from datetime import datetime, timedelta import talib.abstract as ta import pandas_ta as pta from freqtrade.persistence import Trade from freqtrade.strategy.interface import IStrategy from pandas import DataFrame from freqtrade.strategy import DecimalParameter, IntParameter from functools import reduce import warnings warnings.simplefilter(action="ignore", category=RuntimeWarning) TMP_HOLD = [] TMP_HOLD1 = [] class PrintMoney_v1(IStrategy): minimal_roi = { # minutes since entry : target ROI "0": 0.12, # 12% immediate "30": 0.05, # 5% after 30m "60": 0.02, # 2% after 1h "120": 0.01, # 1% after 2h "240": 0.005, # 0.5% after 4h "480": 0.0 # 0% (break even) after 8h } timeframe = '15m' process_only_new_candles = True startup_candle_count = 240 # order_types = { # 'entry': 'market', # 'exit': 'market', # 'emergency_exit': 'market', # 'force_entry': 'market', # 'force_exit': "market", # 'stoploss': 'market', # 'stoploss_on_exchange': False, # 'stoploss_on_exchange_interval': 60, # 'stoploss_on_exchange_market_ratio': 0.99 # } leverage_level = 5 stoploss = -0.28 trailing_stop = True trailing_stop_positive = 0.009 trailing_stop_positive_offset = 0.026 trailing_only_offset_is_reached = True is_optimize_32 = True buy_rsi_fast_32 = IntParameter(20, 70, default=40, space='buy', optimize=is_optimize_32) buy_rsi_32 = IntParameter(15, 50, default=42, space='buy', optimize=is_optimize_32) buy_sma15_32 = DecimalParameter(0.900, 1, default=0.973, decimals=3, space='buy', optimize=is_optimize_32) buy_cti_32 = DecimalParameter(-1, 1, default=0.69, decimals=2, space='buy', optimize=is_optimize_32) sell_fastx = IntParameter(50, 100, default=84, space='sell', optimize=True) cci_opt = True sell_loss_cci = IntParameter(low=0, high=600, default=120, space='sell', optimize=cci_opt) sell_loss_cci_profit = DecimalParameter(-0.15, 0, default=-0.15, decimals=2, space='sell', optimize=cci_opt) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['sma_15'] = ta.SMA(dataframe, timeperiod=15) dataframe['cti'] = pta.cti(dataframe["close"], length=20) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4) dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20) stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0) dataframe['fastk'] = stoch_fast['fastk'] dataframe['cci'] = ta.CCI(dataframe, timeperiod=20) dataframe['ma120'] = ta.MA(dataframe, timeperiod=120) dataframe['ma240'] = ta.MA(dataframe, timeperiod=240) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] dataframe.loc[:, 'enter_tag'] = '' buy_1 = ( (dataframe['rsi_slow'] < dataframe['rsi_slow'].shift(1)) & (dataframe['rsi_fast'] < self.buy_rsi_fast_32.value) & (dataframe['rsi'] > self.buy_rsi_32.value) & (dataframe['close'] < dataframe['sma_15'] * self.buy_sma15_32.value) & (dataframe['cti'] < self.buy_cti_32.value) ) buy_new = ( (dataframe['rsi_slow'] < dataframe['rsi_slow'].shift(1)) & (dataframe['rsi_fast'] < 34) & (dataframe['rsi'] > 28) & (dataframe['close'] < dataframe['sma_15'] * 0.96) & (dataframe['cti'] < self.buy_cti_32.value) ) conditions.append(buy_1) dataframe.loc[buy_1, 'enter_tag'] += 'buy_1' conditions.append(buy_new) dataframe.loc[buy_new, 'enter_tag'] += 'buy_new' # # ——— BTC‐specific entry ——— # if metadata['pair'] == 'BTC/USDT': # buy_btc = ( # # require a small pullback + early momentum # (dataframe['rsi_slow'] < dataframe['rsi_slow'].shift(1)) # & (dataframe['rsi_fast'] < 45) # allow higher fast-RSI # & (dataframe['rsi'] > 32) # slightly lower main RSI floor # & (dataframe['close'] < dataframe['sma_15'] * 0.99) # within 1% of 15-SMA # & (dataframe['cti'] < 0.8) # looser CTI threshold # ) # conditions.append(buy_btc) # dataframe.loc[buy_btc, 'enter_tag'] += 'buy_btc' if conditions: dataframe.loc[ reduce(lambda x, y: x | y, conditions), 'enter_long'] = 1 return dataframe def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs): dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) current_candle = dataframe.iloc[-1].squeeze() min_profit = trade.calc_profit_ratio(trade.min_rate) if current_candle['close'] > current_candle["ma120"] or current_candle['close'] > current_candle["ma240"]: if trade.id not in TMP_HOLD: TMP_HOLD.append(trade.id) else: if trade.id not in TMP_HOLD1: TMP_HOLD1.append(trade.id) if current_profit > 0: if current_candle["fastk"] > self.sell_fastx.value: return "fastk_profit_sell" if current_candle["cci"] > 80: if current_candle["high"] >= trade.open_rate: return "cci_high_sell" # if min_profit <= -0.1: # if current_profit > self.sell_loss_cci_profit.value: # if current_candle["cci"] > self.sell_loss_cci.value: # return "cci_loss_sell" if current_profit <= -0.15: if current_candle["cci"] > 200: return "cci_loss_sell" # if trade.id in TMP_HOLD and current_candle["close"] < current_candle["ma120"] and current_candle["close"] < \ # current_candle["ma240"]: # if current_time - timedelta(minutes=10) > trade.open_date_utc: # TMP_HOLD.remove(trade.id) # return "ma120_sell" # if trade.id in TMP_HOLD1: # if current_candle["high"] > current_candle["ma120"] or current_candle["high"] > current_candle["ma240"]: # if min_profit <= -0.05: # TMP_HOLD1.remove(trade.id) # return "cross_120_or_240_sell" # — ATR stop-loss — atr = talib.ATR(dataframe, timeperiod=14).iloc[-1] if current_rate <= (trade.open_rate - 3 * atr): return "atr_stop_loss" return None def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, ['exit_long', 'exit_tag']] = (0, 'long_out') return dataframe def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, side: str, **kwargs) -> float: """ Optional leverage handling for futures trading. """ return self.leverage_level