# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# --- Do not remove these libs ---
import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame
from freqtrade.strategy.interface import IStrategy
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from technical.util import resample_to_interval, resampled_merge
from freqtrade.strategy import IStrategy, merge_informative_pair
from technical.indicators import ichimoku

class Ichimoku_v31(IStrategy):
    INTERFACE_VERSION = 3
    # ROI table:
    minimal_roi = {'0': 100}
    # Stoploss:
    stoploss = -0.99
    # Optimal timeframe for the strategy.
    timeframe = '1h'
    inf_tf = '4h'
    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = True
    # These values can be overridden in the "ask_strategy" section in the config.
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = True
    # Number of candles the strategy requires before producing valid signals
    startup_candle_count = 150
    # Optional order type mapping.
    order_types = {'entry': 'market', 'exit': 'market', 'stoploss': 'market', 'stoploss_on_exchange': False}

    def informative_pairs(self):
        if not self.dp:
            # Don't do anything if DataProvider is not available.
            return []
        # Get access to all pairs available in whitelist.
        pairs = self.dp.current_whitelist()
        # Assign tf to each pair so they can be downloaded and cached for strategy.
        informative_pairs = [(pair, '4h') for pair in pairs]
        return informative_pairs

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        if not self.dp:
            # Don't do anything if DataProvider is not available.
            return dataframe
        dataframe_inf = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_tf)
        #Heiken Ashi Candlestick Data
        heikinashi = qtpylib.heikinashi(dataframe_inf)
        dataframe_inf['ha_open'] = heikinashi['open']
        dataframe_inf['ha_close'] = heikinashi['close']
        dataframe_inf['ha_high'] = heikinashi['high']
        dataframe_inf['ha_low'] = heikinashi['low']
        ha_ichi = ichimoku(heikinashi, conversion_line_period=20, base_line_periods=60, laggin_span=120, displacement=30)
        #Required Ichi Parameters
        dataframe_inf['senkou_a'] = ha_ichi['senkou_span_a']
        dataframe_inf['senkou_b'] = ha_ichi['senkou_span_b']
        dataframe_inf['cloud_green'] = ha_ichi['cloud_green']
        dataframe_inf['cloud_red'] = ha_ichi['cloud_red']
        # Merge timeframes
        dataframe = merge_informative_pair(dataframe, dataframe_inf, self.timeframe, self.inf_tf, ffill=True)
        '\n    Senkou Span A > Senkou Span B = Cloud Green\n    Senkou Span B > Senkou Span A = Cloud Red\n    '
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[dataframe['ha_close_4h'].crossed_above(dataframe['senkou_a_4h']) & (dataframe['ha_close_4h'].shift() < dataframe['senkou_a_4h']) & (dataframe['cloud_green_4h'] == True) | dataframe['ha_close_4h'].crossed_above(dataframe['senkou_b_4h']) & (dataframe['ha_close_4h'].shift() < dataframe['senkou_b_4h']) & (dataframe['cloud_red_4h'] == True), 'entry'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[(dataframe['ha_close_4h'] < dataframe['senkou_a_4h']) | (dataframe['ha_close_4h'] < dataframe['senkou_b_4h']), 'exit'] = 1
        return dataframe