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Compound Simulation – Exploring Portfolio Uncertainty

Sat, 22 Nov 2025 00:00:00 +0000

Introduction

Financial planning is often built on a deterministic story: “If I invest X € each month at 5 % per year, I’ll have Y € in 20 years.” But real markets are anything but deterministic. Price fluctuations, volatility, and unexpected shocks can significantly change outcomes.

This new tool builds on the foundation of the Compound Interest Calculator, which takes a deterministic view of capital growth. This new tool introduces a probabilistic perspective by using Monte Carlo simulation to explore a spectrum of possible portfolio trajectories based on the users assumptions. Instead of a single projected curve, it generates a fan chart that visualizes uncertainty bands, the likelihood of reaching specific targets, and how sensitive outcomes are to your savings rate.

This is not a crystal ball. It’s a scenario explorer — a way to understand how uncertain markets shape financial trajectories.

You can try the web tool here:
Open the Compound Simulation Tool

What the Tool Does

The simulation is based on a small set of input parameters — initial capital, monthly contributions, expected return (μ), volatility (σ), investment horizon, and optionally a target value.

Using these assumptions, the app runs multiple simulation paths and provides:

Fan chart of portfolio trajectories – median, expected path, and uncertainty bands (percentiles).
Distribution of end values – showing the spread of possible outcomes at the horizon.
Target probability – the likelihood of reaching (or exceeding) your goal.
Stress test – a downside scenario with halved returns and doubled volatility.
Savings elasticity – the effect on median outcomes from marginally increasing monthly contributions.

This shifts the focus from a single deterministic projection to a probabilistic view of potential futures.

How to Use It Online

Running the hosted app is straightforward:

Open the simulation tool.
Enter your core parameters:
- Initial Capital [€]
- Monthly Savings [€]
- Annual Return μ
- Volatility σ
- Time Horizon (years)
- (Optional) Define a target and target date.
- (Optional) Enable the Stress Test to explore adverse scenarios.
- (Optional) Add a Savings Elasticity Increment (e.g. +€50/month) to assess sensitivity.

The output includes:

A fan chart showing uncertainty over time.
A distribution histogram of end values.
A target probability indicator.
A sensitivity summary for additional contributions.

Run Locally

If you want to host or modify the simulation app yourself:

Clone the repository

git clone https://github.com/smichard/compound_simulation

Navigate to the project directory

cd compound_simulation

Build the container image Run the following command to build an image named compound_simulation_app (or choose any name you prefer):

podman build -t compound_simulation_app -f Containerfile .

This command uses the provided Containerfile to set up the environment, including all required R packages for running the Shiny app.

Start the app locally

podman run --rm -p 3838:3838 compound_simulation_app

This launches a container and maps port 3838 inside the container to the same port on your host system.

Access the app in your browser

http://localhost:3838/

You should now see the Compound Simulation app running locally.

Why This Matters

Uncertainty is real — any deterministic projection hides the range of plausible outcomes. Markets fluctuate, assumptions shift, and unexpected events can have a lasting impact. Probabilistic thinking helps make better decisions by accounting for both upside and downside scenarios instead of focusing on a single expected path.

Goal probability provides a tangible measure: “What are the chances I’ll reach €X by year Y?”
Savings elasticity reveals whether increasing contributions might be more effective than simply chasing higher returns.
For investors, educators, or anyone exploring financial planning under uncertainty, this tool complements the Compound Interest Calculator by adding a probabilistic layer to previously deterministic projections.

Summary

Compound Simulation brings uncertainty to the forefront. By combining Monte Carlo simulation, sensitivity analysis, and clear visualizations, it highlights that financial projections aren’t fixed—they’re distributions. The tool helps explore not only expected growth but also the range of potential outcomes and their probabilities.

It can be used as a teaching aid, a scenario testing environment, or a personal planning companion. And since it’s open source, you can easily adapt it to your own assumptions, risk parameters, or visualization preferences.

References

Related Post - Compound Interest Calculator - link
Web App - link
GitHub Repository - link

Compound Interest Calculator – Visualizing Capital Growth

Sat, 25 Oct 2025 00:00:00 +0000

Introduction

Understanding how capital develops over time is a cornerstone of financial planning. While compound interest formulas are straightforward on paper, the interplay between savings rate, interest, and time is often less intuitive. To address this, I built the Compound Interest Calculator – a Shiny app that visualizes how capital grows based on different input parameters.

The tool illustrates not only the raw numbers but also the dynamics of savings and interest accumulation. It allows you to model different scenarios, compare strategies, and identify milestones such as when your savings generate more returns than your yearly contributions.

The idea for this tool was sparked after watching a YouTube video that explains why the first €100,000 is such a critical milestone in building wealth. There are many excellent videos and articles that explore this concept in depth. But to make it truly tangible — and to experiment interactively with savings rates, interest assumptions, and time horizons — I decided to build a tool of my own. The result: a simple, hands-on way to see compound interest in action and explore how various strategies may impact the growth of your capital over time.

You can try the web tool here:
Open the Compound Interest Calculator

Prerequisites

The easiest way to use the calculator is online (see above).
If you want to run it locally, you’ll need:

An environment capable of running containers, e.g. Podman, or
R with Shiny installed.

Getting Started

The online version is straightforward:

Open the Compound Interest Calculator.
Enter your investment parameters, e.g. start year, savings rate, interest rate, investment period.
Click Calculate and explore the generated charts and tables.

Input Parameters

Start Year: The year when the investment begins.
Initial Capital: The amount of money you start with.
Savings Rate: The amount of money you plan to save regularly.
Savings Interval: The frequency at which you save the specified savings rate (either monthly or yearly).
Investment Period: The total number of years you plan to invest.
Interest Rate: The annual interest rate (as a percentage) that your capital will earn.
Adjustment Rate: The annual rate (as a percentage) at which your savings rate will increase.
Savings Suspension: The number of years after which you plan to stop saving money.
Target Value: A specific capital value you aim to achieve. The app will indicate when (or if) this value is reached.

Generated Diagrams

Overview: Shows the growth of accumulated savings and total capital over time.
Distribution: Displays a pie chart showing the distribution between total savings and total interest earned.
Savings Rate: Represents the annual savings rate in relation to the value of the generated interest each year. This visualization illustrates the development of both the savings rate and the generated interest over time. Additionally, it highlights the year when the generated interest surpasses the annual savings rate.
Normalized Values: Displays the values of the savings rate and generated interests, both normalized based on the annual growth comprised of the savings rate and yearly interests. This provides a clearer perspective on how each component contributes to the overall growth each year.
Goals: Displays the development of total capital and highlights specific milestones, such as when the capital doubles from the initial investment. It also indicates when the user-defined target value is achieved.
Values: A table that provides a detailed breakdown of the capital at the beginning of the year, savings amount per year, generated interest per year, and capital at the end of the year.

Run Locally

If you want to host the calculator yourself:

Clone this repository

git clone https://github.com/smichard/compound_interest_calculator.git

Navigate to the project directory:

cd compound_interest_calculator

Build the container image: Run the following command to build a Docker image. Replace my_app with a name of your choice for the image.

podman build -t my_app -f Containerfile

This command will use the provided Containerfile to build an image named my_app. The process will install the necessary R packages and set up the environment for the Shiny app.

Run the Shiny app locally:

After building the image, you can run the Shiny app locally using the following command:

podman run --rm -p 3838:3838 my_app

This command will start a container from the my_app image and map port 3838 of the container to port 3838 of your local machine.

Access the Shiny app in a browser Open a web browser and navigate to:

http://localhost:3838/

You should now see your Shiny app running!

Summary

The Compound Interest Calculator helps bridge the gap between abstract formulas and practical insights. It turns the often-theoretical concept of compound growth into something tangible and interactive. By visualizing how capital evolves over time, it allows users to experiment with different savings rates, investment horizons, and interest assumptions — and to see immediately how these variables influence the trajectory of their capital.

Whether used for personal financial planning, educational purposes, or illustrating investment concepts, the tool provides a clear and structured way to explore “what-if” scenarios. It highlights key inflection points — such as when generated interest surpasses annual savings — making the dynamics of compounding easier to grasp and communicate.

Ultimately, the calculator is designed to make complex relationships between time, capital, and interest transparent, empowering users to make more informed, data-driven decisions about their long-term financial strategies.

References

YouTube Video - Nischa: Why Net Worth Skyrockets After $100K - link
Web App - link
GitHub Repository - link

graphR. - Visualizing RV Tool exports

Sun, 29 Jan 2023 00:00:00 +0000

Introduction

With the advent of hyper-converged infrastructure (HCI), the initial question was how to design such a solution. Unfortunately, sizing tools were missing at the beginning. I found the evaluation of RV Tools very helpful to design HCI solutions, unfortunately, a detailed analysis could take a lot of time and be very error-prone. Therefore I developed a small tool called graphR. which automates the evaluation of RV Tools and compiles a visual presentation of the information contained within one Excel export.
RV Tools is a VMware utility that connects to a vCenter and gathers information with an impressive level of detail on the VMware environment (e. g. on virtual machines, on ESX hosts, on the network configuration). The data collection is fast and easy. The end result can be stored in a Microsoft Excel file. RV Tools exports are a great way to collect data on VMware environments. However, analyzing RV Tool exports, especially of complex environments can be time-consuming, error-prone, and cumbersome.
That’s where graphR. steps in. GraphR. processes RV Tool exports which are saved as Microsoft Excel or as comma-separated files. It performs statistical analysis on the data contained within the Microsoft Excel file. The dataset is visualized through some beautiful-looking diagrams. Finally, all tables and charts are assembled in one downloadable PDF report. Hence graphR. enables the generation of a concise report with some great graphics in order to derive meaningful insights on the analyzed VMware environment.

The tool can be adjusted to your specific needs (see below) or used through the web tool:
visit the graphR. website

Info

The web tool is deployed using a non-persistent container. The uploaded file is cached for evaluation. A cron job ensures that the generated PDF files are deleted at 15-minute intervals. Therefore, no data is stored permanently.

Prerequisites

To run graphR. you just need an environment that supports Docker containers. To customize graphR. according to your needs the installation of the open-source programming language R is recommended.

Getting Started

The easiest way to use graphR. is to pull the latest pre-build Docker container from Dockerhub and run it within your environment. The following commands will download graphR. from Dockerhub and make it available in your environment on port 80:

docker pull smichard/graphr
docker run -d -p 80:3838 smichard/graphr

Customize

To customize graphR. according to your needs, e.g. by adding new ways to plot the data, altering threshold values, or adding a custom design just clone this repository:

git clone https://github.com/smichard/graphR.git

Since the core of graphR. is written in R the installation of R is recommended to see the changes taking effect. If you are using R-Studio as a code editor the graphr_dashboard.Rproj file contains all necessary files to adjust graphR.

Following is a short description of the most important files:

app.R - the main file, which is needed by the Shiny web framework to display the web app. Here the GUI of the web app is described, also the libraries.R and the server_rv.R files are sourced
server_rv.R - contains all necessary functions to ingest the raw data, perform some basic analysis, generate diagrams, and finally generate the pdf report
plottingFunctions.R - a set of functions to display text, data frames, and diagrams on slides
libraries.R - contains a list of all required R packages, and also sources the plottingFunctions.R file

In case you want to use custom backgrounds according to your corporate identity just replace the image files within the /graphr/backgrounds folder and make sure to use the .png file format. The recommended image dimensions are 960 px times 540 px.

Once all changes are done you can build your own custom graphR. container using the following commands:

docker build -t <project name> .
docker run -d -p 80:3838 <project name>

How to use graphR.

The use of graphR. is designed to be simple:

Collect the data with the RV Tools and save the export as .xls, .xlsx or as .csv file
Upload the .xls / .xlsx file (recommended) or the tabvInfo.csv to graphR. and hit Generate Report
Enjoy your report

Demo

Get a glimpse through this YouTube video:

Summary

The tool provides an easy way to systematically analyze RV tools. The evaluation is fast and straightforward. This evaluation helps to get a quick overview of existing VMware environments.

References

RVTools - link
graphR. website - link
GitHub repository - link
R - The open source programming language for statistical computing - link
R-Studio - Used as code editor for R and for debugging and visualization - link
Shiny - Used as web application framework for R - link
Docker - Used to package all dependencies into one container - link