# source: https://raw.githubusercontent.com/RudoRonuma/freqtrade/46306a338fb33f67bb31d5c6155c5dc592caad93/user_data/strategies/master_strategy.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 functools import reduce from datetime import datetime, timedelta, timezone from pandas import DataFrame from typing import Optional, Union 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 from technical import qtpylib # This class is a sample. Feel free to customize it. class Github_RudoRonuma_freqtrade__master_strategy__20250511_005108(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 # Can this strategy go short? can_short: bool = False minimal_roi = { "0": 0.99 # Let TSL and exit signals do most of the work # "2880": 0.05, # "1440": 0.03 } # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". stoploss = -0.10 # Trailing stoploss trailing_stop = False trailing_only_offset_is_reached = True trailing_stop_positive = 0.05 trailing_stop_positive_offset = 0.15 # Optimal timeframe for the strategy. timeframe = "4h" # Run "populate_indicators()" only for new candle. 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 # Hyperoptable parameters buy_fishRsiNorma = IntParameter(low=3, high=10, default=9, space='buy', optimize=True) buy_rsi_floor = IntParameter(low=10, high=20, default=12, space='buy', optimize=True) buy_stoch_fastd_level = IntParameter(low=25, high=35, default=34, space='buy', optimize=True) buy_volumeAVG = IntParameter(low=50, high=300, default=136, space='buy', optimize=True) buy_volume_multiplier = IntParameter(low=3, high=6, default=4, space='buy', optimize=True) # Buy hyperspace params: buy_params = { "buy_fishRsiNorma": 9, "buy_rsi_floor": 12, "buy_stoch_fastd_level": 34, "buy_volumeAVG": 136, "buy_volume_multiplier": 4, } sell_fisherRsiNorma_exit = IntParameter( low=1, high=100, default=70, space='sell', optimize=True) sell_minusDI_exit = IntParameter( low=1, high=100, default=4, space='sell', optimize=True) sell_rsi_exit = IntParameter( low=60, high=90, default=74, space='sell', optimize=True) sell_trigger = CategoricalParameter( ["rsi-macd-minusdi", "sar-fisherRsi"], default="rsi-macd-minusdi", space='sell', optimize=True) # Sell hyperspace params: sell_params = { "sell_fisherRsiNorma_exit": 70, "sell_minusDI_exit": 4, "sell_rsi_exit": 74, "sell_trigger": "rsi-macd-minusdi", } # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 100 # 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"} plot_config = { "main_plot": { "tema": {}, "sar": {"color": "white"}, }, "subplots": { "MACD": { "macd": {"color": "blue"}, "macdsignal": {"color": "orange"}, }, "RSI": { "rsi": {"color": "red"}, }, }, } 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: """ Adds several different TA indicators to the given DataFrame Performance Note: For the best performance be frugal on the number of indicators you are using. Let uncomment only the indicator you are using in your strategies or your hyperopt configuration, otherwise you will waste your memory and CPU usage. :param dataframe: Dataframe with data from the exchange :param metadata: Additional information, like the currently traded pair :return: a Dataframe with all mandatory indicators for the strategies """ # MACD macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] # Minus Directional Indicator / Movement dataframe['minus_di'] = ta.MINUS_DI(dataframe) # RSI dataframe['rsi'] = ta.RSI(dataframe) # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy) rsi = 0.1 * (dataframe['rsi'] - 50) dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) # Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy) dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) # Stoch fast stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] # Overlap Studies # ------------------------------------ # SAR Parabol dataframe['sar'] = ta.SAR(dataframe) # SMA - Simple Moving Average dataframe['sma'] = ta.SMA(dataframe, timeperiod=40) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the entry signal for the given dataframe :param dataframe: DataFrame :param metadata: Additional information, like the currently traded pair :return: DataFrame with entry columns populated """ dataframe.loc[ # Prod ( (dataframe['close'] > 0.00000200) & (dataframe['volume'] > dataframe['volume'].rolling( self.buy_volumeAVG.value).mean() * self.buy_volume_multiplier.value) & (dataframe['close'] < dataframe['sma']) & (qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd'])) & (dataframe['fastd'] < self.buy_stoch_fastd_level.value) & (dataframe['rsi'] > self.buy_rsi_floor.value) & (dataframe['fisher_rsi_norma'] < self.buy_fishRsiNorma.value) ), 'enter_long'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the exit signal for the given dataframe :param dataframe: DataFrame :param metadata: Additional information, like the currently traded pair :return: DataFrame with exit columns populated """ conditions = [] if self.sell_trigger.value == 'rsi-macd-minusdi': conditions.append(qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi_exit.value)) conditions.append(dataframe['macd'] < 0) conditions.append(dataframe['minus_di'] > self.sell_minusDI_exit.value) if self.sell_trigger.value == 'sar-fisherRsi': conditions.append(dataframe['sar'] > dataframe['close']) conditions.append(dataframe['fisher_rsi_norma'] > self.sell_fisherRsiNorma_exit.value) if conditions: dataframe.loc[reduce(lambda x, y: x & y, conditions), 'exit_long'] = 1 return dataframe