# source: https://raw.githubusercontent.com/HeyMrRobot/Freqtrade-Adaptive-Renko-Strategy/d8c383f049712ce921a16ecbb57925094dd0d47f/AdaptiveRenkoStrategy.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file # --- Do not remove these libs --- import logging 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, IStrategy, IntParameter, informative, merge_informative_pair # -------------------------------- # Add your lib to import here from freqtrade.exchange import timeframe_to_minutes # https://github.com/quantroom-pro/pyrenko import pyrenko import pandas_ta as pta from technical import qtpylib import scipy.optimize as opt logger = logging.getLogger(__name__) def evaluate_renko(brick, history, column_name): renko_obj = pyrenko.renko() renko_obj.set_brick_size(brick_size = brick, auto = False) renko_obj.build_history(prices = history) return renko_obj.evaluate()[column_name] class github_HeyMrRobot_Freqtrade_Adaptive_Renko_Strategy__AdaptiveRenkoStrategy__20230524_120158(IStrategy): """ This strategy is based on research work by Sergey Malchevskiy and uses Renko bricks to identify trends. The brick size is optimized using ATR to find out boundaries from an informative period. For more information on the brick size optimization, see the following article: https://towardsdatascience.com/renko-brick-size-optimization-34d64400f60e This strategy is designed to work with FreqTrade and can be used in conjunction with FreqAI. To match with research, brick size optimization should change from ATR to IQR from source data For more information on using Bayesian optimization on this strategy as a training model, see the following article: https://towardsdatascience.com/bayesian-optimization-in-trading-77202ffed530 Author: Mr Robot (@heymrrobot) """ INTERFACE_VERSION = 3 timeframe = '3m' timeframe_minutes = timeframe_to_minutes(timeframe) can_short: bool = True trailing_stop = False startup_candle_count: int = 7*2 use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False process_only_new_candles = True # Initialize renko_obj_obs as a dictionary custom_renkodict = {} def informative_pairs(self): pairs = self.dp.current_whitelist() informative_pairs = [(pair, '15m') for pair in pairs] return informative_pairs def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: inf_tf = '15m' informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf) # Calculate the ATR (Average True Range) for the informative DataFrame atr = pta.atr(informative['high'], informative['low'], informative['close'], timeperiod=10) # Add the ATR values as a new column to the informative DataFrame informative['atr'] = [0 if np.isnan(x) else x for x in atr] dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True) # If the pair is not in the custom_renkodict dictionary, add it with a new pyrenko.renko() object if metadata['pair'] not in self.custom_renkodict: self.custom_renkodict[metadata['pair']] = pyrenko.renko() # Optimize the Renko brick size using fminbound for the new pyrenko.renko() object using the fminbound optimization function opt_bs = opt.fminbound(lambda x: -evaluate_renko(brick=x, history=dataframe.close, column_name='score'), np.min(dataframe['atr_15m']), np.max(dataframe['atr_15m']), disp=0) # Create a new pyrenko.renko() object for the pair with the optimized brick size and add it to the custom_renkodict dictionary self.custom_renkodict[metadata['pair']].set_brick_size(brick_size=opt_bs, auto=False) self.custom_renkodict[metadata['pair']].build_history(prices=dataframe.close) logger.info(f'Created a new pyrenko.renko() object for pair {metadata["pair"]}') # Get the renko object for the given pair from custom_renkodict renko_obj = self.custom_renkodict[metadata['pair']] # Get the list of renko prices from the renko object prices = renko_obj.get_renko_prices() logger.info(f'Got the list of renko prices for pair {metadata["pair"]}') # Get the list of renko directions from the renko object directions = renko_obj.get_renko_directions() self.prev_brick_direction = directions[-2] if len(directions) >= 2 else None self.last_brick_direction = directions[-1] if len(directions) >= 1 else None # Check if there are any prices in the prices list if len(prices) > 0: # Create the next Renko bars using the last close price from the dataframe and get the number of created bars num_created_bars = renko_obj.do_next(dataframe.close.iloc[-1]) logger.info(f'Created {num_created_bars} new Renko bars for pair {metadata["pair"]}') # If new bars were created if num_created_bars != 0: # If the previous brick direction is not the same as the last brick direction if self.prev_brick_direction != self.last_brick_direction: opt_bs = opt.fminbound(lambda x: -evaluate_renko(brick=x, history=dataframe.close, column_name='score'), np.min(dataframe['atr_15m']), np.max(dataframe['atr_15m']), disp=0) if opt_bs != renko_obj.brick_size: renko_obj.set_brick_size(brick_size=opt_bs, auto=False) self.opt_bs = opt_bs # Evaluate the Renko bars and update the score and directions dictionaries renko_eval = renko_obj.evaluate() logger.info(f'Evaluated the Renko bars for pair {metadata["pair"]}, values: {renko_eval}, brick size: {renko_obj.brick_size}') self.score = {metadata['pair']: renko_eval['score']} self.directions = {metadata['pair']: directions} return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Add two new columns to the DataFrame for entering a long and short position, and initialize them to zero. dataframe['enter_long'] = 0 dataframe['enter_short'] = 0 # Check if the current pair is not in the custom_renkodict, or if the score for the pair is less than 4, or if the length of the directions list for the pair is less than 2. # If any of these conditions is true, return the original DataFrame without making any changes. if metadata['pair'] not in self.custom_renkodict or self.score.get(metadata['pair'], -1) < 4 or len(self.directions[metadata['pair']]) < 2: return dataframe # Get the direction of the last brick and the direction of the second-to-last brick for the current pair from the directions list. last_brick_direction = self.directions[metadata['pair']][-1] prev_brick_direction = self.directions[metadata['pair']][-2] # If the direction of the last brick is up (1) and the direction of the previous brick is down (-1), # set the value of enter_long to 1 and enter_tag to 'buy_direction' for all rows in the enter_long and enter_tag columns vice versa. if last_brick_direction == 1 and prev_brick_direction == -1: dataframe.loc[:, ['enter_long', 'enter_tag']] = (1, 'buy_direction') logger.info(f'Set the value of enter_long to 1 and enter_tag to "buy_direction" for pair {metadata["pair"]}') elif last_brick_direction == -1 and prev_brick_direction == 1: dataframe.loc[:, ['enter_short', 'enter_tag']] = (1, 'short_direction') logger.info(f'Set the value of enter_short to 1 and enter_tag to "short_direction" for pair {metadata["pair"]}') return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: if metadata['pair'] not in self.custom_renkodict: return dataframe last_brick_direction = self.directions[metadata['pair']][-1] prev_brick_direction = self.directions[metadata['pair']][-2] if last_brick_direction == -1 and prev_brick_direction == 1: dataframe.loc[:, ['exit_long', 'enter_tag']] = (1, 'direction_long_exit') logger.info(f'Set the value of exit_long to 1 and enter_tag to "direction_long_exit" for pair {metadata["pair"]}') # Flush the custom_renkodict for the corresponding pair # del self.custom_renkodict[metadata['pair']] elif last_brick_direction == 1 and prev_brick_direction == -1: dataframe.loc[:, ['exit_short', 'enter_tag']] = (1, 'direction_short_exit') logger.info(f'Set the value of exit_short to 1 and enter_tag to "direction_short_exit" for pair {metadata["pair"]}') # Flush the custom_renkodict for the corresponding pair # del self.custom_renkodict[metadata['pair']] return dataframe