# source: https://raw.githubusercontent.com/RoboticAutomations/freqtradestrategies/52c4bd40414645f079a980cf4486dc7af4061efb/AIRA%20-%20open%20code%20v2/strategies/rsi-momentum/E0V1E__2.py # directory_url: https://github.com/RoboticAutomations/freqtradestrategies/blob/main/AIRA - open code v2/strategies/rsi-momentum/ # User: RoboticAutomations # Repository: freqtradestrategies # --------------------# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement, trailing-whitespace, unused-import, wrong-import-order, line-too-long, logging-fstring-interpolation # flake8: noqa: F401 # isort: skip_file # from freqtrade.strategy.interface import IStrategy #### import pandas as pd from pandas import DataFrame import numpy as np from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter, stoploss_from_absolute, informative) from freqtrade.exchange import timeframe_to_prev_date # from technical.util import resample_to_interval, resampled_merge # -------------------------------- # Add your lib to import here import logging import talib.abstract as ta import pandas_ta as pta from datetime import datetime, timedelta from typing import List, Optional from technical.indicators import PMAX, SSLChannels, VIDYA from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal from freqtrade.persistence import Trade import freqtrade.vendor.qtpylib.indicators as qtpylib logger = logging.getLogger(__name__) TMP_HOLD = [] TMP_HOLD1 = [] class Github_RoboticAutomations_freqtradestrategies__E0V1E__2__20260915_233254(IStrategy): INTERFACE_VERSION = 3 STRATEGY_VERSION = 1 startup_candle_count: int = 200 minimal_roi = { "0": 1 } timeframe = '5m' strategy_leverage = 5 stoploss = -0.25 # disabled use_custom_stoploss = False max_open_trades = 10 can_short = False # Trailing stop: trailing_stop = False # Optional order type mapping. order_types = { 'entry': 'market', 'exit': 'market', 'emergency_exit': 'market', 'force_entry': 'market', 'force_exit': "market", 'stoploss': 'market', 'stoploss_on_exchange': True, } @property def protections(self): return [ { "method": "CooldownPeriod", "stop_duration_candles": 18 } ] is_optimize_32 = True buy_rsi_fast_32 = IntParameter(20, 70, default=40, space='buy', optimize=is_optimize_32) buy_rsi_32 = IntParameter(15, 50, default=42, space='buy', optimize=is_optimize_32) buy_sma15_32 = DecimalParameter(0.900, 1, default=0.973, decimals=3, space='buy', optimize=is_optimize_32) buy_cti_32 = DecimalParameter(-1, 1, default=0.69, decimals=2, space='buy', optimize=is_optimize_32) sell_fastx = IntParameter(50, 100, default=84, space='sell', optimize=True) # rsi_exit = IntParameter(low=49, high=80, default=70, space='sell', optimize=True) cci_opt = True sell_loss_cci = IntParameter(low=0, high=600, default=120, space='sell', optimize=cci_opt) sell_loss_cci_profit = DecimalParameter(-0.15, 0, default=-0.15, decimals=2, space='sell', optimize=cci_opt) def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) current_candle = dataframe.iloc[-1].squeeze() previous_candle = dataframe.iloc[-2].squeeze() min_profit = trade.calc_profit_ratio(trade.min_rate) if current_candle['close'] > current_candle["ma120"] or current_candle['close'] > current_candle["ma240"]: if trade.id not in TMP_HOLD: TMP_HOLD.append(trade.id) else: if trade.id not in TMP_HOLD1: TMP_HOLD1.append(trade.id) if current_profit > 0: if current_candle["fastk"] > self.sell_fastx.value: return "fastk_profit_sell" if current_candle["cci"] > 80: if current_candle["high"] >= trade.open_rate: return "cci_high_sell" if min_profit <= -0.15: if current_profit > self.sell_loss_cci_profit.value: if current_candle["cci"] > self.sell_loss_cci.value: return "cci_loss_sell" if trade.id in TMP_HOLD and current_candle["close"] < current_candle["ma120"] and current_candle["close"] < \ current_candle["ma240"]: if current_time - timedelta(minutes=12) > trade.open_date_utc: TMP_HOLD.remove(trade.id) return "ma120_sell" if trade.id in TMP_HOLD1: if current_candle["high"] > current_candle["ma120"] or current_candle["high"] > current_candle["ma240"]: if -0.1<= min_profit <= -0.05: TMP_HOLD1.remove(trade.id) return "cross_120_or_240_sell" # if (current_candle['rsi'] >= self.rsi_exit.value) and (previous_candle['rsi'] >= current_candle['rsi']): # return "rsi_reversal" return None 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) # profit sell indicators stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0) dataframe['fastk'] = stoch_fast['fastk'] dataframe['cci'] = ta.CCI(dataframe, timeperiod=20) dataframe['ma120'] = ta.MA(dataframe, timeperiod=120) dataframe['ma240'] = ta.MA(dataframe, timeperiod=240) return dataframe def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: """ Customize leverage for each new trade. This method is only called in futures mode. :param pair: Pair that's currently analyzed :param current_time: datetime object, containing the current datetime :param current_rate: Rate, calculated based on pricing settings in exit_pricing. :param proposed_leverage: A leverage proposed by the bot. :param max_leverage: Max leverage allowed on this pair :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal. :param side: 'long' or 'short' - indicating the direction of the proposed trade :return: A leverage amount, which is between 1.0 and max_leverage. """ return self.strategy_leverage def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: 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) & (dataframe['volume'] > 0) ) dataframe.loc[buy_1, ['enter_long', 'enter_tag']] = (1, 'buy_1') buy_new = ( (dataframe['rsi_slow'] < dataframe['rsi_slow'].shift(1)) & (dataframe['rsi_fast'] < 34) & (dataframe['rsi'] > 28) & (dataframe['close'] < dataframe['sma_15'] * 0.96) & (dataframe['cti'] < self.buy_cti_32.value) & (dataframe['volume'] > 0) ) dataframe.loc[buy_new, ['enter_long', 'enter_tag']] = (1, 'buy_new') return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, ['exit_long', 'exit_tag']] = (0, 'long_out') return dataframe