# source: https://raw.githubusercontent.com/hamidreza07/freqai-strategy/5bd42947b5300922a342d153182906e7cdbe8a40/classic/mind.py # https://github.com/Yodolescrypto/yodostrats import numpy as np # noqa import pandas as pd # noqa from pandas import DataFrame from datetime import datetime, timedelta, timezone from freqtrade.exchange import timeframe_to_minutes import freqtrade.exchange as exchange from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter, merge_informative_pair, informative) from technical.util import resample_to_interval, resampled_merge from freqtrade.persistence import Trade from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal, Real # noqa # -------------------------------- # Add your lib to import here import talib.abstract as ta import pandas_ta as pta import freqtrade.vendor.qtpylib.indicators as qtpylib from technical.pivots_points import pivots_points from typing import Any, Dict, List, Optional # 13% APR 1 year backtest class Github_hamidreza07_freqai_strategy__mind__20231226_132626(IStrategy): custom_info = {} """ 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 = '1d' # Can this strategy go short? can_short: bool = True # Minimal ROI designed for the strategy. # This attribute will be overridden if the config file contains "minimal_roi". minimal_roi = { "0": 100 } # Optimal stoploss designed for the strategy. # This attribute will be overridden if the config file contains "stoploss". stoploss = -0.3 # Trailing stoploss trailing_stop= True trailing_stop_positive=0.99 trailing_stop_positive_offset= 1 trailing_only_offset_is_reached= True custom_price_max_distance_ratio = 1 # 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 = 50 # Optional order type mapping. order_types = { 'entry': 'market', 'exit': 'market', 'stoploss': 'market', 'stoploss_on_exchange': True } # Optional order time in force. order_time_in_force = { 'entry': 'gtc', 'exit': 'gtc' } @property def plot_config(self): return { # Main plot indicators (Moving averages, ...) 'main_plot': { }, 'subplots': { "MACD": { 'macdh': {'color': 'blue'}, 'macdd': {'color': 'cyan'}, 'macdf': {'color': 'purple'}, }, "CCI": { 'cci': {'color': 'red'}, }, } } @property def protections(self): return [ { "method": "CooldownPeriod", "stop_duration_candles": 1 } ] def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, proposed_stake: float, min_stake: float, max_stake: float, entry_tag: str, **kwargs) -> float: return (100) def leverage(self, pair: str, current_time: 'datetime', current_rate: float, proposed_leverage: float, max_leverage: float, side: str, **kwargs) -> float: return 10 def informative_pairs(self): return [] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: macd, macdsignal, macdhist = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9) dataframe['macdf'] = macd dataframe['macdd'] = macdsignal dataframe['macdh'] = macdhist stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['adx'] = ta.ADX(dataframe) bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['lower'] = bollinger['lower'] dataframe['middle'] = bollinger['mid'] dataframe['upper'] = bollinger['upper'] return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # 1d # dataframe.loc[ ( # Reversal Incomming (Trigger) (dataframe['lower'] < dataframe['close']) & (dataframe['lower'].rolling(3).mean() > dataframe['low'].rolling(3).mean()) & (dataframe['fastk'] > dataframe['fastd']) & #10 days going down avg (dataframe['macdh'].rolling(10).mean() < dataframe['macdh']) & (dataframe['volume'] > 0) ), ['enter_long', 'enter_tag']] = (1, "bullish_lower_bb") dataframe.loc[ ( # Reversal Incomming (Trigger) (dataframe['middle'] < dataframe['close']) & (dataframe['middle'].rolling(3).mean() > dataframe['low'].rolling(3).mean()) & (dataframe['fastk'] < dataframe['fastd']) & #5 days going down avg (dataframe['macdh'].rolling(10).mean() < dataframe['macdh']) & (dataframe['volume'] > 0) ), ['enter_long', 'enter_tag']] = (1, "bullish_middle_bb") dataframe.loc[ ( # Reversal Incomming (Trigger) (dataframe['upper'] > dataframe['close']) & (dataframe['upper'].rolling(3).mean() < dataframe['high'].rolling(3).mean()) & (dataframe['fastk'] < dataframe['fastd']) & #5 days going down avg (dataframe['macdh'].rolling(10).mean() > dataframe['macdh']) & (dataframe['volume'] > 0) ), ['enter_short', 'enter_tag']] = (1, "bearish_upper_bb") dataframe.loc[ ( # Reversal Incomming (Trigger) (dataframe['middle'] > dataframe['close']) & (dataframe['middle'].rolling(3).mean() < dataframe['high'].rolling(3).mean()) & (dataframe['fastk'] < dataframe['fastd']) & #5 days going down avg (dataframe['macdh'].rolling(10).mean() > dataframe['macdh']) & (dataframe['volume'] > 0) ), ['enter_short', 'enter_tag']] = (1, "bearish_middle_bb") return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe.loc[ ( (dataframe['volume'] > 0) # Make sure Volume is not 0 ), ['exit_long', 'exit_tag']] = (0, "open_ai_told_me_to_exit") dataframe.loc[ ( (dataframe['volume'] > 0) # Make sure Volume is not 0 ), ['exit_short', 'exit_tag']] = (0, 'yodo_knows_better_ex') return dataframe