Leveraging Industry Sectors for Smarter Value Investing with Python Scraping & FastAPI

1. Industry Sectors

Industry sectors differ from concept stocks; concept stocks are riskier and driven by short‑term news, while industry sectors are classified by stock industry and focus on long‑term stability.

In practice, short‑term selection can use market hotspots and concept stocks, but for medium‑to‑long term investment it is recommended to select stocks based on industry sectors.

2. Scraping sector and stock lists

Using the Toggle JavaScript plugin we discover that sector data comes from a request like http://**/?q=cn|bk|17&n=hqa&c=l&o=pl,d&p=1&_dc=1650680429759. Parameter p is the page number (starting at 1) and _dc is a 13‑digit timestamp.

We then write Python code to fetch the response, extract the sector list with a regular expression, and filter by keyword.

self.ps_url = 'http://**/?q=cn|bk|17&n=hqa&c=l&o=pl,d&p={}&_dc={}'

def __get_timestramp(self):
    """获取13位的时间戳"""
    return int(round(time.time() * 1000))

def get_plates_list(self, plate_keyword):
    plates = []
    index = 0
    while True:
        url = self.ps_url.format(index + 1, self.__get_timestramp())
        resp = self.session.get(url, headers=self.headers).text
        match = re.compile(r'HqData:(.*?)};', re.S)
        result = json.loads(re.findall(match, resp)[0].strip().replace("
", ""))
        if not result:
            break
        temp_plate_list = [item for item in result if plate_keyword in item[2]]
        index += 1
        for item in temp_plate_list:
            plates.append({
                "name": item[2],
                "plate_path": item[1],
                "up_or_down": str(item[10]) + "%",
                "top_stock": item[-6]
            })
    return plates

Similarly, stock data for a given sector is obtained from http://**/?q=cn|s|bk{plate_path}&c=m&n=hqa&o=pl,d&p=1&_dc={}. The code extracts stock name, PE, price, and change percentage, then sorts by PE.

self.stock_url = 'http://**/?q=cn|s|bk{}&c=m&n=hqa&o=pl,d&p={}020&_dc={}'

def get_stock_list(self, plate_path):
    stocks = []
    index = 0
    while True:
        url = self.stock_url.format(plate_path, index + 1, self.__get_timestramp())
        resp = self.session.get(url, headers=self.headers).text
        match = re.compile(r'HqData:(.*?)};', re.S)
        result = json.loads(re.findall(match, resp)[0].strip().replace("
", ""))
        if not result:
            break
        index += 1
        for item in result:
            if item[-1] < 0:
                continue
            stocks.append({
                "stock_name": item[2],
                "pe": item[-1],
                "price": item[8],
                "up_or_down": str(item[12]) + "%"
            })
    stocks.sort(key=lambda x: x["pe"])
    return stocks

By combining the sector path (e.g., 400128925) with the stock URL template, we can construct URLs such as http://summary.**/hybk/400128925.shtml to retrieve the stock list for a specific sector.

3. Serviceization

We wrap the scraping logic into FastAPI endpoints so that the front‑end can request sector lists and stock lists on demand.

@app.post("/xag/plate_list")
async def get_plate_list(plate: Plate):
    pstock = PStock()
    try:
        result = pstock.get_plates_list(plate.content)
        return success(data=result, message="查询成功！")
    except Exception:
        return fail()
    finally:
        pstock.teardown()

@app.post("/xag/plate_stock_list")
async def get_plate_stock_list(plateStock: PlateStock):
    pstock = PStock()
    try:
        result = pstock.get_stock_list(plateStock.plate_path)
        return success(data=result, message="查询成功！")
    except Exception:
        return fail()
    finally:
        pstock.teardown()

The front‑end, built with Uniapp, uses a form to submit a sector keyword, calls /xag/plate_list, and displays the returned sectors in a uni-table. Selecting a sector triggers /xag/plate_stock_list to show the stock list with columns for sector name, change percentage, and top stock.

// Simplified Uniapp template
<view class="box">
  <uni-forms ref="baseForm" :modelValue="baseFormData" :rules="rules">
    <uni-forms-item label="关键字" required name="content">
      <uni-easyinput v-model="baseFormData.content" placeholder="板块关键字"/>
    </uni-forms-item>
  </uni-forms>
  <button type="primary" @click="submit('baseForm')">提交</button>

  <view class="result" v-show="result.length>0">
    <uni-table border stripe emptyText="暂无数据">
      <uni-tr class="uni-item">
        <uni-th width="100%">板块</uni-th>
        <uni-th width="100%">涨跌幅</uni-th>
        <uni-th width="100%">强势股</uni-th>
      </uni-tr>
      <uni-tr v-for="(item, index) in result" :key="index" @row-click="rowclick(item)">
        <uni-td>{{ item.name }}</uni-td>
        <uni-td>{{ item.up_or_down }}</uni-td>
        <uni-td>{{ item.top_stock }}</uni-td>
      </uni-tr>
    </uni-table>
  </view>
</view>

After deployment, users can input a sector keyword, retrieve matching sectors, and then view the low‑PE stocks within the chosen sector for potential investment.

4. Summary

Industry sectors are more suitable for medium‑ to long‑term investing; by filtering sectors with a keyword and then sorting the stocks in the sector by low PE, investors can quickly identify attractive candidates.