# source: https://raw.githubusercontent.com/Tarasyanich87/Micro-Services_FT_Project/88c02d0891259057371bf5c8f8a8a295fbe9bff0/user_data/strategies/FreqaiExampleStrategy.py import numpy as np import pandas as pd from pandas import DataFrame import talib.abstract as ta from freqtrade.strategy import ( IStrategy, DecimalParameter, IntParameter, BooleanParameter, ) import freqtrade.vendor.qtpylib.indicators as qtpylib class Github_Tarasyanich87_Micro_Services_FT_Project__FreqaiExampleStrategy__20251210_163936(IStrategy): """ Example strategy showing how to use FreqAI. This strategy uses FreqAI to make predictions on price movements. """ INTERFACE_VERSION: int = 3 # Strategy metadata minimal_roi = {"0": 0.1, "15": 0.05, "30": 0.025, "60": 0} stoploss = -0.1 timeframe = "3m" # Strategy parameters 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 # 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"} def feature_engineering_expand_all( self, dataframe: DataFrame, period: int, metadata: dict, **kwargs ) -> DataFrame: """ *Only functional with FreqAI enabled strategies* This function will automatically expand the defined features on the base timeframe (3m in this case) to other higher timeframes """ dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=period) dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period) dataframe["%-adx-period"] = ta.ADX(dataframe, timeperiod=period) dataframe["%-sma-period"] = ta.SMA(dataframe, timeperiod=period) dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period) dataframe["%-close-period"] = dataframe["close"] dataframe["%-high-period"] = dataframe["high"] dataframe["%-low-period"] = dataframe["low"] dataframe["%-volume-period"] = dataframe["volume"] return dataframe def feature_engineering_expand_basic( self, dataframe: DataFrame, metadata: dict, **kwargs ) -> DataFrame: """ *Only functional with FreqAI enabled strategies* This function will automatically expand the defined features on the base timeframe (3m in this case) to other higher timeframes """ dataframe["%-pct-change"] = dataframe["close"].pct_change() dataframe["%-raw_volume"] = dataframe["volume"] dataframe["%-raw_price"] = dataframe["close"] return dataframe def feature_engineering_standard( self, dataframe: DataFrame, metadata: dict, **kwargs ) -> DataFrame: """ *Only functional with FreqAI enabled strategies* This function is called once with the dataframe intended to be used for the features """ # All indicators must be populated before this function is called dataframe["%-day_of_week"] = (dataframe["date"].dt.dayofweek + 1) / 7 dataframe["%-hour_of_day"] = (dataframe["date"].dt.hour + 1) / 25 # RSI dataframe["%-rsi"] = ta.RSI(dataframe, timeperiod=14) # MFI dataframe["%-mfi"] = ta.MFI(dataframe) # SMA dataframe["%-sma_200"] = ta.SMA(dataframe, timeperiod=200) dataframe["%-sma_50"] = ta.SMA(dataframe, timeperiod=50) # EMA dataframe["%-ema_50"] = ta.EMA(dataframe, timeperiod=50) dataframe["%-ema_200"] = ta.EMA(dataframe, timeperiod=200) # Williams %R dataframe["%-willr"] = ta.WILLR(dataframe) # Bollinger Bands bollinger = qtpylib.bollinger_bands( qtpylib.typical_price(dataframe), window=20, stds=2 ) dataframe["%-bb_lowerband"] = bollinger["lower"] dataframe["%-bb_upperband"] = bollinger["upper"] dataframe["%-bb_middleband"] = bollinger["mid"] # Volume dataframe["%-volume"] = dataframe["volume"] # Price dataframe["%-close"] = dataframe["close"] dataframe["%-open"] = dataframe["open"] dataframe["%-high"] = dataframe["high"] dataframe["%-low"] = dataframe["low"] return dataframe def set_freqai_targets( self, dataframe: DataFrame, metadata: dict, **kwargs ) -> DataFrame: """ *Only functional with FreqAI enabled strategies* Required function to set the targets for the model. All targets must be prepended with `&` to be recognized by the FreqAI internals. More details about feature engineering available: https://www.freqtrade.io/en/stable/freqai-feature-engineering """ dataframe["&-s_close"] = ( dataframe["close"] .shift(-self.freqai_info["feature_parameters"]["label_period_candles"]) .rolling(self.freqai_info["feature_parameters"]["label_period_candles"]) .mean() / dataframe["close"] - 1 ) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Add all the indicators to the dataframe. """ # RSI dataframe["rsi"] = ta.RSI(dataframe) # MFI dataframe["mfi"] = ta.MFI(dataframe) # ADX dataframe["adx"] = ta.ADX(dataframe) # MACD macd, macdsignal, macdhist = ta.MACD( dataframe, fastperiod=12, slowperiod=26, signalperiod=9 ) dataframe["macd"] = macd dataframe["macdsignal"] = macdsignal dataframe["macdhist"] = macdhist # Bollinger Bands bollinger = qtpylib.bollinger_bands( qtpylib.typical_price(dataframe), window=20, stds=2 ) dataframe["bb_lowerband"] = bollinger["lower"] dataframe["bb_upperband"] = bollinger["upper"] dataframe["bb_middleband"] = bollinger["mid"] # EMA - Exponential Moving Average dataframe["ema50"] = ta.EMA(dataframe, timeperiod=50) dataframe["ema200"] = ta.EMA(dataframe, timeperiod=200) # SMA - Simple Moving Average dataframe["sma50"] = ta.SMA(dataframe, timeperiod=50) dataframe["sma200"] = ta.SMA(dataframe, timeperiod=200) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the entry signal for the given dataframe """ dataframe.loc[ ( (dataframe["rsi"] < 30) & (dataframe["close"] < dataframe["bb_lowerband"]) & (dataframe["volume"] > 0) ), "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 """ dataframe.loc[ ( (dataframe["rsi"] > 70) & (dataframe["close"] > dataframe["bb_upperband"]) ), "exit_long", ] = 1 return dataframe def leverage( self, pair: str, current_time, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: str | None, side: str, **kwargs, ) -> float: """ Customize leverage for each new trade. This method is only called in futures mode. :param pair: Pair that's currently analyzed :param current_time: datetime object, containing the current datetime :param current_rate: Rate, calculated based on pricing settings in exit_pricing. :param proposed_leverage: A leverage proposed by the bot. :param max_leverage: Max leverage allowed on this pair :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal. :param side: 'long' or 'short' - indicating the direction of the proposed trade :return: A leverage amount, which is between 1.0 and max_leverage. """ return 1.0