Fourier Spectrometer of Price w/ Extrapolation Forecast is a forecasting indicator that forecasts the sinusoidal frequency of input price. This method uses Linear Regression with a Fast Fourier Transform function for the forecast and is different from previous forecasting methods I've posted. Dotted lines are the forecast frequencies. You can change the UI...
Fourier Extrapolator of 'Caterpillar' SSA of Price is a forecasting indicator that applies Singular Spectrum Analysis to input price and then injects that transformed value into the Quinn-Fernandes Fourier Transform algorithm to generate a price forecast. The indicator plots two curves: the green/red curve indicates modeled past values and the yellow/fuchsia...
This is a tool designed to translate the data from the expected volatility of different assets, such as for example VIX, which measures the volatility of SP500 index. Once get the data from the volatility asset we want to measure(for this test I have used VIX), we are going to translate it the required timeframe expected move by dividing the initial value into...
Draws a volatility cone on the chart, using the contract's realized volatility (rv). The inputs are: - window: the number of past periods to use for computing the realized volatility. VIX uses 30 calendar days, which is 21 trading days, so 21 is the default. - stdevs: the number of standard deviations that the cone will cover. - periods to project: the length of...
Polynomial Regression Bands w/ Extrapolation of Price is a moving average built on Polynomial Regression. This indicator paints both a non-repainting moving average and also a projection forecast based on the Polynomial Regression. I've included 33 source types and 38 moving average types to smooth the price input before it's run through the Polynomial...
I wasn't going to post this because I don't like how this calculates by puling in the Open price, but I'm posting it anyway. This does work in it's current form but there is a. better way to do this. I'll revisit this in the future. Anyway... The k-Nearest Neighbor algorithm (k-NN) searches for k past patterns (neighbors) that are most similar to the current...
Hodrick-Prescott Extrapolation of Price is a Hodrick-Prescott filter used to extrapolate price. The distinctive feature of the Hodrick-Prescott filter is that it does not delay. It is calculated by minimizing the objective function. F = Sum((y(i) - x(i))^2,i=0..n-1) + lambda*Sum((y(i+1)+y(i-1)-2*y(i))^2,i=1..n-2) where x() - prices, y() - filter values....
What is the Modified Covariance AR Estimator? The Modified Covariance AR Estimator uses the modified covariance method to fit an autoregressive (AR) model to the input data. This method minimizes the forward and backward prediction errors in the least squares sense. The input is a frame of consecutive time samples, which is assumed to be the output of an AR...
Helme-Nikias Weighted Burg AR-SE Extra. of Price is an indicator that uses an autoregressive spectral estimation called the Weighted Burg Algorithm, but unlike the usual WB algo, this one uses Helme-Nikias weighting. This method is commonly used in speech modeling and speech prediction engines. This is a linear method of forecasting data. You'll notice that...
Weighted Burg AR Spectral Estimate Extrapolation of Price is an indicator that uses an autoregressive spectral estimation called the Weighted Burg Algorithm. This method is commonly used in speech modeling and speech prediction engines. This method also includes Levinson–Durbin algorithm. As was already discussed previously in the following indicator: ...
Levinson-Durbin Autocorrelation Extrapolation of Price is an indicator that uses the Levinson recursion or Levinson–Durbin recursion algorithm to predict price moves. This method is commonly used in speech modeling and prediction engines. What is Levinson recursion or Levinson–Durbin recursion? Is a linear algebra prediction analysis that is performed once...
Fourier Extrapolation of Variety Moving Averages is a Fourier Extrapolation (forecasting) indicator that has for inputs 38 different types of moving averages along with 33 different types of sources for those moving averages. This is a forecasting indicator of the selected moving average of the selected price of the underlying ticker. This indicator will repaint,...
Fourier Extrapolator of Variety RSI w/ Bollinger Bands is an RSI indicator that shows the original RSI, the Fourier Extrapolation of RSI in the past, and then the projection of the Fourier Extrapolated RSI for the future. This indicator has 8 different types of RSI including a new type of RSI called T3 RSI. The purpose of this indicator is to demonstrate the...
A simple script to draw a realized volatility forecast, in the form of a box. The script calculates realized volatility using the EWMA method, using a number of periods of your choosing. Using the "periods per year", you can adjust the script to work on any time frame. For example, if you are using an hourly chart with bitcoin, there are 24 periods * 365 = 8760...
The following script allows for the extrapolation of a Cubic Bézier Curve fit using custom set control points and can be used as a drawing tool allowing users to estimate underlying price trends or to forecast future price trends. Settings Extrapolation Length: Number of extrapolated observations. Source: Source input of the script. Style Width:...
A probability cone is an indicator that forecasts a statistical distribution from a set point in time into the future. Features Forecast a Standard or Laplace distribution. Change the how many bars the cones will lookback and sample in their calculations. Set how many bars to forecast the cones. Let the cones follow price from a set number of bars back. ...
This indicator consists of five moving averages. 7, 20, 50, 100 and 200. Moving averages usually represent dynamic supports or resistances and are very useful in trading. In addition, this indicator predicts where these moving averages will be located three candlesticks ahead and predicts their projected movement. I hope you enjoy it and enjoy using it.
Logarithmic regression is used to model data where growth or decay accelerates rapidly at first and then slows over time. This model is for the long term series data (such as 10 years time span). The user can consider entering the market when the price below 25% or 5% confidence and consider take profit when the price goes above 75% or 95% confidence line. This...