Timeframe
5m
Direction
Long Only
Stoploss
-50.0%
Trailing Stop
No
ROI
0m: 1.0%
Interface Version
3
Startup Candles
30
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 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
SMA = 'SMA'
EMA = 'EMA'
# Buy hyperspace params:
#entry_params = {
# "base_nb_candles_entry": 20,
# "ewo_high": 6,
# "fast_ewo": 50,
# "slow_ewo": 200,
# "low_offset": 0.958,
# "entry_trigger": "EMA",
# "ewo_high": 2.0,
# "ewo_low": -16.062,
# "rsi_entry": 51,
#}
# value loaded from strategy
# value loaded from strategy
# value loaded from strategy
entry_params = {'base_nb_candles_entry': 20, 'ewo_high': 5.499, 'ewo_low': -19.881, 'low_offset': 0.975, 'rsi_entry': 67, 'entry_trigger': 'EMA', 'fast_ewo': 50, 'slow_ewo': 200, 'entry_trigger': 'EMA'}
# Sell hyperspace params:
#exit_params = {
# "base_nb_candles_exit": 20,
# "high_offset": 1.012,
# "exit_trigger": "EMA",
#}
# Sell hyperspace params:
exit_params = {'base_nb_candles_exit': 24, 'high_offset': 1.012, 'exit_trigger': 'EMA'}
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['close'] * 100
return emadif
class SMAOffsetProtectOptV0(IStrategy):
INTERFACE_VERSION = 3
# ROI table:
minimal_roi = {'0': 0.01}
# Stoploss:
stoploss = -0.5
# 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)
high_offset = DecimalParameter(0.99, 1.1, default=exit_params['high_offset'], space='exit', optimize=True)
entry_trigger = CategoricalParameter([SMA, EMA], default=entry_params['entry_trigger'], space='entry', optimize=False)
exit_trigger = CategoricalParameter([SMA, EMA], default=exit_params['exit_trigger'], space='exit', optimize=False)
# Protection
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)
fast_ewo = IntParameter(10, 50, default=entry_params['fast_ewo'], space='entry', optimize=False)
slow_ewo = IntParameter(100, 200, default=entry_params['slow_ewo'], space='entry', optimize=False)
rsi_entry = IntParameter(30, 70, default=entry_params['rsi_entry'], space='entry', optimize=True)
# slow_ema = IntParameter(
# 10, 50, default=entry_params['fast_ewo'], space='entry', optimize=True)
# fast_ema = IntParameter(
# 100, 200, default=entry_params['slow_ewo'], space='entry', optimize=True)
# Trailing stop:
trailing_stop = False
trailing_stop_positive = 0.001
trailing_stop_positive_offset = 0.01
trailing_only_offset_is_reached = True
# Sell signal
use_exit_signal = True
exit_profit_only = True
exit_profit_offset = 0.01
ignore_roi_if_entry_signal = True
# Optimal timeframe for the strategy
timeframe = '5m'
informative_timeframe = '1h'
use_exit_signal = True
exit_profit_only = False
process_only_new_candles = True
startup_candle_count = 30
plot_config = {'main_plot': {'ma_offset_entry': {'color': 'orange'}, 'ma_offset_exit': {'color': 'orange'}}}
use_custom_stoploss = False
def informative_pairs(self):
pairs = self.dp.current_whitelist()
informative_pairs = [(pair, self.informative_timeframe) for pair in pairs]
# EMA
informative_pairs['ema_50'] = ta.EMA(informative_pairs, timeperiod=50)
informative_pairs['ema_100'] = ta.EMA(informative_pairs, timeperiod=100)
informative_pairs['ema_200'] = ta.EMA(informative_pairs, timeperiod=200)
# SMA
informative_pairs['sma_200'] = ta.SMA(informative_pairs, timeperiod=200)
informative_pairs['sma_200_dec'] = informative_pairs['sma_200'] < informative_pairs['sma_200'].shift(20)
# RSI
informative_pairs['rsi'] = ta.RSI(informative_pairs, timeperiod=14)
return informative_pairs
def get_informative_indicators(self, metadata: dict):
dataframe = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe)
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# informative = self.get_informative_indicators(metadata)
# dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe,
# ffill=True)
# Calculate all base_nb_candles_entry values
for val in self.base_nb_candles_entry.range:
dataframe[f'ma_entry_{val}'] = ta.EMA(dataframe, timeperiod=val)
# Calculate all base_nb_candles_entry values
for val in self.base_nb_candles_exit.range:
dataframe[f'ma_exit_{val}'] = ta.EMA(dataframe, timeperiod=val)
# ---------------- original code -------------------
##SMAOffset
#if self.entry_trigger.value == 'EMA':
# dataframe['ma_entry'] = ta.EMA(dataframe, timeperiod=self.base_nb_candles_entry.value)
#else:
# dataframe['ma_entry'] = ta.SMA(dataframe, timeperiod=self.base_nb_candles_entry.value)
#
#if self.exit_trigger.value == 'EMA':
# dataframe['ma_exit'] = ta.EMA(dataframe, timeperiod=self.base_nb_candles_exit.value)
#else:
# dataframe['ma_exit'] = ta.SMA(dataframe, timeperiod=self.base_nb_candles_exit.value)
#
#dataframe['ma_offset_entry'] = dataframe['ma_entry'] * self.low_offset.value
#dataframe['ma_offset_exit'] = dataframe['ma_exit'] * self.high_offset.value
# ------------ end original code --------------------
# Elliot
dataframe['EWO'] = EWO(dataframe, self.fast_ewo.value, self.slow_ewo.value)
# RSI
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
conditions.append((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))
conditions.append((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))
# ---------------- original code -------------------
#conditions.append(
# (
# (dataframe['close'] < dataframe['ma_offset_entry']) &
# (dataframe['EWO'] > self.ewo_high.value) &
# (dataframe['rsi'] < self.rsi_entry.value) &
# (dataframe['volume'] > 0)
# )
#)
#conditions.append(
# (
# (dataframe['close'] < dataframe['ma_offset_entry']) &
# (dataframe['EWO'] < self.ewo_low.value) &
# (dataframe['volume'] > 0)
# )
#)
# ------------ end original code --------------------
if conditions:
dataframe.loc[reduce(lambda x, y: x | y, conditions), 'enter_long'] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
conditions.append((dataframe['close'] > dataframe[f'ma_exit_{self.base_nb_candles_exit.value}'] * self.high_offset.value) & (dataframe['volume'] > 0))
# ---------------- original code -------------------
#conditions.append(
# (
# (dataframe['close'] > dataframe['ma_offset_exit']) &
# (dataframe['volume'] > 0)
# )
#)
# ------------ end original code --------------------
if conditions:
dataframe.loc[reduce(lambda x, y: x | y, conditions), 'exit_long'] = 1
return dataframe