# source: https://raw.githubusercontent.com/RoboticAutomations/freqtradestrategies/52c4bd40414645f079a980cf4486dc7af4061efb/AIRA%20-%20open%20code%20v2/strategies/elliott-wave/overwatch.py # directory_url: https://github.com/RoboticAutomations/freqtradestrategies/blob/main/AIRA - open code v2/strategies/elliott-wave/ # User: RoboticAutomations # Repository: freqtradestrategies # --------------------# --- Do not remove these libs --- from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame # -------------------------------- import talib.abstract as ta import numpy as np import freqtrade.vendor.qtpylib.indicators as qtpylib import datetime from technical.util import resample_to_interval, resampled_merge from datetime import datetime, timedelta from freqtrade.persistence import Trade from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter, BooleanParameter import technical.indicators as ftt from freqtrade.exchange import timeframe_to_minutes # Buy hyperspace params: buy_params = { "base_nb_candles_buy": 46, "ewo_high": 2.239, "ewo_low": -8.029, "low_offset": 0.938, "rsi_buy": 64 } # Sell hyperspace params: sell_params = { "base_nb_candles_sell": 49, "high_offset": 1.019 } def EWO(dataframe, ema_length=5, ema2_length=30): df = dataframe.copy() ema1 = ta.EMA(df, timeperiod=ema_length) ema2 = ta.EMA(df, timeperiod=ema2_length) emadif = (ema1 - ema2) / df['close'] * 100 return emadif class Github_RoboticAutomations_freqtradestrategies__overwatch__20260915_233254(IStrategy): INTERFACE_VERSION = 2 # ROI table: minimal_roi = { "0": 0.078, "13": 0.044, "60": 0.016, "162": 0 } # Stoploss: stoploss = -0.329 # SMAOffset base_nb_candles_buy = IntParameter(5, 80, default=buy_params['base_nb_candles_buy'], space='buy', optimize=True) base_nb_candles_sell = IntParameter(5, 80, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True) low_offset = DecimalParameter(0.9, 0.99, default=buy_params['low_offset'], space='buy', optimize=True) high_offset = DecimalParameter(0.99, 1.1, default=sell_params['high_offset'], space='sell', optimize=True) # Protection fast_ewo = 50 slow_ewo = 200 ewo_low = DecimalParameter(-20.0, -8.0, default=buy_params['ewo_low'], space='buy', optimize=True) ewo_high = DecimalParameter(2.0, 12.0, default=buy_params['ewo_high'], space='buy', optimize=True) rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=True) ### BTC fast_1h = IntParameter(3, 8, default=3, space='buy', optimize=True) slow_1h = IntParameter(25, 40, default=39, space='buy', optimize=True) # Trailing stop: trailing_stop = True trailing_stop_positive = 0.005 trailing_stop_positive_offset = 0.03 trailing_only_offset_is_reached = True # Sell signal use_sell_signal = True sell_profit_only = False sell_profit_offset = 0.01 ignore_roi_if_buy_signal = True ## Optional order time in force. order_time_in_force = { 'buy': 'gtc', 'sell': 'ioc' } # Optimal timeframe for the strategy timeframe = '1h' informative_timeframe = '1d' process_only_new_candles = True startup_candle_count = 79 plot_config = { 'main_plot': { 'ma_buy': {'color': 'orange'}, 'ma_sell': {'color': 'orange'}, }, } # Define the parameter spaces # CooldownPeriod cooldown_lookback = IntParameter(0, 48, default=5, space="protection", optimize=True) # StoplossGuard use_stop_protection = BooleanParameter(default=True, space="protection", optimize=True) stop_duration = IntParameter(12, 200, default=5, space="protection", optimize=True) stop_protection_only_per_pair = BooleanParameter(default=False, space="protection", optimize=True) stop_protection_only_per_side = BooleanParameter(default=False, space="protection", optimize=True) stop_protection_trade_limit = IntParameter(1, 10, default=4, space="protection", optimize=True) stop_protection_required_profit = DecimalParameter(-1.0, 3.0, default=0.0, space="protection", optimize=True) # LowProfitPairs use_lowprofit_protection = BooleanParameter(default=True, space="protection", optimize=True) lowprofit_protection_lookback = IntParameter(1, 10, default=6, space="protection", optimize=True) lowprofit_trade_limit = IntParameter(1, 10, default=4, space="protection", optimize=True) lowprofit_stop_duration = IntParameter(1, 100, default=60, space="protection", optimize=True) lowprofit_required_profit = DecimalParameter(-1.0, 3.0, default=0.0, space="protection", optimize=True) lowprofit_only_per_pair = BooleanParameter(default=False, space="protection", optimize=True) # MaxDrawdown use_maxdrawdown_protection = BooleanParameter(default=True, space="protection", optimize=True) maxdrawdown_protection_lookback = IntParameter(1, 10, default=6, space="protection", optimize=True) maxdrawdown_trade_limit = IntParameter(1, 20, default=10, space="protection", optimize=True) maxdrawdown_stop_duration = IntParameter(1, 100, default=6, space="protection", optimize=True) maxdrawdown_allowed_drawdown = DecimalParameter(0.01, 0.10, default=0.0, space="protection", optimize=True) @property def protections(self): prot = [] prot.append({ "method": "CooldownPeriod", "stop_duration_candles": self.cooldown_lookback.value }) if self.use_stop_protection.value: prot.append({ "method": "StoplossGuard", "lookback_period_candles": 24 * 3, "trade_limit": self.stop_protection_trade_limit.value, "stop_duration_candles": self.stop_duration.value, "only_per_pair": self.stop_protection_only_per_pair.value, "required_profit": self.stop_protection_required_profit.value, "only_per_side": self.stop_protection_only_per_side.value }) if self.use_lowprofit_protection.value: prot.append({ "method": "LowProfitPairs", "lookback_period_candles": self.lowprofit_protection_lookback.value, "trade_limit": self.lowprofit_trade_limit.value, "stop_duration_candles": self.lowprofit_stop_duration.value, "required_profit": self.lowprofit_required_profit.value, "only_per_pair": self.lowprofit_only_per_pair.value }) if self.use_maxdrawdown_protection.value: prot.append({ "method": "MaxDrawdown", "lookback_period_candles": self.maxdrawdown_protection_lookback.value, "trade_limit": self.maxdrawdown_trade_limit.value, "stop_duration_candles": self.maxdrawdown_stop_duration.value, "max_allowed_drawdown": self.maxdrawdown_allowed_drawdown.value }) return prot def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.informative_timeframe) for pair in pairs] return informative_pairs def get_informative_indicators(self, metadata: dict): dataframe = self.dp.get_pair_dataframe( pair=metadata['pair'], timeframe=self.informative_timeframe) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Calculate all ma_buy values for val in self.base_nb_candles_buy.range: dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val) # Calculate all ma_sell values for val in self.base_nb_candles_sell.range: dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val) # Elliot #dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo) dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo) # RSI dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_ma'] = ta.SMA(dataframe['rsi'], timeperiod=5) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_buy.value) & (dataframe['volume'] > 0) ), ['enter_long', 'enter_tag']] = (1, 'EWO above high') dataframe.loc[ ( (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value)) & (dataframe['EWO'] < self.ewo_low.value) & (dataframe['volume'] > 0) ), ['enter_long', 'enter_tag']] = (1, 'EWO below low') return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)) & (dataframe['volume'] > 0) ), ['exit_long', 'exit_tag']] = (1, 'Close > MA-Sell') return dataframe