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.