strategy sponsored by user BinH from slack
Timeframe
5m
Direction
Long & Short
Stoploss
-99.0%
Trailing Stop
No
ROI
0m: 100.0%
Interface Version
N/A
Startup Candles
240
Indicators
4
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
from datetime import datetime
from functools import reduce
from freqtrade.persistence import Trade
from freqtrade.strategy import IntParameter, DecimalParameter, stoploss_from_open, CategoricalParameter, informative
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
import freqtrade.vendor.qtpylib.indicators as qtpylib
import talib.abstract as ta
import numpy # noqa
class Bins(IStrategy):
"""
strategy sponsored by user BinH from slack
"""
minimal_roi = {
"0": 1
}
buy_params = {
"buy_adx1": 62,
"buy_adx2": 29,
"buy_adx3": 33,
"buy_adx4": 88,
"buy_emarsi1": 29,
"buy_emarsi2": 30,
"buy_emarsi3": 22,
"buy_emarsi4": 57
}
sell_params = {
"adx2": 21,
"emarsi1": 30,
"emarsi2": 71,
"emarsi3": 72,
"leverage_num": 3,
"sell_1": False,
"sell_2": True,
"sell_3": True,
"sell_4": True,
"sell_5": False,
"pHSL": -0.75,
"pPF_1": 0.03,
"pPF_2": 0.09,
"pSL_1": 0.028,
"pSL_2": 0.08
}
stoploss = -0.99
timeframe = '5m'
process_only_new_candles = True
startup_candle_count = 240
# default False
use_custom_stoploss = True
can_short = True
order_types = {
'entry': 'market',
'exit': 'market',
'emergency_exit': 'market',
'force_entry': 'market',
'force_exit': "market",
'stoploss': 'market',
'stoploss_on_exchange': False,
'stoploss_on_exchange_interval': 60,
'stoploss_on_exchange_limit_ratio': 0.99
}
# buy params
buy_optimize = True
buy_adx1 = IntParameter(low=10, high=100, default=25, space='buy', optimize=buy_optimize)
buy_emarsi1 = IntParameter(low=10, high=100, default=20, space='buy', optimize=buy_optimize)
buy_adx2 = IntParameter(low=20, high=100, default=30, space='buy', optimize=buy_optimize)
buy_emarsi2 = IntParameter(low=20, high=100, default=20, space='buy', optimize=buy_optimize)
buy_adx3 = IntParameter(low=10, high=100, default=35, space='buy', optimize=buy_optimize)
buy_emarsi3 = IntParameter(low=10, high=100, default=20, space='buy', optimize=buy_optimize)
buy_adx4 = IntParameter(low=20, high=100, default=30, space='buy', optimize=buy_optimize)
buy_emarsi4 = IntParameter(low=20, high=100, default=25, space='buy', optimize=buy_optimize)
# trailing stoploss
trailing_optimize = False
pHSL = DecimalParameter(-0.990, -0.040, default=-0.08, decimals=3, space='sell', optimize=trailing_optimize)
pPF_1 = DecimalParameter(0.008, 0.100, default=0.016, decimals=3, space='sell', optimize=trailing_optimize)
pSL_1 = DecimalParameter(0.008, 0.100, default=0.011, decimals=3, space='sell', optimize=trailing_optimize)
pPF_2 = DecimalParameter(0.040, 0.200, default=0.080, decimals=3, space='sell', optimize=trailing_optimize)
pSL_2 = DecimalParameter(0.040, 0.200, default=0.040, decimals=3, space='sell', optimize=trailing_optimize)
# sell params
sell_optimize = True
adx2 = IntParameter(low=10, high=100, default=30, space='sell', optimize=sell_optimize)
emarsi1 = IntParameter(low=10, high=100, default=75, space='sell', optimize=sell_optimize)
emarsi2 = IntParameter(low=20, high=100, default=80, space='sell', optimize=sell_optimize)
emarsi3 = IntParameter(low=20, high=100, default=75, space='sell', optimize=sell_optimize)
sell2_optimize = True
sell_1 = CategoricalParameter([True, False], default=True, space="sell", optimize=sell2_optimize)
sell_2 = CategoricalParameter([True, False], default=True, space="sell", optimize=sell2_optimize)
sell_3 = CategoricalParameter([True, False], default=True, space="sell", optimize=sell2_optimize)
sell_4 = CategoricalParameter([True, False], default=True, space="sell", optimize=sell2_optimize)
sell_5 = CategoricalParameter([True, False], default=True, space="sell", optimize=sell2_optimize)
leverage_optimize = False
leverage_num = IntParameter(low=1, high=20, default=1, space='sell', optimize=leverage_optimize)
def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
# hard stoploss profit
HSL = self.pHSL.value
PF_1 = self.pPF_1.value
SL_1 = self.pSL_1.value
PF_2 = self.pPF_2.value
SL_2 = self.pSL_2.value
# For profits between PF_1 and PF_2 the stoploss (sl_profit) used is linearly interpolated
# between the values of SL_1 and SL_2. For all profits above PL_2 the sl_profit value
# rises linearly with current profit, for profits below PF_1 the hard stoploss profit is used.
if current_profit > PF_2:
sl_profit = SL_2 + (current_profit - PF_2)
elif current_profit > PF_1:
sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1))
else:
sl_profit = HSL
if self.can_short:
if (-1 + ((1 - sl_profit) / (1 - current_profit))) <= 0:
return 1
else:
if (1 - ((1 + sl_profit) / (1 + current_profit))) <= 0:
return 1
return stoploss_from_open(sl_profit, current_profit, is_short=trade.is_short)
@informative('1h', 'BTC/{stake}', fmt='{base}_{column}_{timeframe}')
def populate_indicators_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['ema_fast'] = ta.EMA(dataframe, timeperiod=20)
dataframe['ema_slow'] = ta.EMA(dataframe, timeperiod=25)
dataframe['uptrend'] = (dataframe['ema_fast'] > dataframe['ema_slow']).astype('int')
dataframe['downtrend'] = (dataframe['ema_fast'] < dataframe['ema_slow']).astype('int')
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi'] = numpy.nan_to_num(ta.RSI(dataframe, timeperiod=5))
rsiframe = DataFrame(dataframe['rsi']).rename(columns={'rsi': 'close'})
dataframe['emarsi'] = numpy.nan_to_num(ta.EMA(rsiframe, timeperiod=5))
dataframe['adx'] = numpy.nan_to_num(ta.ADX(dataframe))
dataframe['minusdi'] = numpy.nan_to_num(ta.MINUS_DI(dataframe))
minusdiframe = DataFrame(dataframe['minusdi']).rename(columns={'minusdi': 'close'})
dataframe['minusdiema'] = numpy.nan_to_num(ta.EMA(minusdiframe, timeperiod=25))
dataframe['plusdi'] = numpy.nan_to_num(ta.PLUS_DI(dataframe))
plusdiframe = DataFrame(dataframe['plusdi']).rename(columns={'plusdi': 'close'})
dataframe['plusdiema'] = numpy.nan_to_num(ta.EMA(plusdiframe, timeperiod=5))
dataframe['lowsma'] = numpy.nan_to_num(ta.EMA(dataframe, timeperiod=60))
dataframe['highsma'] = numpy.nan_to_num(ta.EMA(dataframe, timeperiod=120))
dataframe['fastsma'] = numpy.nan_to_num(ta.SMA(dataframe, timeperiod=120))
dataframe['slowsma'] = numpy.nan_to_num(ta.SMA(dataframe, timeperiod=240))
dataframe['bigup'] = dataframe['fastsma'].gt(dataframe['slowsma']) & (
(dataframe['fastsma'] - dataframe['slowsma']) > dataframe['close'] / 300)
dataframe['bigdown'] = ~dataframe['bigup']
dataframe['trend'] = dataframe['fastsma'] - dataframe['slowsma']
dataframe['preparechangetrend'] = dataframe['trend'].gt(dataframe['trend'].shift())
dataframe['preparechangetrendconfirm'] = dataframe['preparechangetrend'] & dataframe['trend'].shift().gt(
dataframe['trend'].shift(2))
dataframe['continueup'] = dataframe['slowsma'].gt(dataframe['slowsma'].shift()) & dataframe[
'slowsma'].shift().gt(dataframe['slowsma'].shift(2))
dataframe['delta'] = dataframe['fastsma'] - dataframe['fastsma'].shift()
dataframe['slowingdown'] = dataframe['delta'].lt(dataframe['delta'].shift())
dataframe['short'] = (dataframe['btc_downtrend_1h'] == 1)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
dataframe.loc[:, 'enter_tag'] = ''
buy_1 = (
dataframe['slowsma'].gt(0) & (dataframe['short']) &
dataframe['close'].lt(dataframe['highsma']) &
dataframe['close'].lt(dataframe['lowsma']) &
dataframe['minusdi'].gt(dataframe['minusdiema']) &
dataframe['rsi'].ge(dataframe['rsi'].shift()) &
~dataframe['preparechangetrend'] &
~dataframe['continueup'] &
dataframe['adx'].gt(self.buy_adx1.value) &
dataframe['bigdown'] &
dataframe['emarsi'].le(self.buy_emarsi1.value)
)
buy_2 = (
dataframe['slowsma'].gt(0) & (dataframe['short']) &
dataframe['close'].lt(dataframe['highsma']) &
dataframe['close'].lt(dataframe['lowsma']) &
dataframe['minusdi'].gt(dataframe['minusdiema']) &
dataframe['rsi'].ge(dataframe['rsi'].shift()) &
~dataframe['preparechangetrend'] &
dataframe['continueup'] &
dataframe['adx'].gt(self.buy_adx2.value) &
dataframe['bigdown'] &
dataframe['emarsi'].le(self.buy_emarsi2.value)
)
buy_3 = (
dataframe['slowsma'].gt(0) & (dataframe['short']) &
dataframe['close'].lt(dataframe['highsma']) &
dataframe['close'].lt(dataframe['lowsma']) &
dataframe['minusdi'].gt(dataframe['minusdiema']) &
dataframe['rsi'].ge(dataframe['rsi'].shift()) &
~dataframe['continueup'] &
dataframe['adx'].gt(self.buy_adx3.value) &
dataframe['bigup'] &
dataframe['emarsi'].le(self.buy_emarsi3.value)
)
buy_4 = (
dataframe['slowsma'].gt(0) & (dataframe['short']) &
dataframe['close'].lt(dataframe['highsma']) &
dataframe['close'].lt(dataframe['lowsma']) &
dataframe['minusdi'].gt(dataframe['minusdiema']) &
dataframe['rsi'].ge(dataframe['rsi'].shift()) &
dataframe['continueup'] &
dataframe['adx'].gt(self.buy_adx4.value) &
dataframe['bigup'] &
dataframe['emarsi'].le(self.buy_emarsi4.value)
)
conditions.append(buy_1)
dataframe.loc[buy_1, 'enter_tag'] += 'buy_1'
conditions.append(buy_2)
dataframe.loc[buy_2, 'enter_tag'] += 'buy_2'
conditions.append(buy_3)
dataframe.loc[buy_3, 'enter_tag'] += 'buy_3'
conditions.append(buy_4)
dataframe.loc[buy_4, 'enter_tag'] += 'buy_4'
if conditions:
dataframe.loc[
reduce(lambda x, y: x | y, conditions),
'enter_short'] = 1
dataframe.loc[(), ['enter_long', 'enter_tag']] = (0, 'long_in')
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[(), ['exit_short', 'exit_tag']] = (0, 'short_out')
dataframe.loc[(), ['exit_long', 'exit_tag']] = (0, 'long_out')
return dataframe
def custom_exit(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()
if current_profit >= self.pPF_1.value:
return None
if self.sell_1.value:
if (
(~last_candle['preparechangetrendconfirm'])
and (~last_candle['continueup'])
and (last_candle['close'] > last_candle['lowsma'] or last_candle['close'] > last_candle['highsma'])
and (last_candle['highsma'] > 0)
and (last_candle['bigdown'])
):
return "sell_1"
if self.sell_2.value:
if (
(~last_candle['preparechangetrendconfirm'])
and (~last_candle['continueup'])
and (last_candle['close'] > last_candle['highsma'])
and (last_candle['highsma'] > 0)
and (last_candle['emarsi'] > self.emarsi1.value or last_candle['close'] > last_candle['slowsma'])
and (last_candle['bigdown'])
):
return "sell_2"
if self.sell_3.value:
if (
(~last_candle['preparechangetrendconfirm'])
and (last_candle['close'] > last_candle['highsma'])
and (last_candle['highsma'] > 0)
and (last_candle['adx'] > self.adx2.value)
and (last_candle['emarsi'] >= self.emarsi2.value)
and (last_candle['bigup'])
):
return "sell_3"
if self.sell_4.value:
if (
(last_candle['preparechangetrendconfirm'])
and (~last_candle['continueup'])
and (last_candle['slowingdown'])
and (last_candle['emarsi'] >= self.emarsi3.value)
and (last_candle['slowsma'] > 0)
):
return "sell_4"
if self.sell_5.value:
if (
(last_candle['preparechangetrendconfirm'])
and (last_candle['minusdi'] < last_candle['plusdi'])
and (last_candle['close'] > last_candle['lowsma'])
and (last_candle['slowsma'] > 0)
):
return "sell_5"
def leverage(self, pair: str, current_time: datetime, current_rate: float,
proposed_leverage: float, max_leverage: float, side: str,
**kwargs) -> float:
return self.leverage_num.value