# source: https://raw.githubusercontent.com/mrgolfprat/ft_sample/d24c79b0d852e13757c6d523524b1660b19ec6e6/user_data/strategies/AiewStra.py
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401

# --- Do not remove these libs ---
import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame

from freqtrade.strategy import IStrategy
from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter

# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy  # noqa
from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter


def EWO(dataframe, ema_length=5, ema2_length=35):
    df = dataframe.copy()
    ema1 = ta.EMA(df, timeperiod=ema_length)
    ema2 = ta.EMA(df, timeperiod=ema2_length)
    emadif = (ema1 - ema2) / df['close'] * 100
    return emadif


class github_mrgolfprat_ft_sample__AiewStra__20211117_150019(IStrategy):
    """
    This is a strategy template to get you started.
    More information in https://www.freqtrade.io/en/latest/strategy-customization/

    You can:
        :return: a Dataframe with all mandatory indicators for the strategies
    - Rename the class name (Do not forget to update class_name)
    - Add any methods you want to build your strategy
    - Add any lib you need to build your strategy

    You must keep:
    - the lib in the section "Do not remove these libs"
    - the methods: populate_indicators, populate_buy_trend, populate_sell_trend
    You should keep:
    - timeframe, minimal_roi, stoploss, trailing_*
    """
    # Strategy interface version - allow new iterations of the strategy interface.
    # Check the documentation or the Sample strategy to get the latest version.
    INTERFACE_VERSION = 2

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi".
    minimal_roi = {
        "30": 0.01,
        "20": 0.014,
        "10": 0.025,
        "0": 0.03
    }

    # Optimal stoploss designed for the strategy.
    # This attribute will be overridden if the config file contains "stoploss".
    stoploss = -0.035
    # Trailing stoploss
    trailing_stop = True
    trailing_only_offset_is_reached = True
    trailing_stop_positive = 0.02
    trailing_stop_positive_offset = 0.04  # Disabled / not configured

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

    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = False

    # These values can be overridden in the "ask_strategy" section in the config.
    use_sell_signal = False
    sell_profit_only = True
    ignore_roi_if_buy_signal = False

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 30

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

    # Optional order time in force.
    order_time_in_force = {
        'buy': 'gtc',
        'sell': 'gtc'
    }

    plot_config = {
        # Main plot indicators (Moving averages, ...)
        'main_plot': {
            'tema': {},
            'sar': {'color': 'white'},
        },
        'subplots': {
            # Subplots - each dict defines one additional plot
            "MACD": {
                'macd': {'color': 'blue'},
                'macdsignal': {'color': 'orange'},
            },
            "RSI": {
                'rsi': {'color': 'red'},
            }
        }
    }

    # BBPeriod

    def informative_pairs(self):

        return []

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

        return dataframe

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

        dataframe.loc[
            (

            ),
            'buy'] = 1

        return dataframe

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

        dataframe.loc[
            (
                dataframe['sell_signal']
            ),
            'sell'] = 1
        return dataframe