# source: https://raw.githubusercontent.com/RudoRonuma/freqtrade/4c0a67a5c05922c6735558e344d5888e93184fcf/user_data/strategies/useless/combined_bin_h_cluc_and_madv9.py from datetime import datetime, timedelta import numpy as np import talib.abstract as ta from pandas import DataFrame import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.persistence import Trade from freqtrade.strategy import CategoricalParameter, DecimalParameter, merge_informative_pair from freqtrade.strategy.interface import IStrategy # taken from: https://github.com/akivacz/freqtrade/blob/develop/user_data/strategies/Github_RudoRonuma_freqtrade__combined_bin_h_cluc_and_madv9__20241205_172947.py ################################################################################################### ## Github_RudoRonuma_freqtrade__combined_bin_h_cluc_and_madv9__20241205_172947 by ilya ## ## ## ## https://github.com/i1ya/freqtrade-strategies ## ## The stratagy most inspired by iterativ (authors of the CombinedBinHAndClucV6) ## ## ## ## ## ################################################################################################### ## The main point of this strat is: ## ## - make drawdown as low as possible ## ## - buy at dip ## ## - sell quick as fast as you can (release money for the next buy) ## ## - soft check if market if rising ## ## - hard check is market if fallen ## ## - 10 buy signals ## ## - new stoploss function, preventing from big fall ## ## ## ################################################################################################### ## GENERAL RECOMMENDATIONS ## ## ## ## For optimal performance, suggested to use between 2 and 4 open trades, with unlimited stake.## ## With my pairlist which can be found in this repo. ## ## ## ## Ensure that you don't override any variables in your config.json. Especially ## ## the timeframe (must be 5m). ## ## ## ## sell_profit_only: ## ## True - risk more (gives you higher profit and higher Drawdown) ## ## False (default) - risk less (gives you less ~10-15% profit and much lower Drawdown) ## ## ## ################################################################################################### ## DONATIONS 2 @iterativ (author of the original strategy) ## ## ## ## Absolutely not required. However, will be accepted as a token of appreciation. ## ## ## ## BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk ## ## ETH: 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91 ## ## ## ################################################################################################### # SSL Channels def SSLChannels(dataframe, length=7): df = dataframe.copy() df["ATR"] = ta.ATR(df, timeperiod=14) df["smaHigh"] = df["high"].rolling(length).mean() + df["ATR"] df["smaLow"] = df["low"].rolling(length).mean() - df["ATR"] df["hlv"] = np.where( df["close"] > df["smaHigh"], 1, np.where(df["close"] < df["smaLow"], -1, np.nan) ) df["hlv"] = df["hlv"].ffill() df["sslDown"] = np.where(df["hlv"] < 0, df["smaHigh"], df["smaLow"]) df["sslUp"] = np.where(df["hlv"] < 0, df["smaLow"], df["smaHigh"]) return df["sslDown"], df["sslUp"] class Github_RudoRonuma_freqtrade__combined_bin_h_cluc_and_madv9__20241205_172947(IStrategy): INTERFACE_VERSION = 2 minimal_roi = { "0": 0.028, # I feel lucky! "10": 0.018, "40": 0.005, } stoploss = -0.99 # effectively disabled. timeframe = "5m" inf_1h = "1h" # Sell signal use_exit_signal = True exit_profit_only = False exit_profit_offset = ( 0.001 # it doesn't meant anything, just to guarantee there is a minimal profit. ) ignore_roi_if_entry_signal = False # Trailing stoploss trailing_stop = False trailing_only_offset_is_reached = False trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.025 # Custom stoploss use_custom_stoploss = True # Run "populate_indicators()" only for new candle. process_only_new_candles = False # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 200 # Optional order type mapping. order_types = { "entry": "market", "exit": "market", "stoploss": "market", "stoploss_on_exchange": False, } buy_params = { ############# # Enable/Disable conditions "buy_condition_0_enable": False, "buy_condition_1_enable": True, "buy_condition_2_enable": True, "buy_condition_3_enable": True, "buy_condition_4_enable": True, "buy_condition_5_enable": True, "buy_condition_6_enable": True, "buy_condition_7_enable": True, "buy_condition_8_enable": True, "buy_condition_9_enable": True, "buy_condition_10_enable": True, } ############################################################################ # Buy buy_condition_0_enable = CategoricalParameter( [True, False], default=True, space="buy", optimize=False, load=True ) buy_condition_1_enable = CategoricalParameter( [True, False], default=True, space="buy", optimize=False, load=True ) buy_condition_2_enable = CategoricalParameter( [True, False], default=True, space="buy", optimize=False, load=True ) buy_condition_3_enable = CategoricalParameter( [True, False], default=True, space="buy", optimize=False, load=True ) buy_condition_4_enable = CategoricalParameter( [True, False], default=True, space="buy", optimize=False, load=True ) buy_condition_5_enable = CategoricalParameter( [True, False], default=True, space="buy", optimize=False, load=True ) buy_condition_6_enable = CategoricalParameter( [True, False], default=True, space="buy", optimize=False, load=True ) buy_condition_7_enable = CategoricalParameter( [True, False], default=True, space="buy", optimize=False, load=True ) buy_condition_8_enable = CategoricalParameter( [True, False], default=True, space="buy", optimize=False, load=True ) buy_condition_9_enable = CategoricalParameter( [True, False], default=True, space="buy", optimize=False, load=True ) buy_condition_10_enable = CategoricalParameter( [True, False], default=True, space="buy", optimize=False, load=True ) buy_bb20_close_bblowerband_safe_1 = DecimalParameter( 0.7, 1.1, default=0.99, space="buy", optimize=False, load=True ) buy_bb20_close_bblowerband_safe_2 = DecimalParameter( 0.7, 1.1, default=0.982, space="buy", optimize=False, load=True ) buy_volume_pump_1 = DecimalParameter( 0.1, 0.9, default=0.4, space="buy", decimals=1, optimize=False, load=True ) buy_volume_drop_1 = DecimalParameter( 1, 10, default=4, space="buy", decimals=1, optimize=False, load=True ) buy_rsi_1h_1 = DecimalParameter( 10.0, 40.0, default=16.5, space="buy", decimals=1, optimize=False, load=True ) buy_rsi_1h_2 = DecimalParameter( 10.0, 40.0, default=15.0, space="buy", decimals=1, optimize=False, load=True ) buy_rsi_1h_3 = DecimalParameter( 10.0, 40.0, default=20.0, space="buy", decimals=1, optimize=False, load=True ) buy_rsi_1h_4 = DecimalParameter( 10.0, 40.0, default=35.0, space="buy", decimals=1, optimize=False, load=True ) buy_rsi_1 = DecimalParameter( 10.0, 40.0, default=28.0, space="buy", decimals=1, optimize=False, load=True ) buy_rsi_2 = DecimalParameter( 7.0, 40.0, default=10.0, space="buy", decimals=1, optimize=False, load=True ) buy_rsi_3 = DecimalParameter( 7.0, 40.0, default=14.2, space="buy", decimals=1, optimize=False, load=True ) buy_macd_1 = DecimalParameter( 0.01, 0.09, default=0.02, space="buy", decimals=2, optimize=False, load=True ) buy_macd_2 = DecimalParameter( 0.01, 0.09, default=0.03, space="buy", decimals=2, optimize=False, load=True ) def custom_stoploss( self, pair: str, trade: "Trade", current_time: datetime, current_rate: float, current_profit: float, **kwargs, ) -> float: # Manage losing trades and open room for better ones. if current_profit > 0: return 0.99 else: trade_time_60 = current_time - timedelta(minutes=50) # Trade open more then 60 minutes. For this strategy it's means -> loss # Let's try to minimize the loss if trade_time_60 > trade.open_date_utc: number_of_candle_shift = int( (trade_time_60 - trade.open_date_utc).total_seconds() / 300 ) dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) candle = dataframe.iloc[-number_of_candle_shift].squeeze() # Are we still sinking? if current_rate * 1.015 < candle["open"]: return 0.01 return 0.99 def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, "1h") for pair in pairs] return informative_pairs def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if not self.dp: print("DataProvider is required for multiple timeframes.") # Get the informative pair informative_1h = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe=self.inf_1h) # EMA informative_1h["ema_50"] = ta.EMA(informative_1h, timeperiod=50) informative_1h["ema_200"] = ta.EMA(informative_1h, timeperiod=200) # RSI informative_1h["rsi"] = ta.RSI(informative_1h, timeperiod=14) # SSL Channels ssl_down_1h, ssl_up_1h = SSLChannels(informative_1h, 20) informative_1h["ssl_down"] = ssl_down_1h informative_1h["ssl_up"] = ssl_up_1h return informative_1h def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: 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["volume_mean_slow"] = dataframe["volume"].rolling(window=30).mean() # EMA dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200) dataframe["ema_26"] = ta.EMA(dataframe, timeperiod=26) dataframe["ema_12"] = ta.EMA(dataframe, timeperiod=12) # SMA dataframe["sma_5"] = ta.EMA(dataframe, timeperiod=5) # RSI dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # The indicators for the 1h informative timeframe informative_1h = self.informative_1h_indicators(dataframe, metadata) dataframe = merge_informative_pair( dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True ) # The indicators for the normal (5m) timeframe dataframe = self.normal_tf_indicators(dataframe, metadata) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( self.buy_condition_1_enable.value & (dataframe["close"] > dataframe["ema_200"]) & (dataframe["close"] > dataframe["ema_200_1h"]) & ( dataframe["close"] < dataframe["bb_lowerband"] * self.buy_bb20_close_bblowerband_safe_1.value ) & ( dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * self.buy_volume_pump_1.value ) & ( dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value) ) & ( dataframe["open"] - dataframe["close"] < dataframe["bb_upperband"].shift(2) - dataframe["bb_lowerband"].shift(2) ) & (dataframe["volume"] > 0) ) | ( self.buy_condition_2_enable.value & (dataframe["close"] > dataframe["ema_200"]) & ( dataframe["close"] < dataframe["bb_lowerband"] * self.buy_bb20_close_bblowerband_safe_2.value ) & ( dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * self.buy_volume_pump_1.value ) & ( dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value) ) & ( dataframe["open"] - dataframe["close"] < dataframe["bb_upperband"].shift(2) - dataframe["bb_lowerband"].shift(2) ) & (dataframe["volume"] > 0) ) | ( self.buy_condition_3_enable.value & (dataframe["close"] > dataframe["ema_200_1h"]) & (dataframe["close"] < dataframe["bb_lowerband"]) & (dataframe["rsi"] < self.buy_rsi_3.value) & ( dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value) ) & (dataframe["volume"] > 0) ) | ( self.buy_condition_4_enable.value & (dataframe["rsi_1h"] < self.buy_rsi_1h_1.value) & (dataframe["close"] < dataframe["bb_lowerband"]) & ( dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value) ) & (dataframe["volume"] > 0) ) | ( self.buy_condition_5_enable.value & (dataframe["close"] > dataframe["ema_200"]) & (dataframe["close"] > dataframe["ema_200_1h"]) & (dataframe["ema_26"] > dataframe["ema_12"]) & ( (dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * self.buy_macd_1.value) ) & ( (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100) ) & (dataframe["close"] < (dataframe["bb_lowerband"])) & ( dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value) ) & ( dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * self.buy_volume_pump_1.value ) & (dataframe["volume"] > 0) # Make sure Volume is not 0 ) | ( self.buy_condition_6_enable.value & (dataframe["ema_26"] > dataframe["ema_12"]) & ( (dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * self.buy_macd_2.value) ) & ( (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100) ) & (dataframe["close"] < (dataframe["bb_lowerband"])) & ( dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value) ) & (dataframe["volume"] > 0) ) | ( self.buy_condition_7_enable.value & (dataframe["rsi_1h"] < self.buy_rsi_1h_2.value) & (dataframe["ema_26"] > dataframe["ema_12"]) & ( (dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * self.buy_macd_1.value) ) & ( (dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100) ) & ( dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value) ) & ( dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * self.buy_volume_pump_1.value ) & (dataframe["volume"] > 0) ) | ( self.buy_condition_8_enable.value & (dataframe["rsi_1h"] < self.buy_rsi_1h_3.value) & (dataframe["rsi"] < self.buy_rsi_1.value) & ( dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value) ) & ( dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * self.buy_volume_pump_1.value ) & (dataframe["volume"] > 0) ) | ( self.buy_condition_9_enable.value & (dataframe["rsi_1h"] < self.buy_rsi_1h_4.value) & (dataframe["rsi"] < self.buy_rsi_2.value) & ( dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value) ) & ( dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * self.buy_volume_pump_1.value ) & (dataframe["volume"] > 0) ) | ( self.buy_condition_10_enable.value & (dataframe["close"] < dataframe["sma_5"]) & (dataframe["ssl_up_1h"] > dataframe["ssl_down_1h"]) & (dataframe["ema_50_1h"] > dataframe["ema_200_1h"]) & (dataframe["rsi"] < dataframe["rsi_1h"] - 43.276) & (dataframe["volume"] > 0) ), "enter_long", ] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( ( dataframe["close"] > dataframe["bb_middleband"] * 1.01 ) # Don't be gready, sell fast & (dataframe["volume"] > 0) # Make sure Volume is not 0 ), "enter_short", ] = 1 return dataframe