python +baostock +talib 计算移动RSI

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jun 20 14:12:58 2025

@author: lg
"""
import baostock as bs
import pandas as pd
import talib
import numpy as np

import talib
# import yfinance as yf  # 用于获取股票数据
import matplotlib.pyplot as plt


def get_data(code, start, end):
    #### 登陆系统 ####

    # 显示登陆返回信息
    # print('login respond error_code:'+lg.error_code)
    # print('login respond  error_msg:'+lg.error_msg)

    #### 获取沪深A股历史K线数据 ####
    # 详细指标参数，参见“历史行情指标参数”章节；“分钟线”参数与“日线”参数不同。“分钟线”不包含指数。
    # 分钟线指标：date,time,code,open,high,low,close,volume,amount,adjustflag
    # 周月线指标：date,code,open,high,low,close,volume,amount,adjustflag,turn,pctChg
    rs = bs.query_history_k_data_plus(code,
                                      "date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,isST",
                                      start_date=start, end_date=end,
                                      frequency="d", adjustflag="3")
    # print('query_history_k_data_plus respond error_code:'+rs.error_code)
    # print('query_history_k_data_plus respond  error_msg:'+rs.error_msg)

    #### 打印结果集 ####
    data_list = []
    while (rs.error_code == '0') & rs.next():
        # 获取一条记录，将记录合并在一起
        data_list.append(rs.get_row_data())
    result = pd.DataFrame(data_list, columns=rs.fields)

    #### 结果集输出到csv文件 ####
    # result.to_csv("D:\\history_A_stock_k_data.csv", index=False)
    # print(result)

    #### 登出系统 ####

    return result


def turn_df_float(df):

    df1 = df[['open', 'high', 'low', 'close', 'preclose', 'volume',
             'amount', 'adjustflag', 'turn', 'tradestatus', 'pctChg', 'isST']]

    df2 = df1.astype(float)

    return df2


code_list = ['sh.600992',
             'sh.600993',
             'sh.600995',
             'sh.600996',
             'sh.600997',
             'sh.600998',
             'sh.600999',
             'sh.601000',
             'sh.601001',
             'sh.601002',
             'sh.601003',
             'sh.601005']


start = '2021-01-01'

end = '2025-06-21'


data0 = this_data = get_data('sh.601003', start, end)

data = turn_df_float(data0)

close = data['close'].values
high = data['high'].values
low = data['low'].values
volume = data['volume'].values

# # 简单移动平均线（SMA）
# sma_5 = talib.SMA(close, timeperiod=5)
# sma_20 = talib.SMA(close, timeperiod=20)

# # 指数移动平均线（EMA）
# ema_12 = talib.EMA(close, timeperiod=12)
# ema_26 = talib.EMA(close, timeperiod=26)
# rsi_14 = talib.RSI(close, timeperiod=14)
# # 绘制均线与价格对比
# plt.figure(figsize=(20, 12))
# plt.plot(data.index, close*8, label='Close Price')
# plt.plot(data.index, sma_5*8, label='5-day SMA', color='red')
# plt.plot(data.index, sma_20*8, label='20-day SMA', color='green')

# plt.plot(data.index, rsi_14, label='RSI-14')
# plt.axhline(y=70, color='red', linestyle='--', label='Overbought (70)')
# plt.axhline(y=30, color='green', linestyle='--', label='Oversold (30)')

# plt.legend()
# plt.title('AAPL Price with Moving Averages')
# plt.show()

# 计算14日RSI

rsi_14 = talib.RSI(close, timeperiod=14)
# 绘制RSI与超买超卖线
plt.figure(figsize=(12, 6))
plt.plot(data.index, rsi_14, label='RSI-14')
plt.axhline(y=70, color='red', linestyle='--', label='Overbought (70)')
plt.axhline(y=30, color='green', linestyle='--', label='Oversold (30)')
plt.legend()
plt.title('AAPL RSI Technical Indicator')
plt.show()