# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/bestKamStratt.py # --- Do not remove these libs --- from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame, Series, DatetimeIndex, merge # -------------------------------- import talib.abstract as ta import pandas_ta as pta import numpy as np import pandas as pd # noqa import warnings, datetime import freqtrade.vendor.qtpylib.indicators as qtpylib from technical.util import resample_to_interval, resampled_merge from datetime import datetime, timedelta from freqtrade.persistence import Trade, Order from freqtrade.strategy import stoploss_from_open, DecimalParameter, IntParameter, CategoricalParameter import technical.indicators as ftt from functools import reduce from datetime import timedelta pd.options.mode.chained_assignment = None # default='warn' def williams_r(dataframe: DataFrame, period: int = 14) -> Series: """Williams %R, ou %R, est un oscillateur d'analyse technique indiquant la position du cours de clôture par rapport aux plus hauts et aux plus bas sur une période donnée. L'oscillateur est exprimé sur une échelle négative de -100 (le plus bas) à 0 (le plus haut). """ highest_high = dataframe["high"].rolling(center=False, window=period).max() lowest_low = dataframe["low"].rolling(center=False, window=period).min() WR = Series( (highest_high - dataframe["close"]) / (highest_high - lowest_low), name=f"{period} Williams %R", ) return WR * -100 class Github_remiotore_freqtrade__bestKamStratt__20260111_210550(IStrategy): INTERFACE_VERSION = 2 # Paramètres optimisables sell_fastx = IntParameter(50, 100, default=84, space='sell', optimize=True) r_threshold = DecimalParameter(-80, -40, default=-61.3, space='buy', optimize=True) cti_threshold = DecimalParameter(-1, 0, default=-0.715, space='buy', optimize=True) @property def protections(self): return [ { "method": "LowProfitPairs", "lookback_period_candles": 60, "trade_limit": 1, "stop_duration_candles": 60, "required_profit": -0.05 }, { "method": "CooldownPeriod", "stop_duration_candles": 5 } ] minimal_roi = { "0": 1 } cc = {} # Stoploss dynamique via ATR stoploss = -0.25 # Valeur par défaut remplacée par custom_stoploss # Trailing stop activé trailing_stop = True trailing_stop_positive = 0.03 trailing_stop_positive_offset = 0.05 trailing_only_offset_is_reached = True use_custom_stoploss = 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_market_ratio': 0.99 } order_time_in_force = { 'entry': 'gtc', 'exit': 'gtc' } timeframe = '5m' process_only_new_candles = True startup_candle_count = 999 plot_config = { 'main_plot': { "mama": {'color': '#d0da3e'}, "fama": {'color': '#da3eb8'}, "kama": {'color': '#3edad8'}, "ema_50": {'color': '#1f77b4'}, "ema_200": {'color': '#ff7f0e'} }, "subplots": { "fastk": { "fastk": {'color': '#da3e3e'} }, "ATR": { "atr": {'color': '#2ca02c'} }, "Williams %R": { "r_14": {'color': '#9467bd'} } } } def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: # Si le profit est supérieur ou égal à 4 %, appliquer un stoploss très serré if current_profit >= 0.04: return -0.002 # Si la position est ouverte depuis plus de 1,5 jours, forcer la sortie avec un stoploss très serré return None def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Volatilité dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) # PCT CHANGE dataframe['change'] = 100 / dataframe['open'] * dataframe['close'] - 100 # MAMA, FAMA, KAMA dataframe['hl2'] = (dataframe['high'] + dataframe['low']) / 2 dataframe['mama'], dataframe['fama'] = ta.MAMA(dataframe['hl2'], 0.25, 0.025) dataframe['mama_diff'] = ((dataframe['mama'] - dataframe['fama']) / dataframe['hl2']) dataframe['kama'] = ta.KAMA(dataframe['close'], 84) # CTI dataframe['cti'] = pta.cti(dataframe["close"], length=20) # Stochastic Fast stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0) dataframe['fastk'] = stoch_fast['fastk'] # RSI dataframe['rsi_84'] = ta.RSI(dataframe, timeperiod=84) dataframe['rsi_112'] = ta.RSI(dataframe, timeperiod=112) # Williams %R dataframe['r_14'] = williams_r(dataframe, period=14) # Moyennes mobiles dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200) dataframe['ema_300'] = ta.EMA(dataframe, timeperiod=300) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'enter_tag'] = '' buy_condition = ( (dataframe['kama'] > dataframe['fama']) & (dataframe['fama'] > dataframe['mama'] * 0.981) & (dataframe['r_14'] < self.r_threshold.value) & # Paramètre optimisable (dataframe['mama_diff'] < -0.025) & (dataframe['cti'] < self.cti_threshold.value) & # Paramètre optimisable (dataframe['close'].rolling(48).max() >= dataframe['close'] * 1.05) & (dataframe['close'].rolling(288).max() >= dataframe['close'] * 1.125) & (dataframe['rsi_84'] < 60) & (dataframe['rsi_112'] < 60) & (dataframe['ema_200'] > dataframe['ema_300']) # Filtre tendance activé ) conditions.append(buy_condition) dataframe.loc[buy_condition, 'enter_tag'] += 'trend_confirmed' if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), 'enter_long'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, 'exit_long'] = 0 return dataframe 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() # Gestion du temps (timezone-aware) trade_open_date = trade.open_date.replace(tzinfo=current_time.tzinfo) if trade.open_date.tzinfo is None else trade.open_date trade_age = current_time - trade_open_date # Sortie forcée après 60h if trade_age >= timedelta(hours=60): return "60h_exit" # Mise à jour du suivi de prix state = self.cc pc = state.get(trade.id, {'date': current_candle['date'], 'open': current_candle['close'], 'high': current_candle['close'], 'low': current_candle['close'], 'close': current_rate, 'volume': 0}) if current_candle['date'] != pc['date']: pc['date'] = current_candle['date'] pc['high'] = current_candle['close'] pc['low'] = current_candle['close'] pc['open'] = current_candle['close'] pc['close'] = current_rate if current_rate > pc['high']: pc['high'] = current_rate if current_rate < pc['low']: pc['low'] = current_rate if current_rate != pc['close']: pc['close'] = current_rate state[trade.id] = pc # Sortie sur surchauffe (Stochastic) if current_profit > 0: # if min_profit <= -0.015: if current_time > pc['date'] + timedelta(minutes=9) + timedelta(seconds=55): df = dataframe.copy() df = df._append(pc, ignore_index = True) stoch_fast = ta.STOCHF(df, 5, 3, 0, 3, 0) df['fastk'] = stoch_fast['fastk'] cc = df.iloc[-1].squeeze() if cc["fastk"] > self.sell_fastx.value: return "fastk_profit_sell_2" else: if current_candle["fastk"] > self.sell_fastx.value: return "fastk_profit_sell" return None