ptype = 'low'
# op, ra, duration_date, days = pct.get_linear_model_status('999999', filter='y', dl=dl, ptype=ptype, days=1)
# duration_date = int(ct.duration_date * 1.4)
duration_date = ct.duration_date_l
# duration_date = 120
# duration_date = 300
du_date = duration_date
newdays = 5
end_date = None
ptype = 'low'
filter = 'y'
percent_status = 'n'
if len(str(duration_date)) < 4:
# duration_date = tdd.get_duration_price_date('999999', dl=duration_date, end=end_date, ptype='dutype')
du_date = tdd.get_duration_Index_date('999999', dl=duration_date)
if cct.get_today_duration(du_date) <= 3:
duration_date = 5
print("duaration: %s duration_date:%s" %
(cct.get_today_duration(du_date), duration_date))
log.info("duaration: %s duration_date:%s" %
(cct.get_today_duration(du_date), duration_date))
set_duration_console(du_date)
# all_diffpath = tdd.get_tdx_dir_blocknew() + '062.blk'
parser = cct.MoniterArgmain()
parserDuraton = cct.DurationArgmain()
market_sort_value = ct.Market_sort_idx['1']
market_sort_value_key = eval(market_sort_value + '_key')
while 1:
try:
# df = sina_data.Sina().all
# top_now = tdd.getSinaAlldf(market='cx', vol=ct.json_countVol, type=ct.json_countType)
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
status_change = False
lastpTDX_DF = pd.DataFrame()
# dl=30
ptype = 'low'
# op, ra, duration_date, days = pct.get_linear_model_status('999999', filter='y', dl=dl, ptype=ptype, days=1)
# duration_date = ct.duration_date
duration_date = ct.duration_date_l
du_date = duration_date
end_date = None
ptype = 'low'
filter = 'y'
percent_status = 'n'
if len(str(duration_date)) < 4:
# duration_date = tdd.get_duration_price_date('999999', dl=duration_date, end=end_date, ptype='dutype')
du_date = tdd.get_duration_Index_date('999999', dl=duration_date)
if cct.get_today_duration(du_date) <= 3:
duration_date = 5
print("duaration: %s duration_date:%s" % (cct.get_today_duration(du_date), duration_date))
log.info("duaration: %s duration_date:%s" % (cct.get_today_duration(du_date), duration_date))
set_duration_console(du_date)
# all_diffpath = tdd.get_tdx_dir_blocknew() + '062.blk'
parser = cct.MoniterArgmain()
parserDuraton = cct.DurationArgmain()
market_sort_value = ct.Market_sort_idx['1']
market_sort_value_key = eval(market_sort_value + '_key')
while 1:
try:
# df = sina_data.Sina().all
time_Rt = time.time()
top_now = tdd.getSinaAlldf(market='rzrq', vol=ct.json_countVol, vtype=ct.json_countType)
# top_now = tdd.getSinaAlldf(market='all', vol=ct.json_countVol, vtype=ct.json_countType)
def get_tdx_append_now_df_api(code, type="f", start=None, end=None):
import sina_data
# 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 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:
try:
ds = ts.get_hist_data(code, start=tdx_last_day, end=today)
# ds = ts.get_h_data('000001', start=tdx_last_day, end=today,index=index_status)
# df.index = pd.to_datetime(df.index)
except (IOError, EOFError, Exception) as e:
print "Error duration", e
if index_status:
code_t = INDEX_LIST[code][2:]
ds = ts.get_h_data(code_t, start=tdx_last_day, end=today, index=index_status)
df.index = pd.to_datetime(df.index)
if ds is not None and len(ds) > 1:
ds = ds[: len(ds) - 1]
if index_status:
code_t = INDEX_LIST[code][2:]
if code == "sh":
code = "999999"
else:
code = code_t
ds["code"] = code
else:
ds["code"] = code
# ds['vol'] = 0
ds = ds.loc[:, ["code", "open", "high", "low", "close", "volume", "amount"]]
# ds.rename(columns={'volume': 'amount'}, inplace=True)
ds.rename(columns={"volume": "vol"}, 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():
# dm = rl.get_sina_Market_json('all').set_index('code')
if index_status:
code = INDEX_LIST[code]
dm = sina_data.Sina().get_stock_code_data(code).set_index("code")
if dm is not None and not dm.empty:
# dm=dm.drop_duplicates()
log.debug("dm:%s" % dm[-1:])
dm.rename(columns={"volume": "amount", "turnover": "vol"}, inplace=True)
# c_name=dm.loc[code,['name']]
dm_code = dm.loc[:, ["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
def get_linear_model_ratio(asset):
log.info("asset:%s" % asset[-1:])
duration = asset[-1:].index.values[0]
log.debug("duration:%s" % duration)
log.debug("duration:%s" % cct.get_today_duration(duration))
# log.debug("duration:%s"%cct.get_duration_date(duration))
asset = asset.dropna()
X = np.arange(len(asset))
x = sm.add_constant(X)
model = regression.linear_model.OLS(asset, x).fit()
a = model.params[0]
b = model.params[1]
log.info("X:%s a:%0.1f b:%0.1f" % (len(asset), a, b))
Y = np.append(X, X[-1] + int(days))
log.debug("X:%s Y:%s" % (X[-1], Y[-1]))
# print ("X:%s" % (X[-1]))
Y_hat = X * b + a
# Y_hat_t = Y * b + a
# log.info("Y_hat:%s " % (Y_hat))
# log.info("asset:%s " % (asset.values))
ratio = b / a * 100
operation = 0
if Y_hat[-1] > Y_hat[1]:
log.debug("u-Y_hat[-1]:%0.1f" % (Y_hat[-1]))
log.debug("price:%0.1f" % asset.iat[-1])
log.debug("u:%0.1f" % Y_hat[1])
log.debug("price:%0.1f" % asset.iat[1])
if type.upper() == "M":
Y_Future = Y * b + a
# ratio = b/a*100
log.info("ratio: %0.1f %0.1f Y_Mid: %0.1f" % (b, ratio, Y_Future[-1]))
# diff = asset.iat[-1] - Y_hat[-1]
# if diff > 0:
# return True, len(asset), diff
# else:
# return False, len(asset), diff
elif type.upper() == "L":
i = (asset.values.T - Y_hat).argmin()
c_low = X[i] * b + a - asset.values[i]
# Y_hatlow = X * b + a - c_low
Y_Future = Y * b + a - c_low
log.info("b: %0.1f ratio:%0.1f Y_Mid: %0.1f" % (b, ratio, Y_Future[-1]))
# diff = asset.iat[-1] - Y_hatlow[-1]
# if asset.iat[-1] - Y_hatlow[-1] > 0:
# return True, len(asset), diff
# else:
# return False, len(asset), diff
elif type.upper() == "H":
i = (asset.values.T - Y_hat).argmax()
c_high = X[i] * b + a - asset.values[i]
# Y_hathigh = X * b + a - c_high
Y_Future = Y * b + a - c_high
log.info("ratio: %0.1f %0.1f Y_Mid: %0.1f" % (b, ratio, Y_Future[-1]))
diff = asset[-1] - Y_Future[-1]
# print ("as:%s Y:%s"%(asset[-1] ,Y_Future[-1]))
if diff > 0:
operation += 1
log.info("UP !!%s Y_Future: %0.1f b:%0.1f ratio:%0.1f " % (type.upper(), Y_Future[-1], b, ratio))
else:
log.info("Down %s Y_Future: %0.1f b:%0.1f ratio:%0.1f" % (type.upper(), Y_Future[-1], b, ratio))
return operation, ratio
else:
log.debug("down !!! d:%s" % Y_hat[1])
print ("down !!! d:%s" % Y_hat[1])
return 0, 0
