# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/f80d4d8b77c53435e9c0a9045636f1bfb2b8c539/chart/deployed_strategies/binance-futures-k8s-namespace/PRICEFOLLOWING.py # directory_url: https://github.com/DerSalvador/freqtrade-helm-chart/blob/main/chart/deployed_strategies/binance-futures-k8s-namespace/ # User: DerSalvador # Repository: freqtrade-helm-chart # --------------------# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file # --- Do not remove these libs --- import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame from freqtrade.strategy import BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter # -------------------------------- # Add your lib to import here import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib from functools import reduce # This class is a sample. Feel free to customize it. class Github_DerSalvador_freqtrade_helm_chart__PRICEFOLLOWING__20260416_224245(IStrategy): """ This is a sample strategy to inspire you. More information in https://www.freqtrade.io/en/latest/strategy-customization/ You can: :return: a Dataframe with all mandatory indicators for the strategies - Rename the class name (Do not forget to update class_name) - Add any methods you want to build your strategy - Add any lib you need to build your strategy You must keep: - the lib in the section "Do not remove these libs" - the methods: populate_indicators, populate_entry_trend, populate_exit_trend You should keep: - timeframe, minimal_roi, stoploss, trailing_* """ # Strategy interface version - allow new iterations of the strategy interface. # Check the documentation or the Sample strategy to get the latest version. INTERFACE_VERSION = 3 # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". minimal_roi = {'60': 0.025, '30': 0.03, '0': 0.04} # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". stoploss = -0.1 # Trailing stoploss trailing_stop = True trailing_only_offset_is_reached = True trailing_stop_positive = 0.02 trailing_stop_positive_offset = 0.03 # Disabled / not configured # Hyperoptable parameters rsi_value = IntParameter(low=1, high=50, default=30, space='entry', optimize=True, load=True) rsi_enabled = BooleanParameter(default=False, space='entry', optimize=True, load=True) ema_pct = DecimalParameter(0.0001, 0.1, decimals=4, default=0.004, space='entry', optimize=True) ema_exit_pct = DecimalParameter(0.0001, 0.1, decimals=4, default=0.003, space='exit', optimize=True, load=True) exit_rsi_value = IntParameter(low=25, high=100, default=70, space='exit', optimize=True, load=True) exit_rsi_enabled = BooleanParameter(default=True, space='exit', optimize=True, load=True) # Optimal timeframe for the strategy. timeframe = '5m' # Run "populate_indicators()" only for new candle. process_only_new_candles = False # These values can be overridden in the "ask_strategy" section in the config. use_exit_signal = True exit_profit_only = True ignore_roi_if_entry_signal = True # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 15 # Optional order type mapping. order_types = {'entry': 'limit', 'exit': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False} # Optional order time in force. order_time_in_force = {'entry': 'gtc', 'exit': 'gtc'} plot_config = {'main_plot': {'tema': {}, 'ema7': {}, 'ha_open': {}, 'ha_close': {}}, 'subplots': {'MACD': {'macd': {'color': 'blue'}, 'macdsignal': {'color': 'orange'}}, 'RSI': {'rsi': {'color': 'red'}}}} 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) """ return [('ETH/USDT', '15m'), ('BTC/USDT', '15m'), ('RVN/USDT', '15m')] 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. :param dataframe: Dataframe with data from the exchange :param metadata: Additional information, like the currently traded pair :return: a Dataframe with all mandatory indicators for the strategies """ # Momentum Indicators # ------------------------------------ # ADX dataframe['adx'] = ta.ADX(dataframe) # 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'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy) # dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) # Stochastic Fast #stoch_fast = ta.STOCHF(dataframe) #dataframe['fastd'] = stoch_fast['fastd'] #dataframe['fastk'] = stoch_fast['fastk'] # MACD macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] # # EMA - Exponential Moving Average dataframe['ema7'] = ta.EMA(dataframe, timeperiod=7) dataframe['ema24'] = ta.EMA(dataframe, timeperiod=24) #dataframe['ema60'] = ta.EMA(dataframe, timeperiod=60) #dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) # Parabolic SAR dataframe['sar'] = ta.SAR(dataframe) # TEMA - Triple Exponential Moving Average dataframe['tema'] = ta.TEMA(dataframe, timeperiod=7) # Cycle Indicator # ------------------------------------ # Hilbert Transform Indicator - SineWave hilbert = ta.HT_SINE(dataframe) dataframe['htsine'] = hilbert['sine'] dataframe['htleadsine'] = hilbert['leadsine'] # # Chart type # # ------------------------------------ # # Heikin Ashi Strategy heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] # first check if dataprovider is available if self.dp: if self.dp.runmode.value in ('live', 'dry_run'): ob = self.dp.orderbook(metadata['pair'], 1) dataframe['best_bid'] = ob['bids'][0][0] dataframe['best_ask'] = ob['asks'][0][0] return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: #rsi_enabled = BooleanParameter(default=True, space='entry', optimize=True) last_ema7 = dataframe['ema7'].tail() last_tema = dataframe['tema'].tail() haclose = dataframe['ha_close'].tail(4) haclose4thlast, haclose3rdlast, haclose2ndlast, hacloselast = haclose haopen = dataframe['ha_open'] Conditions = [] #GUARDS if self.rsi_enabled.value: Conditions.append(dataframe['rsi'] < self.rsi_value.value) Conditions.append(qtpylib.crossed_below(dataframe['ema7'], dataframe['tema'])) else: Conditions.append(qtpylib.crossed_below(dataframe['ema7'], dataframe['tema'])) #Conditions.append(haclose3rdlast > haclose2ndlast > hacloselast) Conditions.append(dataframe['tema'] < dataframe['tema'].shift(1)) #Conditions.append(qtpylib.crossed_below(dataframe['tema'], dataframe['ema7'])) #Conditions.append((abs(last_tema - last_ema7) / last_tema) > self.ema_pct.value) if Conditions: dataframe.loc[reduce(lambda x, y: x & y, Conditions), 'entry'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: haopen = dataframe['ha_open'] haclose = dataframe['ha_close'] last_ema7 = dataframe['ema7'].tail() last_tema = dataframe['tema'].tail() haclose = dataframe['ha_close'].tail(2) haclose2ndlast, hacloselast = haclose conditions = [] # GUARDS AND TRENDS if self.exit_rsi_enabled.value: conditions.append(dataframe['rsi'] < self.exit_rsi_value.value) conditions.append(qtpylib.crossed_above(dataframe['ema7'], dataframe['tema'])) else: #conditions.append(haclose2ndlast > hacloselast) conditions.append(qtpylib.crossed_above(dataframe['ema7'], dataframe['tema'])) #conditions.append(dataframe['best_bid'] < haclose) #conditions.append(((last_tema - last_ema7) / last_ema7) < self.ema_exit_pct.value) if conditions: dataframe.loc[reduce(lambda x, y: x & y, conditions), 'exit'] = 1 return dataframe