def mfi_calc(self, df, print_log=True):
df['volume_aver'] = (df['volume_ask'] + df['volume_bid']) / 2
for tmprd_mfi in self.mfi_tmprd_arr:
postfix = '_' + processing.digit_to_text(tmprd_mfi)
new_clmn_names = [self.mfi_base_clmn_name + postfix]
if print_log: print('new_clmn_names: ', new_clmn_names)
inp = {
'df': df,
'function': tl.MFI,
'add_columns': new_clmn_names,
'shift': 1,
'high': df['high'].values,
'low': df['low'].values,
'close': df['close'].values,
'volume': df['volume_aver'].values,
'timeperiod': tmprd_mfi
}
df = processing.features_add(**inp)
for item in self.mfi_compare_arr:
clmn_1 = self.mfi_base_clmn_name + '_' + processing.digit_to_text(
item[0])
clmn_2 = self.mfi_base_clmn_name + '_' + processing.digit_to_text(
item[1])
new_clmn_name = self.mfi_base_clmn_name + '_cmpr_' + processing.digit_to_text(item[0]) + '_' + \
processing.digit_to_text(item[1])
df = processing.clmn_compare(df=df,
clmn_1=clmn_1,
clmn_2=clmn_2,
new_clmn_name=new_clmn_name)
return df
def tema_calc(self, df, print_log=True):
for tmprd_tema in self.tema_tmprd_arr:
postfix = '_' + processing.digit_to_text(tmprd_tema)
new_clmn_names = [self.tema_base_clmn_name + postfix]
if print_log: print('new_clmn_names: ', new_clmn_names)
inp = {
'df': df,
'function': tl.TEMA,
'add_columns': new_clmn_names,
'shift': 0,
'real': df['open'].values,
'timeperiod': tmprd_tema
}
df = processing.features_add(**inp)
inp = (df, 'open', new_clmn_names[0], 'tema_open' + postfix)
df = processing.clmn_compare(*inp)
for item in self.tema_compare_arr:
clmn_1 = self.tema_base_clmn_name + '_' + processing.digit_to_text(
item[0])
clmn_2 = self.tema_base_clmn_name + '_' + processing.digit_to_text(
item[1])
new_clmn_name = self.tema_base_clmn_name + '_cmpr_' + processing.digit_to_text(item[0]) + '_' + \
processing.digit_to_text(item[1])
df = processing.clmn_compare(df=df,
clmn_1=clmn_1,
clmn_2=clmn_2,
new_clmn_name=new_clmn_name)
return df
def bb_calc(self, df, print_log=True):
for tmprd_bb in self.bb_tmprd_arr:
for d in self.bb_d_arr:
nbdevup, nbdevdn = d, d
postfix = '_' + processing.digit_to_text(
tmprd_bb) + '_' + processing.digit_to_text(d)
new_clmn_names = [
name + postfix for name in self.bb_base_clmn_names
]
if print_log: print('new_clmn_names: ', new_clmn_names)
inp = {
'df': df,
'function': tl.BBANDS,
'add_columns': new_clmn_names,
'real': df['open'].values,
'timeperiod': tmprd_bb,
'nbdevup': nbdevup,
'nbdevdn': nbdevdn,
'matype': 0
}
df = processing.features_add(**inp)
new_clmn_name = 'bb_rp' + postfix
df = self.bb_price_position(df, 'open', new_clmn_names[0],
new_clmn_names[1],
new_clmn_names[2], new_clmn_name)
# удаляем столбцы со значениями ubb, mbb, lbb. Остаётся только отн. положение цены в BB-канале.
df.drop(columns=new_clmn_names[0], inplace=True)
df.drop(columns=new_clmn_names[1], inplace=True)
df.drop(columns=new_clmn_names[2], inplace=True)
for item in self.bb_compare_arr:
clmn_1 = 'bb_rp' + '_' + processing.digit_to_text(
item[0]) + '_' + processing.digit_to_text(item[2])
clmn_2 = 'bb_rp' + '_' + processing.digit_to_text(
item[1]) + '_' + processing.digit_to_text(item[2])
new_clmn_name = 'bb_rp_cmpr_' + processing.digit_to_text(item[0]) + '_' + \
processing.digit_to_text(item[1]) + '_' + processing.digit_to_text(item[2])
df = processing.clmn_compare(df=df,
clmn_1=clmn_1,
clmn_2=clmn_2,
new_clmn_name=new_clmn_name)
return df
def macd_calc(self, df, print_log=True):
for params_macd in self.macd_params_arr:
postfix = '_' + processing.digit_to_text(params_macd[0]) \
+ '_' + processing.digit_to_text(params_macd[1]) + '_' + processing.digit_to_text(params_macd[2])
new_clmn_names = [
name + postfix for name in self.macd_base_clmn_names
]
if print_log: print('new_clmn_names: ', new_clmn_names)
inp = {
'df': df,
'function': tl.MACD,
'add_columns': new_clmn_names,
'shift': 0,
'real': df['open'].values,
'fastperiod': params_macd[0],
'slowperiod': params_macd[1],
'signalperiod': params_macd[2]
}
df = processing.features_add(**inp)
return df
def so_calc(self, df, print_log=True):
for param_so in self.so_param_arr:
postfix = '_' + processing.digit_to_text(
param_so[0]) + '_' + processing.digit_to_text(param_so[1])
new_clmn_names = [
name + postfix for name in self.so_base_clmn_names
]
if print_log: print('new_clmn_names: ', new_clmn_names)
inp = {
'df': df,
'function': tl.STOCH,
'add_columns': new_clmn_names,
'shift': 1,
'high': df['high'].values,
'low': df['low'].values,
'close': df['close'].values,
'fastk_period': param_so[0],
'slowk_period': int(param_so[0] * self.so_k_slowk_period),
'slowd_period': param_so[1]
}
df = processing.features_add(**inp)
inp = (df, new_clmn_names[0], new_clmn_names[1],
'so_k_d' + postfix)
df = processing.clmn_compare(*inp)
for item in self.so_compare_arr:
clmn_1 = 'so_k' + '_' + processing.digit_to_text(
item[0][0]) + '_' + processing.digit_to_text(item[0][1])
clmn_2 = 'so_k' + '_' + processing.digit_to_text(
item[1][0]) + '_' + processing.digit_to_text(item[1][1])
new_clmn_name = 'so_k_cmpr_' + processing.digit_to_text(item[0][0]) + '_' + \
processing.digit_to_text(item[0][1]) + '_' + processing.digit_to_text(item[1][0]) + '_' + \
processing.digit_to_text(item[1][1])
df = processing.clmn_compare(df=df,
clmn_1=clmn_1,
clmn_2=clmn_2,
new_clmn_name=new_clmn_name)
return df