Backtest – Back-Testing Tool for European Passive Investors

Hacker News

I regularly write on my personal blog, where I do research into my portfolio of index ETFs. For instance, I dived deep into the past performance of my portfolio, or investigated its historical Sharpe ratio. All that required me to find the historical data of the indexes in my portfolio and run simulations on them. For lack of a better tool, I wrote my own simulation software in the form of a web app. Now, that simulation software went through a couple of iterations and got more and more powerful as I extended it for my needs. Finally, I decided it had enough value to release it for others. I first published it on Reddit a month ago, through which I got a lot of feedback and many requests to add additional funds. Now I'm curious what the HN crowd thinks of it! I'm still actively developing the tool so all feedback is super useful to guide the development. Cheers! Yoran

Visit product View on Hacker NewsSep 6, 2019

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: personal, month · Missing: mobile apps, ios, entrepreneurs

57%57% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

17%17% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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