# source: https://raw.githubusercontent.com/gwuml/seas-8414/2ed683185da360b86b72263c214b9233ec04bf09/week-1/application/user_data/strategies/Elmo.py
import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame  # noqa
from datetime import datetime  # noqa
from typing import Optional, Union  # noqa

from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
                                IStrategy, IntParameter)

import talib.abstract as ta
import pandas_ta as pta
import freqtrade.vendor.qtpylib.indicators as qtpylib


class Github_gwuml_seas_8414__Elmo__20221029_010341(IStrategy):
    INTERFACE_VERSION = 3

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

    # Can this strategy go short?
    can_short: bool = True

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi".
    minimal_roi = {
        "60": 0.01,
        "30": 0.02,
        "0": 0.04
    }

    # Optimal stoploss designed for the strategy.
    stoploss = -0.10

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

    # These values can be overridden 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

    # Strategy parameters
    buy_rsi = IntParameter(10, 40, default=30, space="buy")
    sell_rsi = IntParameter(60, 90, default=70, space="sell")
    
    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Adds several different TA indicators to the given DataFrame
        :param dataframe: Dataframe with data from the exchange
        :param metadata: Additional information, like the currently traded pair
        :return: a Dataframe with all mandatory indicators for the strategies
        """

        # RSI
        dataframe['rsi'] = ta.RSI(dataframe)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the entry signal for the given dataframe
        :param dataframe: DataFrame
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with entry columns populated
        """
        dataframe.loc[
            (
                (qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)) &  # Signal: RSI crosses above buy_rsi
                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),
            'enter_long'] = 0

        dataframe.loc[
            (
                (qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value)) &  # Signal: RSI crosses above sell_rsi
                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),
            'enter_short'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the exit signal for the given dataframe
        :param dataframe: DataFrame
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with exit columns populated
        """
        dataframe.loc[
            (
                (qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value)) &  # Signal: RSI crosses above sell_rsi
                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),
            'exit_long'] = 0
        dataframe.loc[
            (
                (qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value))  # Signal: RSI crosses above buy_rsi
            ),
            'exit_short'] = 1
        return dataframe