Golden Level Predictions v1.0Golden Level Predictions (GLP) Trading Indicator
This script introduces a custom trading indicator named "GLP" tailored for the TradingView platform. It offers various price levels derived from Fibonacci calculations and other mathematical models, assisting traders in pinpointing potential overpriced and discounted price levels.
Key Features:
User Inputs : Users have the flexibility to select their desired timeframe, with options ranging from Weekly, Daily, Monthly, and more. Additionally, they can opt to showcase Fibonacci lines and the associated prices within these levels.
Price Level Calculations :
- Employs constants such as the Golden Ratio (PHI) and Pi (PI) to extract various multipliers and factors.
- Assesses if the current asset is a cryptocurrency and tweaks calculations accordingly.
- Determines overpriced and discounted price levels, drawing from the current open price and past data.
Fibonacci Levels :
- For each overpriced and discounted level, the script computes intermediary Fibonacci levels, including 23.6%, 38.2%, 50%, 61.8%, and 78.6% (the 3rd level is excluded due to plot limitations).
- These levels are illustrated on the chart, granting traders a more detailed view of price targets.
Visual Elements :
- Projects horizontal lines to the subsequent selected indicator interval for every calculated price level.
- Exhibits potential percentage gains or losses at each tier, indicating the prospective price alteration upon reaching that level.
- Differentiates overpriced (green) and discounted (red) levels using color codes. A neutral price is depicted in yellow.
Anticipated Close Calculation : Offers a projected closing price for the current timeframe, based on a myriad of factors.
This indicator is particularly effective with cryptocurrencies due to their inherent volatility. It's also compatible with stocks and is most efficient with tickers that provide volume data.
Forecasts
Volume Forecasting [LuxAlgo]The Volume Forecasting indicator provides a forecast of volume by capturing and extrapolating periodic fluctuations. Historical forecasts are also provided to compare the method against volume at time t .
This script will not work on tickers that do not have volume data.
🔶 SETTINGS
Median Memory: Number of days used to compute the median and first/third quartiles.
Forecast Window: Number of bars forecasted in the future.
Auto Forecast Window: Set the forecast window so that the forecast length completes an interval.
🔶 USAGE
The periodic nature of volume on certain securities allows users to more easily forecast using historical volume. The forecast can highlight intervals where volume tends to be more important, that is where most trading activity takes place.
More pronounced periodicity will tend to return more accurate forecasts.
The historical forecast can also highlight intervals where high/low volume is not expected.
The interquartile range is also highlighted, giving an area where we can expect the volume to lie.
🔶 DETAILS
This forecasting method is similar to the time series decomposition method used to obtain the seasonal component.
We first segment the chart over equidistant intervals. Each interval is delimited by a change in the daily timeframe.
To forecast volume at time t+1 we see where the current bar lies in the interval, if the bar is the 78th in interval then the forecast on the next bar is made by taking the median of the 79th bar over N intervals, where N is the median memory.
This method ensures capturing the periodic fluctuation of volume.
The Echo Forecast [LuxAlgo]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 is quantified using the correlation coefficient. Such an assumption can prove to be relatively effective with the forecasting of a periodic time series. We later introduced the ability to select dissimilar series of observations for further experimentation.
This forecasting technique is closely inspired by the analogue method introduced by Lorenz for the prediction of atmospheric data.
1. Settings
Evaluation Window: Window size used for finding historical observations similar/dissimilar to recent observations. The total evaluation window is equal to "Forecast Window" + "Evaluation Window"
Forecast Window: Determines the forecasting horizon.
Forecast Mode: Determines whether to choose historical series similar or dissimilar to the recent price observations.
Forecast Construction: Determines how the forecast is constructed. See "Usage" below.
Src: Source input of the forecast
Other style settings are self-explanatory.
2. Usage
This tool can be used to forecast future trends but also to indicate which historical variations have the highest degree of similarity/dissimilarity between the observations in the orange zone.
The forecasting window determines the prices segment (in orange) to be used as a reference for the search of the most similar/dissimilar historical price segment (in green) within the gray area.
Most forecasting techniques highly benefit from a detrended series. Due to the nature of this method, we highly recommend applying it to a detrended and periodic series.
You can see above the method is applied on a smooth periodic oscillator and a momentum oscillator.
The construction of the forecast is made from the price changes obtained in the green area, denoted as w(t) . Using the "Cumulative" options we construct the forecast from the cumulative sum of w(t) . Finally, we add the most recent price value to this cumulated series.
Using the "Mean" options will add the series w(t) with the mean of the prices within the orange segment.
Finally the "Linreg" will add the series w(t) to an extrapolated linear regression fit to the prices within the orange segment.
