def update_single_basic_ele(ele_name, retry_times):
for year in range(2001, int(year_now) + 1):
for quarter in range(1, 5):
table_name = ele_name + str(year) + '0' + str(quarter)
if not is_table_exist(conn=conn_profit,
database_name=stk_profit_data_db_name,
table_name=table_name):
"""失败达到一定次数便不再尝试"""
failure_time = 0
go_on_flag = True
while go_on_flag & (failure_time < retry_times):
try:
if ele_name == "profit":
ts.get_profit_data(year=year,quarter=quarter)\
.to_sql(con=engine_profit,
name=table_name,
if_exists='append',
schema=stk_profit_data_db_name,
index=False)
elif ele_name == "growth":
ts.get_growth_data(year=year, quarter=quarter) \
.to_sql(con=engine_growth,
name=table_name,
if_exists='append',
schema=stk_growth_data_db_name,
index=False,)
elif ele_name == "operation":
ts.get_operation_data(year=year, quarter=quarter) \
.to_sql(con=engine_operation,
name=table_name,
if_exists='append',
schema=stk_operation_data_db_name,
index=False)
elif ele_name == "debtpaying":
ts.get_debtpaying_data(year=year, quarter=quarter) \
.to_sql(con=engine_debtpaying,
name=table_name,
if_exists='append',
schema=stk_debtpaying_data_db_name,
index=False)
elif ele_name == "cashflow":
ts.get_cashflow_data(year=year, quarter=quarter) \
.to_sql(con=engine_cashflow,
name=table_name,
if_exists='append',
schema=stk_cashflow_data_db_name,
index=False)
go_on_flag = False
except:
print(table_name + "下载失败！重试！\n")
failure_time = failure_time + 1
else:
print(table_name + "已经存在！\n")
def get_debt_data(self, year, loops):
for i in range(1,loops):
print(i)
for j in year:
print(year)
try:
ts.get_debtpaying_data(j, 4).to_csv('debt_data_%d.csv'%j, encoding='utf-8')
print(j)
year.remove(j)
except: pass
def basic_information():
ts.get_cashflow_data(2017, 1).to_sql('cash_flow',
engine,
if_exists='append')
ts.get_debtpaying_data(2017, 1).to_sql('debtpaying',
engine,
if_exists='append')
ts.get_growth_data(2017, 1).to_sql('growth', engine, if_exists='append')
ts.get_operation_data(2017, 1).to_sql('operation',
engine,
if_exists='append')
ts.get_profit_data(2017, 1).to_sql('profit', engine, if_exists='append')
ts.get_report_data(2017, 2).to_sql('report', engine, if_exists='append')
print('basic information over ....')
def debtpaying_data(self, year, quarter): #偿债能力
df = ts.get_debtpaying_data(year, quarter)
for indexs in df.index:
code = str(df.loc[indexs, ["code"]].values[0])
name = str(df.loc[indexs, ["name"]].values[0])
currentratio = str(df.loc[indexs, ["currentratio"]].values[0])
currentratio = '0' if currentratio in ('nan',
'--') else currentratio
quickratio = str(df.loc[indexs, ["quickratio"]].values[0])
quickratio = '0' if quickratio in ('nan', '--') else quickratio
cashratio = str(df.loc[indexs, ["cashratio"]].values[0])
cashratio = '0' if cashratio in ('nan', '--') else cashratio
icratio = str(df.loc[indexs, ["icratio"]].values[0])
icratio = '0' if icratio in ('nan', '--') else icratio
sheqratio = str(df.loc[indexs, ["sheqratio"]].values[0])
sheqratio = '0' if sheqratio in ('nan', '--') else sheqratio
adratio = str(df.loc[indexs, ["adratio"]].values[0])
adratio = '0' if adratio in ('nan', '--') else adratio
insert = (
"insert into debtpaying_data(code,name,currentratio,quickratio,cashratio,icratio,sheqratio,adratio,year,quarter)\
values('" + code + "','" + name + "'," +
currentratio + "," + quickratio + "," + cashratio + "," +
icratio + "," + sheqratio + "," + adratio + ",'" + str(year) +
"','" + str(quarter) + "')")
self.dbA.updateInsertDelete(insert)
self.dbA.commit()
def export(exportType, datePicker):
sb = None
[year, quarter] = getYearQuarter(datePicker)
if exportType == "report":
name = "业绩报表"
sb = ts.get_report_data(year, quarter)
elif exportType == "profit":
name = "盈利能力报表"
sb = ts.get_profit_data(year, quarter)
elif exportType == "operation":
name = "营运能力报表"
sb = ts.get_operation_data(year, quarter)
elif exportType == "growth":
name = "成长能力报表"
sb = ts.get_growth_data(year, quarter)
elif exportType == "debtpaying":
name = "偿债能力报表"
sb = ts.get_debtpaying_data(year, quarter)
elif exportType == "cashflow":
name = "现金流量报表"
sb = ts.get_cashflow_data(year, quarter)
filename = quote(name + str(year) + "Q" + str(quarter) + ".xlsx")
filepath = os.path.join(basedir, app.config['UPLOAD_FOLDER'], filename)
sb.to_excel(filepath)
rtn = send_file(filepath, as_attachment=True)
rtn.headers['Content-Disposition'] += "; filename*=utf-8''%s" % (filename)
return rtn
def update_basics():
basics = ts.get_stock_basics()
f = os.path.join(DATA_DIR, 'basics.h5')
basics.to_hdf(f, 'basics')
length = 4 * 5
year, season = last_report_season()
for i in range(length):
f = os.path.join(DATA_DIR, 'basics-{0}-{1}.h5'.format(year, season))
if os.path.exists(f):
continue
report = ts.get_report_data(year, season)
report.to_hdf(f, 'report')
profit = ts.get_profit_data(year, season)
profit.to_hdf(f, 'profit')
operation = ts.get_operation_data(year, season)
operation.to_hdf(f, 'operation')
growth = ts.get_growth_data(year, season)
growth.to_hdf(f, 'growth')
debtpaying = ts.get_debtpaying_data(year, season)
debtpaying.to_hdf(f, 'debtpaying')
cashflow = ts.get_cashflow_data(year, season)
cashflow.to_hdf(f, 'cashflow')
season -= 1
if season == 0:
season = 4
year -= 1
def get_basic_datas(
data_kind): #datakind 为debtpaying,growth,operation,profit,report
client = pymongo.MongoClient('localhost', 27017)
table_stock = client['stock']
sheet = table_stock[data_kind]
for year in range(STARTYEAR, ENDYEAR + 1):
try:
for season in range(1, 5):
print('getting ' + datakind + ' data at year:' + str(year) +
" season:" + str(season))
if data_kind == 'debtpaying':
tf = ts.get_debtpaying_data(year, season)
elif data_kind == 'growth':
tf = ts.get_growth_data(year, season)
elif data_kind == 'operation':
tf = ts.get_operation_data(year, season)
elif data_kind == 'profit':
tf = ts.get_profit_data(year, season)
elif data_kind == 'report':
tf = ts.get_report_data(year, season)
else:
print('Not available data type of data_kind!')
return
jsonres = json.loads(tf.to_json(orient='records'))
for j in jsonres:
sheet.insert_one(j)
except: #数据缺失，tushare接口会报网络错误
print('the year: ' + str(year) + ' lost data will begin next year')
continue
def valuation_factor(year):
report = ts.get_report_data(year,4)
report = report.sort_values(by = 'code',axis = 0,ascending = True)
report = report.reset_index(drop = True)
report.to_csv("/home/yirui/Desktop/Quant/Report/%s.csv"%year, mode="w")
profit = ts.get_profit_data(year, 4)
profit = profit.sort_values(by='code', axis=0, ascending=True)
profit = profit.reset_index(drop=True)
profit.to_csv("/home/yirui/Desktop/Quant/Profit/%s.csv"%year,mode= "w")
operation = ts.get_operation_data(year,4)
operation = operation.sort_values(by='code', axis=0, ascending=True)
operation = operation.reset_index(drop=True)
operation.to_csv("/home/yirui/Desktop/Quant/Operation/%s.csv" % year, mode="w")
growth = ts.get_growth_data(year,4)
growth = growth.sort_values(by='code', axis=0, ascending=True)
growth = growth.reset_index(drop=True)
growth.to_csv("/home/yirui/Desktop/Quant/Growth/%s.csv" % year, mode="w")
debtpaying = ts.get_debtpaying_data(year,4)
debtpaying = debtpaying.sort_values(by='code', axis=0, ascending=True)
debtpaying = debtpaying.reset_index(drop=True)
debtpaying.to_csv("/home/yirui/Desktop/Quant/Debtpaying/%s.csv" % year, mode="w")
cashflow = ts.get_cashflow_data(year,4)
cashflow = cashflow.sort_values(by='code', axis=0, ascending=True)
cashflow = cashflow.reset_index(drop=True)
cashflow.to_csv("/home/yirui/Desktop/Quant/Cashflow/%s.csv" % year, mode="w")
def _fetch_finance():
for year in range(2004, 2018):
set_year = lambda x: str(year) + '-' + x
for quarter in range(1, 5):
print(year, ' year ', 'quarter ', quarter)
rep = ts.get_report_data(
year, quarter)[['code', 'eps', 'bvps', 'epcf', 'report_date']]
pro = ts.get_profit_data(year, quarter)[[
'code', 'roe', 'net_profit_ratio', 'gross_profit_rate',
'net_profits', 'business_income', 'bips'
]]
ope = ts.get_operation_data(year, quarter)[[
'code', 'arturnover', 'arturndays', 'inventory_turnover',
'currentasset_turnover', 'currentasset_days'
]]
gro = ts.get_growth_data(
year, quarter)[['code', 'mbrg', 'nprg', 'nav', 'epsg', 'seg']]
deb = ts.get_debtpaying_data(year, quarter)[[
'code', 'currentratio', 'quickratio', 'cashratio', 'icratio',
'sheqratio', 'adratio'
]]
cas = ts.get_cashflow_data(year, quarter)[[
'code', 'cf_sales', 'rateofreturn', 'cf_nm', 'cf_liabilities',
'cashflowratio'
]]
rep.rename(columns={'report_date': 'date'}, inplace=True)
rep['date'] = rep['date'].apply(set_year)
rep = rep.merge(pro, on='code', how='left')
rep = rep.merge(ope, on='code', how='left')
rep = rep.merge(gro, on='code', how='left')
rep = rep.merge(deb, on='code', how='left')
rep = rep.merge(cas, on='code', how='left')
finance.insert(rep.to_dict('record'))
print(year, quarter)
def get_temp_data(year, quarter):
df1 = ts.get_report_data(year, quarter)
#print (1)
df1 = df1.merge(ts.get_profit_data(year, quarter),
how='inner',
on=['code', 'name'])
#print (2)
df1 = df1.merge(ts.get_operation_data(year, quarter),
how='inner',
on=['code', 'name'])
#print (3, "n", df1)
df1 = df1.merge(ts.get_growth_data(year, quarter),
how='inner',
on=['code', 'name'])
#print (4)
print(df1)
df1 = df1.merge(ts.get_debtpaying_data(year, quarter),
how='inner',
on=['code', 'name'])
#print (5)
print(df1)
df1 = df1.merge(ts.get_cashflow_data(year, quarter),
how='inner',
on=['code', 'name'])
#print (6)
(row, col) = df1.shape
for i in range(0, row):
df1.iloc[i, 0] = str(df1.iloc[i, 0])
return df1
def get_report_data(year, season):
if not available(year, season):
return None
print("get_report_data")
save(ts.get_report_data(year, season), "basics/report_data", year, season)
print("get_profit_data")
save(ts.get_profit_data(year, season), "basics/profit_data", year, season)
filename = "operation_data"
print("get_operation_data")
save(ts.get_operation_data(year, season), "basics/operation_data", year,
season)
filename = "growth_data"
print("get_growth_data")
save(ts.get_growth_data(year, season), "basics/growth_data", year, season)
filename = "get_debtpaying_data"
print("get_debtpaying_data")
save(ts.get_debtpaying_data(year, season), "basics/debtpaying_data", year,
season)
filename = "get_debtpaying_data"
print("get_cashflow_data")
save(ts.get_cashflow_data(year, season), "basics/cashflow_data", year,
season)
def get_debtpaying_data(self, year, quarter):
tsdata = ts.get_debtpaying_data(
year=year,
quarter=quarter,
)
jsdata = To_Json(tsdata)
return jsdata
def store_fund_data(quarter_list):
stock2year_path = os.path.join(LastFilePath, "stock_fundm_info")
for fun_year, fun_quarter in quarter_list:
#every dataframe you craw down all needs remove the duplicated row. Only need keep the first row of duplicates.
# stock2year_report is tushare:get_report_data (fundamental data).
stock2year_report = ts.get_report_data(
fun_year, fun_quarter).drop_duplicates(keep='first')
# stock2year_prof is tushare.get_profit_data (fundamental data).
stock2year_prof = ts.get_profit_data(
fun_year, fun_quarter).drop_duplicates(keep='first')
# stock2year_opera is tushare.get_operation_data (fundamental data).
stock2year_opera = ts.get_operation_data(
fun_year, fun_quarter).drop_duplicates(keep='first')
#stock2year_grow is tushare.get_growth_data (fundamental data).
stock2year_grow = ts.get_growth_data(
fun_year, fun_quarter).drop_duplicates(keep='first')
#stock2year_debt is tushare.get_debtpaying_data (fundamental data).
stock2year_debt = ts.get_debtpaying_data(
fun_year, fun_quarter).drop_duplicates(keep='first')
#stock2year_cash is tushare.get_cashflow_data (fundamental data).
stock2year_cash = ts.get_cashflow_data(
fun_year, fun_quarter).drop_duplicates(keep='first')
#stock2year_comb is to combine all the stock2year data of same year and quarter in a same stock code.
stock2year_list = [stock2year_report,stock2year_prof,stock2year_opera,stock2year_grow, \
stock2year_debt,stock2year_cash]
for every_fund_element in stock2year_list:
every_fund_element = every_fund_element.set_index('code')
#use pandas concat to combine all the dataframe along columns.
total_fund = pd.concat(stock2year_list, axis=1)
HeadName = fun_year + "/" + fun_quarter + "_" + "fundamt_info"
CsvName = os.path.join(stock2year_path, "{}.csv".format(HeadName))
total_fund.to_csv(CsvName)
def stat_all(tmp_datetime):
# 返回 31 天前的数据，做上个季度数据统计。
tmp_datetime_1month = tmp_datetime + datetime.timedelta(days=-31)
year = int((tmp_datetime_1month).strftime("%Y"))
quarter = int(pd.Timestamp(tmp_datetime_1month).quarter) # 获得上个季度的数据。
print("############ year %d, quarter %d", year, quarter)
# 业绩报告（主表）
data = ts.get_report_data(year, quarter)
# 增加季度字段。
data = concat_quarter(year, quarter, data)
# 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。
data = data.drop_duplicates(subset="code", keep="last")
global db
# 插入数据库。
db.insert_db(data, "ts_report_data", True, "`quarter`,`code`")
# 盈利能力
data = ts.get_profit_data(year, quarter)
# 增加季度字段。
data = concat_quarter(year, quarter, data)
# 处理重复数据，保存最新一条数据。
data = data.drop_duplicates(subset="code", keep="last")
# 插入数据库。
db.insert_db(data, "ts_profit_data", True, "`quarter`,`code`")
# 营运能力
data = ts.get_operation_data(year, quarter)
# 增加季度字段。
data = concat_quarter(year, quarter, data)
# 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。
data = data.drop_duplicates(subset="code", keep="last")
# 插入数据库。
db.insert_db(data, "ts_operation_data", True, "`quarter`,`code`")
# 成长能力
data = ts.get_growth_data(year, quarter)
# 增加季度字段。
data = concat_quarter(year, quarter, data)
# 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。
data = data.drop_duplicates(subset="code", keep="last")
# 插入数据库。
db.insert_db(data, "ts_growth_data", True, "`quarter`,`code`")
# 偿债能力
data = ts.get_debtpaying_data(year, quarter)
# 增加季度字段。
data = concat_quarter(year, quarter, data)
# 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。
data = data.drop_duplicates(subset="code", keep="last")
# 插入数据库。
db.insert_db(data, "ts_debtpaying_data", True, "`quarter`,`code`")
# 现金流量
data = ts.get_cashflow_data(year, quarter)
# 增加季度字段。
data = concat_quarter(year, quarter, data)
# 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。
data = data.drop_duplicates(subset="code", keep="last")
# 插入数据库。
db.insert_db(data, "ts_cashflow_data", True, "`quarter`,`code`")
def get_stock_debtpaying(year, season, engine): frame = ts.get_debtpaying_data(year, season) table_name = 'stock_debtpaying_' + str(year) + 's' + str(season) if useDB == True: frame.to_sql(table_name, engine) else: frame.to_csv(table_name + '.csv')
def fetchByYearQuarter(self, mongo, type):
years = range(self.begin_year, self.end_year_notinclude)
quarters = range(1, 5)
for year in years:
for quarter in quarters:
print(str(type) + '_' + str(year) + '_' + str(quarter))
if (type == 'report_data'):
df = fd.get_report_data(year, quarter)
elif (type == 'profit_data'):
df = ts.get_profit_data(year, quarter)
elif (type == 'operation_data'):
df = ts.get_operation_data(year, quarter)
elif (type == 'growth_data'):
df = ts.get_growth_data(year, quarter)
elif (type == 'debtpaying_data'):
df = ts.get_debtpaying_data(year, quarter)
elif (type == 'cashflow_data'):
df = ts.get_cashflow_data(year, quarter)
else:
df = {}
tmpJson = json.loads(df.to_json(orient='records'))
for i in range(len(tmpJson)):
tmpJson[i][u'year'] = int(year)
tmpJson[i][u'quarter'] = int(quarter)
coll = mongo.fundemental[type]
coll2 = mongo.fundemental[str(type) + '_' + str(year) + '_' +
str(quarter)]
coll2.insert(tmpJson)
coll.insert(tmpJson)
def get_debtpaying_data(year, quarter):
try:
df = ts.get_debtpaying_data(year, quarter)
engine = create_engine('mysql://*****:*****@127.0.0.1/stock?charset=utf8')
df.to_sql('debtpaying_data', engine, if_exists='append')
print "message"
except Exception, e:
e.message
def get_stock_debtpaying(nd, jd):
"""
获取偿债能力
"""
try:
res = ts.get_debtpaying_data(nd, jd)
return res
except:
return None
def get_debtpaying_data_dict(year, season):
ddf = ts.get_debtpaying_data(year, season)
ret_dict = {}
if ddf is None:
return ret_dict
records = ddf.to_dict("records")
for data in records:
ret_dict[data['code']] = data
return ret_dict
def single_stock_report(code, year_start, k_index):
"""
:param code: the valid stock code, for example '002146'
:param year_start: the start date that we want to check the stock report, for example '201801'
:param k_index: the performance of report we want to check
:return: DataFrame table: the index is the quarter from start to end, the
"""
if code is None:
raise ValueError('please assign code')
if year_start is None:
raise ValueError('please assign year')
if k_index is None:
raise ValueError('please assign index')
year_to_market = stock_list('timeToMarket')
ytm = year_to_market[year_to_market.index == code]
ytm = str(ytm.iloc[0])
if ytm >= year_start:
qs = getBetweenQuarter(ytm)
else:
qs = getBetweenQuarter(year_start)
j = len(qs) - 1
results = pd.DataFrame()
new_index = []
for i in range(j):
year = int(qs[i].split('Q')[0])
q = int(qs[i].split('Q')[1])
n = 1
data = []
while n < 10:
if k_index == 'get_profit_data':
data = ts.get_profit_data(int(year), q)
elif k_index == 'get_report_data':
data = ts.get_report_data(int(year), q)
elif k_index == 'get_operation_data':
data = ts.get_operation_data(int(year), q)
elif k_index == 'get_growth_data':
data = ts.get_growth_data(int(year), q)
elif k_index == 'get_debtpaying_data':
data = ts.get_debtpaying_data(int(year), q)
elif k_index == 'get_cashflow_data':
data = ts.get_cashflow_data(int(year), q)
else:
raise Exception('the k_indexs is not correct')
result = data[data['code'] == code]
if len(result) >= 1:
new_index.append('%d0%d' % (year, q))
results = results.append(result[0:1], ignore_index=True)
print(results)
break
elif len(result) == 0:
n += 1
continue
new_index_1 = pd.DataFrame({"Y_Q": new_index})
frames = [results, new_index_1]
return pd.concat(frames, axis=1)
def Deep_Data_Median(self):
# 1/行业利润概览
l1 = list()
for x in self.fd_data_index:
z = ts.get_profit_data(x, 4)
k = z[z["code"].isin(self.idstry_code_list)].iloc[:, 1:].median()
l1.append(k)
all_idt_PF_data = pd.DataFrame(l1, index=self.fd_data_index)
all_idt_PF_data.dropna(how="all", inplace=True)
# 2/行业现金流量概览
l2 = list()
for x in self.fd_data_index:
z = ts.get_cashflow_data(x, 4)
k = z[z["code"].isin(self.idstry_code_list)].iloc[:, 1:].median()
l2.append(k)
all_idt_CS_data = pd.DataFrame(l2, index=self.fd_data_index)
all_idt_CS_data.dropna(how="all", inplace=True)
# 3/行业偿债能力概览
l3 = list()
for x in self.fd_data_index:
z = ts.get_debtpaying_data(x, 4)
k = z[z["code"].isin(self.idstry_code_list)].iloc[:, 1:].median()
l3.append(k)
all_idt_DP_data = pd.DataFrame(l3, index=self.fd_data_index)
all_idt_DP_data.dropna(how="all", inplace=True)
# 4/行业营运能力概览
l4 = list()
for x in self.fd_data_index:
z = ts.get_operation_data(x, 4)
k = z[z["code"].isin(self.idstry_code_list)].iloc[:, 1:].median()
l4.append(k)
all_idt_OP_data = pd.DataFrame(l4, index=self.fd_data_index)
all_idt_OP_data.dropna(how="all", inplace=True)
# 5/行业成长能力概览
l5 = list()
for x in self.fd_data_index:
z = ts.get_growth_data(x, 4)
k = z[z["code"].isin(self.idstry_code_list)].iloc[:, 1:].median()
l5.append(k)
all_idt_GR_data = pd.DataFrame(l5, index=self.fd_data_index)
all_idt_GR_data.dropna(how="all", inplace=True)
# 保存数据
data_writer = pd.ExcelWriter("股票{}的基本面数据全览（中位数视角）.xlsx".format(self.code))
self.All_Ratio_data.T.sort_index(ascending=True).to_excel(data_writer, sheet_name="基本面财务比率")
self.All_Basic_data.T.sort_index(ascending=True).to_excel(data_writer, sheet_name="基本面财务数据")
self.All_Growth_data.sort_index(ascending=True).to_excel(data_writer, sheet_name="基本面数据增长率")
self.All_Valuation_data.T.sort_index(ascending=True).to_excel(data_writer, sheet_name="三项估值指标")
self.Dupon_data.T.sort_index(ascending=True).to_excel(data_writer, sheet_name="杜邦分析表")
all_idt_PF_data.sort_index(ascending=True).to_excel(data_writer, sheet_name="行业利润历史")
all_idt_CS_data.sort_index(ascending=True).to_excel(data_writer, sheet_name="行业现金历史")
all_idt_OP_data.sort_index(ascending=True).to_excel(data_writer, sheet_name="行业营运历史")
all_idt_GR_data.sort_index(ascending=True).to_excel(data_writer, sheet_name="行业成长历史")
all_idt_DP_data.sort_index(ascending=True).to_excel(data_writer, sheet_name="行业偿债历史")
data_writer.save()
def get_fundamental_data(year,quarter):
fundamental = {}
fundamental['basic'] =ts.get_stock_basics()
fundamental['report']=ts.get_report_data(year, quarter)
fundamental['profit']=ts.get_profit_data(year, quarter)
fundamental['operation']=ts.get_operation_data(year, quarter)
fundamental['cashflow']=ts.get_cashflow_data(year, quarter)
fundamental['growth']=ts.get_growth_data(year, quarter)
fundamental['debt']=ts.get_debtpaying_data(year, quarter)
return fundamental
def debt():
"""偿债能力
code,代码 name,名称
currentratio,流动比率 quickratio,速动比率
cashratio,现金比率 icratio,利息支付倍数
sheqratio,股东权益比率 adratio,股东权益增长率
"""
# 获取2014年第3季度的偿债能力数据
df = ts.get_debtpaying_data(2014, 3)
print(df)
def getDebtpaying(self, year, season):
'''
获取股票运营能力数据
'''
print 'getDebtpaying work'
df = ts.get_debtpaying_data(year, season)
df.to_csv(self.__filename)
self.__getDataCommSeason(9, year, season, "Debtpaying")
def year_report(year):
df0 = ts.get_report_data(year, 4)
df0.to_sql(str(year) + '_main', engine)
df1 = ts.get_profit_data(year, 4)
df1.to_sql(str(year) + '_profit', engine)
df2 = ts.get_growth_data(year, 4)
df2.to_sql(str(year) + '_growth', engine)
df3 = ts.get_debtpaying_data(year, 4)
df3.to_sql(str(year) + '_debtpaying', engine)
df4 = ts.get_cashflow_data(year, 4)
df4.to_sql(str(year) + '_cashflow', engine)
def get_debtpaying_data(start=None, end=None):
startArr = start.split("-")
endArr = end.split("-")
seasonList = dataUtil.getListSeason(int(startArr[0]), int(startArr[1]), int(endArr[0]), int(endArr[1]))
for season in seasonList :
df = ts.get_debtpaying_data(season[0],season[1]).drop_duplicates('code')
dt = pd.DataFrame({"date": np.array([str(season[0])+"-"+str(season[1])]*len(df))},index=df.index)
df = pd.concat([df,dt],axis=1)
dataPath = TushareApi.path + "debtpaying_data/" +str(season[0])+"-"+str(season[1])+"-debtpaying_data.csv"
fileUtil.saveDf(df,dataPath)
def get_debtpaying_data(cls, year, quarter): # 偿债能力数据
data = ts.get_debtpaying_data(year, quarter)
data['year'] = year
data['quarter'] = quarter
data.reset_index(drop=True, inplace=True)
ModelData.remove_data(table_name='debtpaying_data',
year=year,
quarter=quarter)
print('remove success')
ModelData.insert_data(table_name='debtpaying_data', data=data)
print('write success')
def debtpaying_data(year):
for i in range(4):
quarter = i + 1
predts = ts.get_debtpaying_data(year, quarter)
if predts is not None:
predts['year'] = year
predts['quarter'] = quarter
predts.to_sql('fundamental_debtpaying_data',
engine,
flavor='mysql',
if_exists='append')
def get_debtpaying_data(year, quarter):
try:
logger.info('get %s year %s quarter debtpaying data starting...' %
(year, quarter))
df = ts.get_debtpaying_data(year, quarter)
logger.info('get %s year %s quarter debtpaying data end...' %
(year, quarter))
except:
logger.exception(
'some errors between get %s year %s quarter debtpaying data end...'
% (year, quarter))
return df
def getDebtReport(year):
for q in [1, 2, 3, 4]:
getDownloaded()
if not isDownloaded(debtByYear(year, q)):
try:
print('Debt of ' + str(year) + ' and quarter is ' + str(q))
debt_df = ts.get_debtpaying_data(year, q)
debt_df.to_csv(path + debtByYear(year, q))
time.sleep(5)
except Exception:
logError('Debt: ' + str(year) + '-' + str(q) +
' there is problem, will skip it. ')
def getDebtReport(year):
for q in [1, 2, 3, 4]:
getDownloaded()
if not isDownloaded(debtByYear(year, q)):
try:
print('Debt of ' + str(year) + ' and quarter is ' + str(q))
debt_df = ts.get_debtpaying_data(year, q)
debt_df.to_csv(path + debtByYear(year, q))
time.sleep(5)
except Exception:
logError('Debt: ' + str(year) + '-' + str(q) +
' there is problem, will skip it. ')
def sync_debtpaying_data(): ''' sync debtpaying data ''' year = datetime.datetime.now().year month = datetime.datetime.now().month seaon = month/3 if month<3: year = year - 1 seaon = 4 monthstr = '%s%s'%(year,seaon) DataFrameToMongo(ts.get_debtpaying_data(year, seaon), MongoClient(mongourl)['stoinfo']['debtpaying_data'], ['code'], monthstr)
def fImportDebtpayingData(year="", quarter=""):
df = ts.get_debtpaying_data(year=year, quarter=quarter)
df['YEAR'] = year
df['QUARTER'] = quarter
df = df.replace("--", np.nan)
#print(df)
df["currentratio"] = df["currentratio"].astype('float64')
df["quickratio"] = df["quickratio"].astype('float64')
df["cashratio"] = df["cashratio"].astype('float64')
df["icratio"] = df["icratio"].astype('float64')
df["sheqratio"] = df["sheqratio"].astype('float64')
df["adratio"] = df["adratio"].astype('float64')
engine = create_engine('oracle://c##tushare:didierg160@myoracle')
df.to_sql('tb_stock_debtpaying_data', engine, if_exists='append')
def report_type_chosen(report_type, year, season):
"""
report_type: 报表种类
year: 报表年份
season: 报表季度
"""
report_type_dict = {
'主表': ts.get_report_data(year, season),
'收益表': ts.get_profit_data(year, season),
'运营表': ts.get_operation_data(year, season),
'现金流表': ts.get_cashflow_data(year, season),
'偿债能力表': ts.get_debtpaying_data(year, season),
}
report_chosen = report_type_dict.get(report_type)
return report_chosen
def debt(engine, year, quarter):
tbl = "basic_debt"
tsl.log(tbl + " start...")
try:
df = ts.get_debtpaying_data(year, quarter)
df = df.set_index('code', drop='true')
df['year'] = year
df['quarter'] = quarter
df = df.fillna(0)
df.to_sql(tbl, engine, if_exists='append')
print
tsl.log(tbl + " done")
except BaseException, e:
print
print e
tsl.log(tbl + " error")
def update_basics():
basics = ts.get_stock_basics()
f = os.path.join(base_dir, 'basics.h5')
basics.to_hdf(f, 'basics')
today = datetime.date.today()
current_year = today.year
current_season = today.month / 3
if current_season == 0:
current_year -= 1
current_season = 4
length = 4 * 5
year = current_year
season = current_season
for i in range(length):
f = os.path.join(base_dir, 'basics-{0}-{1}.h5'.format(year, season))
if os.path.exists(f):
continue
print(f)
report = ts.get_report_data(year, season)
report.to_hdf(f, 'report')
profit = ts.get_profit_data(year, season)
profit.to_hdf(f, 'profit')
operation = ts.get_operation_data(year, season)
operation.to_hdf(f, 'operation')
growth = ts.get_growth_data(year, season)
growth.to_hdf(f, 'growth')
debtpaying = ts.get_debtpaying_data(year, season)
debtpaying.to_hdf(f, 'debtpaying')
cashflow = ts.get_cashflow_data(year, season)
cashflow.to_hdf(f, 'cashflow')
season -= 1
if season == 0:
season = 4
year -= 1
def updatedebtpay():
debtpaydatalist=ts.get_debtpaying_data(2014,1)
debtpaydata=pd.DataFrame(debtpaydatalist)
conn= ms.connect(host='localhost',port = 3306,user='******', passwd='123456',db ='investment',charset="utf8")
cur = conn.cursor()
values=[]
for index,row in debtpaydata.iterrows():
if row['currentratio']=='--':
currentratio=0
else:
currentratio=row['currentratio']
if row['quickratio']=='--':
quickratio=0
else:
quickratio=row['quickratio']
if row['cashratio']=='--':
cashratio=0
else:
cashratio=row['cashratio']
if row['icratio']=='--':
icratio=0
else:
icratio=row['icratio']
if row['sheqratio']=='--':
sheqratio=0
else:
sheqratio=row['sheqratio']
if row['adratio']=='--':
adratio=0
else:
adratio=row['adratio']
values.append((row['code'],row['name'],currentratio,quickratio,cashratio,icratio,sheqratio,adratio))
cur.executemany('insert into debtpay20141 (code,name,currentratio,quickratio,cashratio,icratio,sheqratio,adratio) values(%s,%s,%s,%s,%s,%s,%s,%s)',values)
conn.commit()
cur.close()
conn.close()
import tushare as ts
import pandas as pd
from tushare.stock import cons as ct
import lxml.html
from lxml import etree
import re
from pandas.compat import StringIO
try:
from urllib.request import urlopen, Request
except ImportError:
from urllib2 import urlopen, Request
df = ts.get_debtpaying_data(year=2016,quarter=1,orderby='errors')
print (df)
df.to_excel('/Users/elliot/Downloads/debtpaying_data2016Q1.xlsx')
"""
year=2016
quarter=1
orderby='default'
pageNo=1
urla=(ct.PROFIT_URL % (ct.P_TYPE['http'], ct.DOMAINS['vsf'],
ct.PAGES['fd'], year,
quarter, pageNo, ct.PAGE_NUM[1], orderby))
print (urla)
"""
def pick_data(self, max_num_threads = 20, pause = 0):
"""
pick all necessary data from local database and from internet for loaded stocks. This function will take a while.
"""
logging.info('getting basics from tushare')
self._init_stock_objs()
# self.data_manager.drop_stock()
# self.stocks = {key: self.stocks[key] for key in ['600233', '600130']}
logging.info('totally there are %d listed companies' % len(self.stocks))
logging.info('get indexes from tushare')
self._get_indexes()
# self._pick_hist_data_and_save(self.stocks, False, self.indexes['000001'].hist_start_date, max_num_threads)
logging.info('getting last stock trading data')
df = ts.get_today_all()
self._extract_from_dataframe(df,
ignore=('changepercent', 'open', 'high', 'low', 'settlement', 'volume', 'turnoverratio', 'amount'),
remap={'trade': 'price', 'per': 'pe'})
# calculate the report quarter
report_year, report_quarter = ts.get_last_report_period()
logging.info('getting last report (%d quarter %d) from tushare' % (report_year, report_quarter))
df = ts.get_report_data(report_year, report_quarter)
self._extract_from_dataframe(df)
logging.info('getting last profit data from tushare')
df = ts.get_profit_data(report_year, report_quarter)
self._extract_from_dataframe(df, ignore=('net_profits', 'roe', 'eps'))
logging.info('getting last operation data from tushare')
df = ts.get_operation_data(report_year, report_quarter)
self._extract_from_dataframe(df)
logging.info('getting last growth data from tushare')
df = ts.get_growth_data(report_year, report_quarter)
self._extract_from_dataframe(df)
logging.info('getting last debtpaying data from tushare')
df = ts.get_debtpaying_data(report_year, report_quarter)
self._extract_from_dataframe(df)
logging.info('getting last cashflow data from tushare')
df = ts.get_cashflow_data(report_year, report_quarter)
self._extract_from_dataframe(df)
logging.info('getting history trading data from tushare')
start_from = self.indexes['000001'].hist_start_date
data_full = self._pick_hist_data_and_save(self.stocks, False, start_from, max_num_threads, pause) # anything that pulling data must before here
self._remove_unavailable_stocks()
'''
# calculate qianfuquan data
# deprecated due to precision issue
for code, stock in self.stocks.items():
for i in range(1, len(stock.hist_data.index)-1):
b = stock.hist_data.at[stock.hist_data.index[i], 'close']
a = stock.hist_data.at[stock.hist_data.index[i+1], 'close']
p = stock.hist_data.at[stock.hist_data.index[i+1], 'p_change'] / 100.0
q = (p*a+a)/b
if q > 1.1:
print('%s chuq-uan %s: %s %s %s, 1/%s' % (stock, stock.hist_data.index[i], b, a, p, q))
'''
return data_full
#coding=utf-8 import tushare as ts # 获取沪深上市公司基本情况 df = ts.get_stock_basics() date = df.ix['600848']['timeToMarket']#上市日期YYYYMMDD #获取2014年第3季度的业绩报表数据 ts.get_report_data(2014,3) #获取2014年第3季度的盈利能力数据 ts.get_profit_data(2014,3) #获取2014年第3季度的营运能力数据 ts.get_operation_data(2014,3) #获取2014年第3季度的成长能力数据 ts.get_growth_data(2014,3) #获取2014年第3季度的偿债能力数据 ts.get_debtpaying_data(2014,3) #获取2014年第3季度的现金流量数据 ts.get_cashflow_data(2014,3)
def download_debtpaying_data(file_path, year, quarter):
debtpaying_data = ts.get_debtpaying_data(year, quarter)
if debtpaying_data is not None:
debtpaying_data.to_csv(file_path + 'debtpaying_' + str(year) + '_' + str(quarter) + '.csv', encoding='utf-8')
FinancialData = ts.get_report_data(CURRENT.year, np.floor((CURRENT.month+2)/3)-1)
FinancialData = FinancialData.set_index('code')
FinancialData = FinancialData.drop(['name', 'bvps', 'distrib', 'epcf', 'report_date'], axis = 1)
FinancialData.to_csv('./ASHR/DATA/FinancialData_2015_1.csv', index = True)
ProfitData = ts.get_profit_data(CURRENT.year, np.floor((CURRENT.month+2)/3)-1)
ProfitData = ProfitData.set_index('code')
ProfitData = ProfitData.drop(['name', 'business_income', 'net_profits'], axis = 1)
ProfitData.to_csv('./ASHR/DATA/ProfitData_2015_1.csv', index = True)
GrowthData = ts.get_growth_data(CURRENT.year, np.floor((CURRENT.month+2)/3)-1)
GrowthData = GrowthData.set_index('code')
GrowthData = GrowthData.drop(['name'], axis = 1)
GrowthData.to_csv('./ASHR/DATA/GrowthData_2015_1.csv', index = True)
DebtPayingData = ts.get_debtpaying_data(CURRENT.year, np.floor((CURRENT.month+2)/3)-1)
DebtPayingData = DebtPayingData.set_index('code')
DebtPayingData = DebtPayingData.drop(['name', 'sheqratio', 'adratio'], axis = 1)
DebtPayingData.to_csv('./ASHR/DATA/DebtPayingData_2014_12.csv', index = True)
# Merging data
for subtab in [FinancialData, ProfitData, GrowthData, DebtPayingData]:
StockInfo = pd.merge(StockInfo, subtab, how = 'outer', on = 'code')
# Saving data
StockInfo = StockInfo.to_csv('./ASHR/DATA/StockInfo.csv', index = True)
########################
## Trade Tick Data ##
df = ts.get_growth_data(currentYear,currentSeason)
df = df.assign(quater=currentQuater)
df.to_sql('growth_data',engine, if_exists='append')
# import debt pay data
#code,代码
#name,名称
#currentratio,流动比率
#quickratio,速动比率
#cashratio,现金比率
#icratio,利息支付倍数
#sheqratio,股东权益比率
#adratio,股东权益增长率
df = ts.get_debtpaying_data(lastYear,lastSeason)
df = df.assign(quater=lastQuater)
df.to_sql('debtpay_data',engine, if_exists='replace')
df = ts.get_debtpaying_data(currentYear,currentSeason)
df = df.assign(quater=currentQuater)
df.to_sql('debtpay_data',engine, if_exists='append')
# import cashflow data
#code,代码
#name,名称
#cf_sales,经营现金净流量对销售收入比率
#rateofreturn,资产的经营现金流量回报率
#cf_nm,经营现金净流量与净利润的比率
#cf_liabilities,经营现金净流量对负债比率
def getdebtpaying(year,quarter):
dfdebtpaying = ts.get_debtpaying_data(year,quarter)
dfdebtpaying.insert(0,'uploadtime',nowtime)
dfdebtpaying.insert(1,'year',year)
dfdebtpaying.insert(2,'quarter',quarter)
dfdebtpaying.to_sql('tb_debtpaying',engine,if_exists='append')
import tushare as ts
from sqlalchemy import create_engine
import mysql.connector
conn = create_engine('mysql+mysqlconnector://stockadmin:stock2016@localhost/stock?charset=utf8')
for year in range(2004,2012):
for season in range(1,5):
print(year,season)
df_profit = ts.get_profit_data(year,season)
time.sleep(15)
df_growth = ts.get_growth_data(year,season)
time.sleep(15)
df_operation = ts.get_operation_data(year,season)
time.sleep(15)
df_debtpaying = ts.get_debtpaying_data(year,season)
time.sleep(15)
df_cashflow = ts.get_cashflow_data(year,season)
time.sleep(15)
df_report = ts.get_report_data(year,season)
df_profit.to_sql('profit',conn,if_exists='append')
df_growth.to_sql('growth',conn,if_exists='append')
df_operation.to_sql('operation',conn,if_exists='append')
df_debtpaying.to_sql('debtpaying',conn,if_exists='append')
df_cashflow.to_sql('cashflow',conn,if_exists='append')
df_report.to_sql('report',conn,if_exists='append')
#sys.exit(1)
#追加数据到现有表
#df.to_sql('tick_data',engine,if_exists='append')
import tushare as ts import sys df = ts.get_debtpaying_data(int(sys.argv[1]), int(sys.argv[2])) df.to_csv(sys.argv[3], encoding="utf8")
