上市公司股价数据分析¶
要实现对上市公司股价进行分析，可以使用Python的tushare接口来获取和处理股票数据。以下是一个可能的流程：
- 获取数据：使用tushare接口获取指定上市公司的历史股价数据，包括开盘价、收盘价、最高价、最低价、成交量等信息。
- 持久化数据：将获取到的股价数据存储在本地数据库中，以便后续的数据分析和处理。可以使用SQLite、MySQL等数据库。
- 数据预处理：对获取到的股价数据进行预处理，包括数据清洗、缺失值填充、异常值处理等。可以使用pandas库进行数据预处理。
- 分析数据：
- a. 输出该股票所有收盘比开盘上涨3%以上的日期
- b. 输出该股票所有开盘比前日收盘跌幅超过2%的日期
- c. 计算收益：计算每日收益率和累计收益率
- d. 计算每天的收益变化：计算每日收益率差异值
- 绘图显示股票的价格、均线、RSI等
1. 通过tushare接口获取上市公司最近半年的历史股价数据，并将数据存储在本地数据库中¶
In [1]:
import tushare as ts
import pandas as pd
import numpy as np
import sqlite3
from datetime import datetime, timedelta
import warnings
import matplotlib
import matplotlib.pyplot as plt
warnings.filterwarnings("ignore")
font = {'family': 'Microsoft YaHei', 'weight': 'bold', 'size': '12'}
matplotlib.rc("font", **font)
# 设置tushare token
ts.set_token('d4ade4dc2337bb63733c3ec90deb49bfe013ac6b81452bdba73641e7')
pro = ts.pro_api()
# 获取最近半年的日期范围
end_date = datetime.now().strftime('%Y%m%d')
start_date = (datetime.now() - timedelta(days=180)).strftime('%Y%m%d')
# 获取股票数据（以平安银行000001.SZ为例）
df = pro.daily(ts_code='000001.SZ', start_date=start_date, end_date=end_date)
# 按日期升序排列
df = df.sort_values('trade_date').reset_index(drop=True)
print(f"获取数据时间范围：{start_date} 至 {end_date}")
print(f"共获取 {len(df)} 条记录")
print(df.head(10))
获取数据时间范围：20251031 至 20260429
共获取 119 条记录
ts_code trade_date open high low close pre_close change \
0 000001.SZ 20251031 11.38 11.40 11.30 11.32 11.38 -0.06
1 000001.SZ 20251103 11.34 11.44 11.30 11.43 11.32 0.11
2 000001.SZ 20251104 11.42 11.64 11.40 11.59 11.43 0.16
3 000001.SZ 20251105 11.59 11.60 11.50 11.52 11.59 -0.07
4 000001.SZ 20251106 11.50 11.58 11.47 11.51 11.52 -0.01
5 000001.SZ 20251107 11.52 11.58 11.50 11.55 11.51 0.04
6 000001.SZ 20251110 11.52 11.64 11.45 11.63 11.55 0.08
7 000001.SZ 20251111 11.62 11.69 11.57 11.67 11.63 0.04
8 000001.SZ 20251112 11.70 11.79 11.65 11.68 11.67 0.01
9 000001.SZ 20251113 11.68 11.72 11.57 11.70 11.68 0.02
pct_chg vol amount
0 -0.5272 970192.93 1099179.193
1 0.9717 952326.43 1084422.504
2 1.3998 1503007.48 1737289.514
3 -0.6040 794926.05 918112.474
4 -0.0868 766585.46 882799.462
5 0.3475 734850.55 848177.984
6 0.6926 827208.33 957555.601
7 0.3439 886508.87 1033284.776
8 0.0857 1141325.38 1337963.805
9 0.1712 978991.03 1139765.204
In [2]:
# 将数据存储到SQLite本地数据库
conn = sqlite3.connect('stock_data.db')
df.to_sql('stock_daily', conn, if_exists='replace', index=False)
# 验证数据已存储
df_check = pd.read_sql('SELECT * FROM stock_daily LIMIT 5', conn)
print("数据库中存储的数据：")
print(df_check)
conn.close()
数据库中存储的数据：
ts_code trade_date open high low close pre_close change \
0 000001.SZ 20251031 11.38 11.40 11.30 11.32 11.38 -0.06
1 000001.SZ 20251103 11.34 11.44 11.30 11.43 11.32 0.11
2 000001.SZ 20251104 11.42 11.64 11.40 11.59 11.43 0.16
3 000001.SZ 20251105 11.59 11.60 11.50 11.52 11.59 -0.07
4 000001.SZ 20251106 11.50 11.58 11.47 11.51 11.52 -0.01
pct_chg vol amount
0 -0.5272 970192.93 1099179.193
1 0.9717 952326.43 1084422.504
2 1.3998 1503007.48 1737289.514
3 -0.6040 794926.05 918112.474
4 -0.0868 766585.46 882799.462
2. 对获取到的股价数据进行预处理，包括数据清洗、缺失值填充、异常值处理等¶
In [3]:
# 从数据库读取数据
conn = sqlite3.connect('stock_data.db')
df = pd.read_sql('SELECT * FROM stock_daily', conn)
conn.close()
# 查看数据基本信息
print("数据基本信息：")
print(df.info())
print("\n缺失值统计：")
print(df.isnull().sum())
# 缺失值填充（使用前向填充）
df = df.fillna(method='ffill')
# 异常值处理：去除价格为0或负数的记录
df = df[(df['open'] > 0) & (df['close'] > 0) & (df['high'] > 0) & (df['low'] > 0)]
# 重置索引
df = df.reset_index(drop=True)
print(f"\n预处理后数据量：{len(df)} 条")
print("\n预处理后数据统计描述：")
print(df[['open', 'high', 'low', 'close', 'vol']].describe())
数据基本信息：
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 119 entries, 0 to 118
Data columns (total 11 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 ts_code 119 non-null object
1 trade_date 119 non-null object
2 open 119 non-null float64
3 high 119 non-null float64
4 low 119 non-null float64
5 close 119 non-null float64
6 pre_close 119 non-null float64
7 change 119 non-null float64
8 pct_chg 119 non-null float64
9 vol 119 non-null float64
10 amount 119 non-null float64
dtypes: float64(9), object(2)
memory usage: 10.4+ KB
None
缺失值统计：
ts_code 0
trade_date 0
open 0
high 0
low 0
close 0
pre_close 0
change 0
pct_chg 0
vol 0
amount 0
dtype: int64
预处理后数据量：119 条
预处理后数据统计描述：
open high low close vol
count 119.000000 119.000000 119.000000 119.000000 1.190000e+02
mean 11.238739 11.312437 11.173529 11.242941 9.039411e+05
std 0.316660 0.317607 0.316829 0.318421 3.652367e+05
min 10.520000 10.680000 10.430000 10.450000 4.061041e+05
25% 10.960000 11.035000 10.905000 10.960000 6.883779e+05
50% 11.160000 11.230000 11.110000 11.170000 8.340826e+05
75% 11.520000 11.595000 11.480000 11.530000 1.040694e+06
max 11.810000 11.990000 11.740000 11.850000 3.401276e+06
3. 筛选出该股票收盘价比前一日开盘价上涨超过3%的所有日期¶
In [4]:
# 收盘价比前一日开盘价上涨超过3%的日期
df['prev_open'] = df['open'].shift(1)
df['close_vs_prev_open'] = (df['close'] - df['prev_open']) / df['prev_open'] * 100
rise_3pct = df[df['close_vs_prev_open'] > 3]
print("收盘价比前一日开盘价上涨超过3%的日期：")
print(rise_3pct[['trade_date', 'prev_open', 'close', 'close_vs_prev_open']].to_string(index=False))
收盘价比前一日开盘价上涨超过3%的日期： trade_date prev_open close close_vs_prev_open 20260325 10.52 10.94 3.992395 20260427 10.98 11.38 3.642987
4. 筛选出该股票开盘价比前一日收盘价下跌超过2%的所有日期¶
In [5]:
# 开盘价比前一日收盘价下跌超过2%的日期
df['prev_close'] = df['close'].shift(1)
df['open_vs_prev_close'] = (df['open'] - df['prev_close']) / df['prev_close'] * 100
drop_2pct = df[df['open_vs_prev_close'] < -2]
print("开盘价比前一日收盘价下跌超过2%的日期：")
print(drop_2pct[['trade_date', 'prev_close', 'open', 'open_vs_prev_close']].to_string(index=False))
开盘价比前一日收盘价下跌超过2%的日期： Empty DataFrame Columns: [trade_date, prev_close, open, open_vs_prev_close] Index: []
5. 计算该股票的每日收益率和一段时间内的累计收益率¶
In [6]:
# 计算每日收益率
df['daily_return'] = df['close'].pct_change()
# 计算累计收益率
df['cumulative_return'] = (1 + df['daily_return']).cumprod() - 1
print("每日收益率和累计收益率：")
print(df[['trade_date', 'close', 'daily_return', 'cumulative_return']].to_string(index=False))
print(f"\n期间累计收益率：{df['cumulative_return'].iloc[-1]*100:.2f}%")
每日收益率和累计收益率： trade_date close daily_return cumulative_return 20251031 11.32 NaN NaN 20251103 11.43 0.009717 0.009717 20251104 11.59 0.013998 0.023852 20251105 11.52 -0.006040 0.017668 20251106 11.51 -0.000868 0.016784 20251107 11.55 0.003475 0.020318 20251110 11.63 0.006926 0.027385 20251111 11.67 0.003439 0.030919 20251112 11.68 0.000857 0.031802 20251113 11.70 0.001712 0.033569 20251114 11.75 0.004274 0.037986 20251117 11.67 -0.006809 0.030919 20251118 11.59 -0.006855 0.023852 20251119 11.80 0.018119 0.042403 20251120 11.85 0.004237 0.046820 20251121 11.69 -0.013502 0.032686 20251124 11.60 -0.007699 0.024735 20251125 11.80 0.017241 0.042403 20251126 11.69 -0.009322 0.032686 20251127 11.71 0.001711 0.034452 20251128 11.61 -0.008540 0.025618 20251201 11.69 0.006891 0.032686 20251202 11.64 -0.004277 0.028269 20251203 11.55 -0.007732 0.020318 20251204 11.49 -0.005195 0.015018 20251205 11.53 0.003481 0.018551 20251208 11.52 -0.000867 0.017668 20251209 11.43 -0.007812 0.009717 20251210 11.33 -0.008749 0.000883 20251211 11.37 0.003530 0.004417 20251212 11.35 -0.001759 0.002650 20251215 11.51 0.014097 0.016784 20251216 11.48 -0.002606 0.014134 20251217 11.53 0.004355 0.018551 20251218 11.64 0.009540 0.028269 20251219 11.62 -0.001718 0.026502 20251222 11.52 -0.008606 0.017668 20251223 11.56 0.003472 0.021201 20251224 11.54 -0.001730 0.019435 20251225 11.56 0.001733 0.021201 20251226 11.54 -0.001730 0.019435 20251229 11.56 0.001733 0.021201 20251230 11.48 -0.006920 0.014134 20251231 11.41 -0.006098 0.007951 20260105 11.50 0.007888 0.015901 20260106 11.67 0.014783 0.030919 20260107 11.64 -0.002571 0.028269 20260108 11.51 -0.011168 0.016784 20260109 11.46 -0.004344 0.012367 20260112 11.48 0.001745 0.014134 20260113 11.47 -0.000871 0.013251 20260114 11.36 -0.009590 0.003534 20260115 11.31 -0.004401 -0.000883 20260116 11.19 -0.010610 -0.011484 20260119 11.12 -0.006256 -0.017668 20260120 11.16 0.003597 -0.014134 20260121 11.07 -0.008065 -0.022085 20260122 11.07 0.000000 -0.022085 20260123 10.99 -0.007227 -0.029152 20260126 10.96 -0.002730 -0.031802 20260127 10.94 -0.001825 -0.033569 20260128 10.84 -0.009141 -0.042403 20260129 10.96 0.011070 -0.031802 20260130 10.83 -0.011861 -0.043286 20260202 10.86 0.002770 -0.040636 20260203 10.84 -0.001842 -0.042403 20260204 10.97 0.011993 -0.030919 20260205 11.09 0.010939 -0.020318 20260206 11.05 -0.003607 -0.023852 20260209 11.07 0.001810 -0.022085 20260210 11.06 -0.000903 -0.022968 20260211 11.07 0.000904 -0.022085 20260212 10.96 -0.009937 -0.031802 20260213 10.91 -0.004562 -0.036219 20260224 10.91 0.000000 -0.036219 20260225 10.86 -0.004583 -0.040636 20260226 10.87 0.000921 -0.039753 20260227 10.90 0.002760 -0.037102 20260302 10.85 -0.004587 -0.041519 20260303 10.88 0.002765 -0.038869 20260304 10.71 -0.015625 -0.053887 20260305 10.81 0.009337 -0.045053 20260306 10.82 0.000925 -0.044170 20260309 10.76 -0.005545 -0.049470 20260310 10.81 0.004647 -0.045053 20260311 10.89 0.007401 -0.037986 20260312 10.94 0.004591 -0.033569 20260313 10.93 -0.000914 -0.034452 20260316 10.92 -0.000915 -0.035336 20260317 11.03 0.010073 -0.025618 20260318 10.96 -0.006346 -0.031802 20260319 10.88 -0.007299 -0.038869 20260320 10.77 -0.010110 -0.048587 20260323 10.45 -0.029712 -0.076855 20260324 10.88 0.041148 -0.038869 20260325 10.94 0.005515 -0.033569 20260326 10.94 0.000000 -0.033569 20260327 11.02 0.007313 -0.026502 20260330 10.99 -0.002722 -0.029152 20260331 11.08 0.008189 -0.021201 20260401 11.15 0.006318 -0.015018 20260402 11.27 0.010762 -0.004417 20260403 11.12 -0.013310 -0.017668 20260407 11.03 -0.008094 -0.025618 20260408 11.22 0.017226 -0.008834 20260409 11.10 -0.010695 -0.019435 20260410 11.09 -0.000901 -0.020318 20260413 11.07 -0.001803 -0.022085 20260414 11.17 0.009033 -0.013251 20260415 11.21 0.003581 -0.009717 20260416 11.09 -0.010705 -0.020318 20260417 11.01 -0.007214 -0.027385 20260420 11.06 0.004541 -0.022968 20260421 11.08 0.001808 -0.021201 20260422 10.98 -0.009025 -0.030035 20260423 11.00 0.001821 -0.028269 20260424 11.00 0.000000 -0.028269 20260427 11.38 0.034545 0.005300 20260428 11.46 0.007030 0.012367 期间累计收益率：1.24%
6. 计算该股票每日收益率与前一日收益率之间的差异，并输出差异值¶
In [7]:
# 计算每日收益率变化（当日收益率 - 前一日收益率）
df['return_diff'] = df['daily_return'].diff()
print("每日收益率变化（差异值）：")
print(df[['trade_date', 'daily_return', 'return_diff']].dropna().to_string(index=False))
每日收益率变化（差异值）： trade_date daily_return return_diff 20251104 0.013998 4.280936e-03 20251105 -0.006040 -2.003794e-02 20251106 -0.000868 5.171634e-03 20251107 0.003475 4.343294e-03 20251110 0.006926 3.451168e-03 20251111 0.003439 -3.487026e-03 20251112 0.000857 -2.582483e-03 20251113 0.001712 8.554307e-04 20251114 0.004274 2.561176e-03 20251117 -0.006809 -1.108201e-02 20251118 -0.006855 -4.667359e-05 20251119 0.018119 2.497425e-02 20251120 0.004237 -1.388178e-02 20251121 -0.013502 -1.773940e-02 20251124 -0.007699 5.803222e-03 20251125 0.017241 2.494027e-02 20251126 -0.009322 -2.656341e-02 20251127 0.001711 1.103290e-02 20251128 -0.008540 -1.025057e-02 20251201 0.006891 1.543032e-02 20251202 -0.004277 -1.116777e-02 20251203 -0.007732 -3.454799e-03 20251204 -0.005195 2.537154e-03 20251205 0.003481 8.676093e-03 20251208 -0.000867 -4.348591e-03 20251209 -0.007812 -6.945197e-03 20251210 -0.008749 -9.364064e-04 20251211 0.003530 1.227936e-02 20251212 -0.001759 -5.289465e-03 20251215 0.014097 1.585593e-02 20251216 -0.002606 -1.670335e-02 20251217 0.004355 6.961830e-03 20251218 0.009540 5.184929e-03 20251219 -0.001718 -1.125854e-02 20251222 -0.008606 -6.887639e-03 20251223 0.003472 1.207807e-02 20251224 -0.001730 -5.202326e-03 20251225 0.001733 3.463206e-03 20251226 -0.001730 -3.463206e-03 20251229 0.001733 3.463206e-03 20251230 -0.006920 -8.653517e-03 20251231 -0.006098 8.228542e-04 20260105 0.007888 1.398538e-02 20260106 0.014783 6.894791e-03 20260107 -0.002571 -1.735330e-02 20260108 -0.011168 -8.597691e-03 20260109 -0.004344 6.824336e-03 20260112 0.001745 6.089249e-03 20260113 -0.000871 -2.616281e-03 20260114 -0.009590 -8.719155e-03 20260115 -0.004401 5.188827e-03 20260116 -0.010610 -6.208671e-03 20260119 -0.006256 4.354494e-03 20260120 0.003597 9.852708e-03 20260121 -0.008065 -1.166164e-02 20260122 0.000000 8.064516e-03 20260123 -0.007227 -7.226739e-03 20260126 -0.002730 4.496985e-03 20260127 -0.001825 9.049368e-04 20260128 -0.009141 -7.315950e-03 20260129 0.011070 2.021088e-02 20260130 -0.011861 -2.293142e-02 20260202 0.002770 1.463140e-02 20260203 -0.001842 -4.611704e-03 20260204 0.011993 1.383424e-02 20260205 0.010939 -1.053696e-03 20260206 -0.003607 -1.454578e-02 20260209 0.001810 5.416808e-03 20260210 -0.000903 -2.713297e-03 20260211 0.000904 1.807501e-03 20260212 -0.009937 -1.084093e-02 20260213 -0.004562 5.374722e-03 20260224 0.000000 4.562044e-03 20260225 -0.004583 -4.582951e-03 20260226 0.000921 5.503762e-03 20260227 0.002760 1.839079e-03 20260302 -0.004587 -7.347046e-03 20260303 0.002765 7.352133e-03 20260304 -0.015625 -1.838998e-02 20260305 0.009337 2.496207e-02 20260306 0.000925 -8.411999e-03 20260309 -0.005545 -6.470356e-03 20260310 0.004647 1.019213e-02 20260311 0.007401 2.753715e-03 20260312 0.004591 -2.809187e-03 20260313 -0.000914 -5.505445e-03 20260316 -0.000915 -8.363008e-07 20260317 0.010073 1.098817e-02 20260318 -0.006346 -1.641959e-02 20260319 -0.007299 -9.529419e-04 20260320 -0.010110 -2.811024e-03 20260323 -0.029712 -1.960187e-02 20260324 0.041148 7.086049e-02 20260325 0.005515 -3.563362e-02 20260326 0.000000 -5.514706e-03 20260327 0.007313 7.312614e-03 20260330 -0.002722 -1.003494e-02 20260331 0.008189 1.091159e-02 20260401 0.006318 -1.871573e-03 20260402 0.010762 4.444642e-03 20260403 -0.013310 -2.407200e-02 20260407 -0.008094 5.216147e-03 20260408 0.017226 2.531927e-02 20260409 -0.010695 -2.792094e-02 20260410 -0.000901 9.794286e-03 20260413 -0.001803 -9.025256e-04 20260414 0.009033 1.083685e-02 20260415 0.003581 -5.452403e-03 20260416 -0.010705 -1.428575e-02 20260417 -0.007214 3.491022e-03 20260420 0.004541 1.175503e-02 20260421 0.001808 -2.733008e-03 20260422 -0.009025 -1.083359e-02 20260423 0.001821 1.084676e-02 20260424 0.000000 -1.821494e-03 20260427 0.034545 3.454545e-02 20260428 0.007030 -2.751558e-02
7. 使用matplotlib绘图工具，将股票的价格、均线、RSI等指标绘制成图表¶
In [8]:
# 计算均线
df['MA5'] = df['close'].rolling(window=5).mean()
df['MA10'] = df['close'].rolling(window=10).mean()
df['MA20'] = df['close'].rolling(window=20).mean()
# 计算RSI（14日）
delta = df['close'].diff()
gain = delta.where(delta > 0, 0)
loss = (-delta).where(delta < 0, 0)
avg_gain = gain.rolling(window=14).mean()
avg_loss = loss.rolling(window=14).mean()
rs = avg_gain / avg_loss
df['RSI'] = 100 - (100 / (1 + rs))
# 绘制股票价格和均线图
fig, axes = plt.subplots(3, 1, figsize=(14, 12), sharex=True)
# 子图1：股价和均线
axes[0].plot(df['trade_date'], df['close'], label='收盘价', linewidth=1.5)
axes[0].plot(df['trade_date'], df['MA5'], label='MA5', linewidth=1)
axes[0].plot(df['trade_date'], df['MA10'], label='MA10', linewidth=1)
axes[0].plot(df['trade_date'], df['MA20'], label='MA20', linewidth=1)
axes[0].set_title('股票价格与均线')
axes[0].set_ylabel('价格')
axes[0].legend()
axes[0].grid(True, alpha=0.3)
tick_step = max(1, len(df) // 10)
axes[0].set_xticks(range(0, len(df), tick_step))
# 子图2：成交量
axes[1].bar(df['trade_date'], df['vol'], color='steelblue', alpha=0.7)
axes[1].set_title('成交量')
axes[1].set_ylabel('成交量')
axes[1].grid(True, alpha=0.3)
axes[1].set_xticks(range(0, len(df), tick_step))
# 子图3：RSI指标
axes[2].plot(df['trade_date'], df['RSI'], label='RSI(14)', color='purple', linewidth=1.5)
axes[2].axhline(y=70, color='r', linestyle='--', alpha=0.5, label='超买线(70)')
axes[2].axhline(y=30, color='g', linestyle='--', alpha=0.5, label='超卖线(30)')
axes[2].set_title('RSI指标')
axes[2].set_ylabel('RSI')
axes[2].set_xlabel('日期')
axes[2].legend()
axes[2].grid(True, alpha=0.3)
axes[2].set_xticks(range(0, len(df), tick_step))
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()
