# source: https://raw.githubusercontent.com/Industrial/ft_userdata/cddef4af28d3ee2cb0cf841a410708dfa1d37c49/user_data/strategies/sample_strategy.py from operator import is_ from typing import Optional from datetime import datetime from pandas import DataFrame from freqtrade.exchange import timeframe_to_minutes from freqtrade.strategy import ( IStrategy, IntParameter, DecimalParameter, informative, Trade, ) import freqtrade.vendor.qtpylib.indicators as qtpylib import talib.abstract as ta from freqtrade.exchange import timeframe_to_seconds # ElliottWaveOscilator def EWO(source, sma_length=5, sma2_length=35): sma1 = ta.SMA(source, timeperiod=sma_length) sma2 = ta.SMA(source, timeperiod=sma2_length) smadif = (sma1 - sma2) / source * 100 return smadif def is_positive(dataframe: DataFrame, column: str): return dataframe[column] > 0 def is_trending_up(dataframe: DataFrame, column_first: str, column_second: str): return dataframe[column_first] > dataframe[column_second] def has_crossed_above(dataframe: DataFrame, column: str, threshold: float): return (dataframe[column] > threshold) & (dataframe[column].shift(1) < threshold) class Github_Industrial_ft_userdata__sample_strategy__20250216_195752(IStrategy): INTERFACE_VERSION = 3 can_short: bool = False timeframe = "1h" timeframe_mins = timeframe_to_minutes(timeframe) # minimal_roi = { # str(timeframe_mins * 00): 0.0100, # 10.00% after 0 minutes # str(timeframe_mins * 30): 0.0050, # 7.50% After 30 minutes # str(timeframe_mins * 60): 0.0010, # 0.01% After 60 minutes # } minimal_roi = {"0": 0.446, "303": 0.165, "983": 0.042, "1299": 0} # stoploss = -0.10 stoploss = -0.03 use_custom_stoploss = False trailing_stop = True # trailing_stop_positive = 0.01 # trailing_stop_positive_offset = 0.0 # trailing_only_offset_is_reached = False # ATR Stop Loss # risk_reward_ratio = 1.00 # risk_reward_ratio = 0.5 # atr_distance = 10 # timeframe_minutes = timeframe_to_minutes(timeframe) # custom_info_fixed_rr = dict() # init_fixed_rr_dict = { # "roi": 0, # "sl": 0, # } # process_only_new_candles = True # use_exit_signal = True # exit_profit_only = True # ignore_roi_if_entry_signal = False # 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 # ) rsi_period = IntParameter( low=2, high=100, default=14, space="buy", optimize=False, load=True ) rsi_buy_threshold = IntParameter( low=10, high=70, default=30, space="buy", optimize=False, load=True ) rsi_ema_period = IntParameter( low=2, high=100, default=9, space="buy", optimize=False, load=True ) ewo_low_period = IntParameter( low=2, high=100, default=10, space="buy", optimize=False, load=True ) ewo_high_period = IntParameter( low=51, high=100, default=100, space="buy", optimize=False, load=True ) ewo_buy_threshold = DecimalParameter( low=0.1, high=10, default=3, space="buy", optimize=False, load=True ) tema_small_period = IntParameter( low=1, high=10000, default=283, space="buy", optimize=False, load=True ) tema_large_period = IntParameter( low=284, high=100000, default=10000, space="buy", optimize=False, load=True ) # Optional order time in force. order_time_in_force = {"entry": "GTC", "exit": "GTC"} plot_config = { "main_plot": { "tema_small_btc_1h": { "color": "cyan", }, "tema_large_btc_1h": { "color": "blue", }, }, "subplots": { "RSI": { "rsi": {"color": "red"}, "rsi_ema": {"color": "green"}, }, # "EWO": { # "ewo": {"color": "blue"}, # }, }, } def informative_pairs(self): return [] # @informative("1h", "BTC/{stake}") # def populate_indicators_btc_1h( # self, dataframe: DataFrame, metadata: dict # ) -> DataFrame: # dataframe["tema_small"] = ta.TEMA( # dataframe, timeperiod=int(self.tema_small_period.value) # ) # dataframe["tema_large"] = ta.TEMA( # dataframe, timeperiod=int(self.tema_large_period.value) # ) # return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # dataframe["atr"] = ta.ATR(dataframe, timeperiod=14) dataframe["rsi"] = ta.RSI(dataframe, timeperiod=int(self.rsi_period.value)) dataframe["rsi_ema"] = ta.RSI( dataframe["rsi"], timeperiod=int(self.rsi_ema_period.value) ) dataframe["ewo"] = EWO( dataframe["close"], int(self.ewo_low_period.value), int(self.ewo_high_period.value), ) dataframe["tema_small"] = ta.TEMA( dataframe, timeperiod=int(self.tema_small_period.value) ) dataframe["tema_large"] = ta.TEMA( dataframe, timeperiod=int(self.tema_large_period.value) ) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( is_trending_up(dataframe, "tema_small", "tema_large") # & has_crossed_above(dataframe, "rsi_ema", self.rsi_buy_threshold.value) & has_crossed_above(dataframe, "ewo", self.ewo_buy_threshold.value) & is_positive(dataframe, "volume") ), "enter_long", ] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # is_trending_down_large = ( # dataframe["btc_usdt_tema_small_1h"] < dataframe["btc_usdt_tema_large_1h"] # ) # dataframe.loc[ # (is_trending_down_large), # "exit_long", # ] = 1 return dataframe # def custom_exit( # self, # pair: str, # trade: Trade, # current_time: datetime, # current_rate: float, # current_profit: float, # **kwargs # ) -> Optional[str]: # if (current_time - trade.open_date_utc).total_seconds() >= ( # 60 # * 60 # * 24 # # 50 * timeframe_to_seconds(self.timeframe) # ): # return "exit_timeout" # return None # 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 pair not in self.custom_info_fixed_rr: # self.custom_info_fixed_rr[pair] = self.init_fixed_rr_dict.copy() # dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) # current_candle = dataframe.iloc[-1].squeeze() # self.custom_info_fixed_rr[pair]["roi"] = current_candle["close"] + ( # self.atr_distance * self.risk_reward_ratio * current_candle["atr"] # ) # self.custom_info_fixed_rr[pair]["sl"] = current_candle["close"] - ( # self.atr_distance * current_candle["atr"] # ) # return True # def confirm_trade_exit( # self, # pair: str, # trade: Trade, # order_type: str, # amount: float, # rate: float, # time_in_force: str, # exit_reason: str, # current_time: datetime, # **kwargs # ) -> bool: # if pair in self.custom_info_fixed_rr: # self.custom_info_fixed_rr[pair] = self.init_fixed_rr_dict.copy() # return True # def custom_exit( # self, # pair: str, # trade: Trade, # current_time: datetime, # current_rate: float, # current_profit: float, # **kwargs # ) -> Optional[Union[str, bool]]: # entry_time = timeframe_to_prev_date(self.timeframe, trade.open_date_utc) # dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) # atr_roi = trade.get_custom_data(key="atr_roi", default=0) # atr_sl = trade.get_custom_data(key="atr_sl", default=0) # if atr_roi == 0: # # need to set the roi and sl # signal_time = entry_time - timedelta(minutes=int(self.timeframe_minutes)) # signal_candle = dataframe.loc[dataframe["date"] == signal_time] # if not signal_candle.empty: # # make sure we take only a row # signal_candle = signal_candle.iloc[-1].squeeze() # atr_roi = signal_candle["close"] + ( # self.atr_distance * self.risk_reward_ratio * signal_candle["atr"] # ) # atr_sl = signal_candle["close"] - ( # self.atr_distance * signal_candle["atr"] # ) # trade.set_custom_data(key="atr_roi", value=atr_roi) # trade.set_custom_data(key="atr_sl", value=atr_sl) # if current_time - timedelta(minutes=int(self.timeframe_minutes)) >= entry_time: # current_candle = dataframe.iloc[-1].squeeze() # if atr_roi > 0: # if current_candle["close"] >= atr_roi: # return "atr_roi" # if current_candle["close"] <= atr_sl: # return "atr_sl" # else: # # Signal candle not found, use percentage as exits # current_profit = trade.calc_profit_ratio(current_candle["close"]) # if current_profit > 0.01: # return "emergency_roi" # elif current_profit < -0.04: # return "emergency_sl" # return None