from mcDuck.custom_indicators import klinger_oscilator
from pandas.core.frame import DataFrame
from freqtrade.strategy import IStrategy, merge_informative_pair
from freqtrade.exchange import timeframe_to_minutes
from tabulate import tabulate


class StrategyKlingerSpikes(IStrategy):
    INTERFACE_VERSION = 2

    # Optimal ticker interval for the strategy.
    timeframe = '5m'
    informative_timeframe = '1d'

    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = False

    # These values can be overridden in the "ask_strategy" section in the config.
    use_sell_signal = True
    sell_profit_only = True
    ignore_roi_if_buy_signal = False
    ignore_buying_expired_candle_after = 720
    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 600

    minimal_roi = {
        "0": 100
    }
    # Stoploss:
    stoploss = -100
    # Optional order type mapping.
    order_types = {
        'buy': 'limit',
        'sell': 'limit',
        'stoploss': 'market',
        'stoploss_on_exchange': False
    }

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

    def do_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        [klinger_volume_indicator, signal] = klinger_oscilator(dataframe)
        dataframe["kvo"] = klinger_volume_indicator
        dataframe["ks"] = signal
        # print(find_peaks(klinger_volume_indicator))
        dataframe1 = dataframe.shift(1)
        dataframe2 = dataframe.shift(2)

        dataframe.loc[(dataframe1["kvo"] > dataframe2["kvo"]) &
                      (dataframe1["kvo"] < dataframe["kvo"]) &
                      (dataframe["kvo"] < dataframe["ks"]), "peak_buy"] = True
        dataframe.loc[((dataframe1["kvo"] < dataframe2["kvo"]) &
                       (dataframe1["kvo"] > dataframe["kvo"]) &
                       (dataframe["kvo"] > dataframe["ks"])), "peak_sell"] = True

        print(metadata['pair'])
        print(tabulate(dataframe.copy()[dataframe["peak_sell"] | dataframe["peak_buy"]], headers='keys', tablefmt='psql'))

        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        if self.config['runmode'].value in ('backtest', 'hyperopt'):
            assert (timeframe_to_minutes(self.timeframe) <=
                    5), "Backtest this strategy in 5m or 1m timeframe."

        if self.timeframe == self.informative_timeframe:
            dataframe = self.do_indicators(dataframe, metadata)
        else:
            if not self.dp:
                return dataframe
            informative = self.dp.get_pair_dataframe(
                pair=metadata['pair'], timeframe=self.informative_timeframe)

            informative = self.do_indicators(informative.copy(), metadata)

            dataframe = merge_informative_pair(
                dataframe, informative, self.timeframe, self.informative_timeframe, ffill=True)
            skip_columns = [(s + "_" + self.informative_timeframe)
                            for s in ['date', 'open', 'high', 'low', 'close', 'volume']]
            dataframe.rename(columns=lambda s: s.replace("_{}".format(
                self.informative_timeframe), "") if (not s in skip_columns) else s, inplace=True)

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        minimum_coin_price = 0.0000015

        dataframe.loc[(
            (dataframe["peak_buy"] == True) &
            (dataframe["volume"] > 0) &
            (dataframe["close"] > minimum_coin_price)
        ), "buy"] = 1
        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[(
            (dataframe["peak_sell"] == True) &
            (dataframe["volume"] > 0)
        ), "sell"] = 1
        return dataframe