# source: https://raw.githubusercontent.com/RudoRonuma/freqtrade/577d2b949274ed881d00154139a3ee0f91eb5c6f/user_data/strategies/master_strategy.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # isort: skip_file # --- Do not remove these imports --- import numpy as np import pandas as pd from datetime import datetime, timedelta, timezone from pandas import DataFrame from typing import Optional, Union from freqtrade.strategy import ( IStrategy, Trade, Order, PairLocks, informative, # @informative decorator # Hyperopt Parameters BooleanParameter, CategoricalParameter, DecimalParameter, IntParameter, RealParameter, # timeframe helpers timeframe_to_minutes, timeframe_to_next_date, timeframe_to_prev_date, # Strategy helper functions merge_informative_pair, stoploss_from_absolute, stoploss_from_open, ) # -------------------------------- # Add your lib to import here import talib.abstract as ta from technical import qtpylib from functools import reduce class Github_RudoRonuma_freqtrade__master_strategy__20250511_220746(IStrategy): """ The master strategy. When testing it with hyperopt, make sure to set remove_unrelated_hyperopt_params to true in config file. """ # region General Strategy Params # 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 = { "0": 0.99 # Let TSL and exit signals do most of the work # "2880": 0.05, # "1440": 0.03 } # 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 = True trailing_stop_positive = 0.05 trailing_stop_positive_offset = 0.15 # Optimal timeframe for the strategy. timeframe = "4h" # 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 # Number of candles the strategy requires before producing valid signals startup_candle_count: int = 200 # endregion # region BTC params # buy params for btc buy_adx_threshold_btc = IntParameter(low=1, high=50, default=20, space="buy", optimize=True) buy_fishRsiNorma_btc = IntParameter(low=1, high=50, default=9, space="buy", optimize=True) buy_rsi_floor_btc = IntParameter(low=10, high=20, default=12, space="buy", optimize=True) buy_stoch_fastd_level_btc = IntParameter( low=10, high=35, default=34, space="buy", optimize=True ) buy_volumeAVG_btc = IntParameter(low=1, high=300, default=136, space="buy", optimize=True) buy_volume_multiplier_btc = IntParameter(low=3, high=6, default=4, space="buy", optimize=True) # sell params for btc sell_fisherRsiNorma_exit_btc = IntParameter( low=1, high=100, default=70, space="sell", optimize=True ) sell_minusDI_exit_btc = IntParameter(low=1, high=100, default=4, space="sell", optimize=True) sell_rsi_exit_btc = IntParameter(low=60, high=90, default=74, space="sell", optimize=True) sell_trigger_btc = CategoricalParameter( ["rsi-macd-minusdi", "sar-fisherRsi"], default="rsi-macd-minusdi", space="sell", optimize=True, ) # endregion # region HyperOpt params # Buy hyperspace params: buy_params = { # btc params "buy_adx_threshold_btc": 10, "buy_fishRsiNorma_btc": 27, "buy_rsi_floor_btc": 15, "buy_stoch_fastd_level_btc": 27, "buy_volumeAVG_btc": 111, "buy_volume_multiplier_btc": 3, # eth params } # Sell hyperspace params: sell_params = { # btc params "sell_fisherRsiNorma_exit_btc": 58, "sell_minusDI_exit_btc": 14, "sell_rsi_exit_btc": 70, "sell_trigger_btc": "rsi-macd-minusdi", # eth params } # endregion # region Order and Plot params # 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"}, }, }, } # endregion # region Master-Strategy methods 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 """ target_pair = metadata["pair"] if target_pair == "BTC/USDT": dataframe = self.populate_indicators_btc(dataframe, metadata) elif target_pair == "ETH/USDT": dataframe = self.populate_indicators_eth(dataframe, metadata) else: print(f"WARNING: the pair {target_pair} is not supported yet") return dataframe # do things which are common between all pairs in here 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 """ target_pair = metadata["pair"] if target_pair == "BTC/USDT": dataframe = self.populate_entry_trend_btc(dataframe, metadata) elif target_pair == "ETH/USDT": dataframe = self.populate_entry_trend_eth(dataframe, metadata) else: print(f"WARNING: the pair {target_pair} is not supported yet") return dataframe # do things which are common between all pairs in here 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 """ target_pair = metadata["pair"] if target_pair == "BTC/USDT": dataframe = self.populate_exit_trend_btc(dataframe, metadata) elif target_pair == "ETH/USDT": dataframe = self.populate_exit_trend_eth(dataframe, metadata) else: print(f"WARNING: the pair {target_pair} is not supported yet") return dataframe # do things which are common between all pairs in here return dataframe # endregion # region BTC-stragey methods def populate_indicators_btc(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # ADX dataframe["adx"] = ta.ADX(dataframe, timeperiod=14) # MACD macd = ta.MACD(dataframe) dataframe["macd"] = macd["macd"] dataframe["macdsignal"] = macd["macdsignal"] # Minus Directional Indicator / Movement dataframe["minus_di"] = ta.MINUS_DI(dataframe) # RSI dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["mfi"] = ta.MFI(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, value [0.0, 100.0] (https://goo.gl/2JGGoy) dataframe["fisher_rsi_norma"] = 50 * (dataframe["fisher_rsi"] + 1) # Stoch fast stoch_fast = ta.STOCHF(dataframe) dataframe["fastd"] = stoch_fast["fastd"] dataframe["fastk"] = stoch_fast["fastk"] bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe["bb_lowerband"] = bollinger["lower"] # Overlap Studies # ------------------------------------ # SAR Parabol dataframe["sar"] = ta.SAR(dataframe) # TEMA - Triple Exponential Moving Average dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9) # SMA - Simple Moving Average dataframe["sma"] = ta.SMA(dataframe, timeperiod=40) dataframe["ema21"] = ta.EMA(dataframe, timeperiod=21) dataframe["ema55"] = ta.EMA(dataframe, timeperiod=55) 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"] # Keep your volume calculation as it was impactful dataframe["volume_mean_long"] = ( dataframe["volume"].rolling(self.buy_volumeAVG_btc.value).mean() ) return dataframe def populate_entry_trend_btc(self, dataframe: DataFrame, metadata: dict) -> DataFrame: current_dust_condition = dataframe["close"] > 0.00000200 dataframe.loc[ # Prod ( current_dust_condition & ( dataframe["volume"] > dataframe["volume"].rolling(self.buy_volumeAVG_btc.value).mean() * self.buy_volume_multiplier_btc.value ) & (dataframe["close"] < dataframe["sma"]) & (qtpylib.crossed_above(dataframe["fastk"], dataframe["fastd"])) & (dataframe["fastd"] < self.buy_stoch_fastd_level_btc.value) & (dataframe["adx"] > self.buy_adx_threshold_btc.value) & (dataframe["rsi"] > self.buy_rsi_floor_btc.value) & (dataframe["fisher_rsi_norma"] < self.buy_fishRsiNorma_btc.value) ), "enter_long", ] = 1 return dataframe def populate_exit_trend_btc(self, dataframe: DataFrame, metadata: dict) -> DataFrame: rsi_sell_condition = qtpylib.crossed_above(dataframe["rsi"], self.sell_rsi_exit_btc.value) conditions = [] if self.sell_trigger_btc.value == "rsi-macd-minusdi": conditions.append(rsi_sell_condition) conditions.append(dataframe["macd"] < 0) conditions.append(dataframe["minus_di"] > self.sell_minusDI_exit_btc.value) if self.sell_trigger_btc.value == "sar-fisherRsi": conditions.append(dataframe["sar"] > dataframe["close"]) conditions.append( dataframe["fisher_rsi_norma"] > self.sell_fisherRsiNorma_exit_btc.value ) if conditions: dataframe.loc[reduce(lambda x, y: x & y, conditions), "exit_long"] = 1 return dataframe # endregion # region BTC-stragey methods def populate_indicators_eth(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # ADX dataframe["adx"] = ta.ADX(dataframe, timeperiod=14) # MACD macd = ta.MACD(dataframe) dataframe["macd"] = macd["macd"] dataframe["macdsignal"] = macd["macdsignal"] # Minus Directional Indicator / Movement dataframe["minus_di"] = ta.MINUS_DI(dataframe) # RSI dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14) dataframe["mfi"] = ta.MFI(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, value [0.0, 100.0] (https://goo.gl/2JGGoy) dataframe["fisher_rsi_norma"] = 50 * (dataframe["fisher_rsi"] + 1) # Stoch fast stoch_fast = ta.STOCHF(dataframe) dataframe["fastd"] = stoch_fast["fastd"] dataframe["fastk"] = stoch_fast["fastk"] bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe["bb_lowerband"] = bollinger["lower"] # Overlap Studies # ------------------------------------ # SAR Parabol dataframe["sar"] = ta.SAR(dataframe) # TEMA - Triple Exponential Moving Average dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9) # SMA - Simple Moving Average dataframe["sma"] = ta.SMA(dataframe, timeperiod=40) dataframe["ema21"] = ta.EMA(dataframe, timeperiod=21) dataframe["ema55"] = ta.EMA(dataframe, timeperiod=55) 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"] # Keep your volume calculation as it was impactful dataframe["volume_mean_long"] = ( dataframe["volume"].rolling(self.buy_volumeAVG_btc.value).mean() ) return dataframe def populate_entry_trend_eth(self, dataframe: DataFrame, metadata: dict) -> DataFrame: current_dust_condition = dataframe["close"] > 0.00000200 dataframe.loc[ # Prod ( current_dust_condition & ( dataframe["volume"] > dataframe["volume"].rolling(self.buy_volumeAVG_btc.value).mean() * self.buy_volume_multiplier_btc.value ) & (dataframe["close"] < dataframe["sma"]) & (qtpylib.crossed_above(dataframe["fastk"], dataframe["fastd"])) & (dataframe["fastd"] < self.buy_stoch_fastd_level_btc.value) & (dataframe["adx"] > self.buy_adx_threshold_btc.value) & (dataframe["rsi"] > self.buy_rsi_floor_btc.value) & (dataframe["fisher_rsi_norma"] < self.buy_fishRsiNorma_btc.value) ), "enter_long", ] = 1 return dataframe def populate_exit_trend_eth(self, dataframe: DataFrame, metadata: dict) -> DataFrame: rsi_sell_condition = qtpylib.crossed_above(dataframe["rsi"], self.sell_rsi_exit_btc.value) conditions = [] if self.sell_trigger_btc.value == "rsi-macd-minusdi": conditions.append(rsi_sell_condition) conditions.append(dataframe["macd"] < 0) conditions.append(dataframe["minus_di"] > self.sell_minusDI_exit_btc.value) if self.sell_trigger_btc.value == "sar-fisherRsi": conditions.append(dataframe["sar"] > dataframe["close"]) conditions.append( dataframe["fisher_rsi_norma"] > self.sell_fisherRsiNorma_exit_btc.value ) if conditions: dataframe.loc[reduce(lambda x, y: x & y, conditions), "exit_long"] = 1 return dataframe # endregion