Timeframe
5m
Direction
Long Only
Stoploss
-50.0%
Trailing Stop
No
ROI
0m: 1.0%
Interface Version
2
Startup Candles
30
Indicators
3
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
import talib.abstract as ta
import numpy as np
import freqtrade.vendor.qtpylib.indicators as qtpylib
import datetime
from technical.util import resample_to_interval, resampled_merge
from datetime import datetime, timedelta
from freqtrade.persistence import Trade
from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter
import technical.indicators as ftt
SMA = 'SMA'
EMA = 'EMA'
buy_params = {
"base_nb_candles_buy": 20,
"ewo_high": 5.499,
"ewo_low": -19.881,
"low_offset": 0.975,
"rsi_buy": 67,
"buy_trigger": "EMA", # value loaded from strategy
"fast_ewo": 50, # value loaded from strategy
"slow_ewo": 200, # value loaded from strategy
"buy_trigger": "EMA",
}
sell_params = {
"base_nb_candles_sell": 24,
"high_offset": 1.012,
"sell_trigger": "EMA",
}
def EWO(dataframe, ema_length=5, ema2_length=35):
df = dataframe.copy()
ema1 = ta.EMA(df, timeperiod=ema_length)
ema2 = ta.EMA(df, timeperiod=ema2_length)
emadif = (ema1 - ema2) / df['close'] * 100
return emadif
class 09_SMAOffsetProtectOptV0(IStrategy):
INTERFACE_VERSION = 2
minimal_roi = {
"0": 0.01
}
stoploss = -0.5
base_nb_candles_buy = IntParameter(
5, 80, default=buy_params['base_nb_candles_buy'], space='buy', optimize=True)
base_nb_candles_sell = IntParameter(
5, 80, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True)
low_offset = DecimalParameter(
0.9, 0.99, default=buy_params['low_offset'], space='buy', optimize=True)
high_offset = DecimalParameter(
0.99, 1.1, default=sell_params['high_offset'], space='sell', optimize=True)
buy_trigger = CategoricalParameter(
[SMA, EMA], default=buy_params['buy_trigger'], space='buy', optimize=False)
sell_trigger = CategoricalParameter(
[SMA, EMA], default=sell_params['sell_trigger'], space='sell', optimize=False)
ewo_low = DecimalParameter(-20.0, -8.0,
default=buy_params['ewo_low'], space='buy', optimize=True)
ewo_high = DecimalParameter(
2.0, 12.0, default=buy_params['ewo_high'], space='buy', optimize=True)
fast_ewo = IntParameter(
10, 50, default=buy_params['fast_ewo'], space='buy', optimize=False)
slow_ewo = IntParameter(
100, 200, default=buy_params['slow_ewo'], space='buy', optimize=False)
rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=True)
trailing_stop = False
trailing_stop_positive = 0.001
trailing_stop_positive_offset = 0.01
trailing_only_offset_is_reached = True
use_sell_signal = True
sell_profit_only = True
sell_profit_offset = 0.01
ignore_roi_if_buy_signal = True
timeframe = '5m'
informative_timeframe = '1h'
use_sell_signal = True
sell_profit_only = 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 informative_pairs(self):
pairs = self.dp.current_whitelist()
informative_pairs = [(pair, self.informative_timeframe) for pair in pairs]
return informative_pairs
def get_informative_indicators(self, metadata: dict):
dataframe = self.dp.get_pair_dataframe(
pair=metadata['pair'], timeframe=self.informative_timeframe)
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
for val in self.base_nb_candles_buy.range:
dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val)
for val in self.base_nb_candles_sell.range:
dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val)
dataframe['EWO'] = EWO(dataframe, self.fast_ewo.value, self.slow_ewo.value)
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
conditions.append(
(
(dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) &
(dataframe['EWO'] > self.ewo_high.value) &
(dataframe['rsi'] < self.rsi_buy.value) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
(dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) &
(dataframe['EWO'] < self.ewo_low.value) &
(dataframe['volume'] > 0)
)
)
if conditions:
dataframe.loc[
reduce(lambda x, y: x | y, conditions),
'buy'
]=1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
conditions.append(
(
(dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) &
(dataframe['volume'] > 0)
)
)
if conditions:
dataframe.loc[
reduce(lambda x, y: x | y, conditions),
'sell'
]=1
return dataframe