# pr#agma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# isort: skip_file
# --- Do not remove these libs ---
from datetime import datetime

import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame

from freqtrade.strategy.interface import IStrategy

# --------------------------------
# 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 StrategyPierrick(IStrategy):

    # 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


    # ROI table:
    minimal_roi = {
        #"0": 0.015
        "0": 0.5
    }

    # Stoploss:
    stoploss = -1
    trailing_stop = True
    trailing_stop_positive = 0.001
    trailing_stop_positive_offset = 0.0175 #0.015
    trailing_only_offset_is_reached = True

    #max_open_trades = 3

    # Optimal ticker interval 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 indicators (Moving averages, ...)
        'main_plot': {
            'bb_lowerband': {'color': 'white'},
            'bb_upperband': {'color': 'white'},
        },
        'subplots': {
            # Subplots - each dict defines one additional plot
            "BB": {
                'bb_width': {'color': 'white'},
            },
            "Aaron": {
                'aroonup': {'color': 'blue'},
                'aroondown': {'color': 'red'}
            }
        }
    }

    def informative_pairs(self):

        return []

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

        # MACD
        #macd = ta.MACD(dataframe)
        #dataframe['macd'] = macd['macd']
        #dataframe['macdsignal'] = macd['macdsignal']
        #dataframe['macdhist'] = macd['macdhist']

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

        # Bollinger Bands
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_upperband'] = bollinger['upper']
        dataframe["bb_percent"] = (
            (dataframe["close"] - dataframe["bb_lowerband"]) /
            (dataframe["bb_upperband"] - dataframe["bb_lowerband"])
        )
        dataframe["bb_width"] = (
            (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
        )

        return dataframe

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

        dataframe.loc[
            (
                (
                    (dataframe['close'] < dataframe['bb_lowerband'])
                    & (dataframe['bb_width'] >= 0.065)
                    #& (dataframe['rsi'] < 45) 
                    & (dataframe['volume'] > 0)
                )
            ),
            'buy'] = 1
        return dataframe

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

        dataframe.loc[
            (

            ),

            'sell'] = 1
        return dataframe