def index_price_daily(code: str, source: str = None) -> dict:
if source == 'tx':
df = ak.stock_zh_index_daily_tx(symbol=code)
elif source == 'em':
df = ak.stock_zh_index_daily_em(symbol=code)
else:
df = ak.stock_zh_index_daily(symbol=code)
df['date'] = pd.to_datetime(df['date'])
df = df.rename({'date': '_id'}, axis=1)
print(df)
return json.loads(df.T.to_json()) # use default date_format='epoch' instead of 'iso'
def get_index_price_25(self, code):
# newest_data
df = ak.stock_zh_index_spot()
# print(df)
newest_data = float(
pd.to_numeric(df.loc[df[u'代码'] == code, '最新价']).astype('float'))
# history_data
history_data = ak.stock_zh_index_daily_em(symbol=code)
#print(original_data)
return (newest_data, history_data.iloc[-25, 2])
def index_price_daily(code: str, source: str = None) -> pd.DataFrame:
if source == 'tx':
df = ak.stock_zh_index_daily_tx(symbol=code)
elif source == 'em':
df = ak.stock_zh_index_daily_em(symbol=code)
else:
df = ak.stock_zh_index_daily(symbol=code)
df['date'] = pd.to_datetime(df['date'])
df = df.rename({'date': '_id'}, axis=1)
print(df)
return df
def load_fund_price(self, code):
"""
历史行情数据-东方财富
目标地址: http://quote.eastmoney.com/center/hszs.html
描述: 东方财富股票指数数据, 历史数据按日频率更新
限量: 单次返回具体指数的所有历史行情数据
输入参数
名称 类型 必选 描述
symbol str Y symbol="sz399552"
:param code:
:return:
"""
df = ak.stock_zh_index_daily_em(symbol="sz399812")
return df
def draw(len):
stock_zh_index_daily_em_df = ak.stock_zh_index_daily_em(symbol="sh000300")
data = stock_zh_index_daily_em_df[-1000:]
last_k = data["close"]
last_ten = stock_zh_index_daily_em_df[-len:]["close"]
last_ten = last_ten / last_ten.max()
min = 1000000
index = 0
for i in range(450):
temp = last_k[i:i+len]
temp = temp / temp.max()
current_dist = DtwDist(temp.values, last_ten.values)
if current_dist < min :
min = current_dist
index = i
print(data[index:index+len])
plt.plot(stock_zh_index_daily_em_df[-len:]["close"].values, label='The last 10 days')
plt.plot(data[index:index + len + 20]["close"].values, label='Match')
plt.legend()
plt.show()
import akshare as ak
import pandas as pd
import numpy as np
import random
import time
import os
code_num = 50
minYears = 2 # 最小年份
index_code = 'sh000300' # 指数
codes = pd.read_excel('fund_codes.xlsx', index_col=0, dtype={'基金代码':
'object'})['基金代码']
# 获取指数组合index_code的收益率数据
df_M = ak.stock_zh_index_daily_em(symbol=index_code)
df_M['updown'] = 0.0
for index, row in df_M.iterrows():
if index > 0:
df_M['updown'][index] = (float(row['close']) - float(
df_M['close'][index - 1])) / float(df_M['close'][index - 1])
df_M = df_M[['date', 'updown']]
# 获取最新Shibor作为无风险收益率
df_rf = ak.rate_interbank(market="上海银行同业拆借市场",
symbol="Shibor人民币",
indicator="3月",
need_page="1")
print('无风险利率:' + str(df_rf['利率(%)'][0]) + '%\n')
rf = np.power(1 + df_rf['利率(%)'][0] / 100, 1 / 360) - 1 # 转为日利率
def query_his_index_by_code(code='sz399812') -> pd.DataFrame:
stock_zh_index_daily_em_df = ak.stock_zh_index_daily_em(symbol=code)
stock_zh_index_daily_em_df['code'] = code
stock_zh_index_daily_em_df.rename(columns={'代码': 'code'}, inplace=True)
return stock_zh_index_daily_em_df