Timeframe
5m
Direction
Long Only
Stoploss
-23.0%
Trailing Stop
Yes
ROI
0m: 2.6%
Interface Version
2
Startup Candles
30
Indicators
2
freqtrade/freqtrade-strategies
# --- Do not remove these libs ---
# --------------------------------
from datetime import datetime, timedelta
import talib.abstract as ta
from pandas import DataFrame
from freqtrade.persistence import Trade
from freqtrade.strategy import CategoricalParameter
from freqtrade.strategy import DecimalParameter, IntParameter
from freqtrade.strategy.interface import IStrategy
# author @tirail
ma_types = {
'SMA': ta.SMA,
'EMA': ta.EMA,
}
class SMAIP3(IStrategy):
INTERFACE_VERSION = 2
# hyperopt and paste results here
# Buy hyperspace params:
buy_params = {
"base_nb_candles_buy": 18,
"buy_trigger": "SMA",
"low_offset": 0.968,
"pair_is_bad_1_threshold": 0.130,
"pair_is_bad_2_threshold": 0.075,
}
# Sell hyperspace params:
sell_params = {
"base_nb_candles_sell": 26,
"high_offset": 0.985,
"sell_trigger": "EMA",
}
# Stoploss:
stoploss = -0.23
# ROI table:
minimal_roi = {
"0": 0.026
}
base_nb_candles_buy = IntParameter(16, 60, default=buy_params['base_nb_candles_buy'], space='buy')
base_nb_candles_sell = IntParameter(16, 60, default=sell_params['base_nb_candles_sell'], space='sell')
low_offset = DecimalParameter(0.8, 0.99, default=buy_params['low_offset'], space='buy')
high_offset = DecimalParameter(0.8, 1.1, default=sell_params['high_offset'], space='sell')
buy_trigger = CategoricalParameter(ma_types.keys(), default=buy_params['buy_trigger'], space='buy')
sell_trigger = CategoricalParameter(ma_types.keys(), default=sell_params['sell_trigger'], space='sell')
pair_is_bad_1_threshold = DecimalParameter(0.00, 0.30, default=0.200, space='buy')
pair_is_bad_2_threshold = DecimalParameter(0.00, 0.25, default=0.072, space='buy')
# Trailing stop:
trailing_stop = True
trailing_only_offset_is_reached = True
trailing_stop_positive = 0.003
trailing_stop_positive_offset = 0.018
# Optimal timeframe for the strategy
timeframe = '5m'
use_sell_signal = True
sell_profit_only = False
ignore_roi_if_buy_signal = False
process_only_new_candles = True
startup_candle_count = 30
plot_config = {
'main_plot': {
'ma_offset_buy': {'color': 'orange'},
'ma_offset_sell': {'color': 'orange'},
},
}
use_custom_stoploss = False
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
if not self.config['runmode'].value == 'hyperopt':
dataframe['ma_offset_buy'] = ma_types[self.buy_trigger.value](dataframe,
int(self.base_nb_candles_buy.value)) * self.low_offset.value
dataframe['ma_offset_sell'] = ma_types[self.sell_trigger.value](dataframe,
int(self.base_nb_candles_sell.value)) * self.high_offset.value
dataframe['pair_is_bad'] = (
(((dataframe['open'].shift(12) - dataframe['close']) / dataframe[
'close']) >= self.pair_is_bad_1_threshold.value) |
(((dataframe['open'].shift(6) - dataframe['close']) / dataframe[
'close']) >= self.pair_is_bad_2_threshold.value)).astype('int')
dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
if self.config['runmode'].value == 'hyperopt':
dataframe['ma_offset_buy'] = ma_types[self.buy_trigger.value](dataframe,
int(self.base_nb_candles_buy.value)) * self.low_offset.value
dataframe['pair_is_bad'] = (
(((dataframe['open'].shift(12) - dataframe['close']) / dataframe[
'close']) >= self.pair_is_bad_1_threshold.value) |
(((dataframe['open'].shift(6) - dataframe['close']) / dataframe[
'close']) >= self.pair_is_bad_2_threshold.value)).astype('int')
dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
dataframe.loc[
(
(dataframe['ema_50'] > dataframe['ema_200']) &
(dataframe['close'] > dataframe['ema_200']) &
(dataframe['pair_is_bad'] < 1) &
(dataframe['close'] < dataframe['ma_offset_buy']) &
(dataframe['volume'] > 0)
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
if self.config['runmode'].value == 'hyperopt':
dataframe['ma_offset_sell'] = ma_types[self.sell_trigger.value](dataframe,
int(self.base_nb_candles_sell.value)) * self.high_offset.value
dataframe.loc[
(
(dataframe['close'] > dataframe['ma_offset_sell']) &
(dataframe['volume'] > 0)
),
'sell'] = 1
return dataframe