# 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, IntParameter 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 StrategyPierrick4115(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=0.095, space="buy") buy_bollinger_enabled = BooleanParameter(default=True, 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_bollinger_2_enabled = BooleanParameter(default=True, space="buy") # pourcentage sma à dépasser buy_sma_percent = DecimalParameter(0.95, 1.05, decimals=2, default=0.098, space="buy") buy_sma_percent_enabled = BooleanParameter(default=True, space="buy") # volume à atteindre buy_volume = IntParameter(0, 50, default=0, space="buy") buy_volume_enabled = BooleanParameter(default=True, space="buy") buy_candel_percent = DecimalParameter(1.02, 1.10, decimals=2, default=1.04, space="buy") buy_candel_sma_percent = DecimalParameter(0.97, 1.04, decimals=2, default=0.99, 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 = True trailing_stop_positive = 0.001 trailing_stop_positive_offset = 0.0175 # 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': 'white'}, 'bb_upperband': {'color': 'white'}, 'sma100': {'color': 'green'}, 'sma500': {'color': 'blue'}, 'sma200_98': {'color': 'yellow'}, 'sma200_95': {'color': 'cyan'}, 'rsi': {'color': '#c58893'} }, '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'} }, "Pct": { 'percent': {'color': 'white'} } } } 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['sma21'] = ta.SMA(dataframe, timeperiod=21) # 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"] # 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['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: value = 0 p = dataframe['close'].shift(20) / dataframe['close'] for k, v in p.iteritems(): # print(k, v) value = v # condition1 = np.where(value >= 1.04, True, False) conditions_bb_1 = [] # GUARDS AND TRENDS if self.buy_bollinger_enabled.value: conditions_bb_1.append(dataframe['bb_width'] >= self.buy_bollinger.value) condition1 = False if conditions_bb_1: condition1 = reduce(lambda x, y: x & y, conditions_bb_1) conditions2_bb_2 = [] if self.buy_bollinger_2_enabled.value: conditions2_bb_2.append(dataframe['bb_width'] >= self.buy_bollinger_2.value) # conditions2_bb_2.append(dataframe['bb_width'] >= 0.03) # conditions2_bb_2.append(dataframe['bb_width'] <= 0.1) condition2 = False if conditions2_bb_2: condition2 = reduce(lambda x, y: x & y, conditions2_bb_2) conditions_volume = [] condition_volume = True if self.buy_volume_enabled.value: conditions_volume.append(dataframe['volume'] >= self.buy_volume.value * 1000) if conditions_volume: condition_volume = np.where(conditions_volume, True, False) condition_sma = False conditions_sma = [] if self.buy_sma_percent_enabled.value: # conditions_sma.append(dataframe['close'] <= dataframe['sma200'] * self.buy_sma_percent.value) conditions_sma.append((dataframe['sma100'].shift(36) - dataframe['sma100']) / dataframe[ 'sma100'] > self.buy_sma_percent.value) if conditions_sma: condition_sma = reduce(lambda x, y: x & y, conditions_sma) dataframe.loc[ ( (dataframe['close'] < dataframe['bb_lowerband']) & condition_volume & condition1 ) | ( (dataframe['close'] > dataframe['bb_upperband']) & (dataframe['close'] > dataframe['open'] * 1.04) & (dataframe['close'] < dataframe['open'].shift(4) * 1.06) & (dataframe['close'].shift(4) < dataframe['sma100'].shift(4) * 1.04) ) , '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) ) | ( (dataframe['close'] * 1.04 < dataframe['open']) ), 'sell'] = 1 return dataframe