# source: https://raw.githubusercontent.com/zbchi/strategies/567f68bc2d101a76d2883226d5d03a409d131815/testStrategy.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 from functools import reduce import pandas as pd from datetime import datetime, timedelta, timezone from pandas import DataFrame from typing import Dict, Optional, Union, Tuple 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 def ewo(dataframe, ema_length=5, ema2_length=35): df = dataframe.copy() ema1 = ta.EMA(df, timeperiod=ema_length) ema2 = ta.EMA(df, timeperiod=ema2_length) emadif = (ema1 - ema2) / df['low'] * 100 return emadif class Github_zbchi_strategies__testStrategy__20250530_032102(IStrategy): INTERFACE_VERSION = 3 timeframe = "5m" minimal_roi = { "0": 10 } process_only_new_candles = True startup_candle_count: int = 20 order_types = { "entry": "market", "exit": "market", "emergency_exit":"market", "force_entry":"market", "force_exit":"market", "stoploss": "market", "stoploss_on_exchange": False, "stoploss_on_exchange_interval": 60, "stoploss_on_exchange_market_ratio": 0.99 } stoploss = -0.99 use_custom_stoploss = True # Strategy parameters is_optimize_ewo = True buy_rsi_fast = IntParameter(35, 50, default=50, space='buy', optimize=is_optimize_ewo) buy_rsi = IntParameter(15, 35, default=30, space='buy', optimize=is_optimize_ewo) buy_ewo = DecimalParameter(-6.0, 5, default=-1.238, space='buy', optimize=is_optimize_ewo) buy_ema_low = DecimalParameter(0.9, 0.99, default=0.956, space='buy', optimize=is_optimize_ewo) buy_ema_high = DecimalParameter(0.95, 1.2, default=0.986, space='buy', optimize=is_optimize_ewo) is_optimize_32 = True buy_rsi_fast_32 = IntParameter(20, 70, default=63, space='buy', optimize=is_optimize_32) buy_rsi_32 = IntParameter(15, 50, default=16, space='buy', optimize=is_optimize_32) buy_sma15_32 = DecimalParameter(0.900, 1, default=0.932, decimals=3, space='buy', optimize=is_optimize_32) buy_cti_32 = DecimalParameter(-1, 0, default=-0.8, decimals=2, space='buy', optimize=is_optimize_32) is_optimize_deadfish = True sell_deadfish_bb_width = DecimalParameter(0.03, 0.75, default=0.05, space='sell', optimize=is_optimize_deadfish) sell_deadfish_profit = DecimalParameter(-0.15, -0.05, default=-0.05, space='sell', optimize=is_optimize_deadfish) sell_deadfish_bb_factor = DecimalParameter(0.90, 1.20, default=1.0, space='sell', optimize=is_optimize_deadfish) sell_deadfish_volume_factor = DecimalParameter(1, 2.5, default=1.0, space='sell', optimize=is_optimize_deadfish) sell_fastx = IntParameter(50, 100, default=75, space='sell', optimize=True) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # buy_1 indicators dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15) dataframe['cti'] = pta.cti(dataframe['close'], length=20) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) # ewo indicators dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8) dataframe['ema_16'] = ta.EMA(dataframe, timeperiod=16) dataframe['EWO'] = ewo(dataframe, 50, 200) # profit sell indicators stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] # loss sell indicators bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband2'] = bollinger2['lower'] dataframe['bb_middleband2'] = bollinger2['mid'] dataframe['bb_upperband2'] = bollinger2['upper'] dataframe['bb_width'] = ( (dataframe['bb_upperband2'] - dataframe['bb_lowerband2']) / dataframe['bb_middleband2'] ) dataframe['volume_mean_12'] = dataframe['volume'].rolling(12).mean().shift(1) dataframe['volume_mean_24'] = dataframe['volume'].rolling(24).mean().shift(1) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'enter_tag'] = '' is_ewo = ( (dataframe['rsi_fast'] < self.buy_rsi_fast.value) & (dataframe['close'] < dataframe['ema_8'] * self.buy_ema_low.value) & (dataframe['EWO'] > self.buy_ewo.value) & (dataframe['close'] < dataframe['ema_16'] * self.buy_ema_high.value) & (dataframe['rsi'] < self.buy_rsi.value) ) buy_1 = ( (dataframe['rsi_slow'] < dataframe['rsi_slow'].shift(1)) & (dataframe['rsi_fast'] < self.buy_rsi_fast_32.value) & (dataframe['rsi'] > self.buy_rsi_32.value) & (dataframe['close'] < dataframe['sma_15'] * self.buy_sma15_32.value) & (dataframe['cti'] < self.buy_cti_32.value) ) conditions.append(is_ewo) dataframe.loc[is_ewo, 'enter_tag'] += 'ewo' conditions.append(buy_1) dataframe.loc[buy_1, 'enter_tag'] += 'buy_1' if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'enter_long' ] = 1 return dataframe def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) current_candle = dataframe.iloc[-1].squeeze() if current_time - timedelta(minutes=60) > trade.open_date_utc: if (current_candle["fastk"] > self.sell_fastx.value) and (current_profit > -0.01): return -0.001 if current_time - timedelta(days=1) > trade.open_date_utc: if (current_candle["fastk"] > self.sell_fastx.value) and (current_profit > -0.05): return -0.001 enter_tag = '' if hasattr(trade, 'enter_tag') and trade.enter_tag is not None: enter_tag = trade.enter_tag enter_tags = enter_tag.split() if "ewo" in enter_tags: if current_profit >= 0.05: return -0.005 if current_profit > 0: if current_candle["fastk"] > self.sell_fastx.value: return -0.001 return self.stoploss def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> Optional[Union[str, bool]]: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) current_candle = dataframe.iloc[-1].squeeze() # stoploss - deadfish if ((current_profit < self.sell_deadfish_profit.value) and (current_candle['bb_width'] < self.sell_deadfish_bb_width.value) and (current_candle['close'] > current_candle['bb_middleband2'] * self.sell_deadfish_bb_factor.value) and (current_candle['volume_mean_12'] < current_candle[ 'volume_mean_24'] * self.sell_deadfish_volume_factor.value)): return "sell_stoploss_deadfish" def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: return dataframe