BB_RPB_TSL @author jilv220 Simple bollinger brand strategy inspired by this blog ( https://hacks-for-life.blogspot.com/2020/12/freqtrade-notes.html ) RPB, which stands for Real Pull Back, taken from ( https://github.com/GeorgeMurAlkh/freqtrade-stuff/blob/main/user_data/strategies/TheRealPullbackV2.py ) The trailing custom stoploss taken from BigZ04_TSL from Perkmeister ( modded by ilya ) I modified it to better suit my taste and added Hyperopt for this strategy.
Timeframe
5m
Direction
Long Only
Stoploss
-99.0%
Trailing Stop
No
ROI
0m: 10000.0%
Interface Version
N/A
Startup Candles
120
Indicators
11
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
import pandas_ta as pta
from freqtrade.persistence import Trade
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame, Series, DatetimeIndex, merge
from datetime import datetime, timedelta
from freqtrade.strategy import merge_informative_pair, CategoricalParameter, DecimalParameter, IntParameter, stoploss_from_open
from functools import reduce
from technical.indicators import RMI, zema
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
def williams_r(dataframe: DataFrame, period: int = 14) -> Series:
"""Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the high and low
of the past N days (for a given N). It was developed by a publisher and promoter of trading materials, Larry Williams.
Its purpose is to tell whether a stock or commodity limit is trading near the high or the low, or somewhere in between,
of its recent trading range.
The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest).
"""
highest_high = dataframe["high"].rolling(center=False, window=period).max()
lowest_low = dataframe["low"].rolling(center=False, window=period).min()
WR = Series(
(highest_high - dataframe["close"]) / (highest_high - lowest_low),
name=f"{period} Williams %R",
)
return WR * -100
def vmap_b(dataframe, window_size=20, num_of_std=1):
df = dataframe.copy()
df['vwap'] = qtpylib.rolling_vwap(df,window=window_size)
rolling_std = df['vwap'].rolling(window=window_size).std()
df['vwap_low'] = df['vwap'] - (rolling_std * num_of_std)
df['vwap_high'] = df['vwap'] + (rolling_std * num_of_std)
return df['vwap_low'], df['vwap'], df['vwap_high']
def top_percent_change(dataframe: DataFrame, length: int) -> float:
"""
Percentage change of the current close from the range maximum Open price
:param dataframe: DataFrame The original OHLC dataframe
:param length: int The length to look back
"""
if length == 0:
return (dataframe['open'] - dataframe['close']) / dataframe['close']
else:
return (dataframe['open'].rolling(length).max() - dataframe['close']) / dataframe['close']
class BB_RPB_TSL_RNG_VWAP_0(IStrategy):
'''
BB_RPB_TSL
@author jilv220
Simple bollinger brand strategy inspired by this blog ( https://hacks-for-life.blogspot.com/2020/12/freqtrade-notes.html )
RPB, which stands for Real Pull Back, taken from ( https://github.com/GeorgeMurAlkh/freqtrade-stuff/blob/main/user_data/strategies/TheRealPullbackV2.py )
The trailing custom stoploss taken from BigZ04_TSL from Perkmeister ( modded by ilya )
I modified it to better suit my taste and added Hyperopt for this strategy.
'''
buy_params = {
"buy_btc_safe": -289,
"buy_btc_safe_1d": -0.05,
"buy_threshold": 0.003,
"buy_bb_factor": 0.999,
"buy_bb_delta": 0.025,
"buy_bb_width": 0.095,
"buy_cci": -116,
"buy_cci_length": 25,
"buy_rmi": 49,
"buy_rmi_length": 17,
"buy_srsi_fk": 32,
"buy_closedelta": 12.148,
"buy_ema_diff": 0.022,
"buy_adx": 20,
"buy_fastd": 20,
"buy_fastk": 22,
"buy_ema_cofi": 0.98,
"buy_ewo_high": 4.179,
"buy_ema_high_2": 1.087,
"buy_ema_low_2": 0.970,
}
sell_params = {
"pHSL": -0.25,
"pPF_1": 0.019,
"pPF_2": 0.065,
"pSL_1": 0.019,
"pSL_2": 0.062,
"sell_btc_safe": -389,
"base_nb_candles_sell": 24,
"high_offset": 0.991,
"high_offset_2": 0.997
}
minimal_roi = {
"0": 100,
}
timeframe = '5m'
inf_1h = '1h'
stoploss = -0.99
use_custom_stoploss = True
use_sell_signal = True
process_only_new_candles = True
startup_candle_count = 120
order_types = {
'buy': 'limit',
'sell': 'limit',
'emergencysell': 'limit',
'forcebuy': "limit",
'forcesell': 'limit',
'stoploss': 'limit',
'stoploss_on_exchange': False,
'stoploss_on_exchange_interval': 60,
'stoploss_on_exchange_limit_ratio': 0.99
}
is_optimize_dip = False
buy_rmi = IntParameter(30, 50, default=35, optimize= is_optimize_dip)
buy_cci = IntParameter(-135, -90, default=-133, optimize= is_optimize_dip)
buy_srsi_fk = IntParameter(30, 50, default=25, optimize= is_optimize_dip)
buy_cci_length = IntParameter(25, 45, default=25, optimize = is_optimize_dip)
buy_rmi_length = IntParameter(8, 20, default=8, optimize = is_optimize_dip)
is_optimize_break = False
buy_bb_width = DecimalParameter(0.05, 0.2, default=0.15, optimize = is_optimize_break)
buy_bb_delta = DecimalParameter(0.025, 0.08, default=0.04, optimize = is_optimize_break)
is_optimize_local_dip = False
buy_ema_diff = DecimalParameter(0.022, 0.027, default=0.025, optimize = is_optimize_local_dip)
buy_bb_factor = DecimalParameter(0.990, 0.999, default=0.995, optimize = False)
buy_closedelta = DecimalParameter(12.0, 18.0, default=15.0, optimize = is_optimize_local_dip)
is_optimize_ewo = False
buy_rsi_fast = IntParameter(35, 50, default=45, optimize = False)
buy_rsi = IntParameter(15, 30, default=35, optimize = False)
buy_ewo = DecimalParameter(-6.0, 5, default=-5.585, optimize = is_optimize_ewo)
buy_ema_low = DecimalParameter(0.9, 0.99, default=0.942 , optimize = is_optimize_ewo)
buy_ema_high = DecimalParameter(0.95, 1.2, default=1.084 , optimize = is_optimize_ewo)
is_optimize_ewo_2 = False
buy_ema_low_2 = DecimalParameter(0.96, 0.978, default=0.96 , optimize = is_optimize_ewo_2)
buy_ema_high_2 = DecimalParameter(1.05, 1.2, default=1.09 , optimize = is_optimize_ewo_2)
is_optimize_cofi = False
buy_ema_cofi = DecimalParameter(0.96, 0.98, default=0.97 , optimize = is_optimize_cofi)
buy_fastk = IntParameter(20, 30, default=20, optimize = is_optimize_cofi)
buy_fastd = IntParameter(20, 30, default=20, optimize = is_optimize_cofi)
buy_adx = IntParameter(20, 30, default=30, optimize = is_optimize_cofi)
buy_ewo_high = DecimalParameter(2, 12, default=3.553, optimize = is_optimize_cofi)
is_optimize_btc_safe = False
buy_btc_safe = IntParameter(-300, 50, default=-200, optimize = is_optimize_btc_safe)
buy_btc_safe_1d = DecimalParameter(-0.075, -0.025, default=-0.05, optimize = is_optimize_btc_safe)
buy_threshold = DecimalParameter(0.003, 0.012, default=0.008, optimize = is_optimize_btc_safe)
buy_is_dip_enabled = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_is_break_enabled = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
sell_btc_safe = IntParameter(-400, -300, default=-365, optimize = True)
base_nb_candles_sell = IntParameter(5, 80, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True)
high_offset = DecimalParameter(0.95, 1.1, default=sell_params['high_offset'], space='sell', optimize=True)
high_offset_2 = DecimalParameter(0.99, 1.5, default=sell_params['high_offset_2'], space='sell', optimize=True)
pHSL = DecimalParameter(-0.200, -0.040, default=-0.08, decimals=3, space='sell', load=True)
pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3, space='sell', load=True)
pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', load=True)
pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell', load=True)
pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, space='sell', load=True)
def informative_pairs(self):
informative_pairs = [("BTC/USDT", "5m")]
return informative_pairs
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
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
buy_tag = ''
if hasattr(trade, 'buy_tag') and trade.buy_tag is not None:
buy_tag = trade.buy_tag
buy_tags = buy_tag.split()
if len(buy_tags) == 1 and "vwap" in buy_tags:
PF_1 = 0.01
SL_1 = 0.01
PF_2 = 0.05
SL_2 = 0.042
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
if (sl_profit >= current_profit):
return -0.99
return stoploss_from_open(sl_profit, current_profit)
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
assert self.dp, "DataProvider is required for multiple timeframes."
bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband2'] = bollinger2['lower']
dataframe['bb_middleband2'] = bollinger2['mid']
dataframe['bb_upperband2'] = bollinger2['upper']
bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3)
dataframe['bb_lowerband3'] = bollinger3['lower']
dataframe['bb_middleband3'] = bollinger3['mid']
dataframe['bb_upperband3'] = bollinger3['upper']
vwap_low, vwap, vwap_high = vmap_b(dataframe, 20, 1)
dataframe['vwap_low'] = vwap_low
dataframe['tcp_percent_4'] = top_percent_change(dataframe, 4)
inf_tf = '5m'
informative = self.dp.get_pair_dataframe('BTC/USDT', timeframe=inf_tf)
informative_past = informative.copy().shift(1) # Get recent BTC info
informative_past_source = (informative_past['open'] + informative_past['close'] + informative_past['high'] + informative_past['low']) / 4 # Get BTC price
informative_threshold = informative_past_source * self.buy_threshold.value # BTC dump n% in 5 min
informative_past_delta = informative_past['close'].shift(1) - informative_past['close'] # should be positive if dump
informative_diff = informative_threshold - informative_past_delta # Need be larger than 0
dataframe['btc_threshold'] = informative_threshold
dataframe['btc_diff'] = informative_diff
informative_past_1d = informative.copy().shift(288)
informative_past_source_1d = (informative_past_1d['open'] + informative_past_1d['close'] + informative_past_1d['high'] + informative_past_1d['low']) / 4
dataframe['btc_5m'] = informative_past_source
dataframe['btc_1d'] = informative_past_source_1d
dataframe['bb_width'] = ((dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2'])
dataframe['bb_delta'] = ((dataframe['bb_lowerband2'] - dataframe['bb_lowerband3']) / dataframe['bb_lowerband2'])
dataframe['bb_bottom_cross'] = qtpylib.crossed_below(dataframe['close'], dataframe['bb_lowerband3']).astype('int')
for val in self.buy_cci_length.range:
dataframe[f'cci_length_{val}'] = ta.CCI(dataframe, val)
dataframe['cci'] = ta.CCI(dataframe, 26)
dataframe['cci_long'] = ta.CCI(dataframe, 170)
for val in self.buy_rmi_length.range:
dataframe[f'rmi_length_{val}'] = RMI(dataframe, length=val, mom=4)
stoch = ta.STOCHRSI(dataframe, 15, 20, 2, 2)
dataframe['srsi_fk'] = stoch['fastk']
dataframe['srsi_fd'] = stoch['fastd']
dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15)
dataframe['sma_30'] = ta.SMA(dataframe, timeperiod=30)
dataframe['cti'] = pta.cti(dataframe["close"], length=20)
dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8)
dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12)
dataframe['ema_13'] = ta.EMA(dataframe, timeperiod=13)
dataframe['ema_16'] = ta.EMA(dataframe, timeperiod=16)
dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26)
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)
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)
dataframe['rsi_84'] = ta.RSI(dataframe, timeperiod=84)
dataframe['rsi_112'] = ta.RSI(dataframe, timeperiod=112)
dataframe['EWO'] = EWO(dataframe, 50, 200)
stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
dataframe['adx'] = ta.ADX(dataframe)
dataframe['r_14'] = williams_r(dataframe, period=14)
dataframe['volume_mean_4'] = dataframe['volume'].rolling(4).mean().shift(1)
for val in self.base_nb_candles_sell.range:
dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
dataframe.loc[:, 'buy_tag'] = ''
if self.buy_is_dip_enabled.value:
is_dip = (
(dataframe[f'rmi_length_{self.buy_rmi_length.value}'] < self.buy_rmi.value) &
(dataframe[f'cci_length_{self.buy_cci_length.value}'] <= self.buy_cci.value) &
(dataframe['srsi_fk'] < self.buy_srsi_fk.value)
)
if self.buy_is_break_enabled.value:
is_break = (
( (dataframe['bb_delta'] > self.buy_bb_delta.value) #"buy_bb_delta": 0.025 0.036
& #"buy_bb_width": 0.095 0.133
(dataframe['bb_width'] > self.buy_bb_width.value)
)
&
(dataframe['closedelta'] > dataframe['close'] * self.buy_closedelta.value / 1000 ) & # from BinH
(dataframe['close'] < dataframe['bb_lowerband3'] * self.buy_bb_factor.value)
)
is_local_uptrend = ( # from NFI next gen
(dataframe['ema_26'] > dataframe['ema_12']) &
(dataframe['ema_26'] - dataframe['ema_12'] > dataframe['open'] * self.buy_ema_diff.value) &
(dataframe['ema_26'].shift() - dataframe['ema_12'].shift() > dataframe['open'] / 100) &
(dataframe['close'] < dataframe['bb_lowerband2'] * self.buy_bb_factor.value) &
(dataframe['closedelta'] > dataframe['close'] * self.buy_closedelta.value / 1000 )
)
is_ewo = ( # from SMA offset
(dataframe['rsi_fast'] < self.buy_rsi_fast.value) &
(dataframe['close'] < dataframe['ema_8'] * self.buy_ema_low.value) &
(dataframe['EWO'] > self.buy_ewo.value) &
(dataframe['close'] < dataframe['ema_16'] * self.buy_ema_high.value) &
(dataframe['rsi'] < self.buy_rsi.value)
)
is_nfi_32 = (
(dataframe['rsi_slow'] < dataframe['rsi_slow'].shift(1)) &
(dataframe['rsi_fast'] < 46) &
(dataframe['rsi'] > 19) &
(dataframe['close'] < dataframe['sma_15'] * 0.942) &
(dataframe['cti'] < -0.86)
)
is_nfi_33 = (
(dataframe['close'] < (dataframe['ema_13'] * 0.978)) &
(dataframe['EWO'] > 8) &
(dataframe['cti'] < -0.88) &
(dataframe['rsi'] < 32) &
(dataframe['r_14'] < -98.0) &
(dataframe['volume'] < (dataframe['volume_mean_4'] * 2.5))
)
is_vwap = (
(dataframe['close'] < dataframe['vwap_low']) &
(dataframe['tcp_percent_4'] > 0.04) &
(dataframe['cti'] < -0.8) &
(dataframe['rsi'] < 35) &
(dataframe['rsi_84'] < 60) &
(dataframe['rsi_112'] < 60) &
(dataframe['volume'] > 0)
)
is_BB_checked = is_dip & is_break
conditions.append(is_BB_checked) # ~1.7 89%
dataframe.loc[is_BB_checked, 'buy_tag'] += 'bb '
conditions.append(is_local_uptrend) # ~3.84 90.2%
dataframe.loc[is_local_uptrend, 'buy_tag'] += 'local uptrend '
conditions.append(is_ewo) # ~2.26 93.5%
dataframe.loc[is_ewo, 'buy_tag'] += 'ewo '
conditions.append(is_nfi_32) # ~2.43 91.3%
dataframe.loc[is_nfi_32, 'buy_tag'] += 'nfi 32 '
conditions.append(is_nfi_33) # ~0.11 100%
dataframe.loc[is_nfi_33, 'buy_tag'] += 'nfi 33 '
conditions.append(is_vwap)
dataframe.loc[is_vwap, 'buy_tag'] += 'vwap '
if conditions:
dataframe.loc[reduce(lambda x, y: x | y, conditions), 'buy' ] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
conditions.append(
(
(dataframe['close'] > dataframe['sma_9'])&
(dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value)) &
(dataframe['rsi']>50)&
(dataframe['volume'] > 0)&
(dataframe['rsi_fast'] > dataframe['rsi_slow'])
)
|
(
(dataframe['sma_9'] > (dataframe['sma_9'].shift(1) + dataframe['sma_9'].shift(1)*0.005 )) &
(dataframe['close'] < dataframe['hma_50'])&
(dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.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),
'sell'
]=1
return dataframe