def gp():
print(ts.get_index()[:1])
print(ts.get_index()[12:13])
print(ts.get_index()[17:18])
df = ts.get_realtime_quotes([
'002291', '002939', '002475', '000034', '601607', '000100', '002241',
'601099', '600728', '000998', '601800'
])[['code', 'name', 'price', 'bid', 'ask', 'volume', 'amount', 'time']]
print(df, end='\n{0}\n'.format('-' * 50))
sh = ts.get_index()[:1][['amount']].values[0][0]
sz = ts.get_index()[12:13][['amount']].values[0][0]
print('沪深成交额:{:.2f}亿'.format(float(sh) + float(sz)))
def notrealdata():
cursor.execute(
"""SELECT distinct(tradeDate) FROM mkt_mktindex group by tradeDate order by tradeDate desc
""")
tradetimesql = cursor.fetchone()
count = cursor.rowcount
if count == 0:
dfindex = ts.get_index()
inserttable(dfindex, '15:00')
elif inputtradetime != tradetimesql[0]:
dfindex = ts.get_index()
#dfindex.insert(2,'tradeTimehm','15:00')
inserttable(dfindex, '15:00')
def notrealdatanoon():
cursor.execute(
"""SELECT distinct(tradeTimehm) FROM mkt_mktindex where tradeDate=%s order by tradeTimehm desc
""", (inputtradetime))
tradetimesql = cursor.fetchone()
count = cursor.rowcount
print count
print tradetimesql[0]
if count == 0:
dfindex = ts.get_index()
inserttable(dfindex, '11:30')
elif (tradetimesql[0] != '11:30'):
dfindex = ts.get_index()
inserttable(dfindex, '11:30')
def notrealdatanight():
cursor.execute(
"""SELECT distinct(tradeTimehm) FROM mkt_mktindex where tradeDate=%s order by tradeTimehm desc
""", (inputtradetime))
tradetimesql = cursor.fetchone()
print tradetimesql[0]
count = cursor.rowcount
if count == 0:
dfindex = ts.get_index()
inserttable(dfindex, '15:00')
if (tradetimesql[0] != '15:00'):
dfindex = ts.get_index()
#dfindex.insert(2,'tradeTimehm','15:00')
inserttable(dfindex, '15:00')
def get_last_exchange_day():
from datetime import datetime, date, timedelta
print 'entering get_last_exchange_day'
import sys
reload(sys)
sys_temp = sys
sys_temp.setdefaultencoding('utf-8')
date_time= datetime.now().strftime("%Y-%m-%d %H:%M:%S")
hour = int(date_time[11:13])
minute = int(date_time[14:16])
try:
print 'entering try1'
file_changetime,file_date = get_FileModifyTime(data_const.My_Store_Dir+'sh.csv')
file_hour = int(file_changetime[11:13])
file_minute= int(file_changetime[14:16]);time_thresh=5
if file_date == getDatetimeToday().strftime("%Y-%m-%d") and (hour-file_hour)*60+minute-file_minute <time_thresh:
data_sh_index = pd.read_csv(data_const.My_Store_Dir+'sh.csv')[data_const.Meta_Index_Needing]#避免下面重复计算
prep_d.compute_item(data_sh_index,'MACD_d').to_csv(data_const.My_Store_Dir+'sh'+data_const.Suffix)
last_date = data_sh_index.loc[len(data_sh_index)-1]['date']
print 'today:',hour ,minute,'file_changetime:',file_hour,file_minute,'time_thresh:',time_thresh,'minutes'
print 'within timethresh needing file not need change,leaving get_last_exchange_day',last_date
return last_date
except :
pass
try:
print 'entering try 2'
#环境数据,股票基本资料
basic = 'basic.csv'
ts.get_stock_basics().to_csv(data_const.My_Store_Dir+basic)
print basic ,'ok'
#实时行情数据
realtime = 'realtime_data.csv'
ts.get_today_all().to_csv(data_const.My_Store_Dir+realtime)
print '\n',realtime,'ok'
wholeindex = 'whole_index.csv'
ts.get_index().to_csv(data_const.My_Store_Dir+wholeindex)
print wholeindex,'ok'
for item,_ in data_const.INDEX_LIST.items():
prep_d.compute_item(ts.get_k_data(item),'MACD_d').to_csv(data_const.My_Store_Dir+item+data_const.Suffix)
print item,'ok'
data_sh_index = pd.read_csv(data_const.My_Store_Dir+'sh.csv')
last_date = data_sh_index.loc[len(data_sh_index)-1]['date']
print 'leaving get_last_exchange_day',last_date
return last_date
except Exception,e:
print e
time.sleep(25)
return get_last_exchange_day()
def dump_index():
df = ts.get_index()
df.to_sql('index_data',
engine,
if_exists='append',
index=False,
index_label='code')
def get_code1(conn):
"""
使用常规存储方式存储大盘指数行情数据获取 可获得上证指数、深证指数
:return:
"""
df_index = ts.get_index()
sql_market = """
INSERT INTO middle_news_market(
code, name, change_market, open_market, preclose, close_market, high, low, volume, amount
)
VALUES(%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
"""
for i in range(0, 25):
code = df_index['code'][i]
name = df_index['name'][i]
change_market = str('%.2f' % df_index['change'][i])
open_market = str('%.4f' % df_index['open'][i])
preclose = str('%.4f' % df_index['preclose'][i])
close = str('%.4f' % df_index['close'][i])
high = str('%.4f' % df_index['high'][i])
low = str('%.4f' % df_index['low'][i])
volume = str(df_index['volume'][i])
amount = str('%.4f' % df_index['amount'][i])
sql_params = [
code, name, change_market, open_market, preclose, close, high, low,
volume, amount
]
logger.debug(sql_market)
logger.debug(sql_params)
# 存入数据库
execute_sql(conn, sql_market, sql_params)
def save_big_index_data():
"""获取大盘指数实时行情列表，以表格的形式展示大盘指数实时行情。"""
logger.info('Begin get and save big index data, date is: %s.' % today_line)
try:
data_df = ts.get_index()
except Exception as e:
logger.exception('Error get big index data, date is: %s.' % today_line)
else:
if data_df is not None and not data_df.empty:
data = data_df.values
data_dicts = [{
'date': today_line,
'code': row[0],
'name': row[1],
'change': row[2],
'open': row[3],
'preclose': row[4],
'close': row[5],
'high': row[6],
'low': row[7],
'volume': row[8],
'amount': row[9]
} for row in data]
try:
BigIndexData.insert_many(data_dicts).execute()
except Exception as e:
logger.exception('Error save big index data, date is: %s.' %
today_line)
logger.error('Error data is: %s.' % data)
else:
logger.info('Success save big index data, date is: %s.' %
today_line)
else:
logger.warn('Empty save big index data, date is: %s.' % today_line)
def get_rel_price(self):
df_index = ts.get_index()
# 上证指数
df_sh = df_index[df_index["code"] == '000001']
df_sz = df_index[df_index["code"] == '399001']
df_hs300 = df_index[df_index["code"] == '000300']
df_zz500 = df_index[df_index["code"] == '000905']
hsi_price, hsi_pre_close = yahoo_finance_api.get_real_price('^HSI')
gspc_price, gspc_pre_close = yahoo_finance_api.get_real_price('^GSPC')
ixic_price, ixic_pre_close = yahoo_finance_api.get_real_price('^IXIC')
dict = [{
'sh_direction':
cal_direction(float(df_sh["change"].values[0])),
'sz_direction':
cal_direction(float(df_sz["change"].values[0])),
'hs300_direction':
cal_direction(float(df_hs300["change"].values[0])),
'zz500_direction':
cal_direction(float(df_zz500["change"].values[0])),
'hsi_direction':
cal_direction(hsi_price - hsi_pre_close),
'gspc_direction':
cal_direction(gspc_price - gspc_pre_close),
'ixic_direction':
cal_direction(ixic_price - ixic_pre_close)
}]
df = pd.DataFrame(dict)
return df
def daily(engine, session, cdate):
if not tsu.is_holiday(cdate):
tsl.log("trade_index_today start...")
try:
df = ts.get_index()
df = df.set_index('code', drop='true')
df['date'] = cdate
df.to_sql('trade_index_today', engine, if_exists='append')
tsl.log("trade_index_today done")
except BaseException, e:
print e
tsl.log("trade_index_today error")
tsl.log("trade_market_today start...")
try:
df = ts.get_today_all()
df = df.set_index('code', drop='true')
df['date'] = cdate
df.to_sql('trade_market_today', engine, if_exists='append')
print
tsl.log("trade_market_today done")
except BaseException, e:
print
print e
tsl.log("trade_market_today error")
def stat_today_all(tmp_datetime):
datetime_str = (tmp_datetime).strftime("%Y-%m-%d")
datetime_int = (tmp_datetime).strftime("%Y%m%d")
print("datetime_str:", datetime_str)
print("datetime_int:", datetime_int)
data = ts.get_today_all()
# 处理重复数据，保存最新一条数据。最后一步处理，否则concat有问题。
if not data is None and len(data) > 0:
# 插入数据库。
# del data["reason"]
data["date"] = datetime_int # 修改时间成为int类型。
data = data.drop_duplicates(subset="code", keep="last")
data.head(n=1)
common.insert_db(data, "ts_today_all", False, "`date`,`code`")
else:
print("no data .")
time.sleep(5) # 停止5秒
data = ts.get_index()
# 处理重复数据，保存最新一条数据。最后一步处理，否则concat有问题。
if not data is None and len(data) > 0:
# 插入数据库。
# del data["reason"]
data["date"] = datetime_int # 修改时间成为int类型。
data = data.drop_duplicates(subset="code", keep="last")
data.head(n=1)
common.insert_db(data, "ts_index_all", False, "`date`,`code`")
else:
print("no data .")
print(datetime_str)
def get_index_data(starttime):
sql = "REPLACE INTO algo_today_stock(CODE,changepercent,trade,OPEN,high,low,settlement,TYPE,time,volume,amount) VALUES"
try:
result = ts.get_index()
except Exception, e:
print(e)
return
def work():
db = db_init()
"""
stock_id = "601899"
log_debug("stock: %s", stock_id)
k_index_day_one_stock(stock_id, db)
"""
# step1: get from web
stocks = ts.get_index()
# step2: to db
begin = get_micro_second()
# to db
for row_index, row in stocks.iterrows():
stock_id = row['code']
log_debug("stock: %s", stock_id)
# import to DB
k_index_day_one_stock(stock_id, db)
log_info("save-all costs %d us", get_micro_second()-begin)
db_end(db)
def get_info():
df = ts.get_index()
new_df = df[['name', 'change', 'open', 'preclose', 'close', 'high', 'low', 'volume', 'amount']]
index_name = new_df.columns.values
info = {
'名称': '',
'涨跌幅': 0,
'今日开盘': 0,
'昨日收盘': 0,
'今日收盘': 0,
'今日最高': 0,
'今日最低': 0,
'成交量(手)': 0,
'成交金额(亿元)': 0,
}
seq = 0
for num in range(1): # num=0 => A股指数, 表示目前只取A股指数信息
for k in list(info.keys()):
info[k] = new_df[index_name[seq]][num]
seq += 1
# 显示文本内容
content = '时间：%s\n\n' % datetime.now().time().strftime('%H:%M:%S')
# 字符串输出+显示当日涨幅
for k, v in info.items():
if k == '今日收盘':
if float(v) <= float(info['昨日收盘']):
tip = '📉'
else:
tip = '📈'
v = '%s %s' % (v, tip)
content += '%s: %s\n' % (k, v)
return content, info
def getDataRelateLessWithCode(self, mongo, func):
if (func == "stock_basics"):
df = ts.get_stock_basics()
date = df.ix['600848']['timeToMarket'] # 上市日期YYYYMMDD
# ts.get_h_data('002337') # 前复权
# ts.get_h_data('002337', autype='hfq') # 后复权
# ts.get_h_data('002337', autype=None) # 不复权
# ts.get_h_data('002337', start='2015-01-01', end='2015-03-16') # 两个日期之间的前复权数据
#
# ts.get_h_data('399106', index=True) # 深圳综合指数
print(date)
elif (func == "today_all"):
df = ts.get_today_all()
tmpJson = json.loads(df.to_json(orient='records'))
coll = mongo.trading[func]
for i in range(len(tmpJson)):
tmpJson[i][u'now'] = str(self.now)
coll.insert(tmpJson[i])
return
elif (func == "index"):
df = ts.get_index()
else:
df = {}
insert_string = df.to_json(orient='records')
items = json.loads(insert_string)
coll = mongo.trading[func]
coll.insert(items)
def index():
whole_indicators = json.loads(ts.get_index()[:4].to_json(orient='records'))
getLatestNews()
whole_news = News.query.order_by(News.time.desc()).limit(7)
# 站点公告
notice = Article.query.order_by(Article.updated_time.desc()).limit(7)
dates = datetime.datetime.now()
week = datetime.datetime.now().weekday()
if week in [5, 6]:
days = 4 - week
dates = dates + datetime.timedelta(days=days)
dates = dates.strftime('%Y-%m-%d')
recommend_stocks = Stock.query.filter(Stock.code != 'ALL').limit(8).all()
if current_user.is_authenticated:
for i in range(len(recommend_stocks)):
if Follow_Stock.query.filter_by(user_id=current_user.id,
stock_id=recommend_stocks[i].code).first() is not None:
recommend_stocks[i].flag = True
else:
recommend_stocks[i].flag = False
return render_template('main/index.html',
whole_indicators=whole_indicators,
news=whole_news,
notices=notice,
dates=dates,
stocks=recommend_stocks)
def GetPrice():
print("GetPrice run...")
out = {}
try:
df = ts.get_index()
# print(df)
# 这里有问题，应该是和昨天收盘比涨跌，不是今天开盘
AStock = df.loc[df.code == '000001']
out['code'] = AStock.loc[0, 'code']
out['name'] = AStock.loc[0, 'name']
out['close'] = AStock.loc[0, 'close']
out['open'] = AStock.loc[0, 'open']
out['preclose'] = AStock.loc[0, 'preclose']
out['error'] = 0
# re.code = AStock.loc[0, 'code']
# re.name = AStock.loc[0, 'name']
# re.closePrice = AStock.loc[0, 'close']
# re.openPrice = AStock.loc[0, 'open']
# re.error = 0
except Exception as e:
print(e)
out['error'] = -1
out2 = json.dumps(out)
return out2
def get_indeces():
current = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
try:
index_df = ts.get_index()
except Exception as err:
err = str(err)
print(err)
result = {'status': 'failed', 'content': err}
return Response(json.dumps(result), mimetype='application/json')
index_df.drop_duplicates(subset=["code"], inplace=True)
# index_df.drop(['preclose'], axis=1, inplace=True)
index_df.rename(columns={
'change': 'change_percent',
'preclose': 'settlement'
},
inplace=True)
index_df['time'] = current
try:
index_df.to_sql(name=index_table,
con=engine,
if_exists='append',
index=False)
print(index_df.head())
except Exception as err:
err = str(err)
print(err)
result = {'status': 'failed', 'content': err}
return Response(json.dumps(result), mimetype='application/json')
result = {'status': '200'}
return Response(json.dumps(result), mimetype='application/json')
def show_current_market():
# a = ts.get_today_all()
stf = lambda v: float(v)
codes = ['603188', '002940', '300694', '603156', '600128', '000622']
buy_falg = ['', '', '', '', 'B', '']
while True:
try:
for code, flag in zip(codes, buy_falg):
s1 = ts.get_realtime_quotes(code)
current_p = stf(s1['price'])
open_p = stf(s1['open'])
preclose_p = stf(s1['pre_close'])
change = (current_p - preclose_p) / preclose_p * 100
buy_1 = stf(s1['b1_v'])
print('[{}] code: {}, cp: {}, change: {:.2F}, buy one: {}'.
format(flag, code, current_p, change, buy_1))
print('')
inds = [0, 12, 17]
df = ts.get_index()
for ind in inds:
print('code: {}, change: {:.2F}'.format(
df['code'][ind], df['change'][ind]))
for _ in range(3):
print('')
time.sleep(5)
except:
print('try again!')
finally:
pass
def store_index(self, trading_date=None):
"""获取大盘指数实时行情列表，以表格的形式展示大盘指数实时行情, 收盘后就是日K历史的行情"""
print("store_index()......")
trading_date = self.last_trading_date if trading_date is None else trading_date
df = ts.get_index()
df['date'] = trading_date
self.mysqlUtils.append_data(df, 'index_data')
def __call__(self, conns):
self.base = Base()
self.finacial_data = conns['financial_data']
#清空表
# self.finacial_data.dopost('TRUNCATE TABLE dapan_hangqing_date')
# self.finacial_data.dopost('TRUNCATE TABLE dapan_code_name')
# 实时行情
hangqing = ts.get_index()
today = self.base.gettoday()
hangqing['date'] = today.replace('/', '-')
#大盘指数每日行情数据
self.base.batchwri(hangqing, 'dapan_hangqing_date', self.finacial_data)
#大盘代码-名字对照表
self.base.batchwri(hangqing[['code', 'name']], 'dapan_code_name',
self.finacial_data)
#大盘代码-名字对照表去重
duizhao = self.finacial_data.getdata('dapan_code_name')
# print(duizhao.size)
duizhao_qc = duizhao.drop_duplicates().sort_values(
by='code').reset_index(drop=True)
# print(df)
self.finacial_data.dopost('TRUNCATE TABLE dapan_code_name')
self.base.batchwri(duizhao_qc, 'dapan_code_name', self.finacial_data)
def realtime(request, code):
code_list = code.split(',')
index_change = ts.get_index().loc[0, "change"]
str = "上证涨幅:%s<br/>-----------<br/>" % index_change
code_pattern = re.compile("\d{6}$")
for code in code_list:
a = re.findall(code_pattern, code)
if (len(a) != 1):
db_data = stock_basic.objects.filter(
name=code).values_list('code')[0]
code = db_data[0]
df = ts.get_realtime_quotes(code)
name = df.loc[0, 'name']
pre_close = df.loc[0, 'pre_close']
open_price = df.loc[0, 'open']
price = df.loc[0, 'price']
volumn = df.loc[0, 'volume']
amount = df.loc[0, 'amount']
b1_p = df.loc[0, 'b1_p']
b1_v = df.loc[0, 'b1_v']
a1_p = df.loc[0, 'a1_p']
a1_v = df.loc[0, 'a1_v']
avg_price = float(amount) / int(volumn)
str += '''名字:%s code:%s<br/>昨日收盘价:%s<br/>今日开盘价:%s<br/>当前价:%s<br/>
成交股数:%s<br/>成交总额:%s<br/>均价:%f<br/>卖1:price:%svolumn:%s<br/>买1:price:%svolumn:%s<br/>------------------<br/>''' % (
name, code, pre_close, open_price, price, volumn, amount,
avg_price, a1_p, a1_v, b1_p, b1_v)
return HttpResponse(str)
def get_mainland_index():
df = ts.get_index()
if len(df) == 0:
return
now = datetime.datetime.now()
df['date'] = now.date()
df['time'] = now.time()
df.to_sql('stock_mainland_index', engine, if_exists='append', chunksize=1000, index=False)
def index_catcha(self):
df = pd.DataFrame(ts.get_index())
a = df[df['name'] == '上证指数'].change[0]
b = df[df['name'] == '上证指数'].close[0]
mess = "Change：%s index：%s" % (a, b)
return mess
def avg(request):
all_data = ts.get_index().iterrows()
data = {
'message': 'Hello World!',
'title': '股市大盘',
'all_data': all_data,
}
return render(request, 'stock_market/avg.html', data)
def jjs():
print(ts.get_index()[:1])
for i in jj_list:
a = requests.get(
'http://fundgz.1234567.com.cn/js/{}.js'.format(i)).text
print(re.findall('name":"(.*?)".*?gszzl":"(.*?)"', a)[0])
if jj_list.index(i) == 5:
print('-' * 50)
def get_index():
try:
logger.info('get index data starting...')
df = ts.get_index()
logger.info('get index data end...')
except:
logger.exception('some errors between get index data end...')
return df
def market_index(self):
"""
获取大盘指数实时行情列表，以表格的形式展示大盘指数实时行情
:return:
"""
df = ts.get_index()
df = df[["code", "name", "change", "preclose", "close"]]
return df
def get_trading_data(ticker,startdate,enddate):
data={}
price=ts.get_h_data(ticker,start=startdate,end=enddate)
tick=ts.get_today_ticks(ticker)
index=ts.get_index()
data["price"]=price
data["tick"]=tick
data["index"]=index
return data
def get_stock_index():
"""
获取大盘指数行情
return
"""
df = ts.get_index()
res = df.to_sql(table_index_stock, engine, if_exists='replace')
msg = 'ok' if res is None else res
print('写入大盘指数行情: ' + msg + '\n')
def handle_get_index(self):
"""
更新大盘指数，目前不存
"""
info_logger.info(self.handle_get_index.__doc__)
df = tushare.get_index()
""":type : DataFrame"""
clean_table(IndexInTimeList)
write_dataframe_to_sql(df, TableName.index_in_time, index=False)
def buildIndexList(self):
tableName = 'indexList'
try:
engine = createDbEngine('database')
df = ts.get_index()
df.to_sql(tableName, engine, if_exists='replace')
except:
print_exc()
return False
return True
def get_codes_names(filename):
'''
Get the latest codes which includes stock and index
'''
zs = ts.get_index()
stock = ts.get_today_all()
codes = np.concatenate( (zs['code'].unique(), stock['code'].unique()))
names = np.concatenate( (zs['name'].unique(), stock['name'].unique()))
out = (codes, names)
if filename:
write_pickle(out, filename)
return out
def get_index(engine):
df = ts.get_index()
df.to_sql('index', engine, if_exists='append')
def getIndex():
return ts.get_index()
def download_symbols():
df = ts.get_stock_basics()
df.to_csv(SYM["all"])
index_df = ts.get_index()
index_df.to_csv(SYM["id"])
client = Client()
client.init('77ac42d684c5dedeca8aa6b62a1a8714f7aeaf6def3198d3a8307f8665c9f694')
url1='/api/market/getMktIdxd.json?field=&beginDate=20160101&endDate=20160505&indexID=&ticker=399006&tradeDate='
code, re = client.getData(url1)
if code==200:
print (re)
else:
print (code)
print (re)
url2='/api/market/getMktEqud.json?field=&beginDate=20160101&endDate=20160505&secID=&ticker=000762&tradeDate='
code, result = client.getData(url2)
if(code==200):
file_object = open('thefile.csv', 'w')
file_object.write(str(result))
file_object.close( )
else:
print (code)
print (result)
except Exception as e:
#traceback.print_exc()
raise e
import pandas
import tushare as ts
ts.set_token('77ac42d684c5dedeca8aa6b62a1a8714f7aeaf6def3198d3a8307f8665c9f694')
st=ts.Market()
df = st.MktEqud(tradeDate='20160505', field='ticker,secShortName,preClosePrice,openPrice,highestPrice,lowestPrice,closePrice,turnoverVol,turnoverRate')
df1=st.MktEqud(ticker='600836',beginDate='20160101',endDate='20160507',field='ticker,secShortName,preClosePrice,openPrice,highestPrice,lowestPrice,closePrice,turnoverVol,turnoverVole,turnoverRate,negMarketValue,PE,PE1,PB')
df2= ts.get_index()
print (df1)
df1.to_csv('c:/000876.csv',encoding='gbk')
def sync_market_today(): ''' sync current market data ''' df = ts.get_index() DataFrameToMongo(df, MongoClient(mongourl)['stoinfo']['market'], ['code'])
def realtimeUpdate_from_tushare():
import tushare as ts
#更新股票行情
df = ts.get_today_all()
for i in range(len(df)):
if df.ix[i, 'open'] == 0:
continue #停牌
code = df.ix[i][0]
stock = getStock('sh' + code)
if stock.isNull() == True or stock.type != constant.STOCKTYPE_A:
stock = getStock('sz' + code)
if stock.isNull() == True:
continue
record = KRecord()
record.openPrice = df.ix[i, 'open']
record.highPrice = df.ix[i, 'high']
record.lowPrice = df.ix[i, 'low']
record.closePrice = df.ix[i, 'trade']
record.transAmount = float(df.ix[i, 'amount'])
record.transCount = float(df.ix[i, 'volume'])
from datetime import date
d = date.today()
record.datetime = Datetime(d)
stock.realtimeUpdate(record)
#更新指数行情
df = ts.get_index()
for i in range(len(df)):
code = df.ix[i][0]
stock = getStock('sh' + code)
if stock.isNull() == True or stock.type != constant.STOCKTYPE_INDEX:
stock = getStock('sz' + code)
if stock.isNull() == True:
continue
total = stock.getCount(Query.DAY)
if total == 0:
continue
last_record = stock.getKRecord(total - 1)
record = KRecord()
record.openPrice = df.ix[i, 'open']
record.highPrice = df.ix[i, 'high']
record.lowPrice = df.ix[i, 'low']
record.closePrice = df.ix[i, 'close']
record.transCount = float(df.ix[i, 'volume'])
record.transAmount = float(df.ix[i, 'amount'])
if (last_record.closePrice != record.closePrice
or last_record.highPrice != record.highPrice
or last_record.lowPrice != record.lowPrice
or last_record.openPrice != record.openPrice):
from datetime import date
d = date.today()
record.datetime = Datetime(d)
stock.realtimeUpdate(record)
if not is_exist:
spam_asset_writer_sh.writerow(['date', 'code', 'amount', 'growth'])
is_exist = os.path.exists('./ClassifyHistory/AssetFow' + sz_index + '.csv')
asset_flow_csv_sz = open('./ClassifyHistory/AssetFow' + sz_index + '.csv', 'ab+')
spam_asset_writer_sz = csv.writer(asset_flow_csv_sz, dialect='excel')
if not is_exist:
spam_asset_writer_sz.writerow(['date', 'code', 'amount', 'growth'])
is_exist = os.path.exists('./ClassifyHistory/AssetFow' + cy_index + '.csv')
asset_flow_csv_cy = open('./ClassifyHistory/AssetFow' + cy_index + '.csv', 'ab+')
spam_asset_writer_cy = csv.writer(asset_flow_csv_cy, dialect='excel')
if not is_exist:
spam_asset_writer_cy.writerow(['date', 'code', 'amount', 'growth'])
index_all = ts.get_index()
sh_amount = index_all.loc[index_all['code'] == sh_index, 'amount'].values[0]
sh_growth = index_all.loc[index_all['code'] == sh_index, 'change'].values[0]
cy_amount = index_all.loc[index_all['code'] == cy_index, 'amount'].values[0]
cy_growth = index_all.loc[index_all['code'] == cy_index, 'change'].values[0]
sz_amount = index_all.loc[index_all['code'] == sz_index, 'amount'].values[0]
sz_growth = index_all.loc[index_all['code'] == sz_index, 'change'].values[0]
spam_asset_writer_sh.writerow([format_time_asset, sh_index, sh_amount, sh_growth])
spam_asset_writer_sz.writerow([format_time_asset, sz_index, sz_amount, sz_growth])
spam_asset_writer_cy.writerow([format_time_asset, cy_index, cy_amount, cy_growth])
asset_flow_csv_sh.close()
asset_flow_csv_cy.close()
asset_flow_csv_sz.close()
#coding=utf-8
import tushare as ts
import datetime
df = ts.get_index()
df.to_csv('Index_data.csv')
#d0 = datetime.datetime.today()
#d1 = d0 + datetime.timedelta(days = -730)
#tmp = ts.get_h_data('000001', index=True, start=d1.strftime('%Y-%m-%d'), end=d0.strftime('%Y-%m-%d'))
#while True:
# d0 = d1 + datetime.timedelta(days = -1)
# d1 = d0 + datetime.timedelta(days = -730)
# final = ts.get_h_data('000001', index=True, start=d1.strftime('%Y-%m-%d'), end=d0.strftime('%Y-%m-%d'))
# try:
# tmp = tmp.append(final)
# except:
# break
#tmp.to_csv('IN000001.csv')
for index, row in df.iterrows():
print index, df.ix[index]['code'], df.ix[index]['name']
final = ts.get_h_data('000001', index=True, start='1991-01-01', end='2016-02-20')
final.to_csv('IN000001.csv')
#-*- coding:utf-8 -*-
'''
Created on 2015年11月25日
@author: LeoBrilliant
#获取大盘指数历史行情
'''
import MySQLdb
import tushare as ts
from datetime import datetime
#连接数据库，当前参数为地址、用户、密码（可控）、数据库
#db = MySQLdb.connect("localhost", "root", "", "Data")
db = MySQLdb.connect(host="localhost", user="******", passwd="", db="Data", charset="utf8")
data = ts.get_index()
tradingday = datetime.strftime(datetime.today(), "%Y%m%d")
data['tradingday'] = tradingday
indexdata = data.set_index(['tradingday', 'code'])
ret = indexdata.to_sql("t_index_data", db, flavor="mysql", if_exists="append")
#关闭连接
db.close()
print("Success")