# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame
from freqtrade.strategy import BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
# This class is a sample. Feel free to customize it.

class botbaby(IStrategy):
    # Check the documentation or the Sample strategy to get the latest version.
    INTERFACE_VERSION = 3
    # IMP NOTEEEEEEEE - also change this roi parameter after testing
    minimal_roi = {'0': 0.01}
    # Optimal stoploss designed for the strategy.
    # This attribute will be overridden if the config file contains "stoploss".
    #  IMP NOTE -Hey listen, remeber to change stoploss after testing
    stoploss = -0.007
    # Trailing stoploss
    trailing_stop = False
    # Optimal timeframe for the strategy.
    timeframe = '30m'
    # 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_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False
    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 30
    # Optional order type mapping.
    order_types = {'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False}
    # Optional order time in force.
    order_time_in_force = {'entry': 'gtc', 'exit': 'gtc'}

    def informative_pairs(self):
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # ADX
        # EMA - Exponential Moving Average
        dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
        dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200)
        dataframe['ema13'] = ta.EMA(dataframe, timeperiod=13)
        dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[(dataframe['ema13'] > dataframe['ema50']) & (dataframe['ema13'].shift(1) <= dataframe['ema50'].shift(1)), 'entry'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[dataframe['ema13'] < dataframe['ema50'], 'exit'] = 1
        return dataframe