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 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 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 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 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 __bar(self):
self.data_frame = ts.bar(code=self.code,
conn=TushareConn.conn,
start_date=self.start,
end_date=self.end,
freq=self.freq,
asset=self.asset)
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 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 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 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 get_close_price(code, start, end=datetime.today(), freq='D'):
if (asset_category(code) == 'FUND_TRADED' or asset_category(code) == 'FUND_UNTRADED'):
try:
f = fp_prefix + code + '.csv'
if (asset_category(code) == 'FUND_TRADED'): col_name = '收盘价'
if (asset_category(code) == 'FUND_UNTRADED'): col_name = '净值'
close_price = pd.read_csv(f, header=0, sep=',',
converters={'交易时间': lambda x: datetime.strptime(x, '%Y-%m-%d')}, index_col='交易时间',
usecols=['交易时间', col_name])
except:
print('Cannot find {}.\n'.format(f))
else:
if (asset_category(code) == 'A'): asset = 'E'
if (asset_category(code) == 'HK'): asset = 'X'
if (asset_category(code) == 'XSB'): asset = 'X'
if (asset_category(code) == 'INDEX'): asset = 'INDEX'
try:
#cons = ts.get_apis()
s = ts.bar(code, conn=cons, freq='D', start_date=start, end_date=end, asset=asset)
close_price = s['close']
except:
print('Error when get price of {}.\n'.format(code))
close_price = close_price.sort_index(ascending='ascending')
if (freq == 'Y' or freq == 'END'):
close_price = close_price.iloc[[0, -1]]
return close_price
def collect_single_index_daily_from_ts(code,
table_name='index_k_data_60m',
conn=None):
start = (datetime.now() - timedelta(5)).strftime(
datetime_utils.DATE_FORMAT)
try:
data = ts.bar(conn=conn,
code=code,
freq='60min',
asset="INDEX",
start_date=start,
retry_count=10)
data.rename(columns={'vol': 'volume'}, inplace=True)
data = data.drop(columns=['amount'])
data['date'] = data.index
data['pre_close'] = data['close'].shift(-1)
data = data.head(4)
data.to_sql(table_name,
dataSource.mysql_quant_engine,
if_exists='append',
index=False)
except Exception as e:
logger.error(e)
def fetch_code(code='000001'):
df = ts.bar(code=code, conn=cons, freq=freq, start_date=start_date, end_date=end_date, ma=ma)
columns = ['close', 'high', 'low', 'open', 'vol', 'amount', 'ma5', 'ma10', 'ma20', 'ma30', 'ma60']
df = df.loc[:, columns]
df = df.sort_index(axis=0, ascending=True)
df.to_csv('data/code.csv')
return df
def plot_stock_line(code,start):
fig = plt.figure(figsize=(10,15))
# fig,(ax,ax2)=plt.subplots(2,1,sharex=True,figsize=(16,10))
ax=fig.add_axes([0,0.2,1,0.5])
ax2=fig.add_axes([0,0,1,0.2])
df = ts.bar(code,conn=api,start_date=start)
# df=df.sort_values(by='datetime')
df = df.sort_index()
df =df.reset_index()
# df = ts.get_k_data('300141',start='2018-03-01')
# df['date']=df['date'].dt.strftime('%Y-%m-%d')
df['datetime']=df['datetime'].dt.strftime('%Y-%m-%d')
sma5=talib.SMA(df['close'].values,5)
sma20=talib.SMA(df['close'].values,20)
# ax.set_xticks(range(0,len(df),20))
# # ax.set_xticklabels(df['date'][::5])
# ax.set_xticklabels(df['datetime'][::20])
candlestick2_ochl(ax,df['open'],df['close'],df['high'],df['low'],width=0.5,colorup='r',colordown='g',alpha=0.6)
# ax.set_title(code)
ax.plot(sma5)
ax.plot(sma20)
# df['vol'].plot(kind='bar')
volume_overlay(ax2,df['open'],df['close'],df['vol'],width=0.5,alpha=0.8,colordown='g',colorup='r')
ax2.set_xticks(range(0,len(df),20))
# ax.set_xticklabels(df['date'][::5])
ax2.set_xticklabels(df['datetime'][::20])
# ax2.grid(True)
# plt.setp(ax.get_xticklabels(), rotation=30, horizontalalignment='right')
# plt.grid(True)
# plt.subplots_adjust(hspace=0)
plt.show()
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 __ma(self):
self.data_frame_ma = ts.bar(code=self.code,
conn=TushareConn.conn,
start_date=self.start,
end_date=self.end,
freq=self.freq,
ma=self.ma)
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 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 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 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 get_of_bar(code, conn, freq, start, end):
df = ts.bar(code=code,
conn=conn,
freq=freq,
start_date=start,
end_date=end)
return df
def get_k_frame(cls, con, code):
k_df = tushare.bar(code, con, adj='qfq')
k_df_size = k_df.index.size
if not k_df_size: return k_df
k_df['high'] = pandas.to_numeric(k_df['high'])
k_df['low'] = pandas.to_numeric(k_df['low'])
k_df['open'] = pandas.to_numeric(k_df['open'])
k_df['close'] = pandas.to_numeric(k_df['close'])
k_df['date'] = k_df.index
k_df['date'] = k_df['date'].apply(lambda x: datetime.datetime.strftime(x, "%Y-%m-%d"))
k_df = k_df.drop(['vol', 'amount'], axis=1)
k_df = k_df.reset_index(drop=True)
k_df = k_df.sort_values(by='date').reset_index(drop=True)
k_df['k_pos'] = k_df.index
def _get_per_change(x):
if x == 0:
_pc = (k_df.at[0, "close"] - k_df.at[0, "open"]) / k_df.at[0, "open"] * 100
else:
_pc = (k_df.at[x, "close"] - k_df.at[x-1, "close"]) / k_df.at[x-1, "close"] * 100
return float("%0.2f" % _pc)
k_df["per_change"] = k_df.k_pos.apply(_get_per_change)
return k_df
def isXR(code=""):
global cons
shi_jian = gsd.get_code_min_shi_jian(code)
if shi_jian == "None":
return False
data = gsd.get_stock_data_daily_df_time(code=code,
start=shi_jian,
end=shi_jian)
price = data["price"].values[0]
tsFaile = 1
while tsFaile != 0:
try:
# print(code,shi_jian)
# if tsFaile >=2 :
# cons = ts.get_apis()
data = ts.bar(code,
conn=cons,
adj='qfq',
start_date=shi_jian,
end_date=shi_jian,
retry_count=2)
# print("data",data)
'''
if len(data["close"].values)<1 :
tsFaile += 1
else:
tsFaile = 0
'''
tsFaile = 0
if len(data["close"].values) < 1:
return False
except Exception as e:
#获取连接备用
ts.close_apis(conn=cons)
cons = ts.get_apis()
print(code, shi_jian, "isXR fail:", tsFaile, e)
time.sleep(5)
if tsFaile <= 3:
tsFaile += 1
else:
return False
# print(code,shi_jian)
# data = ts.bar(code, conn=cons, adj='qfq', start_date=shi_jian, end_date=shi_jian,retry_count = 20)
# print("data",data)
close = data["close"].values[0]
if price == close:
return False
else:
return True
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 get_index(symbol):
cons = ts.get_apis()
return ts.bar(symbol,
cons,
freq='D',
start_date='',
end_date='',
asset='INDEX').sort_index(ascending=True)
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 get_hist_data(self,code,name,start_data):
try:
start_data = datetime.datetime.strptime(str(start_data), '%Y%m%d').strftime('%Y-%m-%d')
df = ts.bar(code,conn=conn,start_date=start_data,adj='qfq')
print df
except Exception,e:
print e
return
def get_hist_data(self, code, name, start_data):
try:
start_data = datetime.datetime.strptime(str(start_data), '%Y%m%d').strftime('%Y-%m-%d')
df = ts.bar(code, conn=conn, start_date=start_data, adj='qfq')
print df
except Exception, e:
print e
return
def download():
cons = ts.get_apis()
df = ts.bar('ifl9',
conn=cons,
asset='X',
start_date='2017-6-24',
end_date='')
df1 = df[['close']]
df1.to_excel('c:/if.xlsx')
def get_hs300_last_date_online(freq='d'):
conn = ts.get_apis()
benchmark = ts.bar('000300', conn=conn, asset='INDEX', freq=freq)
ts.close_apis(conn)
if benchmark is None:
return None, None
last_date0 = benchmark.index[0].strftime('%Y-%m-%d')
last_date1 = benchmark.index[1].strftime('%Y-%m-%d')
return last_date0, last_date1
def get_trading_days(cls,
start=None,
end=None,
ndays=None,
ascending=True):
"""
取得交易日列表，分三种方式取得
（1）指定开始、结束日期，即start和end不为None，此时忽略参数ndays
（2）指定开始日期和天数，即start和ndays不为None，而end为None
（3）指定结束日期和天数，即end和ndays为None，而start为None
--------
:param start: datetime-like or str
开始日期，格式：YYYY-MM-DD
:param end: datetime-like or str
结束日期，格式：YYYY-MM-DD
:param ndays: int
交易日天数
:param ascending: bool，默认True
是否升序排列
:return:
--------
Series of pandas.Timestamp，交易日列表，默认按交易日升序排列
"""
if len(Utils.utils_trading_days) == 0:
ts_conn = ts.get_apis()
df_SZZS = ts.bar(code='000001', conn=ts_conn, asset='INDEX')
ts.close_apis(ts_conn)
Utils.utils_trading_days = Series(df_SZZS.index).sort_values()
if start is not None:
start = cls.to_date(start)
if end is not None:
end = cls.to_date(end)
if start is not None and end is not None:
trading_days = Utils.utils_trading_days[
(Utils.utils_trading_days >= start)
& (Utils.utils_trading_days <= end)]
elif start is not None and ndays is not None:
trading_days = Utils.utils_trading_days[
Utils.utils_trading_days >= start].iloc[:ndays]
elif end is not None and ndays is not None:
trading_days = Utils.utils_trading_days[
Utils.utils_trading_days <= end].iloc[-ndays:]
elif start is not None:
trading_days = Utils.utils_trading_days[
Utils.utils_trading_days >= start]
elif end is not None:
trading_days = Utils.utils_trading_days[
Utils.utils_trading_days <= end]
elif ndays is not None:
trading_days = Utils.utils_trading_days.iloc[-ndays:]
else:
trading_days = Utils.utils_trading_days
trading_days = trading_days.reset_index(drop=True)
if not ascending:
trading_days = trading_days.sort_values(ascending=False)
return trading_days
def store(self, code, start_date):
#print code,start_date
try:
print code
print start_date
df = ts.bar(code=code, conn=self.cons,start_date=start_date)
except Exception,e:
print e
return
def fetch_kdata(code, asset, freq):
start_date = get_start_date(freq)
kdata = ts.bar(code,
conn=cons,
asset='INDEX',
adj='qfq',
freq=freq,
start_date=start_date,
end_date='')
return insert_kdata(asset, freq, kdata)
def store(self, code, start_date):
#print code,start_date
try:
print code
print start_date
df = ts.bar(code=code, conn=self.cons,start_date=start_date)
except Exception,e:
print e
return
def _data(self, symbol, start_date='', end_date=''):
try:
conn = self._get_server()
df = ts.bar(symbol, conn=conn, freq='5min')
df = df.reset_index(inplace=True)
df = df[['datetime', 'open', 'close', 'high', 'low', 'volume', 'code']]
self.data = self.data.append(df, ignore_index=True)
return df
except:
return None
def PreClose(L):
ClosePrice = OrderedDict()
for i in L:
ClosePrice[i] = ts.bar(i,
conn=cons,
freq='D',
asset='E',
start_date=Today,
end_date='').close
return ClosePrice
def store(self, code, start_date):
#print(code,start_date)
try:
df = ts.bar(code=code, conn=self.cons,start_date=start_date)
except Exception as e:
print(e)
return
try:
df.to_sql(code, self.engine)
except Exception as e:
print(e)
return
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 get_hist_data(self, code, name, start_data):
try:
start_data = datetime.datetime.strptime(str(start_data), '%Y%m%d').strftime('%Y-%m-%d')
df = ts.bar(code, conn=conn, start_date=start_data, adj='qfq')
print(df)
except Exception as e:
print(e)
return
df.insert(1, 'name', name)
df = df.reset_index()
try:
df.to_sql(code, engine, if_exists='append')
except Exception as e:
print(e)
def get_hist_data(code, name, start_data):
try:
# start_data = datetime.datetime.strptime(str(start_data), '%Y%m%d').strftime('%Y-%m-%d')
df = ts.bar(code, conn=conn, start_date=start_data, adj='qfq')
except Exception as e:
print(e)
return
hist_con = get_engine('history')
df.insert(1, 'name', name)
df = df.reset_index()
#print(df)
df2=pd.read_sql_table(code,hist_con,index_col='index')
try:
new_df = pd.concat([df,df2])
new_df = new_df.reset_index(drop=True)
new_df.to_sql(code, engine, if_exists='replace')
except Exception as e:
print(e)
return
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
df = ts.bar(symbol, freq='1min')
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 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
import json
import pandas as pd
import pymongo
import time
from ggplot import *
import tushare as ts
cons = ts.get_apis()
hs300 = ts.bar('000300', conn=cons, asset='INDEX', start_date='', end_date='')
hs300 = hs300.reset_index();
#print(hs300.head(5));
firstopen = hs300.iloc[-1].values[2];
#print(firstopen);
hs300['value'] = hs300['open']/firstopen;
hs300['time'] = hs300['datetime'];
del hs300['datetime'];
del hs300['open'];
del hs300['close'];
del hs300['high'];
del hs300['low'];
del hs300['vol'];
del hs300['amount'];
#print(hs300.head(5));
client = pymongo.MongoClient('localhost', 27017)
#基金
fddb = client.fddb;
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()
#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();
df1['odc'] = df1['open']>df1['close'];
import tushare as ts
# 0. 获取连接备用
# [](http://mp.weixin.qq.com/s/RhGgTEVHqm8aDh2ZzpLviA)
cons = ts.get_apis()
# 1. 股票日线行情
df = ts.bar('600000', conn=cons, freq='D', start_date='2016-01-01', end_date='')
df.head(5)
# 2. 带因子的行情
# ma=[5, 10, 20, 60] 表示一次性获得5/10/20/60日均线
# factors=[‘vr’, ‘tor’]，vr表示量比，tor表示换手率
df = ts.bar('600000', conn=cons, start_date='2016-01-01', end_date='',
ma=[5, 10, 20], factors=['vr', 'tor'])
df.head(5)
# 3. 复权行情
# adj=qfq(前复权)， hfq（后复权），默认None不复权
df = ts.bar('600000', conn=cons, adj='qfq', start_date='2016-01-01', end_date='')
df.head(5)
# 4. 分钟数据
# 设置freq参数，分别为1min/5min/15min/30min/60min，D(日)/W(周)/M(月)/Q(季)/Y(年)
df = ts.bar('600000', conn=cons, freq='1min', start_date='2016-01-01', end_date='')
df.head(15)
