover (). Here it is implemented in conjunction with the seasonal forecast I showed in one of my previous posts. It takes the calculated QReg curve and extends its last section (Season) into the future (Forecasted periods).
For completeness here is a naive method with seasonality. The idea behind naive method with seasonality is to take last value from same season and treat it as a forecast. Its counterpart, naive method without seasonality, involves taking last mean value, i.e forecast = sma(x, p) .
This is a continuation of my series on forecasting techniques. The idea behind the Simple Mean method is to somehow extend historical mean to the future. In this case a forecast equals to last value plus average change.
Holt's Forecasting method
Holt (1957) extended simple exponential smoothing to allow the forecasting of data with a trend. This method involves a forecast equation and two smoothing equations (one for the level and one for the trend):
Forecast equation: ŷ = l + h * b
Level equation: l = alpha * y + (1 - alpha) * (l + b)
Trend equation: b = beta * (l - l)...
Introduction
The oscillator version of the stationary extrapolated levels indicator. The methodology behind the extrapolated levels where to minimize the risk of making a decision based only on a forecast, therefore the indicator plotted levels in order to determine possible reversal points, signals where generated when the detrended series crossed over/under...
Sometimes it is more than convenient to differ fast from a genuine high or
a B in an expanded flat (a very impulsive counter within a correction, resulting in an higher high than the genuine.)
I tried to use the typical choppiness of Bs in general to indicate them (orange box in example).
Therefore i used a momentum of close, relative to the bar's heights...
The Alpha-Sutte model is an ongoing project run by Ansari Saleh Ahmar, a lecturer and researcher at Universitas Negeri Makassar in Indonesia, that attempts to make forecasts for time series like how Arima and Holt-Winters models do. Currently Ahmar and his team have conducted research and published papers comparing the efficacy of the Alpha-Sutte and other models,...
Basic extrapolator for forecast a time-series, all forecasts are mades length periods ahead.
This is not a estimation of the exact price
This should only be used for forecasting direction, dont expect the price to be at the same value of its forecast.
Bias, Mean absolute error, Mean percentage error...etc look useless here, its better to use...
![WCG: EW Correction on choppiness with CB Composite index [yoxxx]](https://s3.tradingview.com/d/DqKbSFhE_mid.png "EW Correction on choppiness with CB Composite index [yoxxx]")
![EURUSD: [RS]Fractal MA V0](https://s3.tradingview.com/k/kWnrnIeQ_mid.png "[RS]Fractal MA V0")
