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FinSignals delivers real-time financial sentiment analysis via a fast, structured API. 7 classification heads, 5-15 ms latency. Free tier available - get your API key in 60 seconds.

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Website, pricing, platforms and company facts side by side.
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| Website | finsignals.ai | opalstack.com |
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| Company | Startup from the United States · 1 - 9 employees · 2026 | — |
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In their own words, as submitted to SaaSHub.


7 signals per API call — sentiment, directionality, quality, post type, relevance score, author confidence, sarcasm Trained on financial Reddit — handles meme-stock slang, emoji posts, DD formatting, pump-and-dump patterns Batch up to 256 posts at 30% lower cost per item 5–15ms inference — built...
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As answered by people managing FinSignals and Opalstack.
FinSignals's answer
FinSignals is the only API purpose-built for classifying financial Reddit and social media posts. It returns 7 signals per call: sentiment, bullish/bearish directionality, quality filtering (relevant/noise/spam), post type, relevance score, author confidence, and a sarcasm flag, all in a single low-latency inference pass. Generic NLP models fail on financial Reddit slang, meme-stock language, and emoji-heavy posts. FinSignals was fine-tuned specifically on this content.
FinSignals's answer
Most competitors offer pre-computed sentiment scores on news articles. FinSignals classifies raw text in real time for live trading pipelines. It is 6–30x cheaper per classification than using general-purpose LLM APIs (Claude, GPT-4o), eliminates prompt engineering entirely, and delivers consistent structured JSON output on every call with no hallucinations or malformed responses.
FinSignals's answer
Quantitative traders and algo trading developers who need to process Reddit sentiment at scale; fintech startups building market sentiment dashboards; financial data aggregators; researchers studying social media's effect on asset prices.
FinSignals's answer
Built to solve a real gap: existing financial sentiment APIs only cover news, while retail trader sentiment on Reddit has become a demonstrably market-moving signal. Generic NLP models misread the domain. They don't know that "diamond hands" is bullish, "DD" signals a high-quality post, or that "to the moon 🚀" with no supporting text is noise. FinSignals was fine-tuned on labeled financial Reddit data to handle these patterns correctly.
FinSignals's answer
DeBERTa-v3-base fine-tuned model with 7 classification heads; FastAPI served on Google Cloud Run; Python SDK (finsignals-api on PyPI); REST API with JSON responses.
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