Timeframe
N/A
Direction
Long Only
Stoploss
-23.7%
Trailing Stop
Yes
ROI
0m: 17.6%, 53m: 11.5%, 226m: 6.1%, 400m: 0.0%
Interface Version
N/A
Startup Candles
N/A
Indicators
3
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
# --------------------------------
class BbRoi(IStrategy):
minimal_roi = {
"0": 0.17552,
"53": 0.11466,
"226": 0.06134,
"400": 0
}
# Stoploss:
stoploss = -0.23701
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.01007
trailing_stop_positive_offset = 0.01821
trailing_only_offset_is_reached = True
ticker_interval = '15m'
# Experimental settings (configuration will overide these if set)
use_sell_signal = True
ignore_roi_if_buy_signal = False
order_types = {
'buy': 'market',
'sell': 'market',
'stoploss': 'limit',
'stoploss_on_exchange': True
}
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
# EMA
dataframe['ema9'] = ta.EMA(dataframe, timeperiod=9)
dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20)
dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200)
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['close'] > dataframe['bb_middleband']) &
(dataframe['close'] < dataframe['bb_upperband']) &
(dataframe['close'] > dataframe['ema9']) &
(dataframe['close'] > dataframe['ema200']) &
(dataframe['ema20'] > dataframe['ema200'])
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['rsi'] > 75) |
(dataframe['close'] < dataframe['bb_middleband'] * 0.97) &
(dataframe['open'] > dataframe['close']) # red bar
),
'sell'] = 1
return dataframe