# source: https://raw.githubusercontent.com/kitokoh/trad/434fab1fddee113a2b9cf9ab270b019a8cc614ca/user_data/strategies/sample_strategy.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file # --- Do not remove these imports --- import numpy as np import pandas as pd from datetime import datetime, timedelta, timezone from pandas import DataFrame from typing import Optional, Union from freqtrade.strategy import ( IStrategy, Trade, Order, PairLocks, informative, # @informative decorator # Hyperopt Parameters BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, RealParameter, # timeframe helpers timeframe_to_minutes, timeframe_to_next_date, timeframe_to_prev_date, # Strategy helper functions merge_informative_pair, stoploss_from_absolute, stoploss_from_open, ) # -------------------------------- # Add your lib to import here import talib.abstract as ta from technical import qtpylib # This class is a sample. Feel free to customize it. class Github_kitokoh_trad__sample_strategy__20250521_062215(IStrategy): """ This is a sample strategy to inspire you. More information in https://www.freqtrade.io/en/latest/strategy-customization/ You can: :return: a Dataframe with all mandatory indicators for the strategies - Rename the class name (Do not forget to update class_name) - Add any methods you want to build your strategy - Add any lib you need to build your strategy You must keep: - the lib in the section "Do not remove these libs" - the methods: populate_indicators, populate_entry_trend, populate_exit_trend You should keep: - timeframe, minimal_roi, stoploss, trailing_* """ # Strategy interface version - allow new iterations of the strategy interface. # Check the documentation or the Sample strategy to get the latest version. INTERFACE_VERSION = 3 # Can this strategy go short? can_short: bool = False # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". minimal_roi = { # "120": 0.0, # exit after 120 minutes at break even "60": 0.01, "30": 0.02, "0": 0.04, } # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". stoploss = -0.10 # Trailing stoploss trailing_stop = False # trailing_only_offset_is_reached = False # trailing_stop_positive = 0.01 # trailing_stop_positive_offset = 0.0 # Disabled / not configured # Optimal timeframe for the strategy. timeframe = "5m" # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the config. use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # Hyperoptable parameters buy_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True) sell_rsi = IntParameter(low=50, high=100, default=70, space="sell", optimize=True, load=True) short_rsi = IntParameter(low=51, high=100, default=70, space="sell", optimize=True, load=True) exit_short_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True) # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 200 # Optional order type mapping. order_types = { "entry": "limit", "exit": "limit", "stoploss": "market", "stoploss_on_exchange": False, } # Optional order time in force. order_time_in_force = {"entry": "GTC", "exit": "GTC"} plot_config = { "main_plot": { "tema": {}, "sar": {"color": "white"}, }, "subplots": { "MACD": { "macd": {"color": "blue"}, "macdsignal": {"color": "orange"}, }, "RSI": { "rsi": {"color": "red"}, }, }, } def informative_pairs(self): """ Define additional, informative pair/interval combinations to be cached from the exchange. These pair/interval combinations are non-tradeable, unless they are part of the whitelist as well. For more information, please consult the documentation :return: List of tuples in the format (pair, interval) Sample: return [("ETH/USDT", "5m"), ("BTC/USDT", "15m"), ] """ return [] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Adds several different TA indicators to the given DataFrame Performance Note: For the best performance be frugal on the number of indicators you are using. Let uncomment only the indicator you are using in your strategies or your hyperopt configuration, otherwise you will waste your memory and CPU usage. :param dataframe: Dataframe with data from the exchange :param metadata: Additional information, like the currently traded pair :return: a Dataframe with all mandatory indicators for the strategies """ # Momentum Indicators # ------------------------------------ # ADX dataframe["adx"] = ta.ADX(dataframe) # # Plus Directional Indicator / Movement # dataframe['plus_dm'] = ta.PLUS_DM(dataframe) # dataframe['plus_di'] = ta.PLUS_DI(dataframe) # # Minus Directional Indicator / Movement # dataframe['minus_dm'] = ta.MINUS_DM(dataframe) # dataframe['minus_di'] = ta.MINUS_DI(dataframe) # # Aroon, Aroon Oscillator # aroon = ta.AROON(dataframe) # dataframe['aroonup'] = aroon['aroonup'] # dataframe['aroondown'] = aroon['aroondown'] # dataframe['aroonosc'] = ta.AROONOSC(dataframe) # # Awesome Oscillator # dataframe['ao'] = qtpylib.awesome_oscillator(dataframe) # # Keltner Channel # keltner = qtpylib.keltner_channel(dataframe) # dataframe["kc_upperband"] = keltner["upper"] # dataframe["kc_lowerband"] = keltner["lower"] # dataframe["kc_middleband"] = keltner["mid"] # dataframe["kc_percent"] = ( # (dataframe["close"] - dataframe["kc_lowerband"]) / # (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) # ) # dataframe["kc_width"] = ( # (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"] # ) # # Ultimate Oscillator # dataframe['uo'] = ta.ULTOSC(dataframe) # # Commodity Channel Index: values [Oversold:-100, Overbought:100] # dataframe['cci'] = ta.CCI(dataframe) # RSI dataframe["rsi"] = ta.RSI(dataframe) # # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy) # rsi = 0.1 * (dataframe['rsi'] - 50) # dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) # # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy) # dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) # # Stochastic Slow # stoch = ta.STOCH(dataframe) # dataframe['slowd'] = stoch['slowd'] # dataframe['slowk'] = stoch['slowk'] # Stochastic Fast stoch_fast = ta.STOCHF(dataframe) dataframe["fastd"] = stoch_fast["fastd"] dataframe["fastk"] = stoch_fast["fastk"] # # Stochastic RSI # Please read https://github.com/freqtrade/freqtrade/issues/2961 before using this. # STOCHRSI is NOT aligned with tradingview, which may result in non-expected results. # stoch_rsi = ta.STOCHRSI(dataframe) # dataframe['fastd_rsi'] = stoch_rsi['fastd'] # dataframe['fastk_rsi'] = stoch_rsi['fastk'] # MACD macd = ta.MACD(dataframe) dataframe["macd"] = macd["macd"] dataframe["macdsignal"] = macd["macdsignal"] dataframe["macdhist"] = macd["macdhist"] # MFI dataframe["mfi"] = ta.MFI(dataframe) # # ROC # dataframe['roc'] = ta.ROC(dataframe) # Overlap Studies # ------------------------------------ # Bollinger Bands bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe["bb_lowerband"] = bollinger["lower"] dataframe["bb_middleband"] = bollinger["mid"] dataframe["bb_upperband"] = bollinger["upper"] dataframe["bb_percent"] = (dataframe["close"] - dataframe["bb_lowerband"]) / ( dataframe["bb_upperband"] - dataframe["bb_lowerband"] ) dataframe["bb_width"] = (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe[ "bb_middleband" ] # Bollinger Bands - Weighted (EMA based instead of SMA) # weighted_bollinger = qtpylib.weighted_bollinger_bands( # qtpylib.typical_price(dataframe), window=20, stds=2 # ) # dataframe["wbb_upperband"] = weighted_bollinger["upper"] # dataframe["wbb_lowerband"] = weighted_bollinger["lower"] # dataframe["wbb_middleband"] = weighted_bollinger["mid"] # dataframe["wbb_percent"] = ( # (dataframe["close"] - dataframe["wbb_lowerband"]) / # (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) # ) # dataframe["wbb_width"] = ( # (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) / # dataframe["wbb_middleband"] # ) # # EMA - Exponential Moving Average # dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3) # dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) # dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10) # dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21) # dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) # dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) # # SMA - Simple Moving Average # dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3) # dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5) # dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10) # dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21) # dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50) # dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100) # Parabolic SAR dataframe["sar"] = ta.SAR(dataframe) # TEMA - Triple Exponential Moving Average dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9) # Cycle Indicator # ------------------------------------ # Hilbert Transform Indicator - SineWave hilbert = ta.HT_SINE(dataframe) dataframe["htsine"] = hilbert["sine"] dataframe["htleadsine"] = hilbert["leadsine"] # Pattern Recognition - Bullish candlestick patterns # ------------------------------------ # # Hammer: values [0, 100] # dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe) # # Inverted Hammer: values [0, 100] # dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe) # # Dragonfly Doji: values [0, 100] # dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe) # # Piercing Line: values [0, 100] # dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100] # # Morningstar: values [0, 100] # dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100] # # Three White Soldiers: values [0, 100] # dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100] # Pattern Recognition - Bearish candlestick patterns # ------------------------------------ # # Hanging Man: values [0, 100] # dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe) # # Shooting Star: values [0, 100] # dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe) # # Gravestone Doji: values [0, 100] # dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe) # # Dark Cloud Cover: values [0, 100] # dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe) # # Evening Doji Star: values [0, 100] # dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe) # # Evening Star: values [0, 100] # dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe) # Pattern Recognition - Bullish/Bearish candlestick patterns # ------------------------------------ # # Three Line Strike: values [0, -100, 100] # dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe) # # Spinning Top: values [0, -100, 100] # dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100] # # Engulfing: values [0, -100, 100] # dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100] # # Harami: values [0, -100, 100] # dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100] # # Three Outside Up/Down: values [0, -100, 100] # dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100] # # Three Inside Up/Down: values [0, -100, 100] # dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100] # # Chart type # # ------------------------------------ # # Heikin Ashi Strategy # heikinashi = qtpylib.heikinashi(dataframe) # dataframe['ha_open'] = heikinashi['open'] # dataframe['ha_close'] = heikinashi['close'] # dataframe['ha_high'] = heikinashi['high'] # dataframe['ha_low'] = heikinashi['low'] # Retrieve best bid and best ask from the orderbook # ------------------------------------ """ # first check if dataprovider is available if self.dp: if self.dp.runmode.value in ('live', 'dry_run'): ob = self.dp.orderbook(metadata['pair'], 1) dataframe['best_bid'] = ob['bids'][0][0] dataframe['best_ask'] = ob['asks'][0][0] """ return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the entry signal for the given dataframe :param dataframe: DataFrame :param metadata: Additional information, like the currently traded pair :return: DataFrame with entry columns populated """ dataframe.loc[ ( # Signal: RSI crosses above 30 (qtpylib.crossed_above(dataframe["rsi"], self.buy_rsi.value)) & (dataframe["tema"] <= dataframe["bb_middleband"]) # Guard: tema below BB middle & (dataframe["tema"] > dataframe["tema"].shift(1)) # Guard: tema is raising & (dataframe["volume"] > 0) # Make sure Volume is not 0 ), "enter_long", ] = 1 dataframe.loc[ ( # Signal: RSI crosses above 70 (qtpylib.crossed_above(dataframe["rsi"], self.short_rsi.value)) & (dataframe["tema"] > dataframe["bb_middleband"]) # Guard: tema above BB middle & (dataframe["tema"] < dataframe["tema"].shift(1)) # Guard: tema is falling & (dataframe["volume"] > 0) # Make sure Volume is not 0 ), "enter_short", ] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the exit signal for the given dataframe :param dataframe: DataFrame :param metadata: Additional information, like the currently traded pair :return: DataFrame with exit columns populated """ dataframe.loc[ ( # Signal: RSI crosses above 70 (qtpylib.crossed_above(dataframe["rsi"], self.sell_rsi.value)) & (dataframe["tema"] > dataframe["bb_middleband"]) # Guard: tema above BB middle & (dataframe["tema"] < dataframe["tema"].shift(1)) # Guard: tema is falling & (dataframe["volume"] > 0) # Make sure Volume is not 0 ), "exit_long", ] = 1 dataframe.loc[ ( # Signal: RSI crosses above 30 (qtpylib.crossed_above(dataframe["rsi"], self.exit_short_rsi.value)) & # Guard: tema below BB middle (dataframe["tema"] <= dataframe["bb_middleband"]) & (dataframe["tema"] > dataframe["tema"].shift(1)) # Guard: tema is raising & (dataframe["volume"] > 0) # Make sure Volume is not 0 ), "exit_short", ] = 1 return dataframe # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file from functools import reduce import numpy as np import pandas as pd from datetime import datetime from pandas import DataFrame from typing import Optional, Dict, List from freqtrade.strategy import IStrategy, Trade, informative import talib.abstract as ta from technical import qtpylib class Github_kitokoh_trad__sample_strategy__20250521_062215(IStrategy): """ Stratégie optimisée basée sur RSI avec : - Filtrage ADX pour les tendances fortes - Stop-loss dynamique basé sur ATR - Confirmations multiples - Gestion de risque améliorée """ INTERFACE_VERSION = 3 can_short: bool = True # ROI optimisé minimal_roi = { "60": 0.015, "30": 0.025, "0": 0.035 } # Paramètres de stop-loss stoploss = -0.10 # Valeur par défaut use_custom_stoploss = True # Timeframe timeframe = '5m' process_only_new_candles = True startup_candle_count = 200 # Paramètres tradables buy_rsi = IntParameter(20, 40, default=30, space='buy', optimize=True) sell_rsi = IntParameter(60, 90, default=70, space='sell', optimize=True) short_rsi = IntParameter(60, 90, default=70, space='sell', optimize=True) exit_short_rsi = IntParameter(20, 40, default=30, space='buy', optimize=True) buy_adx = IntParameter(20, 40, default=25, space='buy', optimize=True) atr_multiplier = DecimalParameter(1.5, 3.5, default=2.5, decimals=1, space='risk', optimize=True) use_ema_filter = BooleanParameter(default=True, space='buy', optimize=True) use_volume_filter = BooleanParameter(default=True, space='buy', optimize=True) min_volume = DecimalParameter(0.5, 3.0, default=1.0, decimals=1, space='buy', optimize=True) # Types d'ordre order_types = { 'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } order_time_in_force = { 'entry': 'GTC', 'exit': 'GTC' } @informative('1h') def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['rsi_1h'] = ta.RSI(dataframe) dataframe['ema50_1h'] = ta.EMA(dataframe, timeperiod=50) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Indicateurs principaux dataframe['rsi'] = ta.RSI(dataframe) dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9) dataframe['adx'] = ta.ADX(dataframe) dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20) dataframe['volume_mean'] = dataframe['volume'].rolling(window=24).mean() # Bollinger Bands bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2) dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] dataframe['bb_lowerband'] = bollinger['lower'] # Stochastic pour confirmation stoch = ta.STOCHF(dataframe) dataframe['fastd'] = stoch['fastd'] dataframe['fastk'] = stoch['fastk'] return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions_long = [ qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value), dataframe['tema'] <= dataframe['bb_middleband'], dataframe['tema'] > dataframe['tema'].shift(1), dataframe['adx'] > self.buy_adx.value, dataframe['close'] > dataframe['ema20'] if self.use_ema_filter.value else True, dataframe['volume'] > (dataframe['volume_mean'] * self.min_volume.value) if self.use_volume_filter.value else True, dataframe['rsi_1h'] > 50, # Tendance haussière sur 1h dataframe['close'] > dataframe['ema50_1h'] # Au-dessus de la EMA50 1h ] conditions_short = [ qtpylib.crossed_below(dataframe['rsi'], self.short_rsi.value), dataframe['tema'] > dataframe['bb_middleband'], dataframe['tema'] < dataframe['tema'].shift(1), dataframe['adx'] > self.buy_adx.value, dataframe['close'] < dataframe['ema20'] if self.use_ema_filter.value else True, dataframe['volume'] > (dataframe['volume_mean'] * self.min_volume.value) if self.use_volume_filter.value else True, dataframe['rsi_1h'] < 50, # Tendance baissière sur 1h dataframe['close'] < dataframe['ema50_1h'] # En-dessous de la EMA50 1h ] dataframe.loc[ reduce(lambda x, y: x & y, conditions_long), 'enter_long' ] = 1 dataframe.loc[ reduce(lambda x, y: x & y, conditions_short), 'enter_short' ] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions_exit_long = [ qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value), dataframe['tema'] > dataframe['bb_middleband'], dataframe['tema'] < dataframe['tema'].shift(1) ] conditions_exit_short = [ qtpylib.crossed_below(dataframe['rsi'], self.exit_short_rsi.value), dataframe['tema'] < dataframe['bb_middleband'], dataframe['tema'] > dataframe['tema'].shift(1) ] dataframe.loc[ reduce(lambda x, y: x & y, conditions_exit_long), 'exit_long' ] = 1 dataframe.loc[ reduce(lambda x, y: x & y, conditions_exit_short), 'exit_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) last_candle = dataframe.iloc[-1].squeeze() # Stop-loss dynamique basé sur ATR atr_stoploss = self.atr_multiplier.value * last_candle['atr'] / current_rate # Protection contre les gaps if current_profit < -0.25: # Si perte > 25% return -1 # Fermer immédiatement return -atr_stoploss def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs) -> bool: # Empêcher la sortie si le RSI montre encore du momentum dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() if exit_reason == 'roi' and last_candle['rsi'] < 50: return False # Annuler la sortie ROI si RSI < 50 return True plot_config = { 'main_plot': { 'tema': {'color': 'blue'}, 'bb_middleband': {'color': 'grey'}, 'bb_upperband': {'color': 'grey', 'fill_to': 'bb_lowerband'}, }, 'subplots': { "RSI": { 'rsi': {'color': 'purple'}, 'buy_rsi': {'color': 'green', 'plotly': {'opacity': 0.4}}, 'sell_rsi': {'color': 'red', 'plotly': {'opacity': 0.4}} }, "ADX": { 'adx': {'color': 'orange'} } } }