# source: https://raw.githubusercontent.com/viktors1982/trading/e572140b1b8cb550cb244c67d363087b81bd4ab8/freqtrade/strategies/dip_catcher.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file # --- Do not remove these libs --- import numpy as np import pandas as pd from pandas import DataFrame from datetime import datetime from typing import Optional, Union from freqtrade.persistence import Trade from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, IStrategy, merge_informative_pair) # -------------------------------- # Add your lib to import here import talib.abstract as ta import pandas_ta as pta from technical import qtpylib from datetime import timedelta import math class Github_viktors1982_trading__dip_catcher__20240410_103225(IStrategy): """ // // Dip / Retracement Catcher https://github.com/Haehnchen/crypto-trading-bot/tree/master/src/modules/strategy/strategies/Github_viktors1982_trading__dip_catcher__20240410_103225 https://github.com/Haehnchen/crypto-trading-bot/blob/master/src/modules/strategy/strategies/Github_viktors1982_trading__dip_catcher__20240410_103225/Github_viktors1982_trading__dip_catcher__20240410_103225.js translated for freqtrade: viksal1982 viktors.s@gmail.com https://github.com/viktors1982/trading/tree/main/freqtrade/strategies """ INTERFACE_VERSION = 3 timeframe = '5m' # Can this strategy go short? can_short: bool = True minimal_roi = { "60": 0.01, "30": 0.02, "0": 0.03 } stoploss = -0.08 trailing_stop = False trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.0 trailing_only_offset_is_reached = False 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 = 30 length_hma = IntParameter(4, 20, default=9, space="buy", optimize=True) length_hma_high = IntParameter(4, 20, default=9, space="buy", optimize=True) length_hma_low = IntParameter(4, 20, default=9, space="buy", optimize=True) bollinger_window = IntParameter(2, 40, default=20, space="buy", optimize=True) trend_cloud_multiplier = IntParameter(1, 10, default=4, space="buy", optimize=True) # Optional order type mapping. order_types = { 'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } # Optional order time in force. order_time_in_force = { 'entry': 'GTC', 'exit': 'GTC' } @property def plot_config(self): return { 'main_plot': { }, 'subplots': { } } def informative_pairs(self): return [] def ichimoku_cloud(self, dataframe, conversion_periods=9, base_periods=26, lagging_span2_periods=52, displacement=26): def donchian_channel(series, length): return (series.rolling(length).min() + series.rolling(length).max()) / 2 dataframe['conversion_line'] = (donchian_channel(dataframe['high'], conversion_periods) + donchian_channel(dataframe['low'], conversion_periods)) / 2 dataframe['base_line'] = (donchian_channel(dataframe['high'], base_periods) + donchian_channel(dataframe['low'], base_periods)) / 2 dataframe['lead_line1'] = ((dataframe['conversion_line'] + dataframe['base_line']) / 2).shift(displacement) dataframe['lead_line2'] = ((donchian_channel(dataframe['high'], lagging_span2_periods) + donchian_channel(dataframe['low'], lagging_span2_periods)) / 2).shift(displacement) dataframe['lagging_span'] = dataframe['close'].shift(displacement) return dataframe[['conversion_line', 'base_line', 'lead_line1', 'lead_line2', 'lagging_span']] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['hma'] = ta.WMA( 2 * ta.WMA(dataframe['close'], int(math.floor(int(self.length_hma.value)/2))) - ta.WMA(dataframe['close'], int(self.length_hma.value)), int(round(np.sqrt(int(self.length_hma.value)))) ) dataframe['hma_high'] = ta.WMA( 2 * ta.WMA(dataframe['close'], int(math.floor(int(self.length_hma_high.value/2)))) - ta.WMA(dataframe['close'], int(self.length_hma_high.value)), int(round(np.sqrt(int(self.length_hma_high.value)))) ) dataframe['hma_low'] = ta.WMA( 2 * ta.WMA(dataframe['close'], int(math.floor(int(self.length_hma_low.value/2)))) - ta.WMA(dataframe['close'], int(self.length_hma_low.value)), int(round(np.sqrt(int(self.length_hma_low.value)))) ) bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=int(self.bollinger_window.value), stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] dataframe[['conversion_line', 'base_line', 'spanA', 'spanB', 'lagging_span']] = self.ichimoku_cloud(dataframe, conversion_periods= (9 * int(self.trend_cloud_multiplier.value)), base_periods=(26 * int(self.trend_cloud_multiplier.value)) , lagging_span2_periods=(52* int(self.trend_cloud_multiplier.value)), displacement=(10* int(self.trend_cloud_multiplier.value))) dataframe.loc[ ( # Condition for a long entry (dataframe['hma_low'] > dataframe['bb_lowerband']) & (dataframe['hma'] > dataframe['spanB']) ), 'enter_long'] = 1 dataframe.loc[ ( # Condition for a short entry (dataframe['hma_high'] < dataframe['bb_upperband']) & (dataframe['hma'] < dataframe['spanB']) ), 'enter_short'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: return dataframe