# source: https://raw.githubusercontent.com/ObernhumerMichael/crypto_strategies/447c749d8daf8b185d5ff332aec074b00a9990f7/strategies/TF_EMA_MACD.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 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 from functools import reduce class Github_ObernhumerMichael_crypto_strategies__TF_EMA_MACD__20250309_172147(IStrategy): """ This is a strategy template to get you started. More information in https://www.freqtrade.io/en/latest/strategy-customization/ You can: :return: a Dataframe with all mandatory indicators for the strategies - Rename the class name (Do not forget to update class_name) - Add any methods you want to build your strategy - Add any lib you need to build your strategy You must keep: - the lib in the section "Do not remove these libs" - the methods: populate_indicators, populate_entry_trend, populate_exit_trend You should keep: - timeframe, minimal_roi, stoploss, trailing_* """ # 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 # Optimal timeframe for the strategy. timeframe = "1m" # Can this strategy go short? can_short: bool = False # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". minimal_roi = {"0": 0.02} # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". stoploss = -0.02 use_custom_stoploss = True # Trailing stoploss trailing_stop = False # trailing_only_offset_is_reached = True # trailing_stop_positive = 0.01 # trailing_stop_positive_offset = 0.01 # Disabled / not configured # 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 # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 310 # Strategy parameters ema_short = 20 ema_long = 99 adx_guard = 25 atr_multiplier = 6 atr_length = 14 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 indicators (Moving averages, ...) "main_plot": { "ema_short": {"color": "blue"}, "ema_long": {"color": "yellow"}, }, "subplots": { # Subplots - each dict defines one additional plot "MACD": { "macd": {"color": "blue"}, "macdsignal": {"color": "orange"}, "macdhist": {"color": "violet", "type": "bar"}, }, "ADX": { "adx": {"color": "red"}, }, }, } # no trailing stoploss!!!!!!!!!! def custom_stoploss( self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, after_fill: bool, **kwargs, ) -> Optional[float]: """ The custom stoploss can never be below self.stoploss, which serves as a hard maximum loss. """ trade_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc) # only adjust the stoploss at the beginning of the trade. # stoploss is set to what the value of stoploss_percent is on trade entry if current_time == trade_date: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1].squeeze() stoploss_percent = last_candle["stoploss_percent"] return stoploss_percent # don't update stoploss value return None 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 """ # ATR dataframe[f"atr"] = pta.atr( high=dataframe["high"], low=dataframe["low"], close=dataframe["close"], length=self.atr_length, mamode="sma", ) dataframe[f"stoploss_percent"] = ( dataframe["atr"] / dataframe["close"] * self.atr_multiplier * -1 ) # ADX dataframe["adx"] = ta.ADX(dataframe) # MACD macd = ta.MACD(dataframe) dataframe["macd"] = macd["macd"] dataframe["macdsignal"] = macd["macdsignal"] dataframe["macdhist"] = macd["macdhist"] # #MA dataframe[f"ema_short"] = ta.EMA(dataframe, timeperiod=self.ema_short) dataframe[f"ema_long"] = ta.EMA(dataframe, timeperiod=self.ema_long) 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 """ conditions = [] # Trigger conditions.append( qtpylib.crossed_above( dataframe[f"ema_short"], dataframe[f"ema_long"], ) ) # Guard conditions.append(dataframe["adx"] > self.adx_guard) conditions.append(dataframe["macdhist"] > 0) conditions.append(dataframe["macdhist"].shift(1) < dataframe["macdhist"]) # Check that volume is not 0 conditions.append(dataframe["volume"] > 0) if conditions: dataframe.loc[reduce(lambda x, y: x & y, conditions), "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 = [] # Trigger conditions.append( qtpylib.crossed_above( dataframe[f"ema_long"], dataframe[f"ema_short"], ) ) # Guard # Check that volume is not 0 conditions.append(dataframe["volume"] > 0) if conditions: dataframe.loc[reduce(lambda x, y: x & y, conditions), "exit_long"] = 1 return dataframe