# source: https://raw.githubusercontent.com/hamidreza07/freqai-strategy/5bd42947b5300922a342d153182906e7cdbe8a40/freqai/TrendMomoClassifier.py import logging import talib.abstract as ta from pandas import DataFrame from technical import qtpylib import numpy as np from freqtrade.strategy import IStrategy, IntParameter from freqtrade.persistence import Trade from datetime import datetime logger = logging.getLogger(__name__) class Github_hamidreza07_freqai_strategy__TrendMomoClassifier__20231226_132626(IStrategy): plot_config = { "main_plot": {}, "subplots": { "Up_or_down": { '&s-up_or_down': {'color': 'green'}, }, "do_predict": { "do_predict": {"color": "brown"}, }, }, } custom_info = { 'risk_reward_ratio': 2.5, 'set_to_break_even_at_profit': 1, } process_only_new_candles = True stoploss = -0.03 use_custom_stoploss = True use_exit_signal = True can_short = True buy_stoch_rsi = IntParameter( low=1, high=20, default=10, space='buy', optimize=True, load=True) sell_stoch_rsi = IntParameter( low=80, high=100, default=90, space='sell', optimize=True, load=True) short_stoch_rsi = IntParameter( low=80, high=100, default=90, space='sell', optimize=True, load=True) exit_short_stoch_rsi = IntParameter( low=1, high=20, default=10, space='buy', optimize=True, load=True) def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: """ custom_stoploss using a risk/reward ratio https://github.com/freqtrade/freqtrade-strategies/blob/main/user_data/strategies/FixedRiskRewardLoss.py """ result = break_even_sl = takeprofit_sl = -1 custom_info_pair = self.custom_info.get(pair) if custom_info_pair is not None: # using current_time/open_date directly via custom_info_pair[trade.open_daten] # would only work in backtesting/hyperopt. # in live/dry-run, we have to search for nearest row before it open_date_mask = custom_info_pair.index.unique().get_loc( trade.open_date_utc, method='ffill') open_df = custom_info_pair.iloc[open_date_mask] # trade might be open too long for us to find opening candle if(len(open_df) != 1): return -1 # won't update current stoploss initial_sl_abs = open_df['stoploss_rate'] # calculate initial stoploss at open_date initial_sl = initial_sl_abs/current_rate-1 # calculate take profit treshold # by using the initial risk and multiplying it risk_distance = trade.open_rate-initial_sl_abs reward_distance = risk_distance * \ self.custom_info['risk_reward_ratio'] # take_profit tries to lock in profit once price gets over # risk/reward ratio treshold take_profit_price_abs = trade.open_rate+reward_distance # take_profit gets triggerd at this profit take_profit_pct = take_profit_price_abs/trade.open_rate-1 # break_even tries to set sl at open_rate+fees (0 loss) break_even_profit_distance = risk_distance * \ self.custom_info['set_to_break_even_at_profit'] # break_even gets triggerd at this profit break_even_profit_pct = ( break_even_profit_distance+current_rate)/current_rate-1 result = initial_sl if(current_profit >= break_even_profit_pct): break_even_sl = ( trade.open_rate*(1+trade.fee_open+trade.fee_close) / current_rate)-1 result = break_even_sl if(current_profit >= take_profit_pct): takeprofit_sl = take_profit_price_abs/current_rate-1 result = takeprofit_sl return result def feature_engineering_expand_all(self, df: DataFrame, period, **kwargs): df[["%-stoch_rsi_K-period", "%-stoch_rsi_D-period"]] = ta.STOCHRSI( df, timeperiod=period)[["fastk", "fastd"]] mfv = ((df['close'] - df['low']) - (df['high'] - df['close']) ) / (df['high'] - df['low']) * df['volume'] df["%-A/D-period"] = mfv.rolling(period).sum() df["%-relative_volume-period"] = (df["volume"] / df["volume"].rolling(period).mean()) return df def feature_engineering_expand_basic(self, df: DataFrame, **kwargs): df["%-pct-change"] = df["close"].pct_change() df["%-raw_volume"] = df["volume"] df["%-raw_price"] = df["close"] return df def feature_engineering_standard(self, df: DataFrame, **kwargs): df["%-day_of_week"] = df["date"].dt.dayofweek df["%-hour_of_day"] = df["date"].dt.hour return df def set_freqai_targets(self, df: DataFrame, **kwargs): df['&s-up_or_down'] = np.where(df["close"].shift(-50) > df["close"], 'up', 'down') return df def populate_indicators(self, df: DataFrame, metadata: dict) -> DataFrame: df = self.freqai.start(df, metadata, self) df[["stoch_rsi_K", "stoch_rsi_D"]] = ta.STOCHRSI( df)[["fastk", "fastd"]] df['stoploss_rate'] = df['close']-(ta.ATR(df)*2) self.custom_info[metadata['pair']] = df[[ 'date', 'stoploss_rate']].copy().set_index('date') return df def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: df.loc[ ( # Signal: stochRSI below 20 (df["stoch_rsi_K"] <= self.buy_stoch_rsi.value) & (qtpylib.crossed_above(df["stoch_rsi_K"], df["stoch_rsi_D"])) & (df['volume'] > 0) & # Make sure Volume is not 0 # Make sure Freqai is confident in the prediction (df['do_predict'] == 1) & # Only enter trade if Freqai thinks the trend is in this direction (df['&s-up_or_down'] == 'up') ), 'enter_long'] = 1 df.loc[ ( # Signal: stochRSI above 80 (df["stoch_rsi_K"] >= self.short_stoch_rsi.value) & (qtpylib.crossed_above(df["stoch_rsi_D"], df["stoch_rsi_K"])) & (df['volume'] > 0) & # Make sure Volume is not 0 # Make sure Freqai is confident in the prediction (df['do_predict'] == 1) & # Only enter trade if Freqai thinks the trend is in this direction (df['&s-up_or_down'] == 'down') ), 'enter_short'] = 1 return df def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: df.loc[ ( # Signal: stochRSI above 80 (df["stoch_rsi_K"] <= self.sell_stoch_rsi.value) & (qtpylib.crossed_above(df["stoch_rsi_D"], df["stoch_rsi_K"])) & (df['volume'] > 0) ), 'exit_long'] = 1 df.loc[ ( # Signal: stoch RSI below 20 (df["stoch_rsi_K"] <= self.exit_short_stoch_rsi.value) & (qtpylib.crossed_above(df["stoch_rsi_K"], df["stoch_rsi_D"])) & (df['volume'] > 0) ), 'exit_short'] = 1 return df