# source: https://raw.githubusercontent.com/mohsenjfar/trade/17ea10e916b730006e142258d67c307b102faca1/user_data/Archive/XGBoost_Strategy_.py # directory_url: https://github.com/mohsenjfar/trade/blob/main/user_data/Archive/ # User: mohsenjfar # Repository: trade # --------------------import logging from typing import Dict import numpy as np # noqa import pandas as pd # noqa import talib.abstract as ta from pandas import DataFrame from technical import qtpylib from freqtrade.persistence import Trade from datetime import datetime, timedelta from freqtrade.strategy import IntParameter, IStrategy, merge_informative_pair # noqa from typing import Optional, Union logger = logging.getLogger(__name__) class Github_mohsenjfar_trade__XGBoost_Strategy___20260816_043218(IStrategy): plot_config = { 'main_plot': { 'tema': {}, }, 'subplots': { "MACD": { 'macd': {'color': 'blue'}, 'macdsignal': {'color': 'orange'}, }, "RSI": { 'rsi': {'color': 'red'}, }, "Up_or_down": { '&s-up_or_down': {'color': 'green'}, } } } process_only_new_candles = True stoploss = -0.1 use_exit_signal = True startup_candle_count: int = 30 can_short = True use_custom_stoploss = True total_risk = 0.01 custom_info = {} # Hyperoptable parameters buy_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True) sell_rsi = IntParameter(low=50, high=100, default=70, space='sell', optimize=True, load=True) short_rsi = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True) exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True) def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: Dict, **kwargs) -> DataFrame: 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) bollinger = qtpylib.bollinger_bands( qtpylib.typical_price(dataframe), window=period, stds=2.2 ) dataframe["bb_lowerband-period"] = bollinger["lower"] dataframe["bb_middleband-period"] = bollinger["mid"] dataframe["bb_upperband-period"] = bollinger["upper"] dataframe["%-bb_width-period"] = ( dataframe["bb_upperband-period"] - dataframe["bb_lowerband-period"] ) / dataframe["bb_middleband-period"] dataframe["%-close-bb_lower-period"] = ( dataframe["close"] / dataframe["bb_lowerband-period"] ) dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period) dataframe["%-relative_volume-period"] = ( dataframe["volume"] / dataframe["volume"].rolling(period).mean() ) return dataframe def feature_engineering_expand_basic( self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame: 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: dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek dataframe["%-hour_of_day"] = dataframe["date"].dt.hour return dataframe def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame: self.freqai.class_names = ["down", "up"] dataframe['&s-up_or_down'] = np.where(dataframe["close"].shift(-8) > dataframe["close"], 'up', 'down') return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # noqa: C901 dataframe = self.freqai.start(dataframe, metadata, self) dataframe['rsi'] = ta.RSI(dataframe) bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] dataframe["bb_percent"] = ( (dataframe["close"] - dataframe["bb_lowerband"]) / (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) ) dataframe["bb_width"] = ( (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"] ) dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9) dataframe['atr'] = ta.ATR(dataframe) dataframe['stop_loss'] = dataframe['close']-(dataframe['atr']*2) return dataframe def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: df.loc[ ( # Signal: RSI crosses above 30 (qtpylib.crossed_above(df['rsi'], self.buy_rsi.value)) & (df['tema'] <= df['bb_middleband']) & # Guard: tema below BB middle (df['tema'] > df['tema'].shift(1)) & # Guard: tema is raising (df['volume'] > 0) & # Make sure Volume is not 0 (df['do_predict'] == 1) & # Make sure Freqai is confident in the prediction # Only enter trade if Freqai thinks the trend is in this direction (df['&s-up_or_down'] == 'up') ), 'enter_long'] = 1 df.loc[ ( # Signal: RSI crosses above 70 (qtpylib.crossed_above(df['rsi'], self.short_rsi.value)) & (df['tema'] > df['bb_middleband']) & # Guard: tema above BB middle (df['tema'] < df['tema'].shift(1)) & # Guard: tema is falling (df['volume'] > 0) & # Make sure Volume is not 0 (df['do_predict'] == 1) & # Make sure Freqai is confident in the prediction # 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: return df def position_size(self, total_asset, leverage, stop, total_risk): if stop < total_risk: return total_asset else: return (total_asset * total_risk) / (leverage * stop) def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: Optional[float], max_stake: float, leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) previous_candle = dataframe.iloc[-2].squeeze() stop = previous_candle['stop_loss'] stake = self.position_size(max_stake, leverage, stop, self.total_risk) return stake def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) prev_candle = dataframe.iloc[-2].squeeze() if (current_time - timedelta(minutes=60) > trade.open_date_utc): if current_profit >= 0.03: return 0.01 def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> Optional[Union[str, bool]]: dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) current_candle = dataframe.iloc[-1].squeeze() current_profit = trade.calc_profit_ratio(current_candle['close']) if (current_time - timedelta(minutes=51) > trade.open_date_utc): if current_profit < 0: self.custom_info[pair] = {} if trade.is_short: self.custom_info[pair]["short_reverse"] = True else: self.custom_info[pair]["long_reverse"] = True self.custom_info[pair]["unlock_me"] = True message = f"Trade expired, changing direction for {pair}" self.dp.send_msg(message) return True def bot_loop_start(self, current_time: datetime, **kwargs) -> None: for pair in list(self.custom_info): if "unlock_me" in self.custom_info[pair]: message = f"Unlocking {pair}" self.dp.send_msg(message) self.unlock_pair(pair) del self.custom_info[pair] def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str, current_time: datetime, entry_tag: Optional[str], side: str, **kwargs) -> bool: if self.custom_info.get(pair): del self.custom_info[pair] return True