Most brokerage apps show you a return percentage that doesn't account for when you added or withdrew money, which makes it look like you're doing better or worse than you actually are. Stockfly fixes that by calculating your money-weighted return (the number that actually reflects your timing of deposits and withdrawals), tracking dividends with tax already factored in, and pulling all your holdings — even across different brokers and currencies — into one clear view without the jargon. Stockfly is broker-neutral: instead of connecting to a specific brokerage API, it lets you import your holdings from a single spreadsheet and then tracks live pricing, per-holding and portfolio-wide IRR, dividend withholding tax, and performance versus market indices. It includes plain-language stock insights on trends and valuations, a what-if planner for modeling target allocations, and exportable tax summaries in PDF or Excel. The product is built by a solo founder (Sedat Kaan Gozum) with an explicit no-ads, no-jargon design philosophy, and authentication is handled via Google Sign-in.
| # | Tool | Best for | Price |
|---|---|---|---|
| 1 | Étudiants, scientifiques, équipes R&D et organisations sensibles à la confidentialité des données | — | |
| 2 | Organisations entreprise déployant des agents IA et applications nécessitant des données temps réel, notamment en cybersécurité, services financiers et SaaS | — | |
| 3 | Scientifiques et chercheurs cherchant un espace de travail unifié pour l'analyse collaborative | — | |
| 4 | Agents IA et assistants de code analysant les marchés financiers, équipes de trading ayant besoin de recherche sourcée, investisseurs voulant comparer des dépôts d'entreprises | — | |
| 5 | Développeurs et agents IA ayant besoin d'extraire des données structurées de sites web ou d'API sans coder de scraper | — | |
| 6 | Revendeurs et "flippers" cherchant des bonnes affaires sur les enchères de surplus gouvernemental US | — | |
| 7 | Développeurs construisant des dashboards ou outils financiers/scientifiques avec des datasets massifs | — | |
| 8 | Organisations d'ingénierie logicielle, du startup en amorçage aux entreprises Fortune 100, voulant mesurer l'impact réel de l'IA sur leur codebase | — | |
| 9 | Makers indépendants, petits sites, éditeurs SaaS et créateurs cherchant une analytique respectueuse de la vie privée avec attribution de revenu | — | |
| 10 | Équipes RevOps, Finance/FP&A et commerciales pharma dans de grandes entreprises | — | |
| 11 | Développeurs, rédacteurs tech, investisseurs et curieux voulant analyser les tendances historiques sur Hacker News | — | |
| 12 | Entreprises de taille moyenne à grande avec une vraie infrastructure de données et un goulot d'étranglement analystes | — |
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