import os
import pandas as pd
import tushare as ts
import numpy as np
from pathlib import Path
import matplotlib.pyplot as plt
import mplfinance as mpf
import matplotlib as mpl
from cycler import cycler# 用于定制线条颜色
import time
def get_jijin_csv():
pro = ts.pro_api('ee5c0e991e17949cdafbcf8ec42321ef4bac94e9ca3474e4d62313a3')
jiJinFile = 'jiJin.csv'
if not os.path.exists(jiJinFile):
df = pro.fund_basic(market='E')
df.to_csv(jiJinFile, encoding='utf_8_sig')
df = pd.read_csv(jiJinFile)
path = './基金/'
dir = Path(path)
if not os.path.exists(dir):
os.mkdir(dir)
codes = df['ts_code'].values
names = df['name'].values
fund_types = df['fund_type'].values
for code, name, fund_type in zip(codes, names, fund_types):
if fund_type == '商品型' or fund_type == '债券型' or '货币市场型' == fund_type:
continue
jijin_chicang_file = path + code +'_'+ name + '.csv'
if not os.path.exists(jijin_chicang_file):
df = pro.fund_portfolio(ts_code=code)
df.to_csv(jijin_chicang_file, encoding='utf_8_sig')
time.sleep(1)
# 加载股票列表
def load_code_list(market='SZSE', sel=False): #交易所 SSE上交所 SZSE深交所 HKEX港交所(未上线)
path = './data/'
faceDir = Path(path)
if faceDir.exists():
file_dir = path + 'code_list_' + market + '.csv'
else:
os.mkdir(faceDir)
file_dir = path + 'code_list_' + market + '.csv'
# 判断文件是否存在,不存在则通过网络接口获得
if os.path.exists(file_dir):
code_list = pd.read_csv(file_dir)
else:
# 初始化pro接口
pro = ts.pro_api('ee5c0e991e17949cdafbcf8ec42321ef4bac94e9ca3474e4d62313a3')
# 查询某交易所所有上市公司
#code_list = pro.stock_basic(exchange=market, list_status='L', fields='ts_code') # ,symbol,name,market,list_date
#code_list = pro.stock_basic(exchange=market, list_status='L') # ,symbol,name,market,list_date
code_list = pro.stock_basic(exchange=market, list_status='L') # ,symbol,name,market,list_date
# 保存数据到文件
code_list.to_csv(file_dir, index=False, encoding="utf_8_sig")
#code_list = code_list[['ts_code']].values.flatten()
return code_list
#获取代码对应的名字
def get_code_name(ts_code):
code_list = pd.read_csv('all_codes.csv')
codes = code_list['ts_code'].values
names = code_list['name'].values
i = 0
for code in codes:
if code == ts_code:
return names[i]
else:
i = i+1
print('NOName ************************** NoName')
return 'noName'
#获取名字对应代码
def get_name_code(name):
code_list = pd.read_csv('all_codes.csv')
codes = code_list['ts_code'].values
names = code_list['name'].values
i = 0
for find in names:
if name == find:
return codes[i]
else:
i = i+1
print('NOCode ************************** NOCode')
return 'NoCode'
# 获取单个代码的总值##############################################################
def get_code_in_jijin_perValue(ts_code, is_debug = False):
pro = ts.pro_api('ee5c0e991e17949cdafbcf8ec42321ef4bac94e9ca3474e4d62313a3')
jiJinFile = 'jiJin.csv'
if not os.path.exists(jiJinFile):
df = pro.fund_basic(market='E')
df.to_csv(jiJinFile, encoding='utf_8_sig')
df = pd.read_csv(jiJinFile)
path = './基金/'
dir = Path(path)
if not os.path.exists(dir):
os.mkdir(dir)
jijin_codes = df['ts_code'].values
jijin_names = df['name'].values
fund_types = df['fund_type'].values
ts_name = get_code_name(ts_code)
per_value = 0
mkv_value = 0
count = 0
for jijin_code, jiJin_name, fund_type in zip(jijin_codes, jijin_names, fund_types):
if fund_type == '商品型' or fund_type == '债券型' or '货币市场型' == fund_type:
continue
jijin_chicang_file = path + jijin_code + '_' + jiJin_name + '.csv'
if not os.path.exists(jijin_chicang_file):
df = pro.fund_portfolio(ts_code=ts_code)
df.to_csv(jijin_chicang_file, encoding='utf_8_sig')
#time.sleep(1)
jijin_df = pd.read_csv(jijin_chicang_file)
gupiao_codes = jijin_df['symbol'].values #代码
stk_float_ratios = jijin_df['stk_float_ratio'].values #占流通市值比例
ann_dates = jijin_df['ann_date'].values # 公告日
stk_mkv_ratios = jijin_df['stk_mkv_ratio'].values #占股票市值比
isFind = False
for i in range(len(gupiao_codes)):
if ann_dates[i] != ann_dates[0]:
break
if ts_code == gupiao_codes[i]:
per_value = per_value + stk_float_ratios[i]
mkv_value = mkv_value + stk_mkv_ratios[i]
count = count +1
isFind = True
if is_debug and isFind:
print('jiJin', jijin_code, jiJin_name, ts_code, ts_name, '%.2f'% per_value)
print("Count",count, ts_code, ts_name, '%.2f'% per_value)
return per_value, count, mkv_value
def get_allcode_per_value():
code_list = pd.read_csv('all_codes.csv')
codes = code_list['ts_code'].values
names = code_list['name'].values
markets = code_list['market'].values
code_array = np.array([])
name_array = np.array([])
per_array = np.array([])
count_array = np.array([])
mkv_array = np.array([])
writefile = 'all_code_fund_value.csv'
for code, market, name in zip(codes, markets, names):
if market == '科创板':
print(market, code, name)
continue
# 获取单个代码的总值##############################################################
per_value, count, mkv_value = get_code_in_jijin_perValue(code)
code_array = np.append(code_array, code)
name_array = np.append(name_array, name)
per_array = np.append(per_array, per_value)
count_array = np.append(count_array, count)
mkv_array = np.append(mkv_array, mkv_value)
data = [code_array, name_array, count_array, per_array]
data = np.transpose(data)
ser2 = pd.DataFrame(data, columns=['ts_code', 'name', 'count', 'per_value'])
ser2.to_csv(writefile, encoding="utf_8_sig")
print("EEEEEEEEE")
#get_code_in_jijin_perValue('600837.SH', is_debug = True)
get_allcode_per_value()
