elif (len(st.split()[0]) == 1 and st.split()[0].isdigit()) or st.split()[0].startswith('x'):
st_l=st.split()
st_k=st_l[0]
if st_k in list(ct.Market_sort_idx.keys()) and len(top_all) > 0:
st_key_sort=st
market_sort_value, market_sort_value_key=ct.get_market_sort_value_key(
st_key_sort, top_all=top_all)
else:
log.error("market_sort key error:%s" % (st))
cct.sleeprandom(5)
elif st.lower() == 'g' or st.lower() == 'go':
status=True
elif st.lower() == 'clear' or st.lower() == 'c':
top_all=pd.DataFrame()
cct.GlobalValues().setkey('lastbuylogtime', 1)
# cct.set_clear_logtime()
status=False
elif st.startswith('w') or st.startswith('a'):
args=cct.writeArgmain().parse_args(st.split())
codew=stf.WriteCountFilter(top_temp, writecount=args.dl)
if args.code == 'a':
cct.write_to_blocknew(block_path, codew)
# cct.write_to_blocknew(all_diffpath,codew)
else:
cct.write_to_blocknew(block_path, codew, False)
# cct.write_to_blocknew(all_diffpath,codew,False)
print("wri ok:%s" % block_path)
cct.sleeprandom(ct.duration_sleep_time / 2)
# cct.sleep(5)
elif st.lower() == 'r':
def get_hot_countNew(changepercent, rzrq, fibl=None, fibc=10):
global fibcount
INDEX_LIST_TDX = {'999999': 'sh', '399001': 'sz', '399006': 'cyb'}
# {v: k for k, v in m.items()}
# >>> zip(m.values(), m.keys())
# mi = dict(zip(m.values(), m.keys()))
if fibcount == 0 or fibcount >= fibc:
if fibcount >= fibc:
fibcount = 1
else:
fibcount += 1
if fibl is not None:
int = 0
for f in fibl:
code, op, ra, daysData, fib, st = f[0], f[1], f[2], f[3], f[
4], f[5]
# cumin_index[INDEX_LIST_TDX[code]]=cumin
int += 1
if int % 2 != 0:
print "%s op:%s ra:%s d:%s fib:%s m5:%s %s" % (
code, f_print(3, op), f_print(
5, ra), f_print(2, daysData[0]), f_print(
3, fib), f_print(4, daysData[1]), st),
else:
print "%s op:%s ra:%s d:%s fib:%s m5:%s " % (
st, f_print(3, op), f_print(
5, ra), f_print(2, daysData[0]), f_print(
3, fib), f_print(4, daysData[1]))
else:
fibcount += 1
allTop = pd.DataFrame()
indexKeys = ['sh', 'sz', 'cyb']
# ffindex = ffu.get_dfcfw_fund_flow('all')
ffindex = ffu.get_dfcfw_fund_flow2020('all')
ffall = {}
topTen_all = 0
crashTen_all = 0
ffall['zlr'] = 0
ffall['zzb'] = 0
for market in indexKeys:
# market = ct.SINA_Market_KEY()
# df = rd.get_sina_Market_json(market, False)
df = sina_data.Sina().market(market)
# count=len(df.index)
# log.info("market:%s" % df[:1])
df = df.dropna()
df = df[df.close > 0]
if 'percent' not in df.columns:
df['percent'] = map(lambda x, y: round((x - y) / y * 100, 1),
df.close.values, df.llastp.values)
if 'percent' in df.columns.values:
# and len(df[:20][df[:20]['percent']>0])>3:
# if 'code' in df.columns:
# top = df[df['percent'] > changepercent]
# topTen = df[df['percent'] > 9.9]
# crashTen = df[df['percent'] < -9.8]
# crash = df[df['percent'] < -changepercent]
# else:
top = df[df['percent'] > changepercent]
topTen = df[df['percent'] > 9.9]
# dropcode = [ x for x in topTen.index.tolist() if x not in top_Ten_Dropcxg]
# if len(dropcode) >0:
# topT_l = tdd.get_tdx_exp_all_LastDF_DL(dropcode, dt=ct.duration_date_l,newdays=10,showRunTime=False)
# if isinstance(topT_l, pd.DataFrame):
# top_Ten_Dropcxg.extend(topT_l.index.tolist())
crashTen = df[df['percent'] < -9.8]
crash = df[df['percent'] < -changepercent]
else:
log.info("market No Percent:%s" % df[:1])
top = '0'
topTen = '0'
crashTen = '0'
crash = '0'
topTen_all += len(topTen)
crashTen_all += len(crashTen)
# top=df[ df['changepercent'] <6]
# print("\033[1;31;40m您输入的帐号或密码错误！\033[0m")
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)))),
# print(u"-5:%s" %
# (f_print(4, len(crash[crash < -5])))),
ff = ffindex[market]
if len(ff) > 0:
zlr = float(ff['zlr'])
zzb = float(ff['zzb'])
ffall['zlr'] = ffall['zlr'] + zlr
ffall['zzb'] = ffall['zzb'] + 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(6, zlr, 32), f_print(4, zzb, 32))),
if 'close' in ff.keys():
if ff['close'] == 0:
_percent = 0
else:
_percent = round(
(ff['close'] - ff['lastp']) * 100 / ff['close'], 2)
else:
_percent = 0
ff['close'] = 0.0
ff['open'] = 0.0
ff['lastp'] = 0.0
# print (u" %s"%(f_print(2,cumin_index[market],31))),
print(
u"%s %s%% %s%s" %
(f_print(7, ff['close']), f_print(4, _percent, 31),
f_print(1, '!' if ff['open'] > ff['lastp'] else '?'),
f_print(2, '!!' if ff['close'] > ff['lastp'] else '??', 32)))
allTop = allTop.append(df.reset_index(), ignore_index=True)
allTop = allTop.drop_duplicates()
df = allTop
df = tdd.get_single_df_lastp_to_df(df.set_index('code'), resample='d')
count = len(df.index)
top = df[df['percent'] > changepercent]
topTen = df[df['percent'] >= 9.9]
if 'max5' in df.columns:
top_Max = (df[(df.close >= df.hmax) & (df.close >= df.max5)])
# top_low = len(df[df.low < df.min5])
top_min = (df[(df.close <= df.lmin) & (df.close <= df.min5)])
cct.GlobalValues().setkey('top_max', top_Max)
cct.GlobalValues().setkey('top_min', top_min)
else:
top_Max = []
top_low = 0
top_min = []
# topTen = str(len(topTen)) +'('+str(len(top_Ten_Dropcxg))+')' +'(H:'+str(len(top_Max))+')'
topTen = str(topTen_all) + '(' + str(len(topTen)) + ')' + \
'(H:' + str(len(top_Max)) + ')'
# print "top_Ten_Dropcxg:%s",top_Ten_Dropcxg
# crashTen = df[df['percent'] < -9.8]
crashTen = str(crashTen_all) + '(L:' + str(len(top_min)) + ')'
crash = df[df['percent'] < -changepercent]
print(u"AL:%s topT:%s top>%s:%s" %
(f_print(4, count), f_print(
3, (topTen), 31), changepercent, f_print(4, len(top), 31))),
print(
u"crashT:%s crash<-%s:%s" %
(f_print(3,
(crashTen), 32), changepercent, f_print(4, len(crash), 31))),
print(u"-5:%s" % (f_print(4, len(crash[crash.percent < -5]), 32))),
# ff = ffu.get_dfcfw_fund_flow(ct.DFCFW_FUND_FLOW_ALL)
ffall['time'] = ff['time']
ff = ffall
zzb = 0
if len(ff) > 0:
zlr = round(float(ff['zlr']), 1)
zzb = round(float(ff['zzb']) / 3, 1)
zt = str(ff['time'])
print(u"流入: %s亿 占比: %s%% %s" %
(f_print(4, zlr, 31), f_print(4, zzb, 31), f_print(4, zt)))
ff = ffu.get_dfcfw_fund_SHSZ()
# hgt = ffu.get_dfcfw_fund_HGT()
# szt = ffu.get_dfcfw_fund_HGT(url=ct.DFCFW_FUND_FLOW_SZT)
hgt = ffu.get_dfcfw_fund_HGSZ2021('bei')
szt = ffu.get_dfcfw_fund_HGSZ2021('nan')
log.debug("shzs:%s hgt:%s" % (ff, 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']))),
bigcount = rd.getconfigBigCount(count=None, write=True)
if len(ff) > 0:
print(u"\tSh: %s Vr:%s Sz: %s Vr:%s " %
(f_print(4, ff['scent']), f_print(5, ff['svol'], 31),
f_print(4, ff['zcent']), f_print(5, ff['zvol'], 31))),
print(u'B:%s-%s V:%s' %
(bigcount[0], bigcount[2], f_print(4, bigcount[1])))
else:
print(u"\tSh: \t%s Vr: \t%s Sz: \t%s Vr: \t%s ") % (0, 0, 0, 0),
print(u'B:%s-%s V:%s' %
(bigcount[0], bigcount[2], f_print(4, bigcount[1])))
if len(hgt) > 0:
print("\tSgt: %s Gst: %s Hgt: %s Ggt: %s SSVol:%s" %
(hgt['ggt'], szt['ggt'], hgt['hgt'], szt['hgt'],
f_print(10, ff['allvol'], 31)))
else:
print("\t%s Sgt: %s Gst: %s \tHgt: \t%s Ggt: " % (0, 0, 0, 0))
if len(rzrq) > 0:
if 'shrz' not in rzrq.keys() and 'szrz' not in rzrq.keys():
rzrq['shrz'] = 0
rzrq['szrz'] = 0
shpcent = round(
(rzrq['shrz'] / rzrq['sh'] * 100), 1) if rzrq['sh'] > 0 else '?'
szpcent = round(
(rzrq['szrz'] / rzrq['sz'] * 100), 1) if rzrq['sz'] > 0 else '?'
print(u"\tSh: %s rz:%s :%s%% sz: %s rz:%s :%s%% All: %s diff: %s亿" %
(f_print(5, rzrq['sh']), f_print(4, rzrq['shrz']), shpcent,
f_print(5, rzrq['sz']), f_print(4, rzrq['szrz']), szpcent,
f_print(4, rzrq['all'], 31), f_print(5, rzrq['dff'], 31)))
# print "bigcount:",bigcount
cct.set_console(width,
height,
title=[
'B:%s-%s V:%s' %
(bigcount[0], bigcount[2], bigcount[1]),
'ZL: %s' % (zlr if len(ff) > 0 else 0),
'To:%s' % len(topTen),
'D:%s' % len(crash),
'Sh: %s ' % ff['scent'] if len(ff) > 0 else '?',
'Vr:%s%% ' % ff['svol'] if len(ff) > 0 else '?',
'MR: %s' % zzb,
'ZL: %s' % (zlr if len(ff) > 0 else '?')
],
closeTerminal=True)
log.debug("set_console:bigcount[0]%s bigcount[2]:%s" %
(bigcount[0], bigcount[2]))
return allTop
def getBollFilter(df=None,
boll=ct.bollFilter,
duration=ct.PowerCountdl,
filter=True,
ma5d=True,
dl=14,
percent=True,
resample='d',
ene=False,
upper=False,
down=False,
indexdff=True,
cuminTrend=False,
top10=True):
# drop_cxg = cct.GlobalValues().getkey('dropcxg')
# if len(drop_cxg) >0:
# log.info("stf drop_cxg:%s"%(len(drop_cxg)))
# drop_cxg = list(set(drop_cxg))
# drop_t = [ co for co in drop_cxg if co in df.index]
# if len(drop_t) > 0:
# df = df.drop(drop_t,axis=0)top
# log.error("stf drop_cxg:%s"%(len(drop_t)))
# hvdu max量天数 lvdu min天数 hv max量 lv 量
# fib < 2 (max) fibl > 2 lvdu > 3 (hvdu > ? or volume < 5)? ene
time_s = time.time()
# df['upper'] = map(lambda x: round((1 + 11.0 / 100) * x, 1), df.ma10d)
# df['lower'] = map(lambda x: round((1 - 9.0 / 100) * x, 1), df.ma10d)
# df['ene'] = map(lambda x, y: round((x + y) / 2, 1), df.upper, df.lower)
'''
df = df[(df.close > df.ma20d) | (df.close > df.ene)]
if filter:
if 'boll' in df.columns:
df = df[(df.boll >= boll) | (df.buy > df.upper)]
'''
# if 'nlow' in df.columns:
# df = df[ ((df.nlow >= df.llastp) & (df.nlow >= df.low)) | ((df.nlow <= df.low) & (df.open == df.nlow))]
# if cct.get_work_time() and 'df2' in df.columns:
# if 915 < cct.get_now_time_int() < 930:
# df['cumnow'] = map(lambda x, y: 1 if x - y > 0 else 0, df.close, df.cumaxe)
# else:
# df['cumnow'] = map(lambda x, y: 1 if x - y > 0 else 0, df.high, df.cumaxe)
# df['df2'] = (df['df2'] + df['cumnow']).map(lambda x: int(x))
# df = df[df.df2 > 1]
# else:
# df = df[df.df2 > 0]
radio_t = cct.get_work_time_ratio()
df['lvolr%s' % (resample)] = df['volume']
# df['volume'] = (map(lambda x, y: round(x / y / radio_t, 1), df.nvol.values, df.lvolume.values))
if cct.get_now_time_int() > 1530:
df['volume'] = (map(
lambda x, y, z: round((x / y / radio_t)
if z < 9.9 else (x / y), 1), df.nvol.values,
df.ma5vol.values, df.percent.values))
else:
df['volume'] = (map(
lambda x, y, z: round((x / y / radio_t)
if z < 9.9 else (x / y), 1), df.nvol.values,
df.lvol.values, df.percent.values))
# df['volume'] = (map(lambda x, y,z: round((x / y / radio_t) if z < 9.9 else (x / y) , 1), df.nvol.values, df.ma5vol.values,df.percent.values))
# df['volume'] = (map(lambda x, y: round(x / y / radio_t, 1), df.nvol.values, df.lastv1d.values))
indexfibl = cct.GlobalValues().getkey('indexfibl')
sort_value = cct.GlobalValues().getkey('market_sort_value')
market_key = cct.GlobalValues().getkey('market_key')
market_value = cct.GlobalValues().getkey('market_value')
# if int(market_value) > 1 and 930 < cct.get_now_time_int():
df = compute_perd_value(df, market_value, 'perc')
df = compute_perd_value(df, market_value, 'per')
if sort_value <> 'percent' and (market_key in ['2', '3']
and market_value not in ['1']):
# print("sort_value:%s,market_key:%s ,market_value:%s" %
# (sort_value, market_key, market_value))
if market_key is not None and market_value is not None:
# if market_key == '3':
if market_key == '3':
market_value = int(market_value)
log.info("stf market_key:%s" % (market_key))
idx_k = cct.get_col_in_columns(df, 'perc%sd', market_value)
# filter percd > idx
# df= df[(df[("perc%sd" % (idx_k))] >= idx_k) | (df[("perc%sd" % (idx_k))]< -idx_k)]
# if int(market_value) > 1 and 930 < cct.get_now_time_int():
# df= compute_perd_value(df, market_value, 'perc')
# df= compute_perd_value(df, market_value, 'per')
# elif market_key == '2':
# if int(market_value) > 1 and 915 < cct.get_now_time_int():
# # df['per%d'%(market_value)] = compute_perd_value(df,market_value)
# df= compute_perd_value(df, market_value,'per')
# df= compute_perd_value(df, market_value, 'perc')
# if sort_value <> 'percent' and (market_key in ['2','3'] and market_value not in ['1'] ):
# if indexfibl is not None:
# df = df[df.fibl >= (indexfibl - 1)]
# else:
# indexfibl = 1
# if cuminTrend:
# cuminfibl = cct.GlobalValues().getkey('cuminfibl')
# if cuminfibl is None:
# cuminfibl = cct.get_index_fibl()
# if cuminfibl > 1:
# df = df[df.df2 >= cuminfibl]
# print df.loc['300107'][['close','cumine','llastp','cumaxe','cumaxc']]
# df = df[ (df.cumine == df.cumaxc) | (df.close >= df.cumine) | (df.llastp >=df.cumaxc) | (df.close >= df.cumaxe) ]
# else:
# upper = True
if not down:
if upper:
# upper_count = len( df[ ( ( df.buy > df.upper)| ( df.lastp0d > df.upper) | ( df.lastp1d > df.upper) | ( df.lastp2d > df.upper) | ( df.lastp3d > df.upper))])
# # ene_count = len(df[df.buy > df.upper])
# if upper_count > 30:
# df = df[ ( ( df.buy > df.upper)| ( df.lastp0d > df.upper) | ( df.lastp1d > df.upper) | ( df.lastp2d > df.upper) | ( df.lastp3d > df.upper))]
# else:
# df = df[ ( (( df.buy > df.ene ) ) | ( df.buy > df.upper)| ( df.lasth0d > df.upper) | ( df.lasth1d > df.upper) | ( df.lasth2d > df.upper) | ( df.lasth3d > df.upper))]
df = df[(df.low >= df.upper) | (df.close >= df.upper) |
(df.per1d >= 9.5) | (df.lastp1d == df.hmax) |
((df.lastp1d <> df.hmax) & (df.high > df.hmax))]
else:
if 935 < cct.get_now_time_int() < 1400:
df = df[((df.buy > df.ene) | (df.llastp > df.ene) |
(df.llastp >= df.max5) | (df.high >= df.max5) |
(df.max5 >= df.upper))]
else:
df = df[((df.buy > df.ene) | (df.llastp > df.ene) |
(df.llastp >= df.max5) | (df.high >= df.max5) |
(df.max5 >= df.upper))]
# df['cumins'] = round(cum_counts.index[0], 2)
# df['cumine'] = round(cumdf[-1], 2)
# df['cumax']
# df.rename(columns={'cumin': 'df2'}, inplace=True)
if df is None:
print "dataframe is None"
return None
else:
# top10 = df[ (df.percent >= 9.99) & (df.b1_v > df.a1_v)]
if resample in ['d', 'w']:
# if not top10 or (cct.get_now_time_int() < 950 or cct.get_now_time_int() > 1502):
# df.loc[((df.b1_v > df.a1_v) & (df.percent > 9)), 'percent'] = 10.1
# df.loc[((df.percent >= 9.97) & (df.percent < 10.1)), 'percent'] = 10
# else:
# df.loc[((df.b1_v > df.a1_v) & (df.percent > 9)), 'percent'] = 10.1
# df.loc[((df.percent >= 9.97) & (df.percent < 10.1)), 'percent'] = 10
# df.loc[df.per1d >= 9.99, 'per1d'] = 10
df['percent'] = df['percent'].apply(lambda x: round(x, 2))
# time_ss = time.time()
perc_col = [co for co in df.columns if co.find('perc') == 0]
per_col = [co for co in df.columns if co.find('per') == 0]
# per_col = list(set(per_col) - set(perc_col) - set(['per1d', 'perlastp']))
per_col = list(
set(per_col) - set(perc_col) - set(['per1d', 'perlastp']))
perc_col.remove('percent')
for co in perc_col:
df[co] = df[co].apply(lambda x: round(x, 2))
# da, down_zero, down_dn, percent_l = 1, 0, 0, 2
# da, down_zero, down_dn, percent_l = 1, 0, -1, 1
# df['perc_n'] = map((lambda h, lh, l, ll, c, lc: (1 if (h - lh) > 0 else down_dn) + (1 if c - lc > 0 else down_dn) + (1 if (l - ll) > 0 else down_dn) + (2 if (c - lh) > 0 else down_zero) + (2 if (l - lc) > 0 else down_zero) + (0 if (h - lc) > 0 else down_dn)), df['high'], df['lasth%sd' % da], df['low'], df['lastl%sd' % da], df['close'],df['lastp%sd' % da])
# df['perc_n'] = map((lambda c, lc: (1 if (c - lc) > 0 else down_zero) + (1 if (c - lc) / lc * 100 > 3 else down_zero) +
# (down_dn if (c - lc) / lc * 100 < -3 else down_zero)), df['close'], df['lastp%sd' % da])
# idx_rnd = random.randint(0, len(df) - 10) if len(df) > 10 else 0
# print "idx_rnd",idx_rnd,df.ix[idx_rnd].lastp0d ,df.ix[idx_rnd].close,df.ix[idx_rnd].lastp0d != df.ix[idx_rnd].close
# if not upper:
# if cct.get_work_time() or df.ix[idx_rnd].lastp0d <> df.ix[idx_rnd].close:
# nowd, per1d = 0, 1
# if 'nlow' in df.columns:
# df['perc_n'] = map(cct.func_compute_percd2, df['close'], df['llastp'], df['open'],df['lasto%sd' % nowd], df['lasth%sd' %
# (nowd)], df['lastl%sd' % (nowd)], df['nhigh'], df['nlow'],df['nvol']/radio_t,df['lastv%sd'%(nowd)], df['hmax'],df['df2'])
# else:
# df['perc_n'] = map(cct.func_compute_percd2, df['close'], df['llastp'], df['open'],df['lasto%sd' % nowd], df['lasth%sd' %
# (nowd)], df['lastl%sd' % (nowd)], df['high'], df['low'],df['nvol']/radio_t,df['lastv%sd'%(nowd)], df['hmax'],df['df2'])
# else:
# nowd, per1d = 1, 2
# # print df['per%sd' % da+1], df['lastp%sd' % (da)], df['lasth%sd' % (da)], df['lastl%sd' % (da)], df['high'], df['low']
# df['perc_n'] = map(cct.func_compute_percd2, df['close'], df['llastp'], df['open'],df['lasto%sd' % nowd], df['lasth%sd' %
# (nowd)], df['lastl%sd' % (nowd)], df['high'], df['low'], df['hmax'])
'''
idx_rnd = random.randint(0, len(df) - 10) if len(df) > 10 else 0
if not upper:
# if cct.get_work_time() or df.ix[idx_rnd].lastp1d <> df.ix[idx_rnd].close:
if cct.get_work_time() :
nowd, per1d=1, 1
if 'nlow' in df.columns:
df['perc_n']=map(cct.func_compute_percd2020, df['open'], df['close'], df['nhigh'], df['nlow'], df['lasto%sd' % nowd], df['llastp'],
df['lasth%sd' % (nowd)], df['lastl%sd' % (nowd)], df['ma5d'], df['ma10d'], df['nvol'] / radio_t, df['lastv%sd' % (nowd)])
else:
df['perc_n']=map(cct.func_compute_percd2020, df['open'], df['close'], df['high'], df['low'], df['lasto%sd' % nowd], df['llastp'],
df['lasth%sd' % (nowd)], df['lastl%sd' % (nowd)], df['ma5d'], df['ma10d'], df['nvol'] / radio_t, df['lastv%sd' % (nowd)])
else:
nowd, per1d=1, 1
# print df['per%sd' % da+1], df['lastp%sd' % (da)], df['lasth%sd' % (da)], df['lastl%sd' % (da)], df['high'], df['low']
df['perc_n']=map(cct.func_compute_percd2020, df['open'], df['close'], df['high'], df['low'], df['lasto%sd' % nowd], df['llastp'],
df['lasth%sd' % (nowd)], df['lastl%sd' % (nowd)], df['ma5d'], df['ma10d'], df['nvol'] / radio_t, df['lastv%sd' % (nowd)])
for co in perc_col:
df[co] = (df[co] + df['perc_n']).map(lambda x: int(x))
'''
# for co in per_col:
# df[co] = (df[co] + df['percent']).map(lambda x: int(x))
# print "percT:%.2f"%(time.time()-time_ss)
else:
df['percent'] = (map(
lambda x, y: round((x - y) / y * 100, 1)
if int(y) > 0 else 0, df.buy, df.lastp1d))
# df.loc['002204'].percent
# -15.4
# ipdb> df.loc['002204'].ma10d
# 3.2000000000000002
# ipdb> df.loc['002204'].ma5d
# 3.2999999999999998
if 'fib' not in df.columns:
df['fib'] = 0
# else:
co2int = ['boll', 'op', 'ratio', 'fib', 'fibl', 'df2']
# co2int.extend([co for co in df.columns.tolist()
# if co.startswith('perc') and co.endswith('d')])
co2int.extend(['top10', 'topR'])
co2int = [inx for inx in co2int if inx in df.columns]
for co in co2int:
df[co] = df[co].astype(int)
# if down:
# # if 'nlow' in df.columns:
# # if 'max5' in df.columns:
# # # df = df[(df.hmax >= df.ene) & (df.nlow >= df.low) & (df.low <> df.high) & (((df.low < df.hmax) & (df.high > df.cmean)) | (df.high > df.max5))]
# # df = df[ (((df.buy > df.ene) & (df.volume / radio_t > 1.5)) ) | (((df.lastv1d > df.lvol * 1.5) & (df.lastv1d > df.lvol * 1.5)) & (df.lastp1d > df.ene) )]
# # else:
# # df = df[ (((df.buy > df.ene) & (df.volume / radio_t > 1.5)) ) | (((df.lastv1d > df.lvol * 1.5) ) )]
# # else:
# # # df = df[ (df.max5 >= df.ene) & (df.low <> df.high) & (((df.low < df.hmax) & (df.close > df.cmean)) | (df.high > df.cmean))]
# # df = df[ (((df.buy > df.ene) & (df.volume / radio_t > 1.5)) ) | (((df.lastp1d > df.ene) ) | ((df.buy > df.max5)|(df.buy > df.ene)) | (df.max5 >= df.ene) | (df.high > df.cmean) )]
# return df
if cct.get_work_time() and 'b1_v' in df.columns and 'nvol' in df.columns:
df = df[(df.b1_v > 0) | (df.nvol > 0)]
if (cct.get_now_time_int() > 915 and cct.get_now_time_int() < 926):
df['b1_v'] = df['volume']
else:
dd = df[df.percent < 10]
dd['b1_v'] = dd['volume']
df = cct.combine_dataFrame(df, dd.loc[:, ['b1_v']])
# print "t:%0.2f"%(time.time()-time_ss)
# if int(market_value) < 3 and 930 < cct.get_now_time_int():
# if 'nlow' in df.columns:
# # df = df[((df.per1d < 3) & (df.nlow >= df.llastp)) | ((df.per1d > 5) & (df.buy > df.llastp))]
# df = df[((df.per1d < 3) & (df.nlow >= df.llastp)) | ((df.per1d > 5) & (df.buy > df.llastp))]
# else:
# df = df[((df.per1d < 3) & (df.open > df.llastp)) | ((df.per1d > 5) & (df.buy > df.llastp))]
# else:
# if 'fib' in df.columns:
# df = df[df.fib <= 5]
# log.error("perc%sd"%(market_value))
# elif market_key is not None and market_key == '2':
# if market_value in ['1', '2']:
# df['dff'] = (map(lambda x, y, z: round((x + (y if z > 20 else 3 * y)), 1), df.dff.values, df.volume.values, df.ratio.values))
# df['df2'] = (map(lambda x, y, z: w=round((x - y) / z * 100, 1), df.high.values, df.low.values, df.llastp.values))
# df['df2'] = (map(func_compute_df2, df.close.values, df.llastp.values,df.high.values, df.low.values,df.ratio.values))
# df['df2'] = (map(func_compute_df2, df.close.values, df.llastp.values,df.high.values, df.low.values))
'''
filter_up= 'ma5d'
filter_dn= 'ma10d'
if ma5d and filter_up in df.columns:
if filter_dn in df.columns:
if market_key is not None and market_key == '3':
df= df[(df.buy >= df[filter_up]) & (
df.buy * 1.02 >= df[filter_dn] * ct.changeRatio)]
else:
df= df[(df.buy >= df[filter_up])]
else:
df= df[df.buy >= df[filter_up] * ct.changeRatio]
'''
# df = df[(df.lvolume > df.lvol * 0.9) & (df.lvolume > df.lowvol * 1.1)]
# if 'nlow' in df.columns and 932 < cct.get_now_time_int() < 1030:
if ene:
pass
if 'nclose' in df.columns:
# df = df[ ((df.buy > df.ene) ) | ((df.buy > df.upper) & ( df.nclose > df.cmean))]
df = df[((df.buy > df.llastp)) |
((df['nclose'] > df['ene'] * ct.changeRatio) &
(df['percent'] > -3) & (df.volume > 1.5)) |
((df['nclose'] > df['upper'] * ct.changeRatio) &
(df['buy'] > df['upper'] * ct.changeRatioUp)) |
((df['llastp'] > df['upper']) &
(df['nclose'] > df['upper']))]
else:
df = df[((df.buy > df.llastp)) | ((df.buy > df.ene)) |
((df.buy > df.upper) & (df.close > df.cmean))]
if 'nlow' in df.columns and 930 < cct.get_now_time_int():
# for col in ['nhigh', 'nclose', 'nlow','nstd']:
# df[col] = df[col].apply(lambda x: round(x, 2))
if 'nhigh' in df.columns and 'nclose' in df.columns:
# ncloseRatio = map(lambda x, y: x * ct.changeRatio if x * ct.changeRatio > y else y, df.nclose, df.nlow)
df['ncloseRatio'] = map(lambda x: x * 0.99, df.nclose)
df['nopenRatio'] = map(lambda x: x * 0.99, df.open)
# nhighRatio = map(lambda x, y: x * ct.changeRatio if x * ct.changeRatio > y else y, df.nhigh, df.nclose)
# if cct.get_now_time_int() > ct.nlow_limit_time:
# name open low high close nclose nlow nhigh nstd ticktime
# code
# 601899 紫金矿业 4.25 4.24 4.69 4.69 4.52 4.24 4.42 0.12 15:00:00
# 603917 合力科技 27.95 27.69 31.54 31.54 30.46 27.69 30.00 0.70 15:00:00
# 300713 英可瑞 81.00 81.00 89.82 89.77 87.53 80.90 86.36 1.75 15:02:03
# 000933 神火股份 9.07 9.02 10.00 10.00 9.71 9.02 9.44 0.24 15:02:03
# 600050 中国联通 6.49 6.37 6.51 6.41 6.44 6.44 6.51 0.02 15:00:00
# 002350 北京科锐 10.17 10.17 10.75 10.23 10.31 10.17 10.75 0.07 15:02:03
# 603363 傲农生物 17.49 16.91 18.35 18.35 17.35 17.18 18.00 0.34 15:00:00
# 000868 安凯客车 8.77 8.53 8.82 8.82 8.82 8.53 8.82 0.01 15:02:03
# 002505 大康农业 2.91 2.86 2.99 2.89 2.91 2.90 2.99 0.02 15:02:03
# 601939 建设银行 7.50 7.41 7.61 7.55 7.50 7.43 7.52 0.05 15:00:00
# 600392 盛和资源 16.85 16.81 18.63 18.63 17.84 16.81 17.51 0.69 15:00:00 (11, 41)
# 603676 卫信康 17.44 16.95 17.96 17.82 17.34 17.03 17.77 0.18 15:00:00
if 'nstd' in df.columns:
df['stdv'] = map(lambda x, y: round(x / y * 100, 1),
df.nstd, df.open)
# top_temp[:40][['nstd','stdv','name','volume','dff','percent']]
# top_temp.loc['603363'][['nstd','stdv','name','volume','high','nhigh','percent','open','nclose']]
# df['stdv'] = map(lambda x, y, z: round(z / (x / y * 100), 1), df.nstd, df.open, df.volume)
# df.loc['002906'][['open','nclose','close','ncloseRatio','nopenRatio','low','nlow']]
df = df[(df.low > df.upper) |
(((df.nopenRatio <= df.nclose) & (df.low >= df.nlow) &
(df.close >= df.ncloseRatio)) |
((df.nopenRatio <= df.nclose) &
(df.close >= df.ncloseRatio) &
(df.high >= df.nhigh)))]
# else:
# df = df[(((df.low >= df.nlow) & (df.close >= ncloseRatio)) | ((df.close >= ncloseRatio) & (df.close > nhighRatio)))]
else:
df = df[((df.low >= df.nlow) &
(df.close > df.llastp * ct.changeRatio))]
# if 'nhigh' in df.columns and 'nclose' in df.columns:
# if cct.get_now_time_int() > ct.nlow_limit_time:
# df = df[(df.low >= df.nlow) & ((df.open > df.llastp * ct.changeRatio) & (df.nclose > df.llastp * ct.changeRatio)) &
# (((df.low >= df.nlow) & (df.close >= df.nclose)) | ((df.close >= df.nclose) & (df.close > df.nhigh * ct.changeRatio) & (df.high >= df.nhigh)))]
# else:
# df = df[((df.open > df.llastp * ct.changeRatio) & (df.close > df.llastp * ct.changeRatio)) &
# (((df.low >= df.nlow) & (df.close >= df.nclose)) | ((df.close >= df.nclose) & (df.close > df.nhigh * ct.changeRatio)))]
# else:
# df = df[((df.low >= df.nlow) & (df.close > df.llastp))]
if filter:
if cct.get_now_time_int() > 915 and cct.get_now_time_int() <= 1000:
df = df[((df.buy > df.llastp)) |
(df.buy > df.hmax * ct.changeRatio) |
(df.buy < df.lmin * ct.changeRatioUp)]
# df = df[(df.buy > df.hmax * ct.changeRatio) & (df.low > df.llastp)]
# df = df[(df.buy > df.cmean * ct.changeRatioUp) | (df.open > df.llastp)]
# df = df[df.buy > df.cmean]
elif cct.get_now_time_int() > 1000 and cct.get_now_time_int(
) <= 1430:
df = df[((df.buy > df.llastp)) |
(df.buy > df.hmax * ct.changeRatio) |
(df.buy < df.lmin * ct.changeRatioUp)]
# df = df[(df.buy > df.hmax * ct.changeRatio) | (df.low > df.llastp)]
# df = df[(df.buy > df.hmax * ct.changeRatio) & (df.low > df.llastp)]
# df = df[(df.buy > df.cmean * ct.changeRatioUp) | (df.open > df.llastp)]
# df = df[df.buy > df.cmean]
else:
df = df[((df.buy > df.llastp)) |
(df.buy > df.hmax * ct.changeRatio) |
(df.buy < df.lmin * ct.changeRatioUp)]
# df = df[(df.buy > df.hmax * ct.changeRatio) | (df.low > df.llastp)]
# df = df[(df.buy > df.hmax * ct.changeRatio) & (df.low > df.llastp)]
# df = df[(df.buy > df.cmean * ct.changeRatioUp) | (df.open > df.llastp)]
# df = df[df.buy > df.cmean]
if 'vstd' in df.columns:
# df = df[(df.lvol * df.volume > (df.vstd + df.lvol)) | ((df.percent > -10) & (df.hv / df.lv > 1.5))]
# dd = df.loc['000760'] dd.hv/dd.lv,dd.volume
df['hvRatio'] = map(
lambda x, y: round(x / y / cct.get_work_time_ratio(), 1),
df.hv, df.lv)
df['volRatio'] = (map(lambda x, y: round(x / y / radio_t, 1),
df.nvol.values, df.lv.values))
# df = df[(df.lvol * df.volume > (df.vstd + df.lvol)) | ((df.percent > -5) & (df.hv / df.lv > 3))]
# if 1000 < cct.get_now_time_int() < 1450:
# df = df[ (3 < df.ratio) & (df.ratio < 20)]
if 'nclose' in df.columns:
# df = df[((df.hvRatio > df.volRatio) & ((df.buy > df.lastp) & (df.nclose > df.lastp))) | ((
df = df[(((df.buy > df.lastp) & (df.nclose > df.lastp))) |
(((df.percent > -5) &
((df.vstd + df.lvol) * 1.2 < df.nvol) &
(df.nvol > (df.vstd + df.lvol))))]
else:
# df = df[((df.hvRatio > df.volRatio) & ((df.buy > df.lastp))) | ((
df = df[(((df.buy > df.lastp))) |
(((df.percent > -5) &
((df.vstd + df.lvol) * 1.2 < df.nvol) &
(df.nvol > (df.vstd + df.lvol))))]
# [dd.lvol * dd.volume > (dd.vstd + dd.lvol) | dd.lvol * dd.volume >(dd.ldvolume + dd.vstd]
# print df.loc['000801']
if percent:
pass
'''
if 'stdv' in df.columns and 926 < cct.get_now_time_int():
# df = df[((df.volume > 2 * cct.get_work_time_ratio()) & (df.percent > -3)) | ((df.stdv < 1) &
# (df.percent > 2)) | ((df.lvolume > df.lvol * 0.9) & (df.lvolume > df.lowvol * 1.1))]
df_index = tdd.getSinaIndexdf()
if isinstance(df_index, type(pd.DataFrame())):
df_index['volume'] = (map(lambda x, y: round(x / y / radio_t, 1), df_index.nvol.values, df_index.lastv1d.values))
index_vol = df_index.loc['999999'].volume
if 'percent' in df_index.columns and '999999' in df_index.index:
index_percent = df_index.loc['999999'].percent
else:
# import ipdb;ipdb.set_trace()
log.error("index_percent" is None)
index_percent = 0
index_boll = df_index.loc['999999'].boll
index_op = df_index.loc['999999'].op
# df = df[((df.percent > -3) | (df.volume > index_vol)) & (df.percent > index_percent)]
# df = df[((df.percent > -1) | (df.volume > index_vol * 1.5)) & (df.percent > index_percent) & (df.boll >= index_boll)]
df = df[((df.percent > -1) | (df.volume > index_vol * 1.5)) & (df.percent > index_percent)]
else:
print ("df_index is Series",df_index.T)
else:
df = df[((df.volume > 1.2 * cct.get_work_time_ratio()) & (df.percent > -3))]
'''
# df = df[((df['buy'] >= df['ene'])) | ((df['buy'] < df['ene']) & (df['low'] > df['lower'])) | ((df['buy'] > df['upper']) & (df['low'] > df['upper']))]
# df = df[(( df['ene'] * ct.changeRatio < df['open']) & (df['buy'] > df['ene'] * ct.changeRatioUp)) | ((df['low'] > df['upper']) & (df['close'] > df['ene']))]
# df = df[(( df['ene'] < df['open']) & (df['buy'] < df['ene'] * ct.changeRatioUp))]
# df = df[(df.per1d > 9) | (df.per2d > 4) | (df.per3d > 6)]
# df = df[(df.per1d > 0) | (df.per2d > 4) | (df.per3d > 6)]
# time_ss=time.time()
# codel = df.index.tolist()
# dm = tdd.get_sina_data_df(codel)
# results = cct.to_mp_run_async(getab.Get_BBANDS, codel,'d',5,duration,dm)
# bolldf = pd.DataFrame(results, columns=['code','boll'])
# bolldf = bolldf.set_index('code')
# df = cct.combine_dataFrame(df, bolldf)
# print "bollt:%0.2f"%(time.time()-time_ss),
per3d_l = 2
percent_l = -1
op_l = 3
# if 'boll' in df.columns:
# if 915 < cct.get_now_time_int() < 950:
# # df = df[(df.boll >= boll) | ((df.percent > percent_l) & (df.op > 4)) | ((df.percent > percent_l) & (df.per3d > per3d_l))]
# # df = df[((df.percent > percent_l) & (df.op > 4)) | ((df.percent > percent_l) & (df.per3d > per3d_l))]
# pass
# elif 950 < cct.get_now_time_int() < 1501:
# # df = df[(df.boll >= boll) | ((df.low <> 0) & (df.open == df.low) & (((df.percent > percent_l) & (df.op > op_l)) | ((df.percent > percent_l) & (df.per3d > per3d_l))))]
# # df = df[(df.boll >= boll) & ((df.low <> 0) & (df.open >= df.low *
# # ct.changeRatio) & (((df.percent > percent_l)) | ((df.percent >
# # percent_l) & (df.per3d > per3d_l))))]
# df = df[(df.boll >= boll)]
# # else:
# df = df[(df.boll >= boll) | ((df.low <> 0) & (df.open == df.low) &
# (((df.percent > percent_l) & (df.op > op_l)) | ((df.percent > percent_l)
# & (df.per3d > per3d_l))))]
# if 945 < cct.get_now_time_int() and market_key is None:
# df.loc[df.percent >= 9.94, 'percent'] = -10
# else:
# # df['upper'] = map(lambda x: round((1 + 11.0 / 100) * x, 1), df.ma10d)
# # df['lower'] = map(lambda x: round((1 - 9.0 / 100) * x, 1), df.ma10d)
# # df['ene'] = map(lambda x, y: round((x + y) / 2, 1), df.upper, df.lower)
# # df = df[((df['buy'] >= df['ene'])) | ((df['buy'] < df['ene']) & (df['low'] > df['lower'])) | ((df['buy'] > df['upper']) & (df['low'] > df['upper']))]
# # df = df[(( df['ene'] * ct.changeRatio < df['open']) & (df['buy'] > df['ene'] * ct.changeRatioUp)) | ((df['low'] > df['upper']) & (df['close'] > df['ene']))]
# if 'nclose' in df.columns:
# # df = df[((df['nclose'] > df['ene'] * ct.changeRatio) & (df['percent'] > -3) & (df.volume > 1.5)) |
# # ((df['nclose'] > df['upper'] * ct.changeRatio) & (df['buy'] > df['upper'] * ct.changeRatioUp)) | ((df['llastp'] > df['upper']) & (df['nclose'] > df['upper']))]
# df = df[(df.nclose > df.cmean)]
# else:
# df = df[(df.buy > df.cmean)]
print("bo:%0.1f" % (time.time() - time_s)),
df['ral'] = (map(lambda x, y: round((x + y), 1), df.percent, df.ral))
df['ra'] = (map(lambda x, y: round((x + y), 1), df.percent, df.ra))
return df
else:
log.debug('into clean_duration:%s' % (int_time))
if (cct.get_now_time_int() > 1502
and cct.get_now_time_int() < 2400):
while 1:
if cct.get_now_time_int(
) > 1502 and cct.get_now_time_int() < 1510:
print ".",
cct.sleep(60)
elif cct.get_now_time_int() < 2400:
print ".",
print "write dm to file"
if cct.get_work_day_status():
tdd.Write_market_all_day_mp('all')
top_temp = cct.GlobalValues().getkey('top_max')
codew = stf.WriteCountFilter(top_temp,
writecount='all')
cct.write_to_blocknew(block_path,
codew,
append=False,
doubleFile=False)
break
else:
print "."
raise KeyboardInterrupt("Stop Time")
# st = cct.cct_raw_input("status:[go(g),clear(c),quit(q,e)]:")
# if len(st) == 0:
# status = False
# elif st.lower() == 'g' or st.lower() == 'go':