# source: https://raw.githubusercontent.com/sisxmax/Freqtrade-Strategy-Library/e1315c78f00c4d813217ced1e7bc9f8364d3a06d/Donchian_EMA_ADX_CHOP.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 import logging from functools import reduce logger = logging.getLogger(__name__) 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 # ========================================== # I Tested Donchian channel + EMA + ADX + CHOP 163% Profit! Full Strategy Breakdown # https://youtu.be/BeL0hx2BXDY # ========================================== # ================================ # Download Historical Data # ================================ """ freqtrade download-data \ -c user_data/config_binance_futures.json \ --timerange 20230101- \ -t 1m 5m 15m 30m 1h 2h 4h 1d """ # ================================ # Hyperopt Optimization # ================================ """ freqtrade hyperopt \ --strategy Github_sisxmax_Freqtrade_Strategy_Library__Donchian_EMA_ADX_CHOP__20260316_095247 \ --config user_data/config_binance_futures.json \ --timeframe 1h \ --timerange 20250801-20260101 \ --hyperopt-loss MultiMetricHyperOptLoss \ --spaces buy\ -e 50 \ --j 10 \ --random-state 9319 \ --max-open-trades 1 \ -p ETH/USDT:USDT """ # ================================ # Backtesting # ================================ """ freqtrade backtesting \ --strategy Github_sisxmax_Freqtrade_Strategy_Library__Donchian_EMA_ADX_CHOP__20260316_095247 \ --timeframe 1h \ --timerange 20260101-20260201 \ --breakdown month \ -c user_data/config_binance_futures.json \ --max-open-trades 1 \ --cache none \ --timeframe-detail 5m \ -p ETH/USDT:USDT """ # ================================ # Start FreqUI Web Interface # ================================ """ freqtrade webserver \ --config user_data/config_binance_futures.json """ class Github_sisxmax_Freqtrade_Strategy_Library__Donchian_EMA_ADX_CHOP__20260316_095247(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 = "1h" # 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 = -0.25 # 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 use_custom_stoploss = 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 # Strategy parameters donchian_period = CategoricalParameter([15, 20, 25, 30], default=20, space="buy") ema_period = CategoricalParameter([50, 100, 150, 200], default=200, space="buy") adx_threshold = CategoricalParameter([20, 25, 30], default=30, space="buy") chop_threshold = CategoricalParameter([30, 35, 40], default=40, space="buy") atr_mult = CategoricalParameter([2, 2.5, 3], default=2.5, space="buy") leverage_level = IntParameter(1, 10, default=1, space='buy', optimize=False, load=False) @property def plot_config(self): return { "main_plot": { f'dc_upper{self.donchian_period.value}': {"color": "#2962FF"}, f'dc_lower{self.donchian_period.value}': {"color": "#2962FF"} }, "subplots": { "ADX": { "adx": {"color": "#f23645", "type": "line" } }, "CHOP": { "chop": {"color": "#2962FF", "type": "line" } } } } 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"), ] """ return [] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: for val in self.donchian_period.range: dataframe[f'dc_upper{val}'] = dataframe['high'].rolling(window=val).max().shift(1) dataframe[f'dc_lower{val}'] = dataframe['low'].rolling(window=val).min().shift(1) for val in self.ema_period.range: dataframe[f"ema{val}"] = ta.EMA(dataframe, timeperiod=val) dataframe['chop'] = pta.chop( high=dataframe['high'], low=dataframe['low'], close=dataframe['close'], length=14 ) # ADX dataframe["adx"] = ta.ADX(dataframe) # ATR dataframe["atr"] = ta.ATR(dataframe) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (qtpylib.crossed_above(dataframe["close"], dataframe[f'dc_upper{self.donchian_period.value}'])) & (dataframe['close'] > dataframe[f"ema{self.ema_period.value}"]) & (dataframe['adx'] > self.adx_threshold.value) & (dataframe['chop'] < self.chop_threshold.value) & (dataframe['volume'] > 0) ), 'enter_long'] = 1 dataframe.loc[ ( (qtpylib.crossed_below(dataframe["close"], dataframe[f'dc_lower{self.donchian_period.value}'])) & (dataframe['close'] < dataframe[f"ema{self.ema_period.value}"]) & (dataframe['adx'] > self.adx_threshold.value) & (dataframe['chop'] < self.chop_threshold.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_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, after_fill: bool, **kwargs) -> float | None: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) candle = dataframe.iloc[-1].squeeze() side = 1 if trade.is_short else -1 return stoploss_from_absolute(current_rate + (side * candle["atr"] * self.atr_mult.value), current_rate=current_rate, is_short=trade.is_short, leverage=trade.leverage) 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.value