PlusE Financial Analysis

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

Setup

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

# Set API key
export PLUSEFIN_API_KEY=your_api_key

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

Commands