# 𝐼𝓉 𝒾𝓈 𝒟𝒾𝓂𝑜𝓃𝒹 𝒮𝓉𝓇𝒶𝓉𝑒𝑔𝓎.
# 𝒯𝒽𝒶𝓉 𝓉𝒶𝓀𝑒𝓈 𝒽𝑒𝓇 𝑜𝓌𝓃 𝓇𝒾𝑔𝒽𝓉𝓈 𝓁𝒾𝓀𝑒 𝒜𝒻𝑔𝒽𝒶𝓃𝒾𝓈𝓉𝒶𝓃 𝓌𝑜𝓂𝑒𝓃
# 𝒯𝒽𝑜𝓈𝑒 𝓌𝒽𝑜 𝓈𝓉𝒾𝓁𝓁 𝓅𝓇𝑜𝓊𝒹 𝒶𝓃𝒹 𝒽𝑜𝓅𝑒𝒻𝓊𝓁.
# 𝒯𝒽𝑜𝓈𝑒 𝓌𝒽𝑜 𝓉𝒽𝑒 𝓂𝑜𝓈𝓉 𝒷𝑒𝒶𝓊𝓉𝒾𝒻𝓊𝓁 𝒸𝓇𝑒𝒶𝓉𝓊𝓇𝑒𝓈 𝒾𝓃 𝓉𝒽𝑒 𝒹𝑒𝓅𝓉𝒽𝓈 𝑜𝒻 𝓉𝒽𝑒 𝒹𝒶𝓇𝓀𝑒𝓈𝓉.
# 𝒯𝒽𝑜𝓈𝑒 𝓌𝒽𝑜 𝓈𝒽𝒾𝓃𝑒 𝓁𝒾𝓀𝑒 𝒹𝒾𝒶𝓂𝑜𝓃𝒹𝓈 𝒷𝓊𝓇𝒾𝑒𝒹 𝒾𝓃 𝓉𝒽𝑒 𝒽𝑒𝒶𝓇𝓉 𝑜𝒻 𝓉𝒽𝑒 𝒹𝑒𝓈𝑒𝓇𝓉 ...
# 𝒲𝒽𝓎 𝓃𝑜𝓉 𝒽𝑒𝓁𝓅 𝓌𝒽𝑒𝓃 𝓌𝑒 𝒸𝒶𝓃?
# 𝐼𝒻 𝓌𝑒 𝒷𝑒𝓁𝒾𝑒𝓋𝑒 𝓉𝒽𝑒𝓇𝑒 𝒾𝓈 𝓃𝑜 𝓂𝒶𝓃 𝓁𝑒𝒻𝓉 𝓌𝒾𝓉𝒽 𝓉𝒽𝑒𝓂
# (𝒲𝒽𝒾𝒸𝒽 𝒾𝓈 𝓅𝓇𝑜𝒷𝒶𝒷𝓁𝓎 𝓉𝒽𝑒 𝓅𝓇𝑜𝒹𝓊𝒸𝓉 𝑜𝒻 𝓉𝒽𝑒 𝓉𝒽𝑜𝓊𝑔𝒽𝓉 𝑜𝒻 𝓅𝒶𝒾𝓃𝓁𝑒𝓈𝓈 𝒸𝑜𝓇𝓅𝓈𝑒𝓈)
# 𝒲𝒽𝑒𝓇𝑒 𝒽𝒶𝓈 𝑜𝓊𝓇 𝒽𝓊𝓂𝒶𝓃𝒾𝓉𝓎 𝑔𝑜𝓃𝑒?
# 𝒲𝒽𝑒𝓇𝑒 𝒽𝒶𝓈 𝒽𝓊𝓂𝒶𝓃𝒾𝓉𝓎 𝑔𝑜𝓃𝑒?
# 𝒲𝒽𝓎 𝓃𝑜𝓉 𝒽𝑒𝓁𝓅 𝓌𝒽𝑒𝓃 𝓌𝑒 𝒸𝒶𝓃?
# IMPORTANT: This strategy
# designed for "ZERO" loss and "UNDER"
# 15 minuts avg duration.So if you have more
# loss and more avg, Its "NOT" normal result, and
# you will change config.json variables and hyperoption commands
# Thanks To @xmatthias if he was approve the last version of This strategy
# That just a lazy code. I never can reach to this strategy(Now its really a dimond.)
# * freqtrade hyperopt --hyperopt-loss ShortTradeDurHyperOptLoss --spaces all --strategy Dimond -e 700 -j 2 --timerange 20210810-20210813
# * freqtrade backtesting --strategy Dimond
# Author: @Mablue (Masoud Azizi)
# github: https://github.com/mablue/
# (First Hyperopt it.A hyperopt file is available)
# --- Do not remove these libs ---
from freqtrade.strategy.hyper import CategoricalParameter, DecimalParameter, IntParameter
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
# --------------------------------

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

##### SETINGS #####
# It hyperopt just one set of params for all buy and sell strategies if true.
DUALFIT = False
COUNT = 10
GAP = 3
### END SETINGS ###


class Dimond(IStrategy):
    # ###################### RESULT PLACE ######################
    # *    6/700:      1 trades. 1/0/0 Wins/Draws/Losses. Avg profit  17.68%. Median profit  17.68%. Total profit  58.94100000 USDT (   5.89Σ%). Avg duration 0:00:00 min. Objective: 1.79949

    # Buy hyperspace params:
    buy_params = {
        "buy_fast": 31,
        "buy_push": 0.72,
        "buy_shift": -7,
        "buy_slow": 2,
    }

    # Sell hyperspace params:
    sell_params = {
        "sell_fast": 17,
        "sell_push": 1.493,
        "sell_shift": -7,
        "sell_slow": 28,
    }

    # ROI table:
    minimal_roi = {
        "0": 0.177,
        "31": 0.059,
        "61": 0.021,
        "170": 0
    }

    # Stoploss:
    stoploss = -0.241

    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.13
    trailing_stop_positive_offset = 0.189
    trailing_only_offset_is_reached = True
    # Buy hypers
    timeframe = '5m'
    # #################### END OF RESULT PLACE ####################
    buy_push = DecimalParameter(0, 2, decimals=3, default=1, space='buy')
    buy_shift = IntParameter(-10, 0, default=-6, space='buy')
    buy_fast = IntParameter(2, 50, default=9, space='buy')
    buy_slow = IntParameter(2, 50, default=18, space='buy')
    if not DUALFIT:
        sell_push = DecimalParameter(
            0, 2, decimals=3,  default=1, space='sell')
        sell_shift = IntParameter(-10, 0, default=-6, space='sell')
        sell_fast = IntParameter(2, 50, default=9, space='sell')
        sell_slow = IntParameter(2, 50, default=18, space='sell')

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

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['buy_ema_fast'] = ta.SMA(
            dataframe, timeperiod=int(self.buy_fast.value))
        dataframe['buy_ema_slow'] = ta.SMA(
            dataframe, timeperiod=int(self.buy_slow.value))

        conditions = []

        conditions.append(
            qtpylib.crossed_above(
                dataframe['buy_ema_fast'].shift(self.buy_shift.value),
                dataframe['buy_ema_slow'].shift(
                    self.buy_shift.value)*self.buy_push.value
            )
        )

        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:
        push = self.buy_push.value
        shift = self.buy_shift.value
        ema_fast = dataframe['buy_ema_fast']
        ema_slow = dataframe['buy_ema_slow']

        if not DUALFIT:
            push = self.sell_push.value
            shift = self.sell_shift.value
            ema_fast = dataframe['sell_ema_fast'] = ta.SMA(
                dataframe, timeperiod=int(self.buy_fast.value))
            ema_slow = dataframe['sell_ema_slow'] = ta.SMA(
                dataframe, timeperiod=int(self.buy_slow.value))

        conditions = []

        conditions.append(
            qtpylib.crossed_below(
                ema_fast.shift(shift),
                ema_slow.shift(shift)*push
            )
        )

        if conditions:
            dataframe.loc[
                reduce(lambda x, y: x & y, conditions),
                'sell']=1
        return dataframe