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Plusefin Analysis

Financial data research via PlusE API. Provides stock fundamentals, options analysis, market sentiment (Fear & Greed), institutional holdings, insider trades...

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name: plusefin-analysis description: > Financial data research via PlusE API. Provides stock fundamentals, options analysis, market sentiment (Fear & Greed), institutional holdings, insider trades, financial statements, macroeconomic data (FRED), ML price predictions, and market news. Use when user asks about: stock analysis, ticker research, options trading, options Greeks, implied volatility, market sentiment, Fear & Greed index, earnings reports, financial statements (income/ balance/cash flow), insider trading, institutional holders, 13F, GDP, inflation, CPI, unemployment, interest rates, macroeconomics, CNBC news, Reddit stock discussions, or price prediction forecast. metadata: openclaw: emoji: "📈" homepage: "https://github.com/plusefin/plusefin-skill" requires: bins: [python3] env: [PLUSEFIN_API_KEY] primaryEnv: PLUSEFIN_API_KEY

PlusE Financial Analysis

AI-ready financial data research skill. All data is ML-preprocessed and token-optimized for direct AI consumption — no raw JSON parsing needed.

Setup

export PLUSEFIN_API_KEY=your_api_key

Get a free API key at console.plusefin.com.

Usage

There are three ways to access PlusE data. Use whichever your agent supports.

Option A: MCP (Claude Code / OpenCode)

If the PlusE MCP server is connected, call tools directly. MCP server URL:

https://mcp.plusefin.com/mcp/?apikey=$PLUSEFIN_API_KEY

Each tool is listed in the Data Reference below with its MCP tool name. Call tools like: get_ticker_data("AAPL")

Option B: CLI (Any agent — recommended fallback)

python plusefin.py <command> [args]

The plusefin.py script is bundled with this skill directory.

Option C: curl (Any agent)

curl -s -H "Authorization: Bearer $PLUSEFIN_API_KEY" \
  "https://mcp.plusefin.com/api/tools/<endpoint>"

Data Reference

📊 Company Fundamentals

DataMCP ToolCLI Commandcurl Endpoint
Overview, valuation, ratingsget_ticker_data("AAPL")python plusefin.py ticker AAPL/tools/ticker/AAPL
Price history + TA indicatorsget_price_history("AAPL", "1y")python plusefin.py price-history AAPL 1y/tools/price-history?ticker=AAPL&period=1y
Financial statementsget_financial_statements("AAPL", "income", "annual")python plusefin.py statements AAPL income/tools/statements/AAPL?type=income&frequency=annual
Earnings historyget_earnings_history("AAPL")python plusefin.py earnings AAPL/tools/earnings/AAPL
Stock newsget_ticker_news_tool("AAPL")python plusefin.py news AAPL/tools/news/AAPL

📈 Options

DataMCP ToolCLI Commandcurl Endpoint
Options analysis (Greeks, IV, OI)super_option_tool("TSLA")python plusefin.py options-analyze TSLA/tools/options/analyze/TSLA
Options chainpython plusefin.py options TSLA 20/tools/options/TSLA?num_options=20

🏛️ Institutional Activity

DataMCP ToolCLI Commandcurl Endpoint
Top 25 institutional holdersget_top25_holders("AAPL")python plusefin.py top25 AAPL/tools/top25/AAPL
Insider tradesget_insider_trades("AAPL")python plusefin.py insiders AAPL/tools/insiders/AAPL
Institutional holders(same as top25)python plusefin.py holders AAPL/tools/holders/AAPL

😱 Market Sentiment

DataMCP ToolCLI Commandcurl Endpoint
Fear & Greed, VIX, market breadthget_overall_sentiment_tool()python plusefin.py sentiment/tools/sentiment
Historical Fear & Greedpython plusefin.py sentiment-history 30/tools/sentiment/history?days=30
Sentiment trend analysispython plusefin.py sentiment-trend 30/tools/sentiment/trend?days=30
CNBC market newscnbc_news_feed()python plusefin.py news-market/tools/news/market
Reddit discussionssocial_media_feed(["AAPL","TSLA"])python plusefin.py news-social AAPL/tools/news/social?keywords=AAPL

🌍 Macroeconomic Data (FRED)

DataMCP ToolCLI Commandcurl Endpoint
FRED series by IDget_fred_series("GDP")python plusefin.py fred GDP/tools/fred/GDP
Search FRED seriessearch_fred_series("CPI")python plusefin.py fred-search CPI/tools/fred/search?q=CPI

Common FRED series IDs: GDP (GDP), CPIAUCSL (CPI), UNRATE (unemployment), FEDFUNDS (interest rate), DGS10 (10Y Treasury), SP500 (S&P 500), T10YIE (10Y breakeven inflation).

🔮 Price Prediction

DataMCP ToolCLI Commandcurl Endpoint
ML price forecast + probabilityprice_prediction("AAPL")python plusefin.py prediction AAPL/tools/prediction/AAPL

🧮 Calculator

DataMCP Tool
Execute Python expressionscalculate("2 + 2")

No CLI/curl equivalent needed. Use the calculate tool directly in MCP-native agents.

⏰ Time

DataMCP Tool
Current time (ISO 8601)get_current_time()

Research Workflows

Workflow 1: Stock Deep Dive

When user asks "analyze AAPL" or "what do you think about TSLA":

1. Fundamentals    → ticker(symbol)           → overview, valuation, ratings
2. Technicals      → price-history(symbol, 1y) → price data + TA indicators
3. Sentiment check → sentiment()               → Fear & Greed, VIX
4. Institution     → top25(symbol)             → who holds it, recent changes
5. Options market  → options-analyze(symbol)   → IV, Greeks, OI
6. Macro context   → fred(GDP), fred(UNRATE)   → economic backdrop
7. Synthesize into structured report with bull/base/bear cases

Workflow 2: Earnings Preparation

When user asks "earnings coming up for MSFT" or "what to expect from NVDA earnings":

1. Past earnings   → earnings(symbol)          → surprise history, trend
2. Recent news     → news(symbol)              → developments, catalysts
3. Options market  → options-analyze(symbol)    → IV crush, expected move
4. Social buzz     → news-social(symbol)        → retail sentiment
5. ML forecast     → prediction(symbol)         → probability of decline
6. Summarize expectations with key levels to watch

Workflow 3: Market Pulse

When user asks "how's the market looking today":

1. Fear & Greed    → sentiment()                → overall market mood
2. Market news     → news-market()              → CNBC headlines
3. Social pulse    → news-social("market,economy,stocks") → Reddit sentiment
4. Key indicators  → fred(DGS10), fred(FEDFUNDS), fred(T10YIE)
5. Quick summary of risk-on/risk-off environment

Workflow 4: Macroeconomic Context

When user asks "what's the macro picture" or "how's the economy":

1. GDP             → fred(GDP)                   → economic growth
2. Inflation       → fred(CPIAUCSL)              → CPI trend
3. Employment      → fred(UNRATE)                → unemployment
4. Rates           → fred(FEDFUNDS), fred(DGS10) → monetary policy
5. Markets         → fred(SP500)                 → market level context
6. Synthesize macro regime and implications for equities

Workflow 5: Options Strategy Research

When user asks "analyze options for AAPL" or "find options opportunities":

1. Options analysis → options-analyze(symbol)   → full Greeks, IV, OI
2. Options chain    → options(symbol, 20)       → specific strikes/expiry
3. Price context    → price-history(symbol, 6mo) → recent price action
4. Sentiment check  → sentiment()                → market mood alignment
5. Report: IV rank, put/call skew, key strikes, implied move

Analysis Framework

When producing a research report, structure output with these sections:

Core Thesis

  • Direction: bullish / bearish / neutral
  • Key drivers: valuation, earnings growth, catalyst, sentiment reversal
  • Confidence level and time horizon

Evidence Summary

  • Cite specific data points from tools used (fundamentals, technicals, options, sentiment)
  • Note conflicting signals if any

Valuation Scenarios

  • Bull case: upside catalysts, target valuation, key levels
  • Base case: expected outcome under current conditions
  • Bear case: downside risks, key levels to watch
  • Assign probability weights to each scenario

Risk Assessment

  • Company-specific risks
  • Macro/industry risks
  • Key assumptions that, if wrong, change the thesis

Actionable Recommendation

  • Directional view with conviction level
  • Suggested position sizing guidance
  • Key levels and triggers to monitor

如何使用「Plusefin Analysis」?

  1. 打开小龙虾AI(Web 或 iOS App)
  2. 点击上方「立即使用」按钮,或在对话框中输入任务描述
  3. 小龙虾AI 会自动匹配并调用「Plusefin Analysis技能完成任务
  4. 结果即时呈现,支持继续对话优化

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