# --- Do not remove these libs ---
import threading

from freqtrade.strategy import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
# --------------------------------
from datetime import datetime
import subprocess

import talib.abstract as ta

from user_data.strategies.util import IS_BACKTEST, back_tester, launcher


class Strategy004(IStrategy):
    """
    Strategy 004
    author@: Gerald Lonlas
    github@: https://github.com/freqtrade/freqtrade-strategies

    How to use it?
    > python3 ./freqtrade/main.py -s Strategy004
    """

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi"
    minimal_roi = {
        "60": 0.01,
        "30": 0.03,
        "20": 0.04,
        "0": 0.05
    }

    # Optimal stoploss designed for the strategy
    # This attribute will be overridden if the config file contains "stoploss"
    stoploss = -0.10

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

    # trailing stoploss
    trailing_stop = False
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.02

    # run "populate_indicators" only for new candle
    process_only_new_candles = False

    # Experimental settings (configuration will overide these if set)
    use_sell_signal = True
    sell_profit_only = True
    ignore_roi_if_buy_signal = False

    # Optional order type mapping
    order_types = {
        'buy': 'limit',
        'sell': 'limit',
        'stoploss': 'market',
        'stoploss_on_exchange': 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 []

    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.
        """

        # ADX
        dataframe['adx'] = ta.ADX(dataframe)
        dataframe['slowadx'] = ta.ADX(dataframe, 35)

        # Commodity Channel Index: values Oversold:<-100, Overbought:>100
        dataframe['cci'] = ta.CCI(dataframe)

        # Stoch
        stoch = ta.STOCHF(dataframe, 5)
        dataframe['fastd'] = stoch['fastd']
        dataframe['fastk'] = stoch['fastk']
        dataframe['fastk-previous'] = dataframe.fastk.shift(1)
        dataframe['fastd-previous'] = dataframe.fastd.shift(1)

        # Slow Stoch
        slowstoch = ta.STOCHF(dataframe, 50)
        dataframe['slowfastd'] = slowstoch['fastd']
        dataframe['slowfastk'] = slowstoch['fastk']
        dataframe['slowfastk-previous'] = dataframe.slowfastk.shift(1)
        dataframe['slowfastd-previous'] = dataframe.slowfastd.shift(1)

        # EMA - Exponential Moving Average
        dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)

        dataframe['mean-volume'] = dataframe['volume'].mean()

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the buy signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                    (
                            (dataframe['adx'] > 50) |
                            (dataframe['slowadx'] > 26)
                    ) &
                    (dataframe['cci'] < -100) &
                    (
                            (dataframe['fastk-previous'] < 20) &
                            (dataframe['fastd-previous'] < 20)
                    ) &
                    (
                            (dataframe['slowfastk-previous'] < 30) &
                            (dataframe['slowfastd-previous'] < 30)
                    ) &
                    (dataframe['fastk-previous'] < dataframe['fastd-previous']) &
                    (dataframe['fastk'] > dataframe['fastd']) &
                    (dataframe['mean-volume'] > 0.75) &
                    (dataframe['close'] > 0.00000100)
            ),
            'buy'] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the sell signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                    (dataframe['slowadx'] < 25) &
                    ((dataframe['fastk'] > 70) | (dataframe['fastd'] > 70)) &
                    (dataframe['fastk-previous'] < dataframe['fastd-previous']) &
                    (dataframe['close'] > dataframe['ema5'])
            ),
            'sell'] = 1
        return dataframe

    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
                            time_in_force: str, current_time: datetime, **kwargs) -> bool:
        """
        Called right before placing a buy order.
        Timing for this function is critical, so avoid doing heavy computations or
        network requests in this method.

        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/

        When not implemented by a strategy, returns True (always confirming).

        :param pair: Pair that's about to be bought.
        :param order_type: Order type (as configured in order_types). usually limit or market.
        :param amount: Amount in target (quote) currency that's going to be traded.
        :param rate: Rate that's going to be used when using limit orders
        :param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
        :param current_time: datetime object, containing the current datetime
        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
        :return bool: When True is returned, then the buy-order is placed on the exchange.
            False aborts the process
        """
        mode = "test"
        coin = pair.split("/")[0]
        brain = "Freq_" + self.__class__.__name__
        launcher(mode, current_time, coin, brain)
        return True