# source: https://raw.githubusercontent.com/arpanvyas/freqtrade_turtle/5a7b303b7e98a44fed39300d0aca36b9f67e3e63/Feb2023/TurtleTrader_S1.py
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401

# --- Do not remove these libs ---
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)

# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import pandas_ta as pta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.persistence import Trade
import os

class Github_arpanvyas_freqtrade_turtle__TurtleTrader_S1__20250530_210658(IStrategy):

    # 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 = '5m'

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

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi".
    # Disabled by setting a very high value 1000,00%
    minimal_roi = {
        "0": 1000
    }

    # Optimal stoploss designed for the strategy.
    # This attribute will be overridden if the config file contains "stoploss".
    stoploss = -0.02

    # Trailing stoploss
    trailing_stop = False

    # 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

    # 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'
    }
    
    use_exit_signal = True

    #use_custom_stoploss = True
    
    @property
    def plot_config(self):
        return {
            # Main plot indicators (Moving averages, ...)
            'main_plot': {
                'tema': {},
                'sar': {'color': 'white'},
            },
            'subplots': {
                # Subplots - each dict defines one additional plot
                "MACD": {
                    'macd': {'color': 'blue'},
                    'macdsignal': {'color': 'orange'},
                },
                "RSI": {
                    'rsi': {'color': 'red'},
                }
            }
        }


    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        
        mult = 288

        S1_big = 6 * mult + 144
        S1_small = 10 * mult 

        ## Breakouts
        ### Long
        dataframe["S1_big_high"] = dataframe.close.rolling(S1_big).max()
        dataframe["S1_small_low"] = dataframe.close.rolling(S1_small).min()
        dataframe.loc[(dataframe['S1_big_high'] == dataframe['close']),'S1_big_high_this'] = 1
        dataframe.loc[(dataframe['S1_small_low'] == dataframe['close']),'S1_small_low_this'] = 1

        ### Short
        dataframe["S1_big_low"] = dataframe.close.rolling(S1_big).min()
        dataframe["S1_small_high"] = dataframe.close.rolling(S1_small).max()
        dataframe.loc[(dataframe['S1_big_low'] == dataframe['close']),'S1_big_low_this'] = 1
        dataframe.loc[(dataframe['S1_small_high'] == dataframe['close']),'S1_small_high_this'] = 1


        return dataframe

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

        dataframe.loc[
            ( dataframe['S1_big_high_this'] == 1 ),
            'enter_long'] = 1
        dataframe.loc[
            ( dataframe['S1_big_low_this'] == 1 ),
            'enter_short'] = 1

        return dataframe

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

        dataframe.loc[
            ( dataframe['S1_small_low_this'] == 1 ),
            'exit_long'] = 1
        dataframe.loc[
            ( dataframe['S1_small_high_this'] == 1 ),
            'exit_short'] = 1

        return dataframe