# source: https://raw.githubusercontent.com/kanaxe/freqtrade_kanaxe/b674562eaa4915192ec3a58cf3f86b64f3fba6e4/test/binclucmad-combined/BinClucMad.py import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy as np import talib.abstract as ta from freqtrade.persistence import Trade from freqtrade.strategy.interface import IStrategy from pandas import DataFrame from datetime import datetime, timedelta from freqtrade.strategy import merge_informative_pair, CategoricalParameter, DecimalParameter, IntParameter from functools import reduce # 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_kanaxe_freqtrade_kanaxe__BinClucMad__20211115_132756(IStrategy): INTERFACE_VERSION = 2 minimal_roi = { "0": 0.038, # I feel lucky! "10": 0.028, "40": 0.015, "180": 0.018, # We're going up? } stoploss = -0.99 # effectively disabled. timeframe = "5m" informative_timeframe = "1h" # Sell signal use_sell_signal = True sell_profit_only = False sell_profit_offset = 0.001 # it doesn't meant anything, just to guarantee there is a minimal profit. ignore_roi_if_buy_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 = { "buy": "market", "sell": "market", "stoploss": "market", "stoploss_on_exchange": False, } buy_params = { ############# # Enable/Disable conditions "v9_buy_condition_0_enable": False, "v9_buy_condition_1_enable": True, "v9_buy_condition_2_enable": True, "v9_buy_condition_3_enable": True, "v9_buy_condition_4_enable": True, "v9_buy_condition_5_enable": True, "v9_buy_condition_6_enable": True, "v9_buy_condition_7_enable": True, "v9_buy_condition_8_enable": True, "v9_buy_condition_9_enable": True, "v9_buy_condition_10_enable": True, "v6_buy_condition_0_enable": True, "v6_buy_condition_1_enable": True, "v6_buy_condition_2_enable": True, "v6_buy_condition_3_enable": True, "v6_buy_condition_4_enable": True, } sell_params = { ############# # Enable/Disable conditions "v9_sell_condition_0_enable": False, "v8_sell_condition_1_enable": True, "v8_sell_condition_2_enable": False, } ############################################################################ # Buy CombinedBinHClucAndMADV9 v9_buy_condition_0_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v9_buy_condition_1_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v9_buy_condition_2_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v9_buy_condition_3_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v9_buy_condition_4_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v9_buy_condition_5_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v9_buy_condition_6_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v9_buy_condition_7_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v9_buy_condition_8_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v9_buy_condition_9_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v9_buy_condition_10_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v6_buy_condition_0_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v6_buy_condition_1_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v6_buy_condition_2_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v6_buy_condition_3_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) v6_buy_condition_4_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True) # Sell v9_sell_condition_0_enable = CategoricalParameter([True, False], default=True, space="sell", optimize=False, load=True) v8_sell_condition_0_enable = CategoricalParameter([True, False], default=True, space="sell", optimize=False, load=True) v8_sell_condition_1_enable = CategoricalParameter([True, False], default=True, space="sell", optimize=False, load=True) buy_bb20_close_bblowerband_safe_1 = DecimalParameter(0.7, 1.1, default=0.99, space="buy", optimize=True, load=True) buy_bb20_close_bblowerband_safe_2 = DecimalParameter(0.7, 1.1, default=0.982, space="buy", optimize=True, load=True) buy_volume_pump_1 = DecimalParameter(0.1, 0.9, default=0.4, space="buy", decimals=1, optimize=True, load=True) buy_volume_drop_1 = DecimalParameter(1, 10, default=4, space="buy", decimals=1, optimize=True, load=True) buy_rsi_1h_1 = DecimalParameter(10.0, 40.0, default=16.5, space="buy", decimals=1, optimize=True, load=True) buy_rsi_1h_2 = DecimalParameter(10.0, 40.0, default=15.0, space="buy", decimals=1, optimize=True, load=True) buy_rsi_1h_3 = DecimalParameter(10.0, 40.0, default=20.0, space="buy", decimals=1, optimize=True, load=True) buy_rsi_1h_4 = DecimalParameter(10.0, 40.0, default=35.0, space="buy", decimals=1, optimize=True, load=True) buy_rsi_1 = DecimalParameter(10.0, 40.0, default=28.0, space="buy", decimals=1, optimize=True, load=True) buy_rsi_2 = DecimalParameter(7.0, 40.0, default=10.0, space="buy", decimals=1, optimize=True, load=True) buy_rsi_3 = DecimalParameter(7.0, 40.0, default=14.2, space="buy", decimals=1, optimize=True, load=True) buy_macd_1 = DecimalParameter(0.01, 0.09, default=0.02, space="buy", decimals=2, optimize=True, load=True) buy_macd_2 = DecimalParameter(0.01, 0.09, default=0.03, space="buy", decimals=2, optimize=True, load=True) v8_sell_rsi_main = DecimalParameter(72.0, 90.0, default=80, space="sell", decimals=2, optimize=True, 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_50 = trade.open_date_utc + timedelta(minutes=50) # trade_time_240 = trade.open_date_utc + timedelta(minutes=240) # Trade open more then 60 minutes. For this strategy it's means -> loss # Let's try to minimize the loss if current_time > trade_time_50: try: number_of_candle_shift = int((current_time - trade_time_50).total_seconds() / 300) dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) candle = dataframe.iloc[-number_of_candle_shift].squeeze() # We are at bottom. Wait... if candle["rsi_1h"] < 30: return 0.99 # Are we still sinking? if candle["close"] > candle["ema_200"]: if current_rate * 1.025 < candle["open"]: return 0.01 if current_rate * 1.015 < candle["open"]: return 0.01 except IndexError as error: # Whoops, set stoploss at 10% return 0.1 return 0.99 def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, self.informative_timeframe) for pair in pairs] return informative_pairs def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: assert self.dp, "DataProvider is required for multiple timeframes." # Get the informative pair informative_1h = self.dp.get_pair_dataframe(pair=metadata["pair"], timeframe=self.informative_timeframe) # 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_50"] = ta.EMA(dataframe, timeperiod=50) 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 = self.informative_1h_indicators(dataframe, metadata) dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe, ffill=True) # The indicators for the normal (5m) timeframe dataframe = self.normal_tf_indicators(dataframe, metadata) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] # START VERSION9 if self.v9_buy_condition_1_enable.value: conditions.append( ( (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) ) ) if self.v9_buy_condition_2_enable.value: conditions.append( ( (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) ) ) if self.v9_buy_condition_3_enable.value: conditions.append( ( (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) ) ) if self.v9_buy_condition_4_enable.value: conditions.append( ( (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) ) ) if self.v9_buy_condition_5_enable.value: conditions.append( ( (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 ) ) if self.v9_buy_condition_6_enable.value: conditions.append( ( (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) ) ) if self.v9_buy_condition_7_enable.value: conditions.append( ( (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) ) ) if self.v9_buy_condition_8_enable.value: conditions.append( ( (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) ) ) if self.v9_buy_condition_9_enable.value: conditions.append( ( (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) ) ) if self.v9_buy_condition_10_enable.value: conditions.append( ( (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) ) ) # END V9 # START V6 if self.v6_buy_condition_0_enable.value: conditions.append( ( # strategy ClucMay72018 (dataframe["close"] > dataframe["ema_200"]) & (dataframe["close"] > dataframe["ema_200_1h"]) & (dataframe["close"] < dataframe["ema_50"]) & (dataframe["close"] < 0.99 * dataframe["bb_lowerband"]) & ( (dataframe["volume"] < (dataframe["volume_mean_slow"].shift(1) * 21)) | (dataframe["volume_mean_slow"] > (dataframe["volume_mean_slow"].shift(30) * 0.4)) ) & (dataframe["volume"] > 0) ) ) if self.v6_buy_condition_1_enable.value: conditions.append( ( # strategy ClucMay72018 (dataframe["close"] < dataframe["ema_50"]) & (dataframe["close"] < 0.975 * dataframe["bb_lowerband"]) & ( (dataframe["volume"] < (dataframe["volume_mean_slow"].shift(1) * 20)) | (dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * 0.4) ) & (dataframe["rsi_1h"] < 15) # Don't buy if someone drop the market. & (dataframe["volume"] < (dataframe["volume"].shift() * 4)) & (dataframe["volume"] > 0) # Try to exclude pumping # Make sure Volume is not 0 ) ) if self.v6_buy_condition_2_enable.value: conditions.append( ( # strategy MACD Low buy (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"] * 0.02)) & ((dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100)) & ( (dataframe["volume"] < (dataframe["volume"].shift() * 4)) | (dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * 0.4) ) & (dataframe["close"] < (dataframe["bb_lowerband"])) & # (dataframe["volume"] > 0) ) ) if self.v6_buy_condition_3_enable.value: conditions.append( ( # strategy MACD Low buy (dataframe["ema_26"] > dataframe["ema_12"]) & ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * 0.03)) & ((dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100)) & (dataframe["volume"] < (dataframe["volume"].shift() * 4)) & (dataframe["close"] < (dataframe["bb_lowerband"])) # Don't buy if someone drop the market. & (dataframe["volume"] > 0) # Make sure Volume is not 0 ) ) # END V6 if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), "buy"] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] if self.v9_sell_condition_0_enable.value: conditions.append( ( (dataframe["close"] > dataframe["bb_middleband"] * 1.01) & (dataframe["volume"] > 0) # Don't be gready, sell fast # Make sure Volume is not 0 ) ) if self.v8_sell_condition_0_enable.value: conditions.append( ( (dataframe["close"] > dataframe["bb_upperband"]) & (dataframe["close"].shift(1) > dataframe["bb_upperband"].shift(1)) & (dataframe["close"].shift(2) > dataframe["bb_upperband"].shift(2)) & (dataframe["close"].shift(2) > dataframe["bb_upperband"].shift(2)) & (dataframe["volume"] > 0) ) ) if self.v8_sell_condition_1_enable.value: conditions.append(((dataframe["rsi"] > self.v8_sell_rsi_main.value) & (dataframe["volume"] > 0))) if conditions: dataframe.loc[reduce(lambda x, y: x | y, conditions), "sell"] = 1 return dataframe