def market_index_percent(self,start):
'''
1.默认是上海指数,False,为深圳指数 2. 起始时间
return:这段时间内大盘的变化幅度
二期，考虑如何返回板块指数
'''
sh = TS.memchaced_data(ts.get_h_data,'sh','000001', index=True) #上证指数
sz = TS.memchaced_data(ts.get_h_data,'sz','399001', index=True) #深圳综合指数
self.delta_compute(sh,'上海指数','2015-12-01')
self.delta_compute(sz,'深圳指数','2015-12-01')
def market_index_percent(self, start):
'''
1.默认是上海指数,False,为深圳指数 2. 起始时间
return:这段时间内大盘的变化幅度
二期，考虑如何返回板块指数
'''
sh = TS.memchaced_data(ts.get_h_data, 'sh', '000001',
index=True) #上证指数
sz = TS.memchaced_data(ts.get_h_data, 'sz', '399001',
index=True) #深圳综合指数
self.delta_compute(sh, '上海指数', '2015-12-01')
self.delta_compute(sz, '深圳指数', '2015-12-01')
def process(self):
fo = open('shrink_code', 'w')
oversold = open('oversold', 'w')
week_shrink = open('week_shrink', 'w')
raw_data = TS.memchaced_data(ts.get_stock_basics, 'get_stock_basics')
raw_data['earn_ratio'] = raw_data['esp'] / raw_data['bvps']
for code in raw_data.index:
if code in bad_container:
continue
try:
daydata = ts.get_k_data(code, ktype='D')
ratio = judge_oversold(daydata)
if ratio < -0.45:
oversold.write("{0}\t{1}\n".format(code, ratio))
if ratio < 0.2:
weekdata = ts.get_k_data(code, ktype='W')
if judge_week_shrinkage(code, 0.02):
week_shrink.write(code + '\n')
if ratio > -0.3:
continue
if raw_data.ix[code]['earn_ratio'] < 0.05:
continue
totals = raw_data.ix[code]['totals']
close = np.array(daydata['close'])[-1]
if close < 6.5:
continue
if close * totals > 320:
continue
flag, mean = judge_shrinkage(daydata, 0.012)
if ratio > -0.3:
continue
if flag != -1:
fo.write("{0}\t{1}\t{2:.1%}\n".format(flag, code, mean))
except Exception, e:
print e
def concept_analysis(self, concept_code):
#1. 获取概念相关的股票
concept = TS.memchaced_data(ts.get_concept_classified,
'get_concept_classified')
concept.index = concept['code']
concept = concept[concept['c_name'] == concept_code]
#2
raw_data = copy.deepcopy(self.foundmental_data)
raw_data = DataFrame(raw_data, index=concept.index)
#排序
#raw_data = raw_data.sort(columns='reservedPerShare',ascending=False)
print '当前排序1. 资本公积金大 2. 股本小 ，策略待思考 3. 去掉了创业板'
#raw_data = raw_data.sort(columns='totals')
raw_data = raw_data.sort_values(by=['totals'])
raw_data = raw_data.sort_values(by=['reservedPerShare'],
ascending=False)
for code in raw_data.index:
#if code.startswith('3'):
# continue
gross_profit_rate = None
if code in self.profit_data.index:
gross_profit_rate = self.profit_data.ix[code][
'gross_profit_rate']
self.print_stock_info(code, raw_data.ix[code], gross_profit_rate)
def init_profit_data(self):
#1. 初始化财报时间
season_table = [0,3,4,4,4,1,1,1,2,2,2,3,3]
now_time = time.localtime()
season = season_table[now_time.tm_mon]
year = now_time.tm_year
if now_time.tm_mon < 5:
year -= 1
self.profit_data = TS.memchaced_data(ts.get_profit_data,'get_profit_data',year,season)
self.profit_data.index = self.profit_data['code']
print "获取毛利润率财报表时间:{0}年{1}季度".format(year, season)
def init_profit_data(self):
#1. 初始化财报时间
season_table = [0, 3, 4, 4, 4, 1, 1, 1, 2, 2, 2, 3, 3]
now_time = time.localtime()
season = season_table[now_time.tm_mon]
year = now_time.tm_year
if now_time.tm_mon < 5:
year -= 1
self.profit_data = TS.memchaced_data(ts.get_profit_data,
'get_profit_data', year, season)
self.profit_data.index = self.profit_data['code']
print "获取毛利润率财报表时间:{0}年{1}季度".format(year, season)
def concept_analysis(self,concept_code):
#1. 获取概念相关的股票
concept = TS.memchaced_data(ts.get_concept_classified,'get_concept_classified')
concept.index = concept['code']
concept = concept[concept['c_name'] == concept_code]
#2
raw_data = copy.deepcopy(self.foundmental_data)
raw_data = DataFrame(raw_data,index=concept.index)
#排序
#raw_data = raw_data.sort(columns='reservedPerShare',ascending=False)
print '当前排序1. 资本公积金大 2. 股本小 ，策略待思考 3. 去掉了创业板'
#raw_data = raw_data.sort(columns='totals')
raw_data = raw_data.sort_values(by=['totals'])
raw_data = raw_data.sort_values(by=['reservedPerShare'],ascending=False)
for code in raw_data.index:
#if code.startswith('3'):
# continue
gross_profit_rate = None
if code in self.profit_data.index:
gross_profit_rate = self.profit_data.ix[code]['gross_profit_rate']
self.print_stock_info(code,raw_data.ix[code],gross_profit_rate)
def init_foundmental_data(self):
self.foundmental_data = TS.memchaced_data(ts.get_stock_basics,
'get_stock_basics')
self.foundmental_data['earn_ratio'] = self.foundmental_data[
'esp'] / self.foundmental_data['bvps']
print "获取基本数据，并且计算净资产收益率"
def list_stock_percent(self, stock_list, start):
for stock, name in stock_list.iteritems():
stock_data = TS.memchaced_data(ts.get_h_data,stock,stock) #股票数据
if stock_data is not None:
self.delta_compute(stock_data, name, start)
def init_foundmental_data(self):
self.foundmental_data = TS.memchaced_data(ts.get_stock_basics,'get_stock_basics')
self.foundmental_data['earn_ratio'] = self.foundmental_data['esp'] / self.foundmental_data['bvps']
print "获取基本数据，并且计算净资产收益率"
def list_stock_percent(self, stock_list, start):
for stock, name in stock_list.iteritems():
stock_data = TS.memchaced_data(ts.get_h_data, stock, stock) #股票数据
if stock_data is not None:
self.delta_compute(stock_data, name, start)