# source: https://raw.githubusercontent.com/Haydn-King/Algorithmic-Trading-Crypto-Bot/d199294adc4d17ebb25c8189b4d662ae531fe725/user_data/strategies/SuperTrend.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

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

# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import pandas_ta as pd_ta
import freqtrade.vendor.qtpylib.indicators as qtpylib


class github_Haydn_King_Algorithmic_Trading_Crypto_Bot__SuperTrend__20220324_055046(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_buy_trend, populate_sell_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 = 2

    # Optimal timeframe for the strategy.
    timeframe = '5m'

    # 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 = -0.05

    # 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 = False

    # These values can be overridden in the "ask_strategy" section in the config.
    use_sell_signal = True
    sell_profit_only = False
    ignore_roi_if_buy_signal = False

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 30

    # Strategy parameters
    buy_rsi = IntParameter(10, 40, default=30, space="buy")
    sell_rsi = IntParameter(60, 90, default=70, space="sell")

    # Optional order type mapping.
    order_types = {
        'buy': 'limit',
        'sell': 'limit',
        'stoploss': 'market',
        'stoploss_on_exchange': False
    }

    # Optional order time in force.
    order_time_in_force = {
        'buy': 'gtc',
        'sell': 'gtc'
    }
    
    @property
    def plot_config(self):
        return {
            # Main plot indicators (Moving averages, ...)
            'main_plot': {
                'ST_long': {'color': 'green'},
                'ST_short': {'color': 'red'}
            }
        }

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

            period = 7
            atr_multiplier = 3.0
        
            dataframe['ST_long'] = pd_ta.supertrend(dataframe['high'], dataframe['low'], dataframe['close'], length=period, 
                                                    multiplier=atr_multiplier)[f'SUPERTl_{period}_{atr_multiplier}']

            dataframe['ST_short'] = pd_ta.supertrend(dataframe['high'], dataframe['low'], dataframe['close'], length=period, 
                                                    multiplier=atr_multiplier)[f'SUPERTs_{period}_{atr_multiplier}']

            return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
       
        dataframe.loc[
            (
                (dataframe['ST_long'] < dataframe['close']) &
                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),
            'buy'] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
       
        dataframe.loc[
            (
                (dataframe['ST_short'] > dataframe['close']) &
                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),
            'sell'] = 1
        return dataframe