This strategy is only an experiment using candlestick pattern to be used as buy or sell indicator. Do not use this strategy live.
Timeframe
1h
Direction
Long Only
Stoploss
-10.0%
Trailing Stop
No
ROI
0m: 1000.0%
Interface Version
N/A
Startup Candles
N/A
Indicators
1
freqtrade/freqtrade-strategies
this is an example class, implementing a PSAR based trailing stop loss you are supposed to take the `custom_stoploss()` and `populate_indicators()` parts and adapt it to your own strategy
freqtrade/freqtrade-strategies
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
# --- Do not remove these libs --- freqtrade backtesting --strategy SmoothScalp --timerange 20210110-20210410
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 freqtrade.vendor.qtpylib.indicators as qtpylib
from typing import Dict, List
from functools import reduce
from pandas import DataFrame, DatetimeIndex, merge
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy # noqa
class hansencandlepatternV1(IStrategy):
"""
This strategy is only an experiment using candlestick pattern to be used as buy or sell indicator. Do not use this strategy live.
"""
timeframe = '1h'
minimal_roi = {
"0": 10,
}
stoploss = -0.1
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe['open'], dataframe['high'], dataframe['low'], dataframe['close'])
dataframe['EVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe['open'], dataframe['high'], dataframe['low'], dataframe['close'])
dataframe['ABANDONEDBABY'] = ta.CDLEVENINGSTAR(dataframe['open'], dataframe['high'], dataframe['low'], dataframe['close'])
dataframe['HARAMI'] = ta.CDLHARAMI(dataframe['open'], dataframe['high'], dataframe['low'], dataframe['close'])
dataframe['INVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe['open'], dataframe['high'], dataframe['low'], dataframe['close'])
dataframe['ENGULFING'] = ta.CDLENGULFING(dataframe['open'], dataframe['high'], dataframe['low'], dataframe['close'])
dataframe['hclose']=(dataframe['open'] + dataframe['high'] + dataframe['low'] + dataframe['close']) / 4
dataframe['hopen']= ((dataframe['open'].shift(2) + dataframe['close'].shift(2))/ 2)
dataframe['hhigh']=dataframe[['open','close','high']].max(axis=1)
dataframe['hlow']=dataframe[['open','close','low']].min(axis=1)
dataframe['emac'] = ta.SMA(dataframe['hclose'], timeperiod=6)
dataframe['emao'] = ta.SMA(dataframe['hopen'], timeperiod=6)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
((dataframe['3LINESTRIKE'] < 0)|(dataframe['EVENINGSTAR'] > 0)|(dataframe['ABANDONEDBABY'] > 0)|(dataframe['HARAMI'] > 0)|(dataframe['ENGULFING'] > 0))&
(dataframe['emao'] < dataframe['emac'])
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['emao'] > dataframe['emac'])
),
'sell'] = 1
return dataframe