# source: https://raw.githubusercontent.com/arpanvyas/freqtrade_turtle/5a7b303b7e98a44fed39300d0aca36b9f67e3e63/Feb2023/TurtleTrader_hypopt.py # pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement # flake8: noqa: F401 # --- Do not remove these libs --- import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame # noqa from datetime import datetime # noqa from typing import Optional, Union # noqa from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter) # -------------------------------- # Add your lib to import here import talib.abstract as ta import pandas_ta as pta import freqtrade.vendor.qtpylib.indicators as qtpylib from freqtrade.persistence import Trade import os class Github_arpanvyas_freqtrade_turtle__TurtleTrader_hypopt__20250530_210658(IStrategy): """ This is a strategy template to get you started. 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 # Optimal timeframe for the strategy. timeframe = '5m' # Can this strategy go short? can_short: bool = True #can_short: bool = False # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". # Disabled by setting a very high value 1000,00% minimal_roi = { "0": 1000 } # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". #stoploss = -0.002 stoploss = -0.02 #stoploss = -0.1 # 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 # 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 = 30 # 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' } use_exit_signal = True S1_big_param = IntParameter(1,80,default = 40,space="buy") S1_small_param = IntParameter(1,80,default = 40,space="sell") #use_custom_stoploss = True @property def plot_config(self): return { # Main plot indicators (Moving averages, ...) 'main_plot': { 'tema': {}, 'sar': {'color': 'white'}, }, 'subplots': { # Subplots - each dict defines one additional plot "MACD": { 'macd': {'color': 'blue'}, 'macdsignal': {'color': 'orange'}, }, "RSI": { 'rsi': {'color': 'red'}, } } } 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 # ------------------------------------ mult = 72 #same-same #S1_big = self.S1_big_param #S1_small = self.S1_small_param #print(S1_big) #print(S1_small) # ADX #dataframe['adx'] = ta.ADX(dataframe) # Breakouts ## Breakouts ### Long dataframe["S1_big_high"] = dataframe.close.rolling(self.S1_big_param.value*mult).max() dataframe["S1_small_low"] = dataframe.close.rolling(self.S1_small_param.value*mult).min() dataframe.loc[(dataframe['S1_big_high'] == dataframe['close']),'S1_big_high_this'] = 1 dataframe.loc[(dataframe['S1_small_low'] == dataframe['close']),'S1_small_low_this'] = 1 ### Short dataframe["S1_big_low"] = dataframe.close.rolling(self.S1_big_param.value*mult).min() dataframe["S1_small_high"] = dataframe.close.rolling(self.S1_small_param.value*mult).max() dataframe.loc[(dataframe['S1_big_low'] == dataframe['close']),'S1_big_low_this'] = 1 dataframe.loc[(dataframe['S1_small_high'] == dataframe['close']),'S1_small_high_this'] = 1 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['S1_big_high_this'] == 1 ), 'enter_long'] = 1 dataframe.loc[ ( dataframe['S1_big_low_this'] == 1 ), 'enter_short'] = 1 #print("from inside populate entry trend") #print(dataframe.to_string()) 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[ ( dataframe['S1_small_low_this'] == 1 ), 'exit_long'] = 1 dataframe.loc[ ( dataframe['S1_small_high_this'] == 1 ), 'exit_short'] = 1 return dataframe