Friday, October 3, 2025

Can you make stock forecasting with Montecarlo simulation?

The short answer is yes. Here is python based stock forecast hosted on a Stremlit server. The problem that you may perceive is the slowness of the application from opening to waking up as it is hosted on a 'free' server. 

Simulating the Uncertain: A Monte Carlo Tool for Portfolio Forecasting

Predicting financial outcomes is never linear. Markets shift, volatility spikes, and even the most well-balanced portfolios can drift from expected paths. This app is my attempt to model that uncertainty — an interactive Monte Carlo simulator built with Python and Streamlit that visualizes how portfolios might behave over time.


Why Monte Carlo?

Monte Carlo simulations offer a powerful way to understand not just one possible outcome — but thousands. By layering randomized simulations over time, they help investors grasp the range of potential returns, risks, and deviations from baseline expectations.

What the App Does 

This Streamlit-based simulator allows users to:

  • Input initial price, volatility, time horizon, and number of simulations
  • Generate thousands of price paths using statistical modeling
  • Visualize results through intuitive charts and summary metrics
  • Explore distributions of both absolute prices and percent changes

Key Visuals

Price Distribution

Shows how simulated final prices are distributed after a given time horizon.

Median, min, and max lines help anchor expectations

Useful for gauging downside risk and tail probability

Percent Change Distribution

Highlights performance relative to the initial investment.

See how often gains or losses occur over thousands of iterations

Spot asymmetry or skew in outcomes depending on volatility input

The Interactive app

Press enter or click to view image in full size


Under the Hood

Built with:

Front end: Streamlit 

NumPy + Matplotlib for simulation and plotting

Modular architecture: simulator.py for logic, charts_mobile.py for visualization

The app is mobile-optimized and deploys seamlessly via Streamlit Cloud. It’s fast, responsive, and intuitive across devices.

Take a look

🚀Simulator

https://montecarlo-jsim.streamlit.app/

Experiment with parameters, observe outcome shifts, and explore the wide — and often surprising — spectrum of investment futures.


Conclusions

Financial modeling rarely gives certainty. But with tools like Monte Carlo simulations, we gain context — and context builds confidence. Whether you’re stress testing a strategy or simply curious about volatility’s effect on performance, this app gives you an interactive window into risk.