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How to pre-validate stock trades with objective-driven AI

Hacker News

How to pre-validate stock trades with objective-driven AI

Hello HackerNews. We’re a team of scrappy financial risk managers and engineers using objective-driven AI to cut through the noise in today’s data-packed financial landscape. Hedge funds rely on data-driven information to validate their instincts prior to making stock trades. But it takes more time and resources to crunch more data (and there’s a LOT more noise). The biggest banks are investing big and beating up on smaller firms. Our platform helps hedge funds quickly validate their instincts by analyzing loads of traditional, alternative and custom data to provide immutable predictions supported with confidence levels. Results are back-tested and retrained to refine the models and improve future predictions. While our tech uses some proprietary AI models (including an energy based model), we are keenly aware that hedge funds need customized solutions to match their unique strategies. That’s why our tool enables traders to easily customize a variety of elements like investment horizons, market regimes and prices… and even include their own data sets into our models. Give it a try and let us know what you think.

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
94%94% 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 HuntUnlikely to reach the leaderboard · Strong signals: model, new, models · Missing: mac, agents, macos
48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
39%39% 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, including · Missing: https docs, excited, just released
38%38% 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 · Strong signals: platform · Missing: plus, intuitive, reviews
35%35% 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
10%10% 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.

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