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stock-screener

stock-screener

github.com/nicepkg数据与数据库28894 次安装41 周前更新
代码仓库

关于 stock-screener

Stock Screener 是一款基于股票财务指标筛选和比较股票的工具，适合研究人员通过 CSV 或 DataFrame 数据构建筛选条件。它支持预设筛选、评分排名、行业分析及多种格式导出。

主要功能

  1. 01按估值、规模、收益等指标筛选
  2. 02保存并复用自定义筛选
  3. 03并列比较股票
  4. 04按行业汇总分析
  5. 05单指标及综合评分排名
  6. 06导出 CSV、JSON 和报告

适用场景

  1. 01筛选低估值或高股息股票
  2. 02寻找符合增长指标的股票
  3. 03比较同一行业的候选股票
  4. 04导出筛选结果供后续分析

安装

复制下面的命令，在终端中运行。

$npx skills add https://github.com/nicepkg/ai-workflow --skill stock-screener

安装后在 Claude Code 中输入/stock-screener确认已加载。

附带文件scripts/requirements.txtscripts/stock_screener.py

详细介绍

股票筛选器

根据财务指标筛选股票并进行比较分析。

功能

  • 多指标筛选：市盈率、市净率、市值、股息率等。
  • 自定义筛选：保存并重复使用筛选条件组合
  • 比较分析：并排比较股票
  • 行业分析：按行业分组和分析
  • 排名：根据标准对股票评分和排名
  • 导出：CSV、JSON、格式化报告

快速开始

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 用法

# 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

输入格式

股票 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 参考

StockScreener 类
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

筛选标准

估值指标
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
)
规模指标
screener.filter(
    market_cap_min=1e9,    # Min $1B market cap
    market_cap_max=10e9,   # Max $10B (mid-cap)
    revenue_min=500e6      # Min $500M revenue
)
收入指标
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
)
增长指标
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
)
质量指标
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
)

预设筛选条件

价值筛选
results = screener.value_screen()
# Finds undervalued stocks:
# - P/E < 15
# - P/B < 2
# - Dividend yield > 2%
# - Profit margin > 10%
增长筛选
results = screener.growth_screen()
# Finds growth stocks:
# - Revenue growth > 15%
# - Earnings growth > 20%
# - PEG ratio < 2
股息筛选
results = screener.dividend_screen()
# Finds dividend stocks:
# - Dividend yield 2-8%
# - Payout ratio < 75%
# - 5+ years dividend history
质量筛选
results = screener.quality_screen()
# Finds high-quality stocks:
# - ROE > 15%
# - Profit margin > 15%
# - D/E < 0.5
# - Current ratio > 2

股票比较

comparison = screener.compare(["AAPL", "MSFT", "GOOGL"])
# Returns:
#                  AAPL    MSFT    GOOGL
# price           175.50  380.00  140.00
# pe_ratio        28.50   35.20   25.30
# market_cap      2.8T    2.8T    1.7T
# dividend_yield  0.50    0.80    0.00
# profit_margin   25.30   36.70   22.50
# ...

排名与评分

按单项指标排名
# Top 20 by dividend yield
top_dividend = screener.rank_by("dividend_yield", ascending=False).head(20)
综合评分
# Score stocks with custom weights
scores = screener.score_stocks({
    "pe_ratio": -0.2,        # Lower is better
    "dividend_yield": 0.3,   # Higher is better
    "profit_margin": 0.3,    # Higher is better
    "revenue_growth": 0.2    # Higher is better
})
# Returns stocks ranked by composite score
百分位排名
# See where each stock ranks on multiple metrics
ranked = screener.percentile_rank(["pe_ratio", "dividend_yield", "profit_margin"])
# Returns percentile (0-100) for each metric

行业分析

sector_stats = screener.sector_summary()
# Returns:
#   sector        | count | avg_pe | avg_div | avg_margin
#   Technology    | 45    | 28.5   | 1.2     | 22.3
#   Healthcare    | 32    | 18.2   | 2.1     | 18.7
#   Financials    | 28    | 12.5   | 3.2     | 25.1

工作流示例

查找被低估的股息股票
screener = StockScreener()
screener.load_csv("sp500.csv")

# Apply filters
results = screener.filter(
    pe_ratio=(5, 15),
    dividend_yield_min=3.0,
    payout_ratio_max=70,
    profit_margin_min=10
)

# Rank by dividend yield
top = results.sort_values("dividend_yield", ascending=False).head(10)
print(top[["symbol", "name", "pe_ratio", "dividend_yield", "payout_ratio"]])
合理价格增长（GARP）
results = screener.filter(
    revenue_growth_min=15,
    earnings_growth_min=15,
    peg_ratio_max=1.5,
    pe_ratio_max=25
)
行业比较
# Filter to technology sector
tech = screener.filter_by_sector(["Technology"]).filter(
    market_cap_min=10e9,
    profit_margin_min=15
)

# Compare top tech stocks
comparison = screener.compare(tech["symbol"].head(5).tolist())

输出格式

CSV 导出
screener.filter(pe_ratio_max=20).to_csv("value_stocks.csv")
JSON 导出
screener.filter(dividend_yield_min=3).to_json("dividend_stocks.json")
摘要报告
report = screener.summary_report()
# Returns formatted text summary of screening results

依赖项

  • pandas>=2.0.0
  • numpy>=1.24.0

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