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Investment Journey Simulator
Simulate how real-life decisions reshape long-term wealth
Investment Journey Simulator is an open-source Python + Streamlit tool for modelling how real-life decisions change long-term investment outcomes. Simulate SIPs, step-ups, pauses, lump sums, withdrawals, rebalancing, inflation, taxes, historical scenarios and Monte Carlo paths. Compare complete journeys and use Shapley-based attribution to explain which decisions created the difference. It does not predict markets or recommend investments.
Hi Product Hunt - I’m Sambit, the maker of Investment Journey Simulator.
This project started from a very personal frustration.
I had used SIP calculators, maturity calculators, portfolio tools and spreadsheets, but I kept running into the same limitation: they could tell me what might happen if everything went perfectly, but they struggled to represent what actually happens over a long financial life.
What if I lose a job and pause investing for a year?
What if I increase my SIP later?
Add a lump sum?
Start an SWP?
Withdraw midway?
Rebalance?
Add another investment?
Change several of these decisions at different points in time?
I first tried modelling these situations manually for my own planning. As the scenarios became more complicated, I started writing functions, then built a full simulation backend around them.
Over time it grew into something much larger: event-based investment journeys, multiple portfolios, withdrawals, rebalancing, taxation, inflation-adjusted purchasing power, historical scenarios, Monte Carlo simulation, reports & side-by-side journey comparisons.
One problem became particularly interesting: when several decisions change at once, their effects interact.
Measuring each decision independently can double-count those interactions. For supported comparisons, I therefore added Shapley-based attribution so the individual effects reconcile with the actual difference between two journeys.
In 2026, AI/LLM tools also helped me accelerate parts of the interface and productization, but the underlying modelling problem came from something I genuinely wanted for my own investing decisions.
The project is fully open source, built with Python + Streamlit and available for anyone to inspect, run & challenge.
It does not predict markets, recommend investments or connect to your brokerage account.
You define the assumptions; the simulator explores their consequences.
A lot of effort has gone into making the calculations inspectable, the assumptions visible and the results useful for both someone asking a simple “what if?” and someone building a much more detailed investment journey.
The feedback I would value most is this:
What real-life investment event - or combination of events - have I still failed to model?
I’d also love feedback on the assumptions, edge cases, journey comparison & anything in the UX that feels unnecessarily complex.
Thanks for taking a look - happy to answer technical or modelling questions here.
About Investment Journey Simulator on Product Hunt
“Simulate how real-life decisions reshape long-term wealth”
Investment Journey Simulator was submitted on Product Hunt and earned 4 upvotes and 2 comments, placing #47 on the daily leaderboard. Investment Journey Simulator is an open-source Python + Streamlit tool for modelling how real-life decisions change long-term investment outcomes. Simulate SIPs, step-ups, pauses, lump sums, withdrawals, rebalancing, inflation, taxes, historical scenarios and Monte Carlo paths. Compare complete journeys and use Shapley-based attribution to explain which decisions created the difference. It does not predict markets or recommend investments.
On the analytics side, Investment Journey Simulator competes within Open Source, Fintech, Investing and GitHub — topics that collectively have 184.6k followers on Product Hunt. The dashboard above tracks how Investment Journey Simulator performed against the three products that launched closest to it on the same day.
Who hunted Investment Journey Simulator?
Investment Journey Simulator was hunted by Sambit Supriya Dash. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Investment Journey Simulator including community comment highlights and product details, visit the product overview.
Sambit Supriya Dash
Hi Product Hunt - I’m Sambit, the maker of Investment Journey Simulator.
This project started from a very personal frustration.
I had used SIP calculators, maturity calculators, portfolio tools and spreadsheets, but I kept running into the same limitation: they could tell me what might happen if everything went perfectly, but they struggled to represent what actually happens over a long financial life.
What if I lose a job and pause investing for a year?
What if I increase my SIP later?
Add a lump sum?
Start an SWP?
Withdraw midway?
Rebalance?
Add another investment?
Change several of these decisions at different points in time?
I first tried modelling these situations manually for my own planning. As the scenarios became more complicated, I started writing functions, then built a full simulation backend around them.
Over time it grew into something much larger: event-based investment journeys, multiple portfolios, withdrawals, rebalancing, taxation, inflation-adjusted purchasing power, historical scenarios, Monte Carlo simulation, reports & side-by-side journey comparisons.
One problem became particularly interesting: when several decisions change at once, their effects interact.
Measuring each decision independently can double-count those interactions. For supported comparisons, I therefore added Shapley-based attribution so the individual effects reconcile with the actual difference between two journeys.
In 2026, AI/LLM tools also helped me accelerate parts of the interface and productization, but the underlying modelling problem came from something I genuinely wanted for my own investing decisions.
The project is fully open source, built with Python + Streamlit and available for anyone to inspect, run & challenge.
A lot of effort has gone into making the calculations inspectable, the assumptions visible and the results useful for both someone asking a simple “what if?” and someone building a much more detailed investment journey.
The feedback I would value most is this:
What real-life investment event - or combination of events - have I still failed to model?
I’d also love feedback on the assumptions, edge cases, journey comparison & anything in the UX that feels unnecessarily complex.
Thanks for taking a look - happy to answer technical or modelling questions here.