# MCarloRisk — Monte Carlo & factor models

> Monte Carlo risk analyzer for stock and crypto price probabilities. (iPhone/iPad app by differential enterprises.)

- Source: https://appshunter.io/ios/app/mcarlorisk/id412346415 (this page in markdown: same URL + `.md`)
- Developer: [differential enterprises](https://appshunter.io/developer/323713853)
- Category: Finance, Productivity
- Price: Free (on sale, was $4.99)
- Rating: 4.80/5 from 5 App Store ratings · 4 written reviews indexed
- Age rating: 4+
- Requires: iOS 15.6 · 2 MB
- Languages: American English
- Released: 2010-12-31
- Data updated: 2026-07-31
- Monetization: free
- User reviews in markdown: https://appshunter.io/ios/app/mcarlorisk/id412346415/reviews.md

## What is MCarloRisk?

Stock price / probability risk analyzer & optimizer for the common man.   See also our new support for top cryptocurrencies.  Now with portfolio support, pairwise correlation/regression analysis of daily returns, and portfolio optimization.  Computes forward (price,probability) for your share-weighted portfolio. 

Unlike other folio optimizers, this code does not assume normality of returns, nor does it require to you enter volatility estimates...these are computed from public historical return data, and you can tell it how far back to look to compute the volatility.  Try some optimizations and compare to results from other codes!

Main data feed is the innovative IEX.

Why rely on the tea leaves of chart reading when you can apply real statistics and historical resampled data to your analysis?  While charting tools such as Bollinger bands, moving averages, and candlesticks are generated only on historical data, this app takes past data and remixes it via Monte Carlo methods to generate thousands of possible future price walks, then computes the probabilities of those price outcomes.  Also works for stock-like ETFs and short ETFs (e.g. SH = short SPY).

Estimates future price distribution using random walk theory, where random samples are chosen from the history of the stock in question.  

User can control how far back in time to use historical data to capture only the current "epoch" or to take into account long term historical behavior.
 
Built-in backtesting, verification, and model tuning tools.

-- Details --

This app models daily stock returns as a stable stochastic process and estimates a future price distribution by Monte Carlo re-sampling from an "empirical distribution" of a user-specified subset of prior (known) daily returns.  

Be sure to press the Run Monte button on the Monte Carlo tab after changing settings or downloading a new data set.  

This app downloads historical data from IEX as base data to resample.  Prices are converted to daily returns [P(t)/P(t-1)] before resampling.  The user can choose how far back to resample.  By estimating a probability distribution of future prices at the user-specified investment horizon in this manner, we can give risk-of-loss estimates in thumb-rule fashion, to a first approximation.  

Reports out estimated price and %loss estimates at the commonly used levels of 1st percentile and 5th percentile (1% and 5% risk).  Also reports out median (50th percentile) price estimates at the given number of days forward.  Calculations are performed on  daily Closing price data.  An artificial shock filter is provided, which can be used to reject the resampling of prior returns that are artificially large (due to splits or other artificial re-valuations that do not affect the underlying value of the asset).  Theory of operation is described in detail under the Theory tab.

The stochastic model may be tuned or calibrated by adjusting the maximum number of days backwards to sample and/or a back in time linear weighting.

Stochastic Model Validation (backtest) features:

On the Monte Carlo tab, you can withhold any number of recent days from the model and then plot the results of the stochastic risk forecast as lower-bound envelopes at 1% and %5 and all other estimated probability (risk) levels dynamically after the model run is completed. 

Validate tab:

This allows you to perform an exhaustive validation on your model by withholding several points, computing the model, comparing the forward prediction of the model versus the actual reserved data, and repeating this over time for all withheld points.

The app provider makes no claims as to the suitability of this app for any purpose whatsoever, and the user should consult an investment advisor before making investment decisions.

## Key features

- Monte Carlo price simulations
- Portfolio risk analysis
- Cryptocurrency support
- Pairwise correlation/regression
- Customizable historical data lookback
- Empirical distribution resampling
- Built-in backtesting

## Recent user reviews (4 of 4)

All indexed reviews: https://appshunter.io/ios/app/mcarlorisk/id412346415/reviews.md

### 5/5 — Great app.

*2022-03-30*

Please restore PDF export.

Thanks

**Developer response:** Thanks for the review!  We will look into restoring PDF export.  That feature got removed due to some internal library problems that we were unable to quickly fix.  Now that we know there is interest, we will try to restore that feature.  *** Update April 30, we have submitted a version to Apple with the PDF export back in, look for it in a couple of days.

### 5/5 — Extremely helpful

*2019-04-30, version 14.2*

A wonderful and informative app! If the app crashes, erase your whole portfolio and re-enter your symbols. Thank you for the app!

### 5/5 — Impressive, stable and powerful

*2013-04-04, version 4.5*

Definitely worth a look

### 4/5 — I've been waiting for this

*2011-02-24, version 3.2*

Pretty cool. I have some suggestions:
1. Adjustment option  series for drift (Libor perhaps). 
2.  show the upperbounds as well.

## Frequently asked questions about MCarloRisk

### What is MCarloRisk's primary function?

MCarloRisk analyzes stock and cryptocurrency price probabilities using Monte Carlo simulations. It estimates future price distributions and risk of loss by resampling historical return data without assuming normality.

### Does MCarloRisk require manual volatility estimates?

No, MCarloRisk computes volatility estimates directly from public historical return data. You can also specify how far back the app should look to capture relevant historical behavior.

### What kind of financial assets can MCarloRisk analyze?

MCarloRisk supports analysis for common stocks, top cryptocurrencies, stock-like ETFs, and short ETFs. It also offers portfolio support for these assets.

### How does MCarloRisk generate future price predictions?

It models daily stock returns as a stable stochastic process and estimates future price distributions by resampling from an empirical distribution of prior daily returns. This creates thousands of possible future price walks.

### What risk metrics does MCarloRisk report?

MCarloRisk reports estimated price and %loss at the 1st and 5th percentile levels (1% and 5% risk). It also provides median (50th percentile) price estimates at a user-specified investment horizon.

### Can I customize the historical data used for analysis in MCarloRisk?

Yes, users can control how far back in time the app samples historical data. This allows for capturing current market "epochs" or long-term historical behavior.

### Is MCarloRisk available on multiple devices?

Based on the data, MCarloRisk is supported on iPhone and iPod devices. It is designed for iOS users.

### How often is MCarloRisk updated?

The latest version of MCarloRisk is 20.5, and it was last updated on September 28, 2025. This indicates a recent update and ongoing development.

## Version history (last 5 releases)

### 20.8 — 2026-07-26

Patch for iOS27 beta UI bug.

### 20.7 — 2026-07-24

Move multi asset portfolio data pull and time series align to the app instead of via an intermediate server.

### 20.6 — 2026-06-15

Add data entry field for cryptocompare API key due to new requirement from data provider.

### 20.5 — 2025-09-28

Allow longer monte carlo runs for more refined models.  Tested to 5 million.  Limited to 10 million.

### 20.4 — 2025-05-12

Fix typo on alert popup.

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All apps by differential enterprises: https://appshunter.io/developer/323713853

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*Data collected daily from the US App Store and indexed by [AppsHunter](https://appshunter.io/). User reviews are verbatim App Store reviews. Ratings, prices and chart positions refresh continuously; this snapshot is from 2026-07-31.*