Market Analysis Skill

When to Use

Activate this skill when the user asks to:

• Analyze a specific stock ticker (e.g., "analyze NVDA")

• Perform technical analysis

• Evaluate market conditions

• Get stock recommendations

• Understand price movements

• Compare fundamental metrics

Available Framework: TradingAgents

Located in refs/TradingAgents/ , this provides:

1. Data Access Tools (refs/TradingAgents/tradingagents/agents/utils/agent_utils.py)

Import the abstracted data tools

from tradingagents.agents.utils.agent_utils import ( get_stock_data, # Price data via yfinance/Alpha Vantage get_indicators, # Technical indicators get_fundamentals, # Company fundamentals get_balance_sheet, # Balance sheet data get_cashflow, # Cash flow statements get_income_statement,# Income statement get_news, # Company news get_global_news, # Market-wide news get_insider_sentiment, # Insider trading sentiment get_insider_transactions # Insider transactions )

2. Analyst Agents (refs/TradingAgents/tradingagents/agents/analysts/)

Market Analyst (market_analyst.py)

Purpose: Technical analysis with indicators

Key indicators to select (choose 8 complementary ones):

• Moving Averages: close_50_sma, close_200_sma, close_10_ema

• MACD: macd, macds, macdh

• Momentum: rsi

• Volatility: boll, boll_ub, boll_lb, atr

• Volume: vwma

Process:

• Call get_stock_data(ticker, start_date, end_date) first

• Then call get_indicators(ticker, indicator_list, start_date, end_date)

• Analyze trends, momentum, volatility

• Provide detailed interpretation (not just "mixed trends")

Fundamentals Analyst (fundamentals_analyst.py)

Purpose: Analyze company financials and health

Key metrics:

• P/E ratio, EPS growth

• Revenue growth, profit margins

• Debt-to-equity ratio

• Cash flow health

• Insider activity patterns

News Analyst (news_analyst.py)

Purpose: Analyze news impact and sentiment

Process:

• Get recent company news via get_news(ticker)

• Get market-wide news via get_global_news()

• Assess sentiment (bullish/bearish/neutral)

• Identify catalysts and upcoming events

Social Media Analyst (social_media_analyst.py)

Purpose: Gauge retail investor sentiment

Data sources:

• Reddit sentiment (refs/TradingAgents/tradingagents/dataflows/reddit_utils.py)

• News aggregation for sentiment scoring

Analysis Workflow

Step 1: Data Collection

Get price data (ALWAYS call this first)

stock_data = get_stock_data(ticker, start_date, end_date)

Calculate technical indicators

indicators = get_indicators( ticker, ["rsi", "macd", "boll_ub", "boll_lb", "close_50_sma", "close_200_sma", "atr", "vwma"], start_date, end_date )

Get fundamentals

fundamentals = get_fundamentals(ticker) balance_sheet = get_balance_sheet(ticker)

Get news

news = get_news(ticker) global_news = get_global_news()

Step 2: Multi-Dimensional Analysis

Analyze across these dimensions:

Technical:

• Trend direction (bullish/bearish/sideways)

• Momentum strength (RSI, MACD)

• Support/resistance levels

• Volatility assessment

• Volume trends

Fundamental:

• Valuation (overvalued/fair/undervalued)

• Financial health score

• Growth trajectory

• Red flags or concerns

Sentiment:

• News impact (positive/negative/neutral)

• Market mood

• Social sentiment

• Upcoming catalysts

Step 3: Generate Report

Required Format:

Market Analysis Report: {TICKER}

Date: {current_date}

Executive Summary

[One paragraph with key takeaway]

Technical Analysis

Trend: [Bullish/Bearish/Neutral] Key Signals:

• RSI ({value}): {interpretation}
• MACD ({value}): {interpretation}
• Bollinger Bands: {position relative to bands}
• Support: ${level}, Resistance: ${level}

Volume Analysis: {increasing/decreasing/stable}

Fundamental Analysis

Valuation: P/E {value} (vs industry avg {value}) Financial Health: [Strong/Moderate/Weak] Growth Metrics:

• Revenue: {YoY %}
• EPS: {YoY %}
• Margins: {%}

Concerns: {list any red flags}

News & Sentiment

Recent Headlines:

1. {headline 1}
2. {headline 2}
3. {headline 3}

Overall Sentiment: [Positive/Neutral/Negative] Catalysts: {upcoming events}

Key Metrics Table

Trading Recommendation

[Detailed reasoning combining all analysis] Action: BUY/HOLD/SELL Confidence: High/Medium/Low Risk Level: High/Medium/Low

Important Guidelines

• Always call get_stock_data FIRST before requesting indicators

• Select complementary indicators - avoid redundancy (e.g., don't use both RSI and StochRSI)

• Provide detailed, nuanced analysis - never just say "trends are mixed" without elaboration

• Cross-reference signals - technical should align with fundamental analysis

• Include markdown table at the end for quick reference

• Consider multiple timeframes - short-term vs long-term trends

• Document reasoning clearly for ModelChat logging

Code References

All code located in refs/TradingAgents/ :

• Market Analyst: tradingagents/agents/analysts/market_analyst.py

• Fundamentals Analyst: tradingagents/agents/analysts/fundamentals_analyst.py

• News Analyst: tradingagents/agents/analysts/news_analyst.py

• Social Media Analyst: tradingagents/agents/analysts/social_media_analyst.py

• Data Tools: tradingagents/agents/utils/agent_utils.py

• Data Flows: tradingagents/dataflows/

Example Usage

User: "Analyze NVDA stock"

Response:

• Fetch NVDA price data from yfinance

• Calculate 8 complementary technical indicators

• Get fundamentals from Alpha Vantage

• Fetch recent news

• Perform comprehensive analysis across all dimensions

• Generate detailed report with recommendation

• Include metrics table for quick reference

Integration with Multi-Model System

When multiple AI models use this skill:

• Each model analyzes independently

• Results aggregated by decision_aggregator

• Consensus and disagreements highlighted

• All reasoning logged to ModelChat for transparency