# source: https://raw.githubusercontent.com/mpicard/lucy/34591dd9f5365682e709091c567051d63d749552/user_data/strategies/SwingHigh.py
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement

# --- Do not remove these libs ---
import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame

from freqtrade.strategy.interface import IStrategy

# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib


class github_mpicard_lucy__SwingHigh__20210225_202849(IStrategy):
    # Disable ROI
    # Could be replaced with new ROI from hyperopt.
    minimal_roi = {"0": 0.16035, "23": 0.03218, "54": 0.01182, "173": 0}

    stoploss = -0.22274

    ### Do extra hyperopt for trailing seperat. Use "--spaces default" and then "--spaces trailing".
    ### See here for more information: https://www.freqtrade.io/en/latest/hyperopt
    trailing_stop = True
    trailing_stop_positive = 0.08
    trailing_stop_positive_offset = 0.10
    trailing_only_offset_is_reached = True

    timeframe = "30m"

    def informative_pairs(self):
        return []

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

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

        ### Add timeperiod from hyperopt (replace xx with value):
        ### "xx" must be replaced even before the first hyperopt is run,
        ### else "xx" would be a syntax error because it must be a Integer value.
        dataframe["cci-buy"] = ta.CCI(dataframe, timeperiod=13)
        dataframe["cci-sell"] = ta.CCI(dataframe, timeperiod=76)

        return dataframe

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

        dataframe.loc[
            (
                (dataframe["macd"] > dataframe["macdsignal"])
                & (dataframe["cci-buy"] <= -188.0)
                & (dataframe["volume"] > 0)
            ),
            "buy",
        ] = 1

        return dataframe

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

        dataframe.loc[
            (
                (dataframe["macd"] < dataframe["macdsignal"])
                & (dataframe["cci-sell"] >= 231.0)
                & (dataframe["volume"] > 0)
            ),
            "sell",
        ] = 1

        return dataframe