# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame, Series, concat
# --------------------------------
from freqtrade.strategy import (merge_informative_pair,DecimalParameter, IntParameter, CategoricalParameter)
import numpy as np

import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib


class TTF(IStrategy):
    

    INTERFACE_VERSION = 2
    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi"
    minimal_roi = {
        "240" : 0.02,
        "120" : 0.03,
        "60" : 0.05,
        "0"  : 0.08
    }

    # Optimal stoploss designed for the strategy
    # This attribute will be overridden if the config file contains "stoploss"
    stoploss = -0.90

    # Optimal timeframe for the strategy
    timeframe = '5m'
    inf_1h = '1h'

    # trailing stoploss
    trailing_stop = False
    trailing_only_offset_is_reached = False
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.03

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

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

    ttf_length       = IntParameter(1, 50, default=15)
    ttf_upperTrigger = IntParameter(1, 400, default=100)
    ttf_lowerTrigger = IntParameter(1, -400, default=-100)


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

    def get_ticker_indicator(self):
        return int(self.timeframe[:-1])

    def informative_pairs(self):
        # get access to all pairs available in whitelist.
        pairs = self.dp.current_whitelist()
        # Assign tf to each pair so they can be downloaded and cached for strategy.
        informative_pairs = [(pair, '1h') for pair in pairs]
        return informative_pairs

    def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        assert self.dp, "DataProvider is required for multiple timeframes."
        # Get the informative pair
        informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h)

        #Heikin Ashi Smoothed V4
        informative_1h['ttf'] = ttf(informative_1h, int(self.ttf_length.value))

        return informative_1h

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        informative_1h = self.informative_1h_indicators(dataframe, metadata)
        dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True)

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        conditions.append(
            (
                (dataframe['ttf_1h'] >= self.ttf_upperTrigger.value) &
                (dataframe['volume'] > 0)
            )
        )
        if conditions:
            dataframe.loc[
                reduce(lambda x, y: x | y, conditions),
                'buy'
            ] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        conditions.append(
            (
                (dataframe['ttf_1h'] <= self.ttf_lowerTrigger.value) &
                (dataframe['volume'] > 0)
            )
        )
        if conditions:
            dataframe.loc[
                reduce(lambda x, y: x | y, conditions),
                'sell'
            ] = 1
        return dataframe


def ttf(df, ttf_length):
    df = df.copy()
    high, low = df['high'], df['low']
    i = len(high) - ttf_length - 1
    buyPower = high[i : -1].max() - low.shift(ttf_length)[i : -1].fillna(99999).min()
    sellPower = high.shift(ttf_length)[i : -1].fillna(0).max() - low[i : -1].min()
    
    #loggers.logger.info()
    ttf = 200 * (buyPower - sellPower) / (buyPower + sellPower)
    return Series(ttf, name ='ttf')