# source: https://raw.githubusercontent.com/jameszaa52/freqtrade/28f9dad9ebcc1e2d56d7286488ac02148ab2358c/user_data/strategies/brain.py
# github_jameszaa52_freqtrade__brain__20210820_105637 Strategy
# Author: @Mablue (Masoud Azizi)
# github: https://github.com/mablue/
# IMPORTANT: INSTALL TA BEFOUR RUN(pip install ta)
# freqtrade hyperopt --hyperopt github_jameszaa52_freqtrade__brain__20210820_105637Ho --hyperopt-loss SharpeHyperOptLossDaily --spaces buy sell roi --strategy github_jameszaa52_freqtrade__brain__20210820_105637 -j 3 -e 700

# --- 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 github_jameszaa52_freqtrade__brain__20210820_105637(IStrategy):
    ##################### SETTINGS #########################
    # this is your trading github_jameszaa52_freqtrade__brain__20210820_105637 nodes count
    # you can change it and see the results...
    # Importand will same with github_jameszaa52_freqtrade__brain__20210820_105637Ho.py
    nodes = 4

    # 1 means 1, 10 means 0.1, 100 means 0.01
    decimals = 2
    #################### END SETTINGS ######################

    ##################### HYPEROPT RESULTS PASTE PLACE #########################
    # *   10/700:    178 trades. 103/59/16 Wins/Draws/Losses. Avg profit   1.35%. Median profit   2.30%. Total profit  0.02400559 BTC (  24.01Σ%). Avg duration 1 day, 18:31:00 min. Objective: -5.31583

    # Buy hyperspace params:
    buy_params = {
        "buy-node-input-0": "trend_mass_index",
        "buy-node-enabled-0": 0,
        "buy-node-reversed-0": 1,
        "buy-node-wight-0": 81,
        "buy-node-input-1": "momentum_ao",
        "buy-node-enabled-1": 0,
        "buy-node-reversed-1": -1,
        "buy-node-wight-1": 36,
        "buy-node-input-2": "volatility_ui",
        "buy-node-enabled-2": 1,
        "buy-node-reversed-2": -1,
        "buy-node-wight-2": 59,
        "buy-node-input-3": "volatility_kcp",
        "buy-node-enabled-3": 1,
        "buy-node-reversed-3": 1,
        "buy-node-wight-3": 76,
    }

    # Sell hyperspace params:
    sell_params = {
        "sell-node-input-0": "volatility_bbp",
        "sell-node-enabled-0": 0,
        "sell-node-reversed-0": -1,
        "sell-node-wight-0": 25,
        "sell-node-input-1": "trend_vortex_ind_diff",
        "sell-node-enabled-1": 0,
        "sell-node-reversed-1": 1,
        "sell-node-wight-1": 1,
        "sell-node-input-2": "trend_macd",
        "sell-node-enabled-2": 0,
        "sell-node-reversed-2": -1,
        "sell-node-wight-2": 4,
        "sell-node-input-3": "momentum_ppo_hist",
        "sell-node-enabled-3": 0,
        "sell-node-reversed-3": -1,
        "sell-node-wight-3": 72,
    }

    # ROI table:
    minimal_roi = {
        "0": 0.347,
        "392": 0.126,
        "727": 0.023,
        "1411": 0
    }
    # Stoploss:
    stoploss = -0.256

    #################### END HYPEROPT RESULTS PASTE PLACE #######################

    # Buy hypers
    timeframe = '1h'

    # do not edit this line:
    decimals = 10 ** decimals

    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=False)
        return dataframe

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

        conditions = []
        RESULT = 0

        for i in range(self.nodes):
            DFINP = dataframe[self.buy_params[f'buy-node-input-{i}']]
            ENABLED = self.buy_params[f'buy-node-enabled-{i}']
            REVERSE = self.buy_params[f'buy-node-reversed-{i}']
            WIGHT = self.buy_params[f'buy-node-wight-{i}']/self.decimals
            RESULT += DFINP*ENABLED*REVERSE*WIGHT

        conditions.append(RESULT > 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 = []
        RESULT = 0
        for i in range(self.nodes):
            DFINP = dataframe[self.sell_params[f'sell-node-input-{i}']]
            ENABLED = self.sell_params[f'sell-node-enabled-{i}']
            REVERSE = self.sell_params[f'sell-node-reversed-{i}']
            WIGHT = self.sell_params[f'sell-node-wight-{i}']/self.decimals
            RESULT += DFINP*ENABLED*REVERSE*WIGHT

        conditions.append(RESULT > 0)

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

        return dataframe