Concept
Monte Carlo Price Paths
Monte Carlo Price Paths, also known as trade-sequence Monte Carlo, are Statistics concepts. The Library holds 5 implementations, each one a working definition you can pull into Quant.
Top Monte Carlo Price Paths indicators
5 total
What are Monte Carlo Price Paths?
Monte Carlo price paths are simulated futures: many hypothetical forward trajectories generated by drawing random per-bar returns and compounding them from the current price. The draws come either from a fitted model (a drift and a volatility, often estimated from recent realized volatility, driving a geometric random walk) or from bootstrapping, meaning resampling or shuffling the instrument's own historical returns. No single path is a forecast. The information lives in the ensemble: percentile envelopes, the distribution of terminal prices, and frequencies such as the share of paths that touch a level within the horizon.
A second flavor, trade-sequence Monte Carlo, applies the same idea to a strategy instead of a market: reshuffling or resampling the order of historical trades shows the range of equity curves and drawdowns the same trade population could have produced. Both flavors inherit their assumptions. Independent draws ignore volatility clustering and autocorrelation, so standard implementations tend to understate how extreme and how clustered real moves can be.
How traders use it
- To draw forward scenario envelopes: percentile bands over the simulated ensemble form probability cones that frame how far price could plausibly travel within a horizon under the stated volatility.
- To estimate level-touch odds: the fraction of paths that reach a stop or target within N bars serves as a rough, assumption-dependent probability for trade planning.
- To stress a backtest: shuffling trade order, one of the standard resampling tests, reveals how much of the realized drawdown was sequence luck and what worse orderings look like.
More Monte Carlo Price Paths implementations
Related concepts · Simulation
Concept family
Statistics
45 concepts mapped · 37 in the Library
Monte Carlo Price Paths FAQ
Are Monte Carlo price paths a prediction of where price is going?
No. Each path is one random draw under explicit assumptions about drift, volatility, and independence, and the useful output is the range and its frequencies, not any single trajectory. The results are only as reliable as those inputs: a volatility regime shift, or the clustering that independent draws ignore, will make real outcomes fall outside the simulated ranges more often than the labels suggest.
How many Monte Carlo simulations are enough?
Enough that the statistic you care about stops moving when you rerun the batch. Central estimates such as the median path stabilize with modest run counts, while rare-event frequencies deep in the tails need far more. More runs only reduce simulation noise, though: they sharpen precision around the model's answer and do nothing to fix a wrong volatility or distribution assumption.
Build Monte Carlo Price Paths your way.
Quant writes, tests, and refines it with you — then it runs on LuxAlgo charting or ports to TradingView.