# source: https://raw.githubusercontent.com/cyberjunky/freqtrade-cyber-bots/f9e2b6bd5ffd160b6a2c27e7f39e389bc9645e16/base_strategy.py # 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 import pandas as pd from pandas import DataFrame from datetime import datetime from typing import Optional, Union from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, IStrategy, merge_informative_pair) # -------------------------------- # Add your lib to import here import talib.abstract as ta import pandas_ta as pta from technical import qtpylib # Strategy lib imports import os # Strategy local search path for own modules import sys from pathlib import Path sys.path.append(str(Path(__file__).parent)) # Strategy modules import logging from freqtrade.constants import Config class github_cyberjunky_freqtrade_cyber_bots__base_strategy__20230822_153408(IStrategy): """ This is a strategy template to get you started. More information in https://www.freqtrade.io/en/latest/strategy-customization/ You can: :return: a Dataframe with all mandatory indicators for the strategies - Rename the class name (Do not forget to update class_name) - Add any methods you want to build your strategy - Add any lib you need to build your strategy You must keep: - the lib in the section "Do not remove these libs" - the methods: populate_indicators, populate_entry_trend, populate_exit_trend You should keep: - timeframe, minimal_roi, stoploss, trailing_* """ # Logger used for specific logging for this strategy logger = None # Strategy interface version - allow new iterations of the strategy interface. # Check the documentation or the Sample strategy to get the latest version. INTERFACE_VERSION = 3 STRATEGY_VERSION = "1.0.1" # Optimal timeframe for the strategy. timeframe = '1h' # Can this strategy go short? can_short = False # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". # Exit trade at profit of 1% minimal_roi = { "0": 0.01 } # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". # Set to -99% to actually disable the stoploss stoploss = -0.99 # Stoploss configuration use_custom_stoploss = True stoploss_configuration = {} # Trailing stoploss trailing_stop = False # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the config. use_exit_signal = False exit_profit_only = False ignore_roi_if_entry_signal = False # Number of candles the strategy requires before producing valid signals startup_candle_count = 1 # Leverage configuration leverage_configuration = {} def __init__(self, config: Config) -> None: """ Called upon construction of this class. Validate data and initialize all attributes, """ # Initialize logger self.logger = logging.getLogger("freqtrade.strategy") # Make sure the contents of the Leverage configuration is correct for k, v in self.leverage_configuration.items(): self.leverage_configuration[k] = float(v) # Make sure the contents of the Stoploss configuration is correct for k, v in self.stoploss_configuration.items(): self.stoploss_configuration[k] = float(v) # Update minimum ROI table keeping leverage into account # TODO: improve later on with custom exit with profit and leverage calculation for each pair leverage = min(self.leverage_configuration.values()) if len(self.leverage_configuration) > 0 else 1.0 self.logger.info( f"Update minimal ROI keeping leverage of {leverage} into account." ) for k, v in self.minimal_roi.items(): self.minimal_roi[k] = round(v * leverage, 4) # Call to super super().__init__(config) def bot_start(self, **kwargs) -> None: """ Called only once after bot instantiation. :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. """ # Call to super first super().bot_start() self.logger.info(f"Running with stoploss configuration: '{self.stoploss_configuration}'") self.logger.info(f"Running with leverage configuration: '{self.leverage_configuration}'") def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Adds several different TA indicators to the given DataFrame Performance Note: For the best performance be frugal on the number of indicators you are using. Let uncomment only the indicator you are using in your strategies or your hyperopt configuration, otherwise you will waste your memory and CPU usage. :param dataframe: Dataframe with data from the exchange :param metadata: Additional information, like the currently traded pair :return: a Dataframe with all mandatory indicators for the strategies """ return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the entry signal for the given dataframe :param dataframe: DataFrame :param metadata: Additional information, like the currently traded pair :return: DataFrame with entry columns populated """ return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the exit signal for the given dataframe :param dataframe: DataFrame :param metadata: Additional information, like the currently traded pair :return: DataFrame with exit columns populated """ return dataframe def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: """ Customize leverage for each new trade. This method is only called in futures mode. :param pair: Pair that's currently analyzed :param current_time: datetime object, containing the current datetime :param current_rate: Rate, calculated based on pricing settings in exit_pricing. :param proposed_leverage: A leverage proposed by the bot. :param max_leverage: Max leverage allowed on this pair :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal. :param side: 'long' or 'short' - indicating the direction of the proposed trade :return: A leverage amount, which is between 1.0 and max_leverage. """ pairkey = f"{pair}_{side}" if pairkey in self.leverage_configuration: return self.leverage_configuration[pairkey] else: return 1.0 def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: """ Custom stoploss logic, returning the new distance relative to current_rate (as ratio). e.g. returning -0.05 would create a stoploss 5% below current_rate. The custom stoploss can never be below self.stoploss, which serves as a hard maximum loss. For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/ When not implemented by a strategy, returns the initial stoploss value Only called when use_custom_stoploss is set to True. :param pair: Pair that's currently analyzed :param trade: trade object. :param current_time: datetime object, containing the current datetime :param current_rate: Rate, calculated based on pricing settings in exit_pricing. :param current_profit: Current profit (as ratio), calculated based on current_rate. :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. :return float: New stoploss value, relative to the current rate """ sl = self.stoploss pairkey = f"{pair}_{trade.trade_direction}" if pairkey in self.stoploss_configuration: sl = self.stoploss_configuration[pairkey] * self.leverage_configuration[pairkey] return sl