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) +...
This is combo strategies for get a cumulative signal.
This System was created from the Book "How I Tripled My Money In The
Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
The strategy buys at market, if close price is higher than the previous close
during 2 days and the meaning of 9-days Stochastic Slow...
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.