# source: https://raw.githubusercontent.com/mkajnar/HighProfitStrategy/f024f0c3243492295e32597ca0ed1e949c0230e9/HPStrategyV7_Kucoin.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__HPStrategyV7_Kucoin__20240227_155817(IStrategy): INTERFACE_VERSION = 3 timeframe = '5m' stoploss = -0.03 minimal_roi = { "0": 0.01 } 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 = 0.001 trailing_stop_positive_offset = 0.003 use_exit_signal = True ignore_roi_if_entry_signal = True donchian_period = IntParameter(10, 100, default=20, space='buy', optimize=True) total_positive_profit_threshold = DecimalParameter(0.1, 30, default=5.0, space='sell', decimals=1, optimize=True) total_negative_profit_threshold = DecimalParameter(-10.0, -0.1, default=-1, space='sell', decimals=1, optimize=True) exit_profit_offset_par = DecimalParameter(0.001, 0.05, default=0.001, space='sell', decimals=3, optimize=True) exit_profit_offset = exit_profit_offset_par.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 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 'swing' in exit_reason or 'trailing' in exit_reason: confirm_sl = profit_ratio < -self.stoploss confirm_pf = profit_ratio > self.exit_profit_offset #if confirm_sl: # logging.info(f"[CTE] {pair} profit ratio: {profit_ratio}, confirmed stoploss {self.stoploss}") if confirm_pf: logging.info(f"[CTE] {pair} profit ratio: {profit_ratio}, confirmed profit {profit_ratio}") #return confirm_sl or confirm_pf 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