# --- Do not remove these libs ---
from freqtrade.strategy import IStrategy, merge_informative_pair
from pandas import DataFrame
import talib.abstract as ta
import logging
import freqtrade.vendor.qtpylib.indicators as qtpylib
# --------------------------------
import pandas as pd
import numpy as np
import technical.indicators as ftt
from freqtrade.exchange import timeframe_to_minutes
from technical.util import resample_to_interval, resampled_merge
logger = logging.getLogger(__name__)

def pivots_points(dataframe: pd.DataFrame, timeperiod=1, levels=4) -> pd.DataFrame:
    """
    Pivots Points
    https://www.tradingview.com/support/solutions/43000521824-pivot-points-standard/
    Formula:
    Pivot = (Previous High + Previous Low + Previous Close)/3
    Resistance #1 = (2 x Pivot) - Previous Low
    Support #1 = (2 x Pivot) - Previous High
    Resistance #2 = (Pivot - Support #1) + Resistance #1
    Support #2 = Pivot - (Resistance #1 - Support #1)
    Resistance #3 = (Pivot - Support #2) + Resistance #2
    Support #3 = Pivot - (Resistance #2 - Support #2)
    ...
    :param dataframe:
    :param timeperiod: Period to compare (in ticker)
    :param levels: Num of support/resistance desired
    :return: dataframe
    """
    data = {}
    low = qtpylib.rolling_mean(series=pd.Series(index=dataframe.index, data=dataframe['low']), window=timeperiod)
    high = qtpylib.rolling_mean(series=pd.Series(index=dataframe.index, data=dataframe['high']), window=timeperiod)
    # Pivot
    data['pivot'] = qtpylib.rolling_mean(series=qtpylib.typical_price(dataframe), window=timeperiod)
    # Resistance #1
    # data["r1"] = (2 * data["pivot"]) - low ... Standard
    # R1 = PP + 0.382 * (HIGHprev - LOWprev) ... fibonacci
    data['r1'] = data['pivot'] + 0.382 * (high - low)
    data['rS1'] = data['pivot'] + 0.0955 * (high - low)
    # Resistance #2
    # data["s1"] = (2 * data["pivot"]) - high ... Standard
    # S1 = PP - 0.382 * (HIGHprev - LOWprev) ... fibonacci
    data['s1'] = data['pivot'] - 0.382 * (high - low)
    # Calculate Resistances and Supports >1
    for i in range(2, levels + 1):
        prev_support = data['s' + str(i - 1)]
        prev_resistance = data['r' + str(i - 1)]
        # Resitance
        data['r' + str(i)] = data['pivot'] - prev_support + prev_resistance
        # Support
        data['s' + str(i)] = data['pivot'] - (prev_resistance - prev_support)
    return pd.DataFrame(index=dataframe.index, data=data)

def create_ichimoku(dataframe, conversion_line_period, displacement, base_line_periods, laggin_span):
    ichimoku = ftt.ichimoku(dataframe, conversion_line_period=conversion_line_period, base_line_periods=base_line_periods, laggin_span=laggin_span, displacement=displacement)
    dataframe[f'tenkan_sen_{conversion_line_period}'] = ichimoku['tenkan_sen']
    dataframe[f'kijun_sen_{conversion_line_period}'] = ichimoku['kijun_sen']
    dataframe[f'senkou_a_{conversion_line_period}'] = ichimoku['senkou_span_a']
    dataframe[f'senkou_b_{conversion_line_period}'] = ichimoku['senkou_span_b']

class Miku_PP_v3(IStrategy):
    INTERFACE_VERSION = 3
    '\n     Miku_PP_v3\n     La base de la Estrategia es: Miku_PP_v2 y Miku_1m_5m_CSen44_1_5m\n    \n    Provando en:\n     Miku_1m_5m_CSen444v2_N_1_5\n     SymphonIK\n    '
    # Optimal timeframe for the strategy
    timeframe = '5m'
    # generate signals from the 1h timeframe
    informative_timeframe = '1d'
    # WARNING: ichimoku is a long indicator, if you remove or use a
    # shorter startup_candle_count your results will be unstable/invalid
    # for up to a week from the start of your backtest or dry/live run
    # (180 candles = 7.5 days)
    startup_candle_count = 444  # MAXIMUM ICHIMOKU
    # NOTE: this strat only uses candle information, so processing between
    # new candles is a waste of resources as nothing will change
    process_only_new_candles = True
    minimal_roi = {'0': 10}
    plot_config = {'main_plot': {'pivot_1d': {}, 'rS1_1d': {}, 'r1_1d': {}, 's1_1d': {}, 'senkou_b_88': {}}, 'subplots': {'MACD': {'macd_1h': {'color': 'blue'}, 'macdsignal_1h': {'color': 'orange'}}}}
    # WARNING setting a stoploss for this strategy doesn't make much sense, as it will entry
    # back into the trend at the next available opportunity, unless the trend has ended,
    # in which case it would exit anyway.
    # Stoploss:
    stoploss = -0.1

    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, self.informative_timeframe) for pair in pairs]
        if self.dp:
            for pair in pairs:
                informative_pairs += [(pair, '1d')]
        return informative_pairs

    def slow_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        # dataframe "1d"
        """
        dataframe1d = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1d')
        # Pivots Points
        pp = pivots_points(dataframe1d)
        dataframe1d['pivot'] = pp['pivot']
        dataframe1d['r1'] = pp['r1']
        dataframe1d['s1'] = pp['s1']
        dataframe1d['rS1'] = pp['rS1']
        # Pivots Points
        dataframe = merge_informative_pair(dataframe, dataframe1d, self.timeframe, '1d', ffill=True)
        '\n        # dataframe normal\n        '
        '\n        create_ichimoku(dataframe, conversion_line_period=9, \n                        displacement=26, base_line_periods=26, laggin_span=52)\n        '
        create_ichimoku(dataframe, conversion_line_period=20, displacement=88, base_line_periods=88, laggin_span=88)
        create_ichimoku(dataframe, conversion_line_period=88, displacement=444, base_line_periods=88, laggin_span=88)
        create_ichimoku(dataframe, conversion_line_period=355, displacement=880, base_line_periods=175, laggin_span=175)
        dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20)
        "\n        Notes: Start Trading\n\n        * En 1m\n        dataframe['ichimoku_ok'] = (\n            (dataframe['kijun_sen_355_5m'] >= dataframe['tenkan_sen_355_5m']) &\n            (dataframe['senkou_a_100'] > dataframe['senkou_b_100']) &\n            (dataframe['senkou_a_20'] > dataframe['senkou_b_20']) &\n            (dataframe['kijun_sen_20'] > dataframe['tenkan_sen_444']) &\n            (dataframe['senkou_a_9'] > dataframe['senkou_a_20']) &\n            (dataframe['tenkan_sen_20'] >= dataframe['kijun_sen_20']) &\n            (dataframe['tenkan_sen_9'] >= dataframe['tenkan_sen_20']) &\n            (dataframe['tenkan_sen_9'] >= dataframe['kijun_sen_9'])\n        ).astype('int')\n\n        * En 5m\n        dataframe['ichimoku_ok'] = (\n            (dataframe['close'] > dataframe['pivot_1d']) &\n            (dataframe['r1_1d'] > dataframe['close']) &\n            (dataframe['kijun_sen_355'] >= dataframe['tenkan_sen_355']) &\n            (dataframe['senkou_a_20'] > dataframe['senkou_b_20']) &\n            (dataframe['kijun_sen_20'] > dataframe['tenkan_sen_88']) &\n            (dataframe['senkou_a_9'] > dataframe['senkou_a_20']) &\n            (dataframe['tenkan_sen_20'] >= dataframe['kijun_sen_20']) &\n            (dataframe['tenkan_sen_9'] >= dataframe['tenkan_sen_20']) &\n            (dataframe['tenkan_sen_9'] >= dataframe['kijun_sen_9'])\n        ).astype('int')\n\n\n            (dataframe['pivot_1d'] > dataframe['ema20_5m']) anulo ema20_5m para ver si hace entradas en Dry Run\n\n        dataframe['trending_over'] = (\n            (\n            (dataframe['senkou_b_444'] > dataframe['close'])\n            )\n            |\n            (\n            (dataframe['pivot_1d'] > dataframe['close'])\n            )\n            \n        ).astype('int')\n\n        return dataframe\n        "
        # Start Trading
        dataframe['pivots_ok'] = ((dataframe['close'] > dataframe['pivot_1d']) & (dataframe['rS1_1d'] > dataframe['close']) & (dataframe['kijun_sen_355'] >= dataframe['tenkan_sen_355']) & (dataframe['senkou_a_20'] > dataframe['senkou_b_20'])).astype('int')
        dataframe['trending_over'] = (dataframe['senkou_b_88'] > dataframe['close']).astype('int')
        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe = self.slow_tf_indicators(dataframe, metadata)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[dataframe['pivots_ok'] > 0, 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[dataframe['trending_over'] > 0, 'exit_long'] = 1
        return dataframe