Timeframe
5m
Direction
Long Only
Stoploss
-99.0%
Trailing Stop
No
ROI
N/A
Interface Version
2
Startup Candles
N/A
Indicators
1
freqtrade/freqtrade-strategies
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from freqtrade.strategy import IStrategy
from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import math
"""
https://tr.tradingview.com/script/SlCjQY3v/
translated for freqtrade: viksal1982 viktors.s@gmail.com
"""
def SuperSM(dataframe, p = 'close', len = 8, len2 = 13, len3 = 3):
df = dataframe.copy()
f = (1.414*3.14159)/len
a = math.exp(-f)
c2 = 2*a*math.cos(f)
c3 = -a*a
c1 = 1-c2-c3
def calc_ssmooth(dfr, init=0):
global calc_ssmooth_value
global calc_src_value
if init == 1:
calc_ssmooth_value = [0.0] * 2
calc_src_value = [0.0] * 2
return
calc_src_value.pop(0)
calc_src_value.append(dfr[p])
ssm = c1*(calc_src_value[-1]+calc_src_value[-2])*0.5+c2*(calc_ssmooth_value[-1])+c3*(calc_ssmooth_value[-2])
calc_ssmooth_value.pop(0)
calc_ssmooth_value.append(ssm)
return ssm
calc_ssmooth(None, init=1)
df['ssmooth'] = df.apply(calc_ssmooth, axis = 1)
f2 = (1.414*3.14159)/len2
a2 = math.exp(-f2)
c22 = 2*a2*math.cos(f2)
c32 = -a2*a2
c12 = 1-c22-c32
def calc_ssmooth2(dfr, init=0):
global calc_ssmooth_value
global calc_src_value
if init == 1:
calc_ssmooth_value = [0.0] * 2
calc_src_value = [0.0] * 2
return
calc_src_value.pop(0)
calc_src_value.append(dfr[p])
ssm = c12*(calc_src_value[-1]+calc_src_value[-2])*0.5+c22*(calc_ssmooth_value[-1])+c32*(calc_ssmooth_value[-2])
calc_ssmooth_value.pop(0)
calc_ssmooth_value.append(ssm)
return ssm
calc_ssmooth2(None, init=1)
df['ssmooth2'] = df.apply(calc_ssmooth2, axis = 1)
df['macd'] = (df['ssmooth'] - df['ssmooth2'])*10000000
f3 = (1.414*3.14159)/len3
a3 = math.exp(-f3)
c23 = 2*a3*math.cos(f3)
c33 = -a3*a3
c13 = 1-c23-c33
def calc_ssmooth3(dfr, init=0):
global calc_ssmooth_value
global calc_src_value
if init == 1:
calc_ssmooth_value = [0.0] * 2
calc_src_value = [0.0] * 2
return
calc_src_value.pop(0)
calc_src_value.append(dfr['macd'])
ssm = c13*(calc_src_value[-1]+calc_src_value[-2])*0.5+c23*(calc_ssmooth_value[-1])+c33*(calc_ssmooth_value[-2])
calc_ssmooth_value.pop(0)
calc_ssmooth_value.append(ssm)
return ssm
calc_ssmooth3(None, init=1)
df['ssmooth3'] = df.apply(calc_ssmooth3, axis = 1)
return df['ssmooth3'], df['macd']
class SuperSmoothedMACDforCRYPTO(IStrategy):
p1_buy = IntParameter(1, 100, default= 8, space='buy')
p2_buy = IntParameter(1, 100, default= 13, space='buy')
p3_buy = IntParameter(1, 100, default= 3, space='buy')
p1_sell = IntParameter(1, 100, default= 8, space='sell')
p2_sell = IntParameter(1, 100, default= 13, space='sell')
p3_sell = IntParameter(1, 100, default= 3, space='sell')
INTERFACE_VERSION = 2
stoploss = -0.99
# Trailing stoploss
trailing_stop = False
# Optimal timeframe for the strategy.
timeframe = '5m'
process_only_new_candles = False
# These values can be overridden in the "ask_strategy" section in the config.
use_sell_signal = True
sell_profit_only = False
ignore_roi_if_buy_signal = False
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 30
# Optional order type mapping.
order_types = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optional order time in force.
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc'
}
plot_config = {
# Main plot indicators (Moving averages, ...)
'main_plot': {
},
'subplots': {
# Subplots - each dict defines one additional plot
"OSC": {
'macd': {'color': 'blue'},
'ssmooth3': {'color': 'orange'},
}
}
}
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# dataframe.to_csv('aaa.csv')
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
p = 'close'
len = self.p1_buy.value
len2 = self.p2_buy.value
len3 = self.p3_buy.value
dataframe['ssmooth3_buy'], dataframe['macd_buy'] = SuperSM(dataframe,p,len,len2,len3)
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['ssmooth3_buy'], dataframe['macd_buy'])) &
(dataframe['ssmooth3_buy'] > dataframe['ssmooth3_buy'].shift(1)) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
p = 'close'
len = self.p1_sell.value
len2 = self.p2_sell.value
len3 = self.p3_sell.value
dataframe['ssmooth3_sell'], dataframe['macd_sell'] = SuperSM(dataframe,p,len,len2,len3)
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['macd_sell'], dataframe['ssmooth3_sell'])) &
(dataframe['ssmooth3_sell'] < dataframe['ssmooth3_sell'].shift(1)) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'sell'] = 1
return dataframe