from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame, Series # -------------------------------- 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_prev_date from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal, Real # Protection hyperspace params: protection_params = { "cooldown_stop_duration_candles": 1, "lowprofit2_lookback_period_candles": 107, "lowprofit2_required_profit": 0.026, "lowprofit2_stop_duration_candles": 8, "lowprofit2_trade_limit": 6, "lowprofit_lookback_period_candles": 34, "lowprofit_required_profit": 0.027, "lowprofit_stop_duration_candles": 126, "lowprofit_trade_limit": 36, "maxdrawdown_lookback_period_candles": 36, "maxdrawdown_max_allowed_drawdown": 0.34, "maxdrawdown_stop_duration_candles": 54, "maxdrawdown_trade_limit": 17, "stoplossguard_lookback_period_candles": 3, "stoplossguard_stop_duration_candles": 8, "stoplossguard_trade_limit": 11, } # Buy hyperspace params: buy_params = { "base_nb_candles_buy": 17, "ewo_high": 2.182, "ewo_high_2": -3.44, "ewo_low": -10.26, "low_offset": 1.066, "low_offset_2": 0.961, "rsi_buy": 68, "min_profit": 1.03 } # Sell hyperspace params: sell_params = { "base_nb_candles_sell": 9, "high_offset": 1.01, "high_offset_2": 1.233, "high_offset_ema": 0.931 } class abbas(IStrategy): INTERFACE_VERSION = 2 cooldown_stop_duration_candles = IntParameter(0, 20, default=protection_params["cooldown_stop_duration_candles"], space="protection", optimize=True) maxdrawdown_optimize = True maxdrawdown_lookback_period_candles = IntParameter(5, 40, default=protection_params["maxdrawdown_lookback_period_candles"], space="protection", optimize=maxdrawdown_optimize) maxdrawdown_trade_limit = IntParameter(1, 20, default=protection_params["maxdrawdown_trade_limit"], space="protection", optimize=maxdrawdown_optimize) maxdrawdown_stop_duration_candles = IntParameter(10, 60, default=protection_params["maxdrawdown_stop_duration_candles"], space="protection", optimize=maxdrawdown_optimize) maxdrawdown_max_allowed_drawdown = DecimalParameter(0.05, 0.40, default=protection_params["maxdrawdown_max_allowed_drawdown"], space="protection", decimals=2, optimize=maxdrawdown_optimize) stoplossguard_optimize = True stoplossguard_lookback_period_candles = IntParameter(1, 300, default=protection_params["stoplossguard_lookback_period_candles"], space="protection", optimize=stoplossguard_optimize) stoplossguard_trade_limit = IntParameter(1, 20, default=protection_params["stoplossguard_trade_limit"], space="protection", optimize=stoplossguard_optimize) stoplossguard_stop_duration_candles = IntParameter(1, 10, default=protection_params["stoplossguard_stop_duration_candles"], space="protection", optimize=stoplossguard_optimize) lowprofit_optimize = True lowprofit_lookback_period_candles = IntParameter(10, 60, default=protection_params["lowprofit_lookback_period_candles"], space="protection", optimize=lowprofit_optimize) lowprofit_trade_limit = IntParameter(1, 50, default=protection_params["lowprofit_trade_limit"], space="protection", optimize=lowprofit_optimize) lowprofit_stop_duration_candles = IntParameter(10, 200, default=protection_params["lowprofit_stop_duration_candles"], space="protection", optimize=lowprofit_optimize) lowprofit_required_profit = DecimalParameter(0.000, 0.050, default=protection_params["lowprofit_required_profit"], space="protection", decimals=3, optimize=lowprofit_optimize) lowprofit2_optimize = True lowprofit2_lookback_period_candles = IntParameter(10, 300, default=protection_params["lowprofit2_lookback_period_candles"], space="protection", optimize=lowprofit2_optimize) lowprofit2_trade_limit = IntParameter(1, 70, default=protection_params["lowprofit2_trade_limit"], space="protection", optimize=lowprofit2_optimize) lowprofit2_stop_duration_candles = IntParameter(1, 40, default=protection_params["lowprofit2_stop_duration_candles"], space="protection", optimize=lowprofit2_optimize) lowprofit2_required_profit = DecimalParameter(0, 0.030, default=protection_params["lowprofit2_required_profit"], space="protection", decimals=3, optimize=lowprofit2_optimize) @property def protections(self): prot = [] prot.append({ "method": "CooldownPeriod", "stop_duration_candles": self.cooldown_stop_duration_candles.value }) prot.append({ "method": "MaxDrawdown", "lookback_period_candles": self.maxdrawdown_lookback_period_candles.value, "trade_limit": self.maxdrawdown_trade_limit.value, "stop_duration_candles": self.maxdrawdown_stop_duration_candles.value, "max_allowed_drawdown": self.maxdrawdown_max_allowed_drawdown.value }) prot.append({ "method": "StoplossGuard", "lookback_period_candles": self.stoplossguard_lookback_period_candles.value, "trade_limit": self.stoplossguard_trade_limit.value, "stop_duration_candles": self.stoplossguard_stop_duration_candles.value, "only_per_pair": False }) prot.append({ "method": "LowProfitPairs", "lookback_period_candles": self.lowprofit_lookback_period_candles.value, "trade_limit": self.lowprofit_trade_limit.value, "stop_duration_candles": self.lowprofit_stop_duration_candles.value, "required_profit": self.lowprofit_required_profit.value }) prot.append({ "method": "LowProfitPairs", "lookback_period_candles": self.lowprofit2_lookback_period_candles.value, "trade_limit": self.lowprofit2_trade_limit.value, "stop_duration_candles": self.lowprofit2_stop_duration_candles.value, "required_profit": self.lowprofit2_required_profit.value }) return prot class HyperOpt: # Define a custom stoploss space. def stoploss_space(): return [SKDecimal(-0.080, -0.030, decimals=3, name="stoploss")] # Define custom trailing space def trailing_space() -> List[Dimension]: return[ Categorical([True, False], name="trailing_stop"), SKDecimal(0.00010, 0.00100, decimals=5, name="trailing_stop_positive"), SKDecimal(0.0080, 0.0180, decimals=4, name="trailing_stop_positive_offset_p1"), Categorical([True, False], name="trailing_only_offset_is_reached"), ] # ROI table: minimal_roi = { "200": 0 } # Stoploss: stoploss = -0.04 # Trailing stop: trailing_stop = True trailing_stop_positive = 0.00013 trailing_stop_positive_offset = 0.00963 trailing_only_offset_is_reached = True # Sell signal use_sell_signal = False ignore_roi_if_buy_signal = False # SMAOffset smaoffset_optimize = True high_offset_ema = DecimalParameter(0.90, 1.1, default=sell_params["high_offset_ema"], load=True, space="sell", decimals=3, optimize=smaoffset_optimize) base_nb_candles_buy = IntParameter(15, 30, default=buy_params["base_nb_candles_buy"], space="buy", optimize=smaoffset_optimize) base_nb_candles_sell = IntParameter(5, 30, default=sell_params["base_nb_candles_sell"], space="sell", optimize=smaoffset_optimize) low_offset = DecimalParameter(1.0, 1.1, default=buy_params["low_offset"], space="buy", decimals=3, optimize=smaoffset_optimize) low_offset_2 = DecimalParameter(0.94, 0.98, default=buy_params["low_offset_2"], space="buy", decimals=3, optimize=smaoffset_optimize) high_offset = DecimalParameter(1.0, 1.1, default=sell_params["high_offset"], space="sell", decimals=3, optimize=smaoffset_optimize) high_offset_2 = DecimalParameter(1.2, 1.5, default=sell_params["high_offset_2"], space="sell", decimals=3, optimize=smaoffset_optimize) # Protection fast_ewo = 50 slow_ewo = 200 protection_optimize = True ewo_low = DecimalParameter(-12.0, -8.0,default=buy_params["ewo_low"], space="buy", decimals=2, optimize=protection_optimize) ewo_high = DecimalParameter(1.0, 2.2, default=buy_params["ewo_high"], space="buy", decimals=3, optimize=protection_optimize) ewo_high_2 = DecimalParameter(-4.0, -2.0, default=buy_params["ewo_high_2"], space="buy", decimals=2, optimize=protection_optimize) rsi_buy = IntParameter(55, 85, default=buy_params["rsi_buy"], space="buy", optimize=protection_optimize) min_profit = DecimalParameter(0.70, 1.20, default=buy_params["min_profit"], space="buy", decimals=2, optimize=protection_optimize) # Optional order time in force. order_time_in_force = { "entry": "gtc", "exit": "ioc" } # Optimal timeframe for the strategy timeframe = "5m" inf_1h = "1h" process_only_new_candles = True startup_candle_count = 200 plot_config = { "main_plot": { "bb_upperband28": {"color": "#bc281d","type": "line"}, "bb_midband28": {"color": "orange", "type": "line"}, "bb_lowerband28": {"color": "#792bbb","type": "line"} }, "subplots": { "RSI": { "rsi": {"color": "yellow"}, } } } slippage_protection = { "retries": 3, "max_slippage": -0.002 } buy_signals = {} def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, sell_reason: str, current_time: datetime, **kwargs) -> bool: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) # slippage try: state = self.slippage_protection["__pair_retries"] except KeyError: state = self.slippage_protection["__pair_retries"] = {} candle = dataframe.iloc[-1].squeeze() slippage = (rate / candle["close"]) - 1 if slippage < self.slippage_protection["max_slippage"]: pair_retries = state.get(pair, 0) if pair_retries < self.slippage_protection["retries"]: state[pair] = pair_retries + 1 return False state[pair] = 0 return True def informative_pairs(self): # get access to all pairs available in whitelist. pairs = self.dp.current_whitelist() # Assign tf to each pair so they can be downloaded and cached for strategy. informative_pairs = [(pair, "1h") for pair in pairs] return informative_pairs def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." # Get the informative pair informative_1h = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe=self.inf_1h) informative_1h["hma_50"] = qtpylib.hull_moving_average(informative_1h["close"], window=50) informative_1h["ema_100"] = ta.EMA(informative_1h, timeperiod=100) informative_1h["ema_12"] = ta.EMA(informative_1h, timeperiod=12) informative_1h["ema_20"] = ta.EMA(informative_1h, timeperiod=20) informative_1h["ema_26"] = ta.EMA(informative_1h, timeperiod=26) informative_1h["ema_50"] = ta.EMA(informative_1h, timeperiod=50) informative_1h["ema_200"] = ta.EMA(informative_1h, timeperiod=200) informative_1h["sma_200"] = ta.SMA(informative_1h, timeperiod=200) informative_1h["sma_200_dec"] = informative_1h["sma_200"] < informative_1h["sma_200"].shift(20) informative_1h["sma_9"] = ta.SMA(informative_1h, timeperiod=9) # Elliot informative_1h["EWO"] = EWO(informative_1h, self.fast_ewo, self.slow_ewo) # RSI informative_1h["rsi"] = ta.RSI(informative_1h, timeperiod=14) informative_1h["rsi_fast"] = ta.RSI(informative_1h, timeperiod=4) informative_1h["rsi_slow"] = ta.RSI(informative_1h, timeperiod=20) return informative_1h 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) dataframe["hma_50"] = qtpylib.hull_moving_average(dataframe["close"], window=50) dataframe["ema_100"] = ta.EMA(dataframe, timeperiod=100) dataframe["ema_12"] = ta.EMA(dataframe, timeperiod=12) dataframe["ema_20"] = ta.EMA(dataframe, timeperiod=20) dataframe["ema_26"] = ta.EMA(dataframe, timeperiod=26) dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50) dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200) dataframe["sma_200"] = ta.SMA(dataframe, timeperiod=200) dataframe["sma_200_dec"] = dataframe["sma_200"] < dataframe["sma_200"].shift(20) dataframe["sma_9"] = ta.SMA(dataframe, timeperiod=9) # Elliot dataframe["EWO"] = EWO(dataframe, self.fast_ewo, self.slow_ewo) # RSI dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["rsi_fast"] = ta.RSI(dataframe, timeperiod=4) dataframe["rsi_slow"] = ta.RSI(dataframe, timeperiod=20) # Bollinger bands bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2.8) dataframe["bb_lowerband28"] = bollinger2["lower"] dataframe["bb_middleband28"] = bollinger2["mid"] dataframe["bb_upperband28"] = bollinger2["upper"] informative_1h = self.informative_1h_indicators(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe["rsi_fast"] < 35) & (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) & (dataframe["close"] < (dataframe[f"ma_sell_{self.base_nb_candles_sell.value}"] * self.high_offset.value)) ), ["buy", "buy_tag"]] = (1, "ewo1") dataframe.loc[ ( (dataframe["rsi_fast"] < 35) & (dataframe["close"] < (dataframe[f"ma_buy_{self.base_nb_candles_buy.value}"] * self.low_offset_2.value)) & (dataframe["EWO"] > self.ewo_high_2.value) & (dataframe["rsi"] < self.rsi_buy.value) & (dataframe["volume"] > 0) & (dataframe["close"] < (dataframe[f"ma_sell_{self.base_nb_candles_sell.value}"] * self.high_offset.value)) & (dataframe["rsi"] < 25) ), ["buy", "buy_tag"]] = (1, "ewo2") dataframe.loc[ ( (dataframe["rsi_fast"] < 35) & (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) & (dataframe["close"] < (dataframe[f"ma_sell_{self.base_nb_candles_sell.value}"] * self.high_offset.value)) ), ["buy", "buy_tag"]] = (1, "ewolow") dont_buy_conditions = [] dont_buy_conditions.append( ( (dataframe["close_1h"].rolling(24).max() < (dataframe["close"] * self.min_profit.value )) ) ) if dont_buy_conditions: for condition in dont_buy_conditions: dataframe.loc[condition, "buy"] = 0 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: return dataframe def EWO(dataframe, ema_length=5, ema2_length=35): df = dataframe.copy() ema1 = ta.EMA(df, timeperiod=ema_length) ema2 = ta.EMA(df, timeperiod=ema2_length) emadif = (ema1 - ema2) / df["low"] * 100 return emadif