# source: https://raw.githubusercontent.com/mkajnar/HighProfitStrategy/8d68a9ad4006971f2994b037eb077d522afc3045/HPStrategyV7Ultra.py import logging import os import sys from functools import reduce import numpy import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame from typing import Optional, Union, List from pandas_ta import stdev from freqtrade.enums import ExitCheckTuple from freqtrade.persistence import Trade, Order from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter, informative) from datetime import timedelta, datetime, timezone import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib import pandas_ta as pta class Github_mkajnar_HighProfitStrategy__HPStrategyV7Ultra__20240302_141821(IStrategy): INTERFACE_VERSION = 3 timeframe = '5m' leverage_value = 3 stoploss = -0.08 * leverage_value minimal_roi = { "0": 0.007 * leverage_value } allprofits = {} process_only_new_candles = True startup_candle_count = 50 position_adjustment_enable = False trailing_stop = True trailing_only_offset_is_reached = False trailing_stop_positive = 0.003 * leverage_value trailing_stop_positive_offset = 0.007 * leverage_value use_exit_signal = True ignore_roi_if_entry_signal = True donchian_period = IntParameter(5, 50, default=20, space='buy', optimize=True) total_positive_profit_threshold = IntParameter(1, 30, default=16, space='sell', optimize=True) total_negative_profit_threshold = IntParameter(-30, -1, default=-10, space='sell', optimize=True) exit_profit_offset_par = DecimalParameter(0.001, 0.05, default=0.021, space='sell', decimals=3, optimize=True) exit_profit_offset = exit_profit_offset_par.value * leverage_value exit_profit_only = False order_types = { 'entry': 'market', 'exit': 'market', 'stoploss': 'market', 'stoploss_on_exchange': False } def calc_donchian_channels(self, dataframe, period: int): dataframe["upperDon"] = dataframe["high"].rolling(period).max() dataframe["lowerDon"] = dataframe["low"].rolling(period).min() dataframe["midDon"] = (dataframe["upperDon"] + dataframe["lowerDon"]) / 2 return dataframe def mid_don_cross_over(self, dataframe): dataframe["position_m"] = np.nan dataframe["position_m"] = np.where(dataframe["close"] > dataframe["midDon"], 1, dataframe["position_m"]) dataframe["position_m"] = dataframe["position_m"].ffill().fillna(0) return dataframe def don_channel_breakout(self, dataframe): dataframe["position_b"] = np.nan dataframe["position_b"] = np.where(dataframe["close"] > dataframe["upperDon"].shift(1), 1, dataframe["position_b"]) dataframe["position_b"] = dataframe["position_b"].ffill().fillna(0) return dataframe def don_reversal(self, dataframe): dataframe["position_r"] = np.nan dataframe["position_r"] = np.where(dataframe["close"] < dataframe["lowerDon"].shift(1), 1, dataframe["position_r"]) dataframe["position_r"] = dataframe["position_r"].ffill().fillna(0) 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: return self.leverage_value def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Swing high/low dataframe = self.calc_swings(dataframe) dataframe = self.calc_donchian_channels(dataframe=dataframe, period=self.donchian_period.value) dataframe = self.mid_don_cross_over(dataframe=dataframe) dataframe = self.don_reversal(dataframe=dataframe) dataframe = self.don_channel_breakout(dataframe=dataframe) return dataframe def calc_swings(self, dataframe): dataframe['swing_low'] = (dataframe['close'].shift(2) > dataframe['close'].shift(1)) & \ (dataframe['close'].shift(1) < dataframe['close']).astype(int) dataframe['swing_high'] = (dataframe['close'].shift(2) < dataframe['close'].shift(1)) & \ (dataframe['close'].shift(1) > dataframe['close']).astype(int) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['position_r'] == 1) | (dataframe['swing_low'] == 1) | (dataframe['position_m'] == 1) | (dataframe['position_b'] == 1) ), ['enter_long', 'enter_tag'] ] = (1, 'swing_low') return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['swing_high'] == 1) ), ['exit_long', 'exit_tag'] ] = (1, 'swing_high') return dataframe def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs) -> bool: logging.info(f"[CTE] {pair} exit reason: {exit_reason}") dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) profit_ratio = trade.calc_profit_ratio(rate) logging.info(f"[CTE] {pair} profit ratio: {profit_ratio}, exit reason: {exit_reason}") if (exit_reason.startswith('stop') and 'loss' in exit_reason) or 'trailing' in exit_reason: confirm_sl = profit_ratio < -self.stoploss if confirm_sl: logging.info(f"[CTE] {pair} profit ratio: {profit_ratio}, confirmed stoploss {self.stoploss}") exit_reason = "stoploss" return confirm_sl if 'swing' in exit_reason or 'trailing' in exit_reason: confirm_pf = profit_ratio > self.exit_profit_offset if confirm_pf: logging.info(f"[CTE] {pair} profit ratio: {profit_ratio}, confirmed profit {profit_ratio}") return confirm_pf return True def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> Optional[Union[str, bool]]: logging.info(f"[CE] {pair} current profit: {current_profit}") total_profit = 0 if len(self.allprofits.keys()) >= 0: total_profit = sum(self.allprofits.values()) if current_profit != self.allprofits.get(pair, 0): logging.info(f"[CE] Current profit: {current_profit} for {pair} is set to all profits dictionary") self.allprofits[pair] = current_profit logging.info(f"[CE] Total profit: {total_profit}") c = len(self.allprofits.keys()) >= self.max_open_trades if total_profit > self.total_positive_profit_threshold.value and c: logging.info( f"[CE] Total profit: {total_profit} is bigger than {self.total_positive_profit_threshold.value}, sell all positions...") return True if total_profit < self.total_negative_profit_threshold.value and c: logging.info( f"[CE] Total profit: {total_profit} is less than {self.total_negative_profit_threshold.value}, sell all positions...") return True return None