Timeframe
5m
Direction
Long Only
Stoploss
-15.0%
Trailing Stop
Yes
ROI
0m: 21.5%, 40m: 13.2%, 87m: 8.6%, 201m: 3.0%
Interface Version
2
Startup Candles
200
Indicators
3
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
#############################################################################################################
## ElliotV5_SMA Strategy mod 1 by @zxjason & DS ##
## https://github.com/zxjason/Freqtrade_Strategies ##
## Strategy for Freqtrade https://github.com/freqtrade/freqtrade ##
#############################################################################################################
## GENERAL RECOMMENDATIONS ##
## For optimal performance, suggested to use only BTC and ETH. ##
#############################################################################################################
## DONATIONS ##
## usdc: 0xedB85Ce16ba8268F32925f6fF48190Cea97a9b81 ##
## REFERRAL LINKS ##
#############################################################################################################
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 numpy as np
from datetime import datetime
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.persistence import Trade
from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter
# Updated buy and sell parameters with narrowed ranges
buy_params = {
"base_nb_candles_buy": 17,
"ewo_high": 3.34,
"ewo_low": -17.457,
"low_offset": 0.978,
"rsi_buy": 65,
"atr_mult": 2.0 # Added ATR multiplier for dynamic stoploss
}
sell_params = {
"base_nb_candles_sell": 49,
"high_offset": 1.019,
"rsi_sell": 70 # Added RSI sell threshold
}
def EWO(dataframe, ema_length=50, ema2_length=200):
df = dataframe.copy()
ema1 = ta.EMA(df, timeperiod=ema_length)
ema2 = ta.EMA(df, timeperiod=ema2_length)
emadif = (ema1 - ema2) / df['close'] * 100
return emadif
class ElliotV5_SMA_mod1(IStrategy):
INTERFACE_VERSION = 2
minimal_roi = {
"0": 0.215,
"40": 0.132,
"87": 0.086,
"201": 0.03
}
stoploss = -0.15 # Adjusted to -15% (will be overridden by dynamic ATR)
use_custom_stoploss = True # Enable custom stoploss
# Narrowed parameter ranges for efficiency
base_nb_candles_buy = IntParameter(10, 30, default=buy_params['base_nb_candles_buy'], space='buy', optimize=True)
base_nb_candles_sell = IntParameter(20, 60, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True)
low_offset = DecimalParameter(0.95, 0.99, default=buy_params['low_offset'], space='buy', optimize=True)
high_offset = DecimalParameter(1.0, 1.05, default=sell_params['high_offset'], space='sell', optimize=True)
ewo_low = DecimalParameter(-20.0, -8.0, default=buy_params['ewo_low'], space='buy', optimize=True)
ewo_high = DecimalParameter(2.0, 12.0, default=buy_params['ewo_high'], space='buy', optimize=True)
rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=True)
rsi_sell = IntParameter(65, 90, default=sell_params['rsi_sell'], space='sell', optimize=True)
atr_mult = DecimalParameter(1.5, 3.0, default=buy_params['atr_mult'], space='buy', optimize=True)
trailing_stop = True
trailing_stop_positive = 0.01 # Increased from 0.005
trailing_stop_positive_offset = 0.05 # Increased from 0.03
trailing_only_offset_is_reached = True
timeframe = '5m'
informative_timeframe = '1h'
process_only_new_candles = True
startup_candle_count = 200
def informative_pairs(self):
pairs = self.dp.current_whitelist()
return [(pair, self.informative_timeframe) for pair in pairs]
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Calculate EMAs for buy and sell signals
for val in self.base_nb_candles_buy.range:
dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val)
for val in self.base_nb_candles_sell.range:
dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val)
# Calculate EWO with EMA
dataframe['EWO'] = EWO(dataframe)
# Add RSI and ATR for dynamic stoploss
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
# Merge informative timeframe (1h) data
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe)
informative['ema_200'] = ta.EMA(informative, timeperiod=200)
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe, ffill=True)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
# Ensure 1h trend is bullish
bullish_1h = dataframe[f"ema_200_{self.informative_timeframe}"] < dataframe['close']
# Entry condition 1: EWO high with RSI
conditions.append(
(bullish_1h) &
(dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) &
(dataframe['EWO'] > self.ewo_high.value) &
(dataframe['rsi'] < self.rsi_buy.value) &
(dataframe['volume'] > 0)
)
# Entry condition 2: EWO low
conditions.append(
(bullish_1h) &
(dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) &
(dataframe['EWO'] < self.ewo_low.value) &
(dataframe['volume'] > 0)
)
if conditions:
dataframe.loc[reduce(lambda x, y: x | y, conditions), 'buy'] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = [
(dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) &
(dataframe['rsi'] > self.rsi_sell.value) &
(dataframe['volume'] > 0)
]
dataframe.loc[reduce(lambda x, y: x | y, conditions), 'sell'] = 1
return dataframe
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
# Dynamic stoploss using 2*ATR
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1]
atr_stop = last_candle['atr'] * self.atr_mult.value
return current_rate - atr_stop