canslim-screener

Files (22)

• README.md
• SKILL.md
• canslim_methodology.md · references/
• fmp_api_endpoints.md · references/
• interpretation_guide.md · references/
• scoring_system.md · references/
• earnings_calculator.py · scripts/calculators/
• growth_calculator.py · scripts/calculators/
• institutional_calculator.py · scripts/calculators/
• leadership_calculator.py · scripts/calculators/
• market_calculator.py · scripts/calculators/
• new_highs_calculator.py · scripts/calculators/
• supply_demand_calculator.py · scripts/calculators/
• check_institutional_endpoint.py · scripts/
• finviz_stock_client.py · scripts/
• fmp_client.py · scripts/
• report_generator.py · scripts/
• scorer.py · scripts/
• screen_canslim.py · scripts/
• conftest.py · scripts/tests/
• test_canslim_fixes.py · scripts/tests/
• test_fmp_fallback.py · scripts/tests/

SKILL.md (first lines)

# CANSLIM Stock Screener - Phase 3 (Full CANSLIM)

## Overview

This skill screens US stocks using William O'Neil's proven CANSLIM methodology, a systematic approach for identifying growth stocks with strong fundamentals and price momentum. CANSLIM analyzes 7 key components: **C**urrent Earnings, **A**nnual Growth, **N**ewness/New Highs, **S**upply/Demand, **L**eadership/RS Rank, **I**nstitutional Sponsorship, and **M**arket Direction.

**Phase 3** implements all 7 of 7 components (C, A, N, S, L, I, M), representing **100% of the full methodology**.

**Two-Stage Approach:**
1. **Stage 1 (FMP API + Finviz)**: Analyze stock universe with all 7 CANSLIM components
2. **Stage 2 (Reporting)**: Rank by composite score and generate actionable reports

**Key Features:**
- Composite scoring (0-100 scale) with weighted components
- **Finviz fallback** for institutional ownership data (automatic when FMP data incomplete)
- Progressive filtering to optimize API usage
- JSON + Markdown output formats
- Interpretation bands: Exceptional+ (90+), Exceptional (80-89), Strong (70-79), Above Average (60-69)
- Bear market protection (M component gating)

**Phase 3 Component Weights (Original O'Neil weights):**
- C (Current Earnings): 15%
- A (Annual Growth): 20%
- N (Newness): 15%
- S (Supply/Demand): 15%
- L (Leadership/RS Rank): 20%
- I (Institutional): 10%
- M (Market Direction): 5%

**Future Phases:**
- Phase 4: FINVIZ Elite integration → 10x faster execution

---

## When to Use This Skill

**Explicit Triggers:**
- "Find CANSLIM stocks"
- "Screen for growth stocks using O'Neil's method"
- "Which stocks have strong earnings and momentum?"
- "Identify stocks near 52-week highs with accelerating earnings"
- "Run a CANSLIM screener on [sector/universe]"

**Implicit Triggers:**
- User wants to identify multi-bagger candidates
- User is looking for growth stocks with proven fundamentals
- User wants systematic stock selection based on historical winners
- User needs a ranked list of stocks meeting O'Neil's criteria

**When NOT to Use:**
- Value investing focus (use value-dividend-screener instead)
- Income/dividend focus (use dividend-growth-pullback-screener instead)
- Bear market conditions (M component will flag - consider raising cash)

---

## Prerequisites

**API Requirements:**
- **FMP API key** (free tier: 250 calls/day, sufficient for 35 stocks; Starter tier $29.99/mo for 40+ stocks)
  - Sign up: https://site.financialmodelingprep.com/developer/docs
  - Set via environment variable: `export FMP_API_KEY=your_key_here`

**Python Dependencies:**
- Python 3.7+
- `requests` (FMP API calls)
- `beautifulsoup4` (Finviz web scraping)
- `lxml` (HTML parsing)

**Installation:**
```bash
pip install requests beautifulsoup4 lxml
```

---

## Output

**Output Directory:** `reports/` (default) or custom via `--output-dir`

**Generated Files:**
- `canslim_screener_YYYY-MM-DD_HHMMSS.json` - Structured data for programmatic use
- `canslim_screener_YYYY-MM-DD_HHMMSS.md` - Human-readable report

**Report Contents:**
- Market Condition Summary (trend, M score, warnings)
- Top N CANSLIM Candidates (ranked by composite score)
- Component Breakdown for each stock (C, A, N, S, L, I, M scores with details)
- Rating interpretation (Exceptional+/Exceptional/Strong/Above Average)
- Quality warnings and data source notes
- Summary statistics (rating distribution)

**Rating Bands:**
- **Exceptional+ (90-100):** All components near-perfect, aggressive buy
- **Exceptional (80-89):** Outstanding fundamentals + momentum, strong buy
- **Strong (70-79):** Solid across components, standard buy
- **Above Average (60-69):** Meets thresholds with minor weaknesses, buy on pullback

---

## Workflow

### Step 1: Verify API Access and Requirements

Check if user has FMP API key configured:

```bash
# Check environment variable
echo $FMP_API_KEY

# If not set, prompt user to provide it
```

**Requirements:**
- **FMP API key** (free tier: 250 calls/day, sufficient for 40 stocks)
- **Python 3.7+** with required libraries:
  - `requests` (FMP API calls)
  - `beautifulsoup4` (Finviz web scraping)
  - `lxml` (HTML parsing)

From mphinance/alpha-skills, MIT.

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