# Sentiment Analysis > A Claude skill from brainbytes-dev/everything-claude-trading. - **Type:** Skill - **Install:** `agentstack add skill-brainbytes-dev-everything-claude-trading-sentiment-analysis` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [brainbytes-dev](https://agentstack.voostack.com/s/brainbytes-dev) - **Installs:** 0 - **Category:** [Finance & Payments](https://agentstack.voostack.com/c/finance-and-payments) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [brainbytes-dev](https://github.com/brainbytes-dev) - **Source:** https://github.com/brainbytes-dev/everything-claude-trading/tree/main/skills/data/sentiment-analysis ## Install ```sh agentstack add skill-brainbytes-dev-everything-claude-trading-sentiment-analysis ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Sentiment Analysis for Trading name: sentiment-analysis description: Sentiment analysis for trading — news NLP, social media, earnings calls. origin: ECT ## When to Activate - User wants to build sentiment-based trading signals from text data - Applying NLP to financial news, earnings calls, or social media - Evaluating FinBERT or other financial language models for alpha generation - Constructing sentiment indices or contrarian sentiment signals - Analyzing earnings call tone for forward return prediction ## First Questions 1. What text source (news articles, social media, earnings transcripts, SEC filings)? 2. What is the target signal (directional, event-driven, risk indicator)? 3. What is the asset universe (single stock, sector, index, macro)? 4. What is the signal horizon (intraday, daily, weekly)? 5. Is this a standalone signal or combined with other alpha factors? ## Core Concepts ### NLP for Finance Natural language processing extracts structured signals from unstructured financial text. Financial text has domain-specific vocabulary, tone, and conventions that require specialized models. ``` Evolution of financial NLP: Dictionary-based (2000s): - Loughran-McDonald Financial Sentiment Dictionary (2011) - 354 negative words, 75 positive words specific to finance - "Liability" is negative in finance but neutral in general English - Simple: count positive/negative words, compute ratio - Limitation: ignores context, negation, sarcasm Machine learning (2010s): - Naive Bayes, SVM, Random Forest on bag-of-words features - TF-IDF features with labeled financial text - Better than dictionaries but still context-limited Transformer models (2019+): - BERT, FinBERT, GPT-based models - Pre-trained on general text, fine-tuned on financial corpus - Capture context, negation, complex sentence structures - State of the art for financial sentiment classification LLM-based (2023+): - GPT-4, Claude for zero-shot or few-shot sentiment analysis - Can handle nuanced analysis without fine-tuning - Flexible: extract sentiment, entities, key topics simultaneously - Cost: API pricing per token, latency for real-time applications ``` ### FinBERT and Financial Language Models ``` FinBERT (Araci 2019 / Huang et al. 2020): - BERT model fine-tuned on financial text (news, earnings calls, analyst reports) - Three-class output: positive, negative, neutral - Accuracy: ~85-90% on financial sentiment benchmarks - Captures financial nuance: "revenue declined less than expected" = positive Usage: Input: "The company reported a significant decline in operating margins" Output: {negative: 0.89, neutral: 0.08, positive: 0.03} Input: "Despite headwinds, management raised full-year guidance" Output: {positive: 0.82, neutral: 0.12, negative: 0.06} Other financial LMs: - BloombergGPT: trained on Bloomberg's proprietary financial data - FinGPT: open-source financial language model - SEC-BERT: fine-tuned on SEC filings - ChatGPT/Claude: general LLMs with strong financial comprehension Signal construction from FinBERT: 1. Process each news article/headline through FinBERT 2. Compute sentiment score: S = P(positive) - P(negative) 3. Aggregate by company: daily sentiment = mean(S) over all articles for company 4. Normalize: z-score across universe 5. Signal: buy positive sentiment, sell negative sentiment IC: 0.02-0.04 for daily sentiment on US equities ``` ### Social Media Sentiment ``` Sources: Twitter/X: real-time, high volume, noisy Reddit (r/wallstreetbets, r/stocks): retail sentiment, meme stock signals StockTwits: dedicated stock discussion, ticker-tagged Seeking Alpha: longer-form, semi-professional analysis Telegram/Discord: crypto-focused communities Signal types from social: Volume-based: - Abnormal mention volume: spike in mentions of a ticker - Volume z-score: (mentions_today - mean_30d) / std_30d - High volume often precedes large price moves (either direction) Sentiment-based: - Bullish/bearish ratio of posts mentioning a ticker - Weighted by author credibility (follower count, historical accuracy) - Combined sentiment: volume * average_sentiment Engagement-based: - Retweet/like ratios (viral content = extreme sentiment) - Comment-to-post ratio (controversy indicator) Network-based: - Who is posting (smart money accounts vs noise) - Information cascade detection (when does a narrative go viral) Performance: Social sentiment alpha: IC 0.01-0.03 (weak but additive) Best for: small/mid-cap stocks with retail following Horizon: 1-5 days (very short-lived alpha) Risk: meme stock episodes (GME Jan 2021) cause extreme outliers Limitation: manipulable (bot armies, coordinated pumps) ``` ### Earnings Call Tone Analysis ``` Why earnings calls matter: - Managers' spoken tone reveals information beyond the numbers - Tone in Q&A section is more informative than prepared remarks - Deviation from historical tone is more predictive than absolute tone - Academic evidence: Mayew and Venkatachalam (2012), Price et al. (2012) Analysis approaches: Text-based (NLP on transcript): - Apply FinBERT or dictionary to each sentence - Compute: avg sentiment in prepared remarks vs Q&A - Q&A tone change: current call vs last 4 calls (deviation signal) - Hedging language: "approximately," "potentially," "might" = uncertainty - Forward-looking vs backward-looking sentence ratio Audio-based (voice analysis): - Vocal stress: pitch variation, speaking rate changes - Hesitation markers: "um," "uh," pauses before answering - Emotion detection: confidence, anxiety, evasion - Requires audio processing (not just transcripts) Combined signals: - Text sentiment + audio stress = stronger predictor - Disagreement between what is said (positive) and how it sounds (stressed) is a particularly strong negative signal Signal construction: 1. Process transcript within hours of call completion 2. Score: overall tone, Q&A tone, tone change from prior quarter 3. Combine with earnings surprise (SUE) for enhanced PEAD signal 4. Holding period: 5-20 days post-call IC: 0.03-0.05 for tone change combined with SUE ``` ### Sentiment Indices ``` Market-level sentiment indicators: AAII Investor Sentiment Survey: - Weekly survey of individual investors (bullish/bearish/neutral) - Contrarian indicator: extreme bullishness precedes pullbacks - Bull-bear spread > 30: historically bearish for forward returns Put/Call Ratio: - CBOE equity put/call ratio - High ratio (>1.0): excessive fear, contrarian bullish - Low ratio (75 extreme greed News Sentiment Index (Federal Reserve): - Daily index based on NLP of major financial news articles - Aggregated positive/negative economic news - Leading indicator of economic activity Contrarian signal construction: 1. Compute sentiment indicator (AAII bull-bear, put/call, etc.) 2. Calculate z-score relative to trailing 52-week distribution 3. Contrarian: go long when z +2 (extreme greed) 4. Combine multiple sentiment indicators for robustness Historical: contrarian sentiment has IC 0.02-0.04 at monthly horizon ``` ## Detailed Methodology ### Building a News Sentiment Pipeline ``` Architecture: Data ingestion: - News API: RavenPack, Benzinga, NewsAPI, Reuters Machine Readable News - Frequency: real-time streaming or batch (every 5-15 minutes) - Filter: financial news only, deduplicate (same story from multiple sources) NLP processing: - Entity extraction: identify which companies are mentioned (NER) - Relevance scoring: is the company the subject or just mentioned? - Sentiment scoring: FinBERT or LLM-based classification - Novelty detection: is this new information or recycled content? Signal aggregation: - Per company: exponentially weighted sentiment (halflife = 3 days) - Cross-sectional: z-score sentiment within sector - Event detection: flag extreme sentiment shifts (>3 sigma) Alpha integration: - Combine with price momentum, earnings estimates, other factors - Optimal weight: determined by IC contribution and correlation to existing signals - Rebalance: daily for short-horizon sentiment strategies Monitoring: - Track IC over time (sentiment alpha decays as more funds use NLP) - Monitor for data quality issues (missing feeds, entity mapping errors) - A/B test: sentiment model A vs model B on live data ``` ### Avoiding Sentiment Traps ``` Common pitfalls: 1. Look-ahead bias in news timestamps: - News article published time vs first available time - Some APIs backdate articles; use API receipt timestamp 2. Survivorship in ticker mapping: - Delisted companies may not map correctly in historical data - Ensure NER handles ticker changes, M&A, spin-offs 3. Regime dependency: - Sentiment alpha works differently in bull vs bear markets - Contrarian signals fail during secular trends - Adapt: use momentum sentiment in trends, contrarian at extremes 4. Crowding: - As more funds use the same NLP models, alpha decays - Differentiate: custom models, unique data sources, proprietary processing - Edge comes from speed (real-time), depth (multi-source), or novelty 5. Manipulation: - Social media can be manipulated (bots, coordinated posts) - Filter: author credibility scoring, bot detection, anomaly detection - Weight by source quality (WSJ headline > random tweet) ``` ## Quality Gate Before deploying a sentiment-based trading strategy: - [ ] NLP model validated on labeled financial text (accuracy > 80%) - [ ] Sentiment signal tested out-of-sample with positive IC - [ ] Look-ahead bias eliminated (use data receipt timestamps, not publication dates) - [ ] Entity mapping validated (correct company attribution, handle ambiguity) - [ ] Signal horizon matched to strategy holding period - [ ] Contrarian vs momentum sentiment regime identified and handled - [ ] Data quality monitoring in place (missing feeds, model drift, mapping errors) - [ ] Crowding risk assessed (is this signal widely used by other quant funds?) - [ ] Transaction costs deducted (sentiment signals often require fast execution) - [ ] Manipulation risk mitigated (social media bot filtering, source quality weighting) ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [brainbytes-dev](https://github.com/brainbytes-dev) - **Source:** [brainbytes-dev/everything-claude-trading](https://github.com/brainbytes-dev/everything-claude-trading) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-brainbytes-dev-everything-claude-trading-sentiment-analysis - Seller: https://agentstack.voostack.com/s/brainbytes-dev - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. 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