# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file # --- Do not remove these libs --- import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame from freqtrade.strategy import (merge_informative_pair, BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter, informative) # -------------------------------- # Add your lib to import here import talib.abstract as ta from functools import reduce import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.persistence import PairLocks, Trade from datetime import datetime # This class is a sample. Feel free to customize it. class TrixV23Strategy(IStrategy): """ Sources : Cripto Robot : https://www.youtube.com/watch?v=uE04UROWkjs&list=PLpJ7cz_wOtsrqEQpveLc2xKLjOBgy4NfA&index=4 Github : https://github.com/CryptoRobotFr/TrueStrategy/blob/main/TrixStrategy/Trix_Complete_backtest.ipynb """ # 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 # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". minimal_roi = { "0": 0.553, "423": 0.144, "751": 0.059, "1342": 0 } # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". stoploss = -0.31 # Trailing stoploss trailing_stop = False # trailing_only_offset_is_reached = True # trailing_stop_positive = 0.02 # trailing_stop_positive_offset = 0.9 # Disabled / not configured # Optimal timeframe for the strategy. timeframe = '1h' # 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 = True ignore_roi_if_buy_signal = False use_custom_stoploss = True # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 200 # 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' } #---------------------------# #-- Hyperspace parameters --# #---------------------------# buy_params = { "buy_trix_signal_type": "trigger", "buy_trix_src": "low", "buy_trix_timeperiod": 8, "buy_trix_signal_timeperiod": 19, "buy_stoch_rsi_enabled": True, "buy_rsi_timeperiod": 14, "buy_stoch_rsi_timeperiod": 14, "buy_stoch_rsi": 0.901, "buy_ema_enabled": True, "buy_ema_src": "open", "buy_ema_timeperiod": 10, "buy_ema_multiplier": 0.85, "buy_btc_ema_enabled": True, "buy_btc_ema_multiplier": 0.996, "buy_btc_ema_timeperiod": 184, } sell_params = { "sell_trix_signal_type": "trailing", "sell_trix_src": "high", "sell_trix_timeperiod": 10, "sell_trix_signal_timeperiod": 19, "sell_stoch_rsi_enabled": True, "sell_rsi_timeperiod": 14, "sell_stoch_rsi_timeperiod": 14, "sell_stoch_rsi": 0.183, "sell_atr_enabled": True, "sell_atr_timeperiod": 30, "sell_atr_multiplier": 4.99, } #------------------------------# #-- Hyperoptables parameters --# #------------------------------# # buy buy_trix_signal_type = CategoricalParameter(['trailing', 'trigger'], default='trigger', space="buy", optimize=False, load=True) buy_trix_src = CategoricalParameter(['open', 'high', 'low', 'close'], default='close', space="buy", optimize=False, load=True) buy_trix_timeperiod = IntParameter(5, 25, default=9, space="buy", optimize=False, load=True) buy_trix_signal_timeperiod = IntParameter(5, 25, default=21, space="buy", optimize=False, load=True) buy_stoch_rsi_enabled = BooleanParameter(default=True, space="buy", optimize=False, load=True) buy_rsi_timeperiod = IntParameter(5, 25, default=14, space="buy", optimize=False, load=True) buy_stoch_rsi = DecimalParameter(0.6, 0.99, decimals=3, default=0.987, space="buy", optimize=False, load=True) buy_stoch_rsi_timeperiod = IntParameter(5, 25, default=14, space="buy", optimize=False, load=True) buy_ema_enabled = BooleanParameter(default=False, space="buy", optimize=False, load=True) buy_ema_timeperiod = IntParameter(9, 100, default=21, space="buy", optimize=False, load=True) buy_ema_multiplier = DecimalParameter(0.8, 1.2, decimals=2, default=1.00, space="buy", optimize=False, load=True) buy_ema_src = CategoricalParameter(['open', 'high', 'low', 'close'], default='close', space="buy", optimize=False, load=True) buy_btc_ema_enabled = BooleanParameter(default=False, space="buy", optimize=True, load=True) buy_btc_ema_timeperiod = IntParameter(150, 250, default=200, space="buy", optimize=True, load=True) buy_btc_ema_multiplier = DecimalParameter(0.8, 1.0, decimals=3, default=0.97, space="buy", optimize=True, load=True) # sell sell_trix_signal_type = CategoricalParameter(['trailing', 'trigger'], default='trailing', space="sell", optimize=False, load=True) sell_trix_src = CategoricalParameter(['open', 'high', 'low', 'close'], default='close', space="sell", optimize=False, load=True) sell_trix_timeperiod = IntParameter(5, 25, default=9, space="sell", optimize=False, load=True) sell_trix_signal_timeperiod = IntParameter(5, 25, default=21, space="sell", optimize=False, load=True) sell_stoch_rsi_enabled = BooleanParameter(default=True, space="sell", optimize=False, load=True) sell_rsi_timeperiod = IntParameter(5, 25, default=14, space="sell", optimize=False, load=True) sell_stoch_rsi = DecimalParameter(0.01, 0.4, decimals=3, default=0.048, space="sell", optimize=False, load=True) sell_stoch_rsi_timeperiod = IntParameter(5, 25, default=14, space="sell", optimize=False, load=True) sell_atr_enabled = BooleanParameter(default=True, space="sell", optimize=False, load=True) sell_atr_timeperiod = IntParameter(9, 30, default=14, space="sell", optimize=False, load=True) sell_atr_multiplier = DecimalParameter(0.7, 9.0, decimals=3, default=4.0, space="sell", optimize=False, load=True) plot_config = { 'main_plot': { 'trix_b_8': {'color': 'blue'}, 'trix_s_10': {'color': 'orange'}, 'ema_b_signal': {'color': 'red'}, 'btc_usdt_close_1h': {'color': 'purple'}, 'btc_usdt_ema_184_1h': {'color': 'yellow'}, }, 'subplots': { "TRIX BUY": { 'trix_b_pct': {'color': 'blue'}, 'trix_b_signal_19': {'color': 'orange'}, }, "TRIX SELL": { 'trix_s_pct': {'color': 'blue'}, 'trix_s_signal_19': {'color': 'orange'}, }, "STOCH RSI": { 'b_stoch_rsi': {'color': 'blue'}, 's_stoch_rsi': {'color': 'orange'}, }, } } @informative('1h', 'BTC/{stake}') def populate_indicators_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame: #---------# #-- BTC --# #---------# for val in self.buy_btc_ema_timeperiod.range: dataframe[f'ema_{val}'] = ta.EMA(dataframe, timeperiod=val) return dataframe def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: #------------------------# #-- ATR based stoploss --# #------------------------# if self.sell_atr_enabled.value == False: return 1 dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() stoploss_price = last_candle['low'] - last_candle[f'atr_{self.sell_atr_timeperiod.value}'] * self.sell_atr_multiplier.value if stoploss_price < current_rate: return (stoploss_price / current_rate) - 1 # return maximum stoploss value, keeping current stoploss price unchanged return 1 def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: #----------------# #-- Indicators --# #----------------# # Trix Indicator for val in self.buy_trix_timeperiod.range: dataframe[f'trix_b_{val}'] = ta.EMA(ta.EMA(ta.EMA(dataframe[self.buy_trix_src.value], timeperiod=val), timeperiod=val), timeperiod=val) dataframe['trix_b_pct'] = dataframe[f'trix_b_{self.buy_trix_timeperiod.value}'].pct_change() * 100 for val in self.buy_trix_signal_timeperiod.range: dataframe[f'trix_b_signal_{val}'] = ta.SMA(dataframe['trix_b_pct'], timeperiod=val) for val in self.sell_trix_timeperiod.range: dataframe[f'trix_s_{val}'] = ta.EMA(ta.EMA(ta.EMA(dataframe[self.sell_trix_src.value], timeperiod=val), timeperiod=val), timeperiod=val) dataframe['trix_s_pct'] = dataframe[f'trix_s_{self.sell_trix_timeperiod.value}'].pct_change() * 100 for val in self.sell_trix_signal_timeperiod.range: dataframe[f'trix_s_signal_{val}'] = ta.SMA(dataframe['trix_s_pct'], timeperiod=val) # Stochastic RSI for val in self.buy_rsi_timeperiod.range: dataframe['b_rsi'] = ta.RSI(dataframe, timeperiod=val) for val in self.buy_stoch_rsi_timeperiod.range: dataframe['b_stoch_rsi'] = (dataframe['b_rsi'] - dataframe['b_rsi'].rolling(val).min()) / (dataframe['b_rsi'].rolling(val).max() - dataframe['b_rsi'].rolling(val).min()) for val in self.sell_rsi_timeperiod.range: dataframe['s_rsi'] = ta.RSI(dataframe, timeperiod=val) for val in self.sell_stoch_rsi_timeperiod.range: dataframe['s_stoch_rsi'] = (dataframe['s_rsi'] - dataframe['s_rsi'].rolling(val).min()) / (dataframe['s_rsi'].rolling(val).max() - dataframe['s_rsi'].rolling(val).min()) # EMA for val in self.buy_ema_timeperiod.range: dataframe[f'ema_b_{val}'] = ta.EMA(dataframe[self.buy_ema_src.value], timeperiod=val) dataframe['ema_b_signal'] = dataframe[f'ema_b_{self.buy_ema_timeperiod.value}'] * self.buy_ema_multiplier.value # ATR for val in self.sell_atr_timeperiod.range: dataframe[f'atr_{val}'] = ta.ATR(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod=val) dataframe['stoploss_price'] = dataframe['low'] - dataframe[f'atr_{self.sell_atr_timeperiod.value}'] * self.sell_atr_multiplier.value return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] stake = self.config['stake_currency'].lower() #-----------------------# #-- Guards and trends --# #-----------------------# # For backtesting & Hyperopt conditions.append(dataframe['volume'] > 0) # Can't send a buy signal at the same time as a sell signal conditions.append(dataframe['trix_s_pct'] > dataframe[f'trix_s_signal_{self.sell_trix_signal_timeperiod.value}']) # If BTC is not going well, don't buy if self.buy_btc_ema_enabled.value: conditions.append(dataframe[f'btc_{stake}_close_1h'] > (dataframe[f'btc_{stake}_ema_{self.buy_btc_ema_timeperiod.value}_1h'] * self.buy_btc_ema_multiplier.value)) # Stoch RSI if self.buy_stoch_rsi_enabled.value: conditions.append(dataframe['b_stoch_rsi'] < self.buy_stoch_rsi.value) # Trend check if self.buy_ema_enabled.value: conditions.append(dataframe['close'] > dataframe['ema_b_signal']) # Probably less efficient than trigger mode if self.buy_trix_signal_type.value == 'trailing': conditions.append(dataframe['trix_b_pct'] > dataframe[f'trix_b_signal_{self.buy_trix_signal_timeperiod.value}']) #--------------# #-- Triggers --# #--------------# # Main trigger : trix indicator if self.buy_trix_signal_type.value == 'trigger': conditions.append(qtpylib.crossed_above(dataframe['trix_b_pct'], dataframe[f'trix_b_signal_{self.buy_trix_signal_timeperiod.value}'])) dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] #-----------------------# #-- Guards and trends --# #-----------------------# # For backtesting & Hyperopt conditions.append(dataframe['volume'] > 0) # Stoch RSI if self.sell_stoch_rsi_enabled.value: conditions.append(dataframe['s_stoch_rsi'] > self.sell_stoch_rsi.value) # Main indicator : Trix if self.sell_trix_signal_type.value == 'trailing': conditions.append(dataframe['trix_s_pct'] < dataframe[f'trix_s_signal_{self.sell_trix_signal_timeperiod.value}']) #--------------# #-- Triggers --# #--------------# # Main indicator. We probably want trailing mode if self.sell_trix_signal_type.value == 'trigger': conditions.append(qtpylib.crossed_below(dataframe['trix_s_pct'], dataframe[f'trix_s_signal_{self.sell_trix_signal_timeperiod.value}'])) dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'sell'] = 1 return dataframe