Monte Carlo Methods using Google Cloud Dataproc and Apache Spark

Google Cloud Dataproc and Apache Spark provide infrastructure and capacity that you can use to run Monte Carlo simulations written in Java, Python, or Scala.

Monte Carlo methods can help answer a wide range of questions in business, engineering, science, mathematics, and other fields. By using repeated random sampling to create a probability distribution for a variable, a Monte Carlo simulation can provide answers to questions that might otherwise be impossible to answer. In finance, for example, pricing an equity option requires analyzing the thousands of ways the price of the stock could change over time. Monte Carlo methods provide a way to simulate those stock price changes over a wide range of possible outcomes, while maintaining control over the domain of possible inputs to the problem.

In the past, running thousands of simulations could take a very long time and accrue high costs. Cloud Dataproc enables you to provision capacity on demand and pay for it by the minute. Apache Spark lets you utilize clusters of tens, hundreds, or thousands of servers to run simulations in a way that is intuitive and scales to meet your needs. This means that you can run more simulations more quickly, which can help your business innovate faster and manage risk better.

Security is always important when working with financial data. Cloud Dataproc runs on Google Cloud Platform, which helps to keep your data safe, secure, and private in several ways. For example, all data is encrypted during transmission and when at rest, and the Cloud Platform is ISO 27001, SOC3, FINRA, and PCI compliant.


  • Create a managed Cloud Dataproc cluster (with Apache Spark pre-installed).
  • Run a Monte Carlo simulation using Python that estimates the growth of a stock portfolio over time.
  • Run a Monte Carlo simulation using Scala that simulates how a casino makes money.


Create a Cloud Dataproc cluster

Follow the steps to Create a Dataproc Cluster from the Google Cloud Platform Console. The default cluster settings, which includes two-worker nodes, should be sufficient for this tutorial.

Disable logging for warnings

By default, Apache Spark prints verbose logging in the console window. It will be easier to follow this tutorial if you change the logging level to log only errors. Follow these steps:

SSH into the Cloud Dataproc cluster's master node

  1. In the Cloud Platform Console, go to the VM Instances page.

    Go to the VM Instances page

  2. In the list of virtual machine instances, click the SSH button in the row of the instance to which you want to connect.

A browser window opens at your home directory on the master node.

Connected, host fingerprint: ssh-rsa 2048 ...

Change the logging setting

Edit /etc/spark/conf/

sudo nano /etc/spark/conf/

Set log4j.rootCategory equal to ERROR.

    # Set only errors to be logged to the console
    log4j.rootCategory=ERROR, console
    log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n

Save the changes and exit the editor. If you want to enable verbose logging again, reverse the change by restoring the value for .rootCategory to its original (INFO) value.

Spark programming languages

Spark supports Python, Scala, and Java as programming languages for standalone applications, and provides interactive interpreters for Python and Scala. The language you choose is a matter of personal preference. This tutorial uses the interactive interpreters because they provide a quick, easy way for you to experiment by changing the code, trying different input values, then viewing the results.

Estimating portfolio growth

In finance, Monte Carlo methods are sometimes used to run simulations that try to predict how an investment might perform. By producing random samples of outcomes over a range of probable market conditions, a Monte Carlo simulation can answer questions about how a portfolio might perform on average or in worst-case scenarios.

Follow these steps to create a simulation that uses Monte Carlo methods to try to estimate the growth of a financial investment based on a few common market factors.

  1. Start the Python interpreter from the Cloud Dataproc master node.

    $ pyspark

    Wait for the Spark prompt (>>>).

  2. Enter the following code. As always when programming with Python, be careful to maintain the indentation in the function definition.

    >>> import random
    >>> import time
    >>> from operator import add
    >>> def grow(seed):
            portfolio_value = INVESTMENT_INIT
            for i in range(TERM):
                growth = random.normalvariate(MKT_AVG_RETURN, MKT_STD_DEV)
                portfolio_value += portfolio_value * growth + INVESTMENT_ANN
            return portfolio_value
  3. Press return until you see the Spark prompt again.

    The preceding code defines a function that models what might happen when an investor has an existing retirement account that is invested in the stock market, to which they add additional money each year. The function generates a random return on the investment, as a percentage, every year for the duration of a specified term. The function takes a seed value as a parameter. This value is used to reseed the random number generator, which ensures that the function doesn't get the same list of random numbers each time it runs. The random.normalvariate function ensures that random values occur across a normal distribution for the specified mean and standard deviation. The function increases the value of the portfolio by the growth amount, which could be positive or negative, and adds a yearly sum that represents further investment.

    You will define the required constants in an upcoming step.

  4. The next step is to create many seeds to feed to the function. At the Spark prompt, enter the following code, which generates 10,000 seeds:

    >>> seeds = sc.parallelize([time.time() + i for i in xrange(10000)])

    The result of the parallelize operation is a resilient distributed dataset (RDD), which is a collection of elements that are optimized for parallel processing. In this case, the RDD contains seeds that are based on the current system time.

    When creating the RDD, Spark slices the data based on the number of workers and cores available. In this case, Spark chooses to use eight slices, one slice for each core. That's fine for this simulation, which has 10,000 items of data. For larger simulations, each slice might be larger than the default limit. In that case, specifying a second parameter to parallelize can increase the number slices, which can help to keep the size of each slice manageable, while Spark still takes advantage of all eight cores.

  5. Feed the RDD that contains the seeds to the growth function. Enter the following code:

    >>> results =

    The map method passes each seed in the RDD to the grow function and appends each result to a new RDD, which is stored in results. Note that this operation, which performs a transformation, doesn't produce its results right away. Spark won't do this work until the results are needed. This lazy evaluation is why you can enter code without the constants being defined.

  6. Specify some values for the function to work with. Enter the following code:

    >>> INVESTMENT_INIT = 100000  # starting amount
    >>> INVESTMENT_ANN = 10000  # yearly new investment
    >>> TERM = 30  # number of years
    >>> MKT_AVG_RETURN = 0.11 # percentage
    >>> MKT_STD_DEV = 0.18  # standard deviation
  7. Call reduce to aggregate the values in the RDD. Enter the following code to sum the results in the RDD:

    >>>  sum = results.reduce(add)
  8. Enter the following code to estimate and display the average return:

    >>> print sum / 10000.

    Be sure to include the dot (.) character at the end. It signifies floating-point arithmetic.

  9. Now change an assumption and see how the results change. For example, you can enter a new value for the market's average return:

    >>> MKT_AVG_RETURN = 0.07
  10. Run the simulation again. You can run everything with the following code:

    >>> print sc.parallelize([time.time() + i for i in xrange(10000)]) \
  11. When you're done experimenting, press CTRL+D to exit the Python interpreter.

Programming a Monte Carlo simulation in Scala

Monte Carlo, of course, is famous as a gambling destination. In this section, you will use Scala to create a simulation that models the mathematical advantage that a casino enjoys in a game of chance. The "house edge" at a real casino varies widely from game to game; it can be over 20% in keno, for example. This tutorial creates a simple game where the house has only a one-percent advantage. Here's how the game works:

  • The player places a bet, consisting of a number of chips from a bankroll fund.
  • The player rolls a 100-sided die (how cool would that be?).
  • If the result of the roll is a number from 1 to 49, the player wins even money.
  • For results 50 to 100, the player loses the bet.

You can see that this game creates a one-percent disadvantage for the player: in 51 of the 100 possible outcomes for each roll, the player loses.

Follow these steps to create and run the game:

  1. Start the Scala interpreter from the Cloud Dataproc master node.

  2. Copy and paste the following code to create the game. Scala doesn't have the same requirements as Python when it comes to indentation, so you can simply copy and paste this code at the scala> prompt.

    val STARTING_FUND = 10
    val STAKE = 1   // the amount of the bet
    val NUMBER_OF_GAMES = 25
    def rollDie: Int = {
        val r = scala.util.Random
        r.nextInt(99) + 1
    def playGame(stake: Int): (Int) = {
        val faceValue = rollDie
        if (faceValue < 50)
    // Function to play the game multiple times
    // Returns the final fund amount
    def playSession(
       startingFund: Int = STARTING_FUND,
       stake: Int = STAKE,
       numberOfGames: Int = NUMBER_OF_GAMES):
       (Int) = {
        // Initialize values
        var (currentFund, currentStake, currentGame) = (startingFund, 0, 1)
        // Keep playing until number of games is reached or funds run out
        while (currentGame <= numberOfGames && currentFund > 0) {
            // Set the current bet and deduct it from the fund
            currentStake = math.min(stake, currentFund)
            currentFund -= currentStake
            // Play the game
            val (winnings) = playGame(currentStake)
            // Add any winnings
            currentFund += winnings
            // Increment the loop counter
            currentGame += 1
  3. Press return until you see the scala> prompt.

  4. Enter the following code to play the game 25 times, which is the default value for NUMBER_OF_GAMES.


    Your bankroll started with a value of 10 units. Is it higher or lower, now?

  5. Now simulate 10,000 players betting 100 chips per game. Play 10,000 games in a session. This Monte Carlo simulation will calculate the probability of losing all your money before the end of the session. Enter the follow code:

    (sc.parallelize(1 to 10000, 500)
      .map(i => playSession(100000, 100, 250000))
      .map(i => if (i == 0) 1 else 0)

    Note that the syntax .reduce(_+_) is shorthand in Scala for aggregating by using a summing function. It is functionally equivalent to the .reduce(add) syntax that you saw in the Python example.

    The preceding code performs the following steps:

    • Creates an RDD with the results of playing the session.
    • Replaces bankrupt players' results with the number 1 and non-zero results with the number 0.
    • Sums the count of bankrupt players.
    • Divides the count by the number of players.

A typical result might be:


Which represents a near guarantee of losing all your money, even though the casino had only a one-percent advantage.

Next steps

See Cloud Dataproc—Submit a Job for information on submitting Spark jobs to the Cloud Dataproc service without having to SSH into the cluster.

  • Try out other Google Cloud Platform features for yourself. Have a look at our tutorials.
  • Learn how to use Google Cloud Platform products to build end-to-end solutions.

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