# source: https://raw.githubusercontent.com/rmallarapu-bc/brahma/9287745fc036c2f4c00c586e598290885c2883b9/archive/IndianaJones1.py from datetime import datetime from functools import reduce from freqtrade.persistence import Trade from freqtrade.strategy import IntParameter, DecimalParameter, stoploss_from_open, CategoricalParameter, informative from freqtrade.strategy.interface import IStrategy from pandas import DataFrame import freqtrade.vendor.qtpylib.indicators as qtpylib import talib.abstract as ta import numpy # noqa class Github_rmallarapu_bc_brahma__IndianaJones1__20240229_213751(IStrategy): minimal_roi = { "0": 0.1, } # Trailing stoploss trailing_stop = False trailing_stop_positive = 0.0025 trailing_stop_positive_offset = 0.0075 trailing_only_offset_is_reached = False buy_params = { "buy_adx1": 62, "buy_adx2": 29, "buy_adx3": 33, "buy_adx4": 88, "buy_emarsi1": 29, "buy_emarsi2": 30, "buy_emarsi3": 22, "buy_emarsi4": 57 } sell_params = { "adx2": 21, "emarsi1": 30, "emarsi2": 71, "emarsi3": 72, "leverage_num": 3, "sell_1": False, "sell_2": True, "sell_3": True, "sell_4": True, "sell_5": False, "pHSL": -0.75, "pPF_1": 0.03, "pPF_2": 0.09, "pSL_1": 0.028, "pSL_2": 0.08 } stoploss = -0.99 timeframe = '5m' process_only_new_candles = True startup_candle_count = 240 # default False use_custom_stoploss = True can_short = True order_types = { 'entry': 'market', 'exit': '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 } # buy params buy_optimize = True buy_adx1 = IntParameter(low=10, high=100, default=25, space='buy', optimize=buy_optimize) buy_emarsi1 = IntParameter(low=10, high=100, default=20, space='buy', optimize=buy_optimize) buy_adx2 = IntParameter(low=20, high=100, default=30, space='buy', optimize=buy_optimize) buy_emarsi2 = IntParameter(low=20, high=100, default=20, space='buy', optimize=buy_optimize) buy_adx3 = IntParameter(low=10, high=100, default=35, space='buy', optimize=buy_optimize) buy_emarsi3 = IntParameter(low=10, high=100, default=20, space='buy', optimize=buy_optimize) buy_adx4 = IntParameter(low=20, high=100, default=30, space='buy', optimize=buy_optimize) buy_emarsi4 = IntParameter(low=20, high=100, default=25, space='buy', optimize=buy_optimize) # trailing stoploss trailing_optimize = False pHSL = DecimalParameter(-0.990, -0.040, default=-0.08, decimals=3, space='sell', optimize=trailing_optimize) pPF_1 = DecimalParameter(0.008, 0.100, default=0.016, decimals=3, space='sell', optimize=trailing_optimize) pSL_1 = DecimalParameter(0.008, 0.100, default=0.011, decimals=3, space='sell', optimize=trailing_optimize) pPF_2 = DecimalParameter(0.040, 0.200, default=0.080, decimals=3, space='sell', optimize=trailing_optimize) pSL_2 = DecimalParameter(0.040, 0.200, default=0.040, decimals=3, space='sell', optimize=trailing_optimize) # sell params sell_optimize = True adx2 = IntParameter(low=10, high=100, default=30, space='sell', optimize=sell_optimize) emarsi1 = IntParameter(low=10, high=100, default=75, space='sell', optimize=sell_optimize) emarsi2 = IntParameter(low=20, high=100, default=80, space='sell', optimize=sell_optimize) emarsi3 = IntParameter(low=20, high=100, default=75, space='sell', optimize=sell_optimize) sell2_optimize = True sell_1 = CategoricalParameter([True, False], default=True, space="sell", optimize=sell2_optimize) sell_2 = CategoricalParameter([True, False], default=True, space="sell", optimize=sell2_optimize) sell_3 = CategoricalParameter([True, False], default=True, space="sell", optimize=sell2_optimize) sell_4 = CategoricalParameter([True, False], default=True, space="sell", optimize=sell2_optimize) sell_5 = CategoricalParameter([True, False], default=True, space="sell", optimize=sell2_optimize) leverage_optimize = False leverage_num = IntParameter(low=1, high=20, default=1, space='sell', optimize=leverage_optimize) 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 PL_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 if self.can_short: if (-1 + ((1 - sl_profit) / (1 - current_profit))) <= 0: return 1 else: if (1 - ((1 + sl_profit) / (1 + current_profit))) <= 0: return 1 return stoploss_from_open(sl_profit, current_profit, is_short=trade.is_short) @informative('1h', 'BTC/{stake}:{stake}', fmt='{base}_{column}_{timeframe}') def populate_indicators_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['ema_fast'] = ta.EMA(dataframe, timeperiod=20) dataframe['ema_slow'] = ta.EMA(dataframe, timeperiod=25) dataframe['uptrend'] = (dataframe['ema_fast'] > dataframe['ema_slow']).astype('int') dataframe['downtrend'] = (dataframe['ema_fast'] < dataframe['ema_slow']).astype('int') return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['rsi'] = numpy.nan_to_num(ta.RSI(dataframe, timeperiod=5)) rsiframe = DataFrame(dataframe['rsi']).rename(columns={'rsi': 'close'}) dataframe['emarsi'] = numpy.nan_to_num(ta.EMA(rsiframe, timeperiod=5)) dataframe['adx'] = numpy.nan_to_num(ta.ADX(dataframe)) dataframe['minusdi'] = numpy.nan_to_num(ta.MINUS_DI(dataframe)) minusdiframe = DataFrame(dataframe['minusdi']).rename(columns={'minusdi': 'close'}) dataframe['minusdiema'] = numpy.nan_to_num(ta.EMA(minusdiframe, timeperiod=25)) dataframe['plusdi'] = numpy.nan_to_num(ta.PLUS_DI(dataframe)) plusdiframe = DataFrame(dataframe['plusdi']).rename(columns={'plusdi': 'close'}) dataframe['plusdiema'] = numpy.nan_to_num(ta.EMA(plusdiframe, timeperiod=5)) dataframe['lowsma'] = numpy.nan_to_num(ta.EMA(dataframe, timeperiod=60)) dataframe['highsma'] = numpy.nan_to_num(ta.EMA(dataframe, timeperiod=120)) dataframe['fastsma'] = numpy.nan_to_num(ta.SMA(dataframe, timeperiod=120)) dataframe['slowsma'] = numpy.nan_to_num(ta.SMA(dataframe, timeperiod=240)) dataframe['bigup'] = dataframe['fastsma'].gt(dataframe['slowsma']) & ( (dataframe['fastsma'] - dataframe['slowsma']) > dataframe['close'] / 300) dataframe['bigdown'] = ~dataframe['bigup'] dataframe['trend'] = dataframe['fastsma'] - dataframe['slowsma'] dataframe['preparechangetrend'] = dataframe['trend'].gt(dataframe['trend'].shift()) dataframe['preparechangetrendconfirm'] = dataframe['preparechangetrend'] & dataframe['trend'].shift().gt( dataframe['trend'].shift(2)) dataframe['continueup'] = dataframe['slowsma'].gt(dataframe['slowsma'].shift()) & dataframe[ 'slowsma'].shift().gt(dataframe['slowsma'].shift(2)) dataframe['delta'] = dataframe['fastsma'] - dataframe['fastsma'].shift() dataframe['slowingdown'] = dataframe['delta'].lt(dataframe['delta'].shift()) dataframe['short'] = (dataframe['btc_downtrend_1h'] == 1) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'enter_tag'] = '' buy_1 = ( dataframe['slowsma'].gt(0) & (dataframe['short']) & dataframe['close'].lt(dataframe['highsma']) & dataframe['close'].lt(dataframe['lowsma']) & dataframe['minusdi'].gt(dataframe['minusdiema']) & dataframe['rsi'].ge(dataframe['rsi'].shift()) & ~dataframe['preparechangetrend'] & ~dataframe['continueup'] & dataframe['adx'].gt(self.buy_adx1.value) & dataframe['bigdown'] & dataframe['emarsi'].le(self.buy_emarsi1.value) ) buy_2 = ( dataframe['slowsma'].gt(0) & (dataframe['short']) & dataframe['close'].lt(dataframe['highsma']) & dataframe['close'].lt(dataframe['lowsma']) & dataframe['minusdi'].gt(dataframe['minusdiema']) & dataframe['rsi'].ge(dataframe['rsi'].shift()) & ~dataframe['preparechangetrend'] & dataframe['continueup'] & dataframe['adx'].gt(self.buy_adx2.value) & dataframe['bigdown'] & dataframe['emarsi'].le(self.buy_emarsi2.value) ) buy_3 = ( dataframe['slowsma'].gt(0) & (dataframe['short']) & dataframe['close'].lt(dataframe['highsma']) & dataframe['close'].lt(dataframe['lowsma']) & dataframe['minusdi'].gt(dataframe['minusdiema']) & dataframe['rsi'].ge(dataframe['rsi'].shift()) & ~dataframe['continueup'] & dataframe['adx'].gt(self.buy_adx3.value) & dataframe['bigup'] & dataframe['emarsi'].le(self.buy_emarsi3.value) ) buy_4 = ( dataframe['slowsma'].gt(0) & (dataframe['short']) & dataframe['close'].lt(dataframe['highsma']) & dataframe['close'].lt(dataframe['lowsma']) & dataframe['minusdi'].gt(dataframe['minusdiema']) & dataframe['rsi'].ge(dataframe['rsi'].shift()) & dataframe['continueup'] & dataframe['adx'].gt(self.buy_adx4.value) & dataframe['bigup'] & dataframe['emarsi'].le(self.buy_emarsi4.value) ) conditions.append(buy_1) dataframe.loc[buy_1, 'enter_tag'] += 'buy_1' conditions.append(buy_2) dataframe.loc[buy_2, 'enter_tag'] += 'buy_2' conditions.append(buy_3) dataframe.loc[buy_3, 'enter_tag'] += 'buy_3' conditions.append(buy_4) dataframe.loc[buy_4, 'enter_tag'] += 'buy_4' if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'enter_long'] = 1 dataframe.loc[(), ['enter_short', 'enter_tag']] = (0, 'long_in') return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[(), ['exit_short', 'exit_tag']] = (0, 'short_out') dataframe.loc[(), ['exit_long', 'exit_tag']] = (0, 'long_out') return dataframe def custom_exit_2(self, pair: str, trade: Trade, current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() if current_profit >= self.pPF_1.value: return None if self.sell_1.value: if ( (~last_candle['preparechangetrendconfirm']) and (~last_candle['continueup']) and (last_candle['close'] > last_candle['lowsma'] or last_candle['close'] > last_candle['highsma']) and (last_candle['highsma'] > 0) and (last_candle['bigdown']) ): return "sell_1" if self.sell_2.value: if ( (~last_candle['preparechangetrendconfirm']) and (~last_candle['continueup']) and (last_candle['close'] > last_candle['highsma']) and (last_candle['highsma'] > 0) and (last_candle['emarsi'] > self.emarsi1.value or last_candle['close'] > last_candle['slowsma']) and (last_candle['bigdown']) ): return "sell_2" if self.sell_3.value: if ( (~last_candle['preparechangetrendconfirm']) and (last_candle['close'] > last_candle['highsma']) and (last_candle['highsma'] > 0) and (last_candle['adx'] > self.adx2.value) and (last_candle['emarsi'] >= self.emarsi2.value) and (last_candle['bigup']) ): return "sell_3" if self.sell_4.value: if ( (last_candle['preparechangetrendconfirm']) and (~last_candle['continueup']) and (last_candle['slowingdown']) and (last_candle['emarsi'] >= self.emarsi3.value) and (last_candle['slowsma'] > 0) ): return "sell_4" if self.sell_5.value: if ( (last_candle['preparechangetrendconfirm']) and (last_candle['minusdi'] < last_candle['plusdi']) and (last_candle['close'] > last_candle['lowsma']) and (last_candle['slowsma'] > 0) ): return "sell_5" # 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 = -0.03 # loss_upper_limit = -0.10 # # # leverage # leverage_num = 3 # # # 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 #