Monte Carlo Price Forecasts with Naive Bayes Directional Signals

Summary

The indicator estimates a range of possible future prices by simulating paths from recent log returns. At a user-selected chart time, it uses a rolling historical window to calculate the average and standard deviation of returns, then generates simulated prices over a configurable forecast horizon. It plots the 5th, 25th, median, 75th, and 95th percentile paths as central tendency and dispersion guides.

A separate Naive Bayes calculation estimates the probability of an up move from relative volume and ten-period price momentum. It models these features with normal probability densities conditioned on whether the observed return was positive, and displays a directional label and confidence category. The document provides implementation details but no performance tests or evidence of predictive accuracy. The simulation assumes returns follow a distribution summarized by recent mean and volatility, and the Bayesian output depends on its feature and distribution assumptions; neither display is a guarantee of future prices or profitable trades.

Key ideas

• The forecast samples log returns using their rolling historical mean and standard deviation.
• Percentile paths summarize the simulated price distribution at each forecast step.
• The Naive Bayes component uses relative volume and momentum to estimate the chance of an up move.
• The script presents illustrative forecasts and signals without reporting validation or trading results.

Tags

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