# source: https://raw.githubusercontent.com/arpanvyas/freqtrade_turtle/5a7b303b7e98a44fed39300d0aca36b9f67e3e63/Feb2023/TurtleTraderMod2.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
import os

class Github_arpanvyas_freqtrade_turtle__TurtleTraderMod2__20250530_210658(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 = '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 = -0.002
    stoploss = -0.02

    # 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'
    }
    
    use_exit_signal = True

    n_long_entry = 0
    n_long_exit = 0
    n_short_entry = 0
    n_short_exit = 0

    #use_custom_stoploss = True

    ticks_per_day = 288 #5m tick

    days_entry = 20*3
    days_exit = 10*3

    S_entry = days_entry * ticks_per_day
    S_exit = days_exit * ticks_per_day

    allowed_units = 1

    @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 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:
        """
        Adds several different TA indicators to the given DataFrame

        Performance Note: For the best performance be frugal on the number of indicators
        you are using. Let uncomment only the indicator you are using in your strategies
        or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
        :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
        """
        
        # Momentum Indicators
        # ------------------------------------

        # ADX
        #dataframe['adx'] = ta.ADX(dataframe)

        # ATR
        dataframe['atr'] = ta.ATR(dataframe)

        # ATR Ratio
        dataframe['atr_ratio'] = dataframe['atr']/dataframe['close']

        # Breakout Score
        dr = dataframe.iloc[::-1]['close'] 
        dr = dr.reset_index(drop=True)
        
        count = []
        for i, v in dr.iteritems():
            d = dr.iloc[i:]
            d1 = d
            d1[i] = -1 
            try:
                ix = d1[d1 >= v].index[0] - i 
            except IndexError:
                ix = len(d)
        
            count.append(ix - 1)
        dataframe['long_brkt_scr'] = count[::-1]
        
        dr = dataframe.iloc[::-1]['close'] 
        dr = dr.reset_index(drop=True)
        count = []
        for i, v in dr.iteritems():
            d = dr.iloc[i:]
            d1 = d
            d1[i] = 9223372036854775807
            try:
                ix = d1[d1 <= v].index[0] - i 
            except IndexError:
                ix = len(d)
        
            count.append(ix - 1)
        dataframe['short_brkt_scr'] = count[::-1]


        """
        # first check if dataprovider is available
        if self.dp:
            if self.dp.runmode.value in ('live', 'dry_run'):
                ob = self.dp.orderbook(metadata['pair'], 1)
                dataframe['best_bid'] = ob['bids'][0][0]
                dataframe['best_ask'] = ob['asks'][0][0]
        """

        return dataframe

    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                            proposed_stake: float, min_stake: Optional[float], max_stake: float,
                            leverage: float, entry_tag: Optional[str], side: str,
                            **kwargs) -> float:

        dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
        current_candle = dataframe.iloc[-1].squeeze()


        if current_candle['atr_ratio'] < self.allowed_units:
            # Use entire available wallet during favorable conditions when in compounding mode.
            return max_stake
        else:
            return ((self.allowed_units)/(current_candle['atr_ratio']))*(max_stake)

        # Use default stake amount.
        return proposed_stake

    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['long_brkt_scr'] >= self.S_entry ),
            'enter_long'] = 1
        dataframe.loc[
            ( dataframe['long_brkt_scr'] < self.S_entry ),
            'enter_long'] = 0
        # Uncomment to use shorts (Only used in futures/margin mode. Check the documentation for more info)
        dataframe.loc[
            ( dataframe['short_brkt_scr'] >= self.S_entry ),
            'enter_short'] = 1
        dataframe.loc[
            ( dataframe['short_brkt_scr'] < self.S_entry ),
            'enter_short'] = 0

        print("from inside populate entry trend")

        #print(dataframe.to_string()) 
        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['short_brkt_scr'] >= self.S_exit ),
            'exit_long'] = 1
        dataframe.loc[
            ( dataframe['short_brkt_scr'] < self.S_exit),
            'exit_long'] = 0
        # Uncomment to use shorts (Only used in futures/margin mode. Check the documentation for more info)
        dataframe.loc[
            ( dataframe['long_brkt_scr'] >= self.S_exit ),
            'exit_short'] = 1
        dataframe.loc[
            ( dataframe['long_brkt_scr'] < self.S_exit ),
            'exit_short'] = 0 

        print("from inside populate exit trend")
        #print(dataframe.to_string())

        return dataframe