Scalp

# --- Do not remove these libs ---
# --------------------------------
import talib.abstract as ta
from pandas import DataFrame

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


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

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

    Recommended is to only sell based on ROI for this strategy
    """

    # 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 = -0.04
    # 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)

        # 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"]

        return dataframe

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