What data does fundamentals return?

JSON with info (metrics like market cap, PE, EPS, dividend), financials (income statements), and earnings from Yahoo Finance.

What if uv is not installed?

If uv run is unavailable or pyproject.toml is missing, replace 'uv run python' with 'python' in the commands.

What is Piotroski F-Score and how is it used?

Piotroski F-Score is a 0-9 score based on 9 fundamental criteria to assess financial strength; the output includes score, criteria breakdown, interpretation, and data availability for year-over-year comparisons.

fundamentals

npx machina-cli add skill staskh/trading_skills/fundamentals --openclaw

Files (1)

SKILL.md

3.1 KB

Fundamentals

Fetch fundamental financial data from Yahoo Finance.

Instructions

Note: If uv is not installed or pyproject.toml is not found, replace uv run python with python in all commands below.

uv run python scripts/fundamentals.py SYMBOL [--type TYPE]

Arguments

SYMBOL - Ticker symbol
--type - Data type: all, financials, earnings, info (default: all)

Output

Returns JSON with:

info - Key metrics (market cap, PE, EPS, dividend, etc.)
financials - Recent quarterly/annual income statement data
earnings - Historical and estimated earnings

Present key metrics clearly. Compare actual vs estimated earnings if relevant.

Piotroski F-Score

Calculate Piotroski's F-Score to evaluate a company's financial strength using 9 fundamental criteria.

Instructions

uv run python scripts/piotroski.py SYMBOL

What is Piotroski F-Score?

Piotroski's F-Score is a fundamental analysis tool developed by Joseph Piotroski that evaluates a company's financial strength using 9 criteria. Each criterion scores 1 point if passed, 0 if failed, for a maximum score of 9.

The 9 Criteria

Positive Net Income - Company is profitable
Positive ROA - Assets are generating returns
Positive Operating Cash Flow - Company generates cash from operations
Cash Flow > Net Income - High-quality earnings (cash exceeds accounting profit)
Lower Long-Term Debt - Decreasing leverage (improving financial position)
Higher Current Ratio - Improving liquidity
No New Shares Issued - No dilution (or share buybacks)
Higher Gross Margin - Improving profitability efficiency
Higher Asset Turnover - More efficient use of assets

Score Interpretation

8-9: Excellent - Very strong financial health
6-7: Good - Strong financial health
4-5: Fair - Moderate financial health
0-3: Poor - Weak financial health

Output

Returns JSON with:

score - F-Score (0-9)
max_score - Maximum possible score (9)
criteria - Detailed breakdown of each criterion with pass/fail status and values
interpretation - Text description of financial health level
data_available - Boolean indicating if year-over-year comparison data is available for criteria 5-9

Implementation Details

Criteria 1-4 use quarterly financial data (most recent year)
Criteria 5-9 use annual financial data for year-over-year comparisons
Compares most recent fiscal year vs previous fiscal year

Use Cases

Use Piotroski F-Score when:

Evaluating fundamental financial strength
Screening for value stocks with improving fundamentals
Assessing financial health trends
Comparing financial strength across companies
Identifying companies with strong fundamentals but undervalued prices

Dependencies

pandas
yfinance

Source

git clone https://github.com/staskh/trading_skills/blob/main/.claude/skills/fundamentals/SKILL.mdView on GitHub

Overview

Fetch fundamental financial data from Yahoo Finance, including financials, earnings, and key metrics. This skill also provides Piotroski F-Score analysis to gauge financial strength, helping compare profitability, liquidity, and leverage across companies.

How This Skill Works

Use the fundamentals tool to retrieve info, financials, and earnings for a given ticker. You can select the data type with --type (all, financials, earnings, info). For financial strength, run the Piotroski F-Score script to obtain a score, criteria details, and an interpretation.

When to Use It

When the user asks for company financials like revenue, profit, balance sheet, or income statement
When the user needs key metrics such as market cap, P/E, EPS, and dividend information
When earnings history and estimates are required
When evaluating financial strength with Piotroski F-Score for a single company or a set of peers
When screening or comparing multiple companies based on fundamentals

Quick Start

Step 1: uv run python scripts/fundamentals.py SYMBOL [--type TYPE]
Step 2: uv run python scripts/piotroski.py SYMBOL (optional for F-Score)
Step 3: Review the JSON output containing info, financials, earnings, and F-Score details

Best Practices

Use --type all for a comprehensive view, or specify financials, earnings, or info for targeted data
Cross-check earnings against estimates in the earnings section to gauge accuracy
Compute and compare Piotroski F-Scores across candidates to identify stronger fundamentals
Verify the SYMBOL ticker matches the intended company and be mindful of Class A/B tickers
Refresh data after quarterly results to maintain up-to-date insights

Example Use Cases

AAPL: Retrieve Apple’s latest info, financials, and earnings
MSFT: Pull Microsoft’s annual revenue, net income, and key metrics
NVDA: Compute Piotroski F-Score and compare with peers
AMZN: Compare P/E, market cap, and other fundamentals across peers
TSLA: Screen multiple stocks by pulling info, earnings, and F-Score for prioritization

Frequently Asked Questions

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