# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/f80d4d8b77c53435e9c0a9045636f1bfb2b8c539/chart/deployed_strategies/binance-michael-k8s-namespace/Chandem.py
# directory_url: https://github.com/DerSalvador/freqtrade-helm-chart/blob/main/chart/deployed_strategies/binance-michael-k8s-namespace/
# User: DerSalvador
# Repository: freqtrade-helm-chart
# --------------------#!/usr/bin/env python3
# -*- coding: utf-8 -*-
'\nCreated on Wed Dec  2 13:50:49 2020\n\n@author: alex\n'
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib

class Github_DerSalvador_freqtrade_helm_chart__Chandem__20260416_224245(IStrategy):
    INTERFACE_VERSION = 3
    '\n    Sample strategy implementing Informative Pairs - compares stake_currency with USDT.\n    Not performing very well - but should serve as an example how to use a referential pair against USDT.\n    author@: xmatthias\n    github@: https://github.com/freqtrade/freqtrade-strategies\n    How to use it?\n    > python3 freqtrade -s InformativeSample\n    '
    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi"
    minimal_roi = {'0': 0.28396, '974': 0.09268, '1740': 0.06554, '3087': 0}
    # Optimal stoploss designed for the strategy
    # This attribute will be overridden if the config file contains "stoploss"
    stoploss = -0.28031
    # Optimal timeframe for the strategy
    timeframe = '5m'
    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.01013
    trailing_stop_positive_offset = 0.10858
    trailing_only_offset_is_reached = True
    # run "populate_indicators" only for new candle
    ta_on_candle = False
    # Experimental settings (configuration will overide these if set)
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False

    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 [(f"{self.config['stake_currency']}/USDT", self.timeframe)]

    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.
        """
        dataframe['CMO'] = ta.CMO(dataframe, timeperiod=50)
        # Bollinger bands
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=25, stds=3.5)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_upperband'] = bollinger['upper']
        bollingerTA = ta.BBANDS(dataframe, timeperiod=25, nbdevup=3.2, nbdevdn=3.2, matype=0)
        dataframe['bb_lowerbandTA'] = bollingerTA['lowerband']
        dataframe['bb_middlebandTA'] = bollingerTA['middleband']
        dataframe['bb_upperbandTA'] = bollingerTA['upperband']
        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
        :return: DataFrame with entry column
        """
        dataframe.loc[qtpylib.crossed_above(dataframe['CMO'].shift(1), 0) & (dataframe['CMO'] >= 0), 'entry'] = 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
        :return: DataFrame with entry column
        """
        dataframe.loc[qtpylib.crossed_above(dataframe['close'], dataframe['bb_upperband']), 'exit'] = 1
        return dataframe