DISCLAIMER
The codes in this post is just for educational purpose, and not to be used on live trading
If we use existing functions to create our custom indicators on previous post, in this post we will create a new function to calculate our custom indicator.
One of popular indicator that hasn’t been implemented in the existing technical libraries is Elliot Wave Oscillator. Below is the code to calculate it (credit to whoever wrote it first. I’m just using it). Put it just above your strategy class definition.
def EWO(source, sma_length=5, sma2_length=35):
sma1 = ta.SMA(source, timeperiod=sma_length)
sma2 = ta.SMA(source, timeperiod=sma2_length)
smadif = (sma1 - sma2) / source * 100
return smadifAnd to use it, just call it from populate_indicators. The full code will look like this
from freqtrade.strategy import IStrategy, informative
from pandas import DataFrame
import freqtrade.vendor.qtpylib.indicators as qtpylib
import talib.abstract as ta
from freqtrade.persistence import Trade
from datetime import datetime, timedelta
from typing import Optional, Union
def EWO(source, sma_length=5, sma2_length=35):
sma1 = ta.SMA(source, timeperiod=sma_length)
sma2 = ta.SMA(source, timeperiod=sma2_length)
smadif = (sma1 - sma2) / source * 100
return smadif
class strat_template (IStrategy):
def version(self) -> str:
return "template-v1"
INTERFACE_VERSION = 3
minimal_roi = {
"0": 0.05
}
stoploss = -0.05
timeframe = '15m'
process_only_new_candles = True
startup_candle_count = 999
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
sl_new = 1
if (current_time - timedelta(minutes=15) >= trade.open_date_utc):
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
current_candle = dataframe.iloc[-1].squeeze()
current_profit = trade.calc_profit_ratio(current_candle['close'])
if (current_profit >= 0.03):
sl_new = 0.01
return sl_new
def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> Optional[Union[str, bool]]:
if ((current_time - timedelta(minutes=15)) >= trade.open_date_utc):
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
current_candle = dataframe.iloc[-1].squeeze()
current_profit = trade.calc_profit_ratio(current_candle['close'])
if (current_profit >= 0):
if (current_candle['rsi'] >= 70):
return "rsi_overbought"
@informative('30m')
def populate_indicators_inf1(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi'] = ta.RSI(dataframe['close'], 14)
return dataframe
@informative('1h')
def populate_indicators_inf2(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi'] = ta.RSI(dataframe['close'], 14)
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['ema_9'] = ta.EMA(dataframe['close'], 9)
dataframe['ema_20'] = ta.EMA(dataframe['close'], 20)
dataframe['rsi'] = ta.RSI(dataframe['close'], 14)
dataframe['ema_9_rsi'] = ta.EMA(dataframe['rsi'], 9)
dataframe['ewo'] = EWO(dataframe['close'], 50, 200)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
qtpylib.crossed_above(dataframe['ema_9'], dataframe['ema_20'])
&
(dataframe['rsi_30m'] < 50)
&
(dataframe['rsi_1h'] < 30)
&
(dataframe['ema_9_rsi'] < 70)
&
(dataframe['ewo'] > 3)
&
(dataframe['volume'] > 0)
, ['enter_long', 'enter_tag']
] = (1, 'golden cross')
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
qtpylib.crossed_below(dataframe['ema_9'], dataframe['ema_20'])
&
(dataframe['volume'] > 0)
, ['exit_long', 'exit_tag']
] = (1, 'death cross')
return dataframe
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