# source: https://raw.githubusercontent.com/28dec/simple-futures-strategy/cc35621d1b86243df27e67fedb5fb7ef29c0b130/user_data/strategies/SimpleFutures.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file # --- Do not remove these libs --- import math import numpy as np # noqa import pandas as pd # noqa pd.options.mode.chained_assignment = None from pandas import DataFrame from typing import Optional, Union from datetime import datetime from freqtrade.persistence import Trade import logging from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter, stoploss_from_open) import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib logger = logging.getLogger(__name__) def smi_trend(df: DataFrame, k_length=9, d_length=3, smoothing_type='EMA', smoothing=10): ll = df['low'].rolling(window=k_length).min() hh = df['high'].rolling(window=k_length).max() diff = hh - ll rdiff = df['close'] - (hh + ll) / 2 avgrel = rdiff.ewm(span=d_length).mean().ewm(span=d_length).mean() avgdiff = diff.ewm(span=d_length).mean().ewm(span=d_length).mean() smi = np.where(avgdiff != 0, (avgrel / (avgdiff / 2) * 100), 0) if smoothing_type == 'SMA': smi_ma = ta.SMA(smi, timeperiod=smoothing) elif smoothing_type == 'EMA': smi_ma = ta.EMA(smi, timeperiod=smoothing) elif smoothing_type == 'WMA': smi_ma = ta.WMA(smi, timeperiod=smoothing) elif smoothing_type == 'DEMA': smi_ma = ta.DEMA(smi, timeperiod=smoothing) elif smoothing_type == 'TEMA': smi_ma = ta.TEMA(smi, timeperiod=smoothing) else: raise ValueError("Choose an MA Type: 'SMA', 'EMA', 'WMA', 'DEMA', 'TEMA'") conditions = [ (np.greater(smi, 0) & np.greater(smi, smi_ma)), # (2) Bull (np.less(smi, 0) & np.greater(smi, smi_ma)), # (1) Possible Bullish Reversal (np.greater(smi, 0) & np.less(smi, smi_ma)), # (-1) Possible Bearish Reversal (np.less(smi, 0) & np.less(smi, smi_ma)) # (-2) Bear ] smi_trend = np.select(conditions, [2, 1, -1, -2]) return smi, smi_ma, smi_trend class Github_28dec_simple_futures_strategy__SimpleFutures__20230514_063451(IStrategy): INTERFACE_VERSION = 3 timeframe = '1h' can_short = True use_custom_stoploss=False process_only_new_candles = True use_exit_signal = False exit_profit_only = False startup_candle_count: int = 0 stoploss = -0.291 # we use custom stoploss, but no lower this deadline, custom_stoploss will respect this stoploss order_types = { 'entry': 'market', 'exit': 'market', 'stoploss': 'market', 'stoploss_on_exchange': False } minimal_roi = { "360": 0.28, "240": 0.21, "120": 0.14, "0": 0.07 } #pullback_detect_method = CategoricalParameter(['stdev_outlier', 'pct_outlier', 'candle_body'], default = 'pct_outlier', space = 'buy', optimize = True) # SMI params smi_k_length = IntParameter(1, 99, default=9, space="buy", optimize=True) smi_d_length = IntParameter(1, 99, default=3, space="buy", optimize=True) ma_smoothy_type = CategoricalParameter(['SMA', 'EMA', 'WMA', 'DEMA', 'TEMA'], default = 'EMA', space = 'buy', optimize = True) smi_smooth_length = IntParameter(1, 99, default=10, space="buy", optimize=True) ## Trailing params # https://discordapp.com/channels/700048804539400213/852593312116375642/1053608836369502278 # it definitely makes the trailing stop less sensitive to in-candle moves and avoids getting stopped out too early is_optimize_custom_SL = True pHSL = DecimalParameter(-0.200, -0.040, default=-0.08, decimals=3, space='sell', load=True, optimize=is_optimize_custom_SL) pPF_1 = DecimalParameter(0.008, 0.030, default=0.016, decimals=3, space='sell', load=True, optimize=is_optimize_custom_SL) pSL_1 = DecimalParameter(0.008, 0.030, default=0.011, decimals=3, space='sell', load=True, optimize=is_optimize_custom_SL) # profit threshold 2, SL_2 is used pPF_2 = DecimalParameter(0.050, 0.200, default=0.080, decimals=3, space='sell', load=True, optimize=is_optimize_custom_SL) pSL_2 = DecimalParameter(0.030, 0.200, default=0.040, decimals=3, space='sell', load=True, optimize=is_optimize_custom_SL) def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe['smi'], dataframe['smi_ma'], dataframe['smi_trend'] = smi_trend(dataframe, self.smi_k_length.value, self.smi_d_length.value, self.ma_smoothy_type.value) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (qtpylib.crossed_above(dataframe['smi_trend'], 0)) ), ['enter_long', 'enter_tag'] ] = (1, 'smi_long') dataframe.loc[ ( (qtpylib.crossed_below(dataframe['smi_trend'], 0)) ), ['enter_short', 'enter_tag'] ] = (1, 'smi_short') return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: return dataframe def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: return 3.0 def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: HSL = self.pHSL.value PF_1 = self.pPF_1.value SL_1 = self.pSL_1.value PF_2 = self.pPF_2.value SL_2 = self.pSL_2.value dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe) current_candle = dataframe.iloc[-1].squeeze() current_profit = trade.calc_profit_ratio(current_candle['close']) if current_profit > PF_2: sl_profit = SL_2 + (current_profit - PF_2) elif current_profit > PF_1: sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1)) else: sl_profit = HSL if self.can_short: if (-1 + ((1 - sl_profit) / (1 - current_profit))) <= 0: return 1 else: if (1 - ((1 + sl_profit) / (1 + current_profit))) <= 0: return 1 return stoploss_from_open(sl_profit, current_profit, is_short=trade.is_short) or 1