BinHV27

from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame

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

import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from typing import Dict, List
from functools import reduce
from pandas import DataFrame, DatetimeIndex, merge

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

import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy  # noqa


class BinHV27(IStrategy):
    """
    strategy sponsored by user BinH from slack
    """

    minimal_roi = {"0": 1}

    stoploss = -0.50
    timeframe = "5m"

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe["rsi"] = numpy.nan_to_num(ta.RSI(dataframe, timeperiod=5))
        rsiframe = DataFrame(dataframe["rsi"]).rename(columns={"rsi": "close"})
        dataframe["emarsi"] = numpy.nan_to_num(ta.EMA(rsiframe, timeperiod=5))
        dataframe["adx"] = numpy.nan_to_num(ta.ADX(dataframe))
        dataframe["minusdi"] = numpy.nan_to_num(ta.MINUS_DI(dataframe))
        minusdiframe = DataFrame(dataframe["minusdi"]).rename(
            columns={"minusdi": "close"}
        )
        dataframe["minusdiema"] = numpy.nan_to_num(ta.EMA(minusdiframe, timeperiod=25))
        dataframe["plusdi"] = numpy.nan_to_num(ta.PLUS_DI(dataframe))
        plusdiframe = DataFrame(dataframe["plusdi"]).rename(columns={"plusdi": "close"})
        dataframe["plusdiema"] = numpy.nan_to_num(ta.EMA(plusdiframe, timeperiod=5))
        dataframe["lowsma"] = numpy.nan_to_num(ta.EMA(dataframe, timeperiod=60))
        dataframe["highsma"] = numpy.nan_to_num(ta.EMA(dataframe, timeperiod=120))
        dataframe["fastsma"] = numpy.nan_to_num(ta.SMA(dataframe, timeperiod=120))
        dataframe["slowsma"] = numpy.nan_to_num(ta.SMA(dataframe, timeperiod=240))
        dataframe["bigup"] = dataframe["fastsma"].gt(dataframe["slowsma"]) & (
            (dataframe["fastsma"] - dataframe["slowsma"]) > dataframe["close"] / 300
        )
        dataframe["bigdown"] = ~dataframe["bigup"]
        dataframe["trend"] = dataframe["fastsma"] - dataframe["slowsma"]
        dataframe["preparechangetrend"] = dataframe["trend"].gt(
            dataframe["trend"].shift()
        )
        dataframe["preparechangetrendconfirm"] = dataframe[
            "preparechangetrend"
        ] & dataframe["trend"].shift().gt(dataframe["trend"].shift(2))
        dataframe["continueup"] = dataframe["slowsma"].gt(
            dataframe["slowsma"].shift()
        ) & dataframe["slowsma"].shift().gt(dataframe["slowsma"].shift(2))
        dataframe["delta"] = dataframe["fastsma"] - dataframe["fastsma"].shift()
        dataframe["slowingdown"] = dataframe["delta"].lt(dataframe["delta"].shift())
        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            dataframe["slowsma"].gt(0)
            & dataframe["close"].lt(dataframe["highsma"])
            & dataframe["close"].lt(dataframe["lowsma"])
            & dataframe["minusdi"].gt(dataframe["minusdiema"])
            & dataframe["rsi"].ge(dataframe["rsi"].shift())
            & (
                (
                    ~dataframe["preparechangetrend"]
                    & ~dataframe["continueup"]
                    & dataframe["adx"].gt(25)
                    & dataframe["bigdown"]
                    & dataframe["emarsi"].le(20)
                )
                | (
                    ~dataframe["preparechangetrend"]
                    & dataframe["continueup"]
                    & dataframe["adx"].gt(30)
                    & dataframe["bigdown"]
                    & dataframe["emarsi"].le(20)
                )
                | (
                    ~dataframe["continueup"]
                    & dataframe["adx"].gt(35)
                    & dataframe["bigup"]
                    & dataframe["emarsi"].le(20)
                )
                | (
                    dataframe["continueup"]
                    & dataframe["adx"].gt(30)
                    & dataframe["bigup"]
                    & dataframe["emarsi"].le(25)
                )
            ),
            "buy",
        ] = 1
        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (
                    ~dataframe["preparechangetrendconfirm"]
                    & ~dataframe["continueup"]
                    & (
                        dataframe["close"].gt(dataframe["lowsma"])
                        | dataframe["close"].gt(dataframe["highsma"])
                    )
                    & dataframe["highsma"].gt(0)
                    & dataframe["bigdown"]
                )
                | (
                    ~dataframe["preparechangetrendconfirm"]
                    & ~dataframe["continueup"]
                    & dataframe["close"].gt(dataframe["highsma"])
                    & dataframe["highsma"].gt(0)
                    & (
                        dataframe["emarsi"].ge(75)
                        | dataframe["close"].gt(dataframe["slowsma"])
                    )
                    & dataframe["bigdown"]
                )
                | (
                    ~dataframe["preparechangetrendconfirm"]
                    & dataframe["close"].gt(dataframe["highsma"])
                    & dataframe["highsma"].gt(0)
                    & dataframe["adx"].gt(30)
                    & dataframe["emarsi"].ge(80)
                    & dataframe["bigup"]
                )
                | (
                    dataframe["preparechangetrendconfirm"]
                    & ~dataframe["continueup"]
                    & dataframe["slowingdown"]
                    & dataframe["emarsi"].ge(75)
                    & dataframe["slowsma"].gt(0)
                )
                | (
                    dataframe["preparechangetrendconfirm"]
                    & dataframe["minusdi"].lt(dataframe["plusdi"])
                    & dataframe["close"].gt(dataframe["lowsma"])
                    & dataframe["slowsma"].gt(0)
                )
            ),
            "sell",
        ] = 1
        return dataframe