302 lines
9.8 KiB
Python
302 lines
9.8 KiB
Python
# --- Do not remove these libs ---
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# --- Do not remove these libs ---
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from freqtrade.strategy.interface import IStrategy
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from typing import Dict, List
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from functools import reduce
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from pandas import DataFrame
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# --------------------------------
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import talib.abstract as ta
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import numpy as np
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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import datetime
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from technical.util import resample_to_interval, resampled_merge
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from datetime import datetime, timedelta
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from freqtrade.persistence import Trade
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from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter
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import technical.indicators as ftt
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# @Rallipanos
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# # Buy hyperspace params:
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# buy_params = {
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# "base_nb_candles_buy": 14,
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# "ewo_high": 2.327,
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# "ewo_high_2": -2.327,
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# "ewo_low": -20.988,
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# "low_offset": 0.975,
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# "low_offset_2": 0.955,
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# "rsi_buy": 69
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# }
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# # Buy hyperspace params:
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# buy_params = {
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# "base_nb_candles_buy": 18,
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# "ewo_high": 3.422,
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# "ewo_high_2": -3.436,
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# "ewo_low": -8.562,
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# "low_offset": 0.966,
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# "low_offset_2": 0.959,
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# "rsi_buy": 66,
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# }
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# # # Sell hyperspace params:
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# # sell_params = {
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# # "base_nb_candles_sell": 17,
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# # "high_offset": 0.997,
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# # "high_offset_2": 1.01,
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# # }
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# # Sell hyperspace params:
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# sell_params = {
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# "base_nb_candles_sell": 7,
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# "high_offset": 1.014,
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# "high_offset_2": 0.995,
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# }
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# # Buy hyperspace params:
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# buy_params = {
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# "ewo_high_2": -5.642,
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# "low_offset_2": 0.951,
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# "rsi_buy": 54,
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# "base_nb_candles_buy": 16, # value loaded from strategy
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# "ewo_high": 3.422, # value loaded from strategy
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# "ewo_low": -8.562, # value loaded from strategy
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# "low_offset": 0.966, # value loaded from strategy
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# }
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# # Sell hyperspace params:
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# sell_params = {
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# "base_nb_candles_sell": 8,
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# "high_offset_2": 1.002,
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# "high_offset": 1.014, # value loaded from strategy
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# }
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# Buy hyperspace params:
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buy_params = {
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"ewo_high_2": -4.58,
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"low_offset_2": 0.951,
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"rsi_buy": 52,
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"base_nb_candles_buy": 16, # value loaded from strategy
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"ewo_high": 3.422, # value loaded from strategy
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"ewo_low": -8.562, # value loaded from strategy
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"low_offset": 0.966, # value loaded from strategy
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}
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# Sell hyperspace params:
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sell_params = {
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"base_nb_candles_sell": 10,
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"high_offset_2": 1.002,
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"high_offset": 1.014, # value loaded from strategy
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}
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def EWO(dataframe, ema_length=5, ema2_length=35):
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df = dataframe.copy()
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ema1 = ta.EMA(df, timeperiod=ema_length)
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ema2 = ta.EMA(df, timeperiod=ema2_length)
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emadif = (ema1 - ema2) / df['low'] * 100
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return emadif
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class NotAnotherSMAOffsetStrategyHO(IStrategy):
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INTERFACE_VERSION = 2
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# ROI table:
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minimal_roi = {
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"0": 0.289,
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"25": 0.105,
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"80": 0.038,
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"140": 0
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}
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# Stoploss:
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stoploss = -0.35
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# SMAOffset
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base_nb_candles_buy = IntParameter(
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5, 80, default=buy_params['base_nb_candles_buy'], space='buy', optimize=False)
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base_nb_candles_sell = IntParameter(
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5, 80, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True)
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low_offset = DecimalParameter(
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0.9, 0.99, default=buy_params['low_offset'], space='buy', optimize=False)
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low_offset_2 = DecimalParameter(
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0.9, 0.99, default=buy_params['low_offset_2'], space='buy', optimize=True)
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high_offset = DecimalParameter(
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0.95, 1.1, default=sell_params['high_offset'], space='sell', optimize=False)
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high_offset_2 = DecimalParameter(
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0.99, 1.5, default=sell_params['high_offset_2'], space='sell', optimize=True)
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# Protection
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fast_ewo = 50
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slow_ewo = 200
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ewo_low = DecimalParameter(-20.0, -8.0,
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default=buy_params['ewo_low'], space='buy', optimize=False)
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ewo_high = DecimalParameter(
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2.0, 12.0, default=buy_params['ewo_high'], space='buy', optimize=False)
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ewo_high_2 = DecimalParameter(
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-6.0, 12.0, default=buy_params['ewo_high_2'], space='buy', optimize=True)
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rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=True)
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# Trailing stop:
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trailing_stop = True
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trailing_stop_positive = 0.005
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trailing_stop_positive_offset = 0.03
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trailing_only_offset_is_reached = True
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# Sell signal
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use_sell_signal = True
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sell_profit_only = False
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sell_profit_offset = 0.01
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ignore_roi_if_buy_signal = False
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# Optional order time in force.
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order_time_in_force = {
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'buy': 'gtc',
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'sell': 'ioc'
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}
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# Optimal timeframe for the strategy
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timeframe = '5m'
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inf_1h = '1h'
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process_only_new_candles = True
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startup_candle_count = 200
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plot_config = {
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'main_plot': {
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'ma_buy': {'color': 'orange'},
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'ma_sell': {'color': 'orange'},
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},
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}
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slippage_protection = {
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'retries': 3,
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'max_slippage': -0.02
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}
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buy_signals = {}
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def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
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rate: float, time_in_force: str, sell_reason: str,
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current_time: datetime, **kwargs) -> bool:
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dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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last_candle = dataframe.iloc[-1]
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if (last_candle is not None):
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if (sell_reason in ['sell_signal']):
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if (last_candle['hma_50']*1.149 > last_candle['ema_100']) and (last_candle['close'] < last_candle['ema_100']*0.951): # *1.2
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return False
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# slippage
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try:
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state = self.slippage_protection['__pair_retries']
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except KeyError:
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state = self.slippage_protection['__pair_retries'] = {}
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candle = dataframe.iloc[-1].squeeze()
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slippage = (rate / candle['close']) - 1
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if slippage < self.slippage_protection['max_slippage']:
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pair_retries = state.get(pair, 0)
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if pair_retries < self.slippage_protection['retries']:
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state[pair] = pair_retries + 1
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return False
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state[pair] = 0
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return True
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# Calculate all ma_buy values
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for val in self.base_nb_candles_buy.range:
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dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val)
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# Calculate all ma_sell values
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for val in self.base_nb_candles_sell.range:
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dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val)
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dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50)
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dataframe['ema_100'] = ta.EMA(dataframe, timeperiod=100)
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dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9)
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# Elliot
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dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo)
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# RSI
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dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
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dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
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dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)
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return dataframe
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe.loc[
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(
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(dataframe['rsi_fast'] < 35) &
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(dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) &
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(dataframe['EWO'] > self.ewo_high.value) &
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(dataframe['rsi'] < self.rsi_buy.value) &
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(dataframe['volume'] > 0) &
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(dataframe['close'] < (
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dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value))
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),
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['buy', 'buy_tag']] = (1, 'ewo1')
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dataframe.loc[
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(
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(dataframe['rsi_fast'] < 35) &
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(dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset_2.value)) &
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(dataframe['EWO'] > self.ewo_high_2.value) &
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(dataframe['rsi'] < self.rsi_buy.value) &
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(dataframe['volume'] > 0) &
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(dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) &
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(dataframe['rsi'] < 25)
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),
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['buy', 'buy_tag']] = (1, 'ewo2')
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dataframe.loc[
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(
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(dataframe['rsi_fast'] < 35) &
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(dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) &
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(dataframe['EWO'] < self.ewo_low.value) &
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(dataframe['volume'] > 0) &
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(dataframe['close'] < (
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dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value))
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),
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['buy', 'buy_tag']] = (1, 'ewolow')
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return dataframe
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def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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conditions = []
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conditions.append(
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((dataframe['close'] > dataframe['sma_9']) &
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(dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value)) &
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(dataframe['rsi'] > 50) &
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(dataframe['volume'] > 0) &
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(dataframe['rsi_fast'] > dataframe['rsi_slow'])
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)
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(
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(dataframe['close'] < dataframe['hma_50']) &
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(dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) &
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(dataframe['volume'] > 0) &
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(dataframe['rsi_fast'] > dataframe['rsi_slow'])
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)
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)
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if conditions:
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dataframe.loc[
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reduce(lambda x, y: x | y, conditions),
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'sell'
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]=1
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return dataframe
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