# source: https://raw.githubusercontent.com/behradhesami/Projects/ae7930082f7237367db61e66bfac7e356528dfcc/Freqtrade/TrendLine/Fractal.py
import numpy as np
import pandas as pd
from pandas import DataFrame
from freqtrade.strategy.interface import IStrategy

class Github_behradhesami_Projects__Fractal__20240626_115130(IStrategy):
    INTERFACE_VERSION = 3
    can_short: bool = False
    minimal_roi = {
        "60": 0.01,
        "30": 0.02,
        "0": 0.04
    }
    stoploss = -0.10
    trailing_stop = False
    timeframe = '5m'
    process_only_new_candles = True
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False
    startup_candle_count: int = 200
    order_types = {
        'entry': 'limit',
        'exit': 'limit',
        'stoploss': 'market',
        'stoploss_on_exchange': False
    }
    order_time_in_force = {
        'entry': 'GTC',
        'exit': 'GTC'
    }

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['fractal_up'] = self.fractal_up(dataframe)
        dataframe['fractal_down'] = self.fractal_down(dataframe)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                dataframe['fractal_up']
            ),
            'enter_long'] = 1

        dataframe.loc[
            (
                dataframe['fractal_down']
            ),
            'enter_short'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # This should contain conditions to exit trades, currently just placeholders
        dataframe.loc[
            (
                # Add your exit conditions here
            ),
            'exit_long'] = 1

        dataframe.loc[
            (
                # Add your exit conditions here
            ),
            'exit_short'] = 1
        return dataframe

    def fractal_up(self, dataframe: DataFrame) -> pd.Series:
        n = 10  # Number of rows to consider for identifying a fractal
        roll_n = 2 * n + 1
        highest = dataframe['high'].rolling(window=roll_n, center=True).max()
        return highest == dataframe['high']

    def fractal_down(self, dataframe: DataFrame) -> pd.Series:
        n = 10  # Number of rows to consider for a fractal
        roll_n = 2 * n + 1
        lowest = dataframe['low'].rolling(window=roll_n, center=True).min()
        return lowest == dataframe['low']