基于Python的指数基金量化投资-股票数据源baostock

作者: 小将前行 | 来源:发表于2021-02-23 17:14 被阅读0次

课程参考：网易云课堂基于Python的量化指数基金投资

量化用到的数据源来自baostock，可以通过 www.baostock.com网址进行访问，它是一个免费、开源的证券数据平台（无需注册），提供大量准确、完整的证券历史行情数据、上市公司财务数据等。

可以通过提供的python API获取证券数据信息，满足量化交易投资、金融数据分析、计量经济数据需求。

返回的数据格式为pandas DataFrame类型，以便于用pandas/NumPy/Matplotlib进行数据分析和可视化。同时支持通过BaoStock的数据存储功能，将数据全部保存到本地后进行分析。目前版本BaoStock.com只支持Python3.5及以上，暂不支持python 2.x。

安装方式pip install baostock

也可以使用国内源安装：

pip install baostock -i https://pypi.tuna.tsinghua.edu.cn/simple/--trusted-host pypi.tuna.tsinghua.edu.cn

baostock提供了完备的API进行数据获取

例如获取历史A股K线数据：query_history_k_data_plus()

import baostock as bs

import pandas as pd

#### 登陆系统####

lg = bs.login()

# 显示登陆返回信息

print('login responderror_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("sh.600000",

"date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,isST",

start_date='2010-07-01', end_date='2021-02-19',

frequency="d", adjustflag="3")

print('query_history_k_data_plus responderror_code:'+rs.error_code)

print('query_history_k_data_plusrespond 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####

bs.logout()

程序运行完后会返回相应的数据，并按照你指定的文件路径存储为csv文件。

然后可以通过

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

import math as math

stock_info = pd.read_csv("D:\\history_A_stock_k_data.csv")

size_title = 28

size_label = 23

size_text = 35

size_line = 3

size_rotation = 20

size_marker = 23

list_tmp = list(stock_info['close'])

index_max = list_tmp.index(max(list_tmp))

plt_gap = 8

plt.figure(0)

plt.rcParams["axes.grid"] = True

plt.rcParams['font.sans-serif'] =['SimHei']

plt.rcParams["grid.linestyle"] =(3, 5)

plt.plot(stock_info['close'],'tomato',linewidth=size_line)

font = {'size': 30, 'color': 'tomato','weight': 'black'}

plt.text(index_max+30, list_tmp[index_max],str("{:.2f}".format(list_tmp[index_max])), fontdict=font)

plt.plot(index_max, list_tmp[index_max],color='cornflowerblue', marker='o', ms=size_marker)

plt.text(1+30, list_tmp[0]+1,str("{:.2f}".format(list_tmp[0])), fontdict=font)

plt.plot(1, list_tmp[0],color='cornflowerblue', marker='o', ms=size_marker)

plt.text(len(list_tmp)+30, list_tmp[-1]+1,str("{:.2f}".format(list_tmp[-1])), fontdict=font)

plt.plot(len(list_tmp)+30, list_tmp[-1],color='cornflowerblue', marker='o', ms=size_marker)

plt_xticks =stock_info['date'].values[1:len(stock_info['date']):1].tolist()

plt.xticks(range(len(plt_xticks),0,-math.floor(len(plt_xticks)/plt_gap)),plt_xticks[len(plt_xticks):0:-math.floor(len(plt_xticks)/plt_gap)],rotation=size_rotation)

plt.tick_params(labelsize=size_label)

plt.title('股价走势',size=size_title)

plt.figure(1)

plt.rcParams["axes.grid"] = True

plt.rcParams['font.sans-serif'] =['SimHei']

plt.rcParams["grid.linestyle"] =(3, 5)

plt.gca()

# plt.grid(True)

plt.subplot(321)

plt.plot(stock_info['close'],'tomato')

plt_xticks =stock_info['date'].values[1:len(stock_info['date']):1].tolist()

plt.xticks(range(len(plt_xticks),0,-math.floor(len(plt_xticks)/plt_gap)),plt_xticks[len(plt_xticks):0:-math.floor(len(plt_xticks)/plt_gap)],rotation=0)

plt.title('价格')

plt.subplot(322)

plt.plot(stock_info['pbMRQ'] /stock_info['peTTM'],'tomato')

plt.xticks(range(len(plt_xticks),0,-math.floor(len(plt_xticks)/plt_gap)),plt_xticks[len(plt_xticks):0:-math.floor(len(plt_xticks)/plt_gap)],rotation=0)

plt.title('ROE')

plt.subplot(323)

plt.plot(stock_info['pbMRQ'],'tomato')

plt.xticks(range(len(plt_xticks),0,-math.floor(len(plt_xticks)/plt_gap)),plt_xticks[len(plt_xticks):0:-math.floor(len(plt_xticks)/plt_gap)],rotation=0)

data_calc = stock_info['pbMRQ']

xx = np.where(data_calc < data_calc.values[-1])

data_percentage =str("{:.2f}".format(100 * len(xx[0]) / data_calc.shape[0])) + '%'

plt.plot([len(data_calc),0],[data_calc.values[-1],data_calc.values[-1]],color='cornflowerblue',linewidth=1)

plt.title('市净率|' +data_percentage)

plt.subplot(324)

plt.plot(stock_info['peTTM'],'tomato')

plt.xticks(range(len(plt_xticks),0,-math.floor(len(plt_xticks)/plt_gap)),plt_xticks[len(plt_xticks):0:-math.floor(len(plt_xticks)/plt_gap)],rotation=0)

data_calc = stock_info['peTTM']

xx = np.where(data_calc < data_calc.values[-1])

data_percentage =str("{:.2f}".format(100 * len(xx[0]) / data_calc.shape[0])) + '%'

plt.plot([len(data_calc),0],[data_calc.values[-1],data_calc.values[-1]],color='cornflowerblue',linewidth=1)

plt.title('市盈率|' +data_percentage)

运行后可以画出相应的数据图