ForecastingStocks

A backtest gives you one history — the single path that actually happened. But that path was partly luck. Monte Carlo simulation asks: how would my strategy fare across the many histories that could plausibly have happened?

The idea

Monte Carlo generates thousands of alternative scenarios by introducing controlled randomness, then runs the strategy through each. Common techniques:

Resampling the trades — shuffle the order of your historical trades, or sample them with replacement, to build thousands of alternative equity curves.
Simulating prices — generate synthetic price paths with statistical properties matched to the real data (crucially, including fat tails).
Randomising parameters and start dates — to test whether the result depended on one lucky starting point or one precise setting.

What it reveals

Instead of a single 'the strategy returned 30 percent', you get a distribution of outcomes:

A realistic range of returns rather than one point estimate.
The probability and depth of large drawdowns — often far uglier than the single backtest suggested.
The chance of ruin — the odds your account is wiped out somewhere along a bad path.

The sobering value

A strategy that looks great on its one historical path can look terrifying across a thousand plausible ones — revealing that the headline result owed more to a lucky sequence than to a robust edge. Monte Carlo will not save a strategy with no real edge, and it depends entirely on the realism of its assumptions (garbage in, garbage out). But it is one of the few tools that puts an honest confidence interval around a result, instead of the false precision of a single number.