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.
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This indicator uses a simple time series forecasting method derived from the similarity between recent prices and similar/dissimilar historical prices. We named this method "ECHO".
This method originally assumes that future prices can be estimated from a historical series of observations that are most similar to the most recent price variations. This similarity...
Introduction
The last time (as of this publishing) that this indicator detected an inverted interest rate yield curve was on February 20th, 2020 at 12:30pm EST, the afternoon before the S&P500 began one of its largest crashes in US history. The vast majority of major economic recessions since the 1950's have been preceded by an interest rate yield curve...
Returns pivot points high/low alongside the percentage change between one pivot and the previous one (Δ%) and the distance between the same type of pivots in bars (Δt). The trailing mean for each of these metrics is returned on a dashboard on the chart. The indicator also returns an estimate of the future time position of the pivot points.
This indicator by its...
Fibonacci time zones, based on the Fibonacci number sequence, are vertical lines that represent potential areas where a swing high, low, or reversal could occur.
Trend-Based Fib Time shows probable price corrections in an existing trend. A useful tool to use in addition to Elliot Wave counting, Fib Time helps to identify how far the wave is likely to travel ...
This is an experimental study designed to forecast the range of price movement from a specified starting point using a Monte Carlo simulation.
Monte Carlo experiments are a broad class of computational algorithms that utilize random sampling to derive real world numerical results.
These types of algorithms have a number of applications in numerous fields of study...
The Forecast Oscillator is a technical indicator that compares a security close price to its time series forecast. The time series forecast function name is "tsf" and it calculates the projection of the price trend for the next bar.
The Forecast Oscillator and therefore the time series forecast are based on linear regression. The time series forecast indicator...
The Garch (General Autoregressive Conditional Heteroskedasticity) model is a non-linear time series model that uses past data to forecast future variance.
The Garch (1,1) formula is:
Garch = (gamma * Long Run Variance) + (alpha * Squared Lagged Returns) + (beta * Lagged Variance)
The gamma, alpha, and beta values are all weights used in the Garch calculations....
This is the optimized version of my MTFSBB indicator with capability of possible bands prediction in case of negative shifting (to the left).
Make me happy by using it and sending me your ideas about the prediction.
Today we'll link time series forecasting with signal processing in order to provide an original and funny trend forecasting method, the post share lot of information, if you just want to see how to use the indicator then go to the section "Using The Indicator".
Time series forecasting is an area dealing with the prediction of future values of a series by using a...
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.
![BTCUSD: The Echo Forecast [LuxAlgo]](https://s3.tradingview.com/y/Y8v4W3mX_mid.png "The Echo Forecast [LuxAlgo]")
![EURUSD: Pivot High/Low Analysis & Forecast [LuxAlgo]](https://s3.tradingview.com/h/HBuT6e1v_mid.png "Pivot High/Low Analysis & Forecast [LuxAlgo]")
![BTCUSD: Monte Carlo Range Forecast [DW]](https://s3.tradingview.com/u/ujVS2XfG_mid.png "Monte Carlo Range Forecast [DW]")
