MyIchimoku2

from datetime import datetime

import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame
from technical.indicators import ichimoku

# --------------------------------
# Add your lib to import here
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.persistence import Trade
from freqtrade.strategy import (IStrategy)


class MyIchimoku2(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

    # 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 = {
        "240": 0.2,
        "120": 0.1,
        "0": 0.06,
    }

    # "minimal_roi": {
    #     "40": 0.0,    # Exit after 40 minutes if the profit is not negative
    #     "30": 0.01,   # Exit after 30 minutes if there is at least 1% profit
    #     "20": 0.02,   # Exit after 20 minutes if there is at least 2% profit
    #     "0":  0.04    # Exit immediately if there is at least 4% profit
    # },

    stoploss = -0.10
    use_custom_stoploss = True

    def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
                        current_rate: float, current_profit: float, **kwargs) -> float:
        """
        Custom stoploss logic, returning the new distance relative to current_rate (as ratio).
        e.g. returning -0.05 would create a stoploss 5% below current_rate.
        The custom stoploss can never be below self.stoploss, which serves as a hard maximum loss.

        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/

        When not implemented by a strategy, returns the initial stoploss value
        Only called when use_custom_stoploss is set to True.

        :param pair: Pair that's currently analyzed
        :param trade: trade object.
        :param current_time: datetime object, containing the current datetime
        :param current_rate: Rate, calculated based on pricing settings in ask_strategy.
        :param current_profit: Current profit (as ratio), calculated based on current_rate.
        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
        :return float: New stoploss value, relative to the currentrate
        """
        return -0.05

    # Optimal timeframe for the strategy.
    timeframe = '1h'

    # 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

    # Hyperoptable parameters

    # 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'
    }

    plot_config = {
        'main_plot': {
            'tenkan': {'color': 'red'},
            'kijun': {'color': 'green'},
        },
        # 'subplots': {
        #     "MACD": {
        #         'macd': {'color': 'blue'},
        #         'macdsignal': {'color': 'orange'},
        #     },
        #     "RSI": {
        #         'cloud_green': {'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 []

    @property
    def protections(self):
        return [
            {
                "method": "CooldownPeriod",
                "stop_duration_candles": 5
            }
        ]

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        ichi = ichimoku(dataframe)
        dataframe['tenkan'] = ichi['tenkan_sen']
        dataframe['kijun'] = ichi['kijun_sen']
        dataframe['senkou_a'] = ichi['senkou_span_a']
        dataframe['senkou_b'] = ichi['senkou_span_b']
        dataframe['cloud_green'] = ichi['cloud_green']
        dataframe['cloud_red'] = ichi['cloud_red']
        return dataframe

    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['cloud_green']) &
                            (qtpylib.crossed_above(dataframe['close'], dataframe['senkou_a']))
                    )
                    |
                    (
                            (dataframe['cloud_red']) &
                            (qtpylib.crossed_above(dataframe['close'], dataframe['senkou_b']))
                    )
                    |
                    (
                        (qtpylib.crossed_above(dataframe['tenkan'], dataframe['kijun']))
                    )
            ),
            'enter_long'] = 1

        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[
            (
            ),
            'exit_long'] = 0

        return dataframe