# GodStra Strategy
# Author: @Mablue (Masoud Azizi)
# github: https://github.com/mablue/
# IMPORTANT:Add to your pairlists inside config.json (Under StaticPairList):
#   {
#       "method": "AgeFilter",
#       "min_days_listed": 30
#   },
# IMPORTANT: INSTALL TA BEFOUR RUN(pip install ta)
# IMPORTANT: Use Smallest "max_open_trades" for getting best results inside config.json

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

from numpy.lib import math
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
# --------------------------------

# Add your lib to import here
# import talib.abstract as ta
import pandas as pd
# import talib.abstract as ta
from ta import add_all_ta_features
from ta.utils import dropna
import freqtrade.vendor.qtpylib.indicators as qtpylib
from functools import reduce
import numpy as np


class GodStra(IStrategy):

    # Buy hyperspace params:
    buy_params = {
        'buy-cross-0': 'volatility_kcc',
        'buy-indicator-0': 'trend_ichimoku_base',
        'buy-int-0': 42,
        'buy-oper-0': '<R',
        'buy-real-0': 0.06295
    }

    # Sell hyperspace params:
    sell_params = {
        'sell-cross-0': 'volume_mfi',
        'sell-indicator-0': 'trend_kst_diff',
        'sell-int-0': 98,
        'sell-oper-0': '=R',
        'sell-real-0': 0.8779
    }

    # ROI table:
    minimal_roi = {
        "0": 0.3556,
        "4818": 0.21275,
        "6395": 0.09024,
        "22372": 0
    }

    # Stoploss:
    stoploss = -0.34549

    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.22673
    trailing_stop_positive_offset = 0.2684
    trailing_only_offset_is_reached = True
    # Buy hypers
    timeframe = '4h'

    def dna_size(self, dct: dict):
        def int_from_str(st: str):
            str_int = ''.join([d for d in st if d.isdigit()])
            if str_int:
                return int(str_int)
            return -1 # in case if the parameter somehow doesn't have index
        return len({int_from_str(digit) for digit in dct.keys()})

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Add all ta features
        dataframe = dropna(dataframe)
        dataframe = add_all_ta_features(
            dataframe, open="open", high="high", low="low", close="close", volume="volume", fillna=True)
        # dataframe.to_csv("df.csv", index=True)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = list()
        # /5: Cuz We have 5 Group of variables inside buy_param
        for i in range(self.dna_size(self.buy_params)):

            OPR = self.buy_params[f'buy-oper-{i}']
            IND = self.buy_params[f'buy-indicator-{i}']
            CRS = self.buy_params[f'buy-cross-{i}']
            INT = self.buy_params[f'buy-int-{i}']
            REAL = self.buy_params[f'buy-real-{i}']
            DFIND = dataframe[IND]
            DFCRS = dataframe[CRS]

            if OPR == ">":
                conditions.append(DFIND > DFCRS)
            elif OPR == "=":
                conditions.append(np.isclose(DFIND, DFCRS))
            elif OPR == "<":
                conditions.append(DFIND < DFCRS)
            elif OPR == "CA":
                conditions.append(qtpylib.crossed_above(DFIND, DFCRS))
            elif OPR == "CB":
                conditions.append(qtpylib.crossed_below(DFIND, DFCRS))
            elif OPR == ">I":
                conditions.append(DFIND > INT)
            elif OPR == "=I":
                conditions.append(DFIND == INT)
            elif OPR == "<I":
                conditions.append(DFIND < INT)
            elif OPR == ">R":
                conditions.append(DFIND > REAL)
            elif OPR == "=R":
                conditions.append(np.isclose(DFIND, REAL))
            elif OPR == "<R":
                conditions.append(DFIND < REAL)

        print(conditions)
        dataframe.loc[
            reduce(lambda x, y: x & y, conditions),
            'buy'] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = list()
        for i in range(self.dna_size(self.sell_params)):
            OPR = self.sell_params[f'sell-oper-{i}']
            IND = self.sell_params[f'sell-indicator-{i}']
            CRS = self.sell_params[f'sell-cross-{i}']
            INT = self.sell_params[f'sell-int-{i}']
            REAL = self.sell_params[f'sell-real-{i}']
            DFIND = dataframe[IND]
            DFCRS = dataframe[CRS]

            if OPR == ">":
                conditions.append(DFIND > DFCRS)
            elif OPR == "=":
                conditions.append(np.isclose(DFIND, DFCRS))
            elif OPR == "<":
                conditions.append(DFIND < DFCRS)
            elif OPR == "CA":
                conditions.append(qtpylib.crossed_above(DFIND, DFCRS))
            elif OPR == "CB":
                conditions.append(qtpylib.crossed_below(DFIND, DFCRS))
            elif OPR == ">I":
                conditions.append(DFIND > INT)
            elif OPR == "=I":
                conditions.append(DFIND == INT)
            elif OPR == "<I":
                conditions.append(DFIND < INT)
            elif OPR == ">R":
                conditions.append(DFIND > REAL)
            elif OPR == "=R":
                conditions.append(np.isclose(DFIND, REAL))
            elif OPR == "<R":
                conditions.append(DFIND < REAL)

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

        return dataframe