Timeframe
5m
Direction
Long Only
Stoploss
-100.0%
Trailing Stop
No
ROI
0m: 1000.0%
Interface Version
N/A
Startup Candles
20
Indicators
5
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
from datetime import datetime, timedelta
import logging
from typing import Optional, Union
import freqtrade.vendor.qtpylib.indicators as qtpylib
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
from freqtrade.strategy import DecimalParameter, IntParameter
from functools import reduce
logger = logging.getLogger(__name__)
def ewo(dataframe, ema_length=5, ema2_length=35):
#df = dataframe.copy()
df = dataframe
ema1 = ta.EMA(df, timeperiod=ema_length)
ema2 = ta.EMA(df, timeperiod=ema2_length)
emadif = (ema1 - ema2) / df['low'] * 100
return emadif
class Savannah(IStrategy):
minimal_roi = {
"0": 10
}
timeframe = '5m'
# TODO verificare se impostarlo a False, sembra che faccia le operazioni solo ogni 5 minuti
#process_only_new_candles = True
process_only_new_candles = False
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
}
# Trailing stoploss (not used)
trailing_stop = False
trailing_stop_positive = 0
trailing_stop_positive_offset = 0.0
trailing_only_offset_is_reached = False
max_open_trades = 3
# Disabled
stoploss = -1.0
# Max Trade Duration (da rivedere in base alla leva)
max_trade_duration = 300
# Futures
custom_leverage = 1.0
# DCA
position_adjustment_enable = True
max_entry = 2
first_entry_ratio = 0.65
# Custom stoploss
use_custom_stoploss = True
is_optimize_ewo = True
buy_rsi_fast = IntParameter(35, 50, default=42, space='buy', optimize=is_optimize_ewo)
buy_rsi = IntParameter(15, 35, default=35, space='buy', optimize=is_optimize_ewo)
buy_ewo = DecimalParameter(-6.0, 5, default=-5.836, space='buy', optimize=is_optimize_ewo)
buy_ema_low = DecimalParameter(0.9, 0.99, default=0.956, space='buy', optimize=is_optimize_ewo)
buy_ema_high = DecimalParameter(0.95, 1.2, default=1.043, space='buy', optimize=is_optimize_ewo)
is_optimize_32 = True
buy_rsi_fast_32 = IntParameter(20, 70, default=40, space='buy', optimize=is_optimize_32)
buy_rsi_32 = IntParameter(15, 50, default=29, space='buy', optimize=is_optimize_32)
buy_sma15_32 = DecimalParameter(0.900, 1, default=0.975, decimals=3, space='buy', optimize=is_optimize_32)
buy_cti_32 = DecimalParameter(-1, 0, default=-0.55, decimals=2, space='buy', optimize=is_optimize_32)
is_optimize_deadfish = True
sell_deadfish_bb_width = DecimalParameter(0.03, 0.75, default=0.359, space='sell', optimize=is_optimize_deadfish)
sell_deadfish_profit = DecimalParameter(-0.15, -0.05, default=-0.05, space='sell', optimize=is_optimize_deadfish)
sell_deadfish_bb_factor = DecimalParameter(0.90, 1.20, default=0.928, space='sell', optimize=is_optimize_deadfish)
sell_deadfish_volume_factor = DecimalParameter(1, 2.5, default=2.45, space='sell', optimize=is_optimize_deadfish)
sell_fastx = IntParameter(50, 100, default=64, space='sell', optimize=True)
plot_config = {
'main_plot': {
'EWO': {},
'ema_8': {'color': 'red'},
'ema_16': {'color': 'white'},
'sma_15': {'color': 'yellow'},
},
'subplots': {
"RSI": {
'rsi': {'color': 'yellow'},
'rsi_fast': {'color': 'red'},
'rsi_slow': {'color': 'blue'},
}
}
}
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# buy_1 indicators
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)
# ewo indicators
dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8)
dataframe['ema_16'] = ta.EMA(dataframe, timeperiod=16)
dataframe['EWO'] = ewo(dataframe, 50, 200)
# profit sell indicators
stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# loss sell indicators
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']
dataframe['bb_width'] = (
(dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2'])
dataframe['volume_mean_12'] = dataframe['volume'].rolling(12).mean().shift(1)
dataframe['volume_mean_24'] = dataframe['volume'].rolling(24).mean().shift(1)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
dataframe.loc[:, 'enter_tag'] = ''
is_ewo = (
(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)
)
buy_1 = (
(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_ewo)
dataframe.loc[is_ewo, 'enter_tag'] += 'ewo'
conditions.append(buy_1)
dataframe.loc[buy_1, 'enter_tag'] += 'buy_1'
if conditions:
dataframe.loc[
reduce(lambda x, y: x | y, conditions),
'enter_long'] = 1
return dataframe
def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
current_profit: float, **kwargs) -> float:
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
current_candle = dataframe.iloc[-1].squeeze()
enter_tag = ''
if hasattr(trade, 'enter_tag') and trade.enter_tag is not None:
enter_tag = trade.enter_tag
enter_tags = enter_tag.split()
if "ewo" in enter_tags:
if current_profit >= 0.05 * self.custom_leverage:
return -0.005 * self.custom_leverage
if current_profit > 0:
if current_candle["fastk"] > self.sell_fastx.value:
return -0.001 * self.custom_leverage
if current_candle["rsi"] > 80:
return -0.001 * self.custom_leverage
if current_profit < 0:
if current_candle["rsi"] > 90:
return -0.001 * self.custom_leverage
return self.stoploss * self.custom_leverage
def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
current_profit: float, **kwargs) -> Optional[Union[str, bool]]:
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
current_candle = dataframe.iloc[-1].squeeze()
# stoploss - deadfish
if ((current_profit < self.sell_deadfish_profit.value)
and (current_candle['bb_width'] < self.sell_deadfish_bb_width.value)
and (current_candle['close'] > current_candle['bb_middleband2'] * self.sell_deadfish_bb_factor.value)
and (current_candle['volume_mean_12'] < current_candle[
'volume_mean_24'] * self.sell_deadfish_volume_factor.value)):
logger.info(f"{pair} sell_stoploss_deadfish at {current_profit*100}")
return "sell_stoploss_deadfish"
# trade expired
trade_duration = (current_time - trade.open_date_utc).seconds / 60
if trade_duration > self.max_trade_duration:
logger.info(f"{pair} trade_expired at {current_profit*100}")
return "trade_expired"
#TODO liquidation protection
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[(), ['exit_long', 'exit_tag']] = (0, 'long_out')
return dataframe
def leverage(self, pair: str, current_time: datetime, current_rate: float,
proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
**kwargs) -> float:
"""
Customize leverage for each new trade. This method is only called in futures mode.
:param pair: Pair that's currently analyzed
:param current_time: datetime object, containing the current datetime
:param current_rate: Rate, calculated based on pricing settings in exit_pricing.
:param proposed_leverage: A leverage proposed by the bot.
:param max_leverage: Max leverage allowed on this pair
:param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal.
:param side: 'long' or 'short' - indicating the direction of the proposed trade
:return: A leverage amount, which is between 1.0 and max_leverage.
"""
return self.custom_leverage
# Let unlimited stakes leave funds open for DCA orders
def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
proposed_stake: float, min_stake: Optional[float], max_stake: float,
entry_tag: Optional[str], side: str, **kwargs) -> float:
self.proposed_stake = proposed_stake
return proposed_stake * self.first_entry_ratio
def adjust_trade_position(self, trade: Trade, current_time: datetime,
current_rate: float, current_profit: float, min_stake: float,
max_stake: float, **kwargs) -> Optional[float]:
if current_profit > -0.05:
return None
filled_entries = trade.select_filled_orders(trade.entry_side)
count_of_entries = len(filled_entries)
if count_of_entries >= self.max_entry: return None
dca_amount = self.proposed_stake * (1 - self.first_entry_ratio)
logger.info(f"DCA {trade.pair} with stake amount of: {dca_amount}")
return dca_amount