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Plusefin Analysis

AI-ready stock analysis - ticker data, options, sentiment, predictions.

Rating
5 (350 reviews)
Downloads
2,392 downloads
Version
1.0.0

Overview

AI-ready stock analysis - ticker data, options, sentiment, predictions.

Complete Documentation

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PlusE Financial Analysis

AI-ready financial data research skill with structured research methodology.

Setup

bash
export PLUSEFIN_API_KEY=your_api_key

Research Framework

1. Research Setup

  • Define target (ticker) and time range (6mo / 1y / 2y)
  • Set research objective: valuation analysis / technical outlook / event-driven

2. Data Collection

  • Company Fundamentals: ticker - overview, valuation, ratings
  • Market Sentiment: sentiment / sentiment-history
  • Options Data: options / options-analyze (IV, Greeks, OI)
  • Institutional Holdings: holders - major holders changes
  • Financial Statements: statements (income/balance/cash)
  • Earnings & Insider: earnings / insiders
  • Price History: price-history

3. Hypothesis Formation

Based on data, formulate hypotheses:
  • Direction: Bullish / Bearish / Neutral
  • Drivers: Valuation reversion, earnings growth, event catalyst, sentiment reversal

4. Evidence Validation

  • Use search capabilities to gather research reports, news, announcements
  • Cross-validate multi-source data timeline consistency
  • Seek evidence supporting or refuting hypotheses

5. Valuation Scenarios

  • Bull Case: Valuation assuming upside catalysts materialize
  • Base Case: Valuation based on current market expectations
  • Bear Case: Valuation assuming downside risks materialize

6. Risk Assessment

  • Downside risks
  • Key assumption risks
  • Potential catalysts and triggers

7. Report Output

Structured output:
  • Core thesis
  • Evidence summary
  • Valuation scenario comparison
  • Risk warnings
  • Actionable recommendations (if applicable)
Each key conclusion must include source citations.

Usage

bash
# Set API key
export PLUSEFIN_API_KEY=your_api_key

# Run commands
python plusefin.py <command> [args]

Commands

CommandUsageDescription
tickerpython plusefin.py ticker Company overview, valuation, ratings
price-historypython plusefin.py price-history [period]Historical prices (6mo/1y/2y)
sentimentpython plusefin.py sentimentMarket sentiment (Fear & Greed)
sentiment-historypython plusefin.py sentiment-history [days]Historical sentiment
optionspython plusefin.py options [num]Options chain
options-analyzepython plusefin.py options-analyze Options analysis
holderspython plusefin.py holders Institutional holdings
statementspython plusefin.py statements [type]Financial statements (income/balance/cash)
earningspython plusefin.py earnings Earnings history
insiderspython plusefin.py insiders Insider trading
newspython plusefin.py news Stock news
fredpython plusefin.py fred Macroeconomic data

Installation

Terminal bash 

openclaw install plusefin-analysis
Copied!

💻Code Examples

export PLUSEFIN_API_KEY=your_api_key

export-plusefinapikeyyourapikey.txt
## Research Framework

### 1. Research Setup
- Define target (ticker) and time range (6mo / 1y / 2y)
- Set research objective: valuation analysis / technical outlook / event-driven

### 2. Data Collection
- **Company Fundamentals**: `ticker` - overview, valuation, ratings
- **Market Sentiment**: `sentiment` / `sentiment-history`
- **Options Data**: `options` / `options-analyze` (IV, Greeks, OI)
- **Institutional Holdings**: `holders` - major holders changes
- **Financial Statements**: `statements` (income/balance/cash)
- **Earnings & Insider**: `earnings` / `insiders`
- **Price History**: `price-history`

### 3. Hypothesis Formation
Based on data, formulate hypotheses:
- **Direction**: Bullish / Bearish / Neutral
- **Drivers**: Valuation reversion, earnings growth, event catalyst, sentiment reversal

### 4. Evidence Validation
- Use search capabilities to gather research reports, news, announcements
- Cross-validate multi-source data timeline consistency
- Seek evidence supporting or refuting hypotheses

### 5. Valuation Scenarios
- **Bull Case**: Valuation assuming upside catalysts materialize
- **Base Case**: Valuation based on current market expectations
- **Bear Case**: Valuation assuming downside risks materialize

### 6. Risk Assessment
- Downside risks
- Key assumption risks
- Potential catalysts and triggers

### 7. Report Output
Structured output:
- Core thesis
- Evidence summary
- Valuation scenario comparison
- Risk warnings
- Actionable recommendations (if applicable)

Each key conclusion must include source citations.

## Usage
example.sh
# Set API key
export PLUSEFIN_API_KEY=your_api_key

# Run commands
python plusefin.py <command> [args]

Tags

#cli_utilities #data

Quick Info

Category Development
Model Claude 3.5
Complexity One-Click
Author wanghsinche
Last Updated 3/10/2026
🚀
Optimized for
Claude 3.5
🧠

Ready to Install?

Get started with this skill in seconds

openclaw install plusefin-analysis

Resources

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