# source: https://raw.githubusercontent.com/rmallarapu-bc/brahma/9287745fc036c2f4c00c586e598290885c2883b9/archive/PlutoWave1.py from datetime import datetime, timedelta from functools import reduce from typing import Optional, Union import freqtrade.vendor.qtpylib.indicators as qtpylib import pandas_ta as pta import talib.abstract as ta from freqtrade.persistence import Trade from freqtrade.strategy import DecimalParameter, IntParameter from freqtrade.strategy.interface import IStrategy from pandas import 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 class Github_rmallarapu_bc_brahma__PlutoWave1__20240229_213751(IStrategy): minimal_roi = {"0": 0.1} # stoploss = -0.30 # # # Trailing stoploss # trailing_stop = True # trailing_stop_positive = 0.001 # trailing_stop_positive_offset = 0.002 # trailing_only_offset_is_reached = True timeframe = '1h' can_short = True process_only_new_candles = True startup_candle_count = 20 order_types = { 'entry': 'limit', 'exit': 'limit', 'emergency_exit': 'market', 'force_entry': 'limit', 'force_exit': "limit", 'stoploss': 'limit', 'stoploss_on_exchange': False, 'stoploss_on_exchange_interval': 60, 'stoploss_on_exchange_market_ratio': 0.99 } stoploss = -0.99 use_custom_stoploss = False is_optimize_ewo = True buy_rsi_fast = IntParameter(35, 50, default=50, space='buy', optimize=is_optimize_ewo) buy_rsi = IntParameter(15, 35, default=30, space='buy', optimize=is_optimize_ewo) buy_ewo = DecimalParameter(-6.0, 5, default=-1.238, space='buy', optimize=is_optimize_ewo) buy_ema_low = DecimalParameter(0.9, 0.99, default=0.956, space='buy', optimize=is_optimize_ewo) buy_ema_high = DecimalParameter(0.95, 1.2, default=0.986, space='buy', optimize=is_optimize_ewo) is_optimize_32 = True buy_rsi_fast_32 = IntParameter(20, 70, default=63, space='buy', optimize=is_optimize_32) buy_rsi_32 = IntParameter(15, 50, default=16, space='buy', optimize=is_optimize_32) buy_sma15_32 = DecimalParameter(0.900, 1, default=0.932, decimals=3, space='buy', optimize=is_optimize_32) buy_cti_32 = DecimalParameter(-1, 0, default=-0.8, decimals=2, space='buy', optimize=is_optimize_32) is_optimize_deadfish = True sell_deadfish_bb_width = DecimalParameter(0.03, 0.75, default=0.05, space='sell', optimize=is_optimize_deadfish) sell_deadfish_profit = DecimalParameter(-0.15, -0.05, default=-0.05, space='sell', optimize=is_optimize_deadfish) sell_deadfish_bb_factor = DecimalParameter(0.90, 1.20, default=1.0, space='sell', optimize=is_optimize_deadfish) sell_deadfish_volume_factor = DecimalParameter(1, 2.5, default=1.0, space='sell', optimize=is_optimize_deadfish) sell_fastx = IntParameter(50, 100, default=75, space='sell', optimize=True) 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) # ewo indicators dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8) dataframe['ema_16'] = ta.EMA(dataframe, timeperiod=16) dataframe['EWO'] = ewo(dataframe, 50, 200) # profit sell indicators stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] # loss sell indicators bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband2'] = bollinger2['lower'] dataframe['bb_middleband2'] = bollinger2['mid'] dataframe['bb_upperband2'] = bollinger2['upper'] dataframe['bb_width'] = ( (dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2']) dataframe['volume_mean_12'] = dataframe['volume'].rolling(12).mean().shift(1) dataframe['volume_mean_24'] = dataframe['volume'].rolling(24).mean().shift(1) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'enter_tag'] = '' is_ewo = ( (dataframe['rsi_fast'] < self.buy_rsi_fast.value) & (dataframe['close'] < dataframe['ema_8'] * self.buy_ema_low.value) & (dataframe['EWO'] > self.buy_ewo.value) & (dataframe['close'] < dataframe['ema_16'] * self.buy_ema_high.value) & (dataframe['rsi'] < self.buy_rsi.value) ) 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(is_ewo) dataframe.loc[is_ewo, 'enter_tag'] += 'ewo' 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 else: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'enter_short'] = 1 return dataframe def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) current_candle = dataframe.iloc[-1].squeeze() if current_time - timedelta(minutes=60) > trade.open_date_utc: if (current_candle["fastk"] > self.sell_fastx.value) and (current_profit > -0.01): return -0.001 if current_time - timedelta(days=1) > trade.open_date_utc: if (current_candle["fastk"] > self.sell_fastx.value) and (current_profit > -0.05): return -0.001 enter_tag = '' if hasattr(trade, 'enter_tag') and trade.enter_tag is not None: enter_tag = trade.enter_tag enter_tags = enter_tag.split() if "ewo" in enter_tags: if current_profit >= 0.05: return -0.005 if current_profit > 0: if current_candle["fastk"] > self.sell_fastx.value: return -0.001 return self.stoploss def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> Optional[Union[str, bool]]: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) current_candle = dataframe.iloc[-1].squeeze() # stoploss - deadfish if ((current_profit < self.sell_deadfish_profit.value) and (current_candle['bb_width'] < self.sell_deadfish_bb_width.value) and (current_candle['close'] > current_candle['bb_middleband2'] * self.sell_deadfish_bb_factor.value) and (current_candle['volume_mean_12'] < current_candle[ 'volume_mean_24'] * self.sell_deadfish_volume_factor.value)): return "sell_stoploss_deadfish" def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[(), ['exit_long', 'exit_tag']] = (0, 'long_out') return dataframe # from freqtrade.persistence import Trade # from datetime import timedelta, datetime # from typing import Optional # # # DCA options # position_adjustment_enable = True # max_entry_position_adjustment = 10 # initial_order_size = 0.2 # 20% of total capital # incremental_order_size = 0.4 # increase capital by 40% of initial order for every {initial_profit} loss # initial_profit = 0.01 # recurring_loss = -0.01 # loss_upper_limit = -0.10 # # # leverage # leverage_num = 1 # # # This is called when placing the initial order (opening trade) # def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, # proposed_stake: float, min_stake: Optional[float], max_stake: float, # leverage: float, entry_tag: Optional[str], side: str, # **kwargs) -> float: # # # We need to leave most of the funds for possible further DCA orders # # This also applies to fixed stakes # return proposed_stake * self.initial_order_size # # def adjust_trade_position(self, trade: Trade, current_time: datetime, # current_rate: float, current_profit: float, # min_stake: Optional[float], max_stake: float, # current_entry_rate: float, current_exit_rate: float, # current_entry_profit: float, current_exit_profit: float, # **kwargs) -> Optional[float]: # """ # Custom trade adjustment logic, returning the stake amount that a trade should be # increased or decreased. # This means extra buy or sell orders with additional fees. # Only called when `position_adjustment_enable` is set to True. # # For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/ # # When not implemented by a strategy, returns None # # :param trade: trade object. # :param current_time: datetime object, containing the current datetime # :param current_rate: Current buy rate. # :param current_profit: Current profit (as ratio), calculated based on current_rate. # :param min_stake: Minimal stake size allowed by exchange (for both entries and exits) # :param max_stake: Maximum stake allowed (either through balance, or by exchange limits). # :param current_entry_rate: Current rate using entry pricing. # :param current_exit_rate: Current rate using exit pricing. # :param current_entry_profit: Current profit using entry pricing. # :param current_exit_profit: Current profit using exit pricing. # :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. # :return float: Stake amount to adjust your trade, # Positive values to increase position, Negative values to decrease position. # Return None for no action. # """ # # filled_entries = trade.select_filled_orders(trade.entry_side) # count_of_entries = trade.nr_of_successful_entries # count_of_exits = trade.nr_of_successful_exits # stake_amount = filled_entries[0].cost # # if current_profit > self.initial_profit and count_of_exits == 0: # return -(trade.stake_amount / 2) # # # if current_profit < self.loss_upper_limit: return None # if the losses are greater than 10%, stop # # # Only buy when prices are not actively falling. # try: # dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe) # last_candle = dataframe.iloc[-1].squeeze() # previous_candle = dataframe.iloc[-2].squeeze() # if last_candle['close'] < previous_candle['close']: # return None # except: # pass # # # determine the stake amount now # if current_profit < self.recurring_loss and count_of_entries <= self.max_entry_position_adjustment: # return stake_amount * self.incremental_order_size # else: # return None # # def leverage(self, pair: str, current_time: datetime, current_rate: float, # proposed_leverage: float, max_leverage: float, side: str, # **kwargs) -> float: # return self.leverage_num