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.