Timeframe
5m
Direction
Long Only
Stoploss
-35.0%
Trailing Stop
Yes
ROI
0m: 21.5%, 40m: 3.2%, 87m: 1.6%, 201m: 0.0%
Interface Version
3
Startup Candles
200
Indicators
3
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
# --- Do not remove these libs ---
# --- Do not remove these libs ---
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
import freqtrade.vendor.qtpylib.indicators as qtpylib
import datetime
from technical.util import resample_to_interval, resampled_merge
from datetime import datetime, timedelta
from freqtrade.persistence import Trade
from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter
import technical.indicators as ftt
# @Rallipanos
# Buy hyperspace params:
entry_params = {'base_nb_candles_entry': 14, 'ewo_high': 2.327, 'ewo_high_2': -2.327, 'ewo_low': -20.988, 'low_offset': 0.975, 'low_offset_2': 0.955, 'rsi_entry': 69}
# Sell hyperspace params:
exit_params = {'base_nb_candles_exit': 24, 'high_offset': 0.998, 'high_offset_2': 1}
def EWO(dataframe, ema_length=5, ema2_length=35):
df = dataframe.copy()
ema1 = ta.EMA(df, timeperiod=ema_length)
ema2 = ta.EMA(df, timeperiod=ema2_length)
emadif = (ema1 - ema2) / df['low'] * 100
return emadif
class NotAnotherSMAOffSetStrategy_V2(IStrategy):
INTERFACE_VERSION = 3
# ROI table:
minimal_roi = {'0': 0.215, '40': 0.032, '87': 0.016, '201': 0}
# Stoploss:
stoploss = -0.35
# SMAOffset
base_nb_candles_entry = IntParameter(5, 80, default=entry_params['base_nb_candles_entry'], space='entry', optimize=True)
base_nb_candles_exit = IntParameter(5, 80, default=exit_params['base_nb_candles_exit'], space='exit', optimize=True)
low_offset = DecimalParameter(0.9, 0.99, default=entry_params['low_offset'], space='entry', optimize=True)
low_offset_2 = DecimalParameter(0.9, 0.99, default=entry_params['low_offset_2'], space='entry', optimize=True)
high_offset = DecimalParameter(0.95, 1.1, default=exit_params['high_offset'], space='exit', optimize=True)
high_offset_2 = DecimalParameter(0.99, 1.5, default=exit_params['high_offset_2'], space='exit', optimize=True)
# Protection
fast_ewo = 50
slow_ewo = 200
ewo_low = DecimalParameter(-20.0, -8.0, default=entry_params['ewo_low'], space='entry', optimize=True)
ewo_high = DecimalParameter(2.0, 12.0, default=entry_params['ewo_high'], space='entry', optimize=True)
ewo_high_2 = DecimalParameter(-6.0, 12.0, default=entry_params['ewo_high_2'], space='entry', optimize=True)
rsi_entry = IntParameter(30, 70, default=entry_params['rsi_entry'], space='entry', optimize=True)
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.005
trailing_stop_positive_offset = 0.025
trailing_only_offset_is_reached = True
# Sell signal
use_exit_signal = True
exit_profit_only = False
exit_profit_offset = 0.01
ignore_roi_if_entry_signal = False
## Optional order time in force.
order_time_in_force = {'entry': 'gtc', 'exit': 'gtc'}
# Optimal timeframe for the strategy
timeframe = '5m'
inf_1h = '1h'
process_only_new_candles = True
startup_candle_count = 200
plot_config = {'main_plot': {'ma_entry': {'color': 'orange'}, 'ma_exit': {'color': 'orange'}}}
def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs) -> bool:
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1]
if last_candle is not None:
if exit_reason in ['exit_signal']:
if last_candle['hma_50'] > last_candle['ema_100'] and last_candle['rsi'] < 45: #*1.2
return False
if last_candle is not None:
if exit_reason in ['exit_signal']:
if last_candle['hma_50'] * 1.149 > last_candle['ema_100'] and last_candle['close'] < last_candle['ema_100'] * 0.951: #*1.2
return False
return True
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Calculate all ma_entry values
for val in self.base_nb_candles_entry.range:
dataframe[f'ma_entry_{val}'] = ta.EMA(dataframe, timeperiod=val)
# Calculate all ma_exit values
for val in self.base_nb_candles_exit.range:
dataframe[f'ma_exit_{val}'] = ta.EMA(dataframe, timeperiod=val)
dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50)
dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100)
dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9)
# Elliot
dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo)
# RSI
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)
dataframe['vol_7_max'] = dataframe['volume'].rolling(window=20).max()
dataframe['vol_14_max'] = dataframe['volume'].rolling(window=14).max()
dataframe['vol_7_min'] = dataframe['volume'].rolling(window=20).min()
dataframe['vol_14_min'] = dataframe['volume'].rolling(window=14).min()
dataframe['roll_7'] = 100 * ((dataframe['volume'] - dataframe['vol_7_max']) / (dataframe['vol_7_max'] - dataframe['vol_7_min']))
dataframe['vol_base'] = ta.SMA(dataframe['roll_7'], timeperiod=5)
dataframe['vol_ma_26'] = ta.SMA(dataframe['volume'], timeperiod=26)
dataframe['vol_ma_200'] = ta.SMA(dataframe['volume'], timeperiod=100)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[(dataframe['vol_base'] > -96) & (dataframe['vol_base'] < -77) & (dataframe['rsi_fast'] < 35) & (dataframe['close'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset.value) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_entry.value) & (dataframe['volume'] > 0) & (dataframe['close'] < dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value), ['enter_long', 'enter_tag']] = (1, 'ewo1')
dataframe.loc[(dataframe['vol_base'] > -96) & (dataframe['vol_base'] > -20) & (dataframe['rsi_fast'] < 35) & (dataframe['close'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset.value) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_entry.value) & (dataframe['volume'] > 0) & (dataframe['close'] < dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value), ['enter_long', 'enter_tag']] = (1, 'ewo3')
dataframe.loc[(dataframe['vol_base'] > -96) & (dataframe['vol_base'] < -77) & (dataframe['rsi_fast'] < 35) & (dataframe['close'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset_2.value) & (dataframe['EWO'] > self.ewo_high_2.value) & (dataframe['rsi'] < self.rsi_entry.value) & (dataframe['volume'] > 0) & (dataframe['close'] < dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value) & (dataframe['rsi'] < 25), ['enter_long', 'enter_tag']] = (1, 'ewo2')
dataframe.loc[(dataframe['vol_base'] > -96) & (dataframe['vol_base'] < -77) & (dataframe['rsi_fast'] < 35) & (dataframe['close'] < dataframe[f'ma_entry_{self.base_nb_candles_entry.value}'] * self.low_offset.value) & (dataframe['EWO'] < self.ewo_low.value) & (dataframe['volume'] > 0) & (dataframe['close'] < dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value), ['enter_long', 'enter_tag']] = (1, 'ewolow')
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
conditions.append((dataframe['close'] > dataframe['sma_9']) & (dataframe['close'] > dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset_2.value) & (dataframe['rsi'] > 50) & (dataframe['volume'] > 0) & (dataframe['rsi_fast'] > dataframe['rsi_slow']) | (dataframe['close'] < dataframe['hma_50']) & (dataframe['close'] > dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value) & (dataframe['volume'] > 0) & (dataframe['rsi_fast'] > dataframe['rsi_slow']))
if conditions:
dataframe.loc[reduce(lambda x, y: x | y, conditions), 'exit_long'] = 1
return dataframe