# source: https://raw.githubusercontent.com/chunyin820/crypto-trade/598a7a4d33dc43f29c1078d8712a8a2ec1890886/ft_userdata/user_data/strategies/SuperTrend.py
# 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
import pandas as pd
from pandas import DataFrame
from datetime import datetime
from typing import Optional, Union

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

# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import pandas_ta as pta
from technical import qtpylib


class github_chunyin820_crypto_trade__SuperTrend__20230712_142922(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_entry_trend, populate_exit_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 = 3

    # Optimal timeframe for the strategy.
    timeframe = '8h'

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

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi".
    minimal_roi = {
        "0": 100
    }

    # Optimal stoploss designed for the strategy.
    # This attribute will be overridden if the config file contains "stoploss".
    stoploss = -0.10

    # Trailing stoploss
    trailing_stop = False
    # trailing_only_offset_is_reached = False
    # trailing_stop_positive = 0.01
    # trailing_stop_positive_offset = 0.0  # Disabled / not configured

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

    # 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")

    # 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'
    }
    
    @property
    def plot_config(self):
        return {
            # Main plot indicators (Moving averages, ...)
            'main_plot': {
                'ST_long': {'color': 'green'},
                'ST_short': {'color': 'red'}
            },
        }

    def informative_pairs(self):
        """
        Define additional, informative pair/interval combinations to be cached from the exchange.
        These pair/interval combinations are non-tradeable, unless they are part
        of the whitelist as well.
        For more information, please consult the documentation
        :return: List of tuples in the format (pair, interval)
            Sample: return [("ETH/USDT", "5m"),
                            ("BTC/USDT", "15m"),
                            ]
        """
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        
        periodo = 7
        atr_multiplicador = 3.0

        dataframe['ST_long'] = pta.supertrend(dataframe['high'], dataframe['low'], dataframe['close'], 
                                            length=periodo, multiplier=atr_multiplicador)[f'SUPERTl_{periodo}_{atr_multiplicador}']
        dataframe['ST_short'] = pta.supertrend(dataframe['high'], dataframe['low'], dataframe['close'], 
                                            length=periodo, multiplier=atr_multiplicador)[f'SUPERTs_{periodo}_{atr_multiplicador}']
        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[
                (dataframe['ST_long'] < dataframe['close']) &
                (dataframe['volume'] > 0
            ),
            'enter_long'] = 1
            
        # dataframe.loc[
        #     (
        #         (qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)) &  # Signal: RSI crosses above buy_rsi
        #         (dataframe['tema'] <= dataframe['bb_middleband']) &  # Guard: tema below BB middle
        #         (dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard: tema is raising
        #         (dataframe['volume'] > 0)  # Make sure Volume is not 0
        #     ),
        #     'enter_long'] = 1
        # Uncomment to use shorts (Only used in futures/margin mode. Check the documentation for more info)
        """
        dataframe.loc[
            (
                (qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value)) &  # Signal: RSI crosses above sell_rsi
                (dataframe['tema'] > dataframe['bb_middleband']) &  # Guard: tema above BB middle
                (dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard: tema is falling
                (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[
            (
                (dataframe['ST_short'] > dataframe['close']) &
                (dataframe['volume'] > 0)
            ),
            'exit_long'] = 1

        # dataframe.loc[
        #     (
        #         (qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value)) &  # Signal: RSI crosses above sell_rsi
        #         (dataframe['tema'] > dataframe['bb_middleband']) &  # Guard: tema above BB middle
        #         (dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard: tema is falling
        #         (dataframe['volume'] > 0)  # Make sure Volume is not 0
        #     ),
        #     'exit_long'] = 1
        # Uncomment to use shorts (Only used in futures/margin mode. Check the documentation for more info)
        """
        dataframe.loc[
            (
                (qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)) &  # Signal: RSI crosses above buy_rsi
                (dataframe['tema'] <= dataframe['bb_middleband']) &  # Guard: tema below BB middle
                (dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard: tema is raising
                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),
            'exit_short'] = 1
        """
        return dataframe