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个股研报事件策略&选股结果自动推送到微信

基于东方财富网的个股研报数据进行事件策略选股，并实现自动化筛选股票和推送选股结果。

image-20221023210513295

App

策略逻辑：选择当天发布的研报中评级为“买入”（最高评级）的股票，按照“近一个月个股研报数目”降序排列，选择前若干只股票进行等权买入。

将策略代码部署到Streamlit上，定期执行事件选股策略并将选股结果推送到微信，可以大大节省重复运行策略代码的时间，提高交易的执行效率。

演示地址

东方财富网个股研报数据爬虫

Python
from urllib.request import urlopen  # python 自带爬虫库
import json  # python 自带的 JSON 数据库
import pandas as pd
import time
import datetime


def change(x):
    if x[:1] == "6" or x[:3] == "900":
        return "sh" + x
    elif x[:2] == "00" or x[:2] == "30" or x[:3] == "200":
        return "sz" + x
    else:
        return x


def get_research_report_data(begin_time, until_time):
    """
    获取个股研报历史数据，设置起始时间和结束时间
    :param until_time:
    :param begin_time: 起始时间 格式为'2020-06-01'
    :param until_time: 起始时间 格式为'2020-06-01'
    :return:
    """
    # http://data.eastmoney.com/report/stock.jshtml
    if len(begin_time.split("-")) != 3:
        print("begin_tiem_str 格式有误 格式为’%Y-%m-%d‘")
        return None

    try:
        ts = int(time.time() * 1000)
        if until_time is None:
            until_time = now_date.strftime("%Y-%m-%d")
        pageNum = 1
        data = []

        while True:
            url_1 = "http://reportapi.eastmoney.com/report/list?cb=datatable4263982&industryCode=*&pageSize=100&industry=*&rating=*&ratingChange=*&beginTime=%s&endTime=%s&pageNo=%d&fields=&qType=0&orgCode=&code=*&rcode=&_=%d"
            url_stockResearch = url_1 % (begin_time, until_time, pageNum, ts)
            content = urlopen(url_stockResearch).read().decode("utf-8")

            content = content.split("(", maxsplit=1)[-1][:-1]  # 去掉括号
            # content_0 = content_0.split(")", maxsplit=1)[0]  # 去掉右括号
            content_1 = json.loads(content)  # 自己去仔细看下这里面有什么数据
            if content_1["data"]:
                df = pd.DataFrame(content_1["data"])
            else:
                print("今日个股研报无数据")
                break
            df_0 = df[
                [
                    "stockName",
                    "stockCode",
                    "title",
                    "orgName",
                    "orgSName",
                    "predictThisYearPe",
                    "predictThisYearEps",
                    "predictNextYearPe",
                    "predictNextYearEps",
                    "predictNextTwoYearPe",
                    "predictNextTwoYearEps",
                    "indvInduName",
                    "indvInduCode",
                    "emRatingName",
                    "lastEmRatingName",
                    "count",
                    "publishDate",
                ]
            ].copy()
            df_0.rename(
                columns={
                    "stockName": "股票名称",
                    "stockCode": "股票代码",
                    "title": "研报标题",
                    "orgName": "机构",
                    "orgSName": "机构名称",
                    "predictThisYearPe": "预测今年市盈率",
                    "predictThisYearEps": "预测今年每股收益",
                    "predictNextYearPe": "预测明年市盈率",
                    "predictNextYearEps": "预测明年每股收益",
                    "predictNextTwoYearPe": "预测后年市盈率",
                    "predictNextTwoYearEps": "预测后年每股收益",
                    "indvInduName": "行业名称",
                    "indvInduCode": "行业代码",
                    "emRatingName": "评级",
                    "lastEmRatingName": "上次评级",
                    "count": "近一个月个股研报数目",
                    "publishDate": "发布日期",
                },
                inplace=True,
            )
            data.append(df_0)
            print("已爬取第%d页，当前页有%d行数据" % (pageNum, len(df_0)))
            pageNum += 1
            time.sleep(1)
        if not data:
            return None
        else:
            total = pd.concat(data, ignore_index=True)
            total["股票代码"] = total["股票代码"].apply(lambda x: change(x))
            return total
    except Exception as e:
        print("报错，报错内容：", str(e))
        return None

获取指定日期范围的个股研报数据（默认为当日）

Python
now_date = datetime.datetime.now()
begin_time = st.sidebar.date_input("起始日期", now_date)
until_time = st.sidebar.date_input("结束日期", now_date)

begin_time_str = begin_time.strftime("%Y-%m-%d")
until_time_str = until_time.strftime("%Y-%m-%d")

data = get_research_report_data(begin_time_str, until_time_str)

通过Akshare获取股票最新行情数据

Python
import akshare as ak


def get_price():
    return ak.stock_zh_a_spot_em()

设置选股条件

Python
st.sidebar.write("设置选股条件")
# 设置选股条件，对股票进行筛选
condition = None
# 评级为最高等级的买入
if st.sidebar.checkbox("投资评级为买入", True):
    condition = data["评级"] == "买入"
# 应用选股条件
selected_stock = data[condition]
if st.sidebar.checkbox("选择研报数量最多的股票", True):
    selected_stock = selected_stock.sort_values(by="近一个月个股研报数目", ascending=False)
# 删除重复的股票
selected_stock.drop_duplicates(subset=["股票代码"], keep="first", inplace=True)
# 重新索引
selected_stock.index = range(len(selected_stock))

# 设置买入股票数量
buy_num = st.sidebar.number_input("买入股票数量", 1, None, 5)
# 求出买入股票的代码和名称
buy_stock = selected_stock[["股票代码", "股票名称", "近一个月个股研报数目"]][:buy_num]

结合最新价，计算应买入的股票数量

Python
# 每只股票买入金额
buy_money = st.sidebar.number_input("每只股票买入金额", 1, None, 100000, 10000)
# 删除代码中的前缀
buy_stock["代码"] = buy_stock["股票代码"].apply(lambda x: x[2:])
# 获取股票的实时数据
price = get_price()
# 将最新价合并到买入股票的代码和名称中
buy_stock = pd.merge(buy_stock, price, on="代码", how="left")
# 计算每只股票买入的股数
buy_stock["买入股数"] = buy_stock["最新价"].apply(lambda x: int((buy_money // x) // 100 * 100))

通过pushplus推送到微信

Python
# 开始推动到 pushplus
if st.button("推送到微信"):
    # 推送标题
    title = now_date.strftime("%Y-%m-%d") + "个股研报策略选股结果"
    # 将 buy_stock 转换为 Markdown 格式
    buy_stock_md = buy_stock.to_markdown(index=False)
    # 推送内容
    content = buy_stock_md
    template = "markdown"
    headers = {"Content-Type": "application/json"}
    url = (
        "http://www.pushplus.plus/send?token="
        + token
        + "&title="
        + title
        + "&content="
        + content
        + "&template="
        + template
    )
    requests.get(url)
    st.success("推送成功")

推送到微信：

image-20221023210918610

点击消息后可看到选股结果表格：

image-20221023211001264

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