plusefin 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
| Data | MCP Tool | CLI Command | curl Endpoint |
|---|---|---|---|
| Overview, valuation, ratings | get_ticker_data("AAPL") |
python plusefin.py ticker AAPL |
/tools/ticker/AAPL |
| Price history + TA indicators | get_price_history("AAPL", "1y") |
python plusefin.py price-history AAPL 1y |
/tools/price-history?ticker=AAPL&period=1y |
| Financial statements | get_financial_statements("AAPL", "income", "annual") |
python plusefin.py statements AAPL income |
/tools/statements/AAPL?type=income&frequency=annual |
| Earnings history | get_earnings_history("AAPL") |
python plusefin.py earnings AAPL |
/tools/earnings/AAPL |
| Stock news | get_ticker_news_tool("AAPL") |
python plusefin.py news AAPL |
/tools/news/AAPL |
📈 Options
| Data | MCP Tool | CLI Command | curl Endpoint |
|---|---|---|---|
| Options analysis (Greeks, IV, OI) | super_option_tool("TSLA") |
python plusefin.py options-analyze TSLA |
/tools/options/analyze/TSLA |
| Options chain | — | python plusefin.py options TSLA 20 |
/tools/options/TSLA?num_options=20 |
🏛️ Institutional Activity
| Data | MCP Tool | CLI Command | curl Endpoint |
|---|---|---|---|
| Top 25 institutional holders | get_top25_holders("AAPL") |
python plusefin.py top25 AAPL |
/tools/top25/AAPL |
| Insider trades | get_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
| Data | MCP Tool | CLI Command | curl Endpoint |
|---|---|---|---|
| Fear & Greed, VIX, market breadth | get_overall_sentiment_tool() |
python plusefin.py sentiment |
/tools/sentiment |
| Historical Fear & Greed | — | python plusefin.py sentiment-history 30 |
/tools/sentiment/history?days=30 |
| Sentiment trend analysis | — | python plusefin.py sentiment-trend 30 |
/tools/sentiment/trend?days=30 |
| CNBC market news | cnbc_news_feed() |
python plusefin.py news-market |
/tools/news/market |
| Reddit discussions | social_media_feed(["AAPL","TSLA"]) |
python plusefin.py news-social AAPL |
/tools/news/social?keywords=AAPL |
🌍 Macroeconomic Data (FRED)
| Data | MCP Tool | CLI Command | curl Endpoint |
|---|---|---|---|
| FRED series by ID | get_fred_series("GDP") |
python plusefin.py fred GDP |
/tools/fred/GDP |
| Search FRED series | search_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
| Data | MCP Tool | CLI Command | curl Endpoint |
|---|---|---|---|
| ML price forecast + probability | price_prediction("AAPL") |
python plusefin.py prediction AAPL |
/tools/prediction/AAPL |
🧮 Calculator
| Data | MCP Tool |
|---|---|
| Execute Python expressions | calculate("2 + 2") |
No CLI/curl equivalent needed. Use the calculate tool directly in MCP-native agents.
⏰ Time
| Data | MCP 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