CANSLIM Hybrid Analyzer

Analyze US stocks using a three-stage modular pipeline:

1. Run a quantitative screen to filter candidates by earnings, leadership, and price/volume behavior.
2. Use OpenClaw AI enrichment to evaluate qualitative catalysts, float tightness, and institutional quality.
3. Generate a final ranked CANSLIM report using a fixed scoring contract.

When to use

Use this skill when the user asks to:

• Run a comprehensive CANSLIM analysis on US stocks.
• Screen for market leaders combining both quantitative strength and recent catalysts.
• Generate a ranked shortlist with clear met/missed CANSLIM criteria.
• Audit or reproduce the pipeline with deterministic JSON handoffs.

Required files

Expected local files:

• quantitative_analyzer.py
• final_process.py
• requirements.txt

Expected generated files:

• intermediate_canslim.json
• enriched_canslim.json
• final_canslim_report.json
• canslim_analysis.log

Canonical JSON contract

The quantitative phase owns Quantitative_Metrics.

The AI phase must preserve every field already present in Quantitative_Metrics and must only fill AI_Qualitative_Checks.

Intermediate schema

{
  "Metadata": {
    "Schema_Version": "2.1",
    "Date_Run": "2026-03-15",
    "Market_Direction_M": "Confirmed Uptrend",
    "Total_Universe_Scanned": 503,
    "Successfully_Evaluated": 487,
    "Failed_Fetches": 16,
    "Skipped_For_Missing_Fundamentals": 39,
    "Stocks_Passed_To_AI": 27
  },
  "Stocks": [
    {
      "Ticker": "XYZ",
      "Company_Name": "XYZ Corp",
      "Quantitative_Metrics": {
        "C_Met": true,
        "C_Details": "Q EPS Growth: 38.0%",
        "Quarterly_EPS_Growth": 0.38,
        "EPS_Accelerating": false,
        "A_Met": true,
        "A_Details": "Annual EPS CAGR: 31.0%",
        "Annual_EPS_Growth": 0.31,
        "L_Met": true,
        "RS_Rating": 92.4,
        "S_Quant_Met": true,
        "S_Quant_Details": "Today volume >= 1.5x 50-day average, Up-day volume skew positive",
        "S_Score": 2,
        "Today_Volume_Strong": true,
        "Volume_Skew_Positive": true,
        "I_Quant_Flag": true,
        "I_Quant_Details": "78.0% institutional ownership",
        "N_Technical_Met": true,
        "N_Technical_Details": "Within 4.0% of 52-week high",
        "Near_52_Week_High": true,
        "Recent_Breakout": false,
        "Pct_From_High": 0.04,
        "Current_Price": 145.2,
        "Float_Shares": 42000000,
        "Institutional_Ownership": 0.78
      },
      "AI_Qualitative_Checks_Pending": {
        "N_New_Catalyst": null,
        "N_Catalyst_Details": "",
        "S_Float_Tightness": null,
        "S_Float_Details": "",
        "I_Institutional_Quality": null,
        "I_Institutional_Details": ""
      }
    }
  ]
}

Enriched schema

The enriched schema must be the same as the intermediate schema, plus AI_Qualitative_Checks.

{
  "Stocks": [
    {
      "Ticker": "XYZ",
      "Company_Name": "XYZ Corp",
      "Quantitative_Metrics": { "...": "unchanged and preserved" },
      "AI_Qualitative_Checks_Pending": {
        "N_New_Catalyst": null,
        "N_Catalyst_Details": "",
        "S_Float_Tightness": null,
        "S_Float_Details": "",
        "I_Institutional_Quality": null,
        "I_Institutional_Details": ""
      },
      "AI_Qualitative_Checks": {
        "N_New_Catalyst": true,
        "N_Catalyst_Details": "New product launch and raised guidance",
        "S_Float_Tightness": true,
        "S_Float_Details": "Tight float supported by low float and buyback activity",
        "I_Institutional_Quality": true,
        "I_Institutional_Details": "High-quality institutional sponsorship improving"
      }
    }
  ]
}

Execution rules

Follow this checklist exactly:

1. Verify files: Confirm quantitative_analyzer.py, final_process.py, and requirements.txt exist.

2. Create environment:

python3 -m venv canslim_analysis
source canslim_analysis/bin/activate  # Linux/Mac
canslim_analysis\Scripts\activate  # Windows

3. Install dependencies:

pip install --no-cache-dir -r requirements.txt

4. Run quantitative analysis:

python quantitative_analyzer.py

5. Verify intermediate output: Confirm intermediate_canslim.json exists and contains Metadata, Stocks, Quantitative_Metrics, and AI_Qualitative_Checks_Pending.

6. Run OpenClaw AI enrichment:

• Read intermediate_canslim.json.

• For each stock, preserve Ticker, Company_Name, and the entire Quantitative_Metrics object unchanged.

• Add AI_Qualitative_Checks with values for:

  • N_New_Catalyst
  • N_Catalyst_Details
  • S_Float_Tightness
  • S_Float_Details
  • I_Institutional_Quality
  • I_Institutional_Details

7. Write enriched output: enriched_canslim.json

8. Run final processing:

python final_process.py

9. Display results: Read final_canslim_report.json and present the ranked list of stocks, CANSLIM scores, met criteria, missed criteria, price, RS rating, and catalyst note.

10. Cleanup:

deactivate

Scoring contract

Use this exact final scoring model:

C: Quantitative_Metrics.C_Met

A: Quantitative_Metrics.A_Met

L: Quantitative_Metrics.L_Met

M: Metadata.Market_Direction_M == "Confirmed Uptrend"

N: AI_Qualitative_Checks.N_New_Catalyst == true

S: Quantitative_Metrics.S_Quant_Met == true and AI_Qualitative_Checks.S_Float_Tightness == true

I: AI_Qualitative_Checks.I_Institutional_Quality == true

Interpretation guidance

N_Technical_Met is supporting technical context, not the scored N letter by itself.

I_Quant_Flag is reference context, not the scored I letter by itself.

A stock can have strong technical N support and still miss final N if OpenClaw cannot verify a fresh catalyst.

A stock can have strong volume accumulation and still miss final S if OpenClaw cannot verify tight float or buyback support.

Output format

Return the final user-facing answer in this structure:

Market Environment: <1-2 sentence assessment of the M criterion>

Top CANSLIM Candidates:

AI Catalyst Insights:

XYZ: Fresh catalyst confirmed; float tightness and institutional sponsorship also verified.

Notes & Caveats: Mention missing data, lack of catalyst confirmation, or market-trend caution when relevant.

Constraints

Never install dependencies globally.

Always use the canslim_analysis virtual environment.

Use only files generated by the current skill run.

Do not fabricate catalysts, float conclusions, or institutional-quality claims.

If no catalyst is found, set N_New_Catalyst to false and explain briefly.

If the AI phase cannot verify S or I, set the corresponding value to false instead of leaving it ambiguous.

Do not claim results are guaranteed investment advice. Always include disclaimers about risks and the need for further research.

Failure handling

If execution fails:

State exactly which phase failed: Quantitative, AI Enrichment, or Final Processing.

Include the error message when available.

Recommend the smallest next step.

If the failure is a schema mismatch, state which required field is missing or was overwritten.