# source: https://raw.githubusercontent.com/hamidreza07/freqai-strategy/dd7354ccfcf24d50f2a62294e623ccf0a033da2a/startegy%20test/5/**MACDTurn/MACDTurn.py # directory_url: https://github.com/hamidreza07/freqai-strategy/blob/main/startegy test/5/**MACDTurn/ # User: hamidreza07 # Repository: freqai-strategy # --------------------# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # --- Do not remove these libs --- from freqtrade.strategy.interface import IStrategy from typing import Dict, List from functools import reduce from pandas import DataFrame # -------------------------------- from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy # noqa # -------------------------------- # Add your lib to import here from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter import Config class Github_hamidreza07_freqai_strategy__MACDTurn__20241005_141958(IStrategy): """ Detects a direction change in the MACD Histogram 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_* """ buy_mfi = DecimalParameter(10, 100, decimals=0, default=79, space="buy") buy_adx = DecimalParameter(1, 99, decimals=0, default=1, space="buy") buy_fisher = DecimalParameter(-1, 1, decimals=2, default=0.18, space="buy") buy_period = IntParameter(3, 20, default=16, space="buy") buy_bb_gain = DecimalParameter(0.01, 0.10, decimals=2, default=0.04, space="buy") buy_bb_enabled = CategoricalParameter([True, False], default=True, space="buy") buy_neg_macd_enabled = CategoricalParameter([True, False], default=True, space="buy") buy_adx_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_dm_enabled = CategoricalParameter([True, False], default=True, space="buy") buy_mfi_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_sar_enabled = CategoricalParameter([True, False], default=False, space="buy") buy_fisher_enabled = CategoricalParameter([True, False], default=True, space="buy") sell_hold = CategoricalParameter([True, False], default=True, space="sell") sell_macd_enabled = CategoricalParameter([True, False], default=False, space="sell") startup_candle_count = max(2*buy_period.value, 40) # set common parameters minimal_roi = Config.minimal_roi trailing_stop = Config.trailing_stop trailing_stop_positive = Config.trailing_stop_positive trailing_stop_positive_offset = Config.trailing_stop_positive_offset trailing_only_offset_is_reached = Config.trailing_only_offset_is_reached stoploss = Config.stoploss timeframe = Config.timeframe process_only_new_candles = Config.process_only_new_candles use_exit_signal = Config.use_exit_signal exit_profit_only = Config.exit_profit_only ignore_roi_if_entry_signal = Config.ignore_roi_if_entry_signal order_types = Config.order_types 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['dm_plus'] = ta.PLUS_DM(dataframe) dataframe['di_plus'] = ta.PLUS_DI(dataframe) # Minus Directional Indicator / Movement dataframe['dm_minus'] = ta.MINUS_DM(dataframe) dataframe['di_minus'] = ta.MINUS_DI(dataframe) dataframe['dm_delta'] = dataframe['dm_plus'] - dataframe['dm_minus'] dataframe['di_delta'] = dataframe['di_plus'] - dataframe['di_minus'] # # 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'] = (numpy.exp(2 * rsi) - 1) / (numpy.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'] dataframe['macdhist_ave'] = ta.LINEARREG(ta.TEMA(macd['macdhist'], timeperiod=self.buy_period.value), timeperiod=self.buy_period.value ) dataframe['macdhist_slope'] = ta.LINEARREG_SLOPE(dataframe['macdhist_ave'], timeperiod=3) # 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"] ) dataframe["bb_gain"] = ((dataframe["bb_upperband"] - dataframe["close"]) / dataframe["close"]) # 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) #dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200) dataframe['ema7'] = ta.EMA(dataframe, timeperiod=7) dataframe['ema25'] = ta.EMA(dataframe, timeperiod=25) # # 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 # ------------------------------------ return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the buy signal for the given dataframe :param dataframe: DataFrame populated with indicators :param metadata: Additional information, like the currently traded pair :return: DataFrame with buy column """ conditions = [] # GUARDS AND TRENDS if self.buy_adx_enabled.value: conditions.append(dataframe['adx'] >= self.buy_adx.value) if self.buy_dm_enabled.value: conditions.append(dataframe['dm_delta'] > 0) if self.buy_mfi_enabled.value: conditions.append(dataframe['mfi'] > self.buy_mfi.value) # only buy if close is below SAR if self.buy_sar_enabled.value: conditions.append(dataframe['close'] < dataframe['sar']) if self.buy_fisher_enabled.value: conditions.append(dataframe['fisher_rsi'] < self.buy_fisher.value) if self.buy_neg_macd_enabled.value: conditions.append(dataframe['macd'] < 0.0) # potential gain > goal if self.buy_bb_enabled.value: conditions.append(dataframe['bb_gain'] >= self.buy_bb_gain.value) # TRIGGERS # -ve macdhist conditions.append(dataframe['macdhist'] < 0.0) # averaged value, so check it exists conditions.append(dataframe['macdhist_slope'].notnull()) # check for a transition from down to up conditions.append(qtpylib.crossed_above(dataframe['macdhist_slope'], 0)) # # # # n_post candles going up (plus current candle) # if self.buy_num_post.value >= 1: # for i in range((self.buy_num_post.value+1)): # conditions.append(dataframe['macdhist_ave'].shift(i) > dataframe['macdhist_ave'].shift(i+1)) # # # n_pre candles going down # # n_post candles going up (plus current candle) # if self.buy_num_pre.value >= 1: # for i in range((self.buy_num_pre.value)): # j = i + self.buy_num_post.value + 1 # conditions.append(dataframe['macdhist_ave'].shift(j) < dataframe['macdhist_ave'].shift(j+1)) # check that volume is not 0 #conditions.append(dataframe['volume'] > 0) # build the dataframe using the conditions if conditions: dataframe.loc[reduce(lambda x, y: x & y, conditions), 'buy'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the sell signal for the given dataframe :param dataframe: DataFrame populated with indicators :param metadata: Additional information, like the currently traded pair :return: DataFrame with buy column """ conditions = [] # if hold, then don't set a sell signal if self.sell_hold.value: dataframe.loc[(dataframe['close'].notnull() ), 'sell'] = 0 else: # check for transition from up to down if self.sell_macd_enabled: conditions.append((dataframe['macd'] > 0.0)) if conditions: dataframe.loc[reduce(lambda x, y: x & y, conditions), 'sell'] = 1 return dataframe # +--------+---------+----------+--------------------------+--------------+-------------------------------+-----------------+-------------+-------------------------------+ # | Best | Epoch | Trades | Win Draw Loss Win% | Avg profit | Profit | Avg duration | Objective | Max Drawdown (Acct) | # |--------+---------+----------+--------------------------+--------------+-------------------------------+-----------------+-------------+-------------------------------| # | * Best | 1/50 | 2 | 2 0 0 100 | 0.08% | 15.348 USDT (0.15%) | 0 days 03:18:00 | -15.348 | -- | # | * Best | 4/50 | 9 | 8 0 1 88.9 | 0.09% | 79.053 USDT (0.79%) | 0 days 01:50:00 | -79.0527 | -- | # | * Best | 7/50 | 50 | 37 2 11 74.0 | 0.09% | 437.992 USDT (4.38%) | 0 days 09:27:00 | -437.992 | 147.734 USDT (1.40%) | # | * Best | 9/50 | 26 | 10 5 11 38.5 | 0.19% | 515.062 USDT (5.15%) | 0 days 09:41:00 | -515.062 | 14.312 USDT (0.14%) | # | * Best | 11/50 | 37 | 16 3 18 43.2 | 0.28% | 1061.250 USDT (10.61%) | 0 days 15:37:00 | -1,061.24973 | 176.713 USDT (1.57%) | # | Best | 49/50 | 28 | 15 2 11 53.6 | 0.44% | 1278.398 USDT (12.78%) | 0 days 17:11:00 | -1,278.39774 | 157.938 USDT (1.38%) |