Timeframe
N/A
Direction
Long Only
Stoploss
-23.6%
Trailing Stop
Yes
ROI
0m: 55.2%, 414m: 15.2%, 1129m: 7.7%, 2386m: 0.0%
Interface Version
2
Startup Candles
N/A
Indicators
5
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from functools import reduce
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
# Strategy based on Bollinger Bands (BB) and Relative Strength Index (RSI)
class BBRSIStrategy1hSortino(IStrategy):
INTERFACE_VERSION = 2
# Minimal ROI designed for the strategy.
minimal_roi = {
"0": 0.552,
"414": 0.152,
"1129": 0.077,
"2386": 0
}
# Optimal stoploss designed for the strategy.
stoploss = -0.236
# Trailing stoploss
trailing_stop = True
trailing_stop_positive = 0.045
trailing_stop_positive_offset = 0.139
trailing_only_offset_is_reached = False
# Hyperoptable parameters
buy_rsi = IntParameter(low=10, high=50, default=21,
space='buy', optimize=True, load=True)
buy_rsi_enabled = CategoricalParameter([True, False],
default=True,
space='buy')
buy_trigger = CategoricalParameter(['bb_lower_1',
'bb_lower_2',
'bb_lower_3'],
default='bb_lower_1', space='buy')
sell_rsi = IntParameter(low=50, high=90, default=86,
space='sell', optimize=True, load=True)
sell_rsi_enabled = CategoricalParameter([True, False],
default=True,
space='sell')
sell_trigger = CategoricalParameter(['bb_middle_1',
'bb_upper_1',
'bb_upper_2',
'bb_upper_3',],
default='bb_middle_1', space='sell')
# Run "populate_indicators()" only for new candle.
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': True,
'stoploss_on_exchange_limit_ratio': 0.99
}
# Optional order time in force.
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc'
}
plot_config = {
'main_plot': {
'tema': {},
'sar': {'color': 'white'},
},
'subplots': {
"MACD": {
'macd': {'color': 'blue'},
'macdsignal': {'color': 'orange'},
},
"RSI": {
'rsi': {'color': 'red'},
}
}
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return []
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
:param dataframe: Dataframe with data from the exchange
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Bollinger bands 1 standard deviation
bb_1std = ta.BBANDS(dataframe, timeperiod=20, nbdevup=1.0, nbdevdn=1.0)
dataframe['bb_lowerband_1'] = bb_1std['lowerband']
dataframe['bb_middleband_1'] = bb_1std['middleband']
dataframe['bb_upperband_1'] = bb_1std['upperband']
# Bollinger bands 2 standard deviations
bb_2std = ta.BBANDS(dataframe, timeperiod=20, nbdevup=2.0, nbdevdn=2.0)
dataframe['bb_lowerband_2'] = bb_2std['lowerband']
dataframe['bb_upperband_2'] = bb_2std['upperband']
# Bollinger bands 3 standard deviations
bb_3std = ta.BBANDS(dataframe, timeperiod=20, nbdevup=3.0, nbdevdn=3.0)
dataframe['bb_lowerband_3'] = bb_3std['lowerband']
dataframe['bb_upperband_3'] = bb_3std['upperband']
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given
dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
conditions = []
# Guards and trends
if self.buy_rsi_enabled.value:
conditions.append(dataframe['rsi'] <= self.buy_rsi.value)
# Triggers
if self.buy_trigger.value == 'bb_lower_1':
conditions.append(dataframe['close'] < dataframe['bb_lowerband_1'])
elif self.buy_trigger.value == 'bb_lower_2':
conditions.append(dataframe['close'] < dataframe['bb_lowerband_2'])
elif self.buy_trigger.value == 'bb_lower_3':
conditions.append(dataframe['close'] < dataframe['bb_lowerband_3'])
conditions.append(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:
"""
Based on TA indicators, populates the sell signal for the given
dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with sell column
"""
conditions = []
# Guards and trends
if self.sell_rsi_enabled.value:
conditions.append(dataframe['rsi'] > self.sell_rsi.value)
# Triggers
if self.sell_trigger.value == 'bb_middle_1':
conditions.append(dataframe['close'] > dataframe['bb_middleband_1'])
elif self.sell_trigger.value == 'bb_upper_1':
conditions.append(dataframe['close'] > dataframe['bb_upperband_1'])
elif self.sell_trigger.value == 'bb_upper_2':
conditions.append(dataframe['close'] > dataframe['bb_upperband_2'])
elif self.sell_trigger.value == 'bb_upper_3':
conditions.append(dataframe['close'] > dataframe['bb_upperband_3'])
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell'] = 1
return dataframe