Factor.fyi – use SQL to visualize financial, crypto and econ datasets

Hacker News

Hey HN, I'm Steve, co-founder of Factor.fyi, a new data platform for querying and visualizing financial datasets using SQL. During the pandemic, I took much more of an active role in managing my portfolio. I wanted to be able to make informed decisions about the investments I was making, and explore financial data in new ways. Market and Econ data are some of the most talked-about and widely-available datasets out there, but I was frustrated by the lack of options to answer questions I had, like, "What would have happened if I had started dollar-cost averaging VTI back in 2013?"[1] Or, "How many days did BTC and ETH move in the same direction as the S&P500?"[2]. We built Factor to answer these questions. You can use Factor to query disparate datasets in ways that are impossible on any other platform. We currently have 20 datasets [3] that you can use to answer questions you have about the markets and create beautiful visualizations around them. Factor is community-powered and free forever; you can browse our community-built charts here [4]. We'd love to get feedback from the HN community, hear your thoughts on what datasets we can add, and get advice on how we can make the querying experience even easier for those new to SQL. [1]: https://factor.fyi/questions/dollar-cost-averaging-the-marke... [2]: https://factor.fyi/questions/s-p-btc-dia-correlation-factor-... [3]: https://factor.fyi/docs/dataset-reference [4]: https://factor.fyi/browse

Visit product View on Hacker NewsApr 12, 2022

Share card

Actual performance

7points

2comments

Made the leaderboard

Launch Intel predictions

Analyze your own launch →

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

65%65% 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: new, visual, using · Missing: mac, agents, macos

62%62% 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

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

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: visualize, way, para · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

41%41% 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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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

Correct prediction on native model

Similar products

Open Financial Datasets55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Open Financial Datasets

Hacker News5

Visualize and share datasets in seconds57%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Visualize and share datasets in seconds

Hacker News2

Cached Datasets53%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Cached Datasets

Hacker News4

Covid-19 Derived Datasets (JHU, NY Times, ECDC) in JSON, TSV, SQL51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Covid-19 Derived Datasets (JHU, NY Times, ECDC) in JSON, TSV, SQL

Hacker News1

Visualize SQL Queries56%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Visualize SQL Queries

Hacker News259

I Solved N Queens in SQL56%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

I Solved N Queens in SQL

Hacker News2

Lithium::SQL C++17, Header Only MySQL and SQLite Connectors and ORM76%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Lithium::SQL C++17, Header Only MySQL and SQLite Connectors and ORM

Hacker News2

Go Fearless SQL67%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Go Fearless SQL

Hacker News3

Mongita is to MongoDB as SQLite is to SQL77%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Mongita is to MongoDB as SQLite is to SQL

Hacker News126

can we get rid of SQL?67%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

can we get rid of SQL?

Hacker News2

Browse all products Analyze your launch