FSampleStrategy

# 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:

        dataframe.loc[
            (
                # Signal: RSI crosses above 30
                (qtpylib.crossed_above(dataframe["rsi"], 30))
                & (dataframe["tema"] <= dataframe["bb_middleband"])
                & (  # Guard: tema below BB middle
                    dataframe["tema"] > dataframe["tema"].shift(1)
                )
                & (  # Guard: tema is raising
                    dataframe["volume"] > 0
                )  # Make sure Volume is not 0
            ),
            "enter_long",
        ] = 1

        dataframe.loc[
            (
                # Signal: RSI crosses above 70
                (qtpylib.crossed_above(dataframe["rsi"], 70))
                & (dataframe["tema"] > dataframe["bb_middleband"])
                & (  # Guard: tema above BB middle
                    dataframe["tema"] < dataframe["tema"].shift(1)
                )
                & (  # Guard: tema is falling
                    dataframe["volume"] > 0
                )  # Make sure Volume is not 0
            ),
            "enter_short",
        ] = 1

        return dataframe

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

        dataframe.loc[
            (
                # Signal: RSI crosses above 30
                (qtpylib.crossed_above(dataframe["rsi"], 30))
                &
                # Guard: tema below BB middle
                (dataframe["tema"] <= dataframe["bb_middleband"])
                & (dataframe["tema"] > dataframe["tema"].shift(1))
                & (  # Guard: tema is raising
                    dataframe["volume"] > 0
                )  # Make sure Volume is not 0
            ),
            "exit_short",
        ] = 1

        return dataframe