# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/welldone_gotOKX.py import datetime import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame from typing import Optional from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter) import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib import logging logger = logging.getLogger(__name__) pd.options.mode.chained_assignment = None class Github_remiotore_freqtrade__welldone_gotOKX__20260111_210550(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_* """ INTERFACE_VERSION = 3 custom_leverage = 4.0 mr_lev = custom_leverage*0.65 can_short: bool = True def bot_start(self, **kwargs) -> None: """ Called only once after bot instantiation. :param **kwargs: Ensure to keep this here so updates to this won't break your strategy. """ self.optimize = self.config['runmode'].value in ('live', 'dry_run') self.custom_leverage = self.config['custom_leverage'] logger.info(f"--> runmode: {self.config['runmode'].value} | custom_leverage: {self.custom_leverage} ") minimal_roi = { f"0": 0.137, f"{int(7)}": 0.031 * custom_leverage, f"{int(19)}": 0.015 * custom_leverage, f"{int(42)}": 0, f"{int(120)}": -0.005 * custom_leverage, f"{int(240)}": -0.01 * custom_leverage, "480": -0.02 * custom_leverage, "960": -0.04 * custom_leverage, "1920": -0.08 * custom_leverage, "3840": -0.16 * custom_leverage } stoploss = -0.1 trailing_stop = False trailing_stop_positive = 0.0025 trailing_stop_positive_offset = 0.05 trailing_only_offset_is_reached = True timeframe = '15m' process_only_new_candles = False use_exit_signal = False exit_profit_only = False ignore_roi_if_entry_signal = False startup_candle_count: int = 0 order_types = { 'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } 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'}, } } } def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: """ Customize leverage for each new trade. This method is only called in futures mode. :param pair: Pair that's currently analyzed :param current_time: datetime object, containing the current datetime :param current_rate: Rate, calculated based on pricing settings in exit_pricing. :param proposed_leverage: A leverage proposed by the bot. :param max_leverage: Max leverage allowed on this pair :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal. :param side: 'long' or 'short' - indicating the direction of the proposed trade :return: A leverage amount, which is between 1.0 and max_leverage. """ return self.custom_leverage 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 """ pivot_range = int(19) conditions1_minima = np.array([(dataframe["low"].shift(periods = pivot_range) < dataframe["low"].shift(periods = pivot_range + lb)) for lb in range(1, pivot_range + 1)]) conditions2_minima = np.array([(dataframe["low"].shift(periods = pivot_range) < dataframe["low"].shift(periods = pivot_range - lb)) for lb in range(1, pivot_range + 1)]) conditions_minima = conditions1_minima & conditions2_minima conditions_minima_T = np.transpose(conditions_minima) check_minima = np.all(conditions_minima_T, axis = 1) dataframe["check_minima"] = check_minima dataframe["check_minima"][dataframe["check_minima"] == False] = None dataframe["minima"] = dataframe["low"].shift(periods = pivot_range)[check_minima == True] conditions1_maxima = np.array([(dataframe["high"].shift(periods = pivot_range) > dataframe["high"].shift(periods = pivot_range + lb)) for lb in range(1, pivot_range + 1)]) conditions2_maxima = np.array([(dataframe["high"].shift(periods = pivot_range) > dataframe["high"].shift(periods = pivot_range - lb)) for lb in range(1, pivot_range + 1)]) conditions_maxima = conditions1_maxima & conditions2_maxima conditions_maxima_T = np.transpose(conditions_maxima) check_maxima = np.all(conditions_maxima_T, axis = 1) dataframe["check_maxima"] = check_maxima dataframe["check_maxima"][dataframe["check_maxima"] == False] = None dataframe["maxima"] = dataframe["high"].shift(periods = pivot_range)[check_maxima == True] dataframe["maxima"][0] = dataframe["high"][0] * 1.5 # an arbitrarily large value assigned to first row (to make .fillna() function work.) dataframe["minima"][0] = dataframe["low"][0] * 0.5 # an arbitrarily small value assigned to first row (to make .fillna() function work.) dataframe["maxima"] = dataframe["maxima"].fillna(method = "ffill") # Fill NaN with last value. dataframe["minima"] = dataframe["minima"].fillna(method = "ffill") # Fill NaN with last value. dataframe["rolling_max"] = dataframe["close"].rolling(pivot_range * 2).max().shift(periods = 1) dataframe["rolling_min"] = dataframe["close"].rolling(pivot_range * 2).min().shift(periods = 1) dataframe["move"] = dataframe["open"] - dataframe["close"] dataframe["abs_move"] = dataframe["move"].abs() dataframe["abs_move_shifted"] = dataframe["abs_move"].shift(periods = 1) dataframe["volume_shifted"] = dataframe["volume"].shift(periods = 1) dataframe["volume_ma_slow"] = dataframe["volume"].rolling(62).mean() dataframe["volume_ma_fast"] = dataframe["volume"].rolling(5).mean() dataframe['rsi'] = ta.RSI(dataframe) dataframe["rsi_maxima"] = dataframe["rsi"].shift(periods = pivot_range)[check_maxima == True] # RSI of last maxima point. dataframe["rsi_minima"] = dataframe["rsi"].shift(periods = pivot_range)[check_minima == True] # RSI of last minima point. dataframe["rsi_maxima"][0] = 70 # an arbitrarily large value assigned to first row (to make .fillna() function work.) dataframe["rsi_minima"][0] = 30 # an arbitrarily small value assigned to first row (to make .fillna() function work.) dataframe["rsi_maxima"] = dataframe["rsi_maxima"].fillna(method = "ffill") # Fill NaN with last value. dataframe["rsi_minima"] = dataframe["rsi_minima"].fillna(method = "ffill") # Fill NaN with last value. dataframe['mfi'] = ta.MFI(dataframe) dataframe["mfi_maxima"] = dataframe["mfi"].shift(periods = pivot_range)[check_maxima == True] # MFI of last maxima point. dataframe["mfi_minima"] = dataframe["mfi"].shift(periods = pivot_range)[check_minima == True] # MFI of last minima point. dataframe["mfi_maxima"][0] = 70 # an arbitrarily large value assigned to first row (to make .fillna() function work.) dataframe["mfi_minima"][0] = 30 # an arbitrarily small value assigned to first row (to make .fillna() function work.) dataframe["mfi_maxima"] = dataframe["mfi_maxima"].fillna(method = "ffill") # Fill NaN with last value. dataframe["mfi_minima"] = dataframe["mfi_minima"].fillna(method = "ffill") # Fill NaN with last value. dataframe['sma_fast'] = ta.SMA(dataframe, timeperiod=10) """ 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[ ( (dataframe["low"].shift(periods = 1) < (0.99 * dataframe["sma_fast"].shift(periods = 1))) & # Previous low is lower than MA. (dataframe["close"].shift(periods = 1) < dataframe["open"].shift(periods = 1)) & # Previous candle is red. (dataframe["close"] > dataframe["open"]) & # Green candle. (dataframe["abs_move"] > (0.5 * dataframe["abs_move_shifted"])) & # Move is greater than half of previous candle. (dataframe["volume"] < dataframe["volume_ma_fast"]) & # Volume is lower than the fast volume moving average. (dataframe["volume"] < (0.5 * dataframe["volume_shifted"])) & # Volume is lower than half of previous volume. (dataframe["volume_shifted"] > (2 * dataframe["volume_ma_slow"])) & # Previous candle volume is large. (dataframe['volume'] > 0) # Make sure Volume is not 0 ), 'enter_long'] = 1 dataframe.loc[ ( (dataframe["high"].shift(periods = 1) > (1.01 * dataframe["sma_fast"].shift(periods = 1))) & # Previous high is greater than MA. (dataframe["close"].shift(periods = 1) > dataframe["open"].shift(periods = 1)) & # Previous candle is green. (dataframe["close"] < dataframe["open"]) & # Red candle. (dataframe["abs_move"] > (0.5 * dataframe["abs_move_shifted"])) & # Move is greater than half of previous candle. (dataframe["volume"] < dataframe["volume_ma_fast"]) & # Volume is lower than the fast volume moving average. (dataframe["volume"] < (0.5 * dataframe["volume_shifted"])) & # Volume is lower than half of previous volume. (dataframe["volume_shifted"] > (2 * dataframe["volume_ma_slow"])) & # Previous candle volume is large. (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[ ( ((qtpylib.crossed_above(dataframe["high"], dataframe["maxima"])) | (qtpylib.crossed_below(dataframe["rsi"], 50))) & # Candle high swept maxima. (dataframe['volume'] > 0) # Make sure Volume is not 0 ), 'exit_long'] = 1 dataframe.loc[ ( ((qtpylib.crossed_below(dataframe["low"], dataframe["minima"])) | (qtpylib.crossed_above(dataframe["rsi"], 50))) & (dataframe['volume'] > 0) # Make sure Volume is not 0 ), 'exit_short'] = 1 return dataframe