# source: https://raw.githubusercontent.com/remiotore/ccxt-freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/myshortingstrategiembe.py # --- Do not remove these libs --- from freqtrade.strategy import (BooleanParameter, DecimalParameter, IntParameter, IStrategy,RealParameter) from pandas import DataFrame import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib import pandas as pd # noqa pd.options.mode.chained_assignment = None # default='warn' import technical.indicators as ftt from functools import reduce from datetime import datetime # -------------------------------- class Github_remiotore_ccxt_freqtrade__myshortingstrategiembe__20260111_210550(IStrategy): # Buy hyperspace params: buy_params = { "buy_trend_above_senkou_level": 1, "buy_trend_bullish_level": 6, "buy_min_fan_magnitude_gain": 1.002, "buy_volume_multiplier": 1.2, } # Sell hyperspace params: sell_params = { "sell_trend_indicator": "trend_close_2h", } # ROI table: # minimal_roi = { # "0": 0.05, # "10": 0.03, # "40": 0.02, # "80": 0.01, # "200": 0, # } minimal_roi = { "0": 0.079, "15": 0.047, "41": 0.032, "114": 0.11, "180": 0.007, "420": 0.001 } stoploss = -0.32 # Tighter initial stop-loss trailing_stop = True trailing_stop_positive = 0.015 trailing_stop_positive_offset = 0.025 trailing_only_offset_is_reached = True timeframe = "5m" startup_candle_count = 140 can_short = True def __init__(self, config: dict) -> None: super().__init__(config) min_leverage = 1.5 max_leverage = 10 rsi_period = 14 sharpe_period = 120 process_only_new_candles = True position_adjustment_enabled = True use_exit_signal = True exit_profit_only = True # ignore_roi_if_entry_signal = True plot_config = { 'main_plot': { 'senkou_a': { 'color': 'green', 'fill_to': 'senkou_b', 'fill_label': 'Ichimoku Cloud', 'fill_color': 'rgba(255,76,46,0.2)', }, 'senkou_b': {}, 'trend_close_5m': {'color': '#FF5733'}, 'trend_close_2h': {'color': '#E3FF33'}, }, 'subplots': { 'fan_magnitude': {'fan_magnitude': {}}, 'fan_magnitude_gain': {'fan_magnitude_gain': {}}, } } def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Heikin Ashi candles heikinashi = qtpylib.heikinashi(dataframe) dataframe['open'] = heikinashi['open'] dataframe['high'] = heikinashi['high'] dataframe['low'] = heikinashi['low'] # Trend indicators dataframe['trend_close_5m'] = dataframe['close'] dataframe['trend_close_2h'] = ta.EMA(dataframe['close'], timeperiod=24) dataframe['fan_magnitude'] = dataframe['trend_close_5m'] / dataframe['trend_close_2h'] dataframe['fan_magnitude_gain'] = dataframe['fan_magnitude'] / dataframe['fan_magnitude'].shift(1) # Ichimoku ichimoku = ftt.ichimoku(dataframe, conversion_line_period=20, base_line_periods=60, laggin_span=120, displacement=30) dataframe['senkou_a'] = ichimoku['senkou_span_a'] dataframe['senkou_b'] = ichimoku['senkou_span_b'] # Volume filter dataframe['vol_ma'] = dataframe['volume'].rolling(20).mean() # ATR dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) # Calculer le RSI (Relative Strength Index) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=self.rsi_period) # Calculer le TEMA (Triple Exponential Moving Average) dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9) # Calculate Bollinger Bands bollinger = ta.BBANDS(dataframe, timeperiod=20) dataframe['bb_middleband'] = bollinger['middleband'] # # Calculer le ratio de Sharpe # dataframe['sharpe_ratio'] = self.calculate_sharpe_ratio(dataframe, self.sharpe_period) return dataframe # def calculate_sharpe_ratio(self, dataframe: DataFrame, period: int) -> DataFrame: # returns = dataframe['close'].pct_change() # sharpe_ratio = returns.rolling(window=period).mean() / returns.rolling(window=period).std() # return sharpe_ratio def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] # Price above Ichimoku Cloud if self.buy_params['buy_trend_above_senkou_level'] >= 1: conditions.append(dataframe['trend_close_5m'] > dataframe['senkou_a']) conditions.append(dataframe['trend_close_5m'] > dataframe['senkou_b']) # Fan magnitude conditions conditions.append(dataframe['fan_magnitude_gain'] >= self.buy_params['buy_min_fan_magnitude_gain']) conditions.append(dataframe['fan_magnitude'] > 1) # Sufficient volume conditions.append(dataframe['volume'] > dataframe['vol_ma'] * self.buy_params['buy_volume_multiplier']) # Avoid flat markets conditions.append(dataframe['atr'] > dataframe['atr'].rolling(20).mean()) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'buy'] = 1 dataframe.loc[ ( (qtpylib.crossed_above(dataframe['rsi'], 30)) & # Signal: RSI crosses above 30 (dataframe['tema'] <= dataframe['bb_middleband']) & # Guard (dataframe['tema'] > dataframe['tema'].shift(1)) & # Guard (dataframe['volume'] > 0) # Make sure Volume is not 0 ), ['enter_long', 'enter_tag']] = (1, 'rsi_cross') # ['enter_short', 'enter_tag']] = (1, 'rsi_cross') dataframe.loc[ ( (qtpylib.crossed_below(dataframe['rsi'], 70)) & # Signal: RSI crosses below 70 (dataframe['tema'] > dataframe['bb_middleband']) & # Guard (dataframe['tema'] < dataframe['tema'].shift(1)) & # Guard (dataframe['volume'] > 0) # Make sure Volume is not 0 ), ['enter_short', 'enter_tag']] = (1, 'rsi_cross') return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] # Cross below sell trend indicator conditions.append(qtpylib.crossed_below(dataframe['trend_close_5m'], dataframe[self.sell_params['sell_trend_indicator']])) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'sell'] = 1 return dataframe def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: str | None, 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. """ # Paramètres du levier dynamique dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) atr = dataframe['atr'].iloc[-1] # sharpe_ratio = dataframe['sharpe_ratio'].iloc[-1] # rsi = dataframe['rsi'].iloc[-1] # # Calculer le levier basé sur l'ATR, le ratio de Sharpe et le RSI dynamic_leverage = self.min_leverage + (max_leverage - self.min_leverage) * (atr / atr.max()) # if sharpe_ratio > 1: # dynamic_leverage *= 1.5 # Augmenter le levier si le ratio de Sharpe est supérieur à 1 # elif sharpe_ratio < 0: # dynamic_leverage *= 0.5 # Réduire le levier si le ratio de Sharpe est inférieur à 0 # if rsi > 70: # dynamic_leverage *= 0.7 # Réduire le levier si le RSI indique une condition de surachat # elif rsi < 30: # dynamic_leverage *= 2.3 # Augmenter le levier si le RSI indique une condition de survente if proposed_leverage > 1.0: dynamic_leverage *= 1.6 # Augmenter le levier si le levier proposé est supérieur à 1 else: dynamic_leverage *= 0.5 # Réduire le levier si le levier proposé est inférieur à 1 return min(self.max_leverage, max(proposed_leverage, dynamic_leverage/6))