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
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 GPTREV(IStrategy):
    custom_info = {}
    '\n    This is a strategy template to get you started.\n    More information in https://www.freqtrade.io/en/latest/strategy-customization/\n\n    You can:\n        :return: a Dataframe with all mandatory indicators for the strategies\n    - Rename the class name (Do not forget to update class_name)\n    - Add any methods you want to build your strategy\n    - Add any lib you need to build your strategy\n\n    You must keep:\n    - the lib in the section "Do not remove these libs"\n    - the methods: populate_indicators, populate_entry_trend, populate_exit_trend\n    You should keep:\n    - timeframe, minimal_roi, stoploss, trailing_*\n    '
    # 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 = '1m'
    # Can this strategy go short?
    can_short: bool = False
    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi".
    minimal_roi = {'0': 1}
    # Optimal stoploss designed for the strategy.
    # This attribute will be overridden if the config file contains "stoploss".
    stoploss = -0.04
    # Trailing stoploss
    trailing_stop = True
    trailing_stop_positive = 0.02
    trailing_stop_positive_offset = 0.04
    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': False}
    # Optional order time in force.
    order_time_in_force = {'entry': 'gtc', 'exit': 'gtc'}

    @property
    def plot_config(self):
        # Main plot indicators (Moving averages, ...)
        return {'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': 5}]

    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 450

    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"),
                            ]
        """
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, self.informative_timeframe) for pair in pairs]
        return informative_pairs

    @informative('15m')
    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['rsi'] = ta.RSI(dataframe)
        dataframe['adx'] = ta.ADX(dataframe)
        # Can't assign multiple variables & use @informative handle - fuck optimization
        macd, macdsignal, macdhist = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
        dataframe['macdf'] = macd
        dataframe['macdd'] = macdsignal
        dataframe['macdh'] = macdhist
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # GPT #
        # Make sure Volume is not 0
        dataframe.loc[(dataframe['rsi_15m'] < 30) & (dataframe['adx_15m'] > 20) & (dataframe['macdh_15m'] > 0) & (dataframe['volume'] > 0), ['enter_long', 'enter_tag']] = (1, 'openai_told_me_to_enter')
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:  # Make sure Volume is not 0
        dataframe.loc[(dataframe['rsi_15m'] > 70) & (dataframe['adx_15m'] > 20) & (dataframe['macdh_15m'] < 0) & (dataframe['volume'] > 0), ['exit_long', 'exit_tag']] = (1, 'open_ai_told_me_to_exit')
        return dataframe