# --- 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, BooleanParameter, DecimalParameter, IntParameter, CategoricalParameter import math import logging logger = logging.getLogger(__name__) ######################################################################################################################################################## # EWO ######################################################################################################################################################## def EWO(dataframe, ema_length=5, ema2_length=3): 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 ######################################################################################################################################################## # pct_change ######################################################################################################################################################## def pct_change(a, b): return (b - a) / a ######################################################################################################################################################## class DS_Green_5m(IStrategy): ######################################################################################################################################################## # Hyperopt ######################################################################################################################################################## buy_params = { "lambo2_enabled": True, "ewo1_enabled": True, "ewo2_enabled": True, "cofi_enabled": True, # Ewo "base_nb_candles_buy": 12, # EWO 1 "ewo_high": 3.233, "low_offset_1": 0.995, "high_offset_1": 0.969, "rsi_buy": 48, # Ewo 2 "ewo_low": -15.317, "low_offset_2": 0.985, "high_offset_2": 1.08, # Lambo 2 "lambo2_ema_14_factor": 0.979, "lambo2_rsi_14_limit": 44, "lambo2_rsi_4_limit": 31, # Cofi "buy_adx": 28, "buy_fastd": 26, "buy_fastk": 26, "buy_ema_cofi": 0.979, "buy_ewo_high": 2.134, # ? "dca_min_rsi": 64, } sell_params = { "base_nb_candles_sell": 9, # Ewo "high_offset_above": 1.008, "high_offset_below": 1.022, # custom stoploss params, come from BB_RPB_TSL "pHSL": -0.397, "pPF_1": 0.012, "pPF_2": 0.07, "pSL_1": 0.015, "pSL_2": 0.068, } minimal_roi = { "0": 100, } stoploss = -0.99 trailing_stop = False trailing_stop_positive = 0.013 # Offset from the highest profit point to activate the trailing stop. trailing_stop_positive_offset = 0.0205 # Profit necessary to trigger the trailing stop. trailing_only_offset_is_reached = True # Keep stoploss static UNTIL the offset is reached then trigger trailing stop. ######################################################################################################################################################## # Main ######################################################################################################################################################## use_custom_stoploss = True timeframe = '5m' # The primary timeframe for analysis. inf_1h = '1h' # Informative timeframe to gather additional data. # Sell signal configuration. use_exit_signal = True exit_profit_only = True exit_profit_offset = 0.01 # Offset added to exit signal (profitable threshold). ignore_roi_if_entry_signal = False # If True, ignore ROI when the buy signal is still present. # Number of past candles to consider upon startup. process_only_new_candles = True startup_candle_count = 400 # Adjsut trade position initial_safety_order_trigger = -0.018 # Initial trigger for the first safety order. max_safety_orders = 8 # Maximum number of safety orders to prevent overexposure. safety_order_step_scale = 1.2 # How much to increase the trigger for each additional safety order. safety_order_volume_scale = 1.4 # How much to increase the volume of each safety order. # Configuration of order types. order_types = { 'entry': 'limit', 'exit': 'market', 'trailing_stop_loss': 'market', 'emergency_exit': 'market', 'force_entry': 'market', 'force_exit': 'market', 'stoploss': 'market', 'stoploss_on_exchange': False, 'stoploss_on_exchange_interval': 60, 'stoploss_on_exchange_limit_ratio': 0.99 } # Plotting configuration for visualizing indicators in backtesting. plot_config = { 'main_plot': { 'ma_buy': {'color': 'orange'}, # Color for the buy moving average. 'ma_sell': {'color': 'orange'}, # Color for the sell moving average. }, } # Order Time-In-Force defines how long an order will remain active before it is executed or expired. order_time_in_force = { 'entry': 'gtc', 'exit': 'gtc' } ######################################################################################################################################################## # Parameters ######################################################################################################################################################## # Enabled is_optimize_remove = False lambo2_enabled = BooleanParameter(default=buy_params['lambo2_enabled'], space='buy', optimize=is_optimize_remove) ewo1_enabled = BooleanParameter(default=buy_params['ewo1_enabled'], space='buy', optimize=is_optimize_remove) ewo2_enabled = BooleanParameter(default=buy_params['ewo2_enabled'], space='buy', optimize=is_optimize_remove) cofi_enabled = BooleanParameter(default=buy_params['cofi_enabled'], space='buy', optimize=is_optimize_remove) ############################################################################################################################################################################ # Candles is_optimize_base_nb_candles = True base_nb_candles_buy = IntParameter(8, 20, default=buy_params['base_nb_candles_buy'], space='buy', optimize=is_optimize_base_nb_candles) # EWO Protection fast_ewo = 60 slow_ewo = 220 # EWO 1 is_optimize_ewo = False # Optimized 7/11/24 for 2 days low_offset_1 = DecimalParameter(0.985, 0.995, default=buy_params['low_offset_1'], space='buy', optimize=is_optimize_ewo) rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=is_optimize_ewo) ewo_high = DecimalParameter(3.0, 3.4, default=buy_params['ewo_high'], space='buy', optimize=is_optimize_ewo) high_offset_1 = DecimalParameter(0.95, 1.10, default=buy_params['high_offset_1'], space='buy', optimize=is_optimize_ewo) # Ewo 2 is_optimize_ewo2 = False # Optimized 8/11/24 for 6 h ewo_low = DecimalParameter(-20.0, -8.0,default=buy_params['ewo_low'], space='buy', optimize=is_optimize_ewo2) low_offset_2 = DecimalParameter(0.985, 0.995, default=buy_params['low_offset_2'], space='buy', optimize=is_optimize_ewo2) high_offset_2 = DecimalParameter(0.95, 1.10, default=buy_params['high_offset_2'], space='buy', optimize=is_optimize_ewo2) # lambo2 is_optimize_lambo2 = False # Optimized 10/11/24 for 6 h lambo2_ema_14_factor = DecimalParameter(0.8, 1.2, decimals=3, default=buy_params['lambo2_ema_14_factor'], space='buy', optimize=is_optimize_lambo2) lambo2_rsi_4_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_4_limit'], space='buy', optimize=is_optimize_lambo2) lambo2_rsi_14_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_14_limit'], space='buy', optimize=is_optimize_lambo2) #cofi is_optimize_cofi = False # Optimized 8/11/24 for 4h buy_ema_cofi = DecimalParameter(0.96, 0.98, default=0.97, space='buy', optimize = is_optimize_cofi) buy_fastk = IntParameter(20, 30, default=20, space='buy', optimize = is_optimize_cofi) buy_fastd = IntParameter(20, 30, default=20, space='buy', optimize = is_optimize_cofi) buy_adx = IntParameter(20, 30, default=30, space='buy', optimize = is_optimize_cofi) buy_ewo_high = DecimalParameter(2, 12, space='buy', default=3.553, optimize = is_optimize_cofi) # ? dca_min_rsi = IntParameter(35, 75, default=buy_params['dca_min_rsi'], space='buy', optimize=False) # Sell is_optimize_offset_sell = False # Optimized 9/11/24 for 13h base_nb_candles_sell = IntParameter(8, 20, default=sell_params['base_nb_candles_sell'], space='sell', optimize=is_optimize_offset_sell) high_offset_above = DecimalParameter(1.00, 1.10, default=sell_params['high_offset_above'], space='sell', optimize=is_optimize_offset_sell) high_offset_below = DecimalParameter(0.95, 1.05, default=sell_params['high_offset_below'], space='sell', optimize=is_optimize_offset_sell) ############################################################################################################################################################################ # Custom Stoploss is_optimize_stoploss = False # Optimized 7/11/24 # Hard stoploss profit pHSL = DecimalParameter(-0.500, -0.040, default=-0.08, decimals=3, space='sell', optimize=is_optimize_stoploss, load=True) # profit threshold 1, trigger point, SL_1 is used pPF_1 = DecimalParameter(0.008, 0.020, default=0.016, decimals=3, space='sell', optimize=is_optimize_stoploss, load=True) pSL_1 = DecimalParameter(0.008, 0.020, default=0.011, decimals=3, space='sell', optimize=is_optimize_stoploss, load=True) # profit threshold 2, SL_2 is used pPF_2 = DecimalParameter(0.040, 0.100, default=0.080, decimals=3, space='sell',optimize=is_optimize_stoploss, load=True) pSL_2 = DecimalParameter(0.020, 0.070, default=0.040, decimals=3, space='sell', optimize=is_optimize_stoploss,load=True) ######################################################################################################################################################## # Informative Pairs ######################################################################################################################################################## def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '1h') for pair in pairs] if self.config['stake_currency'] in ['USDT','BUSD','USDC','DAI','TUSD','PAX','USD','EUR','GBP']: btc_info_pair = f"BTC/{self.config['stake_currency']}" else: btc_info_pair = "BTC/USDT" informative_pairs.append((btc_info_pair, self.timeframe)) informative_pairs.append((btc_info_pair, self.inf_1h)) return informative_pairs ######################################################################################################################################################## # Informative Pairs ######################################################################################################################################################## def base_tf_btc_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Indicators # ----------------------------------------------------------------------------------------- dataframe['price_trend_long'] = (dataframe['close'].rolling(8).mean() / dataframe['close'].shift(8).rolling(144).mean()) # Add prefix # ----------------------------------------------------------------------------------------- ignore_columns = ['date', 'open', 'high', 'low', 'close', 'volume'] dataframe.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True) return dataframe def info_tf_btc_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Indicators # ----------------------------------------------------------------------------------------- dataframe['rsi_8'] = ta.RSI(dataframe, timeperiod=8) # Add prefix # ----------------------------------------------------------------------------------------- ignore_columns = ['date', 'open', 'high', 'low', 'close', 'volume'] dataframe.rename(columns=lambda s: f"btc_{s}" if s not in ignore_columns else s, inplace=True) return dataframe ######################################################################################################################################################## # Indicators ######################################################################################################################################################## def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if self.config['stake_currency'] in ['USDT','BUSD']: btc_info_pair = f"BTC/{self.config['stake_currency']}" else: btc_info_pair = "BTC/USDT" btc_info_tf = self.dp.get_pair_dataframe(btc_info_pair, self.inf_1h) btc_info_tf = self.info_tf_btc_indicators(btc_info_tf, metadata) dataframe = merge_informative_pair(dataframe, btc_info_tf, self.timeframe, self.inf_1h, ffill=True) drop_columns = [f"{s}_{self.inf_1h}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) btc_base_tf = self.dp.get_pair_dataframe(btc_info_pair, self.timeframe) btc_base_tf = self.base_tf_btc_indicators(btc_base_tf, metadata) dataframe = merge_informative_pair(dataframe, btc_base_tf, self.timeframe, self.timeframe, ffill=True) drop_columns = [f"{s}_{self.timeframe}" for s in ['date', 'open', 'high', 'low', 'close', 'volume']] dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True) # 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['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) #lambo2 dataframe['ema_14'] = ta.EMA(dataframe, timeperiod=14) dataframe['rsi_4'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14) # Pump strength dataframe['dema_30'] = ta.DEMA(dataframe, period=30) dataframe['dema_200'] = ta.DEMA(dataframe, period=200) dataframe['pump_strength'] = (dataframe['dema_30'] - dataframe['dema_200']) / dataframe['dema_30'] # Cofi stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] dataframe['adx'] = ta.ADX(dataframe) dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8) # Dump Protection dataframe = self.pump_dump_protection(dataframe, metadata) # RSI dataframe = super().populate_indicators(dataframe, metadata) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) return dataframe ######################################################################################################################################################## # Pump Dump Protection ######################################################################################################################################################## def pump_dump_protection(self, dataframe: DataFrame, metadata: dict) -> DataFrame: df36h = dataframe.copy().shift( 432 ) # TODO FIXME: This assumes 5m timeframe df24h = dataframe.copy().shift( 288 ) # TODO FIXME: This assumes 5m timeframe dataframe['volume_mean_short'] = dataframe['volume'].rolling(4).mean() dataframe['volume_mean_long'] = df24h['volume'].rolling(48).mean() dataframe['volume_mean_base'] = df36h['volume'].rolling(288).mean() dataframe['volume_change_percentage'] = (dataframe['volume_mean_long'] / dataframe['volume_mean_base']) dataframe['rsi_mean'] = dataframe['rsi'].rolling(48).mean() dataframe['pnd_volume_warn'] = np.where((dataframe['volume_mean_short'] / dataframe['volume_mean_long'] > 5.0), -1, 0) return dataframe ######################################################################################################################################################## # Custom Stoploss ######################################################################################################################################################## def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: # hard stoploss profit HSL = self.pHSL.value PF_1 = self.pPF_1.value SL_1 = self.pSL_1.value PF_2 = self.pPF_2.value SL_2 = self.pSL_2.value # For profits between PF_1 and PF_2 the stoploss (sl_profit) used is linearly interpolated # between the values of SL_1 and SL_2. For all profits above PF_2 the sl_profit value # rises linearly with current profit, for profits below PF_1 the hard stoploss profit is used. if current_profit > PF_2: sl_profit = SL_2 + (current_profit - PF_2) elif current_profit > PF_1: sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1)) else: sl_profit = HSL # Only for hyperopt invalid return if sl_profit >= current_profit: return -0.99 return stoploss_from_open(sl_profit, current_profit) ######################################################################################################################################################## # Buy Trend ######################################################################################################################################################## def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'enter_tag'] = '' dataframe.loc[:, 'enter_long'] = 0 # Lambo2 condition lambo2 = ( bool(self.lambo2_enabled) & (dataframe['close'] < (dataframe['ema_14'] * self.lambo2_ema_14_factor.value)) & (dataframe['rsi_4'] < int(self.lambo2_rsi_4_limit.value)) & (dataframe['rsi_14'] < int(self.lambo2_rsi_14_limit.value)) ) dataframe.loc[lambo2, 'enter_tag'] += 'lambo2_' conditions.append(lambo2) # Buy1 EWO condition ewo = ( (dataframe['rsi_fast'] < 35) & (dataframe['close'] < (dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset_1.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_1.value)) ) dataframe.loc[ewo, 'enter_tag'] += 'eworsi_' conditions.append(ewo) # Buy2 EWO condition ewo2 = ( (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_low.value) & (dataframe['volume'] > 0) & (dataframe['close'] < (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_2.value)) ) dataframe.loc[ewo2, 'enter_tag'] += 'ewo2_' conditions.append(ewo2) # COFI condition cofi = ( (dataframe['open'] < dataframe['ema_8'] * self.buy_ema_cofi.value) & (qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd'])) & (dataframe['fastk'] < self.buy_fastk.value) & (dataframe['fastd'] < self.buy_fastd.value) & (dataframe['adx'] > self.buy_adx.value) & (dataframe['EWO'] > self.buy_ewo_high.value) ) dataframe.loc[cofi, 'enter_tag'] += 'cofi_' conditions.append(cofi) # Applying buy conditions if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), 'enter_long'] = 1 # Additional conditions to avoid buying dont_buy_conditions = [] # don't buy if there seems to be a Pump and Dump event. dont_buy_conditions.append((dataframe['pnd_volume_warn'] < 0.0)) # BTC price protection dont_buy_conditions.append((dataframe['btc_rsi_8_1h'] < 35.0)) # Applying don't buy conditions if dont_buy_conditions: for condition in dont_buy_conditions: dataframe.loc[condition, 'enter_long'] = 0 return dataframe ######################################################################################################################################################## # Adjust Trade Position ######################################################################################################################################################## def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, min_stake: float, max_stake: float, **kwargs): if current_profit > self.initial_safety_order_trigger: return None # credits to reinuvader for not blindly executing safety orders # Obtain pair dataframe. dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe) # Only buy when it seems it's climbing back up last_candle = dataframe.iloc[-1].squeeze() previous_candle = dataframe.iloc[-2].squeeze() if last_candle['close'] < previous_candle['close']: return None count_of_buys = 0 for order in trade.orders: if order.ft_is_open or order.ft_order_side != 'enter_long': continue if order.status == "closed": count_of_buys += 1 if 1 <= count_of_buys <= self.max_safety_orders: safety_order_trigger = abs(self.initial_safety_order_trigger) + (abs(self.initial_safety_order_trigger) * self.safety_order_step_scale * (math.pow(self.safety_order_step_scale,(count_of_buys - 1)) - 1) / (self.safety_order_step_scale - 1)) if current_profit <= (-1 * abs(safety_order_trigger)): try: stake_amount = self.wallets.get_trade_stake_amount(trade.pair, None) stake_amount = stake_amount * math.pow(self.safety_order_volume_scale,(count_of_buys - 1)) amount = stake_amount / current_rate logger.info(f"Initiating safety order buy #{count_of_buys} for {trade.pair} with stake amount of {stake_amount} which equals {amount}") return stake_amount except Exception as exception: logger.info(f'Error occured while trying to get stake amount for {trade.pair}: {str(exception)}') return None return None ######################################################################################################################################################## # Sell Trend ######################################################################################################################################################## def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Initialize 'exit_long' to 0 for all rows dataframe['exit_long'] = 0 dataframe['exit_tag'] = 'no_exit' # Default tag # Define primary condition based on volume being greater than 0 primary_condition = dataframe['volume'] > 0 # Define exit conditions condition_hma50_above = ( (dataframe['close'] > dataframe['hma_50']) & (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_above.value)) & (dataframe['rsi'] > 50) & (dataframe['rsi_fast'] > dataframe['rsi_slow']) ) condition_hma50_below = ( (dataframe['close'] < dataframe['hma_50']) & (dataframe['close'] > (dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset_below.value)) & (dataframe['rsi_fast'] > dataframe['rsi_slow']) ) # Combine conditions with the primary condition combined_conditions_above = primary_condition & condition_hma50_above combined_conditions_below = primary_condition & condition_hma50_below # Apply the conditions to set 'exit_long' to 1 dataframe.loc[combined_conditions_above, 'exit_long'] = 1 dataframe.loc[combined_conditions_below, 'exit_long'] = 1 # Tagging based on which specific condition was met dataframe.loc[combined_conditions_above, 'exit_tag'] = 'hma50_above' dataframe.loc[combined_conditions_below, 'exit_tag'] = 'hma50_below' return dataframe ######################################################################################################################################################## # Confirm Trade Exit ######################################################################################################################################################## def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs) -> bool: if trade and trade.exit_reason: trade.exit_reason = exit_reason + "_" + trade.enter_tag return True ######################################################################################################################################################## # Custom to Sell unclog ######################################################################################################################################################## def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): # Sell any positions at a loss if they are held for more than 9.6 hours (0.4 days). time_held = current_time - trade.open_date_utc time_held_in_hours = time_held.total_seconds() / 3600 # Convert seconds to hours if current_profit < -0.04 and time_held_in_hours >= 5.5: return 'unclog' ######################################################################################################################################################## # Trade Protections ######################################################################################################################################################## @property def protections(self): return [ { "method": "CooldownPeriod", "stop_duration_candles": 5 }, { "method": "MaxDrawdown", "lookback_period_candles": 48, "trade_limit": 20, "stop_duration_candles": 4, "max_allowed_drawdown": 0.2 }, { "method": "StoplossGuard", "lookback_period_candles": 24, "trade_limit": 4, "stop_duration_candles": 2, "only_per_pair": False }, { "method": "LowProfitPairs", "lookback_period_candles": 6, "trade_limit": 2, "stop_duration_candles": 60, "required_profit": 0.02 }, { "method": "LowProfitPairs", "lookback_period_candles": 24, "trade_limit": 4, "stop_duration_candles": 2, "required_profit": 0.01 } ]