def Get_5_10_20_data(self, code, start, end):
data_5_data = ts.get_hist_data(code, start=start, end=end)
#防止过多拉取数据源 网站屏蔽IP
cons = ts.get_apis()
# 增加前复权 adj='qfq' 后复权 adj='hfq'（bob_jie ： 2017-11-01)
data_5_data = ts.bar(code,
conn=cons,
start_date=start,
adj='qfq',
end_date=end,
ma=[5, 10, 20],
factors=['vr', 'tor'])
try:
data_5_data.values[0][0]
except Exception as e:
time.sleep(2)
#防止过多拉取数据源 网站屏蔽IP
cons = ts.get_apis()
# 增加前复权 adj='qfq' 后复权 adj='hfq'（bob_jie ： 2017-11-01)
data_5_data = ts.bar(code,
conn=cons,
start_date=start,
adj='qfq',
end_date=end,
ma=[5, 10, 20],
factors=['vr', 'tor'])
ts.get_h_data('002337', autype='qfq')
Mysql().SaveMySqlTWO(data_5_data, 'Stock_Basics_Info_All',
code + 'stock_basics', code)
def break_monitor(code, warning_vol):
start = True
start_monitor = True
waiting_time = datetime.datetime.now()
conn = ts.get_apis()
while 1:
current = datetime.datetime.now()
try:
if start_monitor:
try:
df = ts.quotes(code, conn=conn)
except Exception as e:
print(e)
time.sleep(EXCEPTION_TIME_OUT)
conn = ts.get_apis()
continue
print('under monitor {}'.format(current))
if df['bid_vol1'].values[0] < warning_vol and start:
title=code+' Buy +1 : '+str(df['bid_vol1'].values[0])
sendmail(title,title)
# msg.send_ceiling(code, df['bid_vol1'].values[0])
start = False
start_monitor = False
waiting_time = current + datetime.timedelta(seconds=TIME_RESET)
time.sleep(NORMAL_TIME_OUT)
except Exception as e:
print(e)
time.sleep(EXCEPTION_TIME_OUT)
conn = ts.get_apis()
continue
if current > waiting_time:
start = True
start_monitor = True
def _get_cons(self):
con1 = ts.get_apis()
con2 = ts.get_apis()
con3 = ts.get_apis()
con4 = ts.get_apis()
con5 = ts.get_apis()
return [con1, con2, con3, con4, con5]
def get_realtime_info(self, codes, has_sent, types, stock, yjl, percent):
try:
price_df = ts.quotes(codes, conn=self.api)
except Exception as e:
logger.error('获取可转债异常 >>>> {}'.format(e))
try:
self.api = ts.get_apis()
except Exception as e:
logger.error('异常中存在异常{}'.format(e))
time.sleep(EXECEPTION_TIME)
else:
if len(price_df) != 0:
price_df = price_df[price_df['cur_vol'] != 0]
price_df['percent'] = (price_df['price'] - price_df['last_close']) / price_df[
'last_close'] * 100
price_df['percent'] = price_df['percent'].map(lambda x: round(x, 2))
ret_dt = \
price_df[
(price_df['percent'] > percent) | (price_df['percent'] < -1 * percent)][
['code', 'price', 'percent']]
if len(ret_dt) > 0:
# 提醒一次后，下一次的间隔为DELTA_TIME分钟后
# sent_list = []
for i in ret_dt['code']:
if has_sent[i] <= datetime.datetime.now():
name_list = []
yjl_list = []
name_list.append(stock[i])
yjl_list.append(yjl[i])
has_sent[i] = datetime.datetime.now() + datetime.timedelta(minutes=DELTA_TIME)
ret_dt1 = ret_dt[ret_dt['code'] == i]
ret_dt1['名称'] = name_list
ret_dt1['溢价率'] = yjl_list
name=ret_dt1['名称'].values[0]
price=ret_dt1['price'].values[0]
percent=ret_dt1['percent'].values[0]
yjl_v = ret_dt1['溢价率'].values[0]
now = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
content0='{t}\n{name}:价格:{price} 涨幅:{percent},溢价率:{yjl}'.format(name=name,price=price,percent=percent,yjl=yjl_v,t=now)
logger.info(content0)
try:
wechat.send_content(content0)
except Exception as e:
logger.info('发送微信失败')
logger.info(e)
def write_all_stock(allAshare, lib=None):
"""
:param allAshare: List，所有股票
:param lib: arctic.store.version_store.VersionStore
:return: succ: List, written stocks; fail: List, failed written stocks
"""
succ = []
fail = []
cons = ts.get_apis()
if not os.path.exists(hqdir):
os.mkdir(hqdir)
for symbol in allAshare:
try:
df = ts.bar(symbol, cons, freq='D', start_date='',
end_date='').sort_index(ascending=True)
if MONGO and lib:
lib.write(symbol, df, metadata={'source': 'Tushare'})
if CSV:
df.to_csv(os.path.abspath("{}/{}.csv".format(hqdir, symbol)))
print(symbol + '写入完成')
succ.append(symbol)
except Exception as e:
fail.append(symbol)
print("Failed for ", symbol, str(e))
sleep(0.1)
if SFL:
write_list(succ, os.path.abspath('./hq_succ_list.txt'))
write_list(fail, os.path.abspath('./hq_fail_list.txt'))
def gjzq(file):
df = pd.read_table(file, encoding='gbk', dtype={'证券代码': np.str})
del df['Unnamed: 15']
code_list = list(df['证券代码'].values)
api = ts.get_apis()
# 移除非法证券代码 中签
t=[code_list.remove(i) for i in code_list.copy() if i.startswith('7') or i[:2] == '07']
price_df = ts.quotes(code_list, conn=api)
# 去除不合法的数据
price_df=price_df.dropna()
filter_df = price_df[price_df['last_close'] != 0]
filter_df = filter_df.reset_index(drop=True)
filter_df['percent'] = (filter_df['price'] - filter_df['last_close']) / filter_df['last_close'] * 100
filter_df['percent'] = filter_df['percent'].map(lambda x: round(x, 2))
ret_df = filter_df[(filter_df['percent'] > ALERT_PERCENTAGE) | (filter_df['percent'] < ALERT_PERCENTAGE*-1)]
d = dict(zip(list(df['证券代码'].values), list(df['证券名称'])))
ret_df['name']=ret_df['code'].map(lambda x:d.get(x))
ret_df['amount']=ret_df['amount'].map(lambda x:round(x/10000,1))
rename_column = {'code':'证券代码','name':'证券名称','price':'当前价格','percent':'涨幅','amount':'成交金额(万)'}
ret_df=ret_df.rename(columns=rename_column)
ret = ret_df[list(rename_column.values())]
ret=ret.reset_index(drop=True)
# 发送推送
print(ret)
ts.close_apis(api)
def get_ticks(self, code): # time, price, volume, type
date = time.strftime('%Y-%m-%d', time.localtime())
ts_api = ts.get_apis()
df = ts.tick(code.split('.')[0], conn=ts_api, date=date, asset='E')
if df is None:
df = pd.DataFrame()
return df
def get_quotes(self, code): # ohl+price+volume+amount+ba1-5vp
ts_api = ts.get_apis()
df = ts.quotes(code.split('.')[0], conn=ts_api, asset='E')
if df is None:
return pd.DataFrame()
df['close'] = df.loc[:, 'price']
return df
def __init__(self):
app = QtWidgets.QApplication(sys.argv)
main_window = QtWidgets.QMainWindow()
self.ui = Ui_dog()
self.ui.setupUi(main_window)
# 银行基本信息目录及文件名
self.bank_basic_info_dir = r"C:\quanttime\src\watch_time\bank_info.csv"
# tushare connect context
token = "17e7755e254f02cc312b8b7e22ded9a308924147f8546fdfbe653ba1"
ts.set_token(token)
self.cons = ts.get_apis()
# ts 授权
self.pro = ts.pro_api()
# 设定默认的行情数据, 通达信默认行情源，tushare，joinquant可选
self.ui.checkBox_4.setCheckState(QtCore.Qt.Unchecked)
self.ui.checkBox_5.setCheckState(QtCore.Qt.Checked)
self.ui.checkBox_7.setCheckState(QtCore.Qt.Unchecked)
# 默认只分析重点关注的券商标的
self.ui.checkBox.setCheckState(QtCore.Qt.Checked)
# 银行基本信息更新按钮信号连接
self.ui.pushButton.clicked.connect(self.update_bank_industry_table)
# 银行分红信息及系统排名表刷新信号连接
self.ui.pushButton_2.clicked.connect(self.update_bank_dividend)
# 券商估值分析pushbutton按钮signal连接
self.ui.pushButton_3.clicked.connect(self.analyze_security_valuation)
main_window.show()
sys.exit(app.exec_())
def get_bar_realtime_code():
ap = ts.get_apis()
data = ts.bar('600570',
ap,
start_date='20190516',
end_date='20190517',
freq='1min')
# if os.path.exists(filename):
# data.to_csv(
# filename,
# mode='a',
# header=False,
# encoding='utf-8',
# na_rep='NA')
# else:
# data.to_csv(filename, encoding='utf-8', na_rep='NA')
# pcolumns = data.columns
# pindex = data.index
# pdata = data[data.columns[0:10]]
# pdata2 = data['首字符'] = data['code'].str[0:1]
data['datetime'] = data.index
# ps = data['time']
# print(ps)
# print(type(data.index))
# data['time'] = pd.to_datetime(data['time'], format='%d.%m.%Y')
# pddata = data.loc[-1] = ptime
# data.loc[data['首字符'] == '6', '市场'] = '1'
# ss = data.to_json(orient='split')
content = data_to_json(data)
# resp = Response_headers(content)
return content
def start():
#获取连接备用
cons = ts.get_apis()
# 导入个股
print("导入个股")
# codes,names,timeToMarket = importStockList()
# codes = ["000801"]
# names = ["四川九洲"]
# timeToMarket = [20171215]
#000002.SZ errcode: -40521008 start_date:1993-01-23,end_date:1993-01-26
codes = ["300251"]
names = ["万科A"]
timeToMarket = [19910102]
print(len(codes), len(names), len(timeToMarket))
importStockDataDailyWind(codes, names, timeToMarket)
# test(codes)
'''
#导入指数
print("导入指数")
df = ts.get_index()
codes = df["code"].values
names = df["name"].values
import import_Stock_Data_Daily
import_Stock_Data_Daily.importStockDataDaily(codes,names,asset='INDEX',adj='None')
'''
ts.close_apis(conn=cons)
print("导入完成")
def insert_kdata(code, asset, freq, start, end):
sheet = get_sheet(freq)
cons = ts.get_apis()
k_data = ts.bar(code,
conn=cons,
freq='5min',
start_date='2016-01-01',
end_date='',
asset=asset)
# 对DataFrame的个别列进行重命名
k_data = k_data.rename(columns={'code': 'code_origin'})
# 代码
k_data['code'] = code
# 周期
k_data['freq'] = freq
# 是否指数
k_data['asset'] = asset
k_data = k_data[[
'code', 'index', 'date', 'ktype', 'open', 'close', 'high', 'low',
'volume', 'code_origin'
]]
# inplace默认为False,如果该值为False，那么原来的pd顺序没变，只是返回的是排序的
k_data.sort_values("date", ascending=False, inplace=True)
records = pandas.io.json.loads(k_data.T.to_json()).values()
global insert_result
insert_result = sheet.insert_many(records, ordered=False)
# except BulkWriteError as e:
# print("insert_many出现重复，忽略错误")
return insert_result
def tushare_bar(code, start, end, freq, **kwargs):
df = ts.bar(code=code, conn = ts.get_apis(), start_date = start, end_date = end, \
freq = freq, asset='E', adj = 'qfq', **kwargs)
if isinstance(df, pd.DataFrame):
return df
else:
raise TypeError('df is unknown type %s' % type(df))
def __init__(self):
self.conn=ts.get_apis()
self.available_bonds = pd.read_sql('tb_bond_jisil', engine, index_col='index')['可转债代码'].values
self.allBonds=ts.new_cbonds(default=0,pause=2)
self.onSellBond=self.allBonds.dropna(subset=['marketprice'])
self.today=datetime.datetime.now().strftime('%Y-%m-%d %H:%M')
self.total = self.onSellBond[self.onSellBond['bcode'].isin(self.available_bonds)]
def Signal(coin_symbol, end_dt):
cons = ts.get_apis()
temp_day = ts.tick(code=coin_symbol, conn=cons, date=end_dt)
print(temp_day)
amount = []
vol = []
close = []
for i in range(len(temp_day)):
temp_list = temp_day.ix[i]
amount.append(float(temp_list[1]) * float(temp_list[2]))
vol.append(float(temp_list[2]))
close.append(float(temp_list[1]))
amount = np.array(amount)
vol = np.array(vol)
close = np.array(close)
price_avg_long = []
price_avg_short = []
price_close = []
delt_list = []
for i in range(1, len(amount)):
price_avg_long.append(float(amount[:i + 1].mean() /
vol[:i + 1].mean()))
price_avg_short.append(float(amount[i] / vol[i]))
price_close.append(close[i])
delt_list.append(
float(amount[:i + 1].mean() / vol[:i + 1].mean()) /
float(amount[i - 1:i].mean() / vol[i - 1:i].mean()))
return price_avg_long, price_avg_short, price_close, delt_list
def stock_plot():
left, width = 0.1, 0.8
rect_vol = [left, 0.1, width, 0.3]
rect_main = [left, 0.4, width, 0.5]
fig = plt.figure()
conn = ts.get_apis()
df = ts.bar('300333', conn=conn, start_date='2018-01-01')
del df['code']
df.reset_index(inplace=True)
dates=df['datetime'].values
df['datetime'] = df['datetime'].map(mdates.date2num)
# dates=df['datetime'].values
vol = df['vol'].values
ax_vol = fig.add_axes(rect_vol)
ax_vol.fill_between(dates, vol,color = 'y')
# 横坐标时间旋转
plt.setp(ax_vol.get_xticklabels(), rotation=30, horizontalalignment='right')
ax_main = fig.add_axes(rect_main)
candlestick_ochl(ax_main,df.values,width=0.6,colordown='g',colorup='r')
ax_main.axes.get_xaxis().set_visible(False)
ax_main.set_title("STOCK LINE")
ts.close_apis(conn)
plt.show()
def __init__(self):
self.conn=ts.get_apis()
self.available_bonds = pd.read_sql('tb_bond_jisilu', engine, index_col='index')['可转债代码'].values
self.allBonds=ts.new_cbonds(default=0,pause=2)
self.onSellBond=self.allBonds.dropna(subset=['marketprice'])
self.today=datetime.datetime.now().strftime('%Y-%m-%d %H:%M')
self.total = self.onSellBond[self.onSellBond['bcode'].isin(self.available_bonds)]
def get_price_diff(self, codes, has_sent_, types, kzz_stocks,
kzz_stocks_yjl):
# 针对可转债
try:
df = ts.quotes(codes, conn=self.api)
except Exception as e:
logger.error('获取可转债异常 >>>> {}'.format(e))
try:
self.api = ts.get_apis()
except Exception as e:
logger.error('异常中存在异常{}'.format(e))
time.sleep(EXECEPTION_TIME)
else:
df['bid1'] = df['bid1'].astype(float)
df['ask1'] = df['ask1'].astype(float)
df['diff'] = np.abs(df['bid1'] - df['ask1'])
result = df[df['diff'] >= DIFF_V]
if result.empty:
# continue
return
else:
for j in result['code']:
if has_sent_[j] <= datetime.datetime.now():
has_sent_[j] = datetime.datetime.now(
) + datetime.timedelta(minutes=DIFF_DELTA_TIME)
name_list = []
yjl_list = []
name_list.append(kzz_stocks[j])
yjl_list.append(kzz_stocks_yjl[j])
ret_dt1 = result[result['code'] == j]
ret_dt1['名称'] = name_list
ret_dt1['溢价率'] = yjl_list
# ret_dt1 = ret_dt1.set_index('code', drop=True)
code = j
name = ret_dt1['名称'].values[0]
price = ret_dt1['price'].values[0]
bid = ret_dt1['bid1'].values[0]
ask = ret_dt1['ask1'].values[0]
diff = round(ret_dt1['diff'].values[0], 2)
now = datetime.datetime.now().strftime(
'%Y-%m-%d %H:%M:%S')
content0 = '{t}\n{code}::{name}:价格:{price} 买1:{bid} 卖1:{ask}差价:{diff}'.format(
code=code,
name=name,
price=price,
bid=bid,
ask=ask,
diff=diff,
t=now)
logger.info(content0)
try:
wechat.send_content(content0)
except Exception as e:
logger.info('发送微信失败')
logger.info(e)
def downMinuteBarBySymbol(symbol):
"""下载某一合约的分钟线数据"""
start = time()
cl = db[symbol]
cl.ensure_index([('datetime', ASCENDING)], unique=True) # 添加索引
cons = ts.get_apis()
df = ts.bar(symbol,
cons,
start_date='2018-01-10',
end_date='2018-04-09',
freq='1min',
asset='X')
df = df.sort_index()
for ix, row in df.iterrows():
bar = generateVtBar(row)
d = bar.__dict__
flt = {'datetime': bar.datetime}
cl.replace_one(flt, d, True)
end = time()
cost = (end - start) * 1000
print u'合约%s数据下载完成%s - %s，耗时%s毫秒' % (symbol, df.index[0], df.index[-1],
cost)
def GetstockData(code,start,end=None):
api=ts.get_apis()
if not end:
end = datetime.datetime.now().strftime('%Y-%m-%d')
print(end)
df = ts.bar(code,conn=api,freq='D',start_date=start,end_date=end)
df.to_excel('data/'+code+'.xls')
def downBarBySymbol(symbol, start_date=None, end_date=None, freq='D'):
"""下载某一合约的分钟线数据"""
start = time()
db_freq = freq
#if freq[0] in ['0', '1', '2','3','4','5','6','7','8','9']:
#db_freq = freq + 'MIN'
db = mc[DB_NAME_DICT[db_freq]] # 数据库
cl = db[symbol]
cl.ensure_index([('datetime', ASCENDING)], unique=True) # 添加索引
if generateExchange(symbol)=="QH":
asset = 'X'
else:
asset = 'E'
asset = 'INDEX'
df = ts.bar(symbol, conn=ts.get_apis(), freq=freq, asset=asset, start_date=start_date, end_date=end_date, adj='qfq')
#df = ts.get_hist_data(symbol,start=start_date, end=end_date, ktype=freq)
df = df.sort_index()
for ix, row in df.iterrows():
bar = generateVtBar(row)
d = bar.__dict__
flt = {'datetime': bar.datetime}
cl.replace_one(flt, d, True)
end = time()
cost = (end - start) * 1000
print u'合约%s 周期%s数据下载完成%s - %s，耗时%s毫秒' %(symbol, freq, df.index[0], df.index[-1], cost)
def stock_plot():
left, width = 0.1, 0.8
rect_vol = [left, 0.1, width, 0.3]
rect_main = [left, 0.4, width, 0.5]
fig = plt.figure()
conn = ts.get_apis()
df = ts.bar('300333', conn=conn, start_date='2018-01-01')
del df['code']
df.reset_index(inplace=True)
dates = df['datetime'].values
df['datetime'] = df['datetime'].map(mdates.date2num)
# dates=df['datetime'].values
vol = df['vol'].values
ax_vol = fig.add_axes(rect_vol)
ax_vol.fill_between(dates, vol, color='y')
# 横坐标时间旋转
plt.setp(ax_vol.get_xticklabels(),
rotation=30,
horizontalalignment='right')
ax_main = fig.add_axes(rect_main)
candlestick_ochl(ax_main, df.values, width=0.6, colordown='g', colorup='r')
ax_main.axes.get_xaxis().set_visible(False)
ax_main.set_title("STOCK LINE")
ts.close_apis(conn)
plt.show()
def __init__(self):
self.engine = get_engine('db_stock')
self.cb_code, self.name, self.yjl = self.bond()
self.stocks = dict(zip(self.cb_code, self.name))
self.stocks_yjl = dict(zip(self.cb_code, self.yjl))
self.api = ts.get_apis()
# python3 这个返回的不是list,需要手工转换
self.kzz_code_list = list(self.stocks.keys())
pool_code, pool_name = self.stock_pool()
self.pool_dict = dict(zip(pool_code, pool_name))
self.pool_list = list(self.pool_dict.keys())
# 添加一部分持仓数据 或者 监测仓
self.df = pd.read_table(file, encoding='gbk', dtype={'证券代码': np.str})
del self.df['Unnamed: 15']
code_list = list(self.df['证券代码'].values)
# 移除非法证券代码 中签
t = [
code_list.remove(i) for i in code_list.copy()
if i.startswith('7') or i[:2] == '07'
]
self.code_lists = code_list
def downMinuteBarBySymbol(symbol, freq):
"""下载某一合约的分钟线数据"""
start = time()
cl = db[symbol]
cl.ensure_index([('datetime', ASCENDING)], unique=True) # 添加索引
df = ts.bar(symbol, ts.get_apis(), freq=freq, asset='X')
#df = ts.bar(symbol, ktype='1min')
df = df.sort_index()
for ix, row in df.iterrows():
bar = generateVtBar(row)
d = bar.__dict__
#有的合约字母变成大写，可以自己放开这个地方
#d['symbol'] = d['symbol'].lower()
flt = {'datetime': bar.datetime}
cl.replace_one(flt, d, True)
end = time()
cost = (end - start) * 1000
print u'合约%s数据下载完成%s - %s，耗时%s毫秒' % (symbol, df.index[0], df.index[-1],
cost)
def plot_stock_line(code, name, table_name, start='2017-10-01', save=False):
today = datetime.datetime.now().strftime('%Y-%m-%d')
title = u'{} {} {} {}'.format(today, code, name, table_name)
if os.path.exists(title + '.png'):
return
engine = get_engine('db_stock', local=True)
fig = plt.figure(figsize=(10, 8))
base_info = pd.read_sql('tb_basic_info', engine, index_col='index')
# fig,(ax,ax2)=plt.subplots(2,1,sharex=True,figsize=(16,10))
ax = fig.add_axes([0, 0.3, 1, 0.55])
ax2 = fig.add_axes([0, 0.1, 1, 0.25])
if code is None and name is not None:
code = base_info[base_info['name'] == name]['code'].values[0]
print code
df = None
for _ in range(4):
api = ts.get_apis()
try:
df = ts.bar(code, conn=api, start_date=start)
break
except Exception, e:
print e
ts.close_apis(api)
time.sleep(random.random() * 3)
continue
def get_realtime_info(self, codes, has_sent, types, stock, yjl, percent):
try:
price_df = ts.quotes(codes, conn=self.api)
except Exception as e:
logger.error('获取可转债异常 >>>> {}'.format(e))
try:
self.api = ts.get_apis()
except Exception as e:
logger.error('异常中存在异常{}'.format(e))
time.sleep(EXECEPTION_TIME)
else:
if len(price_df) != 0:
price_df = price_df[price_df['cur_vol'] != 0]
price_df['percent'] = (price_df['price'] - price_df['last_close']) / price_df[
'last_close'] * 100
price_df['percent'] = price_df['percent'].map(lambda x: round(x, 2))
ret_dt = \
price_df[
(price_df['percent'] > percent) | (price_df['percent'] < -1 * percent)][
['code', 'price', 'percent']]
if len(ret_dt) > 0:
# 提醒一次后，下一次的间隔为5分钟后
# sent_list = []
for i in ret_dt['code']:
if has_sent[i] <= datetime.datetime.now():
name_list = []
yjl_list = []
name_list.append(stock[i])
yjl_list.append(yjl[i])
has_sent[i] = datetime.datetime.now() + datetime.timedelta(minutes=DELTA_TIME)
ret_dt1 = ret_dt[ret_dt['code'] == i]
ret_dt1['名称'] = name_list
ret_dt1['溢价率'] = yjl_list
name=ret_dt1['名称'].values[0]
price=ret_dt1['price'].values[0]
percent=ret_dt1['percent'].values[0]
yjl_v = ret_dt1['溢价率'].values[0]
now = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
content0='{t}\n{name}:价格:{price} 涨幅:{percent},溢价率:{yjl}'.format(name=name,price=price,percent=percent,yjl=yjl_v,t=now)
logger.info(content0)
try:
wechat.send_content(content0)
except Exception as e:
logger.info('发送微信失败')
logger.info(e)
def update_daily(self):
add_cols = u'ALTER TABLE `{}` ADD `{}` FLOAT;'.format(
self.table_name, self.today)
self._exe(add_cols)
# self.conn.commit()
api = ts.get_apis()
cmd = 'SELECT * FROM `{}`'.format(self.table_name)
cur = self._exe(cmd)
for i in cur.fetchall():
(code, name, safe_price, count, profit_ratio, profit, values,
current_price, earn) = i[:9]
df = ts.quotes(code, conn=api)
current_price = round(float(df['price'].values[0]), 2)
values = current_price * count
last_close = df['last_close'].values[0]
earn = (current_price - last_close) * count
profit = (current_price - safe_price) * count
profit_ratio = round(
float(current_price - safe_price) / safe_price * 100, 2)
update_cmd = u'UPDATE {} SET `盈亏比例`={} ,`盈亏`={}, `市值` ={}, `现价` = {},`{}`={} where `证券代码`= {};'.format(
self.table_name, profit_ratio, profit, values, current_price,
self.today, earn, code)
# print update_cmd
self._exe(update_cmd)
ts.close_apis(api)
def __init__(self):
# 初始化日志
self.regularlog = logging.getLogger("dividend_maintenance")
self.cmdconsole = logging.StreamHandler()
self.cmdconsole.setLevel(logging.DEBUG)
self.formatter = logging.Formatter(
"%(asctime)s - %(name)s - %(levelname)s - %(message)s")
self.cmdconsole.setFormatter(self.formatter)
self.regularlog.addHandler(self.cmdconsole)
self.tushare_dir = "C:\\quanttime\\data\\dividend\\tushare\\"
self.errorCodeList = []
# jqdata context 暂时不用先注释
# auth('13811866763', "sam155")
# mongodb client
self.mongo_client = pymongo.MongoClient('mongodb://localhost:27017/')
# tushare connect context
token = "17e7755e254f02cc312b8b7e22ded9a308924147f8546fdfbe653ba1"
ts.set_token(token)
self.cons = ts.get_apis()
# ts 授权
self.pro = ts.pro_api()
def getTickData(self):
"""
通过tushare获取分时数据，并存入数据库，避免后面运行重复获取。
"""
df = None
db = self.connectDb()
flt = {'date': self.date}
result = db[self.collectionName].find_one(flt)
if not result:
print(u'数据库无数据，正在尝试从tushare获取数据..')
cons = ts.get_apis()
df = ts.tick(self.symbol, conn=cons, date=self.date)
if df is not None:
df['date'] = self.date
db[self.collectionName].insert_many(df.to_dict('records'))
print(u'成功获取并写入数据库。')
else:
print(u'获取数据出错，可能接口问题，或者日期设置不正确。')
else:
print(u'数据库有匹配数据')
data = list(db[self.collectionName].find(flt, projection={'_id': False}))
df = pd.DataFrame(data)
if df is not None:
df.drop_duplicates('datetime', keep='last', inplace=True)
df.reset_index(inplace=True, drop=True)
df.datetime = df.datetime.map(lambda dateStr: dateStr[-5:])
self.tickData = df
return df
def collect_index_china_full():
conn = ts.get_apis()
collect_single_index_from_ts('000001', conn=conn)
collect_single_index_from_ts('399001', conn=conn)
collect_single_index_from_ts('000300', conn=conn)
collect_single_index_from_ts('000905', conn=conn)
ts.close_apis(conn)
def fetch(code_list):
tsconn = ts.get_apis()
sqlconn = db.connect()
cursor = sqlconn.cursor()
try:
for code in code_list:
try:
data = ts.bar(code,
conn=tsconn,
start_date="1970-01-01",
freq="5min")
count = data.shape[0]
i = 0
for index, row in data.iterrows():
cursor.execute(
"insert into stock_price (code, time, price) values (%s, %s, %s)",
(code, str(index), float(row["close"])))
i += 1
if i % 1000 == 0:
print("{0} {1}%".format(code, int(i / count * 100)))
sqlconn.commit()
print("done")
except IOError as err:
print(code)
print(err)
finally:
sqlconn.close()
ts.close_apis(tsconn)
def __init__(self):
self.cb_code, self.name, self.yjl = self.bond()
self.stocks = dict(zip(self.cb_code, self.name))
self.stocks_yjl = dict(zip(self.cb_code, self.yjl))
self.api = ts.get_apis()
# python3 这个返回的不是list,需要手工转换
self.code_list = list(self.stocks.keys())
def monitor():
engine = get_engine('db_zdt')
table = '20180409zdt'
api = ts.get_apis()
df = pd.read_sql(table, engine, index_col='index')
# print df
price_list = []
percent_list = []
amplitude_list = []
start = datetime.datetime.now()
for i in df[u'代码'].values:
try:
curr = ts.quotes(i, conn=api)
last_close = curr['last_close'].values[0]
curr_price = curr['price'].values[0]
amplitude = round(
((curr['high'].values[0] - curr['low'].values[0]) * 1.00 /
last_close) * 100, 2)
# if last_close>=curr_price:
# print i,
# print df[df[u'代码']==i][u'名称'].values[0],
# print percent
except Exception, e:
print e
curr_price = 0
if last_close == 0:
percent = np.nan
percent = round((curr_price - last_close) * 100.00 / last_close, 2)
percent_list.append(percent)
price_list.append(curr_price)
amplitude_list.append(amplitude)
def fetch_kdata_lazy(code, asset, freq, end_date=''):
# global在第一行声明，养成好习惯
global cons
start_date = get_date_max(code, asset, freq) + relativedelta(minutes=1)
if isinstance(end_date, datetime.datetime):
end_date_str = end_date.strftime(DT_FMT).replace(
"11:30:00", "13:00:00")
end_date = datetime.datetime.strptime(end_date_str, DT_FMT)
kdata = ts.bar(code,
conn=cons,
asset=asset,
adj='qfq',
freq=freq,
start_date=start_date,
end_date=end_date)
result = 0
try:
result = insert_kdata(asset, freq, kdata)
except pymongo.errors.BulkWriteError as err:
logger.error(err)
except StockError as err:
logger.error(err)
except TypeError as e:
cons = ts.get_apis()
logger.error(e)
except Exception as e:
logger.error(e)
return result
def store_data():
code = '600050'
conn = ts.get_apis()
file_name = os.path.join(data_path, code + '.xls')
df = ts.bar(code, conn=conn, start_date='2015-01-01')
# print(df.head(10))
print(file_name)
df.to_excel(file_name)
def monitor():
engine = get_engine('db_zdt')
table = '20180409zdt'
api = ts.get_apis()
df = pd.read_sql(table, engine, index_col='index')
price_list = []
percent_list = []
amplitude_list = []
start = datetime.datetime.now()
for i in df['代码'].values:
try:
curr = ts.quotes(i, conn=api)
last_close = curr['last_close'].values[0]
curr_price = curr['price'].values[0]
amplitude = round(((curr['high'].values[0] - curr['low'].values[0]) * 1.00 / last_close) * 100, 2)
# if last_close>=curr_price:
# print(i,)
# print(df[df['代码']==i]['名称'].values[0],)
# print( percent)
except Exception as e:
print('this point')
print(e)
api=ts.get_apis()
curr_price = 0
if last_close == 0:
percent = np.nan
percent = round((curr_price - last_close) * 100.00 / last_close, 2)
percent_list.append(percent)
price_list.append(curr_price)
amplitude_list.append(amplitude)
df['今日价格'] = price_list
df['今日涨幅'] = percent_list
df['今日振幅'] = amplitude_list
df['更新时间'] = datetime.datetime.now().strftime('%Y %m %d %H:%M%S')
end = datetime.datetime.now()
print('time use {}'.format(end - start))
df.to_sql(table + 'monitor', engine, if_exists='replace')
ts.close_apis(api)
def __init__(self):
self.engine = get_engine('db_stock')
self.kzz_code, self.kzz_name, self.zg_code, self.name, self.yjl = self.zg_bond()
self.kzz_stocks = dict(zip(self.kzz_code, self.kzz_name))
self.zg_stocks = dict(zip(self.zg_code, self.name))
self.kzz_stocks_yjl = dict(zip(self.kzz_code, self.yjl))
self.zg_stocks_yjl = dict(zip(self.zg_code, self.yjl))
self.api = ts.get_apis()
def get_price_diff(self, codes, has_sent_, types):
# 针对可转债
try:
df = ts.quotes(codes, conn=self.api)
except Exception as e:
logger.error('获取可转债异常 >>>> {}'.format(e))
try:
self.api = ts.get_apis()
except Exception as e:
logger.error('异常中存在异常{}'.format(e))
time.sleep(EXECEPTION_TIME)
else:
df['bid1'] = df['bid1'].astype(float)
df['ask1'] = df['ask1'].astype(float)
df['diff'] = np.abs(df['bid1'] - df['ask1'])
result = df[df['diff'] >= DIFF_V]
if result.empty:
# continue
return
else:
for j in result['code']:
if has_sent_[j] <= datetime.datetime.now():
has_sent_[j] = datetime.datetime.now()+ datetime.timedelta(minutes=DIFF_DELTA_TIME)
name_list = []
yjl_list = []
name_list.append(self.kzz_stocks[j])
yjl_list.append(self.kzz_stocks_yjl[j])
ret_dt1 = result[result['code'] == j]
ret_dt1['名称']=name_list
ret_dt1['溢价率']=yjl_list
# ret_dt1 = ret_dt1.set_index('code', drop=True)
code=j
name = ret_dt1['名称'].values[0]
price = ret_dt1['price'].values[0]
bid = ret_dt1['bid1'].values[0]
ask = ret_dt1['ask1'].values[0]
diff = round(ret_dt1['diff'].values[0],2)
now = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
content0 = '{t}\n{code}::{name}:价格:{price} 买1:{bid} 卖1:{ask}差价:{diff}'.format(code=code,name=name, price=price,
bid=bid, ask=ask,
diff=diff, t=now)
logger.info(content0)
try:
wechat.send_content(content0)
except Exception as e:
logger.info('发送微信失败')
logger.info(e)
def stock_line():
api = ts.get_apis()
df = ts.bar('300141', conn=api, start_date='2018-01-01')
# print(df)
del df['code']
df.reset_index(inplace=True)
print(df)
df['datetime'] = df['datetime'].map(mdates.date2num)
ax1 = plt.subplot2grid((6, 1), (0, 0), rowspan=5)
ax2 = plt.subplot2grid((6, 1), (5, 0), rowspan=5, sharex=ax1)
ax1.xaxis_date()
candlestick_ochl(ax1, df.values, width=1, colorup='r', colordown='g')
# ax2.fill_between(ax1,df['vol'].values,0)
plt.show()
ts.close_apis(api)
def ban_share(code,name):
conn =ts.get_apis()
year_2017 = [2629.218,3970.902,2083.032,1720.327,1999.456,1771.074,2417.082,2904.992,2910.946,2971.483,2350.122,3874.328]
df = ts.bar(code, conn=conn, freq='M', start_date='2016-12-30', end_date='2017-11-01', asset='INDEX')
series = df['close']
'''
diff_series=[]
print series
l = len(series)
for i in range(l-1):
print series[i]
d= series[i]-series[i+1]
diff_series.append(d)
#print len(diff_series)
'''
#s2=Series(series)
s2=series[:len(series)-1]
s3 = s2.sort_index(ascending=True)
#print s3
s1 = Series(year_2017[0:len(s3)])
s3=s3.reset_index(drop=True)
#print s3
#print s1
cor = s3.corr(s1)
#print len(s3)
#print len(s1)
print cor
plt.figure()
plt.subplot(2,1,1)
s1.plot()
plt.subplot(2,1,2)
s3.plot(title=name)
plt.show()
if abs(cor) >0.5:
print 'Great factor: ',code
def api_close_case():
api = ts.get_apis()
print('exit function')
ts.close_apis(api)
exit(0)
from matplotlib import rcParams
import tushare as ts
import matplotlib
import os
import talib
import matplotlib.dates as mdates
from matplotlib.finance import candlestick2_ochl,volume_overlay
# from mpl_finance import candlestick_ochl
# from mpl_finance import candlestick2_ochl
matplotlib.style.use('ggplot')
data_path = os.path.join(os.getcwd(), 'data')
if not os.path.exists(data_path):
os.mkdir(data_path)
os.chdir(data_path)
api = ts.get_apis()
def plot_test1():
x = [1, 2]
y = [2, 4]
# plt.scatter(x,y)
plt.scatter(x, y, color='red', marker='x')
plt.axis([0, 10, 0, 10])
plt.show()
def from_book():
fig = plt.figure()
ax1 = fig.add_subplot(2, 2, 1)
# -*-coding=utf-8-*-
# 获取 不同形态的k线
import random
import time
import tushare as ts
import pandas as pd
import os, datetime, math
import numpy as np
import logging
from setting import get_engine, MYSQL_HOST, MYSQL_PORT, MYSQL_USER, MYSQL_PASSWORD, REDIS_HOST, get_mysql_conn
import redis
from threading import Thread
import MySQLdb
from collect_data import SaveData
engine = get_engine('history')
conn = ts.get_apis()
MYSQL_DB = 'history'
cursor = get_mysql_conn(MYSQL_DB).cursor()
# pd.set_option('display.max_rows', None)
class Kline():
def __init__(self):
logging.info('tushare version: {}'.format(ts.__version__))
path = os.path.join(os.getcwd(), 'data')
self.today_date = datetime.datetime.now().strftime('%Y-%m-%d')
logging.info(self.today_date)
if not os.path.exists(path):
os.mkdir(path)
os.chdir(path)
def plot_stock_line(api,code, name, table_name, current, start='2017-10-01', save=False):
title = '{} {} {} {}'.format(current, code, name, table_name)
title = title.replace('*', '_')
if os.path.exists(title + '.png'):
return
if code is None and name is not None:
code = base_info[base_info['name'] == name]['code'].values[0]
df = None
for _ in range(4):
try:
df = ts.bar(code, conn=api, start_date=start)
except Exception as e:
logger.info(e)
ts.close_apis(api)
time.sleep(random.random() * 30)
api = ts.get_apis()
else:
break
if df is None:
return
df = df.sort_index()
if name is None:
name = base_info[base_info['code'] == code]['name'].values[0]
df = df.reset_index()
df['datetime'] = df['datetime'].dt.strftime('%Y-%m-%d')
sma5 = talib.SMA(df['close'].values, 5)
sma20 = talib.SMA(df['close'].values, 10)
# ax.set_xticks(range(0,len(df),20))
# # ax.set_xticklabels(df['date'][::5])
# ax.set_xticklabels(df['datetime'][::20])
fig = plt.figure(figsize=(10, 8))
# fig,(ax,ax2)=plt.subplots(2,1,sharex=True,figsize=(16,10))
ax = fig.add_axes([0, 0.3, 1, 0.50])
ax2 = fig.add_axes([0, 0.1, 1, 0.20])
candlestick2_ochl(ax, df['open'], df['close'], df['high'], df['low'], width=1, colorup='r', colordown='g',
alpha=0.6)
ax.grid(True)
ax.set_title(title)
ax.plot(sma5, label='MA5')
ax.legend()
ax.plot(sma20, label='MA20')
ax.legend(loc=2)
ax.grid(True)
# df['vol'].plot(kind='bar')
volume_overlay(ax2, df['open'], df['close'], df['vol'], width=1, alpha=0.8, colordown='g', colorup='r')
ax2.set_xticks(range(0, len(df), 5))
# ax.set_xticklabels(df['date'][::5])
ax2.set_xticklabels(df['datetime'][::5])
plt.setp(ax2.get_xticklabels(), rotation=30, horizontalalignment='right')
ax2.grid(True)
plt.subplots_adjust(hspace=0.3)
if save:
# path = os.path.join(os.path.dirname(__file__),'data',today)
fig.savefig(title + '.png')
else:
plt.show()
plt.close()
import tushare as ts
#中证500
#dt = ts.get_zz500s()
#print(dt);
#上证50
#print(ts.get_sz50s());
#沪深300
#print(ts.get_hs300s());
#获取连接备用
cons = ts.get_apis()
df = ts.bar('000300', conn=cons, asset='INDEX', start_date='', end_date='')
df = df.reset_index();
df['value'] = df['open']/2000;
print(df.head(5));
print(df.dtypes);
df1 = ts.bar('000016', conn=cons, asset='INDEX', start_date='', end_date='')
df1 = df1.reset_index();
df1['value'] = df1['open']/2000;
df1['odc'] = df1['open']>df1['close'];
df = pd.concat([df,df1]);
'''
df1 = ts.bar('000905', conn=cons, asset='INDEX', start_date='2016-01-01', end_date='')
df1 = df1.reset_index();
def __init__(self):
self.cons = ts.get_apis()
self.engine = create_engine('mysql+pymysql://root:password@localhost:3306/stock?charset=utf8')
self.all_info = ts.get_stock_basics()
self.all_codes = self.all_info.index
from src.models.Company import Trend, Fundamental
from src.const import BF
file_path = "%s%s%s" % (BASE_DIR, BF['dir']['uploads_dir'], BF['filename']['fundamental'])
if not os.path.exists(file_path):
fun_df = Fundamental.set_frame(file_path)
else:
fun_df = pandas.read_csv(file_path, encoding='gbk')
fun_df.code = fun_df.code.apply(lambda x: str(x).rjust(6, '0'))
con = tushare.get_apis()
code_list = ['600703', '300545', '000014', '300696', '300116', '300094', '002340', '000861',
'601901', '601200', '600079']
for _code in code_list:
_fun_df = fun_df.loc[fun_df['code'] == _code].reset_index(drop=True)
if _fun_df.index.size == 0:
print(_code)
continue
name = _fun_df.at[0, 'name']
name = "%s%s%s" % (BASE_DIR, BF['dir']['company_dir'], name)
Trend.set_frame(con, _code, name)
com_df = pandas.DataFrame(Trend.company_list)
com_df.to_csv("company.csv", index=False)