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BigZ04.py
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BigZ04.py
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import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
from freqtrade.persistence import Trade
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
from datetime import datetime, timedelta
from freqtrade.strategy import merge_informative_pair, CategoricalParameter, DecimalParameter, IntParameter
from functools import reduce
###########################################################################################################
## BigZ04 by ilya ##
## ##
## https://github.com/i1ya/freqtrade-strategies ##
## The stratagy most inspired by iterativ (authors of the CombinedBinHAndClucV6) ##
## ## ##
###########################################################################################################
## The main point of this strat is: ##
## - make drawdown as low as possible ##
## - buy at dip ##
## - sell quick as fast as you can (release money for the next buy) ##
## - soft check if market if rising ##
## - hard check is market if fallen ##
## - 11 buy signals ##
## - stoploss function preventing from big fall ##
## - no sell signal. Whether ROI or stoploss =) ##
## ##
###########################################################################################################
## GENERAL RECOMMENDATIONS ##
## ##
## For optimal performance, suggested to use between 2 and 4 open trades, with unlimited stake. ##
## ##
## As a pairlist you can use VolumePairlist. ##
## ##
## Ensure that you don't override any variables in your config.json. Especially ##
## the timeframe (must be 5m). ##
## ##
## sell_profit_only: ##
## True - risk more (gives you higher profit and higher Drawdown) ##
## False (default) - risk less (gives you less ~10-15% profit and much lower Drawdown) ##
## ##
###########################################################################################################
## DONATIONS 2 @iterativ (author of the original strategy) ##
## ##
## Absolutely not required. However, will be accepted as a token of appreciation. ##
## ##
## BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk ##
## ETH: 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91 ##
## ##
###########################################################################################################
class BigZ04(IStrategy):
INTERFACE_VERSION = 2
minimal_roi = {
"0": 0.028, # I feel lucky!
"10": 0.018,
"40": 0.005,
"180": 0.018, # We're going up?
}
stoploss = -0.99 # effectively disabled.
timeframe = '5m'
inf_1h = '1h'
# Sell signal
use_sell_signal = True
sell_profit_only = False
sell_profit_offset = 0.001 # it doesn't meant anything, just to guarantee there is a minimal profit.
ignore_roi_if_buy_signal = False
# Trailing stoploss
trailing_stop = False
trailing_only_offset_is_reached = False
trailing_stop_positive = 0.01
trailing_stop_positive_offset = 0.025
# Custom stoploss
use_custom_stoploss = True
# Run "populate_indicators()" only for new candle.
process_only_new_candles = True
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 200
# Optional order type mapping.
order_types = {
'buy': 'market',
'sell': 'market',
'stoploss': 'market',
'stoploss_on_exchange': False
}
buy_params = {
#############
# Enable/Disable conditions
"buy_condition_0_enable": True,
"buy_condition_1_enable": True,
"buy_condition_2_enable": True,
"buy_condition_3_enable": True,
"buy_condition_4_enable": True,
"buy_condition_5_enable": True,
"buy_condition_6_enable": True,
"buy_condition_7_enable": True,
"buy_condition_8_enable": True,
"buy_condition_9_enable": True,
"buy_condition_10_enable": True,
"buy_condition_11_enable": True,
"buy_condition_12_enable": True,
"buy_condition_13_enable": False,
}
############################################################################
# Buy
buy_condition_0_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_1_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_2_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_3_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_4_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_5_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_6_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_7_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_8_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_9_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_10_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_11_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_12_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_condition_13_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
buy_bb20_close_bblowerband_safe_1 = DecimalParameter(0.7, 1.1, default=0.989, space='buy', optimize=False, load=True)
buy_bb20_close_bblowerband_safe_2 = DecimalParameter(0.7, 1.1, default=0.982, space='buy', optimize=False, load=True)
buy_volume_pump_1 = DecimalParameter(0.1, 0.9, default=0.4, space='buy', decimals=1, optimize=False, load=True)
buy_volume_drop_1 = DecimalParameter(1, 10, default=3.8, space='buy', decimals=1, optimize=False, load=True)
buy_volume_drop_2 = DecimalParameter(1, 10, default=3, space='buy', decimals=1, optimize=False, load=True)
buy_volume_drop_3 = DecimalParameter(1, 10, default=2.7, space='buy', decimals=1, optimize=False, load=True)
buy_rsi_1h_1 = DecimalParameter(10.0, 40.0, default=16.5, space='buy', decimals=1, optimize=False, load=True)
buy_rsi_1h_2 = DecimalParameter(10.0, 40.0, default=15.0, space='buy', decimals=1, optimize=False, load=True)
buy_rsi_1h_3 = DecimalParameter(10.0, 40.0, default=20.0, space='buy', decimals=1, optimize=False, load=True)
buy_rsi_1h_4 = DecimalParameter(10.0, 40.0, default=35.0, space='buy', decimals=1, optimize=False, load=True)
buy_rsi_1h_5 = DecimalParameter(10.0, 60.0, default=39.0, space='buy', decimals=1, optimize=False, load=True)
buy_rsi_1 = DecimalParameter(10.0, 40.0, default=28.0, space='buy', decimals=1, optimize=False, load=True)
buy_rsi_2 = DecimalParameter(7.0, 40.0, default=10.0, space='buy', decimals=1, optimize=False, load=True)
buy_rsi_3 = DecimalParameter(7.0, 40.0, default=14.2, space='buy', decimals=1, optimize=False, load=True)
buy_macd_1 = DecimalParameter(0.01, 0.09, default=0.02, space='buy', decimals=2, optimize=False, load=True)
buy_macd_2 = DecimalParameter(0.01, 0.09, default=0.03, space='buy', decimals=2, optimize=False, load=True)
def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool:
return True
def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
current_profit: float, **kwargs):
return False
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
# Manage losing trades and open room for better ones.
if (current_profit > 0):
return 0.99
else:
trade_time_50 = trade.open_date_utc + timedelta(minutes=50)
# Trade open more then 60 minutes. For this strategy it's means -> loss
# Let's try to minimize the loss
if (current_time > trade_time_50):
try:
number_of_candle_shift = int((current_time - trade_time_50).total_seconds() / 300)
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
candle = dataframe.iloc[-number_of_candle_shift].squeeze()
# We are at bottom. Wait...
if candle['rsi_1h'] < 30:
return 0.99
# Are we still sinking?
if candle['close'] > candle['ema_200']:
if current_rate * 1.025 < candle['open']:
return 0.01
if current_rate * 1.015 < candle['open']:
return 0.01
except IndexError as error:
# Whoops, set stoploss at 10%
return 0.1
return 0.99
def informative_pairs(self):
pairs = self.dp.current_whitelist()
informative_pairs = [(pair, '1h') for pair in pairs]
return informative_pairs
def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
assert self.dp, "DataProvider is required for multiple timeframes."
# Get the informative pair
informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h)
# EMA
informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50)
informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200)
# RSI
informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14)
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
informative_1h['bb_lowerband'] = bollinger['lower']
informative_1h['bb_middleband'] = bollinger['mid']
informative_1h['bb_upperband'] = bollinger['upper']
return informative_1h
def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=48).mean()
# EMA
dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26)
dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12)
# MACD
dataframe['macd'], dataframe['signal'], dataframe['hist'] = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
# SMA
dataframe['sma_5'] = ta.EMA(dataframe, timeperiod=5)
# RSI
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# The indicators for the 1h informative timeframe
informative_1h = self.informative_1h_indicators(dataframe, metadata)
dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True)
# The indicators for the normal (5m) timeframe
dataframe = self.normal_tf_indicators(dataframe, metadata)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
conditions.append(
(
self.buy_condition_12_enable.value &
(dataframe['close'] > dataframe['ema_200']) &
(dataframe['close'] > dataframe['ema_200_1h']) &
(dataframe['close'] < dataframe['bb_lowerband'] * 0.993) &
(dataframe['low'] < dataframe['bb_lowerband'] * 0.985) &
(dataframe['close'].shift() > dataframe['bb_lowerband']) &
(dataframe['rsi_1h'] < 72.8) &
(dataframe['open'] > dataframe['close']) &
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) &
(dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) &
(dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
((dataframe['open'] - dataframe['close']) < dataframe['bb_upperband'].shift(2) - dataframe['bb_lowerband'].shift(2)) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.buy_condition_11_enable.value &
(dataframe['close'] > dataframe['ema_200']) &
(dataframe['hist'] > 0) &
(dataframe['hist'].shift() > 0) &
(dataframe['hist'].shift(2) > 0) &
(dataframe['hist'].shift(3) > 0) &
(dataframe['hist'].shift(5) > 0) &
(dataframe['bb_middleband'] - dataframe['bb_middleband'].shift(5) > dataframe['close']/200) &
(dataframe['bb_middleband'] - dataframe['bb_middleband'].shift(10) > dataframe['close']/100) &
((dataframe['bb_upperband'] - dataframe['bb_lowerband']) < (dataframe['close']*0.1)) &
((dataframe['open'].shift() - dataframe['close'].shift()) < (dataframe['close'] * 0.018)) &
(dataframe['rsi'] > 51) &
(dataframe['open'] < dataframe['close']) &
(dataframe['open'].shift() > dataframe['close'].shift()) &
(dataframe['close'] > dataframe['bb_middleband']) &
(dataframe['close'].shift() < dataframe['bb_middleband'].shift()) &
(dataframe['low'].shift(2) > dataframe['bb_middleband'].shift(2)) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
)
)
conditions.append(
(
self.buy_condition_0_enable.value &
(dataframe['close'] > dataframe['ema_200']) &
(dataframe['rsi'] < 30) &
(dataframe['close'] * 1.024 < dataframe['open'].shift(3)) &
(dataframe['rsi_1h'] < 71) &
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) &
(dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
)
)
conditions.append(
(
self.buy_condition_1_enable.value &
(dataframe['close'] > dataframe['ema_200']) &
(dataframe['close'] > dataframe['ema_200_1h']) &
(dataframe['close'] < dataframe['bb_lowerband'] * self.buy_bb20_close_bblowerband_safe_1.value) &
(dataframe['rsi_1h'] < 69) &
(dataframe['open'] > dataframe['close']) &
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) &
(dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) &
(dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
((dataframe['open'] - dataframe['close']) < dataframe['bb_upperband'].shift(2) - dataframe['bb_lowerband'].shift(2)) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.buy_condition_2_enable.value &
(dataframe['close'] > dataframe['ema_200']) &
(dataframe['close'] < dataframe['bb_lowerband'] * self.buy_bb20_close_bblowerband_safe_2.value) &
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) &
(dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) &
(dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
(dataframe['open'] - dataframe['close'] < dataframe['bb_upperband'].shift(2) - dataframe['bb_lowerband'].shift(2)) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.buy_condition_3_enable.value &
(dataframe['close'] > dataframe['ema_200_1h']) &
(dataframe['close'] < dataframe['bb_lowerband']) &
(dataframe['rsi'] < self.buy_rsi_3.value) &
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) &
(dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) &
(dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_3.value)) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.buy_condition_4_enable.value &
(dataframe['rsi_1h'] < self.buy_rsi_1h_1.value) &
(dataframe['close'] < dataframe['bb_lowerband']) &
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) &
(dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) &
(dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.buy_condition_5_enable.value &
(dataframe['close'] > dataframe['ema_200']) &
(dataframe['close'] > dataframe['ema_200_1h']) &
(dataframe['ema_26'] > dataframe['ema_12']) &
((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_macd_1.value)) &
((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open']/100)) &
(dataframe['close'] < (dataframe['bb_lowerband'])) &
(dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) &
(dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
)
)
conditions.append(
(
self.buy_condition_6_enable.value &
(dataframe['rsi_1h'] < self.buy_rsi_1h_5.value) &
(dataframe['ema_26'] > dataframe['ema_12']) &
((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_macd_2.value)) &
((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open']/100)) &
(dataframe['close'] < (dataframe['bb_lowerband'])) &
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) &
(dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) &
(dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.buy_condition_7_enable.value &
(dataframe['rsi_1h'] < self.buy_rsi_1h_2.value) &
(dataframe['ema_26'] > dataframe['ema_12']) &
((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_macd_1.value)) &
((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open']/100)) &
(dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) &
(dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.buy_condition_8_enable.value &
(dataframe['rsi_1h'] < self.buy_rsi_1h_3.value) &
(dataframe['rsi'] < self.buy_rsi_1.value) &
(dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.buy_condition_9_enable.value &
(dataframe['rsi_1h'] < self.buy_rsi_1h_4.value) &
(dataframe['rsi'] < self.buy_rsi_2.value) &
(dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) &
(dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) &
(dataframe['volume'] > 0)
)
)
conditions.append(
(
self.buy_condition_10_enable.value &
(dataframe['rsi_1h'] < self.buy_rsi_1h_4.value) &
(dataframe['close_1h'] < dataframe['bb_lowerband_1h']) &
(dataframe['hist'] > 0) &
(dataframe['hist'].shift(2) < 0) &
(dataframe['rsi'] < 40.5) &
(dataframe['hist'] > dataframe['close'] * 0.0012) &
(dataframe['open'] < dataframe['close']) &
(dataframe['volume'] > 0)
)
)
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:
dataframe.loc[
(
(dataframe['close'] > dataframe['bb_middleband'] * 1.01) & # Don't be gready, sell fast
(dataframe['volume'] > 0) # Make sure Volume is not 0
)
,
'sell'
] = 0
return dataframe