qstock Documentation

Install the qstock library using pip. To update to the latest version, use the --upgrade flag.

Note for Wencai users: To use the wencai feature, you must upgrade pywencai (pip install --upgrade pywencai) and install jsdom via Node.js by running npm install jsdom in your terminal.

You can create a word cloud by fetching news data using qs.news_data, processing the text content to handle missing values and convert it to a string, and then using the plot module to process the text with jieba and render the chart.

Note: This workflow assumes you have a plot object available (likely a utility module within the library) that provides cloud_data and chart_wordcloud methods.

Use qs.realtime_data(market, code) to retrieve the latest market indicators for an entire market or specific securities.

Parameters:

• market: The market name or list. Common values include:
  • '沪深京A', '沪深A', '沪A', '深A', '北A' (A-shares)
  • '可转债' (Convertible bonds)
  • '期货' (Futures)
  • '创业板' (GEM)
  • '美股' (US stocks)
  • '港股' (HK stocks)
  • '行业板块', '概念板块' (Sectors/Concepts)
  • 'ETF', 'LOF'
  • '沪深指数', '上证指数', '深证指数'
• code: A single security name/code or a list of names/codes (e.g., '中国平安', '000001', or ['AAPL', '000001']). If omitted, returns data for the entire market.

Retrieve the latest real-time market snapshot for a specific stock in the Shanghai/Shenzhen market.

qs.stock_snapshot('中国平安')

Retrieve real-time market anomaly data (often used for monitoring price action).

Parameters:

• flag: The type of anomaly to filter for. You can use descriptive strings or integer IDs (1-22).

Available Flags (Examples):

• '火箭发射' (Rocket launch)
• '快速反弹' (Rapid rebound)
• '加速下跌' (Accelerated fall)
• '60日新高' (60-day high)
• '60日新低' (60-day low)
• '大笔买入' (Large buy order)
• '封涨停板' (Hit upper limit)

Use plot.scatter() to visualize relationships between two variables.

Parameters:

• trend: Set to 'ols' to add an Ordinary Least Squares regression line.
• marginal_x: Add marginal distributions (e.g., 'histogram', 'violin').
• marginal_y: Add marginal distributions (e.g., 'box').
• size: Column name to scale point size based on value.

Use plot.treemap() to visualize hierarchical data like industry sectors or market concepts.

Parameters:

• data: DataFrame containing the data.
• label: A list of column names or labels to display (e.g., ['Industry', 'Stock Name']).
• weight: The column name used to determine the size/weight of the rectangles.
• value: The column name used for the values.
• color: A list of colors for the visualization.

Access common macro-economic indicators.

Parameters:

• flag: The indicator type:
  • 'gdp': GDP data (default).
  • 'cpi': Consumer Price Index.
  • 'ppi': Producer Price Index.
  • 'pmi': Purchasing Managers' Index.
  • 'ms': Money Supply.
  • 'lpr': Loan Prime Rate.

Alternatively, you can use dedicated convenience functions: qs.cpi(), qs.gdp(), qs.ms(), qs.ppi(), qs.pmi(), or qs.lpr().

Retrieve interbank lending rates for various markets.

Parameters:

• market: The market identifier:
  • 'sh': Shanghai Interbank Market.
  • 'ch': China Interbank Market.
  • 'l': London Interbank Market (Note: use lowercase 'l').
  • 'eu': European Interbank Market.
  • 'hk': Hong Kong Interbank Market.
  • 's': Singapore Interbank Market.
• fc: Foreign currency code (e.g., 'USD', 'GBP', 'EUR', 'JPY', 'HKD', 'SGD', 'CNY').

Returns a pandas DataFrame.

The library provides several tools for statistical analysis:

• plot.hist(data, ...): Histograms. Use histnorm to set normalization: 1:'percent', 2:'probability', 3:'density', 4:'probability density'.
• plot.hist_kde(data, ...): Histogram with Kernel Density Estimation. Use stat to choose: 'count', 'frequency', 'density', or 'probability'. Set kde=True to add the curve.
• plot.box(data, ...): Box plots for visualizing quartiles.
• plot.violin(data, ...): Violin plots, combining box plots with kernel density estimation to show data distribution shape.

Use financial_statement(flag, date) to fetch various types of financial reports.

Parameters:

• flag: The type of report. Supported values:
  • '业绩报表' or 'yjbb': Annual/Quarterly financial indicators.
  • '业绩快报' or 'yjkb': Latest performance previews (Note: currently has a known bug in some versions).
  • '业绩预告' or 'yjyg': Latest performance forecasts.
  • '资产负债表' or 'zcfz': Latest balance sheet indicators.
  • '利润表' or 'lrb': Latest income statement indicators.
  • '现金流量表' or 'xjll': Latest cash flow statement indicators.
• date: Report date in 'YYYYMMDD' format (e.g., '20220630'). Defaults to the latest available report.

Returns a pandas DataFrame.