# 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 typing import Optional, Union 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 # This class is a sample. Feel free to customize it. class SampleStrategyHopt(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 # Can this strategy go short? can_short: bool = False # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". minimal_roi = { "60": 0.01, "30": 0.02, "0": 0.04 } # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". stoploss = -0.10 # Trailing stoploss trailing_stop = False # trailing_only_offset_is_reached = False # trailing_stop_positive = 0.01 # trailing_stop_positive_offset = 0.0 # Disabled / not configured # Optimal timeframe for the strategy. timeframe = '5m' # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the config. use_exit_signal = True exit_profit_only = False 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) short_rsi = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True) exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True) # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 200 # Optional order type mapping. order_types = { 'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } # Optional order time in force. order_time_in_force = { 'entry': 'GTC', 'exit': 'GTC' } plot_config = { 'main_plot': { 'tema': {}, 'sar': {'color': 'white'}, }, 'subplots': { "MACD": { 'macd': {'color': 'blue'}, 'macdsignal': {'color': 'orange'}, }, "RSI": { 'rsi': {'color': 'red'}, } } } # BEGIN - Hyperopt /usr/bin/python3 /root/freqtrade/freqtrade hyperopt --strategy SampleStrategy --timeframe 5m --config /root/freqtrade/user_data/config_obelisk_ichimoku.json --timerange 20240201-20240229 --hyperopt-loss OnlyProfitHyperOptLoss # | Best | Epoch | Trades | Win Draw Loss Win% | Avg profit | Profit | Avg duration | Objective | Max Drawdown (Acct) | # | Best | 89/100 | 183 | 87 90 6 47.5 | 0.44% | 30.642 USDT (30.64%) | 0 days 10:19:00 | -30.6422 | 2.928 USDT (2.19%) | buy_params = { "buy_rsi": 43, "exit_short_rsi": 29, } # Sell hyperspace params: sell_params = { "sell_rsi": 68, "short_rsi": 70, } # ROI table: minimal_roi = { "0": 0.173, "17": 0.036, "77": 0.02, "175": 0 } # Stoploss: stoploss = -0.175 # Trailing stop: trailing_stop = False # value loaded from strategy trailing_stop_positive = None # value loaded from strategy trailing_stop_positive_offset = 0.0 # value loaded from strategy trailing_only_offset_is_reached = False # value loaded from strategy # Max Open Trades: max_open_trades = 3 # value loaded from strategy # END - Hyperopt /usr/bin/python3 /root/freqtrade/freqtrade hyperopt --strategy SampleStrategy --timeframe 5m --config /root/freqtrade/user_data/config_obelisk_ichimoku.json --timerange 20240201-20240229 --hyperopt-loss OnlyProfitHyperOptLoss # BEGIN -/usr/bin/python3 /root/freqtrade/freqtrade hyperopt --strategy SampleStrategy --timeframe 5m --config /root/freqtrade/user_data/config_backtesting.json --timerange 20240101- --hyperopt-loss ShortTradeDurHyperOptLoss # Buy hyperspace params: buy_params = { "buy_rsi": 47, "exit_short_rsi": 12, } # Sell hyperspace params: sell_params = { "sell_rsi": 67, "short_rsi": 100, } # ROI table: minimal_roi = { "0": 0.136, "13": 0.08, "28": 0.04, "140": 0 } # Stoploss: stoploss = -0.334 # Trailing stop: trailing_stop = False # value loaded from strategy trailing_stop_positive = None # value loaded from strategy trailing_stop_positive_offset = 0.0 # value loaded from strategy trailing_only_offset_is_reached = False # value loaded from strategy # Max Open Trades: max_open_trades = 3 # value loaded from strategy # END - /usr/bin/python3 /root/freqtrade/freqtrade hyperopt --strategy SampleStrategy --timeframe 5m --config /root/freqtrade/user_data/config_backtesting.json --timerange 20240101- --hyperopt-loss ShortTradeDurHyperOptLoss # BEGIN ROI - /usr/bin/python3 /root/freqtrade/freqtrade hyperopt --strategy SampleStrategyHopt --timeframe 5m --config /root/freqtrade/user_data/config_backtesting.json --timerange 20240101- --hyperopt-loss ShortTradeDurHyperOptLoss --spaces roi # | Best | Epoch | Trades | Win Draw Loss Win% | Avg profit | Profit | Avg duration | Objective | Max Drawdown (Acct) | # | Best | 56/100 | 228 | 93 124 11 40.8 | 0.30% | 22.148 USDT (22.15%) | 0 days 18:28:00 | 1.96759 | 18.363 USDT (16.42%) | minimal_roi = { "0": 0.253, "21": 0.107, "80": 0.038, "120": 0 } # END ROI - /usr/bin/python3 /root/freqtrade/freqtrade hyperopt --strategy SampleStrategyHopt --timeframe 5m --config /root/freqtrade/user_data/config_backtesting.json --timerange 20240101- --hyperopt-loss ShortTradeDurHyperOptLoss --spaces roi # BEGIN TRAILING - /usr/bin/python3 /root/freqtrade/freqtrade hyperopt --strategy SampleStrategyHopt --timeframe 5m --config /root/freqtrade/user_data/config_backtesting.json --timerange 20240101- --hyperopt-loss ShortTradeDurHyperOptLoss --spaces trailing # | Best | Epoch | Trades | Win Draw Loss Win% | Avg profit | Profit | Avg duration | Objective | Max Drawdown (Acct) | # | * Best | 1/100 | 228 | 93 124 11 40.8 | 0.31% | 22.287 USDT (22.29%) | 0 days 18:28:00 | 1.96713 | 18.297 USDT (16.36%) | # Trailing stop: trailing_stop = True trailing_stop_positive = 0.171 trailing_stop_positive_offset = 0.225 trailing_only_offset_is_reached = False # END TRAILING - /usr/bin/python3 /root/freqtrade/freqtrade hyperopt --strategy SampleStrategyHopt --timeframe 5m --config /root/freqtrade/user_data/config_backtesting.json --timerange 20240101- --hyperopt-loss ShortTradeDurHyperOptLoss --spaces trailing stop_duration_candles = IntParameter(1, 500, default=48, space="protection", optimize=True) only_per_pair = BooleanParameter(default=False, space="protection", optimize=True) only_per_side = BooleanParameter(default=False, space="protection", optimize=True) required_profit = DecimalParameter(0.005, 10, default=0.01, space='protection', optimize=True) look_back = IntParameter(1, 500, default=200, space='protection', optimize=True) use_stop_protection = BooleanParameter(default=True, space="protection", optimize=True) # Protection hyperspace params: protection_params = { "only_per_pair": True, "only_per_side": True, "stop_duration_candles": 155, } 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. :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) # # Plus Directional Indicator / Movement # dataframe['plus_dm'] = ta.PLUS_DM(dataframe) # dataframe['plus_di'] = ta.PLUS_DI(dataframe) # # Minus Directional Indicator / Movement # dataframe['minus_dm'] = ta.MINUS_DM(dataframe) # dataframe['minus_di'] = ta.MINUS_DI(dataframe) # # Aroon, Aroon Oscillator # aroon = ta.AROON(dataframe) # dataframe['aroonup'] = aroon['aroonup'] # dataframe['aroondown'] = aroon['aroondown'] # dataframe['aroonosc'] = ta.AROONOSC(dataframe) # # Awesome Oscillator # dataframe['ao'] = qtpylib.awesome_oscillator(dataframe) # # Keltner Channel # keltner = qtpylib.keltner_channel(dataframe) # dataframe["kc_upperband"] = keltner["upper"] # dataframe["kc_lowerband"] = keltner["lower"] # dataframe["kc_middleband"] = keltner["mid"] # dataframe["kc_percent"] = ( # (dataframe["close"] - dataframe["kc_lowerband"]) / # (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) # ) # dataframe["kc_width"] = ( # (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"] # ) # # Ultimate Oscillator # dataframe['uo'] = ta.ULTOSC(dataframe) # # Commodity Channel Index: values [Oversold:-100, Overbought:100] # dataframe['cci'] = ta.CCI(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 Slow # stoch = ta.STOCH(dataframe) # dataframe['slowd'] = stoch['slowd'] # dataframe['slowk'] = stoch['slowk'] # Stochastic Fast stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] # # Stochastic RSI # Please read https://github.com/freqtrade/freqtrade/issues/2961 before using this. # STOCHRSI is NOT aligned with tradingview, which may result in non-expected results. # stoch_rsi = ta.STOCHRSI(dataframe) # dataframe['fastd_rsi'] = stoch_rsi['fastd'] # dataframe['fastk_rsi'] = stoch_rsi['fastk'] # MACD macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] # MFI dataframe['mfi'] = ta.MFI(dataframe) # # ROC # dataframe['roc'] = ta.ROC(dataframe) # Overlap Studies # ------------------------------------ # Bollinger Bands bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] dataframe["bb_percent"] = ( (dataframe["close"] - dataframe["bb_lowerband"]) / (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) ) dataframe["bb_width"] = ( (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"] ) # Bollinger Bands - Weighted (EMA based instead of SMA) # weighted_bollinger = qtpylib.weighted_bollinger_bands( # qtpylib.typical_price(dataframe), window=20, stds=2 # ) # dataframe["wbb_upperband"] = weighted_bollinger["upper"] # dataframe["wbb_lowerband"] = weighted_bollinger["lower"] # dataframe["wbb_middleband"] = weighted_bollinger["mid"] # dataframe["wbb_percent"] = ( # (dataframe["close"] - dataframe["wbb_lowerband"]) / # (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) # ) # dataframe["wbb_width"] = ( # (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) / # dataframe["wbb_middleband"] # ) # # EMA - Exponential Moving Average # dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3) # dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) # dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10) # dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21) # dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) # dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) # # SMA - Simple Moving Average # dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3) # dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5) # dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10) # dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21) # dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50) # dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100) # Parabolic SAR dataframe['sar'] = ta.SAR(dataframe) # TEMA - Triple Exponential Moving Average dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9) # Cycle Indicator # ------------------------------------ # Hilbert Transform Indicator - SineWave hilbert = ta.HT_SINE(dataframe) dataframe['htsine'] = hilbert['sine'] dataframe['htleadsine'] = hilbert['leadsine'] # Pattern Recognition - Bullish candlestick patterns # ------------------------------------ # # Hammer: values [0, 100] # dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe) # # Inverted Hammer: values [0, 100] # dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe) # # Dragonfly Doji: values [0, 100] # dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe) # # Piercing Line: values [0, 100] # dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100] # # Morningstar: values [0, 100] # dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100] # # Three White Soldiers: values [0, 100] # dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100] # Pattern Recognition - Bearish candlestick patterns # ------------------------------------ # # Hanging Man: values [0, 100] # dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe) # # Shooting Star: values [0, 100] # dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe) # # Gravestone Doji: values [0, 100] # dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe) # # Dark Cloud Cover: values [0, 100] # dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe) # # Evening Doji Star: values [0, 100] # dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe) # # Evening Star: values [0, 100] # dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe) # Pattern Recognition - Bullish/Bearish candlestick patterns # ------------------------------------ # # Three Line Strike: values [0, -100, 100] # dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe) # # Spinning Top: values [0, -100, 100] # dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100] # # Engulfing: values [0, -100, 100] # dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100] # # Harami: values [0, -100, 100] # dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100] # # Three Outside Up/Down: values [0, -100, 100] # dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100] # # Three Inside Up/Down: values [0, -100, 100] # dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100] # # 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'] # Retrieve best bid and best ask from the orderbook # ------------------------------------ """ # 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: """ Based on TA indicators, populates the entry signal for the given dataframe :param dataframe: DataFrame :param metadata: Additional information, like the currently traded pair :return: DataFrame with entry columns populated """ dataframe.loc[ ( # Signal: RSI crosses above 30 (qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)) & (dataframe['tema'] <= dataframe['bb_middleband']) & # Guard: tema below BB middle (dataframe['tema'] > dataframe['tema'].shift(1)) & # Guard: tema is raising (dataframe['volume'] > 0) # Make sure Volume is not 0 ), 'enter_long'] = 1 dataframe.loc[ ( # Signal: RSI crosses above 70 (qtpylib.crossed_above(dataframe['rsi'], self.short_rsi.value)) & (dataframe['tema'] > dataframe['bb_middleband']) & # Guard: tema above BB middle (dataframe['tema'] < dataframe['tema'].shift(1)) & # Guard: tema is falling (dataframe['volume'] > 0) # Make sure Volume is not 0 ), 'enter_short'] = 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 :param metadata: Additional information, like the currently traded pair :return: DataFrame with exit columns populated """ dataframe.loc[ ( # Signal: RSI crosses above 70 (qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value)) & (dataframe['tema'] > dataframe['bb_middleband']) & # Guard: tema above BB middle (dataframe['tema'] < dataframe['tema'].shift(1)) & # Guard: tema is falling (dataframe['volume'] > 0) # Make sure Volume is not 0 ), 'exit_long'] = 1 dataframe.loc[ ( # Signal: RSI crosses above 30 (qtpylib.crossed_above(dataframe['rsi'], self.exit_short_rsi.value)) & # Guard: tema below BB middle (dataframe['tema'] <= dataframe['bb_middleband']) & (dataframe['tema'] > dataframe['tema'].shift(1)) & # Guard: tema is raising (dataframe['volume'] > 0) # Make sure Volume is not 0 ), 'exit_short'] = 1 return dataframe @property def protections(self): prot = [] if self.use_stop_protection.value: prot.append({ "method": "LowProfitPairs", "lookback_period_candles": self.look_back.value, "trade_limit": 4, "stop_duration_candles": self.stop_duration_candles.value, "required_profit": self.required_profit.value, "only_per_pair": self.only_per_pair.value }) prot.append({ "method": "StoplossGuard", "lookback_period_candles": self.look_back.value, "trade_limit": 4, "stop_duration_candles": self.stop_duration_candles.value, "required_profit":self.required_profit.value, "only_per_pair": self.only_per_pair.value, "only_per_side": self.only_per_side.value }) return prot