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name: pywencai description: Tonghuashun WenCai natural language data query tool — query A-share, index, fund, HK/US stock, convertible bond, and other market data using Chinese natural language. version: 1.1.0 homepage: https://github.com/zsrl/pywencai metadata: {"clawdbot":{"emoji":"📈","requires":{"bins":["python3","node"]}}}

PyWenCai (Tonghuashun WenCai Data Query)

Query A-share and other market data from Tonghuashun WenCai using Chinese natural language via Python.

⚠️ Cookie Required: You must provide a valid cookie from the WenCai website. See how to obtain a cookie below.

Requirements

• Python 3.7+
• Node.js v16+ (pywencai internally executes JS code)
• pip package manager

Installation

pip install pywencai --upgrade

How to Obtain a Cookie

1. Open https://www.iwencai.com/ in your browser and log in.
2. Press F12 to open Developer Tools → switch to the Network tab.
3. Perform any query on the WenCai page.
4. Find a request to iwencai.com and copy the Cookie value from the request headers.
5. Use that string as the cookie parameter.

Basic Usage

import pywencai

# Query the top 10 stocks by today's gain; a valid cookie is required
res = pywencai.get(query='今日涨幅前10', cookie='your_cookie_here')
print(res)

API Reference: pywencai.get(**kwargs)

Required Parameters

• query — Chinese natural language query string, e.g. '今日涨停股票', '市盈率小于20的股票'
• cookie — Cookie string obtained from the WenCai website (required)

Optional Parameters

• sort_key — Sort field name, e.g. '退市@退市日期'
• sort_order — Sort direction: 'asc' (ascending) or 'desc' (descending)
• page — Page number (default: 1)
• perpage — Results per page (default and max: 100)
• loop — Set to True to fetch all pages; or set to an integer n to fetch the first n pages
• query_type — Query category (default: 'stock'), possible values:
  • stock — A-share stocks
  • zhishu — Indices
  • fund — Funds
  • hkstock — Hong Kong stocks
  • usstock — US stocks
  • threeboard — New Third Board (NEEQ)
  • conbond — Convertible bonds
  • insurance — Insurance
  • futures — Futures
  • lccp — Wealth management products
• retry — Number of retries on failure (default: 10)
• sleep — Delay in seconds between paginated requests (default: 0)
• log — Set to True to print logs to the console
• pro — Set to True to use the paid version (requires a corresponding cookie)
• no_detail — Set to True to always return a DataFrame or None (never returns a dict)
• find — List of stock codes to prioritize, e.g. ['600519', '000010']
• request_params — Extra parameters passed to requests, e.g. {'proxies': proxies}

Return Values

• List-type queries → returns a pandas.DataFrame
• Detail-type queries → returns a dict (may contain text and DataFrames)

Usage Examples

Query Stocks with P/E Ratio Below 20

import pywencai

# Use natural language to query low P/E ratio stocks
res = pywencai.get(query='市盈率小于20的股票', cookie='xxx')
print(res)

Get Delisted Stocks Sorted by Date

import pywencai

# Query delisted stocks, sorted by delisting date in ascending order
res = pywencai.get(
    query='退市股票',
    sort_key='退市@退市日期',  # Specify the sort field
    sort_order='asc',          # Ascending order
    cookie='xxx'
)
print(res)

Paginate Through All Data with a Proxy

import pywencai

# Configure HTTP proxy
proxies = {'http': 'http://proxy:8080', 'https': 'http://proxy:8080'}

# loop=True auto-paginates to fetch all data; log=True prints request logs
res = pywencai.get(
    query='昨日涨幅',
    sort_order='asc',          # Ascending order
    loop=True,                 # Fetch all pages automatically
    log=True,                  # Print log messages
    cookie='xxx',
    request_params={'proxies': proxies}  # Pass proxy configuration
)
print(res)

Query Index Data

import pywencai

# Set query_type='zhishu' to query index data
res = pywencai.get(
    query='上证指数近5日涨跌幅',
    query_type='zhishu',       # Set query type to index
    cookie='xxx'
)
print(res)

Query Convertible Bond Data

import pywencai

# Set query_type='conbond' to query convertible bond data
res = pywencai.get(
    query='可转债溢价率小于10%',
    query_type='conbond',      # Set query type to convertible bond
    cookie='xxx'
)
print(res)

Tips

• Use sparingly — High-frequency calls may get you blocked by the WenCai server.
• Always use the latest version: pip install pywencai --upgrade
• Query strings use Chinese natural language — write queries as you would search on the WenCai website.
• When loop=True and find is set, loop is ignored and only the first 100 results are returned.
• For paid data, set pro=True and provide a valid cookie.

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Advanced Examples

Query Limit-Up Stock Details

import pywencai

# Query stocks that hit the daily limit up today, get detailed info
res = pywencai.get(
    query='今日涨停的股票',
    cookie='xxx'
)
# Returns a DataFrame with: stock code, name, limit-up time, bid amount, etc.
print(res)

Query Consecutive Limit-Up Stocks

import pywencai

# Query stocks with more than 2 consecutive limit-up days
res = pywencai.get(
    query='连续涨停天数大于2天的股票',
    cookie='xxx'
)
print(res)

Query Financial Data

import pywencai

# Query stocks with ROE > 15% and revenue growth > 20%
res = pywencai.get(
    query='ROE大于15%且营收同比增长率大于20%的股票',
    cookie='xxx'
)
print(res)

# Query stocks with P/E < 10 and P/B < 1 (undervalued screening)
res = pywencai.get(
    query='市盈率小于10且市净率小于1的股票',
    cookie='xxx'
)
print(res)

Query Technical Indicator Data

import pywencai

# Query stocks with a MACD golden cross today
res = pywencai.get(
    query='今日MACD金叉的股票',
    cookie='xxx'
)
print(res)

# Query stocks with KDJ oversold signal
res = pywencai.get(
    query='KDJ的J值小于0的股票',
    cookie='xxx'
)
print(res)

# Query stocks with volume breakout
res = pywencai.get(
    query='今日成交量是5日均量2倍以上且涨幅大于5%的股票',
    cookie='xxx'
)
print(res)

Query Capital Flow Data

import pywencai

# Query top 20 stocks by net main capital inflow today
res = pywencai.get(
    query='今日主力资金净流入前20的股票',
    cookie='xxx'
)
print(res)

# Query stocks with the highest northbound capital holding ratio
res = pywencai.get(
    query='北向资金持股比例最高的前20只股票',
    cookie='xxx'
)
print(res)

Query Fund Data

import pywencai

# Query top 20 funds by return rate over the past year
res = pywencai.get(
    query='近一年收益率最高的前20只基金',
    query_type='fund',     # Set query type to fund
    cookie='xxx'
)
print(res)

Query Hong Kong Stock Data

import pywencai

# Query the largest HK stocks by market cap
res = pywencai.get(
    query='港股市值最大的前20只股票',
    query_type='hkstock',  # Set query type to HK stock
    cookie='xxx'
)
print(res)

Multi-Criteria Stock Screening

import pywencai

# Complex multi-criteria screening: undervalued + high growth + institutional holdings
res = pywencai.get(
    query='市盈率小于20且营收同比增长大于30%且机构持仓比例大于10%的股票',
    cookie='xxx'
)
print(res)

# Technical + fundamental combined screening
res = pywencai.get(
    query='今日站上20日均线且市盈率小于30且ROE大于10%的股票',
    cookie='xxx'
)
print(res)

Retrieve Historical Data

import pywencai

# Query data for a specific date
res = pywencai.get(
    query='2024年1月2日涨幅前10的股票',
    cookie='xxx'
)
print(res)

# Query gain over a date range
res = pywencai.get(
    query='2024年上半年涨幅最大的前20只股票',
    cookie='xxx'
)
print(res)

Full Example: Automated Stock Screening and Export

import pywencai
import pandas as pd
import time

cookie = 'your_cookie_here'

# Define multiple screening strategies
strategies = {
    "低估值高分红": "市盈率小于15且股息率大于3%的股票",
    "高成长": "营收同比增长大于30%且净利润同比增长大于30%的股票",
    "技术突破": "今日放量突破20日均线且涨幅大于3%的股票",
    "机构关注": "近一个月机构调研次数大于3次的股票",
    "北向资金": "北向资金今日净买入前20的股票",
}

results = {}
for name, query in strategies.items():
    try:
        res = pywencai.get(query=query, cookie=cookie, no_detail=True)
        if res is not None and not res.empty:
            results[name] = res
            print(f"Strategy [{name}] selected {len(res)} stocks")
        else:
            print(f"Strategy [{name}] returned no results")
    except Exception as e:
        print(f"Strategy [{name}] query failed: {e}")
    time.sleep(2)  # Wait 2 seconds between queries to avoid being blocked

# Save results to Excel (one sheet per strategy)
if results:
    with pd.ExcelWriter("选股结果.xlsx") as writer:
        for name, df in results.items():
            df.to_excel(writer, sheet_name=name, index=False)
    print("Screening results saved to 选股结果.xlsx")

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