Findata Toolkit Us
Financial data toolkit for US market analysis. Provides scripts to fetch real-time stock data (yfinance), SEC filings and insider trades (EDGAR), financial statement calculators (DuPont, Z-Score, M-Score, F-Score), portfolio analytics (VaR, stress testing, health scoring), multi-factor screening, and macro indicators (FRED). Use when you need live US market data to ground investment analysis. All…
Install
$ agentstack add skill-geeksfino-finskills-findata-toolkit ✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
About
FinData Toolkit — US Market
A self-contained data toolkit providing live financial data and quantitative calculations for US market analysis. All data sources are free and require no API keys.
Setup
Install dependencies (one-time):
pip install -r requirements.txtAvailable Tools
All scripts are in the scripts/ directory. Run from the skill root directory.
1. Stock Data (scripts/stock_data.py)
Fetch stock fundamentals, price history, and financial metrics via yfinance.
| Command | Purpose | |---------|---------| | python scripts/stock_data.py AAPL | Basic company info | | python scripts/stock_data.py AAPL --metrics | Full financial metrics (valuation, profitability, leverage, growth, analyst consensus) | | python scripts/stock_data.py AAPL --history --period 1y | OHLCV price history | | python scripts/stock_data.py AAPL --financials | Income statement, balance sheet, cash flow | | python scripts/stock_data.py AAPL MSFT GOOGL --screen | Screen stocks against value filters |
2. SEC EDGAR (scripts/sec_edgar.py)
Fetch insider trading data (Form 4), company filings, and CIK lookups.
| Command | Purpose | |---------|---------| | python scripts/sec_edgar.py insider AAPL | Recent insider trades | | python scripts/sec_edgar.py insider AAPL --days 90 | Insider trades in last 90 days | | python scripts/sec_edgar.py filings AAPL --form-type 10-K | Recent 10-K filings | | python scripts/sec_edgar.py cik AAPL | Look up CIK number |
3. Financial Calculators (scripts/financial_calc.py)
DuPont decomposition, Altman Z-Score, Beneish M-Score, Piotroski F-Score, earnings quality, and working capital analysis.
| Command | Purpose | |---------|---------| | python scripts/financial_calc.py AAPL --all | All calculations | | python scripts/financial_calc.py AAPL --dupont | 5-factor DuPont decomposition | | python scripts/financial_calc.py AAPL --zscore | Altman Z-Score (bankruptcy risk) | | python scripts/financial_calc.py AAPL --mscore | Beneish M-Score (manipulation detection) | | python scripts/financial_calc.py AAPL --fscore | Piotroski F-Score (financial strength) | | python scripts/financial_calc.py AAPL --quality | Earnings quality assessment | | python scripts/financial_calc.py AAPL --working-capital | Working capital & CCC analysis |
4. Portfolio Analytics (scripts/portfolio_analytics.py)
Portfolio risk analysis: concentration, correlation clusters, VaR/CVaR, stress testing, and health scoring.
| Command | Purpose | |---------|---------| | python scripts/portfolio_analytics.py --holdings "AAPL:30,MSFT:25,GOOGL:20,AMZN:15,META:10" | Full health score (0–100) | | ... --concentration | Concentration analysis (HHI, sector) | | ... --correlation | Correlation clusters & EDR | | ... --risk | VaR/CVaR, Sharpe, Sortino, beta | | ... --stress | Historical stress testing (5 scenarios) |
5. Factor Screener (scripts/factor_screener.py)
Multi-factor stock scoring: value, momentum, quality, low volatility, size, growth.
| Command | Purpose | |---------|---------| | python scripts/factor_screener.py --universe "AAPL,MSFT,GOOGL,AMZN" --top 5 | Screen custom universe | | python scripts/factor_screener.py --sp500-sample --top 10 | Screen S&P 500 sample | | ... --factors value,quality | Use specific factors only |
6. Macro Data (scripts/macro_data.py)
US macroeconomic indicators from FRED.
| Command | Purpose | |---------|---------| | python scripts/macro_data.py --dashboard | Full macro dashboard | | python scripts/macro_data.py --rates | Interest rates & yield curve | | python scripts/macro_data.py --inflation | CPI, PCE, breakevens | | python scripts/macro_data.py --gdp | GDP & leading indicators | | python scripts/macro_data.py --employment | Unemployment, payrolls, JOLTS | | python scripts/macro_data.py --cycle | Business cycle phase assessment |
Data Sources
| Source | Data | API Key | |--------|------|---------| | Yahoo Finance (yfinance) | Stock quotes, financials, history | Not required | | SEC EDGAR | Filings, insider trades (Form 4) | Not required | | FRED | Macro indicators | Not required |
Output Format
All scripts output JSON to stdout for easy parsing. Errors go to stderr.
Configuration
Optional: Edit config/data_sources.yaml to customize rate limits or add API keys for premium data sources.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: Geeksfino
- Source: Geeksfino/finskills
- License: Apache-2.0
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.