# pr#agma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # isort: skip_file # --- Do not remove these libs --- from functools import reduce import numpy as np # noqa import pandas as pd # noqa from freqtrade.strategy.parameters import DecimalParameter from pandas import DataFrame from freqtrade.strategy.interface import IStrategy # -------------------------------- # Add your lib to import here import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib # This class is a sample. Feel free to customize it. class StrategyPierrick2(IStrategy): # Strategy interface version - allow new iterations of the strategy interface. # Check the documentation or the Sample strategy to get the latest version. INTERFACE_VERSION = 2 # ROI table: minimal_roi = { "0": 1, # "600": 0.12, # "1200": 0.08, # "2400": 0.06, # "3600": 0.04, # "7289": 0 } # Stoploss: stoploss = -1 # Buy hypers timeframe = '4h' # Trailing stoploss trailing_stop = False trailing_stop_positive = 0.15 trailing_stop_positive_offset = 0.20 trailing_only_offset_is_reached = True plot_config = { # Main plot indicators (Moving averages, ...) 'main_plot': { 'bb_lowerband': {'color': 'white'}, 'bb_upperband': {'color': 'white'}, }, 'subplots': { # Subplots - each dict defines one additional plot "BB": { 'bb_width': {'color': 'white'}, }, "Aaron": { 'aroonup': {'color': 'blue'}, 'aroondown': {'color': 'red'} } } } def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() previous_last_candle = dataframe.iloc[-2].squeeze() previous_5_candle = dataframe.iloc[-5].squeeze() #print("last_candle", last_candle) #print("previous_last_candle", previous_last_candle) count = 0 for coin, balance in self.wallets.get_all_balances().items(): count = count + 1 # print(coin, " ", balance) # print("count=", count) # (last_candle['percent5'] < -0.005) \ # if (0 < current_profit < 0.005) \ # & ((current_time - trade.open_date_utc).seconds >= 3600 * 2): # # & (previous_last_candle['sma10'] > last_candle['sma10']): # print("too_small_gain", pair, trade, " profit=", current_profit, " rate=", current_rate, " percent5=", # last_candle['percent5']) # return 'too_small_gain' # if (current_profit < -0.05) \ # & ((current_time - trade.open_date_utc).days >= 3): # print("lost_half_profit", pair, trade, " profit=", current_profit, " rate=", current_rate) # return 'stop_loss_profit' # if (current_profit > 0.02) \ # & (last_candle['percent'] < 0.01) \ # & ((current_time - trade.open_date_utc).seconds >= 3600): # print("lost_half_profit", pair, trade, " profit=", current_profit, " rate=", current_rate) # return 'lost_half_profit' # ((current_time - trade.open_date_utc).seconds >= 3600 * 2) \ if (current_profit > 0) \ & (previous_5_candle['sma10'] > last_candle['sma10'] * 1.005) \ & ((last_candle['percent'] < 0) & (last_candle['percent3'] < - (current_profit / 4))): # print("over_bb_band_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate) return 'over_bb_band_sma10_desc' # if (current_profit > 0) \ # & (last_candle['percent'] < -0.02): # # print("over_bb_band_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate) # return 'stop_percent_loss' #if (current_profit > 0) \ # & ((current_time - trade.open_date_utc).seconds >= 3600 * 2) \ # & (previous_last_candle['sma20'] > last_candle['sma20']) \ # & (last_candle['percent'] < 0): # print("over_bb_band_sma20_desc", pair, trade, " profit=", current_profit, " rate=", current_rate) # return 'over_bb_band_sma20_desc' if (current_profit > 0) \ & (last_candle['rsi'] > 88): #| (previous_last_candle['rsi'] > 75 & last_candle['rsi'] < 70)): # print("over_rsi", pair, trade, " profit=", current_profit, " rate=", current_rate) return 'over_rsi' # description trade # Trade(id=0, pair=CAKE/USDT, amount=4.19815281, open_rate=11.91000000, open_since=2021-12-22 17:55:00) # print(last_candle) # if 0.05 < current_profit < 1: # if ( # (previous_last_candle['sma10'] > last_candle['sma10'] * 1.005) & # (current_time - trade.open_date_utc).seconds >= 3600 * 3 # # ) | ( # # (current_time - trade.open_date_utc).seconds >= 3600 * 6 # ): # # self.lock_pair(pair, until=current_time + timedelta(hours=3)) # # print("profit_3h_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate) # return 'profit_3h_sma10_desc' # # if (0 < current_profit < 0.1) \ # & (previous_last_candle['sma20'] > last_candle['sma20']) \ # & ((current_time - trade.open_date_utc).seconds >= 3600 * 5): # print("profit_5h_sma20_desc", pair, trade, " profit=", current_profit, " rate=", current_rate) # return 'profit_5h_sma20_desc' # if (count == self.config['max_open_trades']) & (current_profit < -0.04) \ # & ((current_time - trade.open_date_utc).seconds >= 3600 * 6): # self.lock_pair(pair, until=current_time + timedelta(hours=10)) # print("stop_short_loss", pair, trade, " profit=", current_profit, " rate=", current_rate, # "count=", count, "max=", self.config['max_open_trades']) # return 'stop_short_loss' def informative_pairs(self): return [] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # MACD # macd = ta.MACD(dataframe) # dataframe['macd'] = macd['macd'] # dataframe['macdsignal'] = macd['macdsignal'] # dataframe['macdhist'] = macd['macdhist'] # # # Plus Directional Indicator / Movement # dataframe['plus_dm'] = ta.PLUS_DM(dataframe) # dataframe['plus_di'] = ta.PLUS_DI(dataframe) # # # Minus Directional Indicator / Movement # dataframe['adx'] = ta.ADX(dataframe) # dataframe['minus_dm'] = ta.MINUS_DM(dataframe) # dataframe['minus_di'] = ta.MINUS_DI(dataframe) # dataframe['min'] = ta.MIN(dataframe) # dataframe['max'] = ta.MAX(dataframe) # # Aroon, Aroon Oscillator # aroon = ta.AROON(dataframe) # dataframe['aroonup'] = aroon['aroonup'] # dataframe['aroondown'] = aroon['aroondown'] # dataframe['aroonosc'] = ta.AROONOSC(dataframe) # RSI dataframe['rsi'] = ta.RSI(dataframe) # # 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['sma20'] = ta.SMA(dataframe, timeperiod=20) dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50) dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100) # dataframe['sma200'] = ta.SMA(dataframe, timeperiod=200) # dataframe['sma200_95'] = ta.SMA(dataframe, timeperiod=200) * 0.95 # dataframe['sma200_98'] = ta.SMA(dataframe, timeperiod=200) * 0.98 # dataframe['sma500'] = ta.SMA(dataframe, timeperiod=500) # dataframe['sma500_90'] = ta.SMA(dataframe, timeperiod=500) * 0.9 # dataframe['sma500_95'] = ta.SMA(dataframe, timeperiod=500) * 0.95 # dataframe['sma500_20'] = ta.SMA(dataframe, timeperiod=500) * 0.2 dataframe["percent"] = (dataframe["close"] - dataframe["open"]) / dataframe["open"] dataframe["percent5"] = dataframe["percent"].rolling(5).sum() dataframe["percent3"] = dataframe["percent"].rolling(3).sum() dataframe["percent20"] = dataframe["percent"].rolling(20).sum() dataframe['min'] = ta.MIN(dataframe['close'], timeperiod=200) dataframe['min20'] = ta.MIN(dataframe['close'], timeperiod=20) dataframe['max'] = ta.MAX(dataframe['close'], timeperiod=200) dataframe['max_min'] = dataframe['max'] / dataframe['min'] # 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"] ) dataframe['bb_min'] = ta.MIN(dataframe['bb_lowerband'], timeperiod=36) # 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) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( ( (dataframe['close'] < dataframe['bb_lowerband']) & (dataframe['bb_width'] >= 0.065) #& (dataframe['rsi'] < 45) & (dataframe['volume'] * dataframe['close'] / 1000 >= 100) ) ), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( ), 'sell'] = 1 return dataframe