# source: https://raw.githubusercontent.com/ronmegini/crypto-bot/b23b66a0fb2a50b01dceb4a285708af6310a3b38/user_data/strategies/extreme_rsi_macd_cross.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
import pandas as pd
from pandas import DataFrame
from datetime import datetime
from typing import Optional, Union

from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
                                IntParameter, IStrategy, merge_informative_pair)

# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import pandas_ta as pta
from technical import qtpylib


class github_ronmegini_crypto_bot__extreme_rsi_macd_cross__20230316_064528(IStrategy):
    """
    This strategy get into positions based on extreme RSI values and macd cross.

    :Get in:
    :LONG:
    RSI below 20 and macd cross from below
    :SHORT:
    RSI above 80 and macd cross from above

    :Close:
    trailing_stop of 1 precent
    """
    # 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 = False

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi".
    minimal_roi = {
        "0": 100
    }

    # Stop loss - trailing stoploss of 2%

    # Optimal stoploss designed for the strategy.
    # This attribute will be overridden if the config file contains "stoploss".
    stoploss = -0.02

    # Trailing stoploss
    trailing_stop = True
    # trailing_only_offset_is_reached = False
    # trailing_stop_positive = 0.01
    # trailing_stop_positive_offset = 0.0  # Disabled / not configured

    # *** Might be changed to false ***

    # 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

    # Strategy parameters
    buy_rsi = IntParameter(0, 30, default=30, space="buy")
    sell_rsi = IntParameter(70, 100, default=70, space="sell")

    # Optional order type mapping.
    order_types = {
        'entry': 'limit',
        'exit': 'limit',
        'stoploss': 'market',
        'stoploss_on_exchange': True,
        'stoploss_on_exchange_interval': 60,
        'stoploss_on_exchange_limit_ratio': 0.99
    }

    # 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': {
                # Subplots - each dict defines one additional plot
                "MACD 5m": {
                    'macd': {'color': 'blue'},
                    'macdsignal': {'color': 'red'},
                },
                "RSI 15m": {
                    'rsi_15m': {'color': 'orange'},
                }
            }
        }

    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"),
                            ]
        """
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, '15m') for pair in pairs]
        return informative_pairs

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Adds several different TA indicators to the given DataFrame
        Populate rsi and macd indicators

        :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
        """
        # Populate 15m rsi indicator and merge with main dataframe
        # Informative pairs bars timeframe
        inf_tf='15m'
        # Get the informative pair
        informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
        # Get informative pair macd
        informative['rsi'] = ta.RSI(informative)
        # Use the helper function merge_informative_pair to safely merge the pair
        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)

        # MACD
        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']
        
        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['rsi_15m'] < 30) &
                (qtpylib.crossed_below(dataframe['macdsignal'], dataframe['macd'])) &  # Signal: macdsignal crossed below macd
                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),
            'enter_long'] = 1

        return dataframe

    
    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        return dataframe