Timeframe
5m
Direction
Long Only
Stoploss
-10.0%
Trailing Stop
No
ROI
0m: 10000.0%
Interface Version
N/A
Startup Candles
20
Indicators
4
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
from datetime import datetime, timedelta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import talib.abstract as ta
import pandas_ta as pta
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
from freqtrade.strategy import DecimalParameter, IntParameter
from functools import reduce
class BBModCEV1(IStrategy):
minimal_roi = {
"0": 100
}
timeframe = '5m'
process_only_new_candles = True
startup_candle_count = 20
order_types = {
'entry': 'market',
'exit': 'market',
'emergency_exit': 'market',
'force_entry': 'market',
'force_exit': "market",
'stoploss': 'market',
'stoploss_on_exchange': False,
'stoploss_on_exchange_interval': 60,
'stoploss_on_exchange_market_ratio': 0.99
}
stoploss = -0.1
use_custom_stoploss = True
is_optimize_32 = True
buy_rsi_fast_32 = IntParameter(20, 70, default=46, space='buy', optimize=is_optimize_32)
buy_rsi_32 = IntParameter(15, 50, default=19, space='buy', optimize=is_optimize_32)
buy_sma15_32 = DecimalParameter(0.900, 1, default=0.942, decimals=3, space='buy', optimize=is_optimize_32)
buy_cti_32 = DecimalParameter(-1, 0, default=-0.86, decimals=2, space='buy', optimize=is_optimize_32)
sell_fastx = IntParameter(50, 100, default=75, space='sell', optimize=False)
trailing_optimize = True
sl_1 = DecimalParameter(0.001, 0.01, default=0.005, decimals=3, space='sell', optimize=trailing_optimize)
sl_2 = DecimalParameter(0.005, 0.02, default=0.01, decimals=3, space='sell', optimize=trailing_optimize)
sl_3 = DecimalParameter(0.008, 0.03, default=0.02, decimals=3, space='sell', optimize=trailing_optimize)
sl_4 = DecimalParameter(0.001, 0.008, default=0.002, decimals=3, space='sell', optimize=trailing_optimize)
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
if current_profit > 0.08:
return -self.sl_3.value
elif current_profit > 0.05:
return -self.sl_2.value
elif current_profit >= 0.02:
return -self.sl_1.value
if current_time - timedelta(minutes=60) > trade.open_date_utc:
if 0.02 > current_profit >= 0.01:
return -self.sl_4.value
return 1
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15)
dataframe['cti'] = pta.cti(dataframe["close"], length=20)
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)
stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
dataframe.loc[:, 'enter_tag'] = ''
is_nfi_32 = (
(dataframe['rsi_slow'] < dataframe['rsi_slow'].shift(1)) &
(dataframe['rsi_fast'] < self.buy_rsi_fast_32.value) &
(dataframe['rsi'] > self.buy_rsi_32.value) &
(dataframe['close'] < dataframe['sma_15'] * self.buy_sma15_32.value) &
(dataframe['cti'] < self.buy_cti_32.value)
)
conditions.append(is_nfi_32)
dataframe.loc[is_nfi_32, 'enter_tag'] += 'nfi_32 '
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 = []
dataframe.loc[:, 'exit_tag'] = ''
fastk_cross = (
(qtpylib.crossed_above(dataframe['fastk'], self.sell_fastx.value))
)
conditions.append(fastk_cross)
dataframe.loc[fastk_cross, 'exit_tag'] += 'fastk_cross '
if conditions:
dataframe.loc[
reduce(lambda x, y: x | y, conditions),
'exit_long'] = 1
return dataframe