Timeframe
5m
Direction
Long Only
Stoploss
-99.0%
Trailing Stop
No
ROI
0m: 10000.0%
Interface Version
3
Startup Candles
200
Indicators
3
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
from freqtrade.strategy import IStrategy, informative
from freqtrade.strategy import (merge_informative_pair,
DecimalParameter, IntParameter, BooleanParameter, CategoricalParameter, stoploss_from_open)
from pandas import DataFrame, Series
from typing import Dict, List, Optional, Tuple
from functools import reduce
from freqtrade.persistence import Trade
from datetime import datetime, timedelta, timezone
from freqtrade.exchange import timeframe_to_prev_date
import talib.abstract as ta
import math
import pandas_ta as pta
import logging
from logging import FATAL
import time
import requests
import threading
logger = logging.getLogger(__name__)
###########################################################################################################
## DONATIONS for stash86 ##
## ##
## Real-life money : https://patreon.com/stash86 ##
## BTC: 1FghqtgGLpD9F21BNDMje4iyj4cSzVPZPb ##
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## ##
## REFERRAL LINKS ##
## ##
## Binance: https://accounts.binance.com/en/register?ref=143744527 ##
## Kucoin: https://www.kucoin.com/ucenter/signup?rcode=r3BWY2T ##
## Vultr (you get $100 credit that expires in 14 days) : https://www.vultr.com/?ref=8944192-8H ##
###########################################################################################################
class Cenderawasih_3_kucoin (IStrategy):
def version(self) -> str:
return "cend_3_kucoin"
INTERFACE_VERSION = 3
# ROI table:
minimal_roi = {
"0": 100.0
}
# Buy hyperspace params:
buy_params = {
"base_nb_candles_buy_vwma": 31,
"low_offset_vwma": 0.989,
"buy_rsi_vwma": 52,
"base_nb_candles_buy_ema": 19,
"low_offset_ema": 0.912,
"buy_ema_length_15m": 30,
}
# Sell hyperspace params:
sell_params = {
"base_nb_candles_sell_ema": 61,
"high_offset_ema": 0.942,
"base_nb_candles_sell_ema2": 5,
"high_offset_ema2": 0.908,
"base_nb_candles_sell_ema3": 7,
"high_offset_ema3": 0.947,
"base_nb_candles_sell_ema4": 14,
"high_offset_ema4": 1.088,
}
# Protection hyperspace params:
protection_params = {
"cooldown_lookback": 2, # value loaded from strategy
}
cooldown_lookback = IntParameter(2, 48, default=2, space="protection", optimize=False)
@property
def protections(self):
prot = []
prot.append({
"method": "CooldownPeriod",
"stop_duration_candles": self.cooldown_lookback.value
})
return prot
optimize_buy_ema = False
base_nb_candles_buy_ema = IntParameter(5, 100, default=6, space='buy', optimize=optimize_buy_ema)
low_offset_ema = DecimalParameter(0.9, 0.99, default=0.9, space='buy', optimize=optimize_buy_ema)
optimize_buy_vwma = False
base_nb_candles_buy_vwma = IntParameter(5, 100, default=6, space='buy', optimize=optimize_buy_vwma)
low_offset_vwma = DecimalParameter(0.9, 0.99, default=0.9, space='buy', optimize=optimize_buy_vwma)
optimize_buy_ema_length_15m = False
buy_ema_length_15m = CategoricalParameter([5, 10, 15, 20, 25, 30, 35, 40], default=10, optimize=optimize_buy_ema_length_15m)
buy_rsi_vwma = IntParameter(10, 70, default=50, optimize=False)
optimize_sell_ema = False
base_nb_candles_sell_ema = IntParameter(5, 100, default=6, space='sell', optimize=optimize_sell_ema)
high_offset_ema = DecimalParameter(0.9, 1.1, default=0.95, space='sell', optimize=optimize_sell_ema)
optimize_sell_ema2 = False
base_nb_candles_sell_ema2 = IntParameter(5, 100, default=6, space='sell', optimize=optimize_sell_ema2)
high_offset_ema2 = DecimalParameter(0.9, 1.1, default=0.95, space='sell', optimize=optimize_sell_ema2)
optimize_sell_ema3 = False
base_nb_candles_sell_ema3 = IntParameter(5, 100, default=6, space='sell', optimize=optimize_sell_ema3)
high_offset_ema3 = DecimalParameter(0.9, 1.1, default=0.95, space='sell', optimize=optimize_sell_ema3)
optimize_sell_ema4 = False
base_nb_candles_sell_ema4 = IntParameter(5, 100, default=6, space='sell', optimize=optimize_sell_ema4)
high_offset_ema4 = DecimalParameter(0.9, 1.1, default=0.95, space='sell', optimize=optimize_sell_ema4)
# Stoploss:
stoploss = -0.99
# 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 = False
exit_profit_offset = 0.01
ignore_roi_if_entry_signal = False
timeframe = '5m'
process_only_new_candles = True
startup_candle_count = 200
use_custom_stoploss = True
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
sl_new = 1
if (current_profit > 0.2):
sl_new = 0.05
elif (current_profit > 0.1):
sl_new = 0.03
elif (current_profit > 0.06):
sl_new = 0.02
elif (current_profit > 0.03):
sl_new = 0.01
elif (current_profit > 0.015):
sl_new = 0.005
return sl_new
age_filter = 30
@informative('1d')
def populate_indicators_1d(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['age_filter_ok'] = (dataframe['volume'].rolling(window=self.age_filter, min_periods=self.age_filter).min() > 0)
if not self.config['runmode'].value in ('dry_run', 'live'):
drop_columns = ['open', 'high', 'low', 'close', 'volume']
dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True)
return dataframe
@informative('15m')
def populate_indicators_15m(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
if (self.config['runmode'].value in ('hyperopt')) and self.optimize_buy_ema_length_15m:
for val in self.buy_ema_length_15m.range:
dataframe[f'ema_{val}'] = ta.EMA(dataframe, timeperiod=int(val))
else:
dataframe[f'ema_{self.buy_ema_length_15m.value}'] = ta.EMA(dataframe, timeperiod=int(self.buy_ema_length_15m.value))
if not self.config['runmode'].value in ('dry_run', 'live'):
drop_columns = ['open', 'high', 'low', 'close', 'volume']
dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True)
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# # RSI
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe['live_data_ok'] = (dataframe['volume'].rolling(window=72, min_periods=72).min() > 0)
if not self.optimize_buy_ema:
dataframe['ema_offset_buy'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema.value)) *self.low_offset_ema.value
if not self.optimize_buy_vwma:
dataframe['vwma_offset_buy'] = pta.vwma(dataframe["close"], dataframe["volume"], int(self.base_nb_candles_buy_vwma.value)) *self.low_offset_vwma.value
if not self.optimize_sell_ema:
dataframe['ema_offset_sell'] = ta.EMA(dataframe, int(self.base_nb_candles_sell_ema.value)) *self.high_offset_ema.value
if not self.optimize_sell_ema2:
dataframe['ema_offset_sell2'] = ta.EMA(dataframe, int(self.base_nb_candles_sell_ema2.value)) *self.high_offset_ema2.value
if not self.optimize_sell_ema3:
dataframe['ema_offset_sell3'] = ta.EMA(dataframe, int(self.base_nb_candles_sell_ema3.value)) *self.high_offset_ema3.value
if not self.optimize_sell_ema4:
dataframe['ema_offset_sell4'] = ta.EMA(dataframe, int(self.base_nb_candles_sell_ema4.value)) *self.high_offset_ema4.value
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
if self.optimize_buy_ema:
dataframe['ema_offset_buy'] = ta.EMA(dataframe, int(self.base_nb_candles_buy_ema.value)) *self.low_offset_ema.value
if self.optimize_buy_vwma:
dataframe['vwma_offset_buy'] = pta.vwma(dataframe["close"], dataframe["volume"], int(self.base_nb_candles_buy_vwma.value)) *self.low_offset_vwma.value
dataframe.loc[:, 'enter_tag'] = ''
dataframe.loc[:, 'enter_long'] = 0
add_check = (
dataframe['live_data_ok']
&
dataframe['age_filter_ok_1d']
)
buy_offset_vwma = (
((dataframe['close'] < dataframe['vwma_offset_buy']))
&
(dataframe['close'] < dataframe['ema_offset_buy'])
&
(dataframe['rsi'] < self.buy_rsi_vwma.value)
&
(dataframe['close'] < dataframe[f'ema_{self.buy_ema_length_15m.value}_15m'])
)
dataframe.loc[buy_offset_vwma, 'enter_tag'] += 'vwma '
conditions.append(buy_offset_vwma)
if conditions:
dataframe.loc[
reduce(lambda x, y: x | y, conditions)
&
add_check,
'enter_long',
]= 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
if self.optimize_sell_ema:
dataframe['ema_offset_sell'] = ta.EMA(dataframe, int(self.base_nb_candles_sell_ema.value)) *self.high_offset_ema.value
if self.optimize_sell_ema2:
dataframe['ema_offset_sell2'] = ta.EMA(dataframe, int(self.base_nb_candles_sell_ema2.value)) *self.high_offset_ema2.value
if self.optimize_sell_ema3:
dataframe['ema_offset_sell3'] = ta.EMA(dataframe, int(self.base_nb_candles_sell_ema3.value)) *self.high_offset_ema3.value
if self.optimize_sell_ema4:
dataframe['ema_offset_sell4'] = ta.EMA(dataframe, int(self.base_nb_candles_sell_ema4.value)) *self.high_offset_ema4.value
dataframe.loc[:, 'exit_tag'] = ''
conditions = []
sell_cond_2 = (
(dataframe['close'] > dataframe['ema_offset_sell'])
)
conditions.append(sell_cond_2)
dataframe.loc[sell_cond_2, 'exit_tag'] += 'EMA_1 '
sell_cond_4 = (
(dataframe['close'] < dataframe['ema_offset_sell2'])
)
conditions.append(sell_cond_4)
dataframe.loc[sell_cond_4, 'exit_tag'] += 'EMA_2 '
sell_cond_3 = (
((dataframe['close'] < dataframe['ema_offset_sell3']).rolling(2).min() > 0)
)
conditions.append(sell_cond_3)
dataframe.loc[sell_cond_3, 'exit_tag'] += 'EMA_3 '
sell_cond_1 = (
(dataframe['close'] > dataframe['ema_offset_sell4']).rolling(2).min() > 0
)
conditions.append(sell_cond_1)
dataframe.loc[sell_cond_1, 'exit_tag'] += 'EMA_4 '
add_check = (
(dataframe['volume'] > 0)
)
if conditions:
dataframe.loc[
reduce(lambda x, y: x | y, conditions) & add_check,
'exit_long'
] = 1
return dataframe