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聚宽策略：获取最热门股票，计算5天涨幅大于25%的股票和10天涨幅大于50%的股票

2023-6-22 9:21 morefun 股票量化

聚宽策略：获取最热门股票，计算5天涨幅大于25%的股票和10天涨幅大于50%的股票代码如下：

from jqdata import *
import numpy as np
import pandas as pd
from six import BytesIO

# 读取股票代码列表
body = read_file("hot_stocks.csv")
data = pd.read_csv(BytesIO(body))
column1 = data.iloc[:, 2]
column2 = data.iloc[:, 1]
stocks = column1.tolist()

# 获取10天前和5天前的日期
end_date = '2023-06-19'
trade_days_10 = get_trade_days(end_date=end_date, count=10)
start_date_10 = trade_days_10[0]
trade_days_5 = get_trade_days(end_date=end_date, count=5)
start_date_5 = trade_days_5[0]

# 初始化空列表，存储涨幅超过50%和25%的股票代码和名称
selected_10 = []
selected_5 = []

# 遍历股票代码列表
for name, code in zip(column1, column2):
    # 获取历史行情数据
    df = get_price(code, start_date=start_date_10, end_date=end_date, frequency='daily')
    # 计算10天涨幅和5天涨幅
    change_10 = (df['close'][-1] - df['close'][0]) / df['close'][0] * 100
    df = get_price(code, start_date=start_date_5, end_date=end_date, frequency='daily')
    change_5 = (df['close'][-1] - df['close'][0]) / df['close'][0] * 100
    # 打印结果
    print("{} {} 10天涨幅: {:.2f}%, 5天涨幅: {:.2f}%".format(code, name, change_10, change_5))
    # 判断涨幅是否超过50%和25%
    if change_10 > 50:
        selected_10.append(code + ' ' + name)
    if change_5 > 25:
        selected_5.append(code + ' ' + name)

# 打印符合条件的股票代码和名称列表
print("10天涨幅大于50%的股票: {}".format(selected_10))
print("5天涨幅大于25%的股票: {}".format(selected_5))

运行结果如下：

附用到的文件：hot_stocks.csv

标签: 股票量化