def T3(ohlcv, kw):
""" :return Triple Exponential Moving Average (t3) """
params = {'timeperiod': 5, 'vfactor': 0}
timeperiod, vfactor = _get_params(kw, params, ['timeperiod', 'vfactor'])
result = talib.T3(ohlcv, timeperiod, vfactor)
return {
't3': result
}
def PMAX(dataframe, period=4, multiplier=0.1, length=4, MAtype=7, src=1):
import talib.abstract as ta
df = dataframe.copy()
mavalue = 'MA_' + str(MAtype) + '_' + str(length)
atr = 'ATR_' + str(period)
df[atr] = ta.ATR(df, timeperiod=period)
pm = 'pm_' + str(period) + '_' + str(multiplier) + '_' + str(length) + '_' + str(MAtype)
pmx = 'pmX_' + str(period) + '_' + str(multiplier) + '_' + str(length) + '_' + str(MAtype)
if src == 1:
masrc = df["close"]
elif src == 2:
masrc = (df["high"] + df["low"]) / 2
elif src == 3:
masrc = (df["high"] + df["low"] + df["close"] + df["open"]) / 4
if MAtype == 1:
df[mavalue] = ta.EMA(masrc, timeperiod=length)
elif MAtype == 2:
df[mavalue] = ta.DEMA(masrc, timeperiod=length)
elif MAtype == 3:
df[mavalue] = ta.T3(masrc, timeperiod=length)
elif MAtype == 4:
df[mavalue] = ta.SMA(masrc, timeperiod=length)
elif MAtype == 5:
df[mavalue] = VIDYA(df, length=length)
elif MAtype == 6:
df[mavalue] = ta.TEMA(masrc, timeperiod=length)
elif MAtype == 7:
df[mavalue] = ta.WMA(df, timeperiod=length)
elif MAtype == 8:
df[mavalue] = vwma(df, length)
elif MAtype == 9:
df[mavalue] = zema(df, period=length)
# Compute basic upper and lower bands
df['basic_ub'] = df[mavalue] + (multiplier * df[atr])
df['basic_lb'] = df[mavalue] - (multiplier * df[atr])
# Compute final upper and lower bands
df['final_ub'] = 0.00
df['final_lb'] = 0.00
for i in range(period, len(df)):
df['final_ub'].iat[i] = df['basic_ub'].iat[i] if (
df['basic_ub'].iat[i] < df['final_ub'].iat[i - 1]
or df[mavalue].iat[i - 1] > df['final_ub'].iat[i - 1]) else df['final_ub'].iat[i - 1]
df['final_lb'].iat[i] = df['basic_lb'].iat[i] if (
df['basic_lb'].iat[i] > df['final_lb'].iat[i - 1]
or df[mavalue].iat[i - 1] < df['final_lb'].iat[i - 1]) else df['final_lb'].iat[i - 1]
df[pm] = 0.00
for i in range(period, len(df)):
df[pm].iat[i] = (
df['final_ub'].iat[i] if (df[pm].iat[i - 1] == df['final_ub'].iat[i - 1]
and df[mavalue].iat[i] <= df['final_ub'].iat[i])
else df['final_lb'].iat[i] if (
df[pm].iat[i - 1] == df['final_ub'].iat[i - 1]
and df[mavalue].iat[i] > df['final_ub'].iat[i]) else df['final_lb'].iat[i]
if (df[pm].iat[i - 1] == df['final_lb'].iat[i - 1]
and df[mavalue].iat[i] >= df['final_lb'].iat[i]) else df['final_ub'].iat[i]
if (df[pm].iat[i - 1] == df['final_lb'].iat[i - 1]
and df[mavalue].iat[i] < df['final_lb'].iat[i]) else 0.00)
# up/down belirteçi / main logic
df[pmx] = np.where((df[pm] > 0.00), np.where((df[mavalue] < df[pm]), 'down', 'up'), np.NaN)
df.drop(['basic_ub', 'basic_lb', 'final_ub', 'final_lb'], inplace=True, axis=1)
df.fillna(0, inplace=True)
return df
def slow_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
# dataframe "1d"
"""
dataframe1d = self.dp.get_pair_dataframe(
pair=metadata['pair'], timeframe="1d")
# Pivots Points
pp = pivots_points(dataframe1d)
dataframe1d['pivot'] = pp['pivot']
dataframe1d['r1'] = pp['r1']
dataframe1d['s1'] = pp['s1']
dataframe1d['rS1'] = pp['rS1']
dataframe1d['rS2'] = pp['rS2']
dataframe1d['rS3'] = pp['rS3']
dataframe = merge_informative_pair(
dataframe, dataframe1d, self.timeframe, "1d", ffill=True)
"""
# dataframe "1h"
"""
dataframe1h = self.dp.get_pair_dataframe(
pair=metadata['pair'], timeframe="1h")
create_ichimoku(dataframe1h, conversion_line_period=20,
displacement=9, base_line_periods=88, laggin_span=88)
dataframe = merge_informative_pair(
dataframe, dataframe1h, self.timeframe, "1h", ffill=True)
"""
# dataframe "30m"
"""
dataframe30m = self.dp.get_pair_dataframe(
pair=metadata['pair'], timeframe="30m")
dataframe30m['T33'] = ta.T3(dataframe30m, timeperiod=33)
dataframe = merge_informative_pair(
dataframe, dataframe30m, self.timeframe, "30m", ffill=True)
"""
# dataframe normal "5m"
"""
create_ichimoku(dataframe, conversion_line_period=200,
displacement=88, base_line_periods=200, laggin_span=88)
dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20)
dataframe['ema88'] = ta.EMA(dataframe, timeperiod=88)
dataframe['ema440'] = ta.EMA(dataframe, timeperiod=440)
dataframe['T88'] = ta.T3(dataframe, timeperiod=88)
"""
NOTE: Start Trading
"""
# Start Trading
dataframe['trending_start'] = (
(dataframe['close'] > dataframe['senkou_b_20_1h']) &
(dataframe['close'] > dataframe['tenkan_sen_20_1h']) &
(dataframe['tenkan_sen_20_1h'] > dataframe['senkou_a_200']) &
(dataframe['tenkan_sen_20_1h'] > dataframe['T33_30m']) &
(dataframe['ema88'] > dataframe['ema440']) &
(dataframe['ema88'] > dataframe['T88']) &
(dataframe['ema20'] > dataframe['ema88']) &
(dataframe['close'] > dataframe['pivot_1d']) &
(dataframe['close'] < dataframe['rS1_1d'])
).astype('int')
dataframe['trending_over'] = (
(dataframe['ema88'] < dataframe['T88']) &
(dataframe['close'] < dataframe['tenkan_sen_20_1h'])
).astype('int')
return dataframe