sentiment-analysis

Analyze financial text to generate per-ticker sentiment signals.

10|2|Updated Mar 14, 2026

Search Tags:#finance #nlp #sentiment #trading-signals #finbert #text-analysis

Ready to run

npx skills add https://github.com/brainbytes-dev/everything-claude-trading --skill sentiment-analysis-brainbytes-dev

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: sentiment-analysis
Source: https://github.com/brainbytes-dev/everything-claude-trading/tree/main/skills/data/sentiment-analysis
Command: npx skills add https://github.com/brainbytes-dev/everything-claude-trading --skill sentiment-analysis-brainbytes-dev

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Sentiment analysis for trading translates unstructured financial text into structured, actionable signals that help traders gauge market mood and potential alpha opportunities.

Core Features & Use Cases

Sentiment extraction: classify financial text into positive, negative, or neutral sentiment using FinBERT or advanced LLMs.
Entity-aware scoring: map sentiment to tickers and aggregate by company, sector, or market index.
Signal generation & backtesting readiness: produce per-ticker sentiment scores with timestamps suitable for backtesting and live scoring.
Earnings call and news pipeline: process earnings transcripts, news articles, and social chatter to feed trading decisions.

Quick Start

Process the latest financial news headlines and earnings call transcripts to output a daily sentiment signal per ticker.

Frequently Asked Questions about sentiment-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I extract trading signals from financial news text?▼

To generate trading signals from financial news, classify text sentiment using NLP models like FinBERT, then map sentiment to tickers and aggregate scores with timestamps for actionable trading decisions.

What is FinBERT sentiment analysis and how does it work for finance text?▼

FinBERT sentiment analysis is an NLP technique that classifies financial text into positive, negative, or neutral sentiment. It works by leveraging language models fine-tuned on financial corpora to produce per-ticker sentiment scores suitable for trading signal generation.

Can I use sentiment analysis on earnings call transcripts for trading signals?▼

Yes, you can analyze earnings call transcripts by ingesting the text, extracting entity-specific sentiment, and outputting per-ticker scores with timestamps for short-to-medium holding horizons.

How do I aggregate social media sentiment by ticker for backtesting?▼

Aggregate social media sentiment by ticker by ingesting text, mapping sentiment to entities, and producing timestamped per-ticker scores structured for backtest-ready analysis with quality controls.

Does sentiment analysis work on macro market text or only equities?▼

Sentiment analysis works on both equities and macro markets, processing financial text across news, social media, and earnings calls to generate per-ticker sentiment signals for tradable assets in either category.

What limitations should I expect with NLP sentiment signals for trading?▼

NLP sentiment signals for trading are limited by text quality, model accuracy, and holding horizon suitability. Signals require quality controls during aggregation and are designed for short-to-medium horizons, not long-term fundamental analysis.