BinHV45

# --- Do not remove these libs ---
from pandas import DataFrame

import coingro.vendor.qtpylib.indicators as qtpylib
from coingro.strategy import IntParameter, IStrategy

# --------------------------------


def bollinger_bands(stock_price, window_size, num_of_std):
    rolling_mean = stock_price.rolling(window=window_size).mean()
    rolling_std = stock_price.rolling(window=window_size).std()
    lower_band = rolling_mean - (rolling_std * num_of_std)

    return rolling_mean, lower_band


class BinHV45(IStrategy):
    INTERFACE_VERSION = 2

    minimal_roi = {"0": 0.0125}

    stoploss = -0.05
    timeframe = "1m"

    buy_bbdelta = IntParameter(low=1, high=15, default=30, space="buy", optimize=True)
    buy_closedelta = IntParameter(low=15, high=20, default=30, space="buy", optimize=True)
    buy_tail = IntParameter(low=20, high=30, default=30, space="buy", optimize=True)

    # Hyperopt parameters
    buy_params = {
        "buy_bbdelta": 7,
        "buy_closedelta": 17,
        "buy_tail": 25,
    }

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        bollinger = qtpylib.bollinger_bands(dataframe["close"], window=40, stds=2)

        dataframe["upper"] = bollinger["upper"]
        dataframe["mid"] = bollinger["mid"]
        dataframe["lower"] = bollinger["lower"]
        dataframe["bbdelta"] = (dataframe["mid"] - dataframe["lower"]).abs()
        dataframe["pricedelta"] = (dataframe["open"] - dataframe["close"]).abs()
        dataframe["closedelta"] = (dataframe["close"] - dataframe["close"].shift()).abs()
        dataframe["tail"] = (dataframe["close"] - dataframe["low"]).abs()
        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                dataframe["lower"].shift().gt(0)
                & dataframe["bbdelta"].gt(dataframe["close"] * self.buy_bbdelta.value / 1000)
                & dataframe["closedelta"].gt(dataframe["close"] * self.buy_closedelta.value / 1000)
                & dataframe["tail"].lt(dataframe["bbdelta"] * self.buy_tail.value / 1000)
                & dataframe["close"].lt(dataframe["lower"].shift())
                & dataframe["close"].le(dataframe["close"].shift())
            ),
            "buy",
        ] = 1
        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        no sell signal
        """
        dataframe.loc[:, "sell"] = 0
        return dataframe