mcpField
S
stock-screener

stock-screener

github.com/dkyazzentwatwa数据与数据库126183 次安装29 周前更新
代码仓库

关于 stock-screener

Stock Screener 是用于按财务指标筛选股票并比较结果的 Python 工具和命令行程序。它支持可复用筛选条件、行业分析、排名和导出，适合基于 CSV 文件或 dataframe 分析股票数据。

主要功能

  1. 01按估值、规模、收益、增长和质量指标筛选
  2. 02保存并复用自定义筛选条件
  3. 03并排比较股票
  4. 04按行业汇总数据
  5. 05按指标或综合评分排名
  6. 06导出 CSV、JSON 或报告

适用场景

  1. 01查找符合价值或增长条件的股票
  2. 02比较同一行业的候选股票
  3. 03使用加权指标为股票排名
  4. 04导出筛选结果以供进一步分析

安装

复制下面的命令，在终端中运行。

$npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --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

评论

支持 Markdown