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E0V1E_20240510_1351.py
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E0V1E_20240510_1351.py
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from datetime import datetime, timedelta
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
import warnings
warnings.simplefilter(action="ignore", category=RuntimeWarning)
class E0V1E_20240510_1351(IStrategy):
minimal_roi = {
"0": 1
}
timeframe = '5m'
process_only_new_candles = True
startup_candle_count = 120
order_types = {
'entry': 'market',
'exit': 'market',
'emergency_exit': 'market',
'force_entry': 'market',
'force_exit': "market",
'stoploss': 'market',
'stoploss_on_exchange': True,
'stoploss_on_exchange_interval': 60,
'stoploss_on_exchange_market_ratio': 0.99
}
stoploss = -0.25
is_optimize_32 = True
buy_rsi_fast_32 = IntParameter(20, 70, default=45, space='buy', optimize=is_optimize_32)
buy_rsi_32 = IntParameter(15, 50, default=35, space='buy', optimize=is_optimize_32)
buy_sma15_32 = DecimalParameter(0.900, 1, default=0.961, decimals=3, space='buy', optimize=is_optimize_32)
buy_cti_32 = DecimalParameter(-1, 0, default=-0.58, decimals=2, space='buy', optimize=is_optimize_32)
sell_fastx = IntParameter(50, 100, default=70, space='sell', optimize=True)
sell_loss_cci = IntParameter(low=0, high=600, default=148, space='sell', optimize=False)
sell_loss_cci_profit = DecimalParameter(-0.15, 0, default=-0.04, decimals=2, space='sell', optimize=False)
sell_cci = IntParameter(low=0, high=200, default=90, space='sell', optimize=False)
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)
# profit sell indicators
stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
dataframe['fastk'] = stoch_fast['fastk']
dataframe['cci'] = ta.CCI(dataframe, timeperiod=20)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
dataframe.loc[:, 'enter_tag'] = ''
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(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_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
current_profit: float, **kwargs):
dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
current_candle = dataframe.iloc[-1].squeeze()
if current_time - timedelta(minutes=10) < trade.open_date_utc:
if current_profit >= 0.05:
return "profit_sell_fast"
if current_profit > 0:
if current_candle["fastk"] > self.sell_fastx.value:
return "fastk_profit_sell"
if current_candle["cci"] > self.sell_cci.value:
return "cci_profit_sell"
if current_time - timedelta(hours=2) > trade.open_date_utc:
if current_profit > 0:
return "profit_sell_in_2h"
if current_candle["high"] >= trade.open_rate:
if current_candle["cci"] > self.sell_cci.value:
return "cci_sell"
if current_profit > self.sell_loss_cci_profit.value:
if current_candle["cci"] > self.sell_loss_cci.value:
return "cci_loss_sell"
return None
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[(), ['exit_long', 'exit_tag']] = (0, 'long_out')
return dataframe