Stock Screener

Filter stocks by financial metrics and perform comparative analysis.

Features

• Multi-Metric Filtering: P/E, P/B, market cap, dividend yield, etc.
• Custom Screens: Save and reuse filter combinations
• Comparative Analysis: Side-by-side stock comparison
• Sector Analysis: Group and analyze by sector
• Ranking: Score and rank stocks by criteria
• Export: CSV, JSON, formatted reports

Quick Start

from stock_screener import StockScreener

screener = StockScreener()

# Load stock data
screener.load_csv("stocks.csv")

# Apply filters
results = screener.filter(
    pe_ratio=(0, 20),
    market_cap_min=1e9,
    dividend_yield_min=2.0
)

print(results)

CLI Usage

# Basic screening
python stock_screener.py --input stocks.csv --pe-max 20 --div-min 2.0

# Multiple filters
python stock_screener.py --input stocks.csv --pe 5 25 --pb-max 3 --cap-min 1B

# Sector filter
python stock_screener.py --input stocks.csv --sector Technology --pe-max 30

# Rank by metric
python stock_screener.py --input stocks.csv --rank-by dividend_yield --top 20

# Compare specific stocks
python stock_screener.py --input stocks.csv --compare AAPL MSFT GOOGL

# Export results
python stock_screener.py --input stocks.csv --pe-max 15 --output screened.csv

Input Format

Stock CSV

symbol,name,sector,price,pe_ratio,pb_ratio,market_cap,dividend_yield,eps,revenue_growth,profit_margin
AAPL,Apple Inc,Technology,175.50,28.5,45.2,2.8e12,0.5,6.16,8.5,25.3
MSFT,Microsoft,Technology,380.00,35.2,12.8,2.8e12,0.8,10.79,12.3,36.7
JNJ,Johnson & Johnson,Healthcare,155.00,15.2,5.8,3.8e11,2.9,10.20,5.2,22.1

API Reference

StockScreener Class

class StockScreener:
    def __init__(self)

    # Data Loading
    def load_csv(self, filepath: str) -> 'StockScreener'
    def load_dataframe(self, df: pd.DataFrame) -> 'StockScreener'

    # Filtering
    def filter(self, **criteria) -> pd.DataFrame
    def filter_by_sector(self, sectors: List[str]) -> 'StockScreener'
    def filter_by_metric(self, metric: str, min_val: float = None,
                         max_val: float = None) -> 'StockScreener'

    # Screening Presets
    def value_screen(self) -> pd.DataFrame
    def growth_screen(self) -> pd.DataFrame
    def dividend_screen(self) -> pd.DataFrame
    def quality_screen(self) -> pd.DataFrame
    def custom_screen(self, criteria: Dict) -> pd.DataFrame

    # Analysis
    def compare(self, symbols: List[str]) -> pd.DataFrame
    def rank_by(self, metric: str, ascending: bool = True) -> pd.DataFrame
    def sector_summary(self) -> pd.DataFrame
    def metric_distribution(self, metric: str) -> Dict

    # Scoring
    def score_stocks(self, weights: Dict[str, float] = None) -> pd.DataFrame
    def percentile_rank(self, metrics: List[str]) -> pd.DataFrame

    # Export
    def to_csv(self, filepath: str) -> str
    def to_json(self, filepath: str) -> str
    def summary_report(self) -> str

Filtering Criteria

Valuation Metrics

screener.filter(
    pe_ratio=(5, 20),      # P/E between 5 and 20
    pb_ratio_max=3.0,      # P/B ratio under 3
    ps_ratio_max=5.0,      # Price/Sales under 5
    peg_ratio_max=1.5      # PEG ratio under 1.5
)

Size Metrics

screener.filter(
    market_cap_min=1e9,    # Min $1B market cap
    market_cap_max=10e9,   # Max $10B (mid-cap)
    revenue_min=500e6      # Min $500M revenue
)

Income Metrics

screener.filter(
    dividend_yield_min=2.0,  # Min 2% dividend
    dividend_yield_max=8.0,  # Max 8% (avoid yield traps)
    payout_ratio_max=75      # Sustainable payout
)

Growth Metrics

screener.filter(
    revenue_growth_min=10,   # Min 10% revenue growth
    earnings_growth_min=15,  # Min 15% earnings growth
    eps_growth_min=10        # Min 10% EPS growth
)

Quality Metrics

screener.filter(
    profit_margin_min=15,    # Min 15% profit margin
    roe_min=15,              # Min 15% return on equity
    debt_to_equity_max=1.0,  # Max 1.0 D/E ratio
    current_ratio_min=1.5    # Min 1.5 current ratio