SmoothScalp

# --- Do not remove these libs ---
import numpy  # noqa

# --------------------------------
# --------------------------------
import talib.abstract as ta
from pandas import DataFrame

import coingro.vendor.qtpylib.indicators as qtpylib
from coingro.strategy.interface import IStrategy


class SmoothScalp(IStrategy):
    """
    this strategy is based around the idea of generating a lot of potential buys and making
    tiny profits on each trade

    we recommend to have at least 60 parallel trades at any time to cover non avoidable losses
    """

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi"
    minimal_roi = {"0": 0.01}
    # Optimal stoploss designed for the strategy
    # This attribute will be overridden if the config file contains "stoploss"
    # should not be below 3% loss

    stoploss = -0.5
    # Optimal timeframe for the strategy
    # the shorter the better
    timeframe = "1m"

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe["ema_high"] = ta.EMA(dataframe, timeperiod=5, price="high")
        dataframe["ema_close"] = ta.EMA(dataframe, timeperiod=5, price="close")
        dataframe["ema_low"] = ta.EMA(dataframe, timeperiod=5, price="low")
        stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
        dataframe["fastd"] = stoch_fast["fastd"]
        dataframe["fastk"] = stoch_fast["fastk"]
        dataframe["adx"] = ta.ADX(dataframe)
        dataframe["cci"] = ta.CCI(dataframe, timeperiod=20)
        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
        dataframe["mfi"] = ta.MFI(dataframe)

        # required for graphing
        bollinger = qtpylib.bollinger_bands(dataframe["close"], window=20, stds=2)
        dataframe["bb_lowerband"] = bollinger["lower"]
        dataframe["bb_upperband"] = bollinger["upper"]
        dataframe["bb_middleband"] = bollinger["mid"]

        macd = ta.MACD(dataframe)
        dataframe["macd"] = macd["macd"]
        dataframe["macdsignal"] = macd["macdsignal"]
        dataframe["macdhist"] = macd["macdhist"]
        dataframe["cci"] = ta.CCI(dataframe)

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (
                    (dataframe["open"] < dataframe["ema_low"])
                    & (dataframe["adx"] > 30)
                    & (dataframe["mfi"] < 30)
                    & (
                        (dataframe["fastk"] < 30)
                        & (dataframe["fastd"] < 30)
                        & (qtpylib.crossed_above(dataframe["fastk"], dataframe["fastd"]))
                    )
                    & (dataframe["cci"] < -150)
                )
            ),
            "buy",
        ] = 1
        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (
                    ((dataframe["open"] >= dataframe["ema_high"]))
                    | (
                        (qtpylib.crossed_above(dataframe["fastk"], 70))
                        | (qtpylib.crossed_above(dataframe["fastd"], 70))
                    )
                )
                & (dataframe["cci"] > 150)
            ),
            "sell",
        ] = 1
        return dataframe