Timeframe
5m
Direction
Long Only
Stoploss
-99.0%
Trailing Stop
Yes
ROI
0m: 1.8%
Interface Version
N/A
Startup Candles
N/A
Indicators
2
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.interface import IStrategy
from pandas import DataFrame
from datetime import datetime, timedelta
def bollinger_bands(stock_price, window_size, num_of_std):
rolling_mean = stock_price.rolling(window=window_size).mean()
rolling_std = stock_price.rolling(window=window_size).std()
lower_band = rolling_mean - (rolling_std * num_of_std)
return np.nan_to_num(rolling_mean), np.nan_to_num(lower_band)
class CombinedBinHAndClucV3(IStrategy):
minimal_roi = {
"0": 0.018
}
stoploss = -0.99
timeframe = '5m'
use_sell_signal = True
sell_profit_only = True
sell_profit_offset = 0.001
ignore_roi_if_buy_signal = True
# Trailing stoploss
trailing_stop = True
trailing_only_offset_is_reached = True
trailing_stop_positive = 0.01
trailing_stop_positive_offset = 0.03
use_custom_stoploss = True
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 30
# Optional order type mapping.
order_types = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
if (current_time - timedelta(minutes=2200) > trade.open_date_utc) & (current_profit < 0):
return 0.01
return 0.5
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# strategy BinHV45
mid, lower = bollinger_bands(dataframe['close'], window_size=40, num_of_std=2)
dataframe['lower'] = lower
dataframe['bbdelta'] = (mid - dataframe['lower']).abs()
dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
# strategy ClucMay72018
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['ema_slow'] = ta.EMA(dataframe, timeperiod=50)
dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean()
# EMA
dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
( # strategy BinHV45
dataframe['lower'].shift().gt(0) &
dataframe['bbdelta'].gt(dataframe['close'] * 0.008) &
dataframe['closedelta'].gt(dataframe['close'] * 0.0175) &
dataframe['tail'].lt(dataframe['bbdelta'] * 0.25) &
dataframe['close'].lt(dataframe['lower'].shift()) &
dataframe['close'].le(dataframe['close'].shift())
)
|
( # strategy ClucMay72018
((dataframe['close'] < dataframe['ema_slow']) &
(dataframe['close'] < 0.985 * dataframe['bb_lowerband']) &
(dataframe['volume'] < (dataframe['volume_mean_slow'].shift(1) * 20)))
),
'buy'
] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
"""
dataframe.loc[
# (qtpylib.crossed_below(dataframe['ema_slow'], dataframe['ema_200']))
# |
(
(dataframe['close'] > dataframe['bb_upperband']) &
(dataframe['close'].shift(1) > dataframe['bb_upperband'].shift(1)) &
(dataframe['high'].shift(2) > dataframe['bb_upperband'].shift(2)) &
(dataframe['high'].shift(3) > dataframe['bb_upperband'].shift(3)) &
(dataframe['high'].shift(4) > dataframe['bb_upperband'].shift(4)) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'sell'
] = 1
return dataframe