def diff_svg(stock_a,stock_b):
bk='主板'
# init the figure
pylab.figure(figsize=(9,6))
# get the dates, prepare the ticks, get today, and the mean price
dates,ps,_=zip(*list(x.split() for x in open(bk+'_mean.txt')))
both_ticks=haitai.graph.gen_ticks(dates)
today=dates[0]
ps=numpy.array(list(map(float,ps)))
# sets of stocks
th = set(haitai.get_symbol_list(bk))
outfile='output/tmp.svg'
# data of the stock_a
rtn = netease_daily.load_fresh_stock(stock_a, dates)
if rtn is None : return
name, price, volum = rtn
min_days=len(price)
print(stock_a, name, today,end='\r')
plot(price,outfile,
background=ps[:min_days],
title="%s (%s) %s"%(name, stock_a, today),
both_ticks = both_ticks
)
return (open(outfile).read())
def diff_svg(stock_a, stock_b):
bk = '主板'
# init the figure
pylab.figure(figsize=(9, 6))
# get the dates, prepare the ticks, get today, and the mean price
dates, ps, _ = zip(*list(x.split() for x in open(bk + '_mean.txt')))
both_ticks = haitai.graph.gen_ticks(dates)
today = dates[0]
ps = numpy.array(list(map(float, ps)))
# sets of stocks
th = set(haitai.get_symbol_list(bk))
outfile = 'output/tmp.svg'
# data of the stock_a
rtn = netease_daily.load_fresh_stock(stock_a, dates)
if rtn is None: return
name, price, volum = rtn
min_days = len(price)
print(stock_a, name, today, end='\r')
plot(price,
outfile,
background=ps[:min_days],
title="%s (%s) %s" % (name, stock_a, today),
both_ticks=both_ticks)
return (open(outfile).read())
def diff_all():
bk='主板'
# init the figure
pylab.figure(figsize=(9,6))
# get the dates, prepare the ticks, get today, and the mean price
dates,ps,_=zip(*list(x.split() for x in open(bk+'_mean.txt')))
both_ticks=haitai.graph.gen_ticks(dates)
today=dates[0]
ps=numpy.array(list(map(float,ps)))
# sets of stocks
th = set(haitai.get_symbol_list(bk))
for f in sorted(th) :
outfile='output/figs/'+f+'.svg'
# if the svg file is new, do nothing
if os.path.exists(outfile) :
mtime=os.path.getmtime(outfile)
mtime=datetime.date.fromtimestamp(mtime)
if today < str(mtime) :
continue
rtn = netease_daily.load_fresh_stock(f, dates)
if rtn is None : continue
name, price, volum = rtn
min_days=len(price)
print(f,name, today,end='\r')
plot(price,outfile,
background=ps[:min_days],
title="%s (%s) %s"%(name,f, today),
both_ticks = both_ticks
)
def diff_all():
bk = '主板'
# init the figure
pylab.figure(figsize=(9, 6))
# get the dates, prepare the ticks, get today, and the mean price
dates, ps, _ = zip(*list(x.split() for x in open(bk + '_mean.txt')))
both_ticks = haitai.graph.gen_ticks(dates)
today = dates[0]
ps = numpy.array(list(map(float, ps)))
# sets of stocks
th = set(haitai.get_symbol_list(bk))
for f in sorted(th):
outfile = 'output/figs/' + f + '.svg'
# if the svg file is new, do nothing
if os.path.exists(outfile):
mtime = os.path.getmtime(outfile)
mtime = datetime.date.fromtimestamp(mtime)
if today < str(mtime):
continue
rtn = netease_daily.load_fresh_stock(f, dates)
if rtn is None: continue
name, price, volum = rtn
min_days = len(price)
print(f, name, today, end='\r')
plot(price,
outfile,
background=ps[:min_days],
title="%s (%s) %s" % (name, f, today),
both_ticks=both_ticks)
import haitai.graph
import haitai.common
import haitai
if __name__ == '__main__':
ndays = 3000
bk = '全部'
bk = '中小'
bk = '主板'
# dates
dates = haitai.common.recent_n_days(ndays)
# stocks
th = set(haitai.get_symbol_list(bk))
d = 'data/163_daily'
fs = os.listdir(d)
fs = [f for f in fs if f != '000300.ss']
print(fs)
#th = set(list(th)[:100]) # for debug only
# get data
pss, vols = haitai.common.load_stock_set(fs, ndays, dates)
# compute
p = sum(pss)
print(p)
p = p / p[0]
v = haitai.common.mean_with_nan(vols)
# one diff svg
#svg=get_svg(stock_id)
if '-' in stock_id:
stock_a, _, stock_b = stock_id.partition('-')
else:
stock_a = stock_id
stock_b = None
svg = haitai.diff.diff_svg(stock_a, stock_b)
if svg is None: return "no such stock"
return "<svg>%s</svg>" % (svg)
if __name__ == '__main__':
l = haitai.get_symbol_list()
l = sorted(l)
l = [x.split('.')[0] for x in l]
stock_list = l
stock_order = {k: v for v, k in enumerate(stock_list)}
cherrypy.config.update("server.config")
cherrypy.tree.mount(
Figs(),
'/figs',
{'/': {
'request.dispatch': cherrypy.dispatch.MethodDispatcher()
}},
)
cherrypy.engine.start()
bss.append(bs)
return cal_mean(bss), list(reversed(cal_std(bss)))
if __name__ == '__main__':
ndays=3000
bk='全部'
bk='中小'
bk='主板'
# dates
dates=haitai.common.recent_n_days(ndays)
# stocks
th = set(haitai.get_symbol_list(bk))
d = 'data/163_daily'
fs = os.listdir(d)
fs = [f for f in fs if f != '000300.ss']
print(fs)
#th = set(list(th)[:100]) # for debug only
# get data
pss, vols = haitai.common.load_stock_set(fs,ndays,dates)
# compute
p=sum(pss)
print(p)
p=p/p[0]
v=haitai.common.mean_with_nan(vols)