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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.

Website, pricing, platforms and company facts side by side.
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| Website | opalstack.com | finsignals.ai |
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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.


No description of Opalstack yet.
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 Opalstack and FinSignals.
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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