def fast_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
if self.config['runmode'].value == 'hyperopt':
for len in range(30, 91):
dataframe[f'zema_{len}'] = ftt.zema(dataframe, period=len)
else:
dataframe[f'zema_{self.zema_len_buy.value}'] = ftt.zema(dataframe, period=self.zema_len_buy.value)
dataframe[f'zema_{self.zema_len_sell.value}'] = ftt.zema(dataframe, period=self.zema_len_sell.value)
dataframe[f'zema_buy'] = ftt.zema(dataframe, period=self.zema_len_buy.value) * self.low_offset.value
dataframe[f'zema_sell'] = ftt.zema(dataframe, period=self.zema_len_sell.value) * self.high_offset.value
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
length = 10
MAtype = 9
src_val = 2
multiplier = 3
dataframe['ZLEMA'] = zema(dataframe, period=length)
dataframe = PMAX(dataframe,
period=length,
multiplier=multiplier,
length=length,
MAtype=MAtype,
src=src_val)
print(dataframe.keys)
return dataframe
def populate_indicators(self, dataframe: DataFrame,
metadata: dict) -> DataFrame:
atrperiod = 10
multiplier = 3
matype = 9
malength = 10
source = 2
dataframe['ZLEMA'] = zema(dataframe, period=malength)
dataframe = PMAX(dataframe,
atrperiod=atrperiod,
multiplier=multiplier,
malength=malength,
matype=matype,
source=source)
# print(dataframe.keys)
return dataframe
def populate_indicators(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
This method can also be loaded from the strategy, if it doesn't exist in the hyperopt class.
"""
n = 0
length = 10
dframe = dataframe.copy()
default = dataframe.copy()
jobs = []
tuples = []
def thr(default, length, MAtype, multiplier, src_val, ns):
DAT = PMAX(default,
period=length,
multiplier=multiplier,
length=length,
MAtype=MAtype,
src=src_val)
xdata = ns.df
xdata[list(DAT.keys())[-1]] = DAT[list(DAT.keys())[-1]]
ns.df = xdata
mgr = multiprocessing.Manager()
ns = mgr.Namespace()
ns.df = dataframe
print(dataframe.head())
def wrapper(mpobject):
mpobject.start()
mpobject.join()
for length in range(2, 200):
for MAtype in range(1, 8):
for multiplier in range(1, 30):
for src_val in range(1, 3):
p = multiprocessing.Process(target=thr,
args=(default, length,
MAtype, multiplier,
src_val, ns))
# p.start()
# print(p.exitcode)
# thr(default, length, MAtype, multiplier, src_val, ns)
# jobs.append(p)
# print(ns.df)
# print(dataframe)
jobs.append(p)
# tuples.append(tuple([length, MAtype, multiplier, src_val, default]))
# pool = multiprocessing.Pool(processes=16)
# pool.map_async(wrapper, jobs)
# pool.close()
# pool.join()
for process in jobs:
th = threading.Thread(target=wrapper, args=(process, ))
th.start()
n = n + 1
if n == 512:
th.join()
n = 0
print(sys.getsizeof(ns.df))
dataframe = ns.df
for i in range(2, 200):
EMA = ''
EMA = 'EMA' + str(i)
dataframe[EMA] = zema(dataframe, period=length)
return dataframe