Chande Forecast Oscillator or Tushar Chande Forecast Oscillator (CFO) was developed by Tushar Chande. This oscillator can be said to be an extension of linear regression based indicators. This oscillator plots the difference between the closing price of the stock and the linear regression based price forecast over a specific period of time. By default, the period is considered as a 14-period candle. For a simple explanation, when the forecast price is greater than the closing price of the stock. the oscillator goes above the zero line. On the other hand, if linear regression forecast is below the closing price, the oscillator goes below zero line. This indicator can be attached very easily to Zerodha Kite charts.
Tushar Chande Forecast Oscillator Formula –
How to set up Tushar Chande Oscillator in Zerodha Kite?
- Go to MarketWatch.
- Choose the stock you are going to trade.
- Right click on the stock and select chart from graphic icons.
- Chart window of the stock opens.
- Go to studies. Select studies.
- Go to Chande Forecast Oscillator and click on it.
- A small window opens with default parameters of the indicator.
- Once the parameters are selected, the Done command is chosen, the parameters window goes off the screen and the indicator is plotted on stocks price.
Tushar Chande Forecast Oscillator In Intraday –
- The indicator forecasts price trend and trading opportunities.
- The picture above shows State Bank of India (SBIN) stock price movement with respect to the indicator.
- The chart shows 1 min. timeframe.
- The strategy shows the price trend. Understanding price trend from this indicator is important before creating a position.
- In 1 minute chart, traders may find many whipsaws which will be eliminated if longer time frames are used and this indicator is used with a combination of other indicators.
- Create short position when the oscillator starts coming down after reaching the top.
- Exit from the stock or create fresh buy once the oscillator goes up after bottoming out.
This technical indicator is mainly built on the theory of linear regression. It is an extension of time series forecast and measures the difference between actual price and time series forecast.