# source: https://raw.githubusercontent.com/Mastaaa1987/freqtrade-strategy/bfe7f2274299f758d487034249b32f6a553b4421/user_data/strategies/EV.py from datetime import datetime, timedelta import talib.abstract as ta import pandas_ta as pta from freqtrade.persistence import Trade, Order from freqtrade.strategy.interface import IStrategy from pandas import DataFrame from freqtrade.strategy import DecimalParameter, IntParameter from functools import reduce import warnings import freqtrade.vendor.qtpylib.indicators as qtpylib import pandas as pd # noqa pd.options.mode.chained_assignment = None # default='warn' import technical.indicators as ftt from freqtrade.strategy import merge_informative_pair import numpy as np from freqtrade.strategy import stoploss_from_open warnings.simplefilter(action="ignore", category=RuntimeWarning) TMP_HOLD = [] TMP_HOLD1 = [] class Github_Mastaaa1987_freqtrade_strategy__EV__20241103_060507(IStrategy): minimal_roi = { "0": 1 } timeframe = '5m' process_only_new_candles = True startup_candle_count = 240 order_types = { 'entry': 'limit', 'exit': 'limit', '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 } unfilledtimeout = { "entry": 60 * 25, "exit": 60 * 25 } # buy_params = { # "buy_trend_above_senkou_level": 1, # "buy_trend_bullish_level": 6, # "buy_fan_magnitude_shift_value": 3, # "buy_min_fan_magnitude_gain": 1.009 # # "buy_min_fan_magnitude_gain": 1.002 # NOTE: Good value (Win% ~70%), alot of trades # # "buy_min_fan_magnitude_gain": 1.008 # NOTE: Very save value (Win% ~90%), only the biggest moves 1.008, # } # sell_params = { # "sell_trend_indicator": "trend_close_2h", # } # Stoploss: stoploss = -0.05 trailing_stop = True trailing_stop_positive = 0.001 trailing_stop_positive_offset = 0.037 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_win_cci = IntParameter(low=0, high=600, default=120, space='sell', optimize=cci_opt) sell_loss_cci = IntParameter(low=0, high=600, default=80, space='sell', optimize=cci_opt) sell_loss_profit = DecimalParameter(-1, 1, default=-0.01, decimals=2, space='sell', optimize=cci_opt) sell_win_profit = DecimalParameter(-1, 1, default=0.01, decimals=2, space='sell', optimize=cci_opt) @property def protections(self): return [ { "method": "CooldownPeriod", "stop_duration_candles": 12 } ] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # df = dataframe.copy() # # heikinashi = qtpylib.heikinashi(df) # df['open'] = heikinashi['open'] # #dataframe['close'] = heikinashi['close'] # df['high'] = heikinashi['high'] # df['low'] = heikinashi['low'] # # dataframe['ha_open'] = heikinashi['open'] # dataframe['ha_high'] = heikinashi['high'] # dataframe['ha_low'] = heikinashi['low'] # dataframe['trend_close_5m'] = dataframe['close'] # dataframe['trend_close_15m'] = ta.EMA(dataframe['close'], timeperiod=3) # dataframe['trend_close_30m'] = ta.EMA(dataframe['close'], timeperiod=6) # dataframe['trend_close_1h'] = ta.EMA(dataframe['close'], timeperiod=12) # dataframe['trend_close_2h'] = ta.EMA(dataframe['close'], timeperiod=24) # dataframe['trend_close_4h'] = ta.EMA(dataframe['close'], timeperiod=48) # dataframe['trend_close_6h'] = ta.EMA(dataframe['close'], timeperiod=72) # dataframe['trend_close_8h'] = ta.EMA(dataframe['close'], timeperiod=96) # # dataframe['trend_open_5m'] = dataframe['ha_open'] # dataframe['trend_open_15m'] = ta.EMA(dataframe['ha_open'], timeperiod=3) # dataframe['trend_open_30m'] = ta.EMA(dataframe['ha_open'], timeperiod=6) # dataframe['trend_open_1h'] = ta.EMA(dataframe['ha_open'], timeperiod=12) # dataframe['trend_open_2h'] = ta.EMA(dataframe['ha_open'], timeperiod=24) # dataframe['trend_open_4h'] = ta.EMA(dataframe['ha_open'], timeperiod=48) # dataframe['trend_open_6h'] = ta.EMA(dataframe['ha_open'], timeperiod=72) # dataframe['trend_open_8h'] = ta.EMA(dataframe['ha_open'], timeperiod=96) # dataframe['fan_magnitude'] = (dataframe['trend_close_1h'] / dataframe['trend_close_8h']) # dataframe['fan_magnitude_gain'] = dataframe['fan_magnitude'] / dataframe['fan_magnitude'].shift(1) # # ichimoku = ftt.ichimoku(df, conversion_line_period=20, base_line_periods=60, laggin_span=120, displacement=30) # dataframe['senkou_a'] = ichimoku['senkou_span_a'] # dataframe['senkou_b'] = ichimoku['senkou_span_b'] # buy_1 indicators 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) # profit sell indicators stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0) dataframe['fastk'] = stoch_fast['fastk'] dataframe['fastd'] = stoch_fast['fastd'] # fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0 stoch_slow = ta.STOCH(dataframe, fastk_period=5, slowk_period=3, slowd_period=3) dataframe['slowk'] = stoch_slow['slowk'] dataframe['slowd'] = stoch_slow['slowd'] dataframe['change'] = 100 / dataframe['open'] * dataframe['close'] - 100 dataframe['ma120'] = ta.MA(dataframe, timeperiod=120) dataframe['ma240'] = ta.MA(dataframe, timeperiod=240) dataframe['cci'] = ta.CCI(dataframe, timeperiod=20) # dataframe["cci_mid"] = 0 # # dataframe["rsi_ma"] = ta.SMA(dataframe['rsi'], timeperiod=14) # dataframe["rsi_mid"] = 50 # dataframe['rc'] = 0 # dataframe.loc[ # ( # ((dataframe['rsi'].shift(1) < 50) & (dataframe['rsi'] > 50)) | # ((dataframe['rsi'].shift(2) < 50) & (dataframe['rsi'].shift(1) > 50)) | # ((dataframe['rsi'].shift(3) < 50) & (dataframe['rsi'].shift(2) > 50)) # ), 'rc'] = 1 # # stoch = ta.STOCH(dataframe) # dataframe["slowd"] = stoch["slowd"] # dataframe["slowk"] = stoch["slowk"] # dataframe.loc[:, 'sc'] = np.nan # dataframe.loc[0, 'sc'] = 0 # dataframe.loc[((dataframe['slowd'] > 80) & (dataframe['slowk'] > 80)), 'sc'] = 1 # dataframe.loc[((dataframe['slowd'] < 20) & (dataframe['slowk'] < 20)), 'sc'] = -1 # dataframe['sc'] = dataframe['sc'].ffill() # # dataframe["ema21"] = ta.EMA(dataframe, timeperiod=21) # dataframe["ema50"] = ta.EMA(dataframe, timeperiod=50) # dataframe["ema200"] = ta.EMA(dataframe, timeperiod=200) # # macd = ta.MACD(dataframe) # dataframe["macd"] = macd["macd"] # dataframe["macdsignal"] = macd["macdsignal"] # dataframe["macdhist"] = macd["macdhist"] # dataframe["mc"] = 0 # dataframe["hc"] = 0 # dataframe.loc[( # ((dataframe['macd'].shift(1) < dataframe['macdsignal'].shift(1)) & (dataframe['macd'] > dataframe['macdsignal'])) | # ((dataframe['macd'].shift(2) < dataframe['macdsignal'].shift(2)) & (dataframe['macd'].shift(1) > dataframe['macdsignal'].shift(1))) | # ((dataframe['macd'].shift(3) < dataframe['macdsignal'].shift(3)) & (dataframe['macd'].shift(2) > dataframe['macdsignal'].shift(2))) # ), 'mc'] = 1 # dataframe.loc[( # ((dataframe['macdhist'].shift(1) < 0) & (dataframe['macdhist'] > 0)) | # ((dataframe['macdhist'].shift(2) < 0) & (dataframe['macdhist'].shift(1) > 0)) | # ((dataframe['macdhist'].shift(3) < 0) & (dataframe['macdhist'].shift(2) > 0)) # ), 'hc'] = 1 return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions1 = [] conditions2 = [] conditions3 = [] dataframe.loc[:, 'enter_tag'] = '' # buy_3 = ( # (dataframe['sc'] == -1) & # (dataframe['mc'] == 1) & # (dataframe['rc'] == 1) & # (dataframe['fastk'] < 90) & # (dataframe['close'] > dataframe['open']) & # (dataframe['close'] > dataframe['ema200']) & # (dataframe['ema50'] > dataframe['ema200']) & # (dataframe['ema200'] > dataframe['ema200'].shift(1)) # ) # conditions3.append(buy_3) # dataframe.loc[buy_3, 'enter_tag'] += 'scalp_' # dataframe['tag'] = 0 # dataframe.loc[buy_3, 'tag'] = 1 # if conditions3: # dataframe.loc[ # reduce(lambda x, y: x | y, conditions3), # 'enter_long'] = 1 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) ) conditions1.append(buy_1) dataframe.loc[buy_1, 'enter_tag'] += 'ev_1' # 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) & # ( # (dataframe['ma120'] * 1.001 < dataframe['low']) | # (dataframe['ma120'] > dataframe['high']) # ) & # ( # (dataframe['ma240'] * 1.001 < dataframe['low']) | # (dataframe['ma240'] > dataframe['high']) # ) # ) # # conditions1.append(buy_new) # dataframe.loc[buy_new, 'enter_tag'] += 'ev_new' if conditions1: dataframe.loc[ reduce(lambda x, y: x | y, conditions1), 'enter_long'] = 1 # if self.buy_params['buy_trend_above_senkou_level'] >= 1: # conditions2.append(dataframe['trend_close_5m'] > dataframe['senkou_a']) # conditions2.append(dataframe['trend_close_5m'] > dataframe['senkou_b']) # if self.buy_params['buy_trend_above_senkou_level'] >= 2: # conditions2.append(dataframe['trend_close_15m'] > dataframe['senkou_a']) # conditions2.append(dataframe['trend_close_15m'] > dataframe['senkou_b']) # if self.buy_params['buy_trend_above_senkou_level'] >= 3: # conditions2.append(dataframe['trend_close_30m'] > dataframe['senkou_a']) # conditions2.append(dataframe['trend_close_30m'] > dataframe['senkou_b']) # if self.buy_params['buy_trend_above_senkou_level'] >= 4: # conditions2.append(dataframe['trend_close_1h'] > dataframe['senkou_a']) # conditions2.append(dataframe['trend_close_1h'] > dataframe['senkou_b']) # if self.buy_params['buy_trend_above_senkou_level'] >= 5: # conditions2.append(dataframe['trend_close_2h'] > dataframe['senkou_a']) # conditions2.append(dataframe['trend_close_2h'] > dataframe['senkou_b']) # if self.buy_params['buy_trend_above_senkou_level'] >= 6: # conditions2.append(dataframe['trend_close_4h'] > dataframe['senkou_a']) # conditions2.append(dataframe['trend_close_4h'] > dataframe['senkou_b']) # if self.buy_params['buy_trend_above_senkou_level'] >= 7: # conditions2.append(dataframe['trend_close_6h'] > dataframe['senkou_a']) # conditions2.append(dataframe['trend_close_6h'] > dataframe['senkou_b']) # if self.buy_params['buy_trend_above_senkou_level'] >= 8: # conditions2.append(dataframe['trend_close_8h'] > dataframe['senkou_a']) # conditions2.append(dataframe['trend_close_8h'] > dataframe['senkou_b']) # if self.buy_params['buy_trend_bullish_level'] >= 1: # conditions2.append(dataframe['trend_close_5m'] > dataframe['trend_open_5m']) # if self.buy_params['buy_trend_bullish_level'] >= 2: # conditions2.append(dataframe['trend_close_15m'] > dataframe['trend_open_15m']) # if self.buy_params['buy_trend_bullish_level'] >= 3: # conditions2.append(dataframe['trend_close_30m'] > dataframe['trend_open_30m']) # if self.buy_params['buy_trend_bullish_level'] >= 4: # conditions2.append(dataframe['trend_close_1h'] > dataframe['trend_open_1h']) # if self.buy_params['buy_trend_bullish_level'] >= 5: # conditions2.append(dataframe['trend_close_2h'] > dataframe['trend_open_2h']) # if self.buy_params['buy_trend_bullish_level'] >= 6: # conditions2.append(dataframe['trend_close_4h'] > dataframe['trend_open_4h']) # if self.buy_params['buy_trend_bullish_level'] >= 7: # conditions2.append(dataframe['trend_close_6h'] > dataframe['trend_open_6h']) # if self.buy_params['buy_trend_bullish_level'] >= 8: # conditions2.append(dataframe['trend_close_8h'] > dataframe['trend_open_8h']) # # conditions2.append(dataframe['fan_magnitude_gain'] >= self.buy_params['buy_min_fan_magnitude_gain']) # conditions2.append(dataframe['fan_magnitude_gain'] > dataframe['fan_magnitude_gain'].shift(1)) # conditions2.append(dataframe['fan_magnitude'] > 1) # # for x in range(self.buy_params['buy_fan_magnitude_shift_value']): # conditions2.append(dataframe['fan_magnitude'].shift(x+1) < dataframe['fan_magnitude']) # # if conditions2: # dataframe.loc[ # reduce(lambda x, y: x & y, conditions2), # 'enter_long'] = 1 # dataframe.loc[ # reduce(lambda x, y: x & y, conditions2), # 'enter_tag'] += '[ichi]' 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() last_candle = dataframe.iloc[-2].squeeze() if 'ev' in trade.enter_tag: min_profit = trade.calc_profit_ratio(trade.min_rate) if trade.id not in TMP_HOLD: if len(dataframe.loc[dataframe['date'] < trade.open_date_utc]) > 0: open_candle = dataframe.loc[dataframe['date'] < trade.open_date_utc].iloc[-1].squeeze() if open_candle['close'] > open_candle["ma120"] or open_candle['close'] > open_candle["ma240"]: TMP_HOLD.append(trade.id) elif current_candle['close'] > current_candle["ma120"] or current_candle['close'] > current_candle["ma240"]: TMP_HOLD.append(trade.id) # else: # if trade.id not in TMP_HOLD1: # TMP_HOLD1.append(trade.id) if current_profit > self.sell_win_profit.value: #if current_candle["slowk"] > self.sell_fastx.value: if current_candle["fastk"] > self.sell_fastx.value: return "fastk_profit_sell" if current_candle["cci"] > self.sell_win_cci.value: if current_candle["high"] >= trade.open_rate: return "cci_high_sell" if min_profit <= self.sell_loss_profit.value: if current_profit > self.sell_loss_profit.value: if current_candle["cci"] > self.sell_loss_cci.value: 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=12) > 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 self.stoploss <= min_profit <= self.sell_loss_profit.value: # TMP_HOLD1.remove(trade.id) # return "cross_120_or_240_sell" # elif 'ichi' in trade.enter_tag: # if last_candle['trend_close_5m'] >= last_candle[self.sell_params['sell_trend_indicator']] and current_candle['trend_close_5m'] < current_candle[self.sell_params['sell_trend_indicator']]: # return "long_out" if current_time - timedelta(minutes=120) > trade.open_date_utc and current_profit and current_profit > 0.017: return "roi" elif current_time - timedelta(minutes=180) > trade.open_date_utc and current_profit and current_profit > 0: return "roi" # elif current_time - timedelta(minutes=180) > trade.open_date_utc and current_profit and current_profit > -0.017: # return "roi" return None def check_entry_timeout(self, pair: str, trade: Trade, order: Order, current_time: datetime, **kwargs) -> bool: ob = self.dp.orderbook(pair, 1) current_price = ob["bids"][0][0] print(current_price, order.price) # Cancel buy order if price is more than 2% above the order. if current_price > order.price * 1.02: return True return False def check_exit_timeout(self, pair: str, trade: Trade, order: Order, current_time: datetime, **kwargs) -> bool: ob = self.dp.orderbook(pair, 1) current_price = ob["asks"][0][0] print(current_price, order.price) # Cancel sell order if price is more than 2% below the order. if current_price < order.price * 0.98: return True return False def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[:, ['exit_long', 'exit_tag']] = (0, 'long_out') return dataframe