ReinforcedSmoothScalp

# --- Do not remove these libs ---
from functools import reduce

import numpy  # noqa

# --------------------------------
import talib.abstract as ta
from pandas import DataFrame
from technical.util import resample_to_interval, resampled_merge

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


class ReinforcedSmoothScalp(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
    """

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi"
    minimal_roi = {"0": 0.02}
    # Optimal stoploss designed for the strategy
    # This attribute will be overridden if the config file contains "stoploss"
    # should not be below 3% loss

    stoploss = -0.1
    # Optimal timeframe for the strategy
    # the shorter the better
    timeframe = "1m"

    # resample factor to establish our general trend. Basically don't buy if a trend is not given
    resample_factor = 5

    buy_adx = IntParameter(20, 50, default=32, space="buy")
    buy_fastd = IntParameter(15, 45, default=30, space="buy")
    buy_fastk = IntParameter(15, 45, default=26, space="buy")
    buy_mfi = IntParameter(10, 25, default=22, space="buy")
    buy_adx_enabled = BooleanParameter(default=True, space="buy")
    buy_fastd_enabled = BooleanParameter(default=True, space="buy")
    buy_fastk_enabled = BooleanParameter(default=False, space="buy")
    buy_mfi_enabled = BooleanParameter(default=True, space="buy")

    sell_adx = IntParameter(50, 100, default=53, space="sell")
    sell_cci = IntParameter(100, 200, default=183, space="sell")
    sell_fastd = IntParameter(50, 100, default=79, space="sell")
    sell_fastk = IntParameter(50, 100, default=70, space="sell")
    sell_mfi = IntParameter(75, 100, default=92, space="sell")

    sell_adx_enabled = BooleanParameter(default=False, space="sell")
    sell_cci_enabled = BooleanParameter(default=True, space="sell")
    sell_fastd_enabled = BooleanParameter(default=True, space="sell")
    sell_fastk_enabled = BooleanParameter(default=True, space="sell")
    sell_mfi_enabled = BooleanParameter(default=False, space="sell")

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        tf_res = timeframe_to_minutes(self.timeframe) * 5
        df_res = resample_to_interval(dataframe, tf_res)
        df_res["sma"] = ta.SMA(df_res, 50, price="close")
        dataframe = resampled_merge(dataframe, df_res, fill_na=True)
        dataframe["resample_sma"] = dataframe[f"resample_{tf_res}_sma"]

        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"]

        return dataframe

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

        conditions = []
        if self.buy_mfi_enabled.value:
            conditions.append(dataframe["mfi"] < self.buy_mfi.value)
        if self.buy_fastd_enabled.value:
            conditions.append(dataframe["fastd"] < self.buy_fastd.value)
        if self.buy_fastk_enabled.value:
            conditions.append(dataframe["fastk"] < self.buy_fastk.value)
        if self.buy_adx_enabled.value:
            conditions.append(dataframe["adx"] > self.buy_adx.value)

        # Some static conditions which always apply
        conditions.append(qtpylib.crossed_above(dataframe["fastk"], dataframe["fastd"]))
        conditions.append(dataframe["resample_sma"] < dataframe["close"])

        # Check that volume is not 0
        conditions.append(dataframe["volume"] > 0)

        if conditions:
            dataframe.loc[reduce(lambda x, y: x & y, conditions), "buy"] = 1

        return dataframe

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

        conditions = []

        # Some static conditions which always apply
        conditions.append(dataframe["open"] > dataframe["ema_high"])

        if self.sell_mfi_enabled.value:
            conditions.append(dataframe["mfi"] > self.sell_mfi.value)
        if self.sell_fastd_enabled.value:
            conditions.append(dataframe["fastd"] > self.sell_fastd.value)
        if self.sell_fastk_enabled.value:
            conditions.append(dataframe["fastk"] > self.sell_fastk.value)
        if self.sell_adx_enabled.value:
            conditions.append(dataframe["adx"] < self.sell_adx.value)
        if self.sell_cci_enabled.value:
            conditions.append(dataframe["cci"] > self.sell_cci.value)

        # Check that volume is not 0
        conditions.append(dataframe["volume"] > 0)

        if conditions:
            dataframe.loc[reduce(lambda x, y: x & y, conditions), "sell"] = 1

        return dataframe