mind

# https://github.com/Yodolescrypto/yodostrats

import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame
from datetime import datetime, timedelta, timezone
from freqtrade.exchange import timeframe_to_minutes
import freqtrade.exchange as exchange
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
                                IStrategy, IntParameter, merge_informative_pair, informative)
from technical.util import resample_to_interval, resampled_merge

from freqtrade.persistence import Trade
from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal, Real  # noqa
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import pandas_ta as pta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from technical.pivots_points import pivots_points
from typing import Any, Dict, List, Optional

# 13% APR 1 year backtest
class mind(IStrategy):
    custom_info = {}
    """
    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 = '1d'

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

    # 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 = -0.3

    # Trailing stoploss
    trailing_stop= True
    trailing_stop_positive=0.99
    trailing_stop_positive_offset= 1
    trailing_only_offset_is_reached= True
    
    custom_price_max_distance_ratio = 1

    # 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 = 50


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

    # 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': {

            },
            'subplots': {
                "MACD": {
                    'macdh': {'color': 'blue'},
                    'macdd': {'color': 'cyan'},
                    'macdf': {'color': 'purple'},
                },
                "CCI": {
                    'cci': {'color': 'red'},

                },
            }
        } 

    @property
    def protections(self):
        return  [
            {
                "method": "CooldownPeriod",
                "stop_duration_candles": 1
            }
        ]       
    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                            proposed_stake: float, min_stake: float, max_stake: float,
                            entry_tag: str, **kwargs) -> float:


        return (100)
    
    def leverage(self, pair: str, current_time: 'datetime', current_rate: float,
                 proposed_leverage: float, max_leverage: float, side: str,
                 **kwargs) -> float:

            return 10

    def informative_pairs(self):

        return []
    

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        
        macd, macdsignal, macdhist = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
        
        dataframe['macdf'] = macd
        dataframe['macdd'] = macdsignal
        dataframe['macdh'] = macdhist
        stoch_fast = ta.STOCHF(dataframe)
        dataframe['fastd'] = stoch_fast['fastd']
        dataframe['fastk'] = stoch_fast['fastk']
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['adx'] = ta.ADX(dataframe)
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['lower'] = bollinger['lower']
        dataframe['middle'] = bollinger['mid']
        dataframe['upper'] = bollinger['upper']

        return dataframe



    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # 1d #
        dataframe.loc[
            (            
                # Reversal Incomming (Trigger)
                (dataframe['lower'] < dataframe['close']) &
                (dataframe['lower'].rolling(3).mean() > dataframe['low'].rolling(3).mean()) &
                (dataframe['fastk'] > dataframe['fastd']) &
                #10 days going down avg
                (dataframe['macdh'].rolling(10).mean() < dataframe['macdh']) &
                
                (dataframe['volume'] > 0)  
            ),
            ['enter_long', 'enter_tag']] = (1, "bullish_lower_bb")

        dataframe.loc[
            (            
                # Reversal Incomming (Trigger)
                (dataframe['middle'] < dataframe['close']) &
                (dataframe['middle'].rolling(3).mean() > dataframe['low'].rolling(3).mean()) &
                (dataframe['fastk'] < dataframe['fastd']) &
                #5 days going down avg
                (dataframe['macdh'].rolling(10).mean() < dataframe['macdh']) &

                (dataframe['volume'] > 0)  
            ),
            ['enter_long', 'enter_tag']] = (1, "bullish_middle_bb")

        dataframe.loc[
            (            
                # Reversal Incomming (Trigger)
                (dataframe['upper'] > dataframe['close']) &
                (dataframe['upper'].rolling(3).mean() < dataframe['high'].rolling(3).mean()) &
                (dataframe['fastk'] < dataframe['fastd']) &
                #5 days going down avg
                (dataframe['macdh'].rolling(10).mean() > dataframe['macdh']) &
                
                (dataframe['volume'] > 0)  
            ),
            ['enter_short', 'enter_tag']] = (1, "bearish_upper_bb")

        dataframe.loc[
            (            
                # Reversal Incomming (Trigger)
                (dataframe['middle'] > dataframe['close']) &
                (dataframe['middle'].rolling(3).mean() < dataframe['high'].rolling(3).mean()) &
                (dataframe['fastk'] < dataframe['fastd']) &
                #5 days going down avg
                (dataframe['macdh'].rolling(10).mean() > dataframe['macdh']) &
                
                (dataframe['volume'] > 0)  
            ),
            ['enter_short', 'enter_tag']] = (1, "bearish_middle_bb")

        return dataframe


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

                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),
            ['exit_long', 'exit_tag']] = (0, "open_ai_told_me_to_exit")
        
        dataframe.loc[
            (

                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),
            ['exit_short', 'exit_tag']] = (0, 'yodo_knows_better_ex')
        return dataframe