Plusefin Analysis

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  [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/"

Data Reference

📊 Company Fundamentals

📈 Options

🏛️ Institutional Activity

😱 Market Sentiment

🌍 Macroeconomic Data (FRED)

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

🧮 Calculator

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

⏰ 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