Timeframe
5m
Direction
Long Only
Stoploss
-15.0%
Trailing Stop
No
ROI
0m: 1000.0%, 40m: 8.6%, 99m: 3.6%
Interface Version
2
Startup Candles
200
Indicators
4
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
# --- Do not remove these libs ---
# --- Do not remove these libs ---
from logging import FATAL
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
# Buy hyperspace params:
buy_params = {
"low_offset": 0.981,
"base_nb_candles_buy": 8, # value loaded from strategy
"ewo_high": 3.553, # value loaded from strategy
"ewo_high_2": -5.585, # value loaded from strategy
"ewo_low": -14.378, # value loaded from strategy
"lookback_candles": 32, # value loaded from strategy
"low_offset_2": 0.942, # value loaded from strategy
"profit_threshold": 1.037, # value loaded from strategy
"rsi_buy": 78, # value loaded from strategy
"rsi_fast_buy": 37, # value loaded from strategy
}
# Sell hyperspace params:
sell_params = {
"sell_custom_dec_profit_1": 0.002,
"sell_custom_dec_profit_2": 0.162,
"sell_custom_profit_0": 0.041,
"sell_custom_profit_1": 0.02,
"sell_custom_profit_2": 0.084,
"sell_custom_profit_3": 0.176,
"sell_custom_profit_4": 0.511,
"sell_custom_profit_under_rel_1": 0.015,
"sell_custom_profit_under_rsi_diff_1": 1.337,
"sell_custom_rsi_0": 31.342,
"sell_custom_rsi_1": 36.57,
"sell_custom_rsi_2": 49.69,
"sell_custom_rsi_3": 47.78,
"sell_custom_rsi_4": 42.64,
"sell_custom_stoploss_under_rel_1": 0.015,
"sell_custom_stoploss_under_rsi_diff_1": 12.341,
"sell_custom_under_profit_1": 0.081,
"sell_custom_under_profit_2": 0.078,
"sell_custom_under_profit_3": 0.045,
"sell_custom_under_rsi_1": 45.8,
"sell_custom_under_rsi_2": 62.8,
"sell_custom_under_rsi_3": 51.6,
"sell_trail_down_1": 0.114,
"sell_trail_down_2": 0.146,
"sell_trail_down_3": 0.015,
"sell_trail_profit_max_1": 0.23,
"sell_trail_profit_max_2": 0.22,
"sell_trail_profit_max_3": 0.11,
"sell_trail_profit_min_1": 0.249,
"sell_trail_profit_min_2": 0.088,
"sell_trail_profit_min_3": 0.021,
"base_nb_candles_sell": 16, # value loaded from strategy
"high_offset": 1.097, # value loaded from strategy
"high_offset_2": 1.472, # value loaded from strategy
}
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['low'] * 100
return emadif
class tesla(IStrategy):
INTERFACE_VERSION = 2
# ROI table:
minimal_roi = {
# "0": 0.283,
# "40": 0.086,
# "99": 0.036,
"0": 10,
}
# Stoploss:
stoploss = -0.15
# SMAOffset
base_nb_candles_buy = IntParameter(
2, 20, default=buy_params['base_nb_candles_buy'], space='buy', optimize=False)
base_nb_candles_sell = IntParameter(
2, 25, default=sell_params['base_nb_candles_sell'], space='sell', optimize=False)
low_offset = DecimalParameter(
0.9, 0.99, default=buy_params['low_offset'], space='buy', optimize=False)
low_offset_2 = DecimalParameter(
0.9, 0.99, default=buy_params['low_offset_2'], space='buy', optimize=False)
high_offset = DecimalParameter(
0.95, 1.1, default=sell_params['high_offset'], space='sell', optimize=False)
high_offset_2 = DecimalParameter(
0.99, 1.5, default=sell_params['high_offset_2'], space='sell', optimize=False)
sell_custom_profit_0 = DecimalParameter(0.001, 0.1, default=sell_params['sell_custom_profit_0'], space='sell', decimals=3, optimize=True, load=True)
sell_custom_rsi_0 = DecimalParameter(30.0, 40.0, default=sell_params['sell_custom_rsi_0'], space='sell', decimals=3, optimize=True, load=True)
sell_custom_profit_1 = DecimalParameter(0.005, 0.1, default=sell_params['sell_custom_profit_1'], space='sell', decimals=3, optimize=True, load=True)
sell_custom_rsi_1 = DecimalParameter(30.0, 50.0, default=sell_params['sell_custom_rsi_1'], space='sell', decimals=2, optimize=True, load=True)
sell_custom_profit_2 = DecimalParameter(0.007, 0.1, default=sell_params['sell_custom_profit_2'], space='sell', decimals=3, optimize=True, load=True)
sell_custom_rsi_2 = DecimalParameter(34.0, 50.0, default=sell_params['sell_custom_rsi_2'], space='sell', decimals=2, optimize=True, load=True)
sell_custom_profit_3 = DecimalParameter(0.009, 0.30, default=sell_params['sell_custom_profit_3'], space='sell', decimals=3, optimize=True, load=True)
sell_custom_rsi_3 = DecimalParameter(38.0, 55.0, default=sell_params['sell_custom_rsi_3'], space='sell', decimals=2, optimize=True, load=True)
sell_custom_profit_4 = DecimalParameter(0.01, 0.6, default=sell_params['sell_custom_profit_4'], space='sell', decimals=3, optimize=True, load=True)
sell_custom_rsi_4 = DecimalParameter(40.0, 58.0, default=sell_params['sell_custom_under_profit_1'], space='sell', decimals=2, optimize=True, load=True)
sell_custom_under_profit_1 = DecimalParameter(0.001, 0.10, default=sell_params['sell_custom_under_profit_1'], space='sell', decimals=3, optimize=True, load=True)
sell_custom_under_rsi_1 = DecimalParameter(36.0, 60.0, default=sell_params['sell_custom_under_rsi_1'], space='sell', decimals=1, optimize=True, load=True)
sell_custom_under_profit_2 = DecimalParameter(0.001, 0.10, default=sell_params['sell_custom_under_profit_2'], space='sell', decimals=3, optimize=True, load=True)
sell_custom_under_rsi_2 = DecimalParameter(46.0, 66.0, default=sell_params['sell_custom_under_rsi_2'], space='sell', decimals=1, optimize=True, load=True)
sell_custom_under_profit_3 = DecimalParameter(0.001, 0.10, default=sell_params['sell_custom_under_profit_3'], space='sell', decimals=3, optimize=True, load=True)
sell_custom_under_rsi_3 = DecimalParameter(50.0, 68.0, default=sell_params['sell_custom_under_rsi_3'], space='sell', decimals=1, optimize=True, load=True)
sell_custom_dec_profit_1 = DecimalParameter(0.001, 0.10, default=sell_params['sell_custom_dec_profit_1'], space='sell', decimals=3, optimize=True, load=True)
sell_custom_dec_profit_2 = DecimalParameter(0.05, 0.2, default=sell_params['sell_custom_dec_profit_2'], space='sell', decimals=3, optimize=True, load=True)
sell_trail_profit_min_1 = DecimalParameter(0.001, 0.25, default=sell_params['sell_trail_profit_min_1'], space='sell', decimals=3, optimize=True, load=True)
sell_trail_profit_max_1 = DecimalParameter(0.03, 0.5, default=sell_params['sell_trail_profit_max_1'], space='sell', decimals=2, optimize=True, load=True)
sell_trail_down_1 = DecimalParameter(0.04, 0.2, default=sell_params['sell_trail_down_1'], space='sell', decimals=3, optimize=True, load=True)
sell_trail_profit_min_2 = DecimalParameter(0.004, 0.1, default=sell_params['sell_trail_profit_min_2'], space='sell', decimals=3, optimize=True, load=True)
sell_trail_profit_max_2 = DecimalParameter(0.08, 0.25, default=sell_params['sell_trail_profit_max_2'], space='sell', decimals=2, optimize=True, load=True)
sell_trail_down_2 = DecimalParameter(0.04, 0.2, default=sell_params['sell_trail_down_2'], space='sell', decimals=3, optimize=True, load=True)
sell_trail_profit_min_3 = DecimalParameter(0.006, 0.1, default=sell_params['sell_trail_profit_min_3'], space='sell', decimals=3, optimize=True, load=True)
sell_trail_profit_max_3 = DecimalParameter(0.08, 0.16, default=sell_params['sell_trail_profit_max_3'], space='sell', decimals=2, optimize=True, load=True)
sell_trail_down_3 = DecimalParameter(0.01, 0.04, default=sell_params['sell_trail_down_3'], space='sell', decimals=3, optimize=True, load=True)
sell_custom_profit_under_rel_1 = DecimalParameter(0.01, 0.04, default=sell_params['sell_custom_profit_under_rel_1'], space='sell', optimize=True, load=True)
sell_custom_profit_under_rsi_diff_1 = DecimalParameter(0.0, 20.0, default=sell_params['sell_custom_profit_under_rsi_diff_1'], space='sell', optimize=True, load=True)
sell_custom_stoploss_under_rel_1 = DecimalParameter(0.001, 0.02, default=sell_params['sell_custom_stoploss_under_rel_1'], space='sell', optimize=True, load=True)
sell_custom_stoploss_under_rsi_diff_1 = DecimalParameter(0.0, 20.0, default=sell_params['sell_custom_stoploss_under_rsi_diff_1'], space='sell', optimize=True, load=True)
# Protection
fast_ewo = 50
slow_ewo = 200
lookback_candles = IntParameter(
1, 36, default=buy_params['lookback_candles'], space='buy', optimize=False)
profit_threshold = DecimalParameter(0.99, 1.05,
default=buy_params['profit_threshold'], space='buy', optimize=False)
ewo_low = DecimalParameter(-20.0, -8.0,
default=buy_params['ewo_low'], space='buy', optimize=False)
ewo_high = DecimalParameter(
2.0, 12.0, default=buy_params['ewo_high'], space='buy', optimize=False)
ewo_high_2 = DecimalParameter(
-6.0, 12.0, default=buy_params['ewo_high_2'], space='buy', optimize=False)
rsi_buy = IntParameter(10, 80, default=buy_params['rsi_buy'], space='buy', optimize=False)
rsi_fast_buy = IntParameter(
10, 50, default=buy_params['rsi_fast_buy'], space='buy', optimize=False)
# Trailing stop:
trailing_stop = False
trailing_stop_positive = 0.001
trailing_stop_positive_offset = 0.016
trailing_only_offset_is_reached = True
# Sell signal
use_sell_signal = True
sell_profit_only = False
sell_profit_offset = 0.01
ignore_roi_if_buy_signal = False
# Optional order time in force.
order_time_in_force = {
'buy': 'gtc',
'sell': 'ioc'
}
# Optimal timeframe for the strategy
timeframe = '5m'
inf_15m = '15m'
inf_1h = '1h'
process_only_new_candles = True
startup_candle_count = 200
use_custom_stoploss = True
plot_config = {
'main_plot': {
'ma_buy': {'color': 'orange'},
'ma_sell': {'color': 'orange'},
},
'subplots': {
'rsi': {
'rsi': {'color': 'orange'},
'rsi_fast': {'color': 'red'},
'rsi_slow': {'color': 'green'},
},
'ewo': {
'EWO': {'color': 'orange'}
},
}
}
slippage_protection = {
'retries': 3,
'max_slippage': -0.02
}
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
if (current_profit > 0.3):
return 0.05
elif (current_profit > 0.1):
return 0.03
elif (current_profit > 0.06):
return 0.02
elif (current_profit > 0.04):
return 0.01
elif (current_profit > 0.025):
return 0.005
elif (current_profit > 0.018):
return 0.005
return 0.15
def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
current_profit: float, **kwargs):
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
max_profit = ((trade.max_rate - trade.open_rate) / trade.open_rate)
if(current_time < trade.open_date_utc + timedelta(minutes=360)) : return None
if (last_candle is not None):
if (current_profit > self.sell_custom_profit_4.value) & (last_candle['rsi'] < self.sell_custom_rsi_4.value):
return 'signal_profit_4'
elif (current_profit > self.sell_custom_profit_3.value) & (last_candle['rsi'] < self.sell_custom_rsi_3.value):
return 'signal_profit_3'
elif (current_profit > self.sell_custom_profit_2.value) & (last_candle['rsi'] < self.sell_custom_rsi_2.value):
return 'signal_profit_2'
elif (current_profit > self.sell_custom_profit_1.value) & (last_candle['rsi'] < self.sell_custom_rsi_1.value):
return 'signal_profit_1'
elif (current_profit > self.sell_custom_profit_0.value) & (last_candle['rsi'] < self.sell_custom_rsi_0.value):
return 'signal_profit_0'
elif (current_profit > self.sell_custom_under_profit_1.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_1.value) & (last_candle['close'] < last_candle['ema_200']):
return 'signal_profit_u_1'
elif (current_profit > self.sell_custom_under_profit_2.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_2.value) & (last_candle['close'] < last_candle['ema_200']):
return 'signal_profit_u_2'
elif (current_profit > self.sell_custom_under_profit_3.value) & (last_candle['rsi'] < self.sell_custom_under_rsi_3.value) & (last_candle['close'] < last_candle['ema_200']):
return 'signal_profit_u_3'
elif (current_profit > self.sell_custom_dec_profit_1.value) & (last_candle['sma_200_dec']):
return 'signal_profit_d_1'
elif (current_profit > self.sell_custom_dec_profit_2.value) & (last_candle['close'] < last_candle['ema_100']):
return 'signal_profit_d_2'
elif (current_profit > self.sell_trail_profit_min_1.value) & (current_profit < self.sell_trail_profit_max_1.value) & (max_profit > (current_profit + self.sell_trail_down_1.value)):
return 'signal_profit_t_1'
elif (current_profit > self.sell_trail_profit_min_2.value) & (current_profit < self.sell_trail_profit_max_2.value) & (max_profit > (current_profit + self.sell_trail_down_2.value)):
return 'signal_profit_t_2'
elif (last_candle['close'] < last_candle['ema_200']) & (current_profit > self.sell_trail_profit_min_3.value) & (current_profit < self.sell_trail_profit_max_3.value) & (max_profit > (current_profit + self.sell_trail_down_3.value)):
return 'signal_profit_u_t_1'
return None
def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
rate: float, time_in_force: str, sell_reason: str,
current_time: datetime, **kwargs) -> bool:
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1]
if (last_candle is not None):
if (sell_reason in ['sell_signal']):
if (last_candle['hma_50']*1.149 > last_candle['ema_100']) and (last_candle['close'] < last_candle['ema_100']*0.951): # *1.2
return False
# slippage
try:
state = self.slippage_protection['__pair_retries']
except KeyError:
state = self.slippage_protection['__pair_retries'] = {}
candle = dataframe.iloc[-1].squeeze()
slippage = (rate / candle['close']) - 1
if slippage < self.slippage_protection['max_slippage']:
pair_retries = state.get(pair, 0)
if pair_retries < self.slippage_protection['retries']:
state[pair] = pair_retries + 1
return False
state[pair] = 0
return True
def informative_pairs(self):
pairs = self.dp.current_whitelist()
informative_pairs = [(pair, '15m') for pair in pairs]
return informative_pairs
def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
assert self.dp, "DataProvider is required for multiple timeframes."
# Get the informative pair
informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h)
# EMA
# informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50)
# informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200)
# # RSI
# informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14)
# bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
# informative_1h['bb_lowerband'] = bollinger['lower']
# informative_1h['bb_middleband'] = bollinger['mid']
# informative_1h['bb_upperband'] = bollinger['upper']
return informative_1h
def informative_15m_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
assert self.dp, "DataProvider is required for multiple timeframes."
# Get the informative pair
informative_15m = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_15m)
# EMA
# informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50)
# informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200)
# # RSI
# informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14)
# bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
# informative_1h['bb_lowerband'] = bollinger['lower']
# informative_1h['bb_middleband'] = bollinger['mid']
# informative_1h['bb_upperband'] = bollinger['upper']
return informative_15m
def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Calculate all ma_buy values
for val in self.base_nb_candles_buy.range:
dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val)
# Calculate all ma_sell values
for val in self.base_nb_candles_sell.range:
dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val)
dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50)
dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100)
dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200)
dataframe['sma_200_dec'] = dataframe['sma_200'] < dataframe['sma_200'].shift(20)
dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9)
# Elliot
dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo)
# RSI
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)
dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# informative_1h = self.informative_1h_indicators(dataframe, metadata)
informative_15m = self.informative_15m_indicators(dataframe, metadata)
dataframe = merge_informative_pair(
dataframe, informative_15m, self.timeframe, self.inf_15m, ffill=True)
# The indicators for the normal (5m) timeframe
dataframe = self.normal_tf_indicators(dataframe, metadata)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dont_buy_conditions = []
dont_buy_conditions.append(
(
# don't buy if there isn't 3% profit to be made
(dataframe['close_15m'].rolling(self.lookback_candles.value).max()
< (dataframe['close'] * self.profit_threshold.value))
)
)
dataframe.loc[
(
(dataframe['rsi_fast'] < self.rsi_fast_buy.value) &
(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) &
(dataframe['close'] < (
dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value))
),
['buy', 'buy_tag']] = (1, 'ewo1')
dataframe.loc[
(
(dataframe['rsi_fast'] < self.rsi_fast_buy.value) &
(dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset_2.value)) &
(dataframe['EWO'] > self.ewo_high_2.value) &
(dataframe['rsi'] < self.rsi_buy.value) &
(dataframe['volume'] > 0) &
(dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) &
(dataframe['rsi'] < 25)
),
['buy', 'buy_tag']] = (1, 'ewo2')
dataframe.loc[
(
(dataframe['rsi_fast'] < self.rsi_fast_buy.value) &
(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) &
(dataframe['close'] < (
dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value))
),
['buy', 'buy_tag']] = (1, 'ewolow')
if dont_buy_conditions:
for condition in dont_buy_conditions:
dataframe.loc[condition, 'buy'] = 0
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
conditions.append(
((dataframe['close'] > dataframe['sma_9']) &
(dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value)) &
(dataframe['rsi'] > 50) &
(dataframe['volume'] > 0) &
(dataframe['rsi_fast'] > dataframe['rsi_slow'])
)
|
(
(dataframe['close'] < dataframe['hma_50']) &
(dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) &
(dataframe['volume'] > 0) &
(dataframe['rsi_fast'] > dataframe['rsi_slow'])
)
)
if conditions:
dataframe.loc[
reduce(lambda x, y: x | y, conditions),
'sell'
]=1
return dataframe