# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-futures-k8s-namespace/Seb.py # --- Do not remove these libs --- from freqtrade.strategy import IStrategy from freqtrade.strategy import CategoricalParameter, IntParameter from typing import Dict, List from functools import reduce from pandas import DataFrame import numpy # noqa # -------------------------------- import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib class Github_DerSalvador_freqtrade_helm_chart__Seb__20260408_060519(IStrategy): INTERFACE_VERSION = 3 '\n Strategy 001\n author@: Gerald Lonlas\n github@: https://github.com/freqtrade/freqtrade-strategies\n\n How to use it?\n > python3 ./freqtrade/main.py -s Strategy001\n ' # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi" minimal_roi = {'60': 0.01, '30': 0.03, '20': 0.04, '0': 0.05} # Optimal stoploss designed for the strategy # This attribute will be overridden if the config file contains "stoploss" stoploss = -0.1 # Optimal timeframe for the strategy timeframe = '5m' # trailing stoploss trailing_stop = False trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.02 # run "populate_indicators" only for new candle process_only_new_candles = False # Experimental settings (configuration will overide these if set) use_exit_signal = True exit_profit_only = True ignore_roi_if_entry_signal = False # Optional order type mapping order_types = {'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False} entry_volumeAVG = IntParameter(low=50, high=300, default=70, space='entry', optimize=True) entry_rsi = IntParameter(low=1, high=100, default=30, space='entry', optimize=True) entry_fastd = IntParameter(low=1, high=100, default=30, space='entry', optimize=True) entry_fishRsiNorma = IntParameter(low=1, high=100, default=30, space='entry', optimize=True) exit_rsi = IntParameter(low=1, high=100, default=70, space='exit', optimize=True) exit_minusDI = IntParameter(low=1, high=100, default=50, space='exit', optimize=True) exit_fishRsiNorma = IntParameter(low=1, high=100, default=50, space='exit', optimize=True) exit_trigger = CategoricalParameter(['rsi-macd-minusdi', 'sar-fisherRsi'], default=30, space='exit', optimize=True) # Buy hyperspace params: entry_params = {'entry_fastd': 1, 'entry_fishRsiNorma': 5, 'entry_rsi': 26, 'entry_volumeAVG': 150} # Sell hyperspace params: exit_params = {'exit_fishRsiNorma': 30, 'exit_minusDI': 4, 'exit_rsi': 74, 'exit_trigger': 'rsi-macd-minusdi'} def informative_pairs(self): """ Define additional, informative pair/interval combinations to be cached from the exchange. These pair/interval combinations are non-tradeable, unless they are part of the whitelist as well. For more information, please consult the documentation :return: List of tuples in the format (pair, interval) Sample: return [("ETH/USDT", "5m"), ("BTC/USDT", "15m"), ] """ return [] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Adds several different TA indicators to the given DataFrame Performance Note: For the best performance be frugal on the number of indicators you are using. Let uncomment only the indicator you are using in your strategies or your hyperopt configuration, otherwise you will waste your memory and CPU usage. """ dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20) dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['adx'] = ta.ADX(dataframe) dataframe['rsi'] = ta.RSI(dataframe) macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] bollinger = ta.BBANDS(dataframe, timeperiod=20, nbdevup=2.0, nbdevdn=2.0) dataframe['bb_lowerband'] = bollinger['lowerband'] dataframe['bb_middleband'] = bollinger['middleband'] dataframe['bb_upperband'] = bollinger['upperband'] # Stoch stoch = ta.STOCH(dataframe) dataframe['slowk'] = stoch['slowk'] # Commodity Channel Index: values Oversold:<-100, Overbought:>100 dataframe['cci'] = ta.CCI(dataframe) # Stoch stoch = ta.STOCHF(dataframe, 5) dataframe['fastd'] = stoch['fastd'] dataframe['fastk'] = stoch['fastk'] dataframe['fastk-previous'] = dataframe.fastk.shift(1) dataframe['fastd-previous'] = dataframe.fastd.shift(1) # Slow Stoch slowstoch = ta.STOCHF(dataframe, 50) dataframe['slowfastd'] = slowstoch['fastd'] dataframe['slowfastk'] = slowstoch['fastk'] dataframe['slowfastk-previous'] = dataframe.slowfastk.shift(1) dataframe['slowfastd-previous'] = dataframe.slowfastd.shift(1) # RSI dataframe['rsi'] = ta.RSI(dataframe) # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy) rsi = 0.1 * (dataframe['rsi'] - 50) dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1) # Bollinger bands bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] # SAR Parabol dataframe['sar'] = ta.SAR(dataframe) # Hammer: values [0, 100] dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe) # SMA - Simple Moving Average dataframe['sma'] = ta.SMA(dataframe, timeperiod=40) return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the entry signal for the given dataframe :param dataframe: DataFrame :return: DataFrame with entry column """ # green bar dataframe.loc[qtpylib.crossed_above(dataframe['ema20'], dataframe['ema50']) & (dataframe['ha_close'] > dataframe['ema20']) & (dataframe['ha_open'] < dataframe['ha_close']), 'entry'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the exit signal for the given dataframe :param dataframe: DataFrame :return: DataFrame with entry column """ # red bar dataframe.loc[qtpylib.crossed_above(dataframe['ema50'], dataframe['ema100']) & (dataframe['ha_close'] < dataframe['ema20']) & (dataframe['ha_open'] > dataframe['ha_close']), 'exit'] = 1 return dataframe