# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/ParabolicSAR.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file # --- Do not remove these imports --- import numpy as np import pandas as pd from datetime import datetime, timedelta, timezone from pandas import DataFrame from typing import Dict, Optional, Union, Tuple, List from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal from functools import reduce import json from pathlib import Path from freqtrade.strategy import ( IStrategy, Trade, Order, PairLocks, informative, # @informative decorator # Hyperopt Parameters BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, RealParameter, # timeframe helpers timeframe_to_minutes, timeframe_to_next_date, timeframe_to_prev_date, # Strategy helper functions merge_informative_pair, stoploss_from_absolute, stoploss_from_open, ) # -------------------------------- # Add your lib to import here import talib.abstract as ta import pandas_ta as pta from technical import qtpylib # ========================================== # Boost Your Algo Trading with Freqtrade Parabolic SAR Strategy! # YouTube Link: https://youtu.be/Ssvm9MVqPSE # ========================================== # ================================ # Download Historical Data # ================================ """ freqtrade download-data \ -c user_data/config_binance_futures_Github_remiotore_freqtrade__ParabolicSAR__20260111_210550.json \ --timerange 20230101- \ -t 1m 5m 15m 30m 1h 2h 4h 1d """ # ================================ # Hyperopt Optimization # ================================ """ freqtrade hyperopt \ --strategy Github_remiotore_freqtrade__ParabolicSAR__20260111_210550 \ --config user_data/config_binance_futures_Github_remiotore_freqtrade__ParabolicSAR__20260111_210550.json \ --timeframe 30m \ --timerange 20231001-20240501 \ --hyperopt-loss MultiMetricHyperOptLoss \ --spaces buy\ -e 50 \ --j -2 \ --random-state 9319 \ --min-trades 10 \ -p GRT/USDT:USDT \ --max-open-trades 1 """ # ================================ # Backtesting # ================================ """ freqtrade backtesting \ --strategy Github_remiotore_freqtrade__ParabolicSAR__20260111_210550 \ --timeframe 30m \ --timerange 20231001-20241001 \ --breakdown month \ -c user_data/config_binance_futures_Github_remiotore_freqtrade__ParabolicSAR__20260111_210550.json \ --max-open-trades 1 \ --timeframe-detail 5m \ -p GRT/USDT:USDT \ --cache none """ # ================================ # Start FreqUI Web Interface # ================================ """ freqtrade webserver \ --config user_data/config_binance_futures_Github_remiotore_freqtrade__ParabolicSAR__20260111_210550.json """ class Github_remiotore_freqtrade__ParabolicSAR__20260111_210550(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 = 3 # Optimal timeframe for the strategy. timeframe = "30m" # Can this strategy go short? can_short: bool = True # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". minimal_roi = { } # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". stoploss = -0.20 # Trailing stoploss trailing_stop = False # trailing_only_offset_is_reached = False # trailing_stop_positive = 0.01 # trailing_stop_positive_offset = 0.0 # Disabled / not configured # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the config. use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 200 # Hyperoptable parameters ema_length = CategoricalParameter([50, 100, 150, 200], default=200, space="buy", optimize=False) rsi_upper_threshold = CategoricalParameter([55, 60, 65], default=60, space="buy", optimize=True) rsi_lower_threshold = CategoricalParameter([45, 40, 35], default=40, space="buy", optimize=True) rsi_length = CategoricalParameter([7, 14, 28], default=14, space="buy", optimize=True) stop_loss = DecimalParameter(0.01, 0.05, default=0.01, decimals=2, space='buy') risk_ratio = CategoricalParameter([1, 1.5, 2], default=2, space="buy") leverage_level = 1 @property def plot_config(self): plot_config = {} plot_config['main_plot'] = { f'ema_{self.ema_length.value}': {'color': '#2962ff'}, 'sar': { 'color': '#dedede', 'type': 'scatter', 'scatterSymbolSize': 4 } } plot_config['subplots'] = { 'rsi': { f'rsi_{self.rsi_length.value}': {'color': '#7e57c2'}, } } return plot_config def informative_pairs(self): """ Define additional, informative pair/interval combinations to be cached from the exchange. These pair/interval combinations are non-tradeable, unless they are part of the whitelist as well. For more information, please consult the documentation :return: List of tuples in the format (pair, interval) Sample: return [("ETH/USDT", "5m"), ("BTC/USDT", "15m"), ] """ # get access to all pairs available in whitelist. # pairs = self.dp.current_whitelist() # # Assign tf to each pair so they can be downloaded and cached for strategy. # informative_pairs = [(pair, self.informative_timeframe) for pair in pairs] return [] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe["sar"] = ta.SAR(dataframe) for val in self.rsi_length.range: dataframe[f'rsi_{val}'] = ta.RSI(dataframe, timeperiod=val) for period in self.ema_length.range: dataframe[f'ema_{period}'] = ta.EMA(dataframe, timeperiod=period) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['close'] > dataframe['sar']) & (qtpylib.crossed_above(dataframe[f'rsi_{self.rsi_length.value}'], self.rsi_upper_threshold.value)) & (dataframe['close'] > dataframe[f'ema_{self.ema_length.value}']) & (dataframe['volume'] > 0) ), 'enter_long'] = 1 dataframe.loc[ ( (dataframe['close'] < dataframe['sar']) & (qtpylib.crossed_below(dataframe[f'rsi_{self.rsi_length.value}'], self.rsi_lower_threshold.value)) & (dataframe['close'] < dataframe[f'ema_{self.ema_length.value}']) & (dataframe['volume'] > 0) ), 'enter_short'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, 'exit_long'] = 0 dataframe.loc[:, 'exit_short'] = 0 return dataframe def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs): take_profit = self.stop_loss.value * self.risk_ratio.value if current_profit > take_profit: return 'Take Profit' if current_profit < -self.stop_loss.value: return 'Stop Loss' def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, side: str, **kwargs) -> float: return self.leverage_level