245 lines
7.6 KiB
Python
245 lines
7.6 KiB
Python
import requests
|
||
import pandas as pd
|
||
import json
|
||
from datetime import datetime, timedelta
|
||
import time
|
||
import matplotlib.pyplot as plt
|
||
import matplotlib
|
||
|
||
# 设置全局字体
|
||
matplotlib.rcParams['font.sans-serif'] = ['Microsoft YaHei', 'SimHei', 'SimSun', 'Arial Unicode MS']
|
||
matplotlib.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
|
||
import numpy as np
|
||
import tkinter as tk
|
||
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
|
||
from matplotlib.figure import Figure
|
||
|
||
|
||
def get_stock_data(stock_code, market_code, days=90):
|
||
"""获取股票历史数据，限制为过去90天的数据"""
|
||
url = "http://push2his.eastmoney.com/api/qt/stock/kline/get"
|
||
|
||
# 获取最近90天的数据
|
||
end_date = datetime.now()
|
||
start_date = end_date - timedelta(days=days)
|
||
|
||
# 格式化日期
|
||
beg = start_date.strftime('%Y%m%d')
|
||
end = end_date.strftime('%Y%m%d')
|
||
|
||
params = {
|
||
'secid': f'{market_code}.{stock_code}',
|
||
'fields1': 'f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f11,f12,f13',
|
||
'fields2': 'f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61',
|
||
'klt': '101', # 101表示日K线数据
|
||
'fqt': '0', # 不复权
|
||
'beg': beg, # 起始日期
|
||
'end': end, # 结束日期
|
||
'lmt': days, # 获取的天数
|
||
'_': int(time.time() * 1000)
|
||
}
|
||
|
||
headers = {
|
||
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'
|
||
}
|
||
|
||
try:
|
||
response = requests.get(url, params=params, headers=headers)
|
||
data = json.loads(response.text)
|
||
|
||
if data['data'] is None:
|
||
raise Exception(f"未能获取到{stock_code}的数据")
|
||
|
||
stock_data = data['data']['klines']
|
||
|
||
parsed_data = []
|
||
for item in stock_data:
|
||
values = item.split(',')
|
||
parsed_data.append({
|
||
'日期': values[0],
|
||
'开盘价': float(values[1]),
|
||
'收盘价': float(values[2]),
|
||
'最高价': float(values[3]),
|
||
'最低价': float(values[4]),
|
||
'成交量': float(values[5]),
|
||
'成交额': float(values[6]),
|
||
'振幅': float(values[7]),
|
||
'涨跌幅': float(values[8]),
|
||
'涨跌额': float(values[9]),
|
||
'换手率': float(values[10])
|
||
})
|
||
|
||
df = pd.DataFrame(parsed_data)
|
||
df['RSI'] = calculate_rsi(df['收盘价'].values)
|
||
|
||
# 返回90天的数据，但仅保留最后30天的数据用于可视化
|
||
return df.tail(30) # 只返回最后30天的数据
|
||
|
||
except Exception as e:
|
||
print(f"获取数据时发生错误: {str(e)}")
|
||
return None
|
||
|
||
|
||
def calculate_rsi(prices, periods=14):
|
||
"""计算RSI指标"""
|
||
rsi = np.zeros_like(prices)
|
||
if len(prices) <= periods:
|
||
return rsi
|
||
|
||
changes = np.diff(prices)
|
||
gains = np.where(changes > 0, changes, 0)
|
||
losses = np.where(changes < 0, -changes, 0)
|
||
|
||
avg_gain = np.mean(gains[:periods])
|
||
avg_loss = np.mean(losses[:periods])
|
||
|
||
if avg_loss == 0:
|
||
rsi[periods] = 100
|
||
else:
|
||
rs = avg_gain / avg_loss
|
||
rsi[periods] = 100 - (100 / (1 + rs))
|
||
|
||
for i in range(periods + 1, len(prices)):
|
||
avg_gain = (avg_gain * (periods - 1) + gains[i - 1]) / periods
|
||
avg_loss = (avg_loss * (periods - 1) + losses[i - 1]) / periods
|
||
|
||
if avg_loss == 0:
|
||
rsi[i] = 100
|
||
else:
|
||
rs = avg_gain / avg_loss
|
||
rsi[i] = 100 - (100 / (1 + rs))
|
||
|
||
# 确保RSI值在0到100之间
|
||
rsi[i] = max(0, min(rsi[i], 100))
|
||
|
||
return rsi
|
||
|
||
|
||
def create_visualization_window(rsi_data):
|
||
"""创建可视化窗口"""
|
||
root = tk.Tk()
|
||
root.title("创业板指 RSI走势") # 修改标题为"创业板指 RSI走势"
|
||
root.geometry("1200x800")
|
||
|
||
# 创建Figure
|
||
fig = Figure(figsize=(12, 8), dpi=100)
|
||
ax = fig.add_subplot(111)
|
||
|
||
# 设置样式
|
||
ax.set_facecolor('#f0f0f0')
|
||
fig.patch.set_facecolor('white')
|
||
|
||
# 绘制RSI线
|
||
for index_name, data in rsi_data.items():
|
||
dates = pd.to_datetime(data['日期'])
|
||
ax.plot(dates, data['RSI'], label=index_name, color='#2878B5', linewidth=2, marker='o', markersize=4)
|
||
|
||
# 添加过度买入/卖出区域
|
||
ax.axhline(y=70, color='#C82423', linestyle='--', alpha=0.3)
|
||
ax.axhline(y=30, color='#2878B5', linestyle='--', alpha=0.3)
|
||
ax.fill_between(ax.get_xlim(), 70, 100, color='#C82423', alpha=0.1)
|
||
ax.fill_between(ax.get_xlim(), 0, 30, color='#2878B5', alpha=0.1)
|
||
|
||
# 设置y轴范围
|
||
ax.set_ylim(0, 100)
|
||
|
||
# 设置标题和标签
|
||
ax.set_title('创业板指 RSI走势', fontsize=16, pad=20) # 修改标题为"创业板指 RSI走势"
|
||
ax.set_xlabel('日期', fontsize=12)
|
||
ax.set_ylabel('RSI值', fontsize=12)
|
||
|
||
# 设置图例
|
||
ax.legend(loc='best', fontsize=10)
|
||
|
||
# 设置网格
|
||
ax.grid(True, linestyle='--', alpha=0.3)
|
||
|
||
# 调整x轴标签
|
||
plt.setp(ax.get_xticklabels(), rotation=45)
|
||
|
||
# 调整布局
|
||
fig.tight_layout()
|
||
|
||
# 创建canvas
|
||
canvas = FigureCanvasTkAgg(fig, master=root)
|
||
canvas.draw()
|
||
canvas.get_tk_widget().pack(side=tk.TOP, fill=tk.BOTH, expand=1)
|
||
|
||
# 添加关闭按钮
|
||
close_button = tk.Button(root, text="关闭", command=root.quit, font=('SimHei', 12))
|
||
close_button.pack(side=tk.BOTTOM, pady=10)
|
||
|
||
return root
|
||
|
||
|
||
def save_to_excel(df, filename):
|
||
"""安全地保存数据到Excel文件"""
|
||
try:
|
||
# 尝试在data子目录中保存文件
|
||
import os
|
||
data_dir = 'data'
|
||
|
||
# 创建data目录（如果不存在）
|
||
if not os.path.exists(data_dir):
|
||
os.makedirs(data_dir)
|
||
|
||
# 构建完整的文件路径
|
||
file_path = os.path.join(data_dir, filename)
|
||
|
||
# 如果文件已存在，尝试生成一个新的文件名
|
||
base_name = os.path.splitext(filename)[0]
|
||
extension = os.path.splitext(filename)[1]
|
||
counter = 1
|
||
while os.path.exists(file_path):
|
||
new_filename = f"{base_name}_{counter}{extension}"
|
||
file_path = os.path.join(data_dir, new_filename)
|
||
counter += 1
|
||
|
||
# 保存文件
|
||
df.to_excel(file_path, index=False)
|
||
print(f"数据已成功保存到: {file_path}")
|
||
return True
|
||
except Exception as e:
|
||
print(f"保存文件时发生错误: {str(e)}")
|
||
return False
|
||
|
||
|
||
def main():
|
||
# 只爬取创业板指的数据
|
||
indices = [
|
||
{'code': '399006', 'market': 0, 'name': '创业板指'} # 修改为创业板指的代码和市场代码
|
||
]
|
||
|
||
# 存储创业板指的数据
|
||
rsi_data = {}
|
||
current_date = datetime.now().strftime('%Y%m%d')
|
||
|
||
# 爬取创业板指的数据
|
||
for index in indices:
|
||
df = get_stock_data(index['code'], index['market'], days=90) # 获取90天的数据
|
||
if df is not None:
|
||
# 保存完整数据到Excel
|
||
output_file = f'stock_data_{index["code"]}_{current_date}.xlsx'
|
||
if save_to_excel(df, output_file):
|
||
# 保存RSI数据用于可视化
|
||
rsi_data[index['name']] = df[['日期', 'RSI']]
|
||
|
||
# 保存RSI数据到单独的Excel
|
||
rsi_df = pd.DataFrame()
|
||
first_dates = None
|
||
|
||
for index_name, data in rsi_data.items():
|
||
if first_dates is None:
|
||
first_dates = data['日期']
|
||
rsi_df['日期'] = first_dates
|
||
rsi_df[f'{index_name}_RSI'] = data['RSI']
|
||
|
||
rsi_output = f'RSI_data_{current_date}.xlsx'
|
||
if save_to_excel(rsi_df, rsi_output):
|
||
# 创建并显示创业板指的RSI折线图
|
||
root = create_visualization_window(rsi_data)
|
||
root.mainloop()
|
||
|
||
if __name__ == "__main__":
|
||
main()
|