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

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


# This class is a sample. Feel free to customize it.
class BBRSIStrategy(IStrategy):
    """
    This is a sample strategy to inspire you.
    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

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi".
    minimal_roi = {
        "0": 0.207,
        "14": 0.068,
        "72": 0.015,
        "182": 0.005
    }

    # Optimal stoploss designed for the strategy.
    # This attribute will be overridden if the config file contains "stoploss".
    stoploss = -0.333

    # 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

    # Hyperoptable parameters
    buy_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
    sell_rsi = IntParameter(low=50, high=100, default=70, space='sell', optimize=True, load=True)

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

    # 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

    # 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'
    }

    plot_config = {
        'main_plot': {
            'tema': {},
            'sar': {'color': 'white'},
        },
        'subplots': {
            "MACD": {
                'macd': {'color': 'blue'},
                'macdsignal': {'color': 'orange'},
            },
            "RSI": {
                'rsi': {'color': 'red'},
            }
        }
    }

    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 []

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


        # # EMA - Exponential Moving Average
        # dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
        # dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
        # dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
        # dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
        # dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
        # dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)

        # # SMA - Simple Moving Average
        # dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
        # dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
        # dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
        # dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21)
        # dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
        # dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)

        # RSI
        dataframe['rsi'] = ta.RSI(dataframe)
        #ADX
        dataframe['adx'] = ta.ADX(dataframe)

        dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
        dataframe['ema30'] = ta.EMA(dataframe, timeperiod=30)
        dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)

        stoch_rsi = ta.STOCHRSI(dataframe)
        dataframe['fastd_rsi'] = stoch_rsi['fastd']
        dataframe['fastk_rsi'] = stoch_rsi['fastk']

        # Bollinger bands
        bollinger_1sd = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=1)
        dataframe['bb_lowerband_1sd'] = bollinger_1sd['lower']
        dataframe['bb_middleband_1sd'] = bollinger_1sd['mid']
        dataframe['bb_upperband_1sd'] = bollinger_1sd['upper']

        bollinger_2sd = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband_2sd'] = bollinger_2sd['lower']
        dataframe['bb_middleband_2sd'] = bollinger_2sd['mid']
        dataframe['bb_upperband_2sd'] = bollinger_2sd['upper']

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
            #    (dataframe['rsi'] > 22) &   # Signal: RSI is greater 29
                (dataframe['adx'] > 29) &
                (dataframe['fastd_rsi'] > dataframe['fastk_rsi']) &
                (dataframe['close'] > dataframe['bb_lowerband_2sd'])
            ),
            'buy'] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe['rsi'] > 95) &  # Signal: RSI is greater 83
                (dataframe['close'] > dataframe['bb_middleband_1sd']) # Signal: price is greater than mid bb
            ),
            'sell'] = 1
        return dataframe