plusefin-analysis-x

原始内容

---
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

```bash
export PLUSEFIN_API_KEY=your_api_key
```

Get a free API key at [console.plusefin.com](https://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)

```bash
python plusefin.py <command> [args]
```

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

### Option C: curl (Any agent)

```bash
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

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

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