# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame
from freqtrade.strategy import BooleanParameter, CategoricalParameter, DecimalParameter, IStrategy, IntParameter
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
# This class is a sample. Feel free to customize it.

class FSampleStrategy(IStrategy):
    INTERFACE_VERSION = 3
    timeframe = '1h'
    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi".
    # minimal_roi = {"60": 0.1, "30": 0.2, "0": 0.2}
    minimal_roi = {'0': 1}
    stoploss = -0.05
    can_short = True
    # Trailing stoploss
    trailing_stop = False
    # trailing_only_offset_is_reached = False
    # trailing_stop_positive = 0.01
    # trailing_stop_positive_offset = 0.0  # Disabled / not configured
    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = True
    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 30

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['adx'] = ta.ADX(dataframe)
        # RSI
        dataframe['rsi'] = ta.RSI(dataframe)
        # Stochastic Fast
        stoch_fast = ta.STOCHF(dataframe)
        dataframe['fastd'] = stoch_fast['fastd']
        dataframe['fastk'] = stoch_fast['fastk']
        # MACD
        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']
        dataframe['macdhist'] = macd['macdhist']
        # MFI
        dataframe['mfi'] = ta.MFI(dataframe)
        # Bollinger Bands
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_upperband'] = bollinger['upper']
        dataframe['bb_percent'] = (dataframe['close'] - dataframe['bb_lowerband']) / (dataframe['bb_upperband'] - dataframe['bb_lowerband'])
        dataframe['bb_width'] = (dataframe['bb_upperband'] - dataframe['bb_lowerband']) / dataframe['bb_middleband']
        # Parabolic SAR
        dataframe['sar'] = ta.SAR(dataframe)
        # TEMA - Triple Exponential Moving Average
        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
        # Cycle Indicator
        # ------------------------------------
        # Hilbert Transform Indicator - SineWave
        hilbert = ta.HT_SINE(dataframe)
        dataframe['htsine'] = hilbert['sine']
        dataframe['htleadsine'] = hilbert['leadsine']
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Signal: RSI crosses above 30
        # Guard: tema below BB middle
        # Guard: tema is raising
        # Make sure Volume is not 0
        dataframe.loc[qtpylib.crossed_above(dataframe['rsi'], 30) & (dataframe['tema'] <= dataframe['bb_middleband']) & (dataframe['tema'] > dataframe['tema'].shift(1)) & (dataframe['volume'] > 0), 'enter_long'] = 1
        # Signal: RSI crosses above 70
        # Guard: tema above BB middle
        # Guard: tema is falling
        # Make sure Volume is not 0
        dataframe.loc[qtpylib.crossed_above(dataframe['rsi'], 70) & (dataframe['tema'] > dataframe['bb_middleband']) & (dataframe['tema'] < dataframe['tema'].shift(1)) & (dataframe['volume'] > 0), 'enter_short'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Signal: RSI crosses above 70
        # Guard: tema above BB middle
        # Guard: tema is falling
        # Make sure Volume is not 0
        dataframe.loc[qtpylib.crossed_above(dataframe['rsi'], 70) & (dataframe['tema'] > dataframe['bb_middleband']) & (dataframe['tema'] < dataframe['tema'].shift(1)) & (dataframe['volume'] > 0), 'exit_long'] = 1
        # Signal: RSI crosses above 30
        # Guard: tema below BB middle
        # Guard: tema is raising
        # Make sure Volume is not 0
        dataframe.loc[qtpylib.crossed_above(dataframe['rsi'], 30) & (dataframe['tema'] <= dataframe['bb_middleband']) & (dataframe['tema'] > dataframe['tema'].shift(1)) & (dataframe['volume'] > 0), 'exit_short'] = 1
        return dataframe