# source: https://raw.githubusercontent.com/hamidreza07/freqtrade/2d378aa9ceb8f44e420e507903706b75ac1afb32/freqtrade/templates/BolingerAI.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 typing import Optional, Union from datetime import timedelta import datetime import logging from functools import reduce from typing import Dict from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, merge_informative_pair, IStrategy, IntParameter) # -------------------------------- # Add your lib to import here import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib logger = logging.getLogger(__name__) # This class is a sample. Feel free to customize it. class Github_hamidreza07_freqtrade__BolingerAI__20231127_092233(IStrategy): INTERFACE_VERSION = 3 # 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": 0.065, "30":0.1, "60":0.1 } stoploss = -0.022 # 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 # Optimal timeframe for the strategy. timeframe = '5m' # Run "populate_indicators()" only for new candle. process_only_new_candles = True # These values can be overridden in the config. use_exit_signal = False exit_profit_only = False ignore_roi_if_entry_signal = False startup_candle_count: int = 40 # 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' } std_dev_multiplier_buy = CategoricalParameter( [0.75, 1, 1.25, 1.5, 1.75], default=1.25, space="buy", optimize=True) std_dev_multiplier_sell = CategoricalParameter( [0.75, 1, 1.25, 1.5, 1.75], space="sell", default=1.25, optimize=True) def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: Dict, **kwargs) -> DataFrame: """ *Only functional with FreqAI enabled strategies* This function will automatically expand the defined features on the config defined `indicator_periods_candles`, `include_timeframes`, `include_shifted_candles`, and `include_corr_pairs`. In other words, a single feature defined in this function will automatically expand to a total of `indicator_periods_candles` * `include_timeframes` * `include_shifted_candles` * `include_corr_pairs` numbers of features added to the model. All features must be prepended with `%` to be recognized by FreqAI internals. Access metadata such as the current pair/timeframe with: `metadata["pair"]` `metadata["tf"]` More details on how these config defined parameters accelerate feature engineering in the documentation at: https://www.freqtrade.io/en/latest/freqai-parameter-table/#feature-parameters https://www.freqtrade.io/en/latest/freqai-feature-engineering/#defining-the-features :param dataframe: strategy dataframe which will receive the features :param period: period of the indicator - usage example: :param metadata: metadata of current pair dataframe["%-ema-period"] = ta.EMA(dataframe, timeperiod=period) """ bollinger = qtpylib.bollinger_bands( qtpylib.typical_price(dataframe), window=period, stds=2.2 ) dataframe["bb_lowerband-period"] = bollinger["lower"] dataframe["bb_middleband-period"] = bollinger["mid"] dataframe["bb_upperband-period"] = bollinger["upper"] dataframe["%-bb_width-period"] = ( dataframe["bb_upperband-period"] - dataframe["bb_lowerband-period"] ) / dataframe["bb_middleband-period"] # dataframe["%-close-bb_lower-period"] = ( # dataframe["close"] / dataframe["bb_lowerband-period"] # ) dataframe["%-rsi-period"]=ta.RSI(dataframe, timeperiod=period) return dataframe def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame: """ I Realy Dont Underestand This Sh""" dataframe['&-s_rsi'] = ta.RSI(dataframe, timeperiod = 14) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: dataframe = self.freqai.start(dataframe, metadata, self) blin = ta.BBANDS(dataframe, timeperiod = 20, nbdevup=2.0, nbdevdn=2.0) dataframe['upperband'] = blin['upperband'] dataframe['middleband'] = blin['middleband'] dataframe['lowerband'] = blin['lowerband'] dataframe["percent"] = ( (dataframe["close"] - dataframe["lowerband"]) / (dataframe["upperband"] - dataframe["lowerband"])) return dataframe def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: enter_long_conditions = [ df["do_predict"] == 1, df["&-s_rsi"] > 30, # Adjusted RSI threshold df["percent"] > 0.8, # Adjusted percent threshold ] if enter_long_conditions: df.loc[ reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"] ] = (1, "long") enter_short_conditions = [ df["do_predict"] == 1, df["&-s_rsi"] < 70, # Adjusted RSI threshold df["percent"] < 0.2, # Adjusted percent threshold ] if enter_short_conditions: df.loc[ reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"] ] = (1, "short") return df def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: exit_long_conditions = [ df["do_predict"] == 1, df["&-s_rsi"] < 30, # Adjusted RSI threshold df["percent"] < 0.4, # Adjusted percent threshold ] if exit_long_conditions: df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1 exit_short_conditions = [ df["do_predict"] == 1, df["&-s_rsi"] > 70, # Adjusted RSI threshold df["percent"] > 0.6, # Adjusted percent threshold ] if exit_short_conditions: df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1 return df 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: return 3