# source: https://raw.githubusercontent.com/Lavyk/tradeTestes/825060533a67f135d7bcdc184ee7d014fbf257ef/TPct.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  # noqa
import pandas as pd  # noqa
from pandas import DataFrame
from typing import Optional, Union
from freqtrade.persistence import Trade

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 github_Lavyk_tradeTestes__TPct__20230223_003633(IStrategy):
    """
    This is a sample strategy to inspire you.
    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_*
    """

    INTERFACE_VERSION = 3

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

    # minimal_roi = {
    #     "2880": 0.01,
    #     "1440": 0.02,
    #     "0": 0.03
    # }

    minimal_roi = {
        "0": 0.03
    }

    stoploss = -0.05

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

    timeframe = '1d'

    # 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

    # Hyperoptable parameters
    # buy_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
    # sell_rsi = IntParameter(low=50, high=100, default=70, space='sell', optimize=True, load=True)
    # short_rsi = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True)
    # exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)

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

    # Optional order type mapping.
    order_types = {
        'entry': 'market',
        'exit': 'market',
        'stoploss': 'market',
        'stoploss_on_exchange': False
    }

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        if('venda_alvo' not in dataframe.columns):
            dataframe['venda_alvo'] = 0.0
        return dataframe

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

        dataframe["delta"] = 100 - (dataframe['close'] / ((dataframe['high'] + dataframe['low'])/2)*100)
        dataframe.loc[
            (
                (dataframe['venda_alvo'] == 0.0) &
                (dataframe['delta'] > 2) &
                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),
            ['enter_long', 'venda_alvo']] = (1, np.sum([dataframe['close'], np.multiply(dataframe['close'], 0.01)]))
        
        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
        """

        #print(metadata)  

        dataframe.loc[
            (
                (dataframe['venda_alvo'] != 0.0) &
                (dataframe['close'] > dataframe['venda_alvo']) &
                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),
            ['exit_long', 'venda_alvo']] = (1, 0.0)

        return dataframe