# pr#agma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # isort: skip_file # --- Do not remove these libs --- import numpy as np # noqa import pandas as pd # noqa from freqtrade.strategy.parameters import DecimalParameter, BooleanParameter from pandas import DataFrame import math from functools import reduce 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 StrategyPierrick4121(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 # valeur de bbwidth pour démarrer buy_bollinger = DecimalParameter(0.025, 0.125, decimals=2, default=0.05, space="buy") # Valeur de la deuxième condition bollinger avec condition sma200 # buy_bollinger_2 = DecimalParameter(0.0, 0.08, decimals=2, default=0.04, space="buy") # buy_min = DecimalParameter(1, 1.1, decimals=2, default=1.01, space="buy") # buy_percent = DecimalParameter(1, 1.1, decimals=2, default=1.01, space="buy") # volume à atteindre buy_volume = DecimalParameter(0, 50, decimals=1, default=18, space="buy") # buy_rsi = IntParameter(20, 40, default=30, space="buy") # buy_adx_enabled = BooleanParameter(default=True, space="buy") # buy_rsi_enabled = CategoricalParameter([True, False], default=False, space="buy") # buy_trigger = CategoricalParameter(["bb_lower", "macd_cross_signal"], default="bb_lower", space="buy") # ROI table: minimal_roi = { # "0": 0.015 "0": 0.5 } # Stoploss: stoploss = -1 trailing_stop = False trailing_stop_positive = 0.02 trailing_stop_positive_offset = 0.0275 # 0.015 trailing_only_offset_is_reached = True # max_open_trades = 3 # Optimal ticker interval for the strategy. timeframe = '5m' # Run "populate_indicators()" only for new candle. process_only_new_candles = False # These values can be overridden in the "ask_strategy" section in the config. use_sell_signal = True sell_profit_only = False ignore_roi_if_buy_signal = False # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 30 # Optional order type mapping. order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } # Optional order time in force. order_time_in_force = { 'buy': 'gtc', 'sell': 'gtc' } plot_config = { # Main plot indicators (Moving averages, ...) 'main_plot': { 'bb_lowerband': {'color': 'red'}, 'bb_upperband': {'color': 'green'}, 'sma100': {'color': 'blue'}, # 'sma500': {'color': 'blue'}, 'sma10': {'color': 'yellow'}, 'min': {'color': 'white'}, 'max': {'color': 'white'}, 'sma20': {'color': 'cyan'} }, 'subplots': { # Subplots - each dict defines one additional plot "BB": { 'bb_width': {'color': 'white'}, }, "ADX": { 'adx': {'color': 'white'}, 'minus_dm': {'color': 'blue'}, 'plus_dm': {'color': 'red'} }, "rolling": { 'bb_rolling': {'color': '#87e470'}, "bb_rolling_min": {'color': '#ac3e2a'} } } } 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() # print(last_candle) # Above 20% profit, sell when rsi < 80 if (last_candle['bb_upperband'] > last_candle['max']) & (previous_last_candle['sma20'] > last_candle['sma20']): return 'over_bb_band' # # Between 2% and 10%, sell if EMA-long above EMA-short if 0.05 < current_profit < 0.1: if previous_last_candle['sma20'] > last_candle['sma20'] and (current_time - trade.open_date_utc).seconds >= 3600 * 3: return 'profit_3h' # Sell any positions at a loss if they are held for more than one day. if 0.1 > current_profit > 0.0 and previous_last_candle['sma20'] > last_candle['sma20'] \ and (current_time - trade.open_date_utc).seconds >= 3600 * 5: return 'profit_5h' 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['min'] = ta.MIN(dataframe['close'], timeperiod=200) dataframe['max'] = ta.MAX(dataframe['close'], timeperiod=200) # 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["rolling"] = ( 100 * (dataframe["close"] - dataframe["bb_lowerband"]) / dataframe["bb_lowerband"]).rolling( 3).mean() dataframe["bb_rolling"] = dataframe["rolling"] / dataframe["bb_width"] dataframe['bb_rolling_min'] = ta.MIN(dataframe['bb_rolling'], timeperiod=10) dataframe['bb_buy'] = (dataframe['min'] + (dataframe['max'] - dataframe['min']) / 3) # print(dataframe["rolling"].tolist()) # dataframe['volatility_kcw'] = ta.volatility.keltner_channel_wband( # dataframe['high'], # dataframe['low'], # dataframe['close'], # window=20, # window_atr=10, # fillna=False, # original_version=True # ) # # dataframe['volatility_dcp'] = ta.volatility.donchian_channel_pband( # dataframe['high'], # dataframe['low'], # dataframe['close'], # window=10, # offset=0, # fillna=False # ) # dataframe['bb_lower_reg'] = dataframe["bb_lowerband"] - dataframe["bb_lowerband"].shift(1) return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # condition_bb_rolling = [ # (dataframe['bb_width'].shift(3) >= self.buy_bollinger.value), # (dataframe['volume'].shift(3) * dataframe['close'] / 1000 >= 100), # self.buy_volume.value * 1000), # (dataframe['open'] < dataframe['close']), # (dataframe['open'] * 1.02 < dataframe['close']), # # (dataframe['close'] <= dataframe['sma100'] * 1.01), # (dataframe['close'].shift(1) <= dataframe['bb_lowerband'].shift(1)) | # (dataframe['close'].shift(2) <= dataframe['bb_lowerband'].shift(2)) | # (dataframe['close'].shift(3) <= dataframe['bb_lowerband'].shift(3)), # ] # condition_bb_rolling2 = reduce(lambda x, y: x & y, condition_bb_rolling) # # condition_bb_rolling_inf = [ # (dataframe['bb_width'] >= 0.04), # (dataframe['volume'] * dataframe['close'] / 1000 >= 100), #>= self.buy_volume.value * 1000), # (dataframe['open'] < dataframe['close']), # (dataframe['open'] * 1.02 < dataframe['close']), # # (dataframe['close'] <= dataframe['min'] * 1.01), # (dataframe['close'] * 1.04) <= dataframe['sma100'], # (dataframe['close'].shift(1) <= dataframe['bb_lowerband'].shift(1)) # ] # condition_bb_rolling_inf2 = reduce(lambda x, y: x & y, condition_bb_rolling_inf) condition_bb_rolling = [ # dataframe['bb_width'] >= self.buy_bollinger.value, # (dataframe['open'] < dataframe['close']), (dataframe['bb_rolling_min'].shift(5) <= -6), (dataframe['bb_rolling_min'].shift(5) == dataframe['bb_rolling'].shift(5)), # (dataframe['sma100'].shift(1) <= dataframe['sma100']), (dataframe['close'].shift(5) < dataframe['min'].shift(5) + ( dataframe['max'].shift(5) - dataframe['min'].shift(5)) / 3), (dataframe['min'].shift(5) == dataframe['min']) ] condition_bb_rolling2 = reduce(lambda x, y: x & y, condition_bb_rolling) dataframe.loc[ ( condition_bb_rolling2 # | condition_bb_rolling_inf2 ), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # dataframe.loc[ # ( # (dataframe['close'] < dataframe['open']) & # (dataframe['close'].shift(1) < dataframe['open'].shift(1)) & # (dataframe['close'].shift(2) < dataframe['open'].shift(2)) & # (dataframe['close'] < dataframe['bb_lowerband']) & # (((dataframe['bb_lowerband'].shift(2) - dataframe['bb_lowerband']) / dataframe['bb_lowerband']) >= 0.02) # # (((dataframe['close'].shift(1) - dataframe['close']) / dataframe['close']) >= 0.025) # ), 'sell'] = 1 return dataframe