Monte Carlo Price-Range Forecasts with Weighted Return Simulations

Summary

This indicator estimates a future price range by simulating many paths from a selected starting point over a user-defined horizon. It uses a Wichmann–Hill pseudorandom generator, transforms uniform draws into approximate normal variables, and combines several normal variables with random weights to produce returns with varied tail behavior. The simulated returns are summed, then the outcomes are summarized by their mean and standard deviation. The display plots bands at quarter-standard-deviation increments out to three standard deviations, alongside forecast and simulation information.

The author describes the tool as experimental and more suitable for shorter-term forecasts. It uses simple linear returns for runtime efficiency, and its approximate distribution construction is intended to resemble financial returns rather than fitted to a demonstrated market model. The document offers a method description, not forecast-accuracy evidence. Its ranges therefore depend on assumptions about drift, volatility, seeds, and the suitability of the simulated return distribution; the plotted bands should not be read as validated probabilities.

Key ideas

• The indicator generates many pseudorandom return paths and sums each path across a chosen forecast period.
• It transforms uniform random draws into approximate normal values, then mixes weighted variables to create heavier-tailed simulated returns.
• The forecast is centered on the mean simulated outcome and displayed in standard-deviation bands.
• A lightweight approximation and simple linear returns reduce computation but constrain the modeling approach.
• The script is experimental and supplies no reported evidence that its forecasts match future market outcomes.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.