def get_min_stockQuotes(self, stockcode, period, *adjust):
if stockcode[0:2] == '60' or stockcode[0:2] == '68':
stockcode = 'sh' + stockcode
else:
stockcode = 'sz' + stockcode
if adjust == None:
stock_zh_a_minute_df = ak.stock_zh_a_minute(symbol=stockcode,
period=period,
adjust="qfq")
return stock_zh_a_minute_df
else:
stock_zh_a_minute_df = ak.stock_zh_a_minute(symbol=stockcode,
period=period,
adjust=adjust)
return stock_zh_a_minute_df
def download_ashare_minute_data(symbol):
stock_zh_a_minute_df = ak.stock_zh_a_minute(symbol=symbol,
period='1',
adjust="hfq")
print('type: {0}; data: {1};'.format(type(stock_zh_a_minute_df),
stock_zh_a_minute_df))
stock_zh_a_minute_df.to_csv('./data/{0}.csv'.format(symbol))
def get_minute_data_from_internet(self, code, name, period):
'''获取数据并保存到csv'''
while len(code) < 6:
code = '0' + code
if code.startswith('6'):
code = "sh" + code
if code.startswith('3') or code.startswith('0'):
code = "sz" + code
print(code)
# 获取原始数据
original_data = ak.stock_zh_a_minute(symbol=code,
period=period,
adjust="qfq")
# 取过去50天数据
try:
df = original_data.reset_index().iloc[-200:, :6]
except:
df = original_data.reset_index().iloc[:, :6]
# 去除空值且从零开始编号索引
df = df.dropna(how='any').reset_index(drop=True)
print(df)
# 按日期排序
df = df.sort_values(by='day', ascending=True)
# 均线数据
df['10'] = df.close.rolling(10).mean()
df['150'] = df.close.rolling(150).mean()
df['250'] = df.close.rolling(250).mean()
# 写入csv
file_name = self.data_path + name + ".csv"
df.to_csv(file_name)
print("<" + name + ">数据完成")
return df
# parser.add_argument("-s", "--stock", help="stock code", dest="target_stock", type=str, default="")
# args = parser.parse_args()
# daily_minutes = []
# am_minutes = pd.date_range(start = '09:31:00', end = '11:30:00', freq='min')
# for i in am_minutes:
# daily_minutes.append(str(i)[-8:])
# pm_minutes = pd.date_range(start = '13:01:00', end = '15:00:00', freq='min')
# for i in pm_minutes:
# daily_minutes.append(str(i)[-8:])
# print(daily_minutes)
# stock_zh_ah_daily_df = ak.stock_zh_index_daily(symbol=str(target_stock))
stock_zh_a_minute_df = ak.stock_zh_a_minute(symbol='sz300750',
period='1',
adjust="qfq")
# print(stock_zh_a_minute_df)
stock_zh_a_minute_df.to_csv('daily_details/' + 'sz300750.csv')
# dates = pd.date_range('1/1/2000', periods=8)
# print(dates)
# df = pd.DataFrame(np.random.randn(8, 4), index=dates, columns=['A', 'B', 'C', 'D'])
# print(df)
# if __name__ == "__main__":
# if args.target_stock.startswith('6'):
# target_stock = 'sh' + args.target_stock
# else:
# target_stock = 'sz' + args.target_stock
def get_candle(inst):
str_inst = str(inst)
stock_zh_a_minute_df = ak.stock_zh_a_minute(symbol="sh" + str_inst, period='1', adjust="qfq")
return stock_zh_a_minute_df
def run(self):
stock_zh_a_minute_df = ak.stock_zh_a_minute(symbol='sz000876',
period='1',
adjust="qfq")
print(stock_zh_a_minute_df)
def get_data3():
stock_zh_a_minute_df = ak.stock_zh_a_minute(symbol='sh000300', period='1')
df = pd.DataFrame(stock_zh_a_minute_df)
df.dropna()
df.head()
df.to_csv('thirdStock.csv')
def stock_zh_a_minute(self):
stock_zh_a_minute_df = ak.stock_zh_a_minute(symbol='sh600751',
period='1',
adjust="qfq")
return stock_zh_a_minute_df
# collocations=False,
# font_path='simhei.ttf',
# icon_name='fas fa-heart',
# size=653,
# # palette='matplotlib.Inferno_9',
# output_name='./005827.png')
# Image(filename='./005827.png')
# 用更为量化的方法，计算出每个评论的情感评分
senta = hub.Module(name="senta_bilstm")
texts = df['标题'].tolist()
input_data = {'text': texts}
res = senta.sentiment_classify(data=input_data)
df['投资者情绪'] = [x['positive_probs'] for x in res]
# 重采样至15分钟
df['时间'] = pd.to_datetime(df['时间'])
df.index = df['时间']
data = df.resample('15min').mean().reset_index()
# 通过AkShare这一开源API接口获取上证指数分时数据，AkShare是基于Python的财经数据接口库，
# 可以实现对股票、期货、期权、基金、外汇、债券、指数、
# 数字货币等金融产品的基本面数据、历史行情数据的快速采集和清洗。
sz_index = ak.stock_zh_a_minute(symbol='sh000001', period='15', adjust="qfq")
sz_index['日期'] = pd.to_datetime(sz_index['day'])
sz_index['收盘价'] = sz_index['close'].astype('float')
data = data.merge(sz_index, left_on='时间', right_on='日期', how='inner')
plt.use('Qt5Agg')
data.index = data['时间']
data[['投资者情绪', '收盘价']].plot(secondary_y=['close'])
plt.show()
def load_ds(self):
# 获取指定股票日内1分钟级数据
stock_zh_a_minute_df = ak.stock_zh_a_minute(
symbol=self.symbol, period=self.period
)
print(stock_zh_a_minute_df)