QuantSupport, a Rust-based quant library

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Hi, I wanted to share a project I've building for a while. QuantSupport is a quant library that comtemplates curve construction, simulation and risk analytics for a different asset classes (equity, rates, fx, FI). https://github.com/jmelo11/quantsupport It's goal is to be a realistic production tool for financial institutions, instead of being another analytics library as many other tools are. For this, it leverages automatic differentiation from the ground up to obtain risk functionalities (sensitivites), essential for real trading setups. The library features other engines, like and XVA engine and a scripting language for structuring more complex payoff structures. Also, its available an example in the following repo: https://github.com/jmelo11/derivative_structuring where LLMs leverage the scripting functionalities of the library to provide pricing and insights to user prompted requests, allowing model-consitent results and showing the flexibility of the library. I like to think that is reaching a more mature state, but to know for sure, it would be very helpful to get feedback from people from the quant space. Any feedback is highly appreciated.

Visit product View on Hacker NewsSep 15, 2026

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: model, user · Missing: mac, agents, macos

65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: trading · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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