def get_market_status(mk, type='m', start=None, end=None):
df = rl.get_sina_Market_json(mk).set_index('code')
time_s = time.time()
code_l = []
code_d = []
for code in df.index:
status, lenday, diff = pct.get_linear_model_status_LSH(code,
type=type,
start=start,
end=end)
# op, ra, status, dss = pct.get_linear_model_status(code, type=type, start=start, end=end)
if status:
# print(df.loc[code,:])
# if df.loc[code, 'percent'] > 0 and lenday > 200:
if df.loc[code, 'trade'] != 0 and lenday > 200:
df.loc[code, 'diff'] = abs(diff)
code_l.append(df.loc[code, :])
elif df.loc[code, 'trade'] != 0 and lenday > 200:
df.loc[code, 'diff'] = abs(diff)
code_d.append(df.loc[code, :])
# gold_f = pd.DataFrame(code_l).sort_values(by=['diff'], ascending=[0])
# down_f = pd.DataFrame(code_d).sort_values(by=['diff'], ascending=[0])
gold_f = pd.DataFrame(code_l)
down_f = pd.DataFrame(code_d)
print("timeSearch:%s" % (time.time() - time_s))
print("gold:%s %s :%s" % (mk, len(gold_f), gold_f.iloc[:1, 0:2]))
print("down:%s %s :%s" % (mk, len(down_f), down_f.iloc[:1, 0:2]))
return gold_f, down_f
def get_hot_count(changepercent):
allTop = pd.DataFrame()
for market in ct.SINA_Market_KEY:
df = rd.get_sina_Market_json(market, False)
# count=len(df.index)
# print df[:1]
top = df[df['percent'] > changepercent]['code']
topTen = df[df['percent'] > 9.9]['code']
crashTen = df[df['percent'] < -9.8]['code']
crash = df[df['percent'] < -changepercent]['code']
# top=df[ df['changepercent'] <6]
print (
"%s topT: %s top>%s: %s " % (
f_print(4, market), f_print(3, len(topTen)), changepercent, f_print(4, len(top)))),
url = ct.DFCFW_FUND_FLOW_URL % ct.SINA_Market_KEY_TO_DFCFW[market]
log.debug("ffurl:%s" % url)
ff = ffu.get_dfcfw_fund_flow(url)
if len(ff) > 0:
zlr = float(ff['zlr'])
zzb = float(ff['zzb'])
# zt=str(ff['time'])
print (u"crashT:%s crash<-%s:%s 流入: %0.1f亿 比: %0.1f%%" % (
f_print(4, len(crashTen)), changepercent, f_print(4, len(crash)), zlr, zzb))
else:
print (u"crashT:%s crash<-%s:%s 流入: %0.1f亿 比: %0.1f%% %s" % (
f_print(4, len(crashTen)), changepercent, f_print(4, len(crash))))
allTop = allTop.append(df, ignore_index=True)
df = allTop
count = len(df.index)
top = df[df['percent'] > changepercent]['code']
topTen = df[df['percent'] > 9.9]['code']
crashTen = df[df['percent'] < -9.8]['code']
crash = df[df['percent'] < -changepercent]['code']
print (
u"\t\tA:%s topT:%s top>%s:%s" % (
f_print(4, count), f_print(3, len(topTen)), changepercent, f_print(4, len(top)))),
ff = ffu.get_dfcfw_fund_flow(ct.DFCFW_FUND_FLOW_ALL)
if len(ff) > 0:
zlr = float(ff['zlr'])
zzb = float(ff['zzb'])
zt = str(ff['time'])
print (u"crashT:%s crash<-%s:%s 流入: %0.1f亿 占比: %0.1f%% %s" % (
f_print(3, len(crashTen)), changepercent, f_print(4, (len(crash))), zlr, zzb, zt))
else:
print (u"crashT:%s crash<-%s:%s" % (f_print(3, len(crashTen)), changepercent, f_print(4, len(crash))))
return allTop
def get_tdx_search_day_DF(market="cyb"):
time_t = time.time()
df = rl.get_sina_Market_json(market)
code_list = np.array(df.code)
log.info("code_list:%s" % len(code_list))
results = cct.to_mp_run(get_tdx_day_to_df, code_list)
log.info("get_to_mp_op:%s" % (len(results)))
# df = pd.DataFrame(results, columns=ct.TDX_Day_columns)
# df = df.set_index('code')
# print df[:1]
# print len(df),df[:1]
# print "<2015-08-25",len(df[(df.date< '2015-08-25')])
# print "06-25-->8-25'",len(df[(df.date< '2015-08-25')&(df.date > '2015-06-25')])
print "t:", time.time() - time_t
return results
def main_test():
run = 1
df = rl.get_sina_Market_json("cyb")
df = df.set_index("code")
codelist = df.index.tolist()
duration_date = 20160101
ptype = "low"
dt = duration_date
# codeList='999999'
print ""
for x in xrange(1):
if len(str(dt)) != 8:
df = get_tdx_day_to_df("999999").sort_index(ascending=False)
dt = get_duration_price_date("999999", dt=dt, ptype=ptype, df=df)
dt = df[df.index <= dt].index.values[changedays]
log.info("LastDF:%s" % dt)
else:
dt = int(dt) + changedays
# print dt
# top_now = rl.get_market_price_sina_dd_realTime(df, vol, type)
split_t = timeit.timeit(lambda: get_tdx_all_day_LastDF(codelist, dt=duration_date, ptype=ptype), number=run)
# split_t = timeit.timeit(lambda: get_tdx_day_to_df_last(codeList, 1, type, dt,ptype),number=run)
print ("df Read:", split_t)
dt = duration_date
if len(str(dt)) != 8:
dl = int(dt) + changedays
df = get_tdx_day_to_df("999999").sort_index(ascending=False)
dt = get_duration_price_date("999999", dt=dt, ptype=ptype, df=df)
dt = df[df.index <= dt].index.values[changedays]
log.info("LastDF:%s" % dt)
else:
df = get_tdx_day_to_df("999999").sort_index(ascending=False)
dl = len(get_tdx_Exp_day_to_df("999999", start=dt)) + changedays
dt = cct.day8_to_day10(dt)
# print dt,dl
# strip_tx = timeit.timeit(lambda: get_tdx_exp_low_or_high_price(codeList, dt, ptype, dl), number=run)
strip_tx = timeit.timeit(lambda: get_tdx_exp_all_LastDF(codelist, dt=duration_date, ptype=ptype), number=run)
print ("ex Read:", strip_tx)
def get_market_status(mk, type='m'):
df = rl.get_sina_Market_json(mk).set_index('code')
time_s = time.time()
code_l = []
code_d = []
for code in df.index:
status, lenday, diff = lrh.get_linear_model_status(code, type)
if status:
# print(df.loc[code,:])
# if df.loc[code, 'percent'] > 0 and lenday > 200:
if df.loc[code, 'trade'] != 0 and lenday > 200:
df.loc[code, 'diff'] = abs(diff)
code_l.append(df.loc[code, :])
elif df.loc[code, 'trade'] != 0 and lenday > 200:
df.loc[code, 'diff'] = abs(diff)
code_d.append(df.loc[code, :])
gold_f = pd.DataFrame(code_l).sort_values(by=['diff'], ascending=[0])
down_f = pd.DataFrame(code_d).sort_values(by=['diff'], ascending=[0])
print("timeSearch:%s" % (time.time() - time_s))
print("gold:%s %s :%s" % (mk, len(gold_f), gold_f.iloc[:1, 0:2]))
print("down:%s %s :%s" % (mk, len(down_f), down_f.iloc[:1, 0:2]))
return gold_f, down_f
# cut_num=10000
success = 0
top_all = pd.DataFrame()
time_s = time.time()
# delay_time = 3600
delay_time = cct.get_delay_time()
First = True
base_path = tdd.get_tdx_dir()
block_path = tdd.get_tdx_dir_blocknew() + '062.blk'
status_change = False
# all_diffpath = tdd.get_tdx_dir_blocknew() + '062.blk'
while 1:
try:
# df = sina_data.Sina().all
df = rl.get_sina_Market_json('all')
top_now = rl.get_market_price_sina_dd_realTime(df, vol, type)
# print top_now.loc['300208','name']
# top_now.to_hdf("testhdf5", 'marketDD', format='table', complevel=9)
df_count = len(df)
now_count = len(top_now)
del df
gc.collect()
radio_t = cct.get_work_time_ratio()
time_Rt = time.time()
# top_now = top_now[top_now.buy > 0]
time_d = time.time()
if time_d - time_s > delay_time:
status_change = True
time_s = time.time()
top_all = pd.DataFrame()
def get_tdx_append_now_df(code, type="f", start=None, end=None):
# start=cct.day8_to_day10(start)
# end=cct.day8_to_day10(end)
df = get_tdx_Exp_day_to_df(code, type, start, end).sort_index(ascending=True)
# print df[:1]
if not end == None:
if not end == df.index[-1]:
print (end, df.index[-1])
return df
today = cct.get_today()
if len(df) > 0:
tdx_last_day = df.index[-1]
duration = cct.get_today_duration(tdx_last_day)
log.debug("duration:%s" % duration)
else:
tdx_last_day = None
duration = 1
log.debug("tdx_last_day:%s" % tdx_last_day)
index_status = False
if code == "999999":
code = "sh"
index_status = True
elif code.startswith("399"):
index_status = True
for k in INDEX_LIST.keys():
if INDEX_LIST[k].find(code) > 0:
code = k
log.debug("duration:%s" % duration)
if duration >= 1:
ds = ts.get_hist_data(code, start=tdx_last_day, end=today)
if ds is not None and len(ds) > 1:
ds = ds[: len(ds) - 1]
ds["code"] = code
ds["vol"] = 0
ds = ds.loc[:, ["code", "open", "high", "low", "close", "vol", "volume"]]
ds.rename(columns={"volume": "amount"}, inplace=True)
ds.sort_index(ascending=True, inplace=True)
log.debug("ds:%s" % ds[:1])
df = df.append(ds)
# pd.concat([df,ds],axis=0, join='outer')
# result=pd.concat([df,ds])
if cct.get_work_time() and not index_status:
dm = rl.get_sina_Market_json("all", showtime=False).set_index("code")
# dm=dm.drop_duplicates()
log.debug("dm:%s" % dm[-1:])
dm.rename(columns={"volume": "amount", "trade": "close"}, inplace=True)
# c_name=dm.loc[code,['name']]
dm_code = dm.loc[code, ["open", "high", "low", "close", "amount"]]
log.debug("dm_code:%s" % dm_code["amount"])
dm_code["amount"] = round(float(dm_code["amount"]) / 100, 2)
dm_code["code"] = code
dm_code["vol"] = 0
# dm_code['date']=today
dm_code.name = today
df = df.append(dm_code)
# df['name']=c_name
# log.debug("c_name:%s"%(c_name))
log.debug("df[-3:]:%s" % (df[-2:]))
df["name"] = dm.loc[code, "name"]
log.debug("df:%s" % df[-2:])
return df
from JohnsonUtil import commonTips as cct
if __name__ == "__main__":
# parsehtml(downloadpage(url_s))
# StreamHandler(sys.stdout).push_application()
log = LoggerFactory.getLogger('Autodata')
# log.setLevel(LoggerFactory.DEBUG)
# handler=StderrHandler(format_string='{record.channel}: {record.message) [{record.extra[cwd]}]')
# log.level = log.debug
# error_handler = SyslogHandler('Sina-M-Log', level='ERROR')
try:
if cct.get_work_day_status() and cct.get_now_time_int() < 1505:
# tdx_data=tdd.get_tdx_all_day_DayL_DF(market='cyb', dayl=1)
print sina_data.Sina().get_stock_code_data('300006')
tdx_data = rl.get_sina_Market_json('cyb')
if len(tdx_data) < 1:
raise KeyboardInterrupt("StopTime")
else:
print tdx_data[-2:]
code = '300366'
except (KeyboardInterrupt) as e:
pass
except (IOError, EOFError, Exception) as e:
print "Error", e
#traceback.print_exc()
sleeptime = random.randint(5, 15)
print "Error2sleep:%s" % (sleeptime)
cct.sleep(sleeptime)
'''
def get_hot_countNew(changepercent,rzrq):
allTop = pd.DataFrame()
for market in ct.SINA_Market_KEY:
df = rd.get_sina_Market_json(market, False)
# count=len(df.index)
log.info("market:%s" % df[:1])
df = df.dropna()
if 'percent' in df.columns.values:
# and len(df[:20][df[:20]['percent']>0])>3:
top = df[df['percent'] > changepercent]['code']
topTen = df[df['percent'] > 9.9]['code']
crashTen = df[df['percent'] < -9.8]['code']
crash = df[df['percent'] < -changepercent]['code']
else:
log.info("market No Percent:%s" % df[:1])
top = '0'
topTen = '0'
crashTen = '0'
crash = '0'
# top=df[ df['changepercent'] <6]
print (
"%s topT: %s top>%s: %s " % (
f_print(4, market), f_print(3, len(topTen)), changepercent, f_print(4, len(top)))),
# url = ct.DFCFW_FUND_FLOW_URL % ct.SINA_Market_KEY_TO_DFCFW[market]
# log.debug("ffurl:%s" % url)
print (u"crashT:%s crash<-%s:%s" % (f_print(4, len(crashTen)), changepercent, f_print(4, len(crash)))),
ff = ffu.get_dfcfw_fund_flow(market)
if len(ff) > 0:
zlr = float(ff['zlr'])
zzb = float(ff['zzb'])
# zt=str(ff['time'])
# modfprint=lambda x:f_print(4,x) if x>0 else "-%s"%(f_print(4,str(x).replace('-','')))
# print modfprint(zlr)
# print (u"流入: %s亿 比: %s%%" % (modfprint(zlr), modfprint(zzb))),
print (u"流入: %s亿 比: %s%%" % (f_print(4, zlr), f_print(4, zzb))),
print (u"%s %s%s"%( f_print(4,ff['close']),f_print(1,'!' if ff['open'] > ff['lastp'] else '?'),f_print(2,'!!' if ff['close'] > ff['lastp'] else '??')))
allTop = allTop.append(df, ignore_index=True)
df = allTop
count = len(df.index)
top = df[df['percent'] > changepercent]['code']
topTen = df[df['percent'] > 9.9]['code']
crashTen = df[df['percent'] < -9.8]['code']
crash = df[df['percent'] < -changepercent]['code']
print (
u" \tA:%s topT:%s top>%s:%s" % (
f_print(4, count), f_print(3, len(topTen)), changepercent, f_print(4, len(top)))),
print (u"crashT:%s crash<-%s:%s" % (f_print(3, len(crashTen)), changepercent, f_print(4, len(crash)))),
ff = ffu.get_dfcfw_fund_flow(ct.DFCFW_FUND_FLOW_ALL)
if len(ff) > 0:
zlr = float(ff['zlr'])
zzb = float(ff['zzb'])
zt = str(ff['time'])
print (u"流入: %s亿 占比: %s%% %s" % (f_print(4, zlr), f_print(4, zzb), f_print(4, zt)))
ff = ffu.get_dfcfw_fund_SHSZ()
hgt = ffu.get_dfcfw_fund_HGT()
log.debug("shzs:%s" % ff)
log.debug("hgt:%s" % hgt)
# if len(ff) > 0:
# print ("\tSH: %s u:%s vo: %s sz: %s u:%s vo: %s" % (
# f_print(4, ff['scent']), f_print(4, ff['sup']), f_print(5, ff['svol']), f_print(4, ff['zcent']),
# f_print(4, ff['zup']),
# f_print(5, ff['zvol']))),
if len(ff) > 0:
print (u"\tSh: %s Vr: %s Sz: %s Vr: %s " % (
f_print(4, ff['scent']), f_print(5, ff['svol']), f_print(4, ff['zcent']), f_print(5, ff['zvol']))),
if len(hgt) > 0:
print ("Hgt: %s Ggt: %s" % (hgt['hgt'], hgt['ggt']))
if len(rzrq) > 0:
print (u"\t\t\t\t\t Sh: %s sz: %s All: %s diff: %s亿" % (
f_print(5, rzrq['sh']), f_print(5, rzrq['sz']), f_print(4, rzrq['all']), f_print(5, rzrq['diff'])))
return allTop
status = False
vol = '0'
type = '2'
# cut_num=10000
success = 0
top_all = pd.DataFrame()
time_s = time.time()
# delay_time = 3600
delay_time = cct.get_delay_time()
First = True
base_path = tdd.get_tdx_dir()
block_path = tdd.get_tdx_dir_blocknew() + '067.blk'
# all_diffpath = tdd.get_tdx_dir_blocknew() + '062.blk'
while 1:
try:
df = rl.get_sina_Market_json('cyb')
top_now = rl.get_market_price_sina_dd_realTime(df, vol, type)
# print top_now.loc['300208','name']
df_count = len(df)
now_count = len(top_now)
del df
gc.collect()
radio_t = cct.get_work_time_ratio()
time_Rt = time.time()
time_d = time.time()
if time_d - time_s > delay_time:
status_change = True
log.info("chane clear top")
time_s = time.time()
top_all = pd.DataFrame()
time_s = time.time()
delay_time = 720000
# delay_time = cct.get_delay_time()
First = True
base_path = tdd.get_tdx_dir()
block_path = tdd.get_tdx_dir_blocknew() + "061.blk"
status_change = False
lastpTDX_DF = ""
duration_date = 20151224
ptype = "high"
filter = "y"
# all_diffpath = tdd.get_tdx_dir_blocknew() + '062.blk'
while 1:
try:
# df = sina_data.Sina().all
df = rl.get_sina_Market_json("all")
top_now = rl.get_market_price_sina_dd_realTime(df, vol, type)
top_dif = top_now
# top_now.to_hdf("testhdf5", 'marketDD', format='table', complevel=9)
now_count = len(top_now)
del df
gc.collect()
radio_t = cct.get_work_time_ratio()
# top_now = top_now[top_now.buy > 0]
time_Rt = time.time()
time_d = time.time()
if time_d - time_s > delay_time:
status_change = True
time_s = time.time()
top_all = pd.DataFrame()
else:
