def insert_mongo(self, stockid, stock_concept, id_v):
stock_name = common.QueryStockMap(id=stockid)[0]
self.mongodb.stock.ycj_concept.insert({
"stockid": stockid,
"concept": stock_concept,
"stockname": stock_name,
"id": str(id_v)
})
def queryField(self, fieldName):
print "######## look up #########################################################"
targets = self.query[fieldName]
names = [(common.QueryStockMap(id=x))[0] for x in targets]
names = " ".join(names)
print "%s\n%s" % (fieldName, names)
print targets
print "#########################################################################\n"
def add_L1_element(self, file_dir=u'D:/Money/modeResee/复盘', file_name = u'insert_L1.csv', date=datetime.datetime.today().strftime("%Y%m%d")):
print "Begin to insert L1 alone using file: %s" % os.path.join(file_dir, file_name)
dframe = pd.read_csv(os.path.join(file_dir, file_name), encoding='gbk')
for idx in dframe.index.values:
print idx
concept = dframe.loc[idx, 'group'] # 概念
stockids = [common.QueryStockMap(name=dframe.loc[idx, 'name'])[1]]
try:
if str(dframe.loc[idx, 'anotation']) == 'nan' or len(str(dframe.loc[idx, 'anotation'])) < 2:
anotation_stockids = []
else:
anotations = dframe.loc[idx, 'anotation'].replace(u' ', u'').replace(u'，', u',').split(u',')
anotation_stockids = [common.QueryStockMap(name=x)[1] for x in anotations]
except Exception, err:
print "force to set the anotation stockids to be empty for concept:%s" %concept
anotation_stockids = []
stockids.extend(anotation_stockids) # 该行中，概念相关的股票
for stockid in stockids:
self.insertCache(stockid, concept, date, cache=1) # 往L2中添加股票及对应的概念
def update_L1_from_generated_csv(self, csv_file, date=datetime.datetime.today().strftime("%Y%m%d")):
dframe = pd.read_csv(csv_file, encoding='gbk')
dframe.fillna(value=0, inplace=True)
for idx in dframe.index.values:
stockid = dframe.loc[idx, 'stockid']
stockid = common.regulize_stockid(stockid)
concept = dframe.loc[idx, 'concept']
stockname = dframe.loc[idx, 'stockname']
if_delete = dframe.loc[idx, 'if_delete']
# 如果为1，则删除
if if_delete:
self.removeCache(stockid, concept, cache=1)
print "Delete L1 element, concept:%s, stockid:%s, stockname:%s" % (concept, stockid, stockname)
else:
# 如果该行值不在L1，则插入
if not common.exist_in_cache(stockid, concept, 1):
stockname = common.QueryStockMap(id=stockid)[0]
self.insertCache(stockid, concept, date)
print "Insert new L1 element, concept:%s, stockid:%s, stockname:%s" % (concept, stockid, stockname)
print "Update L1 cache from L1.csv under each day finished!"
def updateAnotationL2CsvByDate(self, date):
# 读入daydayup.csv
dframe = pd.read_csv(os.path.join(self.backsee_dir, u"%s/daydayup.csv" % date), encoding='gbk')
dframe.dropna(subset= ['group'], inplace=True)
for idx in dframe.index.values:
concept = dframe.loc[idx, 'group'] # 概念
# stockids = [dframe.loc[idx, 'stock']]
stockids = [] # 不更新csv中stock列, 在另一个函数中更新
try:
if str(dframe.loc[idx, 'anotation']) == 'nan' or len(str(dframe.loc[idx, 'anotation'])) < 2:
anotation_stockids = []
else:
anotations = dframe.loc[idx, 'anotation'].replace(u' ', u'').replace(u'，', u',').split(u',')
anotation_stockids = [common.QueryStockMap(name=x)[1] for x in anotations]
except Exception, err:
print "force to set the anotation stockids to be empty for concept:%s" %concept
anotation_stockids = []
stockids.extend(anotation_stockids) # 该行中，概念相关的股票
print "updating concept of:%s ..." %concept
for stockid in stockids:
self.insertCache(stockid, concept, date, cache=2) # 往L2中添加股票及对应的概念
def get_zt_details(self):
# '''
# 临时debug
# '''
# self.hiList = ['603108', '000929']
# self.hiList = ['000672']
# self.ztList = []
# self.ztList = ['603819', '603987', '000422', '000022']
targetlist = []
targetlist.extend(self.ztList)
targetlist.extend(self.hiList)
closeZt = {}
openZt = {}
for stock in targetlist:
closetime, nonstable_time = common.zt_time_details(stock, self.day)
stock_name = common.QueryStockMap(stock)[0]
if stock_name == "":
stock_name = stock
self.add_to_dict(closeZt, closetime, stock_name)
self.add_to_dict(openZt, nonstable_time, stock_name)
return closeZt, openZt
def study_plot(tmp_array,dirs, name_tail = u'', end_date_interval = 0, start_date_interval = 0):
## 日线dir
daydir = dirs
minsdir = "%s/mins"%dirs
bothdir = "%s/both"%dirs
if os.path.exists(minsdir):
pass
else:
os.mkdir(minsdir)
if os.path.exists(bothdir):
pass
else:
os.mkdir(bothdir)
[ddate,dtime,code,direction,price]=tmp_array
end_date = common.format_date(ddate,"%Y-%m-%d")
start_date = common.get_lastN_date(end_date, start_date_interval)
day_end_date = common.get_lastN_date(end_date, end_date_interval)
code = "0"*(6-len(str(int(code))))+str(int(code))
# 日线数据
sql = "SELECT TICKER_SYMBOL, SEC_SHORT_NAME, TRADE_DATE, PRE_CLOSE_PRICE, OPEN_PRICE, HIGHEST_PRICE, LOWEST_PRICE, CLOSE_PRICE, \
DEAL_AMOUNT from vmkt_equd where TRADE_DATE >= '%s' and TRADE_DATE <='%s' and TICKER_SYMBOL = '%s'"%(start_date,day_end_date,code)
sub = common.get_mysqlData_sqlquery(sql)
sub = regulate_data(sub)
idxcode = "000001"
idxsql = "SELECT TICKER_SYMBOL, SEC_SHORT_NAME, TRADE_DATE, PRE_CLOSE_INDEX, OPEN_INDEX, HIGHEST_INDEX, LOWEST_INDEX, CLOSE_INDEX, \
TURNOVER_VOL from vmkt_idxd where TRADE_DATE >= '%s' and TRADE_DATE <='%s' and TICKER_SYMBOL = '%s'"%(start_date,day_end_date,idxcode)
idx = common.get_mysqlData_sqlquery(idxsql)
idx.columns=sub.columns
add_mean(sub)
add_mean(idx)
tableTime = common.format_date(ddate,"%Y%m")
endDate = common.format_date(ddate,"%Y%m%d")
endDate = int(endDate)
stockid = int(code)
# table = "equity_pricefenbi%s"%tableTime
table = "MarketDataTDB.equity_pricemin%s"%tableTime
dtsql = "SELECT * from %s where ticker = %s and datadate = %s"%(table,stockid,endDate)
dtv = common.get_mydb_sqlquery(dtsql)
if len(dtv) == 0:
table = "MarketDataL1.equity_pricemin%s"%tableTime
dtsql = "SELECT * from %s where ticker = %s and datadate = %s"%(table,stockid,endDate)
dtv = common.get_mydb_sqlquery(dtsql)
table = "MarketDataTDB.equity_pricemin%s"%tableTime
zssql = 'SELECT * from %s where datadate = %s and ticker = 1 and shortnm = "上证指数"'%(table,endDate)
sh = common.get_mydb_sqlquery(zssql)
if len(sh) == 0:
table = "MarketDataL1.equity_pricemin%s"%tableTime
zssql = 'SELECT * from %s where datadate = %s and ticker = 1 and shortnm = "上证指数"'%(table,endDate)
sh = common.get_mydb_sqlquery(zssql)
# ##临时增加，需要保存数据
# tmp_dir = "D:\Money\lilton_code\Market_Mode\study_fresh_fail_Module\data"
# dtv.to_csv(os.path.join(tmp_dir,"%s_%s.csv"%(ddate,code)),encoding='utf8')
[name,tmp_id]=common.QueryStockMap(id=code)
##日线，daydir
fig1 = plt.figure(figsize=[12,8])
##日线图
point = 10
# plt.title("%s"%code)
plt.title(u"%s_%s_%s_d.png"%(ddate, code, name_tail))
ax1 = fig1.add_subplot(211)
plot_candlestick(sub, ax1, point=10, direction=direction, mount_flag=1, mark_date=end_date)
ax2 = fig1.add_subplot(212)
plot_candlestick(idx, ax2, point=10, mount_flag=1, mark_date=end_date)
# plt.savefig(os.path.join(daydir,"%s_%s_%s.pdf"%(ddate,direction,code)),dpi=300)
plt.savefig(os.path.join(daydir, "%s_%s_%s_%s_m.png"%(ddate, code, name, name_tail)),dpi=300)
plt.close()
##分时图,分钟线，在dir/mins
fig2 = plt.figure(figsize=(12,8))
ax3 = fig2.add_subplot(211)
plt.title("%s"%code)
plot_dealDetail(dtv,ax3,time=dtime,direction=direction,mount_flag=1)
ax4 = fig2.add_subplot(212)
plot_dealDetail(sh,ax4,time=dtime,direction=direction,mount_flag=1)
# plt.savefig(os.path.join(minsdir,"%s_%s_%s_%s.pdf"%(ddate,direction,code,name)),dpi=300)
plt.savefig(os.path.join(minsdir,"%s_%s_%s_%s.png"%(ddate, code, name, name_tail)),dpi=300)
plt.close()
def plot_stocks_html(self):
n_day_before = common.get_lastN_date(self.running_day, 80)
concept_frame_list = []
all_local_frame = common.FindConceptStocks(n_day_before,
self.running_day)
# 收集所有关注概念以及对应的股票代码
for concept in self.concepts:
concept_stocks = []
# 云财经中相关股票
ycj_stocks = common.get_ycj_stocks(concept)
# 金融界中相关股票
jrj_stocks = common.get_jrj_stocks(concept)
# 本地csv中相关股票
csv_stocks = all_local_frame.filter_by_concept(concept)
# 合并到一起
concept_stocks.extend(ycj_stocks)
concept_stocks.extend(jrj_stocks)
concept_stocks.extend(csv_stocks.ticker.values)
concept_stocks = list(set(concept_stocks))
tmp_concept_frame = pd.DataFrame({"stockid": concept_stocks})
tmp_concept_frame['concept'] = concept
concept_frame_list.append(tmp_concept_frame)
# index stockid, concept
concept_frame = pd.concat(concept_frame_list, axis=0)
daily_stock_list = list(np.unique(concept_frame.stockid.values))
daily_stock_list.extend(self.extra_stocks)
daily_stock_list = list(set(daily_stock_list))
# 拿到这些股票，指定日期的所有日线数据
daily_frame = common.get_mysqlData(
daily_stock_list, common.getDate(n_day_before, self.running_day))
# daily_frame.to_csv(u'all_daily_frame.csv', encoding='gbk')
daily_frame['rate'] = 100 * (
daily_frame['CLOSE_PRICE'] -
daily_frame['PRE_CLOSE_PRICE']) / daily_frame['PRE_CLOSE_PRICE']
daily_frame['rate'] = daily_frame['rate'].round(2)
daily_frame['lrate'] = 100 * (
daily_frame['LOWEST_PRICE'] -
daily_frame['PRE_CLOSE_PRICE']) / daily_frame['PRE_CLOSE_PRICE']
daily_frame['lrate'] = daily_frame['lrate'].round(2)
daily_frame['hrate'] = 100 * (
daily_frame['HIGHEST_PRICE'] -
daily_frame['PRE_CLOSE_PRICE']) / daily_frame['PRE_CLOSE_PRICE']
daily_frame['hrate'] = daily_frame['hrate'].round(2)
daily_frame['TRADE_DATE'] = daily_frame['TRADE_DATE'].apply(
lambda x: x.strftime("%Y-%m-%d"))
# 复权数据，以便画k线图
fq_daily_frame = common.get_mysqlData(daily_stock_list,
common.getDate(
n_day_before,
self.running_day),
db_table='vmkt_equd_adj')
fq_daily_frame['rate'] = 100 * (fq_daily_frame['CLOSE_PRICE'] -
fq_daily_frame['PRE_CLOSE_PRICE']
) / fq_daily_frame['PRE_CLOSE_PRICE']
fq_daily_frame['rate'] = fq_daily_frame['rate'].round(2)
fq_daily_frame['lrate'] = 100 * (fq_daily_frame['LOWEST_PRICE'] -
fq_daily_frame['PRE_CLOSE_PRICE']
) / fq_daily_frame['PRE_CLOSE_PRICE']
fq_daily_frame['lrate'] = fq_daily_frame['lrate'].round(2)
fq_daily_frame['hrate'] = 100 * (fq_daily_frame['HIGHEST_PRICE'] -
fq_daily_frame['PRE_CLOSE_PRICE']
) / fq_daily_frame['PRE_CLOSE_PRICE']
fq_daily_frame['hrate'] = fq_daily_frame['hrate'].round(2)
fq_daily_frame['TRADE_DATE'] = fq_daily_frame['TRADE_DATE'].apply(
lambda x: x.strftime("%Y-%m-%d"))
# 筛选出符合条件的股票
# 连续3天以上涨停
# condition1_frame = filter_stock_by_zt_condition(daily_frame, condition='ZT', zt_num=3)
#{"concept":concept_frame, "concept":concept_frame2}
# concept_frame: index TICKER_SYMBOL 5_chg 25_chg N_chg rank
self.concept_rank_dict = {}
for concept in self.concepts:
print concept
concept_stock_list = list(
concept_frame[concept_frame.concept == concept].stockid.values)
# 去除掉rmv对应的股票，以及加上mst对于的股票
if concept in self.rmv_dict.keys():
concept_stock_list = [
x for x in concept_stock_list
if x not in self.rmv_dict[concept]
]
if concept in self.mst_dict.keys():
concept_stock_list.extend(self.mst_dict[concept])
# 概念对应的所有股票的日线数据
concept_daily_frame = daily_frame[daily_frame.TICKER_SYMBOL.isin(
concept_stock_list)]
# 统计出其涨幅排行
concept_rank_chg_frame = add_rank_chg(concept_daily_frame,
chg_days=[5, 25],
sort_v=5)
# 得到连续N个涨停以上的股票
condition1_frame = filter_stock_by_zt_condition(
daily_frame[daily_frame.TICKER_SYMBOL.isin(
concept_stock_list)],
condition='ZT',
zt_num=self.direct10_num[concept])
# print concept,self.direct10_num[concept]
# 得到涨幅排行前N的股票
if len(condition1_frame) < 3:
tail_n = 10 - len(condition1_frame)
else:
tail_n = 5
stock_list = list(
concept_rank_chg_frame.sort_values(
by=['rank'],
ascending=False).tail(tail_n).TICKER_SYMBOL.values)
stock_list.extend(condition1_frame.TICKER_SYMBOL.values)
# 再加上必须关注的股票
if concept in self.mst_dict.keys():
stock_list.extend(self.mst_dict[concept])
stock_list = list(set(stock_list))
# 记录到mongo中daily_interesting_concept_stocks
self.write_mongo_concept_day(stock_list, concept, self.running_day)
# 画图
k_line_frame_list = []
for stock in stock_list:
stock_frame = fq_daily_frame[
fq_daily_frame.TICKER_SYMBOL == stock][[
u'TRADE_DATE', 'OPEN_PRICE', 'CLOSE_PRICE',
'HIGHEST_PRICE', 'LOWEST_PRICE', 'TURNOVER_VOL',
'TICKER_SYMBOL', 'rate', 'lrate', 'hrate',
'SEC_SHORT_NAME'
]]
stock_frame.columns = [
'date', 'open', 'close', 'high', 'low', 'volume', 'code',
'rate', 'lrate', 'hrate', 'name'
]
stock_frame['name'] = stock_frame['name'].apply(
lambda x: x.encode('gb2312'))
# index row，date，open, close, high, low, volume, code
# stock_frame = stock_frame.sort('date', ascending='True')
stock_frame = stock_frame.sort_values(by=['date'],
ascending='True')
stock_frame.reset_index(inplace=True)
stock_frame['name'] = stock_frame['name'].apply(
lambda x: x.decode("gbk").encode("utf-8"))
del stock_frame['index']
k_line_frame_list.append(stock_frame)
# 只显示感兴趣的股票的排行和涨幅数据
concept_rank_chg_frame = concept_rank_chg_frame[
concept_rank_chg_frame.TICKER_SYMBOL.isin(stock_list)]
concept_rank_chg_frame = concept_rank_chg_frame.sort_values(
by=['rank'], ascending=True)
concept_rank_chg_frame['stock_name'] = concept_rank_chg_frame[
'TICKER_SYMBOL'].apply(lambda x: common.QueryStockMap(x)[0])
names = [
x for x in concept_rank_chg_frame.columns
if x not in ['stock_name', 'TICKER_SYMBOL']
]
valid_name = ['stock_name']
valid_name.extend(names)
concept_rank_chg_frame = concept_rank_chg_frame[valid_name]
# 构造一个OPTION4&6，左边为K线图，右边为条形图，以及下面一系列的k线图
plot_frame_list = [[k_line_frame_list[0], concept_rank_chg_frame]]
plot_frame_list.extend(k_line_frame_list[1:])
# 构造对应的stock_list，标识对应的股票代码
plot_stock_list = []
for kline_frame in k_line_frame_list:
plot_stock_list.append(kline_frame['code'].values[0])
html_types = ['4and6']
html_types.extend([4] * len(k_line_frame_list[1:]))
title_list = [['A_stock', 'accum_ratio']]
title_list.extend(['A_stocks'] * len(k_line_frame_list[1:]))
# 在加一个统计区间涨跌幅
# range_ratio = {
# concept_name:[[start1, end1],[start2, end2], [start3, end3], ...],
# concept_name2:[[start1, end1],[start2, end2], [start3, end3], ...],
# }
# 说明该概念需要画出不同时期的涨跌幅对比
if concept in self.range_ratio_dict.keys():
range_ratio_frame = pd.DataFrame()
for [sday, eday] in self.range_ratio_dict[concept]:
col_name = "%s-%s" % (common.format_date(
sday, "%Y%m%d"), common.format_date(eday, "%Y%m%d"))
for stock in stock_list:
range_ratio_frame.loc[
stock, col_name] = common.time_range_accum_ratio(
stock, sday, eday)
range_ratio_frame.reset_index(inplace=True)
range_ratio_frame.rename(columns={"index": "stock_name"},
inplace=True)
range_ratio_frame['stock_name'] = range_ratio_frame[
'stock_name'].apply(lambda x: common.QueryStockMap(x)[0])
# 添加到画图中
plot_frame_list.append(range_ratio_frame)
plot_stock_list.append(0)
html_types.append(7)
title_list.append('ACCUM_RATIO')
# 输出html
common.get_html_curve(plot_frame_list,
'%s' % (concept),
html_types=html_types,
title_list=title_list,
save_dir=os.path.join(
self.dump_dir, self.running_day),
chart_connect=1,
plot_stock_list=plot_stock_list,
generate_date=self.running_day)
logging.getLogger().info("plot html finished for %s, %s" %
(concept, self.running_day))
print "Finished for %s" % self.running_day
def read_concepts_excel(self):
# 读取关注的概念列表
concept_frame = pd.read_excel(os.path.join(self.path, u'战区.xlsx'),
encoding='gbk',
sheetname=u'战区位置')
concept_frame = concept_frame[concept_frame[u'开战状态'] == 1]
ini_concepts = concept_frame[u'战区'].values
top_dns_concept = []
# 将concept化为一级域名的concept
for concept in ini_concepts:
concept = common.get_top_dns_concept(concept)
top_dns_concept.append(concept)
self.concepts = top_dns_concept
# 设置不同概念对应的连板数
self.direct10_num = {}
for idx in concept_frame.index.values:
concept_name = common.get_top_dns_concept(concept_frame.loc[idx,
u'战区'])
self.direct10_num[concept_name] = int(concept_frame.loc[idx,
u'连板数'])
# 读取关注概念统计涨跌幅的时间区间
range_ratio_dict = {}
dframe = pd.read_excel(os.path.join(self.path, u'战区.xlsx'),
encoding='gbk',
sheetname=u'历史战况',
index_col=0)
dframe.fillna(0, inplace=True)
dframe.reset_index(inplace=True)
dframe[u'战区'] = dframe[u'战区'].apply(
lambda x: common.get_top_dns_concept(x))
dframe.set_index(u'战区', inplace=True)
columns = dframe.columns
for concept in dframe.index.values:
if concept == 0:
continue
tcount = 0
concept_list = []
tmp_list = []
for col in columns:
tdate = str(int(dframe.loc[concept, col]))
if tdate == '0':
if len(tmp_list) > 0:
concept_list.append(tmp_list)
break
if tcount % 2 == 0 and tcount != 0:
concept_list.append(tmp_list)
tmp_list = []
tmp_list.append(tdate)
else:
tmp_list.append(tdate)
tcount += 1
if len(concept_list) > 0:
range_ratio_dict[concept] = concept_list
# range_ratio = {
# concept_name:[[start1, end1],[start2, end2], [start3, end3], ...],
# concept_name2:[[start1, end1],[start2, end2], [start3, end3], ...],
# }
self.range_ratio_dict = range_ratio_dict
# 读取特殊关照股票
dframe = pd.read_csv(os.path.join(self.path, u'增减排除.csv'),
encoding='gbk')
dframe.fillna(0, inplace=True)
self.extra_stocks = [
common.QueryStockMap(name=x)[1]
for x in dframe[dframe.status != 'spy']['name'].values
]
rmv_frame = dframe[dframe.status == 'spy']
# rmv_frame 再加上程序自动生成的list, 有时间再弄
# rmv_list =
# rmv_dict = {
# concept_name:[stockid1, stockid2, stockid3, ...]
# }
self.rmv_dict = {}
for idx in rmv_frame.index.values:
concept = rmv_frame.loc[idx, u'concept']
concept = common.get_top_dns_concept(concept)
stockname = rmv_frame.loc[idx, u'name']
if concept not in self.rmv_dict.keys():
self.rmv_dict[concept] = [
common.QueryStockMap(name=stockname)[1]
]
else:
self.rmv_dict[concept].append(
common.QueryStockMap(name=stockname)[1])
mst_frame = dframe[dframe.status != 'spy']
# mst_dict = {
# concept_name:[stockid1, stockid2, stockid3, ...]
# }
self.mst_dict = {}
for idx in mst_frame.index.values:
concept = mst_frame.loc[idx, u'concept']
concept = common.get_top_dns_concept(concept)
stockname = mst_frame.loc[idx, u'name']
if concept not in self.mst_dict.keys():
self.mst_dict[concept] = [
common.QueryStockMap(name=stockname)[1]
]
else:
self.mst_dict[concept].append(
common.QueryStockMap(name=stockname)[1])
# 读取新增的云财经或者金融界的crawl id和对应的concept
dframe = pd.read_excel(os.path.join(self.path, u'战区.xlsx'),
encoding='gbk',
sheetname=u'侦查信号')
dframe.fillna(0, inplace=True)
ycj_dframe = dframe[dframe.source == 'YCJ']
jrj_dframe = dframe[dframe.source == 'JRJ']
# detect_dict = {
# "YCJ": {conceptname1: conceptid1, conceptname2: conceptid2},
# "JRJ": {conceptname1: conceptid1, conceptname2: conceptid2}
# }
detect_dict = {"YCJ": {}, "JRJ": {}}
for idx in ycj_dframe.index.values:
concept = ycj_dframe.loc[idx, u'概念名']
conceptid = ycj_dframe.loc[idx, u'网站id']
concept = common.get_top_dns_concept(concept)
detect_dict['YCJ'][concept] = conceptid
for idx in jrj_dframe.index.values:
concept = jrj_dframe.loc[idx, u'概念名']
conceptid = jrj_dframe.loc[idx, u'网站id']
concept = common.get_top_dns_concept(concept)
detect_dict['JRJ'][concept] = conceptid
self.detect_dict = detect_dict
mongourl = "localhost"
mongodb = pymongo.MongoClient(mongourl)
results = mongodb.stock.ZDT_by_date.find({"date": {"$gte": "20160101"}})
count = 0
dframe = DataFrame()
mysqls = common.mysqldb
global diOut_dict, backTen_dict, tenMonitor_dict
diOut_dict = {} # 开板没有回封的记录
backTen_dict = {} # 专门记录回封情况的dict
tenMonitor_dict = {} # 回封后的每a日监控
for result in results:
dates = result['date']
if "Add_newStocks" in result.keys():
stocks = result['Add_newStocks'].keys()
for stock in stocks:
dframe.loc[count, 'startDate'] = dates
dframe.loc[count, 'stockid'] = stock
dframe.loc[count, 'name'] = common.QueryStockMap(stock)[0]
tmp_result = get_stopDetail(stock, dates, mysqls)
(dframe.loc[count, 'numTen'], dframe.loc[count, 'openDate'],
dframe.loc[count, 'backTen?'], dframe.loc[count, 'backDuring'],
dframe.loc[count,
'stopDate'], dframe.loc[count,
'stopRatio']) = tmp_result
count += 1
print 'will finish'
dframe.to_csv(u"新股开板情况.csv", encoding='GBK')
def insertCache(self, stockid, concept, date, cache=1):
stockid = common.regulize_stockid(stockid)
if cache == 1:
self.mongodb.concepts.L1.update({"stockid":stockid, "concept":concept},
{"$set":{"stockid":stockid, "concept":concept, "name":common.QueryStockMap(id=stockid)[0], "updatetime": date}},upsert=True)
else:
self.mongodb.concepts.L2.update({"stockid":stockid, "concept":concept},
{"$set":{"stockid":stockid, "concept":concept, "name":common.QueryStockMap(id=stockid)[0], "updatetime": date}},upsert=True)
def save_pool_dragon(self, strcontent, concept_name, tdate):
# 分成3类， dragon_list, wind_list, remove_list
str_list = strcontent.split(";")
dragon_list = [
x.split("_")[1] for x in str_list
if (u'Dragon' in x and u'true' in x)
]
wind_list = [
x.split("_")[1] for x in str_list
if (u'Wind' in x and u'true' in x)
]
remove_list = [
x.split("_")[1] for x in str_list
if (u'Remove' in x and u'true' in x)
]
catch_dict = {}
# 读取excel
dframe = pd.read_excel(os.path.join(u'D:/Money/modeResee/彼战',
u'战区.xlsx'),
encoding='gbk',
sheetname=u'类型')
for idx in dframe.index.values:
code_str = dframe.loc[idx, u'源码']
tmp_list = [
x.split("_")[1] for x in str_list
if (code_str in x and u'true' in x)
]
catch_dict[code_str] = tmp_list
# 将remove的存到增减排除.csv中
xframe = pd.read_csv(os.path.join(u'D:/Money/modeResee/彼战',
u'增减排除.csv'),
encoding='gbk')
max_id = max(xframe.index.values) + 1
for ticker in remove_list:
xframe.loc[max_id, 'concept'] = concept_name
xframe.loc[max_id, 'name'] = common.QueryStockMap(id=ticker)[0]
xframe.loc[max_id, 'status'] = 'spy'
max_id += 1
xframe.to_csv(os.path.join(u'D:/Money/modeResee/彼战', u'增减排除.csv'),
encoding='gbk',
index=False)
# 将dragon和wind存到mongo中的concepts, dragon_pool表中
save_dict = {"date": tdate}
results = self.mongodb.concepts.dragon_pool.find({"date": tdate})
if results.count() > 0:
save_dict = results[0]
save_dict[concept_name] = {}
if len(dragon_list) > 0:
save_dict[concept_name]['dragon'] = dragon_list
if len(wind_list) > 0:
save_dict[concept_name]['wind'] = wind_list
self.mongodb.concepts.dragon_pool.update({"date": tdate},
save_dict,
upsert=True)
# 将up, down, flat存放到mongo中的pattern, trend表中
for code_str in catch_dict.keys():
tmp_list = catch_dict[code_str]
for ticker in tmp_list:
self.mongodb.pattern.trend.update(
{
"date": tdate,
"ticker": ticker
}, {
"date": tdate,
"ticker": ticker,
"trend": code_str
},
upsert=True)