def do_index():
_code = request.forms.get('code')
code = _code.split(' ')[0]
if len(_code.split(' ')) == 2:
if _code.split(' ')[1] == 'y':
start_date = dt.date.today() + relativedelta(years=-1)
elif _code.split(' ')[1] == '3y':
start_date = dt.date.today() + relativedelta(years=-3)
else:
start_date = None
else:
start_date = None
jstock(code, start_date)
search = jsm.Quotes().search(code)
Name = search[0].name
financialData = jsm.Quotes().get_finance(code)
finance = [
financialData.market_cap,
financialData.shares_issued,
financialData.dividend_yield,
financialData.dividend_one,
financialData.per,
financialData.pbr,
financialData.price_min,
financialData.round_lot
]
imagepath = 'image/{}.png'.format(code)
txtpath = 'data/{}.txt'.format(code)
return template('result', imagepath=imagepath, txtpath=txtpath, code=code, Name=Name, finance=finance)
def get_candle_data(self):
"""株価データ（始値、高値、安値、終値、出来高、日付）を取得する"""
_quotes = jsm.Quotes()
try:
_target = _quotes.get_historical_prices(self.code, jsm.WEEKLY,
self.start, self.end)
except:
print("存在しないコードです。")
self.is_exist_code = False
return
# 株価情報をリスト形式に変換する
_date = [_data.date.strftime("%Y-%m-%d") for _data in _target]
_open = [_data.open for _data in _target]
_close = [_data.close for _data in _target]
_high = [_data.high for _data in _target]
_low = [_data.low for _data in _target]
_volume = [_data.volume for _data in _target]
# 日付が古い順に並べ替える
self.ohcl["date"] = _date[::-1]
self.ohcl["open"] = _open[::-1]
self.ohcl["close"] = _close[::-1]
self.ohcl["high"] = _high[::-1]
self.ohcl["low"] = _low[::-1]
self.ohcl["volume"] = _volume[::-1]
self.__get_current_candle_data()
def stockgetweekjsm(self, start_date, end_date, stock_num):
q = jsm.Quotes()
stock = q.get_historical_prices(stock_num, jsm.WEEKLY, start_date,
end_date) #指定期間、指定銘柄の株価取得
close_stock = [data.close for data in stock] #終値の取得
date_stock = [data.date for data in stock] #日付の取得
db_sw = Series(close_stock, date_stock) #日付と終値をドッキング
self.add_db_sw = db_sw.sort_index(ascending=True) #古い順に並び替える。
self.add_db_sw = DataFrame(self.add_db_sw,
columns=['Stock']) #列名'Stock'を付与する。
#print("self.add_db_sw")
#print(self.add_db_sw)
#print("self.db_sw")
#print(self.db_sw)
#(try-catch)新規の銘柄の場合はself.db_sが存在しないため、except以降の処理:self.db_sw=self.add_db_swとする
try:
self.db_sw = self.db_sw.append(
self.add_db_sw) #既存の株価に新規に追加した株価を追加する
self.db_sw = self.db_sw.groupby(level=0) #重複行があれば削除する
self.db_sw = self.db_sw.last() #重複行があれば削除する
#print("self.db_sw")
#print(self.db_sw)
except AttributeError:
self.db_sw = self.add_db_sw
self.db_sw.to_csv(setting.HOME_PATH + str(stock_num) + ".week.csv",
header=False)
self.db_sw = DataFrame(self.db_sw, columns=['Stock']) #列名'Stock'を付与する。
return self.db_sw
def main():
con = mysql.connector.connect(
host = config.db['host'],
db = config.db['db'],
user = config.db['user'],
passwd = config.db['passwd'],
charset = "utf8"
)
cur = con.cursor()
q = jsm.Quotes()
"""Brandの取得"""
brand = jsm.Brand()
ids = brand.IDS # 業種コードのリスト
brand_list = []
for industory_code in ids.keys():
industory_name = ids[industory_code]
brand_data = q.get_brand(industory_code) # 業種ごとの属性情報の取得を行う
for data in brand_data:
ts = datetime.datetime.today().strftime("%Y-%m-%d %H:%M:%S")
try:
cur.execute("INSERT INTO brands(ccode, industory_code, industory_name, market, name, info, created_at, updated_at) VALUES(%s, %s, %s, %s, %s, %s, %s, %s) ON DUPLICATE KEY UPDATE market = %s, updated_at = %s", (data.ccode, industory_code, industory_name, data.market, data.name, data.info, ts, ts, data.market, ts))
con.commit()
except:
pass
cur.close()
con.close()
def get_stock(GET_STOCK_CODE, GET_START_DATE, GET_END_DATE):
# Time Span
S_YEAR, S_MONTH, S_DAY = GET_START_DATE.split("-")
TARGET_START = datetime.date(int(S_YEAR), \
int(S_MONTH), \
int(S_DAY))
E_YEAR, E_MONTH, E_DAY = GET_END_DATE.split("-")
TARGET_END = datetime.date(int(E_YEAR), \
int(E_MONTH), \
int(E_DAY))
# Get Stock Price
Q = jsm.Quotes()
TARGET = Q.get_historical_prices(GET_STOCK_CODE, \
jsm.DAILY, \
TARGET_START, \
TARGET_END)
# Return to list each quotes
Q_DATE = [DATA.date for DATA in TARGET]
Q_OPEN = [DATA.open for DATA in TARGET]
Q_HIGH = [DATA.high for DATA in TARGET]
Q_LOW = [DATA.low for DATA in TARGET]
Q_CLOSE = [DATA.close for DATA in TARGET]
Q_VOLUME = [DATA.volume for DATA in TARGET]
return [Q_DATE, Q_OPEN, Q_HIGH, Q_LOW, Q_CLOSE, Q_VOLUME]
def get_stock(code, start_date, end_date):
""" get_stock(stockcode, start_date, end_date) return DataFrame.
index:date,
columns:open, high, low, close, volume
"""
start_date = todate(start_date)
end_date = todate(end_date)
q = jsm.Quotes()
target = q.get_historical_prices(code,
jsm.DAILY,
start_date=start_date,
end_date=end_date)
diccolumns = {}
diccolumns['date'] = [data.date for data in target]
diccolumns['open'] = [data.open for data in target]
diccolumns['high'] = [data.high for data in target]
diccolumns['low'] = [data.low for data in target]
diccolumns['close'] = [data.close for data in target]
diccolumns['volume'] = [data.volume for data in target]
columns = ['open', 'high', 'low', 'close', 'volume']
df = pd.DataFrame(diccolumns, index=diccolumns['date'], columns=columns)
df.index.name = 'date'
df = df[::-1]
return df
def get_stock(code, start_date, end_date):
# 期間設定
year, month, day = start_date.split("-")
start = datetime.date(int(year), int(month), int(day))
year, month, day = end_date.split("-")
end = datetime.date(int(year), int(month), int(day))
# 株価データ取得
q = jsm.Quotes()
target = q.get_historical_prices(code,
jsm.DAILY,
start_date=start,
end_date=end)
# 項目ごとにリストに格納して返す
date = [data.date for data in target]
open = [data.open for data in target]
close = [data.close for data in target]
high = [data.high for data in target]
low = [data.low for data in target]
# 日付が古い順に並び替えて返す
return [date[::-1], open[::-1], close[::-1], high[::-1], low[::-1]]
def stockgetweekjsm(self,start_date,end_date,stock_num):
q = jsm.Quotes()
stock = q.get_historical_prices(stock_num, jsm.WEEKLY, start_date, end_date) #指定期間、指定銘柄の株価取得
open_stock = [data.open for data in stock] # 始値の取得
high_stock = [data.high for data in stock] # 高値の取得
low_stock = [data.low for data in stock] # 低値の取得
close_stock = [data.close for data in stock] # 終値の取得
volume_stock = [data.volume for data in stock] # 出来高の取得
date_stock = [data.date.date() for data in stock] # 日付の取得(date()で時刻の情報を削除している)
db_sw = pd.DataFrame([date_stock,close_stock,open_stock,high_stock,low_stock,volume_stock]).T # 日付,始値,高値,低値,終値,出来高のマトリックスの作成
db_sw.columns = ['DATE','CLOSE','OPEN','HIGH','LOW','VOL']
db_sw=db_sw.set_index('DATE') # DATE列をindexに指定する
self.add_db_sw=db_sw.sort_index() # DATE列で古い順番に並べる
#(try-catch)新規の銘柄の場合はself.db_sが存在しないため、except以降の処理:self.db_sw=self.add_db_swとする
try:
self.db_sw=self.db_sw.append(self.add_db_sw) #既存の株価に新規に追加した株価を追加する
#print("check000")
#self.db_sw=self.db_sw.drop_duplicates().sort_index() #重複行があれば削除する & 日付が古い順番にデータを並べ替える
self.db_sw=self.db_sw.drop_duplicates() #重複行があれば削除する (sort_index()はエラーが出るため削除)
#print("self.db_sw")
#print(self.db_sw)
except AttributeError:
self.db_sw=self.add_db_sw
#self.db_sw.to_csv(setting.HOME_PATH + str(stock_num) +".week.csv",header=True)
return self.db_sw
def get_data_yahoojp(symbols=None,
start=None,
end=None,
retry_count=3,
pause=0.001,
adjust_price=False,
ret_index=False,
chunksize=25):
q = jsm.Quotes()
if not isinstance(symbols, list):
return data2frame(
q.get_historical_prices(symbols, start_date=start, end_date=end))
for i, s in enumerate(symbols):
try:
symbols[i] = int(s)
except:
raise exceptions.ValueError("symbols must be an integer")
prices = []
for symbol in symbols:
prices.append(
data2frame(
q.get_historical_prices(symbol, start_date=start,
end_date=end)))
return prices
def on_click2(self):
gcode = int(self.combo.currentIndex())
if gcode > 0:
brandlist = [
"0050", "1050", "2050", "3050", "3100", "3150", "3200", "3250",
"3300", "3350", "3400", "3450", "3500", "3550", "3600", "3650",
"3700", "3750", "3800", "4050", "5050", "5100", "5150", "5200",
"5250", "6050", "6100", "7050", "7100", "7150", "7200", "8050",
"9050"
]
q = jsm.Quotes()
target = q.get_brand(brandlist[gcode - 1])
ccode = [data.ccode for data in target]
market = [data.market for data in target]
name = [data.name for data in target]
info = [data.info for data in target]
df = DataFrame(index=ccode)
df.index.name = "証券コード"
df["銘柄名"] = name
df["市場"] = market
df["インフォメーション"] = info
df.to_csv(brandlist[gcode - 1] + '.csv')
QMessageBox.question(self, 'Exported list',
brandlist[gcode - 1] + '.csv を出力しました．',
QMessageBox.Ok, QMessageBox.Ok)
self.combo.setCurrentIndex(0)
else:
self.combo.setCurrentIndex(0)
def test_ccode_exception():
q = jsm.Quotes()
try:
q.get_historical_prices('0000', jsm.DAILY)
except CCODENotFoundException:
return
raise Exception('exception is not thrown')
def test_get_stock_split_date():
q = jsm.Quotes()
e = datetime.datetime(2014,6,27)
s = datetime.datetime(2014,6,23)
one = q.get_historical_prices(6858, jsm.DAILY, s, e)
if len(one) != 5:
raise Exception('invalid length')
def get_stock(code, start_date, end_date):
try:
aaa = int(code)
except:
# 米国株
print("米国")
result, date, open1, high, low, close, volume = get_stock_usd(code)
return result, date, open1, high, low, close, volume, 0
try:
# 期間設定
start_date = str(start_date).replace("-", "/")
end_date = str(end_date).replace("-", "/")
year, month, day = start_date.split("/")
start = datetime.date(int(year), int(month), int(day))
year, month, day = end_date.split("/")
end = datetime.date(int(year), int(month), int(day))
# 株価データ取得
q = jsm.Quotes()
target = q.get_historical_prices(code,
jsm.DAILY,
start_date=start,
end_date=end)
# 項目ごとにリストに格納して返す
date = [data.date.strftime("%Y/%m/%d") for data in target]
open = [data.open for data in target]
high = [data.high for data in target]
low = [data.low for data in target]
close = [data.close for data in target]
volume = [data.volume for data in target]
adj_close = [data._adj_close for data in target]
return 1, date, open, high, low, close, volume, adj_close
except:
return -1, 1, 1, 1, 1, 1, 1, 1
def test_get_fund_range_daily():
q = jsm.Quotes()
start_date = datetime.date.fromtimestamp(time.time() - 604800) # 1週間前
end_date = datetime.date.today()
one = q.get_historical_prices(FUND_CODE, jsm.DAILY, start_date, end_date)
if not one:
raise Exception('is None')
def test_get_range():
q = jsm.Quotes()
start_date = datetime.date.fromtimestamp(time.time() - 604800) # 1週間前
end_date = datetime.date.today()
for range_type in (jsm.DAILY, jsm.WEEKLY, jsm.MONTHLY):
one = q.get_historical_prices(CCODE, range_type, start_date, end_date)
if not one:
raise Exception('is None')
def get_jstock(code, freq='D', start=None, end=None, periods=None):
"""get Japanese stock data using jsm
Usage:
`get_jstock(6502)`
To get TOSHIBA daily from today back to 30days except holiday.
`get_jstock(6502, 'W', start=pd.Timestamp('2016'), end=pd.Timestamp('2017'))`
To get TOSHIBA weekly from 2016-01-01 to 2017-01-01.
`get_jstock(6502, end=pd.Timestamp('20170201'), periods=50)`
To get TOSHIBA daily from 2017-02-01 back to 50days except holiday.
`get_jstock(6502, 'M', start='first', end='last')`
To get TOSHIBA monthly from 2000-01-01 (the date of start recording) to today.
"""
# Default args
if com._count_not_none(start, end, periods) == 0: # All of args is None
end, periods = 'last', 30
# Switch frequency Dayly, Weekly or Monthly
freq_dict = {'D': jsm.DAILY, 'W': jsm.WEEKLY, 'M': jsm.MONTHLY}
# 'first' means the start of recording date
if start == 'first':
data = jsm.Quotes().get_historical_prices(code,
range_type=freq_dict[freq],
all=True)
start = [i.date for i in data][-1]
else:
data = None # Temporaly defined
# 'last' means last weekday (or today)
if end == 'last':
end = pd.datetime.today()
# Return "start" and "end"
start, end = (x.date() if hasattr(x, 'date') else x
for x in set_span(start, end, periods, freq))
print('Get data from {} to {}'.format(start, end))
data = jsm.Quotes().get_historical_prices(
code, range_type=freq_dict[freq], start_date=start,
end_date=end) if not data else data
df = _convert_dataframe(data)
return df[start:end]
def test_get():
q = jsm.Quotes()
d = q.get_price(CCODE)
assert isinstance(d, PriceData)
assert isinstance(d.open, (int, float))
assert isinstance(d.close, (int, float))
assert isinstance(d.high, (int, float))
assert isinstance(d.low, (int, float))
assert isinstance(d.volume, int)
def test_search():
q = jsm.Quotes()
result = q.search('グリー')
assert result[0].ccode == '3632'
result = q.search('3632')
assert result[0].ccode == '3632'
result = q.search('NTTドコモ')
assert result[0].ccode == '9437'
result = q.search('0000')
assert len(result) == 0
def get_brand_info(self, classify_list):
q = jsm.Quotes()
print "getting brand info..."
for classify in classify_list:
print "classify:", classify
brands_info = q.get_brand(classify)
for b_info in brands_info:
self.addtoindex(classify, b_info)
self.dbcommit()
def appendstockcsv(code, range_type, start_date, end_date, stockdf):
q = jsm.Quotes()
prices = q.get_historical_prices(code, range_type, start_date, end_date)
for price in prices:
stockdf = stockdf.append(pd.Series(price_to_csvl(price),
index=[
'Date', 'Open', 'High', 'Low',
'Close', 'Volume', 'AdjClose'
]),
ignore_index=True)
stockdf = stockdf.drop_duplicates().sort_values(by=["Date"],
ascending=False)
return stockdf
def getspjsm(self):
cnn = pm.connect(host="127.0.0.1",
user="******",
password="******",
database="TS")
cur = cnn.cursor()
cur.execute("""SELECT BCCode FROM M_Business_Category_YH """)
rcds = cur.fetchall()
cur.execute("""SELECT max([day]) FROM SP_Daily """)
curmaxday = cur.fetchall()
q = j.Quotes()
now = datetime.datetime.now()
nowstr = '{0:%Y%m%d}'.format(now)
curday = curmaxday[0][0]
for cd in rcds:
d = q.get_brand(cd[0])
ccodes = [data.ccode for data in d]
markets = [data.market for data in d]
names = [data.name for data in d]
infos = [data.info for data in d]
data = [ccodes, markets, names, infos]
for (ccode, market, name, info) in zip(*data):
arg2 = (ccode, int(nowstr), market, name, info, cd[0])
cur.callproc('UpdateStockInfo', arg2)
cnn.commit()
a = q.get_finance(ccode)
market_cap = a.market_cap
shares_issued = a.shares_issued
dividend_yield = a.dividend_yield
dividend_one = a.dividend_one
per = a.per
pbr = a.pbr
eps = a.eps
bps = a.bps
price_min = a.price_min
round_lot = a.round_lot
years_high = a.years_high
years_low = a.years_low
arg2 = (ccode, int(curday), market_cap, shares_issued,
dividend_yield, dividend_one, per, pbr, eps, bps,
price_min, round_lot, years_high, years_low)
print('[[' + ccode + ' ]]')
cur.callproc('UpdateStockFinance', arg2)
cnn.commit()
print('[[' + ccode + ' Update Over]]')
cnn.close()
def on_click(self):
code = int(self.textbox.text())
ybegin = int(self.byear.text())
mbegin = int(self.bmonth.text())
dbegin = int(self.bdate.text())
yend = int(self.eyear.text())
mend = int(self.emonth.text())
dend = int(self.edate.text())
self.start = dt.date(ybegin, mbegin, dbegin)
self.end = dt.date(yend, mend, dend)
q = jsm.Quotes()
target = q.get_historical_prices(code,
jsm.DAILY,
start_date=self.start,
end_date=self.end)
self.date = [data.date for data in target]
self.open = [data.open for data in target]
self.high = [data.high for data in target]
self.low = [data.low for data in target]
self.close = [data.close for data in target]
self.volume = [data.volume for data in target]
self.adj_close = [data._adj_close for data in target]
self.Date = self.date[::-1]
self.Open = self.open[::-1]
self.High = self.high[::-1]
self.Low = self.low[::-1]
self.Close = self.close[::-1]
self.Adj = self.adj_close[::-1]
self.Vol = self.volume[::-1]
cdf = DataFrame(index=self.Date)
cdf.index.name = "Date"
cdf["Open"] = self.Open
cdf["High"] = self.High
cdf["Low"] = self.Low
cdf["Close"] = self.Close
cdf["Adj Close"] = self.Adj
cdf["Volume"] = self.Vol
cdf.to_csv(str(code) + '.csv')
QMessageBox.question(self, 'Exported CSV',
str(code) + '.csv を出力しました．', QMessageBox.Ok,
QMessageBox.Ok)
self.textbox.setText("")
def get_price_data(self, code):
""" get new stack price data """
table_name = 'price_master'
q = jsm.Quotes()
if self.isindexed(code):
print 'Ticker symbol:' + str(code) + ' is ' + "updating data..."
try:
self.update_price_data(q, code)
self.brand_info_db.execute(
"update brand_info_master set enable=1 where code=%s" %
code)
except Exception, e:
print "error:", e
self.get_histrical_data(q, code)
def _get_price(text):
try:
q = jsm.Quotes()
price = q.get_price(int(text))
utime = int(time.mktime(price.date.timetuple()))
price_date = datetime.fromtimestamp(utime, JST())
return 'Stock price of {2} at {0} is JPY{1}'.format(
price_date.strftime('%Y/%m/%d %H:%M:%S'),
'{:,.02f}'.format(price.close), text)
"""
for historical data
"""
# prices = q.get_historical_prices(int(text), jsm.DAILY)
# return "\n".join(map(lambda p:"{0}\tJPY{1}".format(p.date.strftime('%Y/%m/%d'), "{:,.02f}".format(p.close)), prices))
except:
return ''
def jpstock(code, start_date, end_date):
year, month, day = start_date.split("-")
start = dt.date(int(year), int(month), int(day))
year, month, day = end_date.split("-")
end = dt.date(int(year), int(month), int(day))
print('CSVを出力中．．．')
q = jsm.Quotes()
target = q.get_historical_prices(code,
jsm.DAILY,
start_date=start,
end_date=end)
date = [data.date for data in target]
open = [data.open for data in target]
high = [data.high for data in target]
low = [data.low for data in target]
close = [data.close for data in target]
volume = [data.volume for data in target]
adj_close = [data._adj_close for data in target]
Date = date[::-1]
Open = open[::-1]
High = high[::-1]
Low = low[::-1]
Close = close[::-1]
Adj = adj_close[::-1]
Vol = volume[::-1]
cdf = DataFrame(index=Date)
cdf.index.name = "Date"
cdf["Open"] = Open
cdf["High"] = High
cdf["Low"] = Low
cdf["Close"] = Close
cdf["Adj Close"] = Adj
cdf["Volume"] = Vol
cdf.to_csv(code + '.csv')
print(code + '.csvを出力しました．')
print('株価データをプロット中．．．')
df = DataFrame(index=Date)
df['Adj Close'] = Adj
return df
def stockgetdayjsm(self, start_date, end_date, stock_num):
q = jsm.Quotes()
stock = q.get_historical_prices(stock_num, jsm.DAILY, start_date, end_date) # 指定期間、指定銘柄の株価取得
date_stock = [data.date.date() for data in stock] # 日付の取得(date()で時刻の情報を削除している)
open_stock = [data.open for data in stock] # 始値の取得
high_stock = [data.high for data in stock] # 高値の取得
low_stock = [data.low for data in stock] # 低値の取得
close_stock = [data.close for data in stock] # 終値の取得
volume_stock = [data.volume for data in stock] # 出来高の取得
db_s=pd.DataFrame([date_stock,close_stock,open_stock,high_stock,low_stock,volume_stock]).T # 日付,始値,高値,低値,終値,出来高のマトリックスの作成
db_s.columns = ['DATE','CLOSE','OPEN','HIGH','LOW','VOL']
db_s=db_s.set_index('DATE') # DATE列をindexに指定する
self.add_db_s=db_s.sort_index() # DATE列で古い順番に並べる
#print("add_db_s")
#print(self.add_db_s)
#self.add_db_s=self.add_db_s(axis='index')
#self.add_db_s = DataFrame(self.add_db_s, columns=['Stock']) #列名'Stock'を付与する。
#print("self.db_s")
#print(self.db_s)
#(try-catch)新規の銘柄の場合はself.db_sが存在しないため、except以降の処理:self.db_s=self.add_db_sとする
try:
#print("self.add_db_s")
#print(self.add_db_s)
#print(self.db_s)
self.db_s=self.db_s.append(self.add_db_s) #既存の株価に新規に追加した株価を追加する
#print("self.db_s")
#print(self.db_s)
#print("d0")
#self.db_s = self.db_s.drop_duplicates().sort_index() #重複行があれば削除する & 日付が古い順番にデータを並べ替える
self.db_s = self.db_s.drop_duplicates() #重複行があれば削除する (sort_index()はエラーが出るため削除)
#print("d1")
#print("self.db_s gb")
#print(self.db_s)
except AttributeError:
self.db_s=self.add_db_s
#print("self.db_s")
#print(self.db_s)
self.db_s.to_csv(setting.HOME_PATH + str(stock_num) + ".csv",header=True) #headerなしでcsvファイルに書き出し
return self.db_s
def __init__(self, corp_cd, start_date, end_date):
q = jsm.Quotes()
self.corp_cd = corp_cd
self.start_date = start_date
self.end_date = end_date
self.close_dict = {}
self.pricedata = q.get_historical_prices(corp_cd, jsm.DAILY,
start_date, end_date)
self.close_list = []
self.date_list = []
for pricedata_eachday in self.pricedata:
self.close_dict[str(pricedata_eachday.date).replace(
' 00:00:00', '')] = pricedata_eachday.close
for k, v in sorted(self.close_dict.items()):
self.close_list.append(v)
self.date_list.append(k)
def main():
con = mysql.connector.connect(host=config.db['host'],
db=config.db['db'],
user=config.db['user'],
passwd=config.db['passwd'])
cur = con.cursor()
q = jsm.Quotes()
dt = datetime.date
start_date = dt.today() - datetime.timedelta(days=3)
end_date = dt.today() - datetime.timedelta(days=1)
cur.execute("SELECT ccode FROM brands")
res = cur.fetchall()
for r in res:
try:
data = q.get_historical_prices(r[0],
jsm.DAILY,
start_date=start_date,
end_date=end_date)
ts = datetime.datetime.today().strftime("%Y-%m-%d %H:%M:%S")
for d in data:
try:
cur.execute(
"INSERT IGNORE INTO prices(ccode, date, open, high, low, close, volume, created_at, updated_at) VALUES(%s, %s, %s, %s, %s, %s, %s, %s, %s)",
[
r[0], d.date, d.open, d.high, d.low, d.close,
d.volume, ts, ts
])
con.commit()
except:
pass
except:
pass
cur.close()
con.close()
def main():
con = mysql.connect(host=config.db['host'],
db=config.db['db'],
user=config.db['user'],
passwd=config.db['passwd'],
charset='utf8')
cur = con.cursor(dictionary=True, buffered=True)
q = jsm.Quotes()
cur.execute("SELECT ccode FROM brands")
res = cur.fetchall()
for r in res:
try:
finance_data = q.get_finance(r['ccode'])
market_cap = finance_data.market_cap
shares_issued = finance_data.shares_issued
dividend_yield = finance_data.dividend_yield
dividend_one = finance_data.dividend_one
per = finance_data.per
pbr = finance_data.pbr
eps = finance_data.eps
bps = finance_data.bps
price_min = finance_data.price_min
round_lot = finance_data.round_lot
ts = datetime.datetime.today().strftime("%Y-%m-%d %H:%M:%S")
cur.execute(
"INSERT INTO finances(ccode, market_cap, shares_issued, dividend_yield, dividend_one, per, pbr, eps, bps, price_min, round_lot, created_at, updated_at) VALUES(%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, '%s', '%s') ON DUPLICATE KEY UPDATE market_cap = %s, shares_issued = %s, dividend_yield = %s, dividend_one = %s, per = %s, pbr = %s, eps = %s, bps = %s, price_min = %s, round_lot = %s, updated_at = '%s'"
% (r['ccode'], market_cap, shares_issued, dividend_yield,
dividend_one, per, pbr, eps, bps, price_min, round_lot, ts,
ts, market_cap, shares_issued, dividend_yield, dividend_one,
per, pbr, eps, bps, price_min, round_lot, ts))
con.commit()
except:
pass
cur.close()
con.close()
def returnList(list):
industries = {
'0050': "農林・水産業",
'1050': "鉱業",
'2050': "建設業",
'3050': "食料品",
'3100': "繊維製品",
'3150': "パルプ・紙",
'3200': "化学",
'3250': "医薬品",
'3300': "石油・石炭製品",
'3350': "ゴム製品",
'3400': "ガラス・土石製品",
'3450': "鉄鋼",
'3500': "非鉄金属",
'3550': "金属製品",
'3600': "機械",
'3650': "電気機器",
'3700': "輸送機器",
'3750': "精密機器",
'3800': "その他製品",
'4050': "電気・ガス業",
'5050': "陸運業",
'5100': "海運業",
'5150': "空運業",
'5200': "倉庫・運輸関連業",
'5250': "情報・通信",
'6050': "卸売業",
'6100': "小売業",
'7050': "銀行業",
'7100': "証券業",
'7150': "保険業",
'7200': "その他金融業",
'8050': "不動産業",
'9050': "サービス業"
}
ind = industries[str(list)]
rowdata = jsm.Quotes().get_brand(str(list))
return template('list', list=list, ind=ind, rowdata=rowdata)