# source: https://raw.githubusercontent.com/khimnguynnn/tqreedsatge-rafstieerq/6dd34c912ae2c1e59815d818475fbe4c746830d0/PRICEFOLLOWINGX.py # 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_khimnguynnn_tqreedsatge_rafstieerq__PRICEFOLLOWINGX__20250515_063843(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_buy_trend, populate_sell_trend You should keep: - timeframe, minimal_roi, stoploss, trailing_* """ @property def protections(self): return [ { "method": "MaxDrawdown", "lookback_period_candles": 48, "trade_limit": 5, "stop_duration_candles": 5, "max_allowed_drawdown": 0.75 }, { "method": "StoplossGuard", "lookback_period_candles": 24, "trade_limit": 3, "stop_duration_candles": 5, "only_per_pair": True }, { "method": "LowProfitPairs", "lookback_period_candles": 30, "trade_limit": 2, "stop_duration_candles": 6, "required_profit": 0.005 }, ] # Strategy interface version - allow new iterations of the strategy interface. # Check the documentation or the Sample strategy to get the latest version. INTERFACE_VERSION = 2 # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". minimal_roi = { "120":0.015, "60": 0.025, "30": 0.03, "0": 0.015 } # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". stoploss = -0.5 # 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_enabled = BooleanParameter(default=True, space='buy', optimize=True, load=True) #buy_rsi = DecimalParameter(0, 50, decimals = 2, default = 40, space="buy", optimize=True, load=True) ema_pct = DecimalParameter(0.001, 0.100, decimals = 3, default = 0.040, space="buy", optimize=True, load=True) buy_frsi = DecimalParameter(-0.71, 0.50, decimals = 2, default = -0.40, space="buy", optimize=True, load=True) frsi_pct = DecimalParameter(0.01, 0.20, decimals = 2, default = 0.10, space="buy", optimize=True, load=True) #sellspace ema_sell_pct = DecimalParameter(0.001, 0.020, decimals = 3, default = 0.003, space="sell", optimize=True, load=True) sell_rsi_enabled = BooleanParameter(default=True, space='sell', optimize=True, load=True) sell_frsi = DecimalParameter(-0.30, 0.70, decimals=2, default=0.2, space="sell", load=True) # Optimal timeframe for the strategy. timeframe = '15m' # 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_sell_signal = True sell_profit_only = True ignore_roi_if_buy_signal = True # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 20 # Optional order type mapping. order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'limit', 'stoploss_on_exchange': False } # Optional order time in force. order_time_in_force = { 'buy': 'gtc', 'sell': 'gtc' } plot_config = { 'main_plot': { 'tema': {}, 'ema7':{}, 'ema7low':{}, 'ema10high':{}, 'ha_open':{}, 'ha_close':{}, }, 'subplots': { #"MACD": { # 'macd': {'color': 'blue'}, # 'macdsignal': {'color': 'orange'}, #}, "RSI": { 'frsi': {'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/BUSD", "1h"), ("LINK/BUSD", "1h"), ("RVN/BUSD", "1h"), ("MATIC/BUSD", "30m") ] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # Momentum Indicators # ------------------------------------ # ADX dataframe['adx'] = ta.ADX(dataframe) # RSI dataframe['rsi'] = ta.RSI(dataframe, window=14) # # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy) rsi = 0.1 * (dataframe['rsi'] - 50) dataframe['frsi'] = (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'] # Parabolic SAR #dataframe['sar'] = ta.SAR(dataframe) #Bollinger Bands bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=19, stds=2.2) 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"] ) # 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'] # # EMA - Exponential Moving Average dataframe['ema7'] = ta.SMA(dataframe, timeperiod=14) dataframe['emalow'] = ta.EMA(dataframe, timeperiod=12, price='low') dataframe['emahigh'] = ta.EMA(dataframe, timeperiod=14, price='high') #dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) # 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_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: #rsi_enabled = BooleanParameter(default=True, space='buy', optimize=True) last_emalow = dataframe['emalow'].tail() last_tema = dataframe['tema'].tail() haclose = dataframe['ha_close'].tail(3) haclose3rdlast, haclose2ndlast, hacloselast = haclose haopen = dataframe['ha_open'] Conditions = [] #GUARDS if self.rsi_enabled.value: Conditions.append(qtpylib.crossed_below(dataframe['frsi'], self.buy_frsi.value)) Conditions.append(dataframe['tema'] < dataframe['bb_lowerband']) Conditions.append(qtpylib.crossed_below(dataframe['tema'], dataframe['emalow'])) #Conditions.append(dataframe['best_bid'] < dataframe['bb_lowerband']) else: Conditions.append(dataframe['tema'] > dataframe['bb_middleband']) Conditions.append(qtpylib.crossed_above(dataframe['tema'], dataframe['ema7'])) #Conditions.append(dataframe['best_bid'] < dataframe['bb_lowerband']) #Conditions.append(((abs(last_emalow - last_tema)) / last_tema) > self.ema_sell_pct.value) if Conditions: dataframe.loc[ reduce(lambda x, y: x & y, Conditions), 'buy'] = 1 return dataframe #if dataframe['buy'] != 1: # dataframe.loc[ # (qtpylib.crossed_above(dataframe['tema'], dataframe['ha_high']))& # (60 < dataframe['rsi'] < 90 )& # (dataframe['macd'] > dataframe['macdsignal']), # 'buy'] = 1 #return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: haopen = dataframe['ha_open'] haclose = dataframe['ha_close'] last_tema = dataframe['tema'].tail() last_emahigh = dataframe['emahigh'].tail() conditions = [] # GUARDS AND TRENDS if self.sell_rsi_enabled.value: conditions.append(qtpylib.crossed_below(dataframe['frsi'], self.sell_frsi.value)) conditions.append(dataframe['tema'] < dataframe['bb_middleband']) #conditions.append(haclose2ndlast > hacloselast) conditions.append(qtpylib.crossed_below(dataframe['tema'], dataframe['ema7'])) #conditions.append(dataframe['best_bid'] < dataframe['ha_close'].shift(1)) else: conditions.append(dataframe['tema'] < dataframe['bb_middleband']) conditions.append(qtpylib.crossed_below(dataframe['tema'], dataframe['ema7'])) #conditions.append(dataframe['best_bid'] < dataframe['ha_close'].shift(1)) #conditions.append(((abs(last_emahigh - last_tema)) / last_tema) > self.ema_sell_pct.value) if conditions: dataframe.loc[ reduce(lambda x, y: x & y, conditions), 'sell'] = 1 return dataframe