Finagg – free and nearly unlimited financial data

Hacker News

finagg is a Python package and CLI that enables you to aggregate and update free financial data from a handful of high quality data sources all within your own SQL database. Here're some quick facts about it: - Implements nearly all of the BEA API, FRED API, and SEC EDGAR APIs (all of which have free and nearly unlimited data access) - Only focused on macrodata (no intraday data support) - Provides methods for transforming data from these APIs into normalized features that're readily useable for analysis, strategy development, and AI/ML - The most popular methods include getting historical price earnings ratios, getting historical price earnings ratios normalized across industries, and sorting companies by their industry-normalized price earnings ratios - Python >= 3.10 only (you should upgrade anyways if you haven't ;) I'm looking for feedback on the overall package and CLI designs. Also, I'd appreciate any recommendations for implementing other financial data sources that're free with no daily API limits. Cheers!

Visit product View on Hacker NewsJan 22, 2024

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

70%70% 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.

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

55%55% 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

49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: mac, apis · Missing: agents, macos, agent

39%39% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

19%19% 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

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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