This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
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.
One simple scenario that explains why I built this:
Start with ₹25,000/month for 25 years.
Now pause investing for 24 months after Year 5 - then resume exactly as before.
Under the same illustrative assumptions, that temporary pause creates a surprisingly large difference decades later.
That’s the kind of question I wanted this simulator to answer: not just “what return will I get?”, but “what did this decision change?”
If you try it, I’d genuinely love to know the first real-life event you would add to the journey.
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.
Investment Journey Simulator was featured in Open Source (68.9k followers), Fintech (47.6k followers), Investing (26.8k followers) and GitHub (41.4k followers) on Product Hunt. Together, these topics include over 75k products, making this a competitive space to launch in.
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.
Want to see how Investment Journey Simulator stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
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.