Fundamental Data Integration

Pyhood provides fundamental data integration via the FundamentalData class, allowing you to screen stocks by financial ratios.

FundamentalData Class

from pyhood.fundamentals import FundamentalData

fd = FundamentalData('AAPL')

# Access individual ratios
print(fd.pe_ratio)         # Trailing P/E ratio
print(fd.revenue_growth)   # Revenue growth (decimal, e.g. 0.15 = 15%)
print(fd.market_cap)       # Market capitalization in dollars
print(fd.sector)           # e.g. 'Technology'

# Get all available data as a dict
summary = fd.summary()
# {'ticker': 'AAPL', 'pe_ratio': 28.5, 'market_cap': 2800000000000, ...}

Data is fetched lazily from yfinance on first access and cached for the lifetime of the object.

Available Properties

Property	yfinance Key	Description
pe_ratio	trailingPE	Trailing price-to-earnings ratio
forward_pe	forwardPE	Forward P/E based on analyst estimates
pb_ratio	priceToBook	Price-to-book ratio
debt_to_equity	debtToEquity	Total debt / total equity
revenue_growth	revenueGrowth	YoY revenue growth (decimal)
profit_margin	profitMargins	Net profit margin (decimal)
market_cap	marketCap	Market capitalisation in USD
beta	beta	Beta coefficient vs market
dividend_yield	dividendYield	Annual dividend yield (decimal)
sector	sector	GICS sector name
industry	industry	Industry classification
insider_buy_pct	heldPercentInsiders	% held by insiders
institutional_pct	heldPercentInstitutions	% held by institutions
short_ratio	shortRatio	Days to cover short interest
earnings_growth	earningsGrowth	YoY earnings growth (decimal)
current_ratio	currentRatio	Current assets / current liabilities
free_cash_flow	freeCashflow	Free cash flow in USD

All properties return None if the data is not available from yfinance.

Fundamental Filtering

Check whether a stock passes a set of criteria:

fd = FundamentalData('AAPL')
passes = fd.passes_filter({
    'pe_ratio': {'max': 30},
    'revenue_growth': {'min': 0.05},
    'market_cap': {'min': 100_000_000_000},
    'beta': {'min': 0.5, 'max': 2.0},
})

Each key is a property name. The value is a dict with 'min' and/or 'max'. If data for a property is missing (None), that filter is skipped (not failed).