Timeframe
5m
Direction
Long Only
Stoploss
-50.0%
Trailing Stop
No
ROI
0m: 2.8%, 10m: 1.8%, 30m: 1.0%, 40m: 0.5%
Interface Version
2
Startup Candles
N/A
Indicators
9
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
# -*- coding: utf-8 -*-
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame, Series
# --------------------------------
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
import math
import pandas_ta as pta
######################################## Warning ########################################
# You won't get a lot of benefits by simply changing to this strategy #
# with the HyperOpt values changed. #
# #
# You should test it closely, trying backtesting and dry running, and we recommend #
# customizing the terms of sale and purchase as well. #
# #
# You should always be careful in real trading! #
#########################################################################################
# Modified Buy / Sell params - 20210619
# Buy hyperspace params:
buy_params = {
"base_nb_candles_buy": 16,
"ewo_high": 5.672,
"ewo_low": -19.931,
"low_offset": 0.973,
"rsi_buy": 59,
"ewo_high_bb": 4.86,
"for_ma_length": 22,
"for_sigma": 1.74,
}
# Sell hyperspace params:
sell_params = {
"base_nb_candles_sell": 20,
"high_offset": 1.010,
"pHSL": -0.178,
"pPF_1": 0.01,
"pPF_2": 0.048,
"pSL_1": 0.009,
"pSL_2": 0.043,
"for_ma_length_sell": 65,
"for_sigma_sell": 1.895,
"rsi_high": 72,
}
# Volume Weighted Moving Average
def vwma(dataframe: DataFrame, length: int = 10):
"""Indicator: Volume Weighted Moving Average (VWMA)"""
# Calculate Result
pv = dataframe['close'] * dataframe['volume']
vwma = Series(ta.SMA(pv, timeperiod=length) / ta.SMA(dataframe['volume'], timeperiod=length))
return vwma
# Modified Elder Ray Index
def moderi(dataframe: DataFrame, len_slow_ma: int = 32) -> Series:
slow_ma = Series(ta.EMA(vwma(dataframe, length=len_slow_ma), timeperiod=len_slow_ma))
return slow_ma >= slow_ma.shift(1) # we just need true & false for ERI trend
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 SMA_BBRSI(IStrategy):
INTERFACE_VERSION = 2
# Modified ROI - 20210620
# ROI table:
minimal_roi = {
"0": 0.028,
"10": 0.018,
"30": 0.010,
"40": 0.005
}
# Stoploss:
stoploss = -0.5
antipump_threshold = DecimalParameter(0, 0.4, default=0.25, space='buy', optimize=True)
# SMAOffset
base_nb_candles_buy = IntParameter(
5, 80, default=buy_params['base_nb_candles_buy'], space='buy', optimize=True)
base_nb_candles_sell = IntParameter(
5, 80, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True)
low_offset = DecimalParameter(
0.9, 0.99, default=buy_params['low_offset'], space='buy', optimize=True)
high_offset = DecimalParameter(
0.99, 1.1, default=sell_params['high_offset'], space='sell', optimize=True)
# Protection
fast_ewo = 50
slow_ewo = 200
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)
# 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_sell_signal = True
sell_profit_only = False
sell_profit_offset = 0.01
ignore_roi_if_buy_signal = False
# Optimal timeframe for the strategy
timeframe = '5m'
informative_timeframe = '1h'
process_only_new_candles = True
startup_candle_count: int = 200
plot_config = {
'main_plot': {
'ma_buy_16': {'color': 'orange'},
'ma_sell_20': {'color': 'purple'},
},
'pump_strength': {
'pump_strength': {'color': 'yellow'}
},
}
protections = [
# {
# "method": "StoplossGuard",
# "lookback_period_candles": 12,
# "trade_limit": 1,
# "stop_duration_candles": 6,
# "only_per_pair": True
# },
# {
# "method": "StoplossGuard",
# "lookback_period_candles": 12,
# "trade_limit": 2,
# "stop_duration_candles": 6,
# "only_per_pair": False
# },
{
"method": "LowProfitPairs",
"lookback_period_candles": 60,
"trade_limit": 1,
"stop_duration": 60,
"required_profit": -0.05
},
{
"method": "MaxDrawdown",
"lookback_period_candles": 24,
"trade_limit": 1,
"stop_duration_candles": 12,
"max_allowed_drawdown": 0.2
},
]
ewo_high_bb = DecimalParameter(0, 7.0, default=buy_params['ewo_high_bb'], space='buy', optimize=True)
for_sigma = DecimalParameter(0, 10.0, default=buy_params['for_sigma'], space='buy', optimize=True)
for_sigma_sell = DecimalParameter(0, 10.0, default=sell_params['for_sigma_sell'], space='sell', optimize=True)
rsi_high = IntParameter(60, 100, default=sell_params['rsi_high'], space='sell', optimize=True)
for_ma_length = IntParameter(5, 80, default=buy_params['for_ma_length'], space='buy', optimize=True)
for_ma_length_sell = IntParameter(5, 80, default=sell_params['for_ma_length_sell'], space='sell', optimize=True)
is_optimize_trailing = True
pHSL = DecimalParameter(-0.200, -0.040, default=-0.08, decimals=3, space='sell', optimize=is_optimize_trailing , load=True)
# profit threshold 1, trigger point, SL_1 is used
pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3, space='sell', optimize=is_optimize_trailing , load=True)
pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', optimize=is_optimize_trailing , load=True)
# profit threshold 2, SL_2 is used
pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell', optimize=is_optimize_trailing , load=True)
pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, space='sell', optimize=is_optimize_trailing , load=True)
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
# hard stoploss profit
HSL = self.pHSL.value
PF_1 = self.pPF_1.value
SL_1 = self.pSL_1.value
PF_2 = self.pPF_2.value
SL_2 = self.pSL_2.value
# For profits between PF_1 and PF_2 the stoploss (sl_profit) used is linearly interpolated
# between the values of SL_1 and SL_2. For all profits above PL_2 the sl_profit value
# rises linearly with current profit, for profits below PF_1 the hard stoploss profit is used.
if (current_profit > PF_2):
sl_profit = SL_2 + (current_profit - PF_2)
elif (current_profit > PF_1):
sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1))
else:
sl_profit = HSL
# Only for hyperopt invalid return
if (sl_profit >= current_profit):
return -0.99
return stoploss_from_open(sl_profit, current_profit)
def informative_pairs(self):
pairs = self.dp.current_whitelist()
informative_pairs = [(pair, self.informative_timeframe) for pair in pairs]
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:
# //@version=3
# study(" RSI + BB (EMA) + Dispersion (2.0)", overlay=false)
#
# // Инициализация параметров
# src = input(title="Source", type=source, defval=close) // Устанавливаем тип цены для расчетов
src = 'close'
# for_rsi = input(title="RSI_period", type=integer, defval=14) // Период для RSI
for_rsi = 14
# for_ma = input(title="Basis_BB", type=integer, defval=20) // Период для MA внутри BB
# for_ma = 20
# for_mult = input(title="Stdev", type=integer, defval=2, minval=1, maxval=5) // Число стандартных отклонений для BB
for_mult = 2
# for_sigma = input(title="Dispersion", type=float, defval=0.1, minval=0.01, maxval=1) // Дисперсия вокруг MA
for_sigma = 0.1
#
# // Условия работы скрипта
# current_rsi = rsi(src, for_rsi) // Текущее положение индикатора RSI
dataframe['rsi'] = ta.RSI(dataframe[src], for_rsi)
dataframe['rsi_4'] = ta.RSI(dataframe[src], 4)
if self.config['runmode'].value == 'hyperopt':
for for_ma in range(5, 81):
# basis = ema(current_rsi, for_ma)
dataframe[f'basis_{for_ma}'] = ta.EMA(dataframe['rsi'], for_ma)
# dev = for_mult * stdev(current_rsi, for_ma)
dataframe[f'dev_{for_ma}'] = ta.STDDEV(dataframe['rsi'], for_ma)
# upper = basis + dev
#dataframe[f'upper_{for_ma}'] = (dataframe[f'basis_{for_ma}'] + (dataframe[f'dev_{for_ma}'] * for_mult))
# lower = basis - dev
#dataframe[f'lower_{for_ma}'] = dataframe[f'basis_{for_ma}'] - (dataframe[f'dev_{for_ma}'] * for_mult)
# disp_up = basis + ((upper - lower) * for_sigma) // Минимально-допустимый порог в области мувинга, который должен преодолеть RSI (сверху)
# dataframe[f'disp_up_{for_ma}'] = dataframe[f'basis_{for_ma}'] + ((dataframe[f'upper_{for_ma}'] - dataframe[f'lower_{for_ma}']) * for_sigma)
# disp_down = basis - ((upper - lower) * for_sigma) // Минимально-допустимый порог в области мувинга, который должен преодолеть RSI (снизу)
# dataframe[f'disp_down_{for_ma}'] = dataframe[f'basis_{for_ma}'] - ((dataframe[f'upper_{for_ma}'] - dataframe[f'lower_{for_ma}']) * for_sigma)
# color_rsi = current_rsi >= disp_up ? lime : current_rsi <= disp_down ? red : #ffea00 // Текущий цвет RSI, в зависимости от его местоположения внутри BB
else:
dataframe[f'basis_{self.for_ma_length.value}'] = ta.EMA(dataframe['rsi'], self.for_ma_length.value)
dataframe[f'basis_{self.for_ma_length_sell.value}'] = ta.EMA(dataframe['rsi'], self.for_ma_length_sell.value)
# dev = for_mult * stdev(current_rsi, for_ma)
dataframe[f'dev_{self.for_ma_length.value}'] = ta.STDDEV(dataframe['rsi'], self.for_ma_length.value)
dataframe[f'dev_{self.for_ma_length_sell.value}'] = ta.STDDEV(dataframe['rsi'], self.for_ma_length_sell.value)
#
# // Дополнительные линии и заливка для областей для RSI
# h1 = hline(70, color=#d4d4d4, linestyle=dotted, linewidth=1)
h1 = 70
# h2 = hline(30, color=#d4d4d4, linestyle=dotted, linewidth=1)
h2 = 30
# fill (h1, h2, transp=95)
#
# // Алерты и условия срабатывания
# rsi_Green = crossover(current_rsi, disp_up)
# rsi_Red = crossunder(current_rsi, disp_down)
# alertcondition(condition=rsi_Green,
# title="RSI cross Above Dispersion Area",
# message="The RSI line closing crossed above the Dispersion area.")
#
# alertcondition(condition=rsi_Red,
# title="RSI cross Under Dispersion Area",
# message="The RSI line closing crossed below the Dispersion area")
#
# // Результаты и покраска
# plot(basis, color=black)
# plot(upper, color=#00fff0, linewidth=2)
# plot(lower, color=#00fff0, linewidth=2)
# s1 = plot(disp_up, color=white)
# s2 = plot(disp_down, color=white)
# fill(s1, s2, color=white, transp=80)
# plot(current_rsi, color=color_rsi, linewidth=2)
#ATR
dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
dataframe['atr_high'] = (dataframe['high'] - (dataframe['atr'] * 3.5)).rolling(2).max()
dataframe['ema_atr'] = ta.EMA(dataframe['atr'], timeperiod=14)
# #HLC3
# dataframe['hlc3'] = (dataframe['high'] + dataframe['low'] + dataframe['close']) / 3
# #OTF
# dataframe['hlc3OTF'] = OTF(dataframe, source='close')
# #ZLEMA BUY
# dataframe['zlema_1'] = dataframe['hlc3OTF']
# dataframe['zlema_1_std'] = dataframe['close']
# dataframe['ema_data'] = dataframe['hlc3OTF'] + (dataframe['hlc3OTF'] - dataframe['hlc3OTF'].shift(2))
# dataframe['ema_data_2'] = dataframe['hlc3OTF'] + (dataframe['hlc3OTF'] - dataframe['hlc3OTF'].shift(1))
# dataframe['zlema_4'] = ta.EMA(dataframe['ema_data'], timeperiod = 4)
# dataframe['zlema_2'] = ta.EMA(dataframe['ema_data_2'], timeperiod = 2)
# dataframe['ema_data_4_std'] = dataframe['close'] + (dataframe['close'] - dataframe['close'].shift(2))
# dataframe['zlema_4_std'] = ta.EMA(dataframe['ema_data_4_std'], timeperiod = 4)
# Modified Elder Ray Index
dataframe['moderi_96'] = moderi(dataframe, 96)
# CTI
dataframe['cti'] = pta.cti(dataframe["close"], length=20)
#CCI
dataframe['cci_slow'] = pta.cci(high=dataframe['high'], low=dataframe['low'], close=dataframe['close'], length=240)
dataframe['cci_fast'] = pta.cci(high=dataframe['high'], low=dataframe['low'], close=dataframe['close'], length=20)
# Calculate all ma_buy values
for val in self.base_nb_candles_buy.range:
dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val)
# Calculate all ma_sell values
for val in self.base_nb_candles_sell.range:
dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val)
# Elliot
dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo)
#pump stregth
dataframe['zema_30'] = ftt.zema(dataframe, period=30)
dataframe['zema_200'] = ftt.zema(dataframe, period=200)
dataframe['pump_strength'] = (dataframe['zema_30'] - dataframe['zema_200']) / dataframe['zema_30']
# RSI
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
dont_buy_conditions = []
dont_buy_conditions.append(
(dataframe['pump_strength'] > self.antipump_threshold.value)
)
conditions.append(
(
(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)
)
)
conditions.append(
(
(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)
)
)
conditions.append(
(
(dataframe['rsi'] < (dataframe[f'basis_{self.for_ma_length.value}'] - (dataframe[f'dev_{self.for_ma_length.value}'] * self.for_sigma.value)))
&
(
(
(dataframe['EWO'] > self.ewo_high_bb.value)
&
(dataframe['EWO'] < 10)
)
|
(
(dataframe['EWO'] >= 10)
&
(dataframe['rsi'] < 40)
)
)
&
(dataframe['rsi_4'] < 25)
&
(dataframe['volume'] > 0)
&
(dataframe['cci_fast'] < 100)
# &
# (qtpylib.crossed_above(dataframe['hlc3OTF'], dataframe['zlema_4']))
# &
# (dataframe["roc_bbwidth_max"] < 70)
) & (dataframe['moderi_96'] == True)
)
if conditions:
dataframe.loc[
reduce(lambda x, y: x | y, conditions),
'buy'
]=1
if dont_buy_conditions:
for condition in dont_buy_conditions:
dataframe.loc[condition, 'buy'] = 0
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
conditions.append(
(
(dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
(((
(dataframe['rsi'] > self.rsi_high.value) |
# upper = basis + dev
# lower = basis - dev
# disp_down = basis - ((upper - lower) * for_sigma) // Минимально-допустимый порог в области мувинга, который должен преодолеть RSI (снизу)
# disp_down = basis - ((2* dev * for_sigma) // Минимально-допустимый порог в области мувинга, который должен преодолеть RSI (снизу)
(dataframe['rsi'] > dataframe[f'basis_{self.for_ma_length_sell.value}'] + ((dataframe[f'dev_{self.for_ma_length_sell.value}'] * self.for_sigma_sell.value)))
) & (dataframe['moderi_96'] == True)) |
(
(qtpylib.crossed_below(dataframe['close'], dataframe['atr_high']))
))&
(dataframe['volume'] > 0)
)
)
if conditions:
dataframe.loc[
reduce(lambda x, y: x | y, conditions),
'sell'
]=1
return dataframe